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How to use to_impl method in Playwright Python

Best Python code snippet using playwright-python

_browser_context.py

Source:_browser_context.py

...355    @property356    def service_workers(self) -> List[Worker]:357        return list(self._service_workers)358    async def new_cdp_session(self, page: Union[Page, Frame]) -> CDPSession:359        page = to_impl(page)360        params = {}361        if isinstance(page, Page):362            params["page"] = page._channel363        elif isinstance(page, Frame):364            params["frame"] = page._channel365        else:366            raise Error("page: expected Page or Frame")367        return from_channel(await self._channel.send("newCDPSession", params))368    @property369    def tracing(self) -> Tracing:370        return self._tracing371    @property372    def request(self) -> "APIRequestContext":373        return self._request

generate_api.py

Source:generate_api.py

...119            "typing.Any" in value_str120            or "typing.Dict" in value_str121            or "Handle" in value_str122        ):123            tokens.append(f"{name}=mapping.to_impl({to_snake_case(name)})")124        elif (125            re.match(r"<class 'playwright\._impl\.[\w]+\.[\w]+", value_str)126            and "_api_structures" not in value_str127        ):128            tokens.append(f"{name}={to_snake_case(name)}._impl_obj")129        else:130            tokens.append(f"{name}={to_snake_case(name)}")131    return split.join(tokens)132def return_type(func: FunctionType) -> str:133    value = get_type_hints(func, globals())["return"]134    return process_type(value)135def short_name(t: Any) -> str:136    match = cast(137        Match[str], re.compile(r"playwright\._impl\.[^.]+\.([^']+)").search(str(t))...

