



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						(       __UpperCamelCase					)						->      str:
			return 1.0 / (1.0 + np.exp(-_outputs					))


def A						(       __UpperCamelCase					)						->      Any:
			A__       =    np.max(_outputs ,	axis=-1 ,	keepdims=__UpperCamelCase					)
			A__       =    np.exp(_outputs - maxes					)
			return shifted_exp / shifted_exp.sum(axis=-1 ,	keepdims=__UpperCamelCase					)


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		List[Any]			       =    "sigmoid"
					A__				:		Tuple			       =    "softmax"
					A__				:		Any			       =    "none"







@add_end_docstrings(
    UpperCAmelCase_							,     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     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		str			       =    False
					A__				:		Tuple			       =    ClassificationFunction.NONE



					def __init__(     self					:       Optional[int]				,					**_snake_case					:       List[Any]					):






								"""simple docstring"""


								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     _a							(     self					:       Tuple				,					_snake_case					:       int=None				,					_snake_case					:       Dict=None				,					_snake_case					:       List[Any]=""				,					**_snake_case					:       Dict					):






								"""simple docstring"""


								A__       =    tokenizer_kwargs

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

								if isinstance(_snake_case				,					_snake_case					) or top_k is None:
											A__       =    top_k
											A__       =    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:
														A__       =    None
											else:
														A__       =    1

								if isinstance(_snake_case				,					_snake_case					):
											A__       =    ClassificationFunction[function_to_apply.upper()]

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



					def __call__(     self					:       int				,					*_snake_case					:       List[str]				,					**_snake_case					:       Tuple					):






								"""simple docstring"""


								A__       =    super().__call__(*_snake_case				,					**_snake_case					)
								# TODO try and retrieve it in a nicer way from _sanitize_parameters.
								A__       =    '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     _a							(     self					:       List[str]				,					_snake_case					:       Tuple				,					**_snake_case					:       Any					):






								"""simple docstring"""


								A__       =    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     _a							(     self					:       Dict				,					_snake_case					:       List[Any]					):






								"""simple docstring"""


								return self.model(**_snake_case					)



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

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

								if function_to_apply == ClassificationFunction.SIGMOID:
											A__       =    sigmoid(_snake_case					)
								elif function_to_apply == ClassificationFunction.SOFTMAX:
											A__       =    softmax(_snake_case					)
								elif function_to_apply == ClassificationFunction.NONE:
											A__       =    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()}

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









import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
							import optax
							from flax.training.common_utils import onehot

							from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
							from transformers.models.ta.modeling_flax_ta import shift_tokens_right


@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class      __lowerCAmelCase     (		unittest.TestCase      ):
					"""simple docstring"""






					@slow
					def     _a							(     self					:       int					):






								"""simple docstring"""


								A__       =    FlaxMTaForConditionalGeneration.from_pretrained('google/mt5-small'					)
								A__       =    AutoTokenizer.from_pretrained('google/mt5-small'					)

								A__       =    tokenizer('Hello there'				,					return_tensors='np'					).input_ids
								A__       =    tokenizer('Hi I am'				,					return_tensors='np'					).input_ids

								A__       =    shift_tokens_right(_snake_case				,					model.config.pad_token_id				,					model.config.decoder_start_token_id					)

								A__       =    model(_snake_case				,					decoder_input_ids=_snake_case					).logits
								A__       =    optax.softmax_cross_entropy(_snake_case				,					onehot(_snake_case				,					logits.shape[-1]					)					).mean()

								A__       =    -(labels.shape[-1] * loss.item())

								A__       =    -84.9127
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE					) < 1E-4					)




