




from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
		import timm


if is_torch_available():
		from torch import Tensor

class        lowercase      (    UpperCAmelCase__     ,	UpperCAmelCase__):
				__lowerCAmelCase    :    int         =   "pixel_values"
				__lowerCAmelCase    :    Dict         =   False
				__lowerCAmelCase    :    Union[str, Any]         =   TimmBackboneConfig







				def __init__(							self   :					List[str]      ,     _lowerCamelCase   :					Dict      ,     **_lowerCamelCase   :					str       ):



					"""simple docstring"""

					requires_backends(self      ,     '''timm'''       )
					super().__init__(lowerCamelCase__       )
					A_  :				Any	   =		config

					if config.backbone is None:
						raise ValueError('''backbone is not set in the config. Please set it to a timm model name.'''       )

					if config.backbone not in timm.list_models():
						raise ValueError(F"""backbone {config.backbone} is not supported by timm."""       )

					if hasattr(lowerCamelCase__      ,     '''out_features'''       ) and config.out_features is not None:
						raise ValueError('''out_features is not supported by TimmBackbone. Please use out_indices instead.'''       )

					A_  :				Optional[Any]	   =		getattr(lowerCamelCase__      ,     '''use_pretrained_backbone'''      ,     lowerCamelCase__       )
					if pretrained is None:
						raise ValueError('''use_pretrained_backbone is not set in the config. Please set it to True or False.'''       )

					# We just take the final layer by default. This matches the default for the transformers models.
					A_  :				int	   =		config.out_indices if getattr(lowerCamelCase__      ,     '''out_indices'''      ,     lowerCamelCase__       ) is not None else (-1,)

					A_  :				List[Any]	   =		timm.create_model(
					    config.backbone      ,     pretrained=lowerCamelCase__      ,     features_only=config.features_only      ,     in_chans=config.num_channels      ,     out_indices=lowerCamelCase__      ,     **lowerCamelCase__      ,     )
					# These are used to control the output of the model when called. If output_hidden_states is True, then
					# return_layers is modified to include all layers.
					A_  :				List[str]	   =		self._backbone.return_layers
					A_  :				Optional[int]	   =		{layer["module"]: str(lowerCamelCase__       ) for i, layer in enumerate(self._backbone.feature_info.info       )}
					super()._init_backbone(lowerCamelCase__       )







				@classmethod
				def 					a_  (							cls   :					List[str]      ,     _lowerCamelCase   :					str      ,     *_lowerCamelCase   :					Tuple      ,     **_lowerCamelCase   :					Optional[Any]       ):



					"""simple docstring"""

					requires_backends(cls      ,     ['''vision''', '''timm''']       )
					from ...models.timm_backbone import TimmBackboneConfig

					A_  :				Any	   =		kwargs.pop('''config'''      ,     TimmBackboneConfig()       )

					A_  :				Dict	   =		kwargs.pop('''use_timm_backbone'''      ,     lowerCamelCase__       )
					if not use_timm:
						raise ValueError('''use_timm_backbone must be True for timm backbones'''       )

					A_  :				str	   =		kwargs.pop('''num_channels'''      ,     config.num_channels       )
					A_  :				Dict	   =		kwargs.pop('''features_only'''      ,     config.features_only       )
					A_  :				str	   =		kwargs.pop('''use_pretrained_backbone'''      ,     config.use_pretrained_backbone       )
					A_  :				Optional[Any]	   =		kwargs.pop('''out_indices'''      ,     config.out_indices       )
					A_  :				Dict	   =		TimmBackboneConfig(
					    backbone=lowerCamelCase__      ,     num_channels=lowerCamelCase__      ,     features_only=lowerCamelCase__      ,     use_pretrained_backbone=lowerCamelCase__      ,     out_indices=lowerCamelCase__      ,     )
					return super()._from_config(lowerCamelCase__      ,     **lowerCamelCase__       )







				def 					a_  (							self   :					Tuple      ,     _lowerCamelCase   :					str       ):



					"""simple docstring"""

					pass







				def 					a_  (							self   :					Union[str, Any]      ,     _lowerCamelCase   :					Tuple      ,     _lowerCamelCase   :					Union[str, Any]=None      ,     _lowerCamelCase   :					List[Any]=None      ,     _lowerCamelCase   :					Union[str, Any]=None      ,     **_lowerCamelCase   :					Dict       ):



					"""simple docstring"""

					A_  :				Any	   =		return_dict if return_dict is not None else self.config.use_return_dict
					A_  :				Optional[int]	   =		(
					    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
					)
					A_  :				Dict	   =		output_attentions if output_attentions is not None else self.config.output_attentions

					if output_attentions:
						raise ValueError('''Cannot output attentions for timm backbones at the moment'''       )

					if output_hidden_states:
						# We modify the return layers to include all the stages of the backbone
						A_  :				Optional[int]	   =		self._all_layers
						A_  :				List[str]	   =		self._backbone(lowerCamelCase__      ,     **lowerCamelCase__       )
						A_  :				List[Any]	   =		self._return_layers
						A_  :				Tuple	   =		tuple(hidden_states[i] for i in self.out_indices       )
					else:
						A_  :				Any	   =		self._backbone(lowerCamelCase__      ,     **lowerCamelCase__       )
						A_  :				Tuple	   =		None

