


'''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.
__UpperCAmelCase      :List[str]			    =       {"LayoutLMv2Config", "LayoutLMv3Config"}



@is_pipeline_test
class      a    (		unittest.TestCase	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Optional[Any]							       =       MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
     SCREAMING_SNAKE_CASE	:			Dict							       =       TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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

     def 			lowerCamelCase__			(    self					:		Any							,			snake_case					:		List[Any]							,			snake_case					:		str							,			snake_case					:		int      )						->							List[Any]:
          __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 			lowerCamelCase__			(    self					:		List[Any]							,			snake_case					:		List[str]							,			snake_case					:		Optional[int]      )						->							Any:
          __UpperCAmelCase     :			Union[str, Any]  							=      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     :			Union[str, Any]  							=      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     :			Any  							=      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     :			Union[str, 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     :			Tuple  							=      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     :			int  							=      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     :			List[str]  							=      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					:		List[str]							,			snake_case					:		Pipeline      )						->							str:
          __UpperCAmelCase     :			str  							=      zero_shot_classifier.model.config
          __UpperCAmelCase     :			int  							=      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     :			int  							=      {'''entailment''': 0, '''neutral''': 1, '''contradiction''': 2}
          self.assertEqual(zero_shot_classifier.entailment_id							,			0      )

          __UpperCAmelCase     :			List[Any]  							=      {'''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     :			Tuple  							=      original_labelaid
          self.assertEqual(snake_case							,			zero_shot_classifier.entailment_id      )

     @require_torch
     def 			lowerCamelCase__			(    self					:		Any      )						->							int:
          __UpperCAmelCase     :			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?''' * 100							,			candidate_labels=['''politics''', '''public health''', '''science''']      )

     @require_torch
     def 			lowerCamelCase__			(    self					:		Any      )						->							Union[str, Any]:
          __UpperCAmelCase     :			Any  							=      pipeline(
              '''zero-shot-classification'''							,			model='''sshleifer/tiny-distilbert-base-cased-distilled-squad'''							,			framework='''pt'''							,			)
          __UpperCAmelCase     :			List[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''': ['''science''', '''public health''', '''politics'''],
                  '''scores''': [0.333, 0.333, 0.333],
              }							,			)

     @require_tf
     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							Any:
          __UpperCAmelCase     :			Tuple  							=      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(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					:		int      )						->							Optional[Any]:
          __UpperCAmelCase     :			List[str]  							=      pipeline('''zero-shot-classification'''							,			model='''roberta-large-mnli'''							,			framework='''pt'''      )
          __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''': ['''politics''', '''public health''', '''science'''],
                  '''scores''': [0.976, 0.015, 0.009],
              }							,			)
          __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''': [0.817, 0.713, 0.018, 0.018],
              }							,			)






     @slow
     @require_tf
     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							int:
          __UpperCAmelCase     :			int  							=      pipeline('''zero-shot-classification'''							,			model='''roberta-large-mnli'''							,			framework='''tf'''      )
          __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''': [0.976, 0.015, 0.009],
              }							,			)
          __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''': [0.817, 0.713, 0.018, 0.018],
              }							,			)



'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class      a    (		_a     ,  _a     ,  unittest.TestCase	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Dict							       =       StableDiffusionXLImgaImgPipeline
     SCREAMING_SNAKE_CASE	:			Tuple							       =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
     SCREAMING_SNAKE_CASE	:			Optional[Any]							       =       PipelineTesterMixin.required_optional_params - {"latents"}
     SCREAMING_SNAKE_CASE	:			int							       =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     SCREAMING_SNAKE_CASE	:			Union[str, Any]							       =       IMAGE_TO_IMAGE_IMAGE_PARAMS
     SCREAMING_SNAKE_CASE	:			Any							       =       IMAGE_TO_IMAGE_IMAGE_PARAMS

