

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__								=		{"""LayoutLMv2Config""", """LayoutLMv3Config"""}







@is_pipeline_test
class 	lowercase    (				unittest.TestCase							):


		"""simple docstring"""




		a__					       =  MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
		a__					       =  TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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


		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		Optional[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 		A__					(				self	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		Tuple			=						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
			_UpperCamelCase   :		Tuple			=						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)]})

			_UpperCamelCase   :		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)]})

			_UpperCamelCase   :		Optional[Any]			=						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)

			_UpperCamelCase   :		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)

			_UpperCamelCase   :		str			=						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
			_UpperCamelCase   :		Tuple			=						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)
			    ]	,       )
			_UpperCamelCase   :		Optional[int]			=						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 		A__					(				self	,       __snake_case):
			_UpperCamelCase   :		str			=						zero_shot_classifier.model.config
			_UpperCamelCase   :		List[str]			=						config.labelaid
			_UpperCamelCase   :		Optional[Any]			=						zero_shot_classifier.entailment_id

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

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

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

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

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


		@require_torch
		def 		A__					(				self):
			_UpperCamelCase   :		Tuple			=						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):
			_UpperCamelCase   :		str			=						pipeline(
			    'zero-shot-classification'	,       model='sshleifer/tiny-distilbert-base-cased-distilled-squad'	,       framework='pt'	,       )
			_UpperCamelCase   :		Optional[int]			=						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': [0.3_3_3, 0.3_3_3, 0.3_3_3],
			    }	,       )


		@require_tf
		def 		A__					(				self):
			_UpperCamelCase   :		str			=						pipeline(
			    'zero-shot-classification'	,       model='sshleifer/tiny-distilbert-base-cased-distilled-squad'	,       framework='tf'	,       )
			_UpperCamelCase   :		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': [0.3_3_3, 0.3_3_3, 0.3_3_3],
			    }	,       )


		@slow
		@require_torch
		def 		A__					(				self):
			_UpperCamelCase   :		Dict			=						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(__snake_case)	,       {
			        '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=__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': [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
			    }	,       )






		@slow
		@require_tf
		def 		A__					(				self):
			_UpperCamelCase   :		str			=						pipeline('zero-shot-classification'	,       model='roberta-large-mnli'	,       framework='tf')
			_UpperCamelCase   :		Dict			=						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': [0.9_7_6, 0.0_1_5, 0.0_0_9],
			    }	,       )
			_UpperCamelCase   :		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': [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
			    }	,       )







import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


lowerCAmelCase__								=		False







class 	lowercase    (				unittest.TestCase							):


		"""simple docstring"""




		def 		A__					(				self	,       __snake_case=32):
			set_seed(0)
			_UpperCamelCase   :		int			=						UNetaDModel(sample_size=__snake_case	,       in_channels=3	,       out_channels=3)
			_UpperCamelCase   :		str			=						torch.optim.SGD(model.parameters()	,       lr=0.0_0_0_1)
			return model, optimizer






		@slow
		def 		A__					(				self):
			_UpperCamelCase   :		Tuple			=						'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
			_UpperCamelCase   :		List[Any]			=						DDPMScheduler(
			    num_train_timesteps=10_00	,       beta_start=0.0_0_0_1	,       beta_end=0.0_2	,       beta_schedule='linear'	,       clip_sample=__snake_case	,       )
			_UpperCamelCase   :		List[Any]			=						DDIMScheduler(
			    num_train_timesteps=10_00	,       beta_start=0.0_0_0_1	,       beta_end=0.0_2	,       beta_schedule='linear'	,       clip_sample=__snake_case	,       )

			assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

			# shared batches for DDPM and DDIM
			set_seed(0)
			_UpperCamelCase   :		Optional[Any]			=						[torch.randn((4, 3, 32, 32)).clip(-1	,       1).to(__snake_case) for _ in range(4)]
			_UpperCamelCase   :		str			=						[torch.randn((4, 3, 32, 32)).to(__snake_case) for _ in range(4)]
			_UpperCamelCase   :		int			=						[torch.randint(0	,       10_00	,       (4,)).long().to(__snake_case) for _ in range(4)]

			# train with a DDPM scheduler
			_UpperCamelCase	,		_UpperCamelCase   :		List[Any]			=						self.get_model_optimizer(resolution=32)
			model.train().to(__snake_case)
			for i in range(4):
				optimizer.zero_grad()
				_UpperCamelCase   :		int			=						ddpm_scheduler.add_noise(clean_images[i]	,       noise[i]	,       timesteps[i])
				_UpperCamelCase   :		Any			=						model(__snake_case	,       timesteps[i]).sample
				_UpperCamelCase   :		str			=						torch.nn.functional.mse_loss(__snake_case	,       noise[i])
				loss.backward()
				optimizer.step()
			del model, optimizer

			# recreate the model and optimizer, and retry with DDIM
			_UpperCamelCase	,		_UpperCamelCase   :		Union[str, Any]			=						self.get_model_optimizer(resolution=32)
			model.train().to(__snake_case)
			for i in range(4):
				optimizer.zero_grad()
				_UpperCamelCase   :		Dict			=						ddim_scheduler.add_noise(clean_images[i]	,       noise[i]	,       timesteps[i])
				_UpperCamelCase   :		Dict			=						model(__snake_case	,       timesteps[i]).sample
				_UpperCamelCase   :		Tuple			=						torch.nn.functional.mse_loss(__snake_case	,       noise[i])
				loss.backward()
				optimizer.step()
			del model, optimizer

			self.assertTrue(torch.allclose(__snake_case	,       __snake_case	,       atol=1e-5))
			self.assertTrue(torch.allclose(__snake_case	,       __snake_case	,       atol=1e-5))