					A_  :				Dict	   =		tuple(lowerCamelCase__       )
					A_  :				Optional[Any]	   =		tuple(lowerCamelCase__       ) if hidden_states is not None else None

					if not return_dict:
						A_  :				Dict	   =		(feature_maps,)
						if output_hidden_states:
							A_  :				List[str]	   =		output + (hidden_states,)
						return output

					return BackboneOutput(feature_maps=lowerCamelCase__      ,     hidden_states=lowerCamelCase__      ,     attentions=lowerCamelCase__       )











"""simple docstring"""

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  :			Optional[Any]        =       {'LayoutLMv2Config', 'LayoutLMv3Config'}

@is_pipeline_test
class        lowercase      (    unittest.TestCase):
				__lowerCAmelCase    :    Optional[Any]         =   MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
				__lowerCAmelCase    :    str         =   TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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







				def 					a_  (							self   :					Optional[Any]      ,     _lowerCamelCase   :					Any      ,     _lowerCamelCase   :					List[Any]      ,     _lowerCamelCase   :					Any       ):



					"""simple docstring"""

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







				def 					a_  (							self   :					List[str]      ,     _lowerCamelCase   :					Any      ,     _lowerCamelCase   :					Union[str, Any]       ):



					"""simple docstring"""

					A_  :				List[Any]	   =		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
					A_  :				Tuple	   =		classifier('''Who are you voting for in 2020?'''      ,     ['''politics''']       )
					self.assertEqual(_lowerCamelCase      ,     {'''sequence''': ANY(_lowerCamelCase       ), '''labels''': [ANY(_lowerCamelCase       )], '''scores''': [ANY(_lowerCamelCase       )]}       )

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

					A_  :				Union[str, Any]	   =		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       )

					A_  :				Tuple	   =		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       )

					A_  :				List[str]	   =		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
					A_  :				str	   =		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       )
					    ]      ,     )
					A_  :				str	   =		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 					a_  (							self   :					Any      ,     _lowerCamelCase   :					Pipeline       ):



					"""simple docstring"""

					A_  :				int	   =		zero_shot_classifier.model.config
					A_  :				Dict	   =		config.labelaid
					A_  :				Optional[int]	   =		zero_shot_classifier.entailment_id

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

					A_  :				Union[str, Any]	   =		{'''entailment''': 0, '''neutral''': 1, '''contradiction''': 2}
					self.assertEqual(zero_shot_classifier.entailment_id      ,     0       )

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

					A_  :				Optional[Any]	   =		{'''ENTAIL''': 2, '''NEUTRAL''': 1, '''CONTR''': 0}
					self.assertEqual(zero_shot_classifier.entailment_id      ,     2       )

					A_  :				List[Any]	   =		original_labelaid
					self.assertEqual(_lowerCamelCase      ,     zero_shot_classifier.entailment_id       )







				@require_torch
				def 					a_  (							self   :					List[Any]       ):



					"""simple docstring"""

					A_  :				List[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_00      ,     candidate_labels=['''politics''', '''public health''', '''science''']       )







				@require_torch
				def 					a_  (							self   :					Dict       ):



					"""simple docstring"""

					A_  :				Optional[Any]	   =		pipeline(
					    '''zero-shot-classification'''      ,     model='''sshleifer/tiny-distilbert-base-cased-distilled-squad'''      ,     framework='''pt'''      ,     )
					A_  :				Optional[Any]	   =		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 					a_  (							self   :					Dict       ):



					"""simple docstring"""

					A_  :				List[str]	   =		pipeline(
					    '''zero-shot-classification'''      ,     model='''sshleifer/tiny-distilbert-base-cased-distilled-squad'''      ,     framework='''tf'''      ,     )
					A_  :				List[str]	   =		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 					a_  (							self   :					int       ):



					"""simple docstring"""

					A_  :				Union[str, Any]	   =		pipeline('''zero-shot-classification'''      ,     model='''roberta-large-mnli'''      ,     framework='''pt'''       )
					A_  :				str	   =		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],
					    }      ,     )
					A_  :				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=_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 					a_  (							self   :					Union[str, Any]       ):



					"""simple docstring"""

					A_  :				Tuple	   =		pipeline('''zero-shot-classification'''      ,     model='''roberta-large-mnli'''      ,     framework='''tf'''       )
					A_  :				str	   =		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],
					    }      ,     )
					A_  :				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=_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],
					    }      ,     )





