

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    :       int        =							{"LayoutLMv2Config", "LayoutLMv3Config"}





@is_pipeline_test
class  __UpperCAmelCase					(	unittest.TestCase						):
			UpperCamelCase       					=					MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
			UpperCamelCase       					=					TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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



			def 	__magic_name__   (							self							:					Tuple,		__A							:					List[str],		__A							:					Tuple,		__A							:					str						):
					UpperCAmelCase	:							Optional[int]						=			ZeroShotClassificationPipeline(
					    model=__A,		tokenizer=__A,		candidate_labels=['''polics''', '''health''']						)
					return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]



			def 	__magic_name__   (							self							:					Dict,		__A							:					Tuple,		__A							:					Optional[Any]						):
					UpperCAmelCase	:							Dict						=			classifier('''Who are you voting for in 2020?''',		candidate_labels='''politics'''						)
					self.assertEqual(__A,		{'''sequence''': ANY(__A						), '''labels''': [ANY(__A						)], '''scores''': [ANY(__A						)]}						)

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

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

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

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

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

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

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

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

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

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

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

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

					self.run_entailment_id(__A						)



			def 	__magic_name__   (							self							:					str,		__A							:					Pipeline						):
					UpperCAmelCase	:							Dict						=			zero_shot_classifier.model.config
					UpperCAmelCase	:							Optional[Any]						=			config.labelaid
					UpperCAmelCase	:							Any						=			zero_shot_classifier.entailment_id

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

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

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

					UpperCAmelCase	:							List[str]						=			{'''ENTAIL''': 2, '''NEUTRAL''': 1, '''CONTR''': 0}
					self.assertEqual(zero_shot_classifier.entailment_id,		2						)

					UpperCAmelCase	:							List[Any]						=			original_labelaid
					self.assertEqual(__A,		zero_shot_classifier.entailment_id						)



			@require_torch
			def 	__magic_name__   (							self							:					Optional[int]						):
					UpperCAmelCase	:							str						=			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?''' * 1_0_0,		candidate_labels=['''politics''', '''public health''', '''science''']						)



			@require_torch
			def 	__magic_name__   (							self							:					Union[str, Any]						):
					UpperCAmelCase	:							Optional[int]						=			pipeline(
					    '''zero-shot-classification''',		model='''sshleifer/tiny-distilbert-base-cased-distilled-squad''',		framework='''pt''',		)
					UpperCAmelCase	:							str						=			zero_shot_classifier(
					    '''Who are you voting for in 2020?''',		candidate_labels=['''politics''', '''public health''', '''science''']						)

					self.assertEqual(
					    nested_simplify(__A						),		{
					        '''sequence''': '''Who are you voting for in 2020?''',
					        '''labels''': ['''science''', '''public health''', '''politics'''],
					        '''scores''': [0.3_3_3, 0.3_3_3, 0.3_3_3],
					    },		)



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

					self.assertEqual(
					    nested_simplify(__A						),		{
					        '''sequence''': '''Who are you voting for in 2020?''',
					        '''labels''': ['''science''', '''public health''', '''politics'''],
					        '''scores''': [0.3_3_3, 0.3_3_3, 0.3_3_3],
					    },		)



			@slow
			@require_torch
			def 	__magic_name__   (							self							:					Dict						):
					UpperCAmelCase	:							Optional[int]						=			pipeline('''zero-shot-classification''',		model='''roberta-large-mnli''',		framework='''pt'''						)
					UpperCAmelCase	:							Union[str, Any]						=			zero_shot_classifier(
					    '''Who are you voting for in 2020?''',		candidate_labels=['''politics''', '''public health''', '''science''']						)

					self.assertEqual(
					    nested_simplify(__A						),		{
					        '''sequence''': '''Who are you voting for in 2020?''',
					        '''labels''': ['''politics''', '''public health''', '''science'''],
					        '''scores''': [0.9_7_6, 0.0_1_5, 0.0_0_9],
					    },		)
					UpperCAmelCase	:							Dict						=			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=__A,		)
					self.assertEqual(
					    nested_simplify(__A						),		{
					        '''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''': [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
					    },		)