     def 			lowerCamelCase__			(    self					:		Any      )						->							Any:
          torch.manual_seed(0      )
          __UpperCAmelCase     :			Optional[Any]  							=      UNetaDConditionModel(
              block_out_channels=(32, 64)							,			layers_per_block=2							,			sample_size=32							,			in_channels=4							,			out_channels=4							,			down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')							,			up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')							,			attention_head_dim=(2, 4)							,			use_linear_projection=snake_case							,			addition_embed_type='''text_time'''							,			addition_time_embed_dim=8							,			transformer_layers_per_block=(1, 2)							,			projection_class_embeddings_input_dim=80							,			cross_attention_dim=64							,			)
          __UpperCAmelCase     :			int  							=      EulerDiscreteScheduler(
              beta_start=0.00_085							,			beta_end=0.012							,			steps_offset=1							,			beta_schedule='''scaled_linear'''							,			timestep_spacing='''leading'''							,			)
          torch.manual_seed(0      )
          __UpperCAmelCase     :			Tuple  							=      AutoencoderKL(
              block_out_channels=[32, 64]							,			in_channels=3							,			out_channels=3							,			down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']							,			up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']							,			latent_channels=4							,			sample_size=128							,			)
          torch.manual_seed(0      )
          __UpperCAmelCase     :			int  							=      CLIPTextConfig(
              bos_token_id=0							,			eos_token_id=2							,			hidden_size=32							,			intermediate_size=37							,			layer_norm_eps=1E-05							,			num_attention_heads=4							,			num_hidden_layers=5							,			pad_token_id=1							,			vocab_size=1000							,			hidden_act='''gelu'''							,			projection_dim=32							,			)
          __UpperCAmelCase     :			Tuple  							=      CLIPTextModel(snake_case      )
          __UpperCAmelCase     :			Optional[int]  							=      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							,			local_files_only=snake_case      )

          __UpperCAmelCase     :			Optional[Any]  							=      CLIPTextModelWithProjection(snake_case      )
          __UpperCAmelCase     :			Union[str, Any]  							=      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							,			local_files_only=snake_case      )

          __UpperCAmelCase     :			Tuple  							=      {
              '''unet''': unet,
              '''scheduler''': scheduler,
              '''vae''': vae,
              '''text_encoder''': text_encoder,
              '''tokenizer''': tokenizer,
              '''text_encoder_2''': text_encoder_a,
              '''tokenizer_2''': tokenizer_a,
              # "safety_checker": None,
              # "feature_extractor": None,
          }
          return components

     def 			lowerCamelCase__			(    self					:		Dict							,			snake_case					:		Optional[int]							,			snake_case					:		List[str]=0      )						->							List[str]:
          __UpperCAmelCase     :			List[str]  							=      floats_tensor((1, 3, 32, 32)							,			rng=random.Random(snake_case      )      ).to(snake_case      )
          __UpperCAmelCase     :			Optional[Any]  							=      image / 2 + 0.5
          if str(snake_case      ).startswith('''mps'''      ):
               __UpperCAmelCase     :			List[str]  							=      torch.manual_seed(snake_case      )
          else:
               __UpperCAmelCase     :			Any  							=      torch.Generator(device=snake_case      ).manual_seed(snake_case      )
          __UpperCAmelCase     :			Optional[int]  							=      {
              '''prompt''': '''A painting of a squirrel eating a burger''',
              '''image''': image,
              '''generator''': generator,
              '''num_inference_steps''': 2,
              '''guidance_scale''': 5.0,
              '''output_type''': '''numpy''',
              '''strength''': 0.75,
          }
          return inputs

     def 			lowerCamelCase__			(    self					:		Tuple      )						->							Optional[int]:
          __UpperCAmelCase     :			Any  							=      '''cpu'''  # ensure determinism for the device-dependent torch.Generator
          __UpperCAmelCase     :			Optional[Any]  							=      self.get_dummy_components()
          __UpperCAmelCase     :			Any  							=      StableDiffusionXLImgaImgPipeline(**snake_case      )
          __UpperCAmelCase     :			Union[str, Any]  							=      sd_pipe.to(snake_case      )
          sd_pipe.set_progress_bar_config(disable=snake_case      )

          __UpperCAmelCase     :			Optional[int]  							=      self.get_dummy_inputs(snake_case      )
          __UpperCAmelCase     :			int  							=      sd_pipe(**snake_case      ).images
          __UpperCAmelCase     :			List[str]  							=      image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)

          __UpperCAmelCase     :			Any  							=      np.array([0.4_656, 0.4_840, 0.4_439, 0.6_698, 0.5_574, 0.4_524, 0.5_799, 0.5_943, 0.5_165]      )

          assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2

     def 			lowerCamelCase__			(    self					:		Any      )						->							int:
          super().test_attention_slicing_forward_pass(expected_max_diff=3E-3      )

     def 			lowerCamelCase__			(    self					:		Optional[Any]      )						->							Optional[int]:
          super().test_inference_batch_single_identical(expected_max_diff=3E-3      )

