






import importlib
import os

import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry

from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem

from .utils import require_lza, require_zstandard






def   a			(						lowerCamelCase_				):






				'''simple docstring'''
				assert "mock" in _fsspec_registry
				assert "bz2" in _fsspec_registry






def   a			(						):






				'''simple docstring'''
				assert "mock" not in _fsspec_registry
				assert "bz2" in _fsspec_registry






def   a			(						):






				'''simple docstring'''
				lowercase__    =			'''mock-s3-bucket'''
				lowercase__    =			F"""s3://{mock_bucket}"""
				lowercase__    =			extract_path_from_uri(lowerCamelCase_				)
				assert dataset_path.startswith('''s3://'''				) is False

				lowercase__    =			'''./local/path'''
				lowercase__    =			extract_path_from_uri(lowerCamelCase_				)
				assert dataset_path == new_dataset_path






def   a			(						lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			is_remote_filesystem(lowerCamelCase_				)
				assert is_remote is True

				lowercase__    =			fsspec.filesystem('''file'''				)

				lowercase__    =			is_remote_filesystem(lowerCamelCase_				)
				assert is_remote is False






@pytest.mark.parametrize('''compression_fs_class'''		,   lowerCamelCase_				)
def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			{'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_file, '''bz2''': bza_file, '''lz4''': lza_file}
				lowercase__    =			input_paths[compression_fs_class.protocol]
				if input_path is None:
								lowercase__    =			F"""for '{compression_fs_class.protocol}' compression protocol, """
								if compression_fs_class.protocol == "lz4":
												reason += require_lza.kwargs["reason"]
								elif compression_fs_class.protocol == "zstd":
												reason += require_zstandard.kwargs["reason"]
								pytest.skip(lowerCamelCase_				)
				lowercase__    =			fsspec.filesystem(compression_fs_class.protocol		,   fo=lowerCamelCase_				)
				assert isinstance(lowerCamelCase_		,   lowerCamelCase_				)
				lowercase__    =			os.path.basename(lowerCamelCase_				)
				lowercase__    =			expected_filename[: expected_filename.rindex('''.'''				)]
				assert fs.glob('''*'''				) == [expected_filename]
				with fs.open(lowerCamelCase_		,   '''r'''		,   encoding='''utf-8'''				) as f, open(lowerCamelCase_		,   encoding='''utf-8'''				) as expected_file:
								assert f.read() == expected_file.read()






@pytest.mark.parametrize('''protocol'''		,   ['''zip''', '''gzip''']				)
def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			{'''zip''': zip_jsonl_path, '''gzip''': jsonl_gz_path}
				lowercase__    =			compressed_file_paths[protocol]
				lowercase__    =			'''dataset.jsonl'''
				lowercase__    =			F"""{protocol}://{member_file_path}::{compressed_file_path}"""
				lowercase__ ,						*lowercase__    =			fsspec.get_fs_token_paths(lowerCamelCase_				)
				assert fs.isfile(lowerCamelCase_				)
				assert not fs.isfile('''non_existing_''' + member_file_path				)






@pytest.mark.integration
def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			hf_api.dataset_info(lowerCamelCase_		,   token=lowerCamelCase_				)
				lowercase__    =			HfFileSystem(repo_info=lowerCamelCase_		,   token=lowerCamelCase_				)
				assert sorted(hffs.glob('''*'''				)				) == [".gitattributes", "data"]
				assert hffs.isdir('''data'''				)
				assert hffs.isfile('''.gitattributes'''				) and hffs.isfile('''data/text_data.txt'''				)
				with open(lowerCamelCase_				) as f:
								assert hffs.open('''data/text_data.txt'''		,   '''r'''				).read() == f.read()






def   a			(						):






				'''simple docstring'''
				lowercase__    =			'''bz2'''

				# Import module
				import datasets.filesystems

				# Overwrite protocol and reload
				register_implementation(lowerCamelCase_		,   lowerCamelCase_		,   clobber=lowerCamelCase_				)
				with pytest.warns(lowerCamelCase_				) as warning_info:
								importlib.reload(datasets.filesystems				)

				assert len(lowerCamelCase_				) == 1
				assert (
				    str(warning_info[0].message				)
				    == F"""A filesystem protocol was already set for {protocol} and will be overwritten."""
				)












from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
							import timm


if is_torch_available():
							from torch import Tensor
class       _UpperCAmelCase					(       A__		,A__	):

				"""simple docstring"""




				lowercase__											=       """pixel_values"""
				lowercase__											=       False
				lowercase__											=       TimmBackboneConfig




				def __init__(     self						:    Tuple,  lowerCamelCase						:    List[str],  **lowerCamelCase						:    List[str]  ):

