


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_      (    SCREAMING_SNAKE_CASE__					:      Union[str, Any]     ):
       '''simple docstring'''





       return 1.0 / (1.0 + np.exp(-_outputs     ))




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Optional[int]     ):
       '''simple docstring'''





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




class A  (   UpperCamelCase_     ):
      UpperCamelCase__							:  Optional[Any]      ='sigmoid'
      UpperCamelCase__							:  Union[str, Any]      ='softmax'
      UpperCamelCase__							:  Union[str, 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 A  (   UpperCamelCase_     ):
      UpperCamelCase__							:  str      =False
      UpperCamelCase__							:  str      =ClassificationFunction.NONE






      def __init__(					self						:     List[Any]			,   **lowercase_						:     Dict )					->						List[Any]:
             """simple docstring"""




             super().__init__(**lowercase_ )

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






      def        lowerCamelCase       (					self						:     List[Any]			,   lowercase_						:     Optional[Any]=None			,   lowercase_						:     Tuple=None			,   lowercase_						:     int=""			,   **lowercase_						:     Any )					->						List[Any]:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=tokenizer_kwargs

             _lowerCamelCase       :		Any		={}
             if hasattr(self.model.config			,   'return_all_scores' ) and return_all_scores is None:
                    _lowerCamelCase       :		Optional[Any]		=self.model.config.return_all_scores

             if isinstance(lowercase_			,   lowercase_ ) or top_k is None:
                    _lowerCamelCase       :		Union[str, Any]		=top_k
                    _lowerCamelCase       :		int		=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`.'			,   lowercase_			,   )
                    if return_all_scores:
                           _lowerCamelCase       :		List[str]		=None
                    else:
                           _lowerCamelCase       :		List[str]		=1

             if isinstance(lowercase_			,   lowercase_ ):
                    _lowerCamelCase       :		List[str]		=ClassificationFunction[function_to_apply.upper()]

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






      def __call__(					self						:     Tuple			,   *lowercase_						:     Optional[int]			,   **lowercase_						:     Dict )					->						str:
             """simple docstring"""




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






      def        lowerCamelCase       (					self						:     Tuple			,   lowercase_						:     List[str]			,   **lowercase_						:     Any )					->						Dict[str, GenericTensor]:
             """simple docstring"""




             _lowerCamelCase       :		List[Any]		=self.framework
             if isinstance(lowercase_			,   lowercase_ ):
                    return self.tokenizer(**lowercase_			,   return_tensors=lowercase_			,   **lowercase_ )
             elif isinstance(lowercase_			,   lowercase_ ) and len(lowercase_ ) == 1 and isinstance(inputs[0]			,   lowercase_ ) 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=lowercase_			,   **lowercase_ )
             elif isinstance(lowercase_			,   lowercase_ ):
                    # 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(lowercase_			,   return_tensors=lowercase_			,   **lowercase_ )






      def        lowerCamelCase       (					self						:     List[Any]			,   lowercase_						:     int )					->						Optional[Any]:
             """simple docstring"""




             return self.model(**lowercase_ )






      def        lowerCamelCase       (					self						:     Tuple			,   lowercase_						:     Dict			,   lowercase_						:     int=None			,   lowercase_						:     Union[str, Any]=1			,   lowercase_						:     Tuple=True )					->						Optional[int]:
             """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       :		List[Any]		=ClassificationFunction.SIGMOID
                    elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
                           _lowerCamelCase       :		List[str]		=ClassificationFunction.SOFTMAX
                    elif hasattr(self.model.config			,   'function_to_apply' ) and function_to_apply is None:
                           _lowerCamelCase       :		Dict		=self.model.config.function_to_apply
                    else:
                           _lowerCamelCase       :		Optional[Any]		=ClassificationFunction.NONE

             _lowerCamelCase       :		Tuple		=model_outputs['logits'][0]
             _lowerCamelCase       :		int		=outputs.numpy()

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






import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase											=       '▁'

lowerCamelCase											=       get_tests_dir('fixtures/test_sentencepiece.model')




