





"""simple docstring"""



from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import numpy as np
       import tensorflow as tf

       from transformers import (
           TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
           FlaubertConfig,
           TFFlaubertForMultipleChoice,
           TFFlaubertForQuestionAnsweringSimple,
           TFFlaubertForSequenceClassification,
           TFFlaubertForTokenClassification,
           TFFlaubertModel,
           TFFlaubertWithLMHeadModel,
       )






class 		__snake_case					:





 """simple docstring"""


 def __init__(    self					,						__lowerCamelCase					,						):



     '''simple docstring'''




     __A    :						Union[str, Any]						 =     parent
     __A    :						List[str]						 =     13
     __A    :						List[Any]						 =     7
     __A    :						Optional[int]						 =     True
     __A    :						Tuple						 =     True
     __A    :						List[str]						 =     True
     __A    :						Union[str, Any]						 =     True
     __A    :						int						 =     True
     __A    :						Tuple						 =     False
     __A    :						str						 =     False
     __A    :						Dict						 =     False
     __A    :						Dict						 =     2
     __A    :						Optional[Any]						 =     99
     __A    :						Optional[int]						 =     0
     __A    :						Tuple						 =     32
     __A    :						List[str]						 =     2
     __A    :						Optional[int]						 =     4
     __A    :						Tuple						 =     0.1
     __A    :						str						 =     0.1
     __A    :						List[Any]						 =     512
     __A    :						Any						 =     16
     __A    :						Dict						 =     2
     __A    :						Union[str, Any]						 =     0.0_2
     __A    :						List[str]						 =     3
     __A    :						Optional[int]						 =     4
     __A    :						Dict						 =     '''last'''
     __A    :						Dict						 =     True
     __A    :						str						 =     None
     __A    :						Dict						 =     0

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Tuple						 =     ids_tensor([self.batch_size, self.seq_length]					,						self.vocab_size  )
     __A    :						Union[str, Any]						 =     random_attention_mask([self.batch_size, self.seq_length]					,						dtype=tf.floataa  )

     __A    :						Dict						 =     None
     if self.use_input_lengths:
         __A    :						Dict						 =     (
             ids_tensor([self.batch_size]					,						vocab_size=2  ) + self.seq_length - 2
         )  # small variation of seq_length

     __A    :						Optional[int]						 =     None
     if self.use_token_type_ids:
         __A    :						Tuple						 =     ids_tensor([self.batch_size, self.seq_length]					,						self.n_langs  )

     __A    :						Optional[Any]						 =     None
     __A    :						str						 =     None
     __A    :						Optional[Any]						 =     None
     if self.use_labels:
         __A    :						str						 =     ids_tensor([self.batch_size]					,						self.type_sequence_label_size  )
         __A    :						Optional[Any]						 =     ids_tensor([self.batch_size, self.seq_length]					,						self.num_labels  )
         __A    :						Dict						 =     ids_tensor([self.batch_size]					,						2					,						dtype=tf.floataa  )
         __A    :						str						 =     ids_tensor([self.batch_size]					,						self.num_choices  )

     __A    :						Dict						 =     FlaubertConfig(
         vocab_size=self.vocab_size					,						n_special=self.n_special					,						emb_dim=self.hidden_size					,						n_layers=self.num_hidden_layers					,						n_heads=self.num_attention_heads					,						dropout=self.hidden_dropout_prob					,						attention_dropout=self.attention_probs_dropout_prob					,						gelu_activation=self.gelu_activation					,						sinusoidal_embeddings=self.sinusoidal_embeddings					,						asm=self.asm					,						causal=self.causal					,						n_langs=self.n_langs					,						max_position_embeddings=self.max_position_embeddings					,						initializer_range=self.initializer_range					,						summary_type=self.summary_type					,						use_proj=self.use_proj					,						bos_token_id=self.bos_token_id					,						)

     return (
         config,
         input_ids,
         token_type_ids,
         input_lengths,
         sequence_labels,
         token_labels,
         is_impossible_labels,
         choice_labels,
         input_mask,
     )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						):



     '''simple docstring'''




     __A    :						List[str]						 =     TFFlaubertModel(config=__lowerCamelCase  )
     __A    :						int						 =     {'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
     __A    :						Optional[int]						 =     model(__lowerCamelCase  )

     __A    :						Tuple						 =     [input_ids, input_mask]
     __A    :						List[Any]						 =     model(__lowerCamelCase  )
     self.parent.assertEqual(result.last_hidden_state.shape					,						(self.batch_size, self.seq_length, self.hidden_size)  )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						):



     '''simple docstring'''




     __A    :						Optional[int]						 =     TFFlaubertWithLMHeadModel(__lowerCamelCase  )

     __A    :						List[str]						 =     {'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
     __A    :						int						 =     model(__lowerCamelCase  )

     self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.seq_length, self.vocab_size)  )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						):



     '''simple docstring'''




