


"""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.
__SCREAMING_SNAKE_CASE							:    List[Any]		=    {'LayoutLMv2Config', 'LayoutLMv3Config'}





@is_pipeline_test
class      __A     (unittest.TestCase):





							'''simple docstring'''

							__lowercase:		Optional[int]							      =    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
							__lowercase:		Any							      =    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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



							def 		lowerCAmelCase       (				self							:      Dict		,       UpperCAmelCase_							:      Optional[int]		,       UpperCAmelCase_							:      Optional[Any]		,       UpperCAmelCase_							:      Dict       )					->List[str]:



														"""simple docstring"""



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



							def 		lowerCAmelCase       (				self							:      int		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      List[Any]       )					->Any:



														"""simple docstring"""



														snake_case_           =		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
														snake_case_           =		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       )]}       )

														snake_case_           =		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       )]}       )

														snake_case_           =		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       )

														snake_case_           =		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       )

														snake_case_           =		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
														snake_case_           =		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       )
														    ]		,       )
														snake_case_           =		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 		lowerCAmelCase       (				self							:      Dict		,       UpperCAmelCase_							:      Pipeline       )					->int:



														"""simple docstring"""



														snake_case_           =		zero_shot_classifier.model.config
														snake_case_           =		config.labelaid
														snake_case_           =		zero_shot_classifier.entailment_id

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

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

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

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

														snake_case_           =		original_labelaid
														self.assertEqual(__snake_case		,       zero_shot_classifier.entailment_id       )



							@require_torch
							def 		lowerCAmelCase       (				self							:      str       )					->Tuple:



														"""simple docstring"""



														snake_case_           =		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 		lowerCAmelCase       (				self							:      Optional[Any]       )					->Union[str, Any]:



														"""simple docstring"""



														snake_case_           =		pipeline(
														    """zero-shot-classification"""		,       model="""sshleifer/tiny-distilbert-base-cased-distilled-squad"""		,       framework="""pt"""		,       )
														snake_case_           =		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 		lowerCAmelCase       (				self							:      Union[str, Any]       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		pipeline(
														    """zero-shot-classification"""		,       model="""sshleifer/tiny-distilbert-base-cased-distilled-squad"""		,       framework="""tf"""		,       )
														snake_case_           =		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 		lowerCAmelCase       (				self							:      Dict       )					->str:



														"""simple docstring"""



														snake_case_           =		pipeline("""zero-shot-classification"""		,       model="""roberta-large-mnli"""		,       framework="""pt"""       )
														snake_case_           =		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],
														    }		,       )
														snake_case_           =		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 		lowerCAmelCase       (				self							:      Tuple       )					->Optional[int]:



														"""simple docstring"""



														snake_case_           =		pipeline("""zero-shot-classification"""		,       model="""roberta-large-mnli"""		,       framework="""tf"""       )
														snake_case_           =		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],
														    }		,       )
														snake_case_           =		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],
														    }		,       )







"""simple docstring"""
import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()





class      __A     (snake_case__						,		snake_case__						,		unittest.TestCase):





							'''simple docstring'''

							__lowercase:		str							      =    VQModel
							__lowercase:		Union[str, Any]							      =    """sample"""



							@property
							def 		lowerCAmelCase       (				self							:      List[str]		,       UpperCAmelCase_							:      List[str]=(32, 32)       )					->Tuple:



														"""simple docstring"""



														snake_case_           =		4
														snake_case_           =		3

														snake_case_           =		floats_tensor((batch_size, num_channels) + sizes       ).to(UpperCAmelCase_       )

														return {"sample": image}



							@property
							def 		lowerCAmelCase       (				self							:      Tuple       )					->str:



														"""simple docstring"""



														return (3, 32, 32)



							@property
							def 		lowerCAmelCase       (				self							:      List[Any]       )					->Any:



														"""simple docstring"""



														return (3, 32, 32)



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Dict:



														"""simple docstring"""



														snake_case_           =		{
														    """block_out_channels""": [32, 64],
														    """in_channels""": 3,
														    """out_channels""": 3,
														    """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
														    """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
														    """latent_channels""": 3,
														}
														snake_case_           =		self.dummy_input
														return init_dict, inputs_dict



							def 		lowerCAmelCase       (				self							:      List[str]       )					->Dict:



														"""simple docstring"""



														pass



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->List[str]:



														"""simple docstring"""



														pass



							def 		lowerCAmelCase       (				self							:      Any       )					->Union[str, Any]:



														"""simple docstring"""



														snake_case_       ,						snake_case_           =		VQModel.from_pretrained("""fusing/vqgan-dummy"""		,       output_loading_info=UpperCAmelCase_       )
														self.assertIsNotNone(UpperCAmelCase_       )
														self.assertEqual(len(loading_info["""missing_keys"""]       )		,       0       )

														model.to(UpperCAmelCase_       )
														snake_case_           =		model(**self.dummy_input       )

														assert image is not None, "Make sure output is not None"



							def 		lowerCAmelCase       (				self							:      Tuple       )					->Optional[Any]:



														"""simple docstring"""



														snake_case_           =		VQModel.from_pretrained("""fusing/vqgan-dummy"""       )
														model.to(UpperCAmelCase_       ).eval()

														torch.manual_seed(0       )
														if torch.cuda.is_available():
																					torch.cuda.manual_seed_all(0       )

														snake_case_           =		torch.randn(1		,       model.config.in_channels		,       model.config.sample_size		,       model.config.sample_size       )
														snake_case_           =		image.to(UpperCAmelCase_       )
														with torch.no_grad():
																					snake_case_           =		model(UpperCAmelCase_       ).sample

														snake_case_           =		output[0, -1, -3:, -3:].flatten().cpu()
														# fmt: off
														snake_case_           =		torch.tensor([-0.0_153, -0.4_044, -0.1_880, -0.5_161, -0.2_418, -0.4_072, -0.1_612, -0.0_633, -0.0_143]       )
														# fmt: on
														self.assertTrue(torch.allclose(UpperCAmelCase_		,       UpperCAmelCase_		,       atol=1E-3       )       )



