






'''simple docstring'''





import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
			from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
			from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING



def        __snake_case						(__UpperCAmelCase    ):





	"""simple docstring"""
	return 1.0 / (1.0 + np.exp(-_outputs    ))



def        __snake_case						(__UpperCAmelCase    ):





	"""simple docstring"""
	lowerCamelCase_					:  List[str]						=    np.max(_outputs       ,	axis=-1       ,	keepdims=SCREAMING_SNAKE_CASE__    )
	lowerCamelCase_					:  Any						=    np.exp(_outputs - maxes    )
	return shifted_exp / shifted_exp.sum(axis=-1       ,	keepdims=SCREAMING_SNAKE_CASE__    )



class   lowerCAmelCase__ ( __SCREAMING_SNAKE_CASE  ):
			A     				= "sigmoid"
			A     				= "softmax"
			A     				= "none"






@add_end_docstrings(
    __SCREAMING_SNAKE_CASE						,R"\n        return_all_scores (`bool`, *optional*, defaults to `False`):\n            Whether to return all prediction scores or just the one of the predicted class.\n        function_to_apply (`str`, *optional*, defaults to `\"default\"`):\n            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n              has several labels, will apply the softmax function on the output.\n            - `\"sigmoid\"`: Applies the sigmoid function on the output.\n            - `\"softmax\"`: Applies the softmax function on the output.\n            - `\"none\"`: Does not apply any function on the output.\n    "						,)
class   lowerCAmelCase__ ( __SCREAMING_SNAKE_CASE  ):
			A     				= False
			A     				= ClassificationFunction.NONE

			def __init__(					self					:		List[Any]     ,		**UpperCamelCase_					:		Any	) ->  List[Any]:







				"""simple docstring"""






				super().__init__(**UpperCamelCase__	)

				self.check_model_type(
				    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
				    if self.framework == '''tf'''
				    else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING	)

			def 						__UpperCamelCase				(					self					:		int     ,		UpperCamelCase_					:		Union[str, Any]=None     ,		UpperCamelCase_					:		List[str]=None     ,		UpperCamelCase_					:		Union[str, Any]=""     ,		**UpperCamelCase_					:		Optional[int]	) ->  Any:







				"""simple docstring"""






				lowerCamelCase_					:  Optional[int]						=    tokenizer_kwargs

				lowerCamelCase_					:  Tuple						=    {}
				if hasattr(self.model.config     ,		'''return_all_scores'''	) and return_all_scores is None:
					lowerCamelCase_					:  int						=    self.model.config.return_all_scores

				if isinstance(UpperCamelCase__     ,		UpperCamelCase__	) or top_k is None:
					lowerCamelCase_					:  int						=    top_k
					lowerCamelCase_					:  Union[str, Any]						=    False
				elif return_all_scores is not None:
					warnings.warn(
					    '''`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'''
					    ''' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.'''     ,		UpperCamelCase__     ,		)
					if return_all_scores:
						lowerCamelCase_					:  Optional[Any]						=    None
					else:
						lowerCamelCase_					:  Union[str, Any]						=    1

				if isinstance(UpperCamelCase__     ,		UpperCamelCase__	):
					lowerCamelCase_					:  int						=    ClassificationFunction[function_to_apply.upper()]

				if function_to_apply is not None:
					lowerCamelCase_					:  Optional[int]						=    function_to_apply
				return preprocess_params, {}, postprocess_params

			def __call__(					self					:		Any     ,		*UpperCamelCase_					:		int     ,		**UpperCamelCase_					:		int	) ->  str:







				"""simple docstring"""






				lowerCamelCase_					:  Optional[Any]						=    super().__call__(*UpperCamelCase__     ,		**UpperCamelCase__	)
				# TODO try and retrieve it in a nicer way from _sanitize_parameters.
				lowerCamelCase_					:  Optional[int]						=    '''top_k''' not in kwargs
				if isinstance(args[0]     ,		UpperCamelCase__	) and _legacy:
					# This pipeline is odd, and return a list when single item is run
					return [result]
				else:
					return result

			def 						__UpperCamelCase				(					self					:		List[Any]     ,		UpperCamelCase_					:		Optional[Any]     ,		**UpperCamelCase_					:		int	) ->  Dict[str, GenericTensor]:







				"""simple docstring"""






				lowerCamelCase_					:  Optional[int]						=    self.framework
				if isinstance(UpperCamelCase__     ,		UpperCamelCase__	):
					return self.tokenizer(**UpperCamelCase__     ,		return_tensors=UpperCamelCase__     ,		**UpperCamelCase__	)
				elif isinstance(UpperCamelCase__     ,		UpperCamelCase__	) and len(UpperCamelCase__	) == 1 and isinstance(inputs[0]     ,		UpperCamelCase__	) and len(inputs[0]	) == 2:
					# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
					return self.tokenizer(
					    text=inputs[0][0]     ,		text_pair=inputs[0][1]     ,		return_tensors=UpperCamelCase__     ,		**UpperCamelCase__	)
				elif isinstance(UpperCamelCase__     ,		UpperCamelCase__	):
					# This is likely an invalid usage of the pipeline attempting to pass text pairs.
					raise ValueError(
					    '''The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'''
					    ''' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.'''	)
				return self.tokenizer(UpperCamelCase__     ,		return_tensors=UpperCamelCase__     ,		**UpperCamelCase__	)

			def 						__UpperCamelCase				(					self					:		List[str]     ,		UpperCamelCase_					:		Tuple	) ->  int:







				"""simple docstring"""






				return self.model(**UpperCamelCase__	)





			def 						__UpperCamelCase				(					self					:		Union[str, Any]     ,		UpperCamelCase_					:		Tuple     ,		UpperCamelCase_					:		Optional[Any]=None     ,		UpperCamelCase_					:		Optional[int]=1     ,		UpperCamelCase_					:		List[Any]=True	) ->  List[str]:







				"""simple docstring"""






				if function_to_apply is None:
					if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
						lowerCamelCase_					:  Any						=    ClassificationFunction.SIGMOID
					elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
						lowerCamelCase_					:  int						=    ClassificationFunction.SOFTMAX
					elif hasattr(self.model.config     ,		'''function_to_apply'''	) and function_to_apply is None:
						lowerCamelCase_					:  List[Any]						=    self.model.config.function_to_apply
					else:
						lowerCamelCase_					:  List[Any]						=    ClassificationFunction.NONE

				lowerCamelCase_					:  Tuple						=    model_outputs['''logits'''][0]
				lowerCamelCase_					:  str						=    outputs.numpy()

				if function_to_apply == ClassificationFunction.SIGMOID:
					lowerCamelCase_					:  int						=    sigmoid(UpperCamelCase__	)
				elif function_to_apply == ClassificationFunction.SOFTMAX:
					lowerCamelCase_					:  Union[str, Any]						=    softmax(UpperCamelCase__	)
				elif function_to_apply == ClassificationFunction.NONE:
					lowerCamelCase_					:  List[str]						=    outputs
				else:
					raise ValueError(F"""Unrecognized `function_to_apply` argument: {function_to_apply}"""	)

				if top_k == 1 and _legacy:
					return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

				lowerCamelCase_					:  Tuple						=    [
				    {'''label''': self.model.config.idalabel[i], '''score''': score.item()} for i, score in enumerate(UpperCamelCase__	)
				]
				if not _legacy:
					dict_scores.sort(key=lambda UpperCamelCase_	: x["score"]     ,		reverse=UpperCamelCase__	)
					if top_k is not None:
						lowerCamelCase_					:  Tuple						=    dict_scores[:top_k]
				return dict_scores








'''simple docstring'''
import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


lowercase__						       =      get_logger(__name__)






class 	snake_case__       (      enum.Enum					):







		"""simple docstring"""

		lowerCamelCase     							=						"""all_checks"""
		lowerCamelCase     							=						"""basic_checks"""
		lowerCamelCase     							=						"""no_checks"""







class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""








class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""








class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""








class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""








def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__=None			)  ->							str:


		'''simple docstring'''


		if expected_checksums is None:
				logger.info('''Unable to verify checksums.'''			)
				return
		if len(set(SCREAMING_SNAKE_CASE__			) - set(SCREAMING_SNAKE_CASE__			)			) > 0:
				raise ExpectedMoreDownloadedFiles(str(set(SCREAMING_SNAKE_CASE__			) - set(SCREAMING_SNAKE_CASE__			)			)			)
		if len(set(SCREAMING_SNAKE_CASE__			) - set(SCREAMING_SNAKE_CASE__			)			) > 0:
				raise UnexpectedDownloadedFile(str(set(SCREAMING_SNAKE_CASE__			) - set(SCREAMING_SNAKE_CASE__			)			)			)
		snake_case  :       Any     			=						[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
		snake_case  :       Union[str, Any]     			=						''' for ''' + verification_name if verification_name is not None else ''''''
		if len(SCREAMING_SNAKE_CASE__			) > 0:
				raise NonMatchingChecksumError(
				    F'Checksums didn\'t match{for_verification_name}:\n'
				    F'{bad_urls}\n'
				    '''Set `verification_mode=\'no_checks\'` to skip checksums verification and ignore this error'''			)
		logger.info('''All the checksums matched successfully''' + for_verification_name			)






class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""








class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""








class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""








class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""








def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							List[str]:


		'''simple docstring'''


		if expected_splits is None:
				logger.info('''Unable to verify splits sizes.'''			)
				return
		if len(set(SCREAMING_SNAKE_CASE__			) - set(SCREAMING_SNAKE_CASE__			)			) > 0:
				raise ExpectedMoreSplits(str(set(SCREAMING_SNAKE_CASE__			) - set(SCREAMING_SNAKE_CASE__			)			)			)
		if len(set(SCREAMING_SNAKE_CASE__			) - set(SCREAMING_SNAKE_CASE__			)			) > 0:
				raise UnexpectedSplits(str(set(SCREAMING_SNAKE_CASE__			) - set(SCREAMING_SNAKE_CASE__			)			)			)
		snake_case  :       Dict     			=						[
		    {'''expected''': expected_splits[name], '''recorded''': recorded_splits[name]}
		    for name in expected_splits
		    if expected_splits[name].num_examples != recorded_splits[name].num_examples
		]
		if len(SCREAMING_SNAKE_CASE__			) > 0:
				raise NonMatchingSplitsSizesError(str(SCREAMING_SNAKE_CASE__			)			)
		logger.info('''All the splits matched successfully.'''			)




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ = True			)  ->							dict:


		'''simple docstring'''


		if record_checksum:
				snake_case  :       Any     			=						shaaaa()
				with open(SCREAMING_SNAKE_CASE__ ,       '''rb'''			) as f:
						for chunk in iter(lambda: f.read(1 << 20			) ,       B''''''			):
								m.update(SCREAMING_SNAKE_CASE__			)
						snake_case  :       Any     			=						m.hexdigest()
		else:
				snake_case  :       Tuple     			=						None
		return {"num_bytes": os.path.getsize(SCREAMING_SNAKE_CASE__			), "checksum": checksum}




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							Dict:


		'''simple docstring'''


		if dataset_size and config.IN_MEMORY_MAX_SIZE:
				return dataset_size < config.IN_MEMORY_MAX_SIZE
		else:
				return False


