

import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
lowerCAmelCase   :      List[str]     =     {'''LayoutLMv2Config''', '''LayoutLMv3Config'''}
@is_pipeline_test
class        _A						(     unittest.TestCase):
  SCREAMING_SNAKE_CASE			:					Union[str, Any]				=     MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
  SCREAMING_SNAKE_CASE			:					List[str]				=     TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

  if model_mapping is not None:
    SCREAMING_SNAKE_CASE			:					str				=     {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
  if tf_model_mapping is not None:
    SCREAMING_SNAKE_CASE			:					List[str]				=     {
        config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
    }

  def        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Any							=   ZeroShotClassificationPipeline(
            model=__snake_case       ,    tokenizer=__snake_case       ,    candidate_labels=['polics', 'health']    )
        return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]

  def        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     str							=   classifier('Who are you voting for in 2020?'       ,    candidate_labels='politics'    )
        self.assertEqual(__snake_case       ,    {'sequence': ANY(__snake_case    ), 'labels': [ANY(__snake_case    )], 'scores': [ANY(__snake_case    )]}    )

        # No kwarg
        SCREAMING_SNAKE_CASE_ :     List[str]							=   classifier('Who are you voting for in 2020?'       ,    ['politics']    )
        self.assertEqual(__snake_case       ,    {'sequence': ANY(__snake_case    ), 'labels': [ANY(__snake_case    )], 'scores': [ANY(__snake_case    )]}    )

        SCREAMING_SNAKE_CASE_ :     str							=   classifier('Who are you voting for in 2020?'       ,    candidate_labels=['politics']    )
        self.assertEqual(__snake_case       ,    {'sequence': ANY(__snake_case    ), 'labels': [ANY(__snake_case    )], 'scores': [ANY(__snake_case    )]}    )

        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   classifier('Who are you voting for in 2020?'       ,    candidate_labels='politics, public health'    )
        self.assertEqual(
            __snake_case       ,    {'sequence': ANY(__snake_case    ), 'labels': [ANY(__snake_case    ), ANY(__snake_case    )], 'scores': [ANY(__snake_case    ), ANY(__snake_case    )]}    )
        self.assertAlmostEqual(sum(nested_simplify(outputs['scores']    )    )       ,    1.0    )

        SCREAMING_SNAKE_CASE_ :     List[str]							=   classifier('Who are you voting for in 2020?'       ,    candidate_labels=['politics', 'public health']    )
        self.assertEqual(
            __snake_case       ,    {'sequence': ANY(__snake_case    ), 'labels': [ANY(__snake_case    ), ANY(__snake_case    )], 'scores': [ANY(__snake_case    ), ANY(__snake_case    )]}    )
        self.assertAlmostEqual(sum(nested_simplify(outputs['scores']    )    )       ,    1.0    )

        SCREAMING_SNAKE_CASE_ :     Optional[int]							=   classifier(
            'Who are you voting for in 2020?'       ,    candidate_labels='politics'       ,    hypothesis_template='This text is about {}'    )
        self.assertEqual(__snake_case       ,    {'sequence': ANY(__snake_case    ), 'labels': [ANY(__snake_case    )], 'scores': [ANY(__snake_case    )]}    )

        # https://github.com/huggingface/transformers/issues/13846
        SCREAMING_SNAKE_CASE_ :     List[Any]							=   classifier(['I am happy']       ,    ['positive', 'negative']    )
        self.assertEqual(
            __snake_case       ,    [
                {'sequence': ANY(__snake_case    ), 'labels': [ANY(__snake_case    ), ANY(__snake_case    )], 'scores': [ANY(__snake_case    ), ANY(__snake_case    )]}
                for i in range(1    )
            ]       ,    )
        SCREAMING_SNAKE_CASE_ :     str							=   classifier(['I am happy', 'I am sad']       ,    ['positive', 'negative']    )
        self.assertEqual(
            __snake_case       ,    [
                {'sequence': ANY(__snake_case    ), 'labels': [ANY(__snake_case    ), ANY(__snake_case    )], 'scores': [ANY(__snake_case    ), ANY(__snake_case    )]}
                for i in range(2    )
            ]       ,    )

        with self.assertRaises(__snake_case    ):
              classifier(''       ,    candidate_labels='politics'    )

        with self.assertRaises(__snake_case    ):
              classifier(__snake_case       ,    candidate_labels='politics'    )

