

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 a(lowercase__	):





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





def a(lowercase__	):





	'''simple docstring'''
	snake_case_      		= np.max(_outputs			, axis=-1			, keepdims=lowercase__	)
	snake_case_      		= np.exp(_outputs - maxes	)
	return shifted_exp / shifted_exp.sum(axis=-1			, keepdims=lowercase__	)


class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			__A						  =  """sigmoid"""
			__A						  =  """softmax"""
			__A						  =  """none"""







@add_end_docstrings(
    __snake_case							,						r"""
        return_all_scores (`bool`, *optional*, defaults to `False`):
            Whether to return all prediction scores or just the one of the predicted class.
        function_to_apply (`str`, *optional*, defaults to `\"default\"`):
            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:

            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model
              has several labels, will apply the softmax function on the output.
            - `\"sigmoid\"`: Applies the sigmoid function on the output.
            - `\"softmax\"`: Applies the softmax function on the output.
            - `\"none\"`: Does not apply any function on the output.
    """							,						)
class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			__A						  =  False
			__A						  =  ClassificationFunction.NONE


			def __init__(    self ,				**__UpperCamelCase							):

				"""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 			__lowerCAmelCase (    self ,				__UpperCamelCase=None ,				__UpperCamelCase=None ,				__UpperCamelCase="" ,				**__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= tokenizer_kwargs

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

				if isinstance(__UpperCamelCase ,				__UpperCamelCase							) or top_k is None:
					snake_case_      		= top_k
					snake_case_      		= 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:
						snake_case_      		= None
					else:
						snake_case_      		= 1

				if isinstance(__UpperCamelCase ,				__UpperCamelCase							):
					snake_case_      		= ClassificationFunction[function_to_apply.upper()]

				if function_to_apply is not None:
					snake_case_      		= function_to_apply
				return preprocess_params, {}, postprocess_params


			def __call__(    self ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= super().__call__(*__UpperCamelCase ,				**__UpperCamelCase							)
				# TODO try and retrieve it in a nicer way from _sanitize_parameters.
				snake_case_      		= '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 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= 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 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				return self.model(**__UpperCamelCase							)







			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase=None ,				__UpperCamelCase=1 ,				__UpperCamelCase=True							):

				"""simple docstring"""


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

				snake_case_      		= model_outputs['logits'][0]
				snake_case_      		= outputs.numpy()

				if function_to_apply == ClassificationFunction.SIGMOID:
					snake_case_      		= sigmoid(__UpperCamelCase							)
				elif function_to_apply == ClassificationFunction.SOFTMAX:
					snake_case_      		= softmax(__UpperCamelCase							)
				elif function_to_apply == ClassificationFunction.NONE:
					snake_case_      		= 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()}

				snake_case_      		= [
				    {'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:
						snake_case_      		= dict_scores[:top_k]
				return dict_scores



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


A											=     {
    'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOnnxConfig'],
    'tokenization_perceiver': ['PerceiverTokenizer'],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A											=     ['PerceiverFeatureExtractor']
		A											=     ['PerceiverImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A											=     [
		    'PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'PerceiverForImageClassificationConvProcessing',
		    'PerceiverForImageClassificationFourier',
		    'PerceiverForImageClassificationLearned',
		    'PerceiverForMaskedLM',
		    'PerceiverForMultimodalAutoencoding',
		    'PerceiverForOpticalFlow',
		    'PerceiverForSequenceClassification',
		    'PerceiverLayer',
		    'PerceiverModel',
		    'PerceiverPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
		from .tokenization_perceiver import PerceiverTokenizer

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_perceiver import PerceiverFeatureExtractor
				from .image_processing_perceiver import PerceiverImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_perceiver import (
				    PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PerceiverForImageClassificationConvProcessing,
				    PerceiverForImageClassificationFourier,
				    PerceiverForImageClassificationLearned,
				    PerceiverForMaskedLM,
				    PerceiverForMultimodalAutoencoding,
				    PerceiverForOpticalFlow,
				    PerceiverForSequenceClassification,
				    PerceiverLayer,
				    PerceiverModel,
				    PerceiverPreTrainedModel,
				)

else:
		import sys

		A											=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
