
import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class 	A__   (						_a				):
							lowerCAmelCase__		:						List[str]    			=							["""image_processor""", """tokenizer"""]
							lowerCAmelCase__		:						str    			=							"""AutoImageProcessor"""
							lowerCAmelCase__		:						Optional[int]    			=							"""AutoTokenizer"""





							def __init__( self  :   str     , _UpperCAmelCase  :   Any=None     , _UpperCAmelCase  :   str=None     , **_UpperCAmelCase  :   Dict				)						->		List[Any]:



											"""simple docstring"""


											__lowercase								=					None
											if "feature_extractor" in kwargs:
															warnings.warn(
															    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
															    ' instead.'     , __UpperCAmelCase     , )
															__lowercase								=					kwargs.pop('feature_extractor'				)

											__lowercase								=					image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
															raise ValueError('You need to specify an `image_processor`.'				)
											if tokenizer is None:
															raise ValueError('You need to specify a `tokenizer`.'				)

											super().__init__(__UpperCAmelCase     , __UpperCAmelCase				)
											__lowercase								=					self.image_processor
											__lowercase								=					False





							def __call__( self  :   Optional[int]     , *_UpperCAmelCase  :   List[Any]     , **_UpperCAmelCase  :   int				)						->		str:



											"""simple docstring"""


											if self._in_target_context_manager:
															return self.current_processor(*__UpperCAmelCase     , **__UpperCAmelCase				)

											__lowercase								=					kwargs.pop('images'     , __UpperCAmelCase				)
											__lowercase								=					kwargs.pop('text'     , __UpperCAmelCase				)
											if len(__UpperCAmelCase				) > 0:
															__lowercase								=					args[0]
															__lowercase								=					args[1:]

											if images is None and text is None:
															raise ValueError('You need to specify either an `images` or `text` input to process.'				)

											if images is not None:
															__lowercase								=					self.image_processor(__UpperCAmelCase     , *__UpperCAmelCase     , **__UpperCAmelCase				)
											if text is not None:
															__lowercase								=					self.tokenizer(__UpperCAmelCase     , **__UpperCAmelCase				)

											if text is None:
															return inputs
											elif images is None:
															return encodings
											else:
															__lowercase								=					encodings["input_ids"]
															return inputs





							def 			a__ ( self  :   Optional[int]     , *_UpperCAmelCase  :   List[str]     , **_UpperCAmelCase  :   Optional[int]				)						->		List[str]:



											"""simple docstring"""


											return self.tokenizer.batch_decode(*__UpperCAmelCase     , **__UpperCAmelCase				)





							def 			a__ ( self  :   str     , *_UpperCAmelCase  :   Optional[int]     , **_UpperCAmelCase  :   Dict				)						->		List[Any]:



											"""simple docstring"""


											return self.tokenizer.decode(*__UpperCAmelCase     , **__UpperCAmelCase				)





							@contextmanager
							def 			a__ ( self  :   List[str]				)						->		Optional[int]:



											"""simple docstring"""


											warnings.warn(
											    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
											    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
											    'your images inputs, or in a separate call.'				)
											__lowercase								=					True
											__lowercase								=					self.tokenizer
											yield
											__lowercase								=					self.image_processor
											__lowercase								=					False





							def 			a__ ( self  :   int     , _UpperCAmelCase  :   List[str]     , _UpperCAmelCase  :   Union[str, Any]=False     , _UpperCAmelCase  :   str=None				)						->		Optional[Any]:



											"""simple docstring"""


											if added_vocab is None:
															__lowercase								=					self.tokenizer.get_added_vocab()

											__lowercase								=					{}

											while tokens:
															__lowercase								=					re.search(R'<s_(.*?)>'     , __UpperCAmelCase     , re.IGNORECASE				)
															if start_token is None:
																			break
															__lowercase								=					start_token.group(1				)
															__lowercase								=					re.search(Rf"""</s_{key}>"""     , __UpperCAmelCase     , re.IGNORECASE				)
															__lowercase								=					start_token.group()
															if end_token is None:
																			__lowercase								=					tokens.replace(__UpperCAmelCase     , ''				)
															else:
																			__lowercase								=					end_token.group()
																			__lowercase								=					re.escape(__UpperCAmelCase				)
																			__lowercase								=					re.escape(__UpperCAmelCase				)
																			__lowercase								=					re.search(f"""{start_token_escaped}(.*?){end_token_escaped}"""     , __UpperCAmelCase     , re.IGNORECASE				)
																			if content is not None:
																							__lowercase								=					content.group(1				).strip()
																							if r"<s_" in content and r"</s_" in content:  # non-leaf node
																											__lowercase								=					self.tokenajson(__UpperCAmelCase     , is_inner_value=__UpperCAmelCase     , added_vocab=__UpperCAmelCase				)
																											if value:
																															if len(__UpperCAmelCase				) == 1:
																																			__lowercase								=					value[0]
																															__lowercase								=					value
																							else:  # leaf nodes
																											__lowercase								=					[]
																											for leaf in content.split(R'<sep/>'				):
																															__lowercase								=					leaf.strip()
																															if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																																			__lowercase								=					leaf[1:-2]  # for categorical special tokens
																															output[key].append(__UpperCAmelCase				)
																											if len(output[key]				) == 1:
																															__lowercase								=					output[key][0]

																			__lowercase								=					tokens[tokens.find(__UpperCAmelCase				) + len(__UpperCAmelCase				) :].strip()
																			if tokens[:6] == r"<sep/>":  # non-leaf nodes
																							return [output] + self.tokenajson(tokens[6:]     , is_inner_value=__UpperCAmelCase     , added_vocab=__UpperCAmelCase				)

											if len(__UpperCAmelCase				):
															return [output] if is_inner_value else output
											else:
															return [] if is_inner_value else {"text_sequence": tokens}





							@property
							def 			a__ ( self  :   str				)						->		Optional[Any]:



											"""simple docstring"""


											warnings.warn(
											    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'     , __UpperCAmelCase     , )
											return self.image_processor_class





							@property
							def 			a__ ( self  :   int				)						->		int:



											"""simple docstring"""


											warnings.warn(
											    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'     , __UpperCAmelCase     , )
											return self.image_processor









"""simple docstring"""







import pytest

from datasets import inspect_metric, list_metrics, load_metric


@pytest.fixture
def      lowercase			(				A_       )->      List[Any]:
  '''simple docstring'''







  monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings"  ,   set()       )


@pytest.fixture
def      lowercase			(				A_       )->      Tuple:
  '''simple docstring'''







  class 						_A  :







      """simple docstring"""




      def __init__(					self				:    str				, __UpperCAmelCase				:    int):
        a						:		List[Any]      							=			metric_id






  class 						_A  :







      """simple docstring"""




      UpperCAmelCase       :							Union[str, Any]	=       [MetricMock(_a  ) for metric_id in ["""accuracy""", """mse""", """precision""", """codeparrot/apps_metric"""]]






      def 	__snake_case							(					self				:    List[str]):
        return self._metrics






  monkeypatch.setattr("datasets.inspect.huggingface_hub"  ,   HfhMock()       )







@pytest.mark.parametrize(
    "func, args"  ,   [(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))]       )
def      lowercase			(				A_  ,   A_  ,   A_  ,   A_  ,   A_       )->      Any:
  '''simple docstring'''







  if "tmp_path" in args:
    a						:		Union[str, Any]      							=			tuple(arg if arg != "tmp_path" else tmp_path for arg in args       )
  with pytest.warns(A_  ,   match="https://huggingface.co/docs/evaluate"       ):
    func(*A_       )