        with self.assertRaises(__snake_case    ):
              classifier('Who are you voting for in 2020?'       ,    candidate_labels=''    )

        with self.assertRaises(__snake_case    ):
              classifier('Who are you voting for in 2020?'       ,    candidate_labels=__snake_case    )

        with self.assertRaises(__snake_case    ):
              classifier(
                  'Who are you voting for in 2020?'       ,    candidate_labels='politics'       ,    hypothesis_template='Not formatting template'       ,    )

        with self.assertRaises(__snake_case    ):
              classifier(
                  'Who are you voting for in 2020?'       ,    candidate_labels='politics'       ,    hypothesis_template=__snake_case       ,    )

        self.run_entailment_id(__snake_case    )

  def        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Tuple							=   zero_shot_classifier.model.config
        SCREAMING_SNAKE_CASE_ :     List[Any]							=   config.labelaid
        SCREAMING_SNAKE_CASE_ :     Any							=   zero_shot_classifier.entailment_id

        SCREAMING_SNAKE_CASE_ :     List[Any]							=   {'LABEL_0': 0, 'LABEL_1': 1, 'LABEL_2': 2}
        self.assertEqual(zero_shot_classifier.entailment_id       ,    -1    )

        SCREAMING_SNAKE_CASE_ :     Dict							=   {'entailment': 0, 'neutral': 1, 'contradiction': 2}
        self.assertEqual(zero_shot_classifier.entailment_id       ,    0    )

        SCREAMING_SNAKE_CASE_ :     List[str]							=   {'ENTAIL': 0, 'NON-ENTAIL': 1}
        self.assertEqual(zero_shot_classifier.entailment_id       ,    0    )

        SCREAMING_SNAKE_CASE_ :     Tuple							=   {'ENTAIL': 2, 'NEUTRAL': 1, 'CONTR': 0}
        self.assertEqual(zero_shot_classifier.entailment_id       ,    2    )

        SCREAMING_SNAKE_CASE_ :     List[str]							=   original_labelaid
        self.assertEqual(__snake_case       ,    zero_shot_classifier.entailment_id    )

  @require_torch
  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Dict							=   pipeline(
            'zero-shot-classification'       ,    model='sshleifer/tiny-distilbert-base-cased-distilled-squad'       ,    framework='pt'       ,    )
        # There was a regression in 4.10 for this
        # Adding a test so we don't make the mistake again.
        # https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
        zero_shot_classifier(
            'Who are you voting for in 2020?' * 100       ,    candidate_labels=['politics', 'public health', 'science']    )

  @require_torch
  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     List[Any]							=   pipeline(
            'zero-shot-classification'       ,    model='sshleifer/tiny-distilbert-base-cased-distilled-squad'       ,    framework='pt'       ,    )
        SCREAMING_SNAKE_CASE_ :     str							=   zero_shot_classifier(
            'Who are you voting for in 2020?'       ,    candidate_labels=['politics', 'public health', 'science']    )

        self.assertEqual(
            nested_simplify(__snake_case    )       ,    {
                'sequence': 'Who are you voting for in 2020?',
                'labels': ['science', 'public health', 'politics'],
                'scores': [0.333, 0.333, 0.333],
            }       ,    )

  @require_tf
  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   pipeline(
            'zero-shot-classification'       ,    model='sshleifer/tiny-distilbert-base-cased-distilled-squad'       ,    framework='tf'       ,    )
        SCREAMING_SNAKE_CASE_ :     List[str]							=   zero_shot_classifier(
            'Who are you voting for in 2020?'       ,    candidate_labels=['politics', 'public health', 'science']    )

        self.assertEqual(
            nested_simplify(__snake_case    )       ,    {
                'sequence': 'Who are you voting for in 2020?',
                'labels': ['science', 'public health', 'politics'],
                'scores': [0.333, 0.333, 0.333],
            }       ,    )

