

import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints

import yaml


lowerCamelCase      				=     NewType('''DataClass''', Any)
lowerCamelCase      				=     NewType('''DataClassType''', Any)


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					if isinstance(__lowerCAmelCase		,					__lowerCAmelCase      ):
										return v
					if v.lower() in ("yes", "true", "t", "y", "1"):
										return True
					elif v.lower() in ("no", "false", "f", "n", "0"):
										return False
					else:
										raise ArgumentTypeError(
										    F"""Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive)."""      )


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							{str(__lowerCAmelCase      ): choice for choice in choices}
					return lambda UpperCAmelCase__      : str_to_choice.get(__lowerCAmelCase		,					__lowerCAmelCase      )


def        __lowercase    (  *,
    UpperCAmelCase__ = None		,					UpperCAmelCase__ = None		,					UpperCAmelCase__ = dataclasses.MISSING		,					UpperCAmelCase__ = dataclasses.MISSING		,					UpperCAmelCase__ = None		,					**UpperCAmelCase__		,					):

					"""simple docstring"""






					if metadata is None:
										# Important, don't use as default param in function signature because dict is mutable and shared across function calls
										__lowerCAmelCase					=							{}
					if aliases is not None:
										__lowerCAmelCase					=							aliases
					if help is not None:
										__lowerCAmelCase					=							help

					return dataclasses.field(metadata=__lowerCAmelCase		,					default=__lowerCAmelCase		,					default_factory=__lowerCAmelCase		,					**__lowerCAmelCase      )





class     snake_case_			(				_a							):


							"""simple docstring"""





							__UpperCAmelCase       =42




							def __init__( self					,						_A					,						**_A							):
												# To make the default appear when using --help
												if "formatter_class" not in kwargs:
																	__lowerCAmelCase					=							ArgumentDefaultsHelpFormatter
												super().__init__(**_lowerCAmelCase							)
												if dataclasses.is_dataclass(_lowerCAmelCase							):
																	__lowerCAmelCase					=							[dataclass_types]
												__lowerCAmelCase					=							list(_lowerCAmelCase							)
												for dtype in self.dataclass_types:
																	self._add_dataclass_arguments(_lowerCAmelCase							)




							@staticmethod
							def        A__  ( _A					,						_A							):
												__lowerCAmelCase					=							F"""--{field.name}"""
												__lowerCAmelCase					=							field.metadata.copy()
												# field.metadata is not used at all by Data Classes,
												# it is provided as a third-party extension mechanism.
												if isinstance(field.type					,						_lowerCAmelCase							):
																	raise RuntimeError(
																	    'Unresolved type detected, which should have been done with the help of '
																	    '`typing.get_type_hints` method by default'							)

												__lowerCAmelCase					=							kwargs.pop('aliases'					,						[]							)
												if isinstance(_lowerCAmelCase					,						_lowerCAmelCase							):
																	__lowerCAmelCase					=							[aliases]

												__lowerCAmelCase					=							getattr(field.type					,						'__origin__'					,						field.type							)
												if origin_type is Union or (hasattr(_lowerCAmelCase					,						'UnionType'							) and isinstance(_lowerCAmelCase					,						types.UnionType							)):
																	if str not in field.type.__args__ and (
																	    len(field.type.__args__							) != 2 or type(_lowerCAmelCase							) not in field.type.__args__
																	):
																						raise ValueError(
																						    'Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because'
																						    ' the argument parser only supports one type per argument.'
																						    F""" Problem encountered in field \'{field.name}\'."""							)
																	if type(_lowerCAmelCase							) not in field.type.__args__:
																						# filter `str` in Union
																						__lowerCAmelCase					=							field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
																						__lowerCAmelCase					=							getattr(field.type					,						'__origin__'					,						field.type							)
																	elif bool not in field.type.__args__:
																						# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
																						__lowerCAmelCase					=							(
																						    field.type.__args__[0] if isinstance(_lowerCAmelCase					,						field.type.__args__[1]							) else field.type.__args__[1]
																						)
																						__lowerCAmelCase					=							getattr(field.type					,						'__origin__'					,						field.type							)