			@slow
			@require_tf
			def 	__magic_name__   (							self							:					Tuple						):
					UpperCAmelCase	:							Tuple						=			pipeline('''zero-shot-classification''',		model='''roberta-large-mnli''',		framework='''tf'''						)
					UpperCAmelCase	:							Tuple						=			zero_shot_classifier(
					    '''Who are you voting for in 2020?''',		candidate_labels=['''politics''', '''public health''', '''science''']						)

					self.assertEqual(
					    nested_simplify(__A						),		{
					        '''sequence''': '''Who are you voting for in 2020?''',
					        '''labels''': ['''politics''', '''public health''', '''science'''],
					        '''scores''': [0.9_7_6, 0.0_1_5, 0.0_0_9],
					    },		)
					UpperCAmelCase	:							Any						=			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=__A,		)
					self.assertEqual(
					    nested_simplify(__A						),		{
					        '''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''': [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
					    },		)








from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

_lowerCamelCase    :       Dict        =							logging.get_logger(__name__)





@add_end_docstrings(lowerCamelCase__						)
class  __UpperCAmelCase					(	lowerCamelCase__						):



   def __init__(							self							:					Optional[Any],		*__A							:					Tuple,		**__A							:					Tuple						):
     super().__init__(*__A,		**__A						)
     self.check_model_type(__A						)



   def 	__magic_name__   (							self							:					Union[str, Any],		__A							:					int=None,		__A							:					Tuple=None,		__A							:					Any=None,		**__A							:					Optional[int]						):
     UpperCAmelCase			,						UpperCAmelCase	:							List[Any]						=			{}, {}
     if padding is not None:
       UpperCAmelCase	:							Optional[int]						=			padding
     if truncation is not None:
       UpperCAmelCase	:							Optional[int]						=			truncation
     if top_k is not None:
       UpperCAmelCase	:							Tuple						=			top_k
     return preprocess_params, {}, postprocess_params



   def __call__(							self							:					Union[str, Any],		__A							:					Union["Image.Image", str],		__A							:					str = None,		**__A							:					Optional[int]						):
     if isinstance(__A,		(Image.Image, str)						) and isinstance(__A,		__A						):
       UpperCAmelCase	:							int						=			{'''image''': image, '''question''': question}
     else:
       UpperCAmelCase	:							str						=			image
     UpperCAmelCase	:							Union[str, Any]						=			super().__call__(__A,		**__A						)
     return results



   def 	__magic_name__   (							self							:					List[str],		__A							:					Union[str, Any],		__A							:					Tuple=False,		__A							:					List[Any]=False						):
     UpperCAmelCase	:							int						=			load_image(inputs['''image''']						)
     UpperCAmelCase	:							List[str]						=			self.tokenizer(
         inputs['''question'''],		return_tensors=self.framework,		padding=__A,		truncation=__A						)
     UpperCAmelCase	:							Union[str, Any]						=			self.image_processor(images=__A,		return_tensors=self.framework						)
     model_inputs.update(__A						)
     return model_inputs



   def 	__magic_name__   (							self							:					Optional[Any],		__A							:					List[Any]						):
     UpperCAmelCase	:							Optional[int]						=			self.model(**__A						)
     return model_outputs





   def 	__magic_name__   (							self							:					Any,		__A							:					List[str],		__A							:					Union[str, Any]=5						):
     if top_k > self.model.config.num_labels:
       UpperCAmelCase	:							Any						=			self.model.config.num_labels

     if self.framework == "pt":
       UpperCAmelCase	:							Any						=			model_outputs.logits.sigmoid()[0]
       UpperCAmelCase			,						UpperCAmelCase	:							Union[str, Any]						=			probs.topk(__A						)
     else:
       raise ValueError(F'''Unsupported framework: {self.framework}'''						)

     UpperCAmelCase	:							str						=			scores.tolist()
     UpperCAmelCase	:							Tuple						=			ids.tolist()
     return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(__A,		__A						)]