  @slow
  @require_torch
  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     str							=   pipeline('zero-shot-classification'       ,    model='roberta-large-mnli'       ,    framework='pt'    )
        SCREAMING_SNAKE_CASE_ :     Any							=   zero_shot_classifier(
            'Who are you voting for in 2020?'       ,    candidate_labels=['politics', 'public health', 'science']    )

        self.assertEqual(
            nested_simplify(__snake_case    )       ,    {
                'sequence': 'Who are you voting for in 2020?',
                'labels': ['politics', 'public health', 'science'],
                'scores': [0.976, 0.015, 0.009],
            }       ,    )
        SCREAMING_SNAKE_CASE_ :     Tuple							=   zero_shot_classifier(
            'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'
            ' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'
            ' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'
            ' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'
            ' machine translation tasks show these models to be superior in quality while being more parallelizable'
            ' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'
            ' English-to-German translation task, improving over the existing best results, including ensembles by'
            ' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'
            ' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'
            ' fraction of the training costs of the best models from the literature. We show that the Transformer'
            ' generalizes well to other tasks by applying it successfully to English constituency parsing both with'
            ' large and limited training data.'       ,    candidate_labels=['machine learning', 'statistics', 'translation', 'vision']       ,    multi_label=__snake_case       ,    )
        self.assertEqual(
            nested_simplify(__snake_case    )       ,    {
                'sequence': (
                    'The dominant sequence transduction models are based on complex recurrent or convolutional neural'
                    ' networks in an encoder-decoder configuration. The best performing models also connect the'
                    ' encoder and decoder through an attention mechanism. We propose a new simple network'
                    ' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'
                    ' and convolutions entirely. Experiments on two machine translation tasks show these models to be'
                    ' superior in quality while being more parallelizable and requiring significantly less time to'
                    ' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'
                    ' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'
                    ' English-to-French translation task, our model establishes a new single-model state-of-the-art'
                    ' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'
                    ' costs of the best models from the literature. We show that the Transformer generalizes well to'
                    ' other tasks by applying it successfully to English constituency parsing both with large and'
                    ' limited training data.'
                ),
                'labels': ['translation', 'machine learning', 'vision', 'statistics'],
                'scores': [0.817, 0.713, 0.018, 0.018],
            }       ,    )



  @slow
  @require_tf
  def        UpperCAmelCase			( self    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   pipeline('zero-shot-classification'       ,    model='roberta-large-mnli'       ,    framework='tf'    )
        SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   zero_shot_classifier(
            'Who are you voting for in 2020?'       ,    candidate_labels=['politics', 'public health', 'science']    )

        self.assertEqual(
            nested_simplify(__snake_case    )       ,    {
                'sequence': 'Who are you voting for in 2020?',
                'labels': ['politics', 'public health', 'science'],
                'scores': [0.976, 0.015, 0.009],
            }       ,    )
        SCREAMING_SNAKE_CASE_ :     List[str]							=   zero_shot_classifier(
            'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'
            ' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'
            ' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'
            ' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'
            ' machine translation tasks show these models to be superior in quality while being more parallelizable'
            ' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'
            ' English-to-German translation task, improving over the existing best results, including ensembles by'
            ' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'
            ' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'
            ' fraction of the training costs of the best models from the literature. We show that the Transformer'
            ' generalizes well to other tasks by applying it successfully to English constituency parsing both with'
            ' large and limited training data.'       ,    candidate_labels=['machine learning', 'statistics', 'translation', 'vision']       ,    multi_label=__snake_case       ,    )
        self.assertEqual(
            nested_simplify(__snake_case    )       ,    {
                'sequence': (
                    'The dominant sequence transduction models are based on complex recurrent or convolutional neural'
                    ' networks in an encoder-decoder configuration. The best performing models also connect the'
                    ' encoder and decoder through an attention mechanism. We propose a new simple network'
                    ' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'
                    ' and convolutions entirely. Experiments on two machine translation tasks show these models to be'
                    ' superior in quality while being more parallelizable and requiring significantly less time to'
                    ' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'
                    ' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'
                    ' English-to-French translation task, our model establishes a new single-model state-of-the-art'
                    ' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'
                    ' costs of the best models from the literature. We show that the Transformer generalizes well to'
                    ' other tasks by applying it successfully to English constituency parsing both with large and'
                    ' limited training data.'
                ),
                'labels': ['translation', 'machine learning', 'vision', 'statistics'],
                'scores': [0.817, 0.713, 0.018, 0.018],
            }       ,    )










import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer


snake_case							:       Dict     			=      logging.get_logger(__name__)