op_lib.py

Source:op_lib.py

1# ------------------------------------------------------------2# Copyright (c) 2017-present, SeetaTech, Co.,Ltd.3#4# Licensed under the BSD 2-Clause License.5# You should have received a copy of the BSD 2-Clause License6# along with the software. If not, See,7#8#     <https://opensource.org/licenses/BSD-2-Clause>9#10# ------------------------------------------------------------11"""Operator library."""12from __future__ import absolute_import13from __future__ import division14from __future__ import print_function15import numpy16from dragon.core.autograph.op_schema import OpSchema17from dragon.core.framework import context18from dragon.core.framework import proto_util19from dragon.core.framework import tapes20from dragon.core.framework import workspace21from dragon.core.framework.tensor import Tensor22from dragon.core.util import nest23class OpExec(object):24    """The executable operator."""25    _created_instances = {}26    def __init__(self, op_type):27        self._op_type = op_type28        self._ignore_keys = {'outputs'}29        def_args = {}30        def_args_getter = OpSchema.get_args(op_type)31        if def_args_getter is not None:32            def_args = def_args_getter()33        for k, v in def_args.items():34            if k.endswith('_desc'):35                self._ignore_keys.add(k.split('_desc')[0])36        self._config_cache = {}37    @classmethod38    def get_instance(cls, op_type):39        """Return the executable operator."""40        try:41            instance = cls._created_instances[op_type]42        except KeyError:43            instance = OpExec(op_type)44            cls._created_instances[op_type] = instance45        return instance46    def get_config(self, **kwargs):47        """Return the execution config."""48        device = context.get_device()49        cache_key = self._op_type + '/' + str(device)50        for k, v in kwargs.items():51            if k not in self._ignore_keys:52                cache_key += '/' + str(v)53        try:54            return self._config_cache[cache_key]55        except KeyError:56            def_args, feed_dict = {}, {}57            def_args_getter = OpSchema.get_args(self._op_type)58            if def_args_getter is not None:59                def_args = def_args_getter(**kwargs)60            device = def_args.pop('device', device)61            no_grad = def_args.pop('no_grad', False)62            for k, v in def_args.items():63                if k.endswith('_desc') and v:64                    name = k.split('_desc')[0]65                    feed_dict[name] = v66                    def_args[k] = '$NAME/' + name67            op_def = proto_util.make_operator_def(68                op_type=self._op_type,69                name=kwargs.get('name', ''),70                device_option=device.to_proto(False),71                cache_key=cache_key,72                to_impl=True, **def_args)73            config = {'def': op_def,74                      'device': device,75                      'no_grad': no_grad,76                      'feed_dict': feed_dict}77            self._config_cache[cache_key] = config78            return config79class OpLib(object):80    """Library to apply the registered operators."""81    @staticmethod82    def add(op_type, inputs, **kwargs):83        """Add operator to output symbols."""84        op_tape = tapes.OrderedTape()85        graph_tape = tapes.get_tape()86        execute_ws = workspace.get_workspace()87        # Add inputs.88        enable_grad = False89        inputs = nest.flatten(inputs)90        for input in inputs:91            op_tape.add_source(input)92            if graph_tape and (input.requires_grad or93                               graph_tape.is_target(id(input))):94                enable_grad = True95        # Add extra inputs.96        for input in nest.flatten(kwargs.pop('extra_inputs', [])):97            op_tape.add_source(input)98            op_tape.add_target(input.id)99        # Add outputs.100        name = kwargs.pop('name', None)101        num_outputs = kwargs.pop('num_outputs', 1)102        outputs = []103        for i in range(num_outputs):104            outputs.append(Tensor(105                impl=execute_ws.create_tensor(scope='Tensor'),106                name=name if name else op_type + ':%d' % i,107                symbolic=True))108        # Create def.109        op_def = proto_util.make_operator_def(110            op_type=op_type,111            inputs=[input.id for input in inputs],112            outputs=[output.id for output in outputs],113            device_option=proto_util.get_default_device_option(),114            name=execute_ws.create_handle('Op'), **kwargs)115        # Record def.116        op_tape.add_element(op_def)117        graph_tape.add_element(op_def) if enable_grad else None118        # Set tape for outputs.119        for output in outputs:120            output._tape = op_tape121            output._requires_grad = enable_grad122        # Add spec to outputs.123        add_output_spec = OpSchema.get_spec(op_type)124        if add_output_spec is None:125            add_output_spec = OpSchema.get_spec('Unchanged')126        outputs = add_output_spec(kwargs, inputs, outputs)127        # Return single or repeated outputs.128        return outputs[0] if num_outputs == 1 else outputs129    @staticmethod130    def execute(op_type, inputs, **kwargs):131        """Execute an operator."""132        op_exec = OpExec.get_instance(op_type)133        run_config = op_exec.get_config(**kwargs)134        return OpLib.run(inputs, run_config, **kwargs)135    @staticmethod136    def run(inputs, run_config, **kwargs):137        """Run operator once."""138        graph_tape = tapes.get_tape()139        execute_ws = workspace.get_workspace()140        # Add inputs.141        input_names = []142        enable_grad = False143        for input in inputs:144            input_names.append(input.id)145            if graph_tape and (input.requires_grad or146                               graph_tape.is_target(id(input))):147                enable_grad = True148        # Unify grad modes.149        no_grad = run_config['no_grad']150        enable_grad = enable_grad and not no_grad151        if hasattr(graph_tape, '_exporting'):152            # Ensure the intermediates saved for the exporting graph.153            no_grad, enable_grad = False, True154        # Add outputs.155        outputs, output_names = [], []156        output_specs = list(kwargs.get('outputs', [None]))157        for i, spec in enumerate(output_specs):158            if spec is None:159                outputs.append(Tensor(160                    device=run_config['device'].copy(),161                    impl=execute_ws.create_tensor(162                        scope=context.get_variable_scope(enable_grad)),163                    deleter=execute_ws._handle_pool))164                output_names.append(outputs[i].id)165            else:166                assert isinstance(spec, Tensor)167                outputs.append(spec)168                output_names.append(spec.id)169                if enable_grad and output_names[-1] not in input_names:170                    raise RuntimeError('Output that requires gradient is not in inputs.')171        # Specialize def for given inputs and outputs.172        op_name = ''  # Optional operator name.173        op_def = run_config['def'].DeriveTo(input_names, output_names)174        # Record def if grad is enabled.175        if len(inputs) > 0 and not no_grad:176            if enable_grad:177                op_name = execute_ws.create_handle(op_def.type)178                op_def.name = op_name179                graph_tape.add_element(op_def)180                graph_tape.add_handle(op_name)181                for input in inputs:182                    graph_tape.add_source(input)183                for output in outputs:184                    output._requires_grad = True185            else:186                for output in outputs:187                    output._requires_grad = False188        # Ensure the named operator for the tracing graph.189        if hasattr(graph_tape, '_tracing') and not op_name:190            op_def.name = op_name = execute_ws.create_handle(op_def.type)191            graph_tape.add_handle(op_name)192        # Emit to dispatch this execution.193        for feed_key, value_type in run_config['feed_dict'].items():194            dest = execute_ws.create_tensor(op_name + '/' + feed_key)195            dest.FromNumpy(numpy.array(kwargs[feed_key], value_type), True)196        execute_ws.run_operator(op_def)197        # Return single or repeated outputs....

