






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.
A__	:			Union[str, Any]			   = {'LayoutLMv2Config', 'LayoutLMv3Config'}
@is_pipeline_test
class       _UpperCAmelCase					(       unittest.TestCase	):

				"""simple docstring"""




				lowercase__											=       MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
				lowercase__											=       TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

				if model_mapping is not None:
								lowercase__											=       {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
				if tf_model_mapping is not None:
								lowercase__											=       {
								    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
								}




				def 				lowercase__   (     self						:    Optional[Any],  lowerCamelCase						:    Tuple,  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    int  ):

								'''simple docstring'''
								lowercase__    =			ZeroShotClassificationPipeline(
								    model=lowerCamelCase,  tokenizer=lowerCamelCase,  candidate_labels=['''polics''', '''health''']  )
								return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]




				def 				lowercase__   (     self						:    int,  lowerCamelCase						:    Optional[int],  lowerCamelCase						:    Optional[int]  ):

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

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

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

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

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

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

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

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

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

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

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

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

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

								self.run_entailment_id(lowerCamelCase  )




				def 				lowercase__   (     self						:    Union[str, Any],  lowerCamelCase						:    Pipeline  ):

								'''simple docstring'''
								lowercase__    =			zero_shot_classifier.model.config
								lowercase__    =			config.labelaid
								lowercase__    =			zero_shot_classifier.entailment_id

								lowercase__    =			{'''LABEL_0''': 0, '''LABEL_1''': 1, '''LABEL_2''': 2}
								self.assertEqual(zero_shot_classifier.entailment_id,  -1  )

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

								lowercase__    =			{'''ENTAIL''': 0, '''NON-ENTAIL''': 1}
								self.assertEqual(zero_shot_classifier.entailment_id,  0  )

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

								lowercase__    =			original_labelaid
								self.assertEqual(lowerCamelCase,  zero_shot_classifier.entailment_id  )




				@require_torch
				def 				lowercase__   (     self						:    Any  ):

								'''simple docstring'''
								lowercase__    =			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 				lowercase__   (     self						:    Any  ):

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

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




				@require_tf
				def 				lowercase__   (     self						:    Dict  ):

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

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




				@slow
				@require_torch
				def 				lowercase__   (     self						:    Tuple  ):

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

								self.assertEqual(
								    nested_simplify(lowerCamelCase  ),  {
								        '''sequence''': '''Who are you voting for in 2020?''',
								        '''labels''': ['''politics''', '''public health''', '''science'''],
								        '''scores''': [0.976, 0.015, 0.009],
								    },  )
								lowercase__    =			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=lowerCamelCase,  )
								self.assertEqual(
								    nested_simplify(lowerCamelCase  ),  {
								        '''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 				lowercase__   (     self						:    Tuple  ):

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

								self.assertEqual(
								    nested_simplify(lowerCamelCase  ),  {
								        '''sequence''': '''Who are you voting for in 2020?''',
								        '''labels''': ['''politics''', '''public health''', '''science'''],
								        '''scores''': [0.976, 0.015, 0.009],
								    },  )
								lowercase__    =			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=lowerCamelCase,  )
								self.assertEqual(
								    nested_simplify(lowerCamelCase  ),  {
								        '''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


A__	:			List[Any]			   = logging.get_logger(__name__)

A__	:			str			   = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

A__	:			int			   = {
    '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'
        ),
    },
}

A__	:			Optional[Any]			   = {
    '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,
}


A__	:			List[str]			   = {
    '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       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				lowercase__											=       VOCAB_FILES_NAMES
				lowercase__											=       PRETRAINED_VOCAB_FILES_MAP
				lowercase__											=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowercase__											=       PRETRAINED_INIT_CONFIGURATION
				lowercase__											=       ["""input_ids""", """attention_mask"""]
				lowercase__											=       DistilBertTokenizer




				def __init__(     self						:    List[Any],  lowerCamelCase						:    List[Any]=None,  lowerCamelCase						:    Dict=None,  lowerCamelCase						:    str=True,  lowerCamelCase						:    Optional[int]="[UNK]",  lowerCamelCase						:    Optional[Any]="[SEP]",  lowerCamelCase						:    List[Any]="[PAD]",  lowerCamelCase						:    Any="[CLS]",  lowerCamelCase						:    Union[str, Any]="[MASK]",  lowerCamelCase						:    str=True,  lowerCamelCase						:    int=None,  **lowerCamelCase						:    Union[str, Any],  ):

								'''simple docstring'''
								super().__init__(
								    lowerCamelCase,  tokenizer_file=lowerCamelCase,  do_lower_case=lowerCamelCase,  unk_token=lowerCamelCase,  sep_token=lowerCamelCase,  pad_token=lowerCamelCase,  cls_token=lowerCamelCase,  mask_token=lowerCamelCase,  tokenize_chinese_chars=lowerCamelCase,  strip_accents=lowerCamelCase,  **lowerCamelCase,  )

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

								lowercase__    =			do_lower_case




				def 				lowercase__   (     self						:    str,  lowerCamelCase						:    Optional[Any],  lowerCamelCase						:    List[Any]=None  ):

								'''simple docstring'''
								lowercase__    =			[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 				lowercase__   (     self						:    Union[str, Any],  lowerCamelCase						:    List[int],  lowerCamelCase						:    Optional[List[int]] = None  ):

								'''simple docstring'''
								lowercase__    =			[self.sep_token_id]
								lowercase__    =			[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 				lowercase__   (     self						:    str,  lowerCamelCase						:    str,  lowerCamelCase						:    Optional[str] = None  ):

								'''simple docstring'''
								lowercase__    =			self._tokenizer.model.save(lowerCamelCase,  name=lowerCamelCase  )
								return tuple(lowerCamelCase  )