								'''simple docstring'''
								requires_backends(self,  '''timm'''  )
								super().__init__(lowerCamelCase  )
								lowercase__    =			config

								if config.backbone is None:
												raise ValueError('''backbone is not set in the config. Please set it to a timm model name.'''  )

								if config.backbone not in timm.list_models():
												raise ValueError(F"""backbone {config.backbone} is not supported by timm."""  )

								if hasattr(lowerCamelCase,  '''out_features'''  ) and config.out_features is not None:
												raise ValueError('''out_features is not supported by TimmBackbone. Please use out_indices instead.'''  )

								lowercase__    =			getattr(lowerCamelCase,  '''use_pretrained_backbone''',  lowerCamelCase  )
								if pretrained is None:
												raise ValueError('''use_pretrained_backbone is not set in the config. Please set it to True or False.'''  )

								# We just take the final layer by default. This matches the default for the transformers models.
								lowercase__    =			config.out_indices if getattr(lowerCamelCase,  '''out_indices''',  lowerCamelCase  ) is not None else (-1,)

								lowercase__    =			timm.create_model(
								    config.backbone,  pretrained=lowerCamelCase,  features_only=config.features_only,  in_chans=config.num_channels,  out_indices=lowerCamelCase,  **lowerCamelCase,  )
								# These are used to control the output of the model when called. If output_hidden_states is True, then
								# return_layers is modified to include all layers.
								lowercase__    =			self._backbone.return_layers
								lowercase__    =			{layer['''module''']: str(lowerCamelCase  ) for i, layer in enumerate(self._backbone.feature_info.info  )}
								super()._init_backbone(lowerCamelCase  )




				@classmethod
				def 				lowercase__   (     cls						:    List[str],  lowerCamelCase						:    List[str],  *lowerCamelCase						:    Optional[int],  **lowerCamelCase						:    List[Any]  ):

								'''simple docstring'''
								requires_backends(cls,  ['''vision''', '''timm''']  )
								from ...models.timm_backbone import TimmBackboneConfig

								lowercase__    =			kwargs.pop('''config''',  TimmBackboneConfig()  )

								lowercase__    =			kwargs.pop('''use_timm_backbone''',  lowerCamelCase  )
								if not use_timm:
												raise ValueError('''use_timm_backbone must be True for timm backbones'''  )

								lowercase__    =			kwargs.pop('''num_channels''',  config.num_channels  )
								lowercase__    =			kwargs.pop('''features_only''',  config.features_only  )
								lowercase__    =			kwargs.pop('''use_pretrained_backbone''',  config.use_pretrained_backbone  )
								lowercase__    =			kwargs.pop('''out_indices''',  config.out_indices  )
								lowercase__    =			TimmBackboneConfig(
								    backbone=lowerCamelCase,  num_channels=lowerCamelCase,  features_only=lowerCamelCase,  use_pretrained_backbone=lowerCamelCase,  out_indices=lowerCamelCase,  )
								return super()._from_config(lowerCamelCase,  **lowerCamelCase  )




				def 				lowercase__   (     self						:    List[Any],  lowerCamelCase						:    Optional[Any]  ):

								'''simple docstring'''
								pass




				def 				lowercase__   (     self						:    int,  lowerCamelCase						:    int,  lowerCamelCase						:    Optional[int]=None,  lowerCamelCase						:    List[Any]=None,  lowerCamelCase						:    int=None,  **lowerCamelCase						:    Optional[int]  ):

								'''simple docstring'''
								lowercase__    =			return_dict if return_dict is not None else self.config.use_return_dict
								lowercase__    =			(
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								lowercase__    =			output_attentions if output_attentions is not None else self.config.output_attentions

								if output_attentions:
												raise ValueError('''Cannot output attentions for timm backbones at the moment'''  )

								if output_hidden_states:
												# We modify the return layers to include all the stages of the backbone
												lowercase__    =			self._all_layers
												lowercase__    =			self._backbone(lowerCamelCase,  **lowerCamelCase  )
												lowercase__    =			self._return_layers
												lowercase__    =			tuple(hidden_states[i] for i in self.out_indices  )
								else:
												lowercase__    =			self._backbone(lowerCamelCase,  **lowerCamelCase  )
												lowercase__    =			None

								lowercase__    =			tuple(lowerCamelCase  )
								lowercase__    =			tuple(lowerCamelCase  ) if hidden_states is not None else None

								if not return_dict:
												lowercase__    =			(feature_maps,)
												if output_hidden_states:
																lowercase__    =			output + (hidden_states,)
												return output

								return BackboneOutput(feature_maps=lowerCamelCase,  hidden_states=lowerCamelCase,  attentions=lowerCamelCase  )