checkpoint.py

Source:checkpoint.py

1# ------------------------------------------------------------2# Copyright (c) 2017-present, SeetaTech, Co.,Ltd.3#4# Licensed under the BSD 2-Clause License.5# You should have received a copy of the BSD 2-Clause License6# along with the software. If not, See,7#8#     <https://opensource.org/licenses/BSD-2-Clause>9#10# ------------------------------------------------------------11"""Checkpoint utilities."""12from __future__ import absolute_import13from __future__ import division14from __future__ import print_function15from dragon.core.framework import context16from dragon.core.framework import proto_util17from dragon.core.framework import tapes18from dragon.core.framework import workspace19from dragon.core.util import decorator20from dragon.core.util import nest21from dragon.vm.torch.core.autograd import grad_mode22from dragon.vm.torch.core.tensor import Tensor23from dragon.vm.torch.core.nn.modules.container import Sequential24class CheckpointFunction(object):25    """Checkpointing function."""26    @staticmethod27    def apply(function, *args, **kwargs):28        """Apply function and create a checkpoint."""29        kwargs.pop('preserve_rng_state', True)30        variable_scope = kwargs.pop('variable_scope', 'Buffer')31        original_variable_scope = context.get_variable_scope(True)32        if kwargs:33            raise ValueError('Unexpected keyword arguments: ' +34                             ','.join(arg for arg in kwargs))35        # Run function.36        graph_tape = tapes.Tape()37        graph_tape._tracing = True  # Enable tracing.38        graph_tape._checkpointing = True  # Enable checkpointing.39        graph_tape._original_variable_scope = original_variable_scope40        with grad_mode.no_grad(), graph_tape:41            with context.variable_scope(variable_scope):42                outputs = function(*args)43        # Collect involving tensors.44        tensor_inputs, tensor_outputs = [], []45        for arg in args:46            if isinstance(arg, Tensor):47                tensor_inputs.append(arg)48        for arg in nest.flatten(outputs):49            if isinstance(arg, Tensor):50                tensor_outputs.append(arg)51        # Fill tape with function context.52        op_tape = tapes.OrderedTape()53        op_handle = workspace.get_workspace().create_handle('Checkpoint')54        op_tape.add_element(proto_util.make_operator_def(55            op_type='Checkpoint',56            name=op_handle,57            inputs=[input.id for input in tensor_inputs],58            outputs=[output.id for output in tensor_outputs],59            defs=[v.SerializeAs() for v in graph_tape.get_elements()],60            buffer_scope=variable_scope,61            to_impl=True))62        op_tape.add_handle(op_handle)63        op_tape.merge_handles(graph_tape.get_handles())64        # Save input tensors for backward.65        for input in tensor_inputs + graph_tape.get_sources():66            op_tape.add_source(input)67        # Save tape for backward.68        for output in tensor_outputs:69            output._tape = op_tape70            output._requires_grad = True71        return outputs72def checkpoint(function, *args, **kwargs):73    """Apply function and create a checkpoint.74    Parameters75    ----------76    function : callable77        The function to apply.78    Returns79    -------80    Any81        The function outputs.82    """83    if not grad_mode.is_grad_enabled():84        return function(*args, **kwargs)85    return CheckpointFunction.apply(function, *args, **kwargs)86def checkpoint_sequential(functions, input, segments=1, **kwargs):87    """Apply functions and create segmental checkpoints.88    Parameters89    ----------90    functions : Union[torch.nn.Sequential, Sequence[callable]]91        The functions to apply sequentially.92    input : dragon.vm.torch.Tensor93        The input tensor.94    segments : Union[int, Sequence[int]], optional95        The number or size of chunked checkpoints.96    Returns97    -------98    Any99        The function outputs.100    """101    def run_function(start, end, functions):102        def forward(input):103            for j in range(start, end):104                input = functions[j](input)105            with no_checkpoint():106                input = functions[end](input)107            return input108        return forward109    preserve_rng_state = kwargs.pop('preserve_rng_state', True)110    variable_scope = kwargs.pop('variable_scope', 'Buffer')111    if kwargs:112        raise ValueError('Unexpected keyword arguments: ' +113                         ','.join(arg for arg in kwargs))114    if isinstance(functions, Sequential):115        functions = list(functions.children())116    start, end = 0, len(functions) - 1117    if not grad_mode.is_grad_enabled():118        return run_function(start, end, functions)(input)119    if nest.is_sequence(segments):120        size_segments = segments121        if sum(size_segments) != len(functions):122            raise ValueError('Failed to chunk {} functions into {} segments.'123                             .format(len(functions), segments))124    else:125        size = (len(functions) + segments - 1) // segments126        last_size = len(functions) - size * (segments - 1)127        if last_size <= 0:128            raise ValueError('Failed to chunk {} functions into {} segments.'129                             .format(len(functions), segments))130        size_segments = [size] * (segments - 1) + [last_size]131    for size in size_segments:132        end = start + size - 1133        input = checkpoint(134            run_function(start, end, functions), input,135            preserve_rng_state=preserve_rng_state,136            variable_scope=variable_scope)137        start = end + 1138    return input139class no_checkpoint(decorator._DecoratorContextManager):140    """Context-manager to disable checkpointing."""141    def __init__(self):142        """Create a ``no_checkpoint`` context manager."""143        self._checkpointing = False144    def __enter__(self):145        graph_tape = tapes.get_tape()146        if hasattr(graph_tape, '_checkpointing'):147            self._checkpointing = True148            context._GLOBAL_VARIABLE_SCOPE_STACK.push(149                graph_tape._original_variable_scope)150    def __exit__(self, *args):151        if self._checkpointing:152            self._checkpointing = False153            context._GLOBAL_VARIABLE_SCOPE_STACK.pop()...

