




import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Any	      =					""
				SCREAMING_SNAKE_CASE__					:							str	      =					(
				    None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
				)
				SCREAMING_SNAKE_CASE__					:							str	      =					None  # compression type in fsspec. ex: "gzip"
				SCREAMING_SNAKE_CASE__					:							str	      =					None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz


				def __init__(							self:   List[str]		,					_lowerCamelCase:   str = ""		,					_lowerCamelCase:   Optional[str] = None		,					_lowerCamelCase:   Optional[dict] = None		,					**_lowerCamelCase:   List[Any]		):
											super().__init__(self		,					**_lowerCamelCase		)
											# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
											SCREAMING_SNAKE_CASE_   	=    fsspec.open(
											    _lowerCamelCase		,					mode='''rb'''		,					protocol=_lowerCamelCase		,					compression=self.compression		,					client_kwargs={
											        '''requote_redirect_url''': False,  # see https://github.com/huggingface/datasets/pull/5459
											        '''trust_env''': True,  # Enable reading proxy env variables.
											        **(target_options or {}).pop('''client_kwargs'''		,					{}		),  # To avoid issues if it was already passed.
											    }		,					**(target_options or {})		,					)
											SCREAMING_SNAKE_CASE_   	=    os.path.basename(self.file.path.split('''::'''		)[0]		)
											SCREAMING_SNAKE_CASE_   	=    (
											    self.compressed_name[: self.compressed_name.rindex('''.'''		)]
											    if '''.''' in self.compressed_name
											    else self.compressed_name
											)
											SCREAMING_SNAKE_CASE_   	=    None


				@classmethod
				def     _A							(							cls:   List[Any]		,					_lowerCamelCase:   Tuple		):
											# compressed file paths are always relative to the archive root
											return super()._strip_protocol(_lowerCamelCase		).lstrip('''/'''		)


				def     _A							(							self:   Any		):
											if self.dir_cache is None:
																		SCREAMING_SNAKE_CASE_   	=    {**self.file.fs.info(self.file.path		), '''name''': self.uncompressed_name}
																		SCREAMING_SNAKE_CASE_   	=    {f['''name''']: f}


				def     _A							(							self:   str		,					_lowerCamelCase:   str		):
											return self.file.open().read()





				def     _A							(							self:   Tuple		,					_lowerCamelCase:   str		,					_lowerCamelCase:   str = "rb"		,					_lowerCamelCase:   Dict=None		,					_lowerCamelCase:   Union[str, Any]=True		,					_lowerCamelCase:   Optional[Any]=None		,					**_lowerCamelCase:   Optional[int]		,					):
											SCREAMING_SNAKE_CASE_   	=    self._strip_protocol(_lowerCamelCase		)
											if mode != "rb":
																		raise ValueError(f"Tried to read with mode {mode} on file {self.file.path} opened with mode 'rb'"		)
											return self.file.open()






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							str	      =					"bz2"
				SCREAMING_SNAKE_CASE__					:							Dict	      =					"bz2"
				SCREAMING_SNAKE_CASE__					:							Any	      =					".bz2"






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							List[Any]	      =					"gzip"
				SCREAMING_SNAKE_CASE__					:							Any	      =					"gzip"
				SCREAMING_SNAKE_CASE__					:							List[Any]	      =					".gz"






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							str	      =					"lz4"
				SCREAMING_SNAKE_CASE__					:							Any	      =					"lz4"
				SCREAMING_SNAKE_CASE__					:							Tuple	      =					".lz4"






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Optional[Any]	      =					"xz"
				SCREAMING_SNAKE_CASE__					:							int	      =					"xz"
				SCREAMING_SNAKE_CASE__					:							Union[str, Any]	      =					".xz"