@require_sentencepiece
@require_tokenizers
class A  (   UpperCamelCase_    ,							unittest.TestCase     ):
      UpperCamelCase__							:  Tuple      =BigBirdTokenizer
      UpperCamelCase__							:  Union[str, Any]      =BigBirdTokenizerFast
      UpperCamelCase__							:  Any      =True
      UpperCamelCase__							:  Optional[Any]      =True






      def        lowerCamelCase       (					self						:     List[Any] )					->						Dict:
             """simple docstring"""




             super().setUp()

             _lowerCamelCase       :		List[Any]		=self.tokenizer_class(lowercase_			,   keep_accents=lowercase_ )
             tokenizer.save_pretrained(self.tmpdirname )






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		List[Any]		='<s>'
             _lowerCamelCase       :		Optional[Any]		=1

             self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase_ )			,   lowercase_ )
             self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase_ )			,   lowercase_ )






      def        lowerCamelCase       (					self						:     int )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		Optional[int]		=list(self.get_tokenizer().get_vocab().keys() )

             self.assertEqual(vocab_keys[0]			,   '<unk>' )
             self.assertEqual(vocab_keys[1]			,   '<s>' )
             self.assertEqual(vocab_keys[-1]			,   '[MASK]' )
             self.assertEqual(len(lowercase_ )			,   1004 )






      def        lowerCamelCase       (					self						:     Any )					->						Union[str, Any]:
             """simple docstring"""




             self.assertEqual(self.get_tokenizer().vocab_size			,   1000 )






      def        lowerCamelCase       (					self						:     Any )					->						Dict:
             """simple docstring"""




             if not self.test_rust_tokenizer:
                    return

             _lowerCamelCase       :		Union[str, Any]		=self.get_tokenizer()
             _lowerCamelCase       :		int		=self.get_rust_tokenizer()

             _lowerCamelCase       :		int		='I was born in 92000, and this is falsé.'

             _lowerCamelCase       :		int		=tokenizer.tokenize(lowercase_ )
             _lowerCamelCase       :		List[Any]		=rust_tokenizer.tokenize(lowercase_ )
             self.assertListEqual(lowercase_			,   lowercase_ )

             _lowerCamelCase       :		Any		=tokenizer.encode(lowercase_			,   add_special_tokens=lowercase_ )
             _lowerCamelCase       :		str		=rust_tokenizer.encode(lowercase_			,   add_special_tokens=lowercase_ )
             self.assertListEqual(lowercase_			,   lowercase_ )

             _lowerCamelCase       :		str		=self.get_rust_tokenizer()
             _lowerCamelCase       :		Union[str, Any]		=tokenizer.encode(lowercase_ )
             _lowerCamelCase       :		List[Any]		=rust_tokenizer.encode(lowercase_ )
             self.assertListEqual(lowercase_			,   lowercase_ )






      def        lowerCamelCase       (					self						:     Dict )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		str		=BigBirdTokenizer(lowercase_			,   keep_accents=lowercase_ )

             _lowerCamelCase       :		int		=tokenizer.tokenize('This is a test' )
             self.assertListEqual(lowercase_			,   ['▁This', '▁is', '▁a', '▁t', 'est'] )

             self.assertListEqual(
                 tokenizer.convert_tokens_to_ids(lowercase_ )			,   [285, 46, 10, 170, 382]			,   )

             _lowerCamelCase       :		Optional[Any]		=tokenizer.tokenize('I was born in 92000, and this is falsé.' )
             self.assertListEqual(
                 lowercase_			,   [
                     SPIECE_UNDERLINE + 'I',
                     SPIECE_UNDERLINE + 'was',
                     SPIECE_UNDERLINE + 'b',
                     'or',
                     'n',
                     SPIECE_UNDERLINE + 'in',
                     SPIECE_UNDERLINE + '',
                     '9',
                     '2',
                     '0',
                     '0',
                     '0',
                     ',',
                     SPIECE_UNDERLINE + 'and',
                     SPIECE_UNDERLINE + 'this',
                     SPIECE_UNDERLINE + 'is',
                     SPIECE_UNDERLINE + 'f',
                     'al',
                     's',
                     'é',
                     '.',
                 ]			,   )
             _lowerCamelCase       :		Any		=tokenizer.convert_tokens_to_ids(lowercase_ )
             self.assertListEqual(
                 lowercase_			,   [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]			,   )