Playwright tutorial

LambdaTest’s Playwright tutorial will give you a broader idea about the Playwright automation framework, its unique features, and use cases with examples to exceed your understanding of Playwright testing. This tutorial will give A to Z guidance, from installing the Playwright framework to some best practices and advanced concepts.

Chapters:

What is Playwright : Playwright is comparatively new but has gained good popularity. Get to know some history of the Playwright with some interesting facts connected with it.
How To Install Playwright : Learn in detail about what basic configuration and dependencies are required for installing Playwright and run a test. Get a step-by-step direction for installing the Playwright automation framework.
Playwright Futuristic Features: Launched in 2020, Playwright gained huge popularity quickly because of some obliging features such as Playwright Test Generator and Inspector, Playwright Reporter, Playwright auto-waiting mechanism and etc. Read up on those features to master Playwright testing.
What is Component Testing: Component testing in Playwright is a unique feature that allows a tester to test a single component of a web application without integrating them with other elements. Learn how to perform Component testing on the Playwright automation framework.
Inputs And Buttons In Playwright: Every website has Input boxes and buttons; learn about testing inputs and buttons with different scenarios and examples.
Functions and Selectors in Playwright: Learn how to launch the Chromium browser with Playwright. Also, gain a better understanding of some important functions like “BrowserContext,” which allows you to run multiple browser sessions, and “newPage” which interacts with a page.
Handling Alerts and Dropdowns in Playwright : Playwright interact with different types of alerts and pop-ups, such as simple, confirmation, and prompt, and different types of dropdowns, such as single selector and multi-selector get your hands-on with handling alerts and dropdown in Playright testing.
Playwright vs Puppeteer: Get to know about the difference between two testing frameworks and how they are different than one another, which browsers they support, and what features they provide.
Run Playwright Tests on LambdaTest: Playwright testing with LambdaTest leverages test performance to the utmost. You can run multiple Playwright tests in Parallel with the LammbdaTest test cloud. Get a step-by-step guide to run your Playwright test on the LambdaTest platform.
Playwright Python Tutorial: Playwright automation framework support all major languages such as Python, JavaScript, TypeScript, .NET and etc. However, there are various advantages to Python end-to-end testing with Playwright because of its versatile utility. Get the hang of Playwright python testing with this chapter.
Playwright End To End Testing Tutorial: Get your hands on with Playwright end-to-end testing and learn to use some exciting features such as TraceViewer, Debugging, Networking, Component testing, Visual testing, and many more.
Playwright Video Tutorial: Watch the video tutorials on Playwright testing from experts and get a consecutive in-depth explanation of Playwright automation testing.