        # A variable to store kwargs for a boolean field, if needed
        # so that we can init a `no_*` complement argument (see below)
												__lowerCAmelCase					=							{}
												if origin_type is Literal or (isinstance(field.type					,						_lowerCAmelCase							) and issubclass(field.type					,						_lowerCAmelCase							)):
																	if origin_type is Literal:
																						__lowerCAmelCase					=							field.type.__args__
																	else:
																						__lowerCAmelCase					=							[x.value for x in field.type]

																	__lowerCAmelCase					=							make_choice_type_function(kwargs['choices']							)

																	if field.default is not dataclasses.MISSING:
																						__lowerCAmelCase					=							field.default
																	else:
																						__lowerCAmelCase					=							True
												elif field.type is bool or field.type == Optional[bool]:
																	# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
																	# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
																	__lowerCAmelCase					=							copy(_lowerCAmelCase							)

																	# Hack because type=bool in argparse does not behave as we want.
																	__lowerCAmelCase					=							string_to_bool
																	if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
																						# Default value is False if we have no default when of type bool.
																						__lowerCAmelCase					=							False if field.default is dataclasses.MISSING else field.default
																						# This is the value that will get picked if we don't include --field_name in any way
																						__lowerCAmelCase					=							default
																						# This tells argparse we accept 0 or 1 value after --field_name
																						__lowerCAmelCase					=							'?'
																						# This is the value that will get picked if we do --field_name (without value)
																						__lowerCAmelCase					=							True
												elif isclass(_lowerCAmelCase							) and issubclass(_lowerCAmelCase					,						_lowerCAmelCase							):
																	__lowerCAmelCase					=							field.type.__args__[0]
																	__lowerCAmelCase					=							'+'
																	if field.default_factory is not dataclasses.MISSING:
																						__lowerCAmelCase					=							field.default_factory()
																	elif field.default is dataclasses.MISSING:
																						__lowerCAmelCase					=							True
												else:
																	__lowerCAmelCase					=							field.type
																	if field.default is not dataclasses.MISSING:
																						__lowerCAmelCase					=							field.default
																	elif field.default_factory is not dataclasses.MISSING:
																						__lowerCAmelCase					=							field.default_factory()
																	else:
																						__lowerCAmelCase					=							True
												parser.add_argument(_lowerCAmelCase					,						*_lowerCAmelCase					,						**_lowerCAmelCase							)

												# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
												# Order is important for arguments with the same destination!
												# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
												# here and we do not need those changes/additional keys.
												if field.default is True and (field.type is bool or field.type == Optional[bool]):
																	__lowerCAmelCase					=							False
																	parser.add_argument(F"""--no_{field.name}"""					,						action='store_false'					,						dest=field.name					,						**_lowerCAmelCase							)




							def        A__  ( self					,						_A							):
												if hasattr(_lowerCAmelCase					,						'_argument_group_name'							):
																	__lowerCAmelCase					=							self.add_argument_group(dtype._argument_group_name							)
												else:
																	__lowerCAmelCase					=							self

												try:
																	__lowerCAmelCase					=							get_type_hints(_lowerCAmelCase							)
												except NameError:
																	raise RuntimeError(
																	    F"""Type resolution failed for {dtype}. Try declaring the class in global scope or """
																	    'removing line of `from __future__ import annotations` which opts in Postponed '
																	    'Evaluation of Annotations (PEP 563)'							)
												except TypeError as ex:
																	# Remove this block when we drop Python 3.9 support
																	if sys.version_info[:2] < (3, 1_0) and "unsupported operand type(s) for |" in str(_lowerCAmelCase							):
																						__lowerCAmelCase					=							'.'.join(map(_lowerCAmelCase					,						sys.version_info[:3]							)							)
																						raise RuntimeError(
																						    F"""Type resolution failed for {dtype} on Python {python_version}. Try removing """
																						    'line of `from __future__ import annotations` which opts in union types as '
																						    '`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To '
																						    'support Python versions that lower than 3.10, you need to use '
																						    '`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of '
																						    '`X | None`.'							) from ex
																	raise

												for field in dataclasses.fields(_lowerCAmelCase							):
																	if not field.init:
																						continue
																	__lowerCAmelCase					=							type_hints[field.name]
																	self._parse_dataclass_field(_lowerCAmelCase					,						_lowerCAmelCase							)




							def        A__  ( self					,						_A=None					,						_A=False					,						_A=True					,						_A=None					,						_A=None					,						):

												if args_file_flag or args_filename or (look_for_args_file and len(sys.argv							)):
																	__lowerCAmelCase					=							[]

																	if args_filename:
																						args_files.append(Path(_lowerCAmelCase							)							)
																	elif look_for_args_file and len(sys.argv							):
																						args_files.append(Path(sys.argv[0]							).with_suffix('.args'							)							)