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Any	      =					"zstd"
				SCREAMING_SNAKE_CASE__					:							Dict	      =					"zstd"
				SCREAMING_SNAKE_CASE__					:							Tuple	      =					".zst"


				def __init__(							self:   Tuple		,					_lowerCamelCase:   str		,					_lowerCamelCase:   str = "rb"		,					_lowerCamelCase:   Optional[str] = None		,					_lowerCamelCase:   Optional[dict] = None		,					_lowerCamelCase:   int = DEFAULT_BLOCK_SIZE		,					**_lowerCamelCase:   int		,					):
											super().__init__(
											    fo=_lowerCamelCase		,					mode=_lowerCamelCase		,					target_protocol=_lowerCamelCase		,					target_options=_lowerCamelCase		,					block_size=_lowerCamelCase		,					**_lowerCamelCase		,					)
											# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
											#
											# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
											#     out.close = close
											# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
											#
											# see https://github.com/intake/filesystem_spec/issues/725
											SCREAMING_SNAKE_CASE_   	=    self.file.__enter__






											class  __magic_name__						:
															'''simple docstring'''







															def __init__(							self:   str		,					_lowerCamelCase:   List[Any]		):
																						SCREAMING_SNAKE_CASE_   	=    file_


															def __enter__(							self:   Tuple		):
																						self._file.__enter__()
																						return self


															def __exit__(							self:   int		,					*_lowerCamelCase:   Tuple		,					**_lowerCamelCase:   int		):
																						self._file.__exit__(*_lowerCamelCase		,					**_lowerCamelCase		)


															def __iter__(							self:   Optional[int]		):
																						return iter(self._file		)


															def     _A							(							self:   str		):
																						return next(self._file		)





															def __getattr__(							self:   Union[str, Any]		,					_lowerCamelCase:   Optional[Any]		):
																						return getattr(self._file		,					_lowerCamelCase		)

											def fixed_enter(*_lowerCamelCase:   Dict		,					**_lowerCamelCase:   Any		):
																		return WrappedFile(_enter(*_lowerCamelCase		,					**_lowerCamelCase		)		)

											SCREAMING_SNAKE_CASE_   	=    fixed_enter








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  __magic_name__						:
				'''simple docstring'''







				def __init__(							self:   Optional[int]		,					_lowerCamelCase:   Tuple		,					_lowerCamelCase:   Any=None		,					_lowerCamelCase:   Any=None		,					_lowerCamelCase:   Tuple=None		,					_lowerCamelCase:   Optional[Any]="resnet50"		,					_lowerCamelCase:   Any=3		,					_lowerCamelCase:   Optional[int]=32		,					_lowerCamelCase:   List[Any]=3		,					_lowerCamelCase:   int=True		,					_lowerCamelCase:   Any=True		,					):
											SCREAMING_SNAKE_CASE_   	=    parent
											SCREAMING_SNAKE_CASE_   	=    out_indices if out_indices is not None else [4]
											SCREAMING_SNAKE_CASE_   	=    stage_names
											SCREAMING_SNAKE_CASE_   	=    out_features
											SCREAMING_SNAKE_CASE_   	=    backbone
											SCREAMING_SNAKE_CASE_   	=    batch_size
											SCREAMING_SNAKE_CASE_   	=    image_size
											SCREAMING_SNAKE_CASE_   	=    num_channels
											SCREAMING_SNAKE_CASE_   	=    use_pretrained_backbone
											SCREAMING_SNAKE_CASE_   	=    is_training


				def     _A							(							self:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_   	=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)
											SCREAMING_SNAKE_CASE_   	=    self.get_config()

											return config, pixel_values


				def     _A							(							self:   List[Any]		):
											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:   str		,					_lowerCamelCase:   str		,					_lowerCamelCase:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_   	=    TimmBackbone(config=_lowerCamelCase		)
											model.to(_lowerCamelCase		)
											model.eval()
											with torch.no_grad():
																		SCREAMING_SNAKE_CASE_   	=    model(_lowerCamelCase		)
											self.parent.assertEqual(
											    result.feature_map[-1].shape		,					(self.batch_size, model.channels[-1], 14, 14)		,					)





				def     _A							(							self:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_   	=    self.prepare_config_and_inputs()
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    config_and_inputs
											SCREAMING_SNAKE_CASE_   	=    {'''pixel_values''': pixel_values}
											return config, inputs_dict