     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							List[Any]:
          pass






     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							int:
          __UpperCAmelCase     :			Union[str, Any]  							=      self.get_dummy_components()
          __UpperCAmelCase     :			List[Any]  							=      StableDiffusionXLImgaImgPipeline(**snake_case      )
          __UpperCAmelCase     :			Tuple  							=      sd_pipe.to(snake_case      )
          __UpperCAmelCase     :			Tuple  							=      sd_pipe.to(snake_case      )
          sd_pipe.set_progress_bar_config(disable=snake_case      )

          # forward without prompt embeds
          __UpperCAmelCase     :			Tuple  							=      self.get_dummy_inputs(snake_case      )
          __UpperCAmelCase     :			Optional[Any]  							=      3 * ['''this is a negative prompt''']
          __UpperCAmelCase     :			Optional[int]  							=      negative_prompt
          __UpperCAmelCase     :			Tuple  							=      3 * [inputs['''prompt''']]

          __UpperCAmelCase     :			Optional[Any]  							=      sd_pipe(**snake_case      )
          __UpperCAmelCase     :			int  							=      output.images[0, -3:, -3:, -1]

          # forward with prompt embeds
          __UpperCAmelCase     :			Union[str, Any]  							=      self.get_dummy_inputs(snake_case      )
          __UpperCAmelCase     :			Tuple  							=      3 * ['''this is a negative prompt''']
          __UpperCAmelCase     :			str  							=      3 * [inputs.pop('''prompt'''      )]

          (
              (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       
          )     :			Any  							=      sd_pipe.encode_prompt(snake_case							,			negative_prompt=snake_case      )

          __UpperCAmelCase     :			Dict  							=      sd_pipe(
              **snake_case							,			prompt_embeds=snake_case							,			negative_prompt_embeds=snake_case							,			pooled_prompt_embeds=snake_case							,			negative_pooled_prompt_embeds=snake_case							,			)
          __UpperCAmelCase     :			List[str]  							=      output.images[0, -3:, -3:, -1]

          # make sure that it's equal
          assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()      ).max() < 1E-4



@slow
@require_torch_gpu
class      a    (		unittest.TestCase	):


     """simple docstring"""






     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							Optional[Any]:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()

     def 			lowerCamelCase__			(    self					:		int							,			snake_case					:		Any							,			snake_case					:		str="cpu"							,			snake_case					:		Tuple=torch.floataa							,			snake_case					:		List[str]=0      )						->							Tuple:
          __UpperCAmelCase     :			Optional[Any]  							=      torch.Generator(device=snake_case      ).manual_seed(snake_case      )
          __UpperCAmelCase     :			int  							=      np.random.RandomState(snake_case      ).standard_normal((1, 4, 64, 64)      )
          __UpperCAmelCase     :			str  							=      torch.from_numpy(snake_case      ).to(device=snake_case							,			dtype=snake_case      )
          __UpperCAmelCase     :			str  							=      {
              '''prompt''': '''a photograph of an astronaut riding a horse''',
              '''latents''': latents,
              '''generator''': generator,
              '''num_inference_steps''': 3,
              '''guidance_scale''': 7.5,
              '''output_type''': '''numpy''',
          }
          return inputs






     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							List[Any]:
          __UpperCAmelCase     :			str  							=      DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''      )
          pipe.to(snake_case      )
          pipe.set_progress_bar_config(disable=snake_case      )

          __UpperCAmelCase     :			str  							=      self.get_inputs(snake_case      )
          __UpperCAmelCase     :			Dict  							=      pipe(**snake_case      ).images
          __UpperCAmelCase     :			List[str]  							=      image[0, -3:, -3:, -1].flatten()

          assert image.shape == (1, 512, 512, 3)
          __UpperCAmelCase     :			Tuple  							=      np.array([0.49_493, 0.47_896, 0.40_798, 0.54_214, 0.53_212, 0.48_202, 0.47_656, 0.46_329, 0.48_506]      )
          assert np.abs(image_slice - expected_slice      ).max() < 7E-3