																	# args files specified via command line flag should overwrite default args files so we add them last
																	if args_file_flag:
																						# Create special parser just to extract the args_file_flag values
																						__lowerCAmelCase					=							ArgumentParser()
																						args_file_parser.add_argument(_lowerCAmelCase					,						type=_lowerCAmelCase					,						action='append'							)

																						# Use only remaining args for further parsing (remove the args_file_flag)
																						__lowerCAmelCase,	__lowerCAmelCase					=							args_file_parser.parse_known_args(args=_lowerCAmelCase							)
																						__lowerCAmelCase					=							vars(_lowerCAmelCase							).get(args_file_flag.lstrip('-'							)					,						_lowerCAmelCase							)

																						if cmd_args_file_paths:
																											args_files.extend([Path(_lowerCAmelCase							) for p in cmd_args_file_paths]							)

																	__lowerCAmelCase					=							[]
																	for args_file in args_files:
																						if args_file.exists():
																											file_args += args_file.read_text().split()

            # in case of duplicate arguments the last one has precedence
            # args specified via the command line should overwrite args from files, so we add them last
																	__lowerCAmelCase					=							file_args + args if args is not None else file_args + sys.argv[1:]
												__lowerCAmelCase,	__lowerCAmelCase					=							self.parse_known_args(args=_lowerCAmelCase							)
												__lowerCAmelCase					=							[]
												for dtype in self.dataclass_types:
																	__lowerCAmelCase					=							{f.name for f in dataclasses.fields(_lowerCAmelCase							) if f.init}
																	__lowerCAmelCase					=							{k: v for k, v in vars(_lowerCAmelCase							).items() if k in keys}
																	for k in keys:
																						delattr(_lowerCAmelCase					,						_lowerCAmelCase							)
																	__lowerCAmelCase					=							dtype(**_lowerCAmelCase							)
																	outputs.append(_lowerCAmelCase							)
												if len(namespace.__dict__							) > 0:
																	# additional namespace.
																	outputs.append(_lowerCAmelCase							)
												if return_remaining_strings:
																	return (*outputs, remaining_args)
												else:
																	if remaining_args:
																						raise ValueError(F"""Some specified arguments are not used by the HfArgumentParser: {remaining_args}"""							)

																	return (*outputs,)




							def        A__  ( self					,						_A					,						_A = False							):
												__lowerCAmelCase					=							set(args.keys()							)
												__lowerCAmelCase					=							[]
												for dtype in self.dataclass_types:
																	__lowerCAmelCase					=							{f.name for f in dataclasses.fields(_lowerCAmelCase							) if f.init}
																	__lowerCAmelCase					=							{k: v for k, v in args.items() if k in keys}
																	unused_keys.difference_update(inputs.keys()							)
																	__lowerCAmelCase					=							dtype(**_lowerCAmelCase							)
																	outputs.append(_lowerCAmelCase							)
												if not allow_extra_keys and unused_keys:
																	raise ValueError(F"""Some keys are not used by the HfArgumentParser: {sorted(_lowerCAmelCase							)}"""							)
												return tuple(_lowerCAmelCase							)




							def        A__  ( self					,						_A					,						_A = False							):
												with open(Path(_lowerCAmelCase							)					,						encoding='utf-8'							) as open_json_file:
																	__lowerCAmelCase					=							json.loads(open_json_file.read()							)
												__lowerCAmelCase					=							self.parse_dict(_lowerCAmelCase					,						allow_extra_keys=_lowerCAmelCase							)
												return tuple(_lowerCAmelCase							)




							def        A__  ( self					,						_A					,						_A = False							):
												__lowerCAmelCase					=							self.parse_dict(yaml.safe_load(Path(_lowerCAmelCase							).read_text()							)					,						allow_extra_keys=_lowerCAmelCase							)
												return tuple(_lowerCAmelCase							)


import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
		import torch

		from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin





class     snake_case_			:


							"""simple docstring"""





							def __init__( self					,						_A					,						_A=None					,						_A=None					,						_A=None					,						_A="resnet50"					,						_A=3					,						_A=3_2					,						_A=3					,						_A=True					,						_A=True					,						):
												__lowerCAmelCase					=							parent
												__lowerCAmelCase					=							out_indices if out_indices is not None else [4]
												__lowerCAmelCase					=							stage_names
												__lowerCAmelCase					=							out_features
												__lowerCAmelCase					=							backbone
												__lowerCAmelCase					=							batch_size
												__lowerCAmelCase					=							image_size
												__lowerCAmelCase					=							num_channels
												__lowerCAmelCase					=							use_pretrained_backbone
												__lowerCAmelCase					=							is_training




							def        A__  ( self							):
												__lowerCAmelCase					=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
												__lowerCAmelCase					=							self.get_config()

												return config, pixel_values




							def        A__  ( self							):
												return TimmBackboneConfig(
												    image_size=self.image_size					,						num_channels=self.num_channels					,						out_features=self.out_features					,						out_indices=self.out_indices					,						stage_names=self.stage_names					,						use_pretrained_backbone=self.use_pretrained_backbone					,						backbone=self.backbone					,						)




							def        A__  ( self					,						_A					,						_A							):
												__lowerCAmelCase					=							TimmBackbone(config=_A							)
												model.to(_A							)
												model.eval()
												with torch.no_grad():
																	__lowerCAmelCase					=							model(_A							)
												self.parent.assertEqual(
												    result.feature_map[-1].shape					,						(self.batch_size, model.channels[-1], 1_4, 1_4)					,						)




							def        A__  ( self							):
												__lowerCAmelCase					=							self.prepare_config_and_inputs()
												__lowerCAmelCase,	__lowerCAmelCase					=							config_and_inputs
												__lowerCAmelCase					=							{'pixel_values': pixel_values}
												return config, inputs_dict





@require_torch
@require_timm
class     snake_case_			(				_a			, _a			, _a			, unittest.TestCase							):


							"""simple docstring"""





							__UpperCAmelCase       =(TimmBackbone,) if is_torch_available() else ()
							__UpperCAmelCase       ={"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False




							def        A__  ( self							):
												__lowerCAmelCase					=							TimmBackboneModelTester(self							)
												__lowerCAmelCase					=							ConfigTester(self					,						config_class=_A					,						has_text_modality=_A							)




							def        A__  ( self							):
												self.config_tester.create_and_test_config_to_json_string()
												self.config_tester.create_and_test_config_to_json_file()
												self.config_tester.create_and_test_config_from_and_save_pretrained()
												self.config_tester.create_and_test_config_with_num_labels()
												self.config_tester.check_config_can_be_init_without_params()
												self.config_tester.check_config_arguments_init()




							def        A__  ( self							):
												__lowerCAmelCase					=							'resnet18'
												__lowerCAmelCase					=							'microsoft/resnet-18'

												__lowerCAmelCase					=							AutoBackbone.from_pretrained(_A					,						use_timm_backbone=_A							)
												__lowerCAmelCase					=							AutoBackbone.from_pretrained(_A							)

												self.assertEqual(len(timm_model.out_features							)					,						len(transformers_model.out_features							)							)
												self.assertEqual(len(timm_model.stage_names							)					,						len(transformers_model.stage_names							)							)
												self.assertEqual(timm_model.channels					,						transformers_model.channels							)
												# Out indices are set to the last layer by default. For timm models, we don't know
												# the number of layers in advance, so we set it to (-1,), whereas for transformers
												# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
												self.assertEqual(timm_model.out_indices					,						(-1,)							)
												self.assertEqual(transformers_model.out_indices					,						[len(timm_model.stage_names							) - 1]							)