snake_case							:       Dict     			=      {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case							:       List[Any]     			=      {
    '''vocab_file''': {
        '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt''',
        '''distilbert-base-uncased-distilled-squad''': (
            '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt'''
        ),
        '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt''',
        '''distilbert-base-cased-distilled-squad''': (
            '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt'''
        ),
        '''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt''',
        '''distilbert-base-multilingual-cased''': (
            '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json''',
        '''distilbert-base-uncased-distilled-squad''': (
            '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json'''
        ),
        '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json''',
        '''distilbert-base-cased-distilled-squad''': (
            '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json'''
        ),
        '''distilbert-base-german-cased''': (
            '''https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json'''
        ),
        '''distilbert-base-multilingual-cased''': (
            '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json'''
        ),
    },
}

snake_case							:       int     			=      {
    '''distilbert-base-uncased''': 5_12,
    '''distilbert-base-uncased-distilled-squad''': 5_12,
    '''distilbert-base-cased''': 5_12,
    '''distilbert-base-cased-distilled-squad''': 5_12,
    '''distilbert-base-german-cased''': 5_12,
    '''distilbert-base-multilingual-cased''': 5_12,
}


snake_case							:       Union[str, Any]     			=      {
    '''distilbert-base-uncased''': {'''do_lower_case''': True},
    '''distilbert-base-uncased-distilled-squad''': {'''do_lower_case''': True},
    '''distilbert-base-cased''': {'''do_lower_case''': False},
    '''distilbert-base-cased-distilled-squad''': {'''do_lower_case''': False},
    '''distilbert-base-german-cased''': {'''do_lower_case''': False},
    '''distilbert-base-multilingual-cased''': {'''do_lower_case''': False},
}







class        snake_case_   (lowerCamelCase_      ):
					UpperCAmelCase__					:    Any          =			VOCAB_FILES_NAMES
					UpperCAmelCase__					:    str          =			PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__					:    int          =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__					:    Tuple          =			PRETRAINED_INIT_CONFIGURATION
					UpperCAmelCase__					:    Optional[Any]          =			['''input_ids''', '''attention_mask''']
					UpperCAmelCase__					:    Optional[int]          =			DistilBertTokenizer
					def __init__(      self   :Dict			,__snake_case   :Dict=None			,__snake_case   :Optional[Any]=None			,__snake_case   :Optional[Any]=True			,__snake_case   :List[Any]="[UNK]"			,__snake_case   :str="[SEP]"			,__snake_case   :List[Any]="[PAD]"			,__snake_case   :Tuple="[CLS]"			,__snake_case   :Optional[int]="[MASK]"			,__snake_case   :Dict=True			,__snake_case   :Dict=None			,**__snake_case   :List[Any]			,)					->    Optional[int]:
												super().__init__(
												    __snake_case			,tokenizer_file=__snake_case			,do_lower_case=__snake_case			,unk_token=__snake_case			,sep_token=__snake_case			,pad_token=__snake_case			,cls_token=__snake_case			,mask_token=__snake_case			,tokenize_chinese_chars=__snake_case			,strip_accents=__snake_case			,**__snake_case			,)

												a__             =     json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
												if (
												    normalizer_state.get('lowercase'			,__snake_case      ) != do_lower_case
												    or normalizer_state.get('strip_accents'			,__snake_case      ) != strip_accents
												    or normalizer_state.get('handle_chinese_chars'			,__snake_case      ) != tokenize_chinese_chars
												):
																			a__             =     getattr(__snake_case			,normalizer_state.pop('type'      )      )
																			a__             =     do_lower_case
																			a__             =     strip_accents
																			a__             =     tokenize_chinese_chars
																			a__             =     normalizer_class(**__snake_case      )

												a__             =     do_lower_case
					def  lowerCamelCase__(      self   :Any			,__snake_case   :List[str]			,__snake_case   :int=None      )					->    Dict:
												a__             =     [self.cls_token_id] + token_ids_a + [self.sep_token_id]

												if token_ids_a:
																			output += token_ids_a + [self.sep_token_id]

												return output
					def  lowerCamelCase__(      self   :List[str]			,__snake_case   :List[int]			,__snake_case   :Optional[List[int]] = None      )					->    List[int]:
												a__             =     [self.sep_token_id]
												a__             =     [self.cls_token_id]
												if token_ids_a is None:
																			return len(cls + token_ids_a + sep      ) * [0]
												return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]



					def  lowerCamelCase__(      self   :Union[str, Any]			,__snake_case   :str			,__snake_case   :Optional[str] = None      )					->    Tuple[str]:
												a__             =     self._tokenizer.model.save(__snake_case			,name=__snake_case      )
												return tuple(__snake_case      )




