






"""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    (					lowerCamelCase__     :   Union[str, Any]  )    ->     int:
							return 1.0 / (1.0 + np.exp(-_outputs  ))

def 			_snake_case    (					lowerCamelCase__     :   Optional[int]  )    ->     Union[str, Any]:
							lowerCamelCase_	:							Any									=np.max(_outputs    ,						axis=-1    ,						keepdims=_lowerCamelCase  )
							lowerCamelCase_	:							Dict									=np.exp(_outputs - maxes  )
							return shifted_exp / shifted_exp.sum(axis=-1    ,						keepdims=_lowerCamelCase  )







class        snake_case__      (							__lowercase       ):
			_UpperCAmelCase		:Optional[Any]             =			'''sigmoid'''
			_UpperCAmelCase		:Tuple             =			'''softmax'''
			_UpperCAmelCase		:Any             =			'''none'''







@add_end_docstrings(
    __lowercase,       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        snake_case__      (							__lowercase       ):
			_UpperCAmelCase		:Union[str, Any]             =			False
			_UpperCAmelCase		:List[str]             =			ClassificationFunction.NONE


			def __init__(				self					:  int       ,	**snake_case__					:  int ):
										super().__init__(**snake_case__ )

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


			def      UpperCAmelCase__						(				self					:  Dict       ,	snake_case__					:  Dict=None       ,	snake_case__					:  Tuple=None       ,	snake_case__					:  List[str]=""       ,	**snake_case__					:  int ):
										lowerCamelCase_	:							List[Any]									=tokenizer_kwargs

										lowerCamelCase_	:							Union[str, Any]									={}
										if hasattr(self.model.config       ,	"return_all_scores" ) and return_all_scores is None:
																	lowerCamelCase_	:							Tuple									=self.model.config.return_all_scores

										if isinstance(snake_case__       ,	snake_case__ ) or top_k is None:
																	lowerCamelCase_	:							Optional[Any]									=top_k
																	lowerCamelCase_	:							Tuple									=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`."       ,	snake_case__       ,	)
																	if return_all_scores:
																								lowerCamelCase_	:							Any									=None
																	else:
																								lowerCamelCase_	:							Optional[Any]									=1

										if isinstance(snake_case__       ,	snake_case__ ):
																	lowerCamelCase_	:							Any									=ClassificationFunction[function_to_apply.upper()]

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


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


			def      UpperCAmelCase__						(				self					:  str       ,	snake_case__					:  int       ,	**snake_case__					:  List[str] ):
										lowerCamelCase_	:							str									=self.framework
										if isinstance(snake_case__       ,	snake_case__ ):
																	return self.tokenizer(**snake_case__       ,	return_tensors=snake_case__       ,	**snake_case__ )
										elif isinstance(snake_case__       ,	snake_case__ ) and len(snake_case__ ) == 1 and isinstance(inputs[0]       ,	snake_case__ ) 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=snake_case__       ,	**snake_case__ )
										elif isinstance(snake_case__       ,	snake_case__ ):
																	# 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(snake_case__       ,	return_tensors=snake_case__       ,	**snake_case__ )


			def      UpperCAmelCase__						(				self					:  Any       ,	snake_case__					:  str ):
										return self.model(**snake_case__ )


			def      UpperCAmelCase__						(				self					:  Any       ,	snake_case__					:  Dict       ,	snake_case__					:  int=None       ,	snake_case__					:  Tuple=1       ,	snake_case__					:  int=True ):

										if function_to_apply is None:
																	if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
																								lowerCamelCase_	:							Optional[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_	:							Optional[int]									=self.model.config.function_to_apply
																	else:
																								lowerCamelCase_	:							Dict									=ClassificationFunction.NONE

										lowerCamelCase_	:							int									=model_outputs["""logits"""][0]
										lowerCamelCase_	:							List[str]									=outputs.numpy()

										if function_to_apply == ClassificationFunction.SIGMOID:
																	lowerCamelCase_	:							Optional[int]									=sigmoid(snake_case__ )
										elif function_to_apply == ClassificationFunction.SOFTMAX:
																	lowerCamelCase_	:							Optional[Any]									=softmax(snake_case__ )
										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_	:							Optional[int]									=[
										    {"""label""": self.model.config.idalabel[i], """score""": score.item()} for i, score in enumerate(snake_case__ )
										]
										if not _legacy:
																	dict_scores.sort(key=lambda snake_case__ : x["score"]       ,	reverse=snake_case__ )
																	if top_k is not None:
																								lowerCamelCase_	:							str									=dict_scores[:top_k]
										return dict_scores











"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


A__	:    Optional[int]			 = {
    'configuration_speech_to_text': ['SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Speech2TextConfig'],
    'processing_speech_to_text': ['Speech2TextProcessor'],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__	:    Dict			 = ['Speech2TextTokenizer']

try:
						if not is_speech_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__	:    Optional[Any]			 = ['Speech2TextFeatureExtractor']

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__	:    Dict			 = [
						    'TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFSpeech2TextForConditionalGeneration',
						    'TFSpeech2TextModel',
						    'TFSpeech2TextPreTrainedModel',
						]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__	:    Optional[int]			 = [
						    'SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'Speech2TextForConditionalGeneration',
						    'Speech2TextModel',
						    'Speech2TextPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
						from .processing_speech_to_text import SpeechaTextProcessor

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_speech_to_text import SpeechaTextTokenizer

						try:
												if not is_speech_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_speech_to_text import (
												    TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFSpeechaTextForConditionalGeneration,
												    TFSpeechaTextModel,
												    TFSpeechaTextPreTrainedModel,
												)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_speech_to_text import (
												    SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SpeechaTextForConditionalGeneration,
												    SpeechaTextModel,
												    SpeechaTextPreTrainedModel,
												)

else:
						import sys

						A__	:    List[Any]			 = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