												__lowerCAmelCase					=							AutoBackbone.from_pretrained(_A					,						use_timm_backbone=_A					,						out_indices=[1, 2, 3]							)
												__lowerCAmelCase					=							AutoBackbone.from_pretrained(_A					,						out_indices=[1, 2, 3]							)

												self.assertEqual(timm_model.out_indices					,						transformers_model.out_indices							)
												self.assertEqual(len(timm_model.out_features							)					,						len(transformers_model.out_features							)							)
												self.assertEqual(timm_model.channels					,						transformers_model.channels							)




							@unittest.skip('TimmBackbone doesn\'t support feed forward chunking'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone doesn\'t have num_hidden_layers attribute'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone initialization is managed on the timm side'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone models doesn\'t have inputs_embeds'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone models doesn\'t have inputs_embeds'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone model cannot be created without specifying a backbone checkpoint'							)
							def        A__  ( self							):
												pass




							@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'							)
							def        A__  ( self							):
												pass




							@unittest.skip('model weights aren\'t tied in TimmBackbone.'							)
							def        A__  ( self							):
												pass




							@unittest.skip('model weights aren\'t tied in TimmBackbone.'							)
							def        A__  ( self							):
												pass




							@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'							)
							def        A__  ( self							):
												pass




							@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone doesn\'t have hidden size info in its configuration.'							)
							def        A__  ( self							):
												pass




							@unittest.skip('TimmBackbone doesn\'t support output_attentions.'							)
							def        A__  ( self							):
												pass




							@unittest.skip('Safetensors is not supported by timm.'							)
							def        A__  ( self							):
												pass




							@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'							)
							def        A__  ( self							):
												pass




							def        A__  ( self							):
												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	__lowerCAmelCase					=							model_class(_A							)
																	__lowerCAmelCase					=							inspect.signature(model.forward							)
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	__lowerCAmelCase					=							[*signature.parameters.keys()]

																	__lowerCAmelCase					=							['pixel_values']
																	self.assertListEqual(arg_names[:1]					,						_A							)




							def        A__  ( self							):
												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()
												__lowerCAmelCase					=							True
												__lowerCAmelCase					=							self.has_attentions

												# no need to test all models as different heads yield the same functionality
												__lowerCAmelCase					=							self.all_model_classes[0]
												__lowerCAmelCase					=							model_class(_A							)
												model.to(_A							)

												__lowerCAmelCase					=							self._prepare_for_class(_A					,						_A							)
												__lowerCAmelCase					=							model(**_A							)
												__lowerCAmelCase					=							outputs[0][-1]

												# Encoder-/Decoder-only models
												__lowerCAmelCase					=							outputs.hidden_states[0]
												hidden_states.retain_grad()

												if self.has_attentions:
																	__lowerCAmelCase					=							outputs.attentions[0]
																	attentions.retain_grad()

												output.flatten()[0].backward(retain_graph=_A							)

												self.assertIsNotNone(hidden_states.grad							)

												if self.has_attentions:
																	self.assertIsNotNone(attentions.grad							)




							def        A__  ( self							):
												__lowerCAmelCase,	__lowerCAmelCase					=							self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	__lowerCAmelCase					=							model_class(_A							)
																	model.to(_A							)
																	model.eval()
																	__lowerCAmelCase					=							model(**_A							)

																	self.assertEqual(len(result.feature_maps							)					,						len(config.out_indices							)							)
																	self.assertEqual(len(model.channels							)					,						len(config.out_indices							)							)

																	# Check output of last stage is taken if out_features=None, out_indices=None
																	__lowerCAmelCase					=							copy.deepcopy(_A							)
																	__lowerCAmelCase					=							None
																	__lowerCAmelCase					=							model_class(_A							)
																	model.to(_A							)
																	model.eval()
																	__lowerCAmelCase					=							model(**_A							)

																	self.assertEqual(len(result.feature_maps							)					,						1							)
																	self.assertEqual(len(model.channels							)					,						1							)

																	# Check backbone can be initialized with fresh weights
																	__lowerCAmelCase					=							copy.deepcopy(_A							)
																	__lowerCAmelCase					=							False
																	__lowerCAmelCase					=							model_class(_A							)
																	model.to(_A							)
																	model.eval()
																	__lowerCAmelCase					=							model(**_A							)