@require_torch
@require_timm
class  __magic_name__						(  __UpperCAmelCase    ,  __UpperCAmelCase    ,  __UpperCAmelCase    ,  unittest.TestCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							List[str]	      =					(TimmBackbone,) if is_torch_available() else ()
				SCREAMING_SNAKE_CASE__					:							List[str]	      =					{"feature-extraction": TimmBackbone} if is_torch_available() else {}
				SCREAMING_SNAKE_CASE__					:							Tuple	      =					False
				SCREAMING_SNAKE_CASE__					:							Optional[int]	      =					False
				SCREAMING_SNAKE_CASE__					:							Any	      =					False
				SCREAMING_SNAKE_CASE__					:							Any	      =					False


				def     _A							(							self:   int		):
											SCREAMING_SNAKE_CASE_   	=    TimmBackboneModelTester(self		)
											SCREAMING_SNAKE_CASE_   	=    ConfigTester(self		,					config_class=_lowerCamelCase		,					has_text_modality=_lowerCamelCase		)


				def     _A							(							self:   str		):
											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:   List[Any]		):
											SCREAMING_SNAKE_CASE_   	=    '''resnet18'''
											SCREAMING_SNAKE_CASE_   	=    '''microsoft/resnet-18'''

											SCREAMING_SNAKE_CASE_   	=    AutoBackbone.from_pretrained(_lowerCamelCase		,					use_timm_backbone=_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    AutoBackbone.from_pretrained(_lowerCamelCase		)

											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]		)

											SCREAMING_SNAKE_CASE_   	=    AutoBackbone.from_pretrained(_lowerCamelCase		,					use_timm_backbone=_lowerCamelCase		,					out_indices=[1, 2, 3]		)
											SCREAMING_SNAKE_CASE_   	=    AutoBackbone.from_pretrained(_lowerCamelCase		,					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:   Optional[Any]		):
											pass


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


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


				@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''		)
				def     _A							(							self:   Optional[Any]		):
											pass


				@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''		)
				def     _A							(							self:   List[Any]		):
											pass


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


				@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''		)
				def     _A							(							self:   List[str]		):
											pass


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


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


				@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''		)
				def     _A							(							self:   Optional[int]		):
											pass


				@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''		)
				def     _A							(							self:   Union[str, Any]		):
											pass


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


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


				@unittest.skip('''Safetensors is not supported by timm.'''		)
				def     _A							(							self:   Optional[int]		):
											pass


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


				def     _A							(							self:   Dict		):
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    self.model_tester.prepare_config_and_inputs_for_common()

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

																		SCREAMING_SNAKE_CASE_   	=    ['''pixel_values''']
																		self.assertListEqual(arg_names[:1]		,					_lowerCamelCase		)


				def     _A							(							self:   Dict		):
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    self.model_tester.prepare_config_and_inputs_for_common()
											SCREAMING_SNAKE_CASE_   	=    True
											SCREAMING_SNAKE_CASE_   	=    self.has_attentions

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

											SCREAMING_SNAKE_CASE_   	=    self._prepare_for_class(_lowerCamelCase		,					_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    model(**_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    outputs[0][-1]

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

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

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

											self.assertIsNotNone(hidden_states.grad		)

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





				def     _A							(							self:   List[str]		):
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		SCREAMING_SNAKE_CASE_   	=    model_class(_lowerCamelCase		)
																		model.to(_lowerCamelCase		)
																		model.eval()
																		SCREAMING_SNAKE_CASE_   	=    model(**_lowerCamelCase		)

																		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
																		SCREAMING_SNAKE_CASE_   	=    copy.deepcopy(_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    None
																		SCREAMING_SNAKE_CASE_   	=    model_class(_lowerCamelCase		)
																		model.to(_lowerCamelCase		)
																		model.eval()
																		SCREAMING_SNAKE_CASE_   	=    model(**_lowerCamelCase		)

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

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