             _lowerCamelCase       :		Optional[int]		=tokenizer.convert_ids_to_tokens(lowercase_ )
             self.assertListEqual(
                 lowercase_			,   [
                     SPIECE_UNDERLINE + 'I',
                     SPIECE_UNDERLINE + 'was',
                     SPIECE_UNDERLINE + 'b',
                     'or',
                     'n',
                     SPIECE_UNDERLINE + 'in',
                     SPIECE_UNDERLINE + '',
                     '<unk>',
                     '2',
                     '0',
                     '0',
                     '0',
                     ',',
                     SPIECE_UNDERLINE + 'and',
                     SPIECE_UNDERLINE + 'this',
                     SPIECE_UNDERLINE + 'is',
                     SPIECE_UNDERLINE + 'f',
                     'al',
                     's',
                     '<unk>',
                     '.',
                 ]			,   )






      @cached_property
      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						str:
             """simple docstring"""




             return BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base' )






      @slow
      def        lowerCamelCase       (					self						:     Any )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		='Hello World!'
             _lowerCamelCase       :		Tuple		=[65, 1_8536, 2260, 101, 66]

             self.assertListEqual(lowercase_			,   self.big_tokenizer.encode(lowercase_ ) )






      @slow
      def        lowerCamelCase       (					self						:     str )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		int		=(
                 'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
                 ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
             )
             # fmt: off
             _lowerCamelCase       :		Tuple		=[65, 871, 419, 358, 946, 991, 2521, 452, 358, 1357, 387, 7751, 3536, 112, 985, 456, 126, 865, 938, 5400, 5734, 458, 1368, 467, 786, 2462, 5246, 1159, 633, 865, 4519, 457, 582, 852, 2557, 427, 916, 508, 405, 3_4324, 497, 391, 408, 1_1342, 1244, 385, 100, 938, 985, 456, 574, 362, 1_2597, 3200, 3129, 1172, 66]  # noqa: E231
             # fmt: on
             self.assertListEqual(lowercase_			,   self.big_tokenizer.encode(lowercase_ ) )






      @require_torch
      @slow
      def        lowerCamelCase       (					self						:     Any )					->						Any:
             """simple docstring"""




             import torch

             from transformers import BigBirdConfig, BigBirdModel

             # Build sequence
             _lowerCamelCase       :		Union[str, Any]		=list(self.big_tokenizer.get_vocab().keys() )[:10]
             _lowerCamelCase       :		List[Any]		=' '.join(lowercase_ )
             _lowerCamelCase       :		List[str]		=self.big_tokenizer.encode_plus(lowercase_			,   return_tensors='pt'			,   return_token_type_ids=lowercase_ )
             _lowerCamelCase       :		Optional[int]		=self.big_tokenizer.batch_encode_plus(
                 [sequence + ' ' + sequence]			,   return_tensors='pt'			,   return_token_type_ids=lowercase_ )

             _lowerCamelCase       :		List[str]		=BigBirdConfig(attention_type='original_full' )
             _lowerCamelCase       :		Optional[Any]		=BigBirdModel(lowercase_ )

             assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

             with torch.no_grad():
                    model(**lowercase_ )
                    model(**lowercase_ )






      @slow
      def        lowerCamelCase       (					self						:     Dict )					->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase       :		Dict		=BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base' )
             _lowerCamelCase       :		int		=tokenizer.decode(tokenizer('Paris is the [MASK].' ).input_ids )

             self.assertTrue(decoded_text == '[CLS] Paris is the[MASK].[SEP]' )






      @slow
      def        lowerCamelCase       (					self						:     Optional[int] )					->						List[Any]:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		={'input_ids': [[65, 3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114, 66], [65, 448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
             # fmt: on

             self.tokenizer_integration_test_util(
                 expected_encoding=lowercase_			,   model_name='google/bigbird-roberta-base'			,   revision='215c99f1600e06f83acce68422f2035b2b5c3510'			,   )