     __A    :						Optional[Any]						 =     TFFlaubertForQuestionAnsweringSimple(__lowerCamelCase  )

     __A    :						Union[str, Any]						 =     {'''input_ids''': input_ids, '''lengths''': input_lengths}

     __A    :						Optional[Any]						 =     model(__lowerCamelCase  )

     self.parent.assertEqual(result.start_logits.shape					,						(self.batch_size, self.seq_length)  )
     self.parent.assertEqual(result.end_logits.shape					,						(self.batch_size, self.seq_length)  )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						):



     '''simple docstring'''




     __A    :						str						 =     TFFlaubertForSequenceClassification(__lowerCamelCase  )

     __A    :						int						 =     {'''input_ids''': input_ids, '''lengths''': input_lengths}

     __A    :						int						 =     model(__lowerCamelCase  )

     self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.type_sequence_label_size)  )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						):



     '''simple docstring'''




     __A    :						Union[str, Any]						 =     self.num_labels
     __A    :						List[Any]						 =     TFFlaubertForTokenClassification(config=__lowerCamelCase  )
     __A    :						Optional[Any]						 =     {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
     __A    :						Dict						 =     model(__lowerCamelCase  )
     self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.seq_length, self.num_labels)  )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						):



     '''simple docstring'''




     __A    :						Tuple						 =     self.num_choices
     __A    :						Union[str, Any]						 =     TFFlaubertForMultipleChoice(config=__lowerCamelCase  )
     __A    :						str						 =     tf.tile(tf.expand_dims(__lowerCamelCase					,						1  )					,						(1, self.num_choices, 1)  )
     __A    :						int						 =     tf.tile(tf.expand_dims(__lowerCamelCase					,						1  )					,						(1, self.num_choices, 1)  )
     __A    :						Tuple						 =     tf.tile(tf.expand_dims(__lowerCamelCase					,						1  )					,						(1, self.num_choices, 1)  )
     __A    :						int						 =     {
         '''input_ids''': multiple_choice_inputs_ids,
         '''attention_mask''': multiple_choice_input_mask,
         '''token_type_ids''': multiple_choice_token_type_ids,
     }
     __A    :						Optional[int]						 =     model(__lowerCamelCase  )
     self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.num_choices)  )







 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						str						 =     self.prepare_config_and_inputs()
     (
         (
         __A
     ) ,       (
         __A
     ) ,       (
         __A
     ) ,       (
         __A
     ) ,       (
         __A
     ) ,       (
         __A
     ) ,       (
         __A
     ) ,       (
         __A
     ) ,       (
         __A
     ) ,       
     )    :						int						 =     config_and_inputs
     __A    :						Dict						 =     {
         '''input_ids''': input_ids,
         '''token_type_ids''': token_type_ids,
         '''langs''': token_type_ids,
         '''lengths''': input_lengths,
     }
     return config, inputs_dict






@require_tf
class 		__snake_case					(		SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__							,							unittest.TestCase     ):





 """simple docstring"""


 _lowerCamelCase        =     (
     (
         TFFlaubertModel,
         TFFlaubertWithLMHeadModel,
         TFFlaubertForSequenceClassification,
         TFFlaubertForQuestionAnsweringSimple,
         TFFlaubertForTokenClassification,
         TFFlaubertForMultipleChoice,
     )
     if is_tf_available()
     else ()
 )
 _lowerCamelCase        =     (
     (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
 )  # TODO (PVP): Check other models whether language generation is also applicable
 _lowerCamelCase        =     (
     {
         """feature-extraction""": TFFlaubertModel,
         """fill-mask""": TFFlaubertWithLMHeadModel,
         """question-answering""": TFFlaubertForQuestionAnsweringSimple,
         """text-classification""": TFFlaubertForSequenceClassification,
         """token-classification""": TFFlaubertForTokenClassification,
         """zero-shot""": TFFlaubertForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 _lowerCamelCase        =     False
 _lowerCamelCase        =     False

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     if (
         pipeline_test_casse_name == "QAPipelineTests"
         and tokenizer_name is not None
         and not tokenizer_name.endswith('''Fast'''  )
     ):
         # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
         # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
         # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
         return True

     return False

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Tuple						 =     TFFlaubertModelTester(self  )
     __A    :						Tuple						 =     ConfigTester(self					,						config_class=__lowerCamelCase					,						emb_dim=37  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     self.config_tester.run_common_tests()

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Optional[Any]						 =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_flaubert_model(*__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Union[str, Any]						 =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_flaubert_lm_head(*__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						int						 =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_flaubert_qa(*__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Tuple						 =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_flaubert_sequence_classif(*__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Dict						 =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_flaubert_for_token_classification(*__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						int						 =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_flaubert_for_multiple_choice(*__lowerCamelCase  )







 @slow
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __A    :						Optional[Any]						 =     TFFlaubertModel.from_pretrained(__lowerCamelCase  )
         self.assertIsNotNone(__lowerCamelCase  )






@require_tf
@require_sentencepiece
@require_tokenizers
class 		__snake_case					(		unittest.TestCase     ):





 """simple docstring"""


 @slow
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						int						 =     TFFlaubertModel.from_pretrained('''jplu/tf-flaubert-small-cased'''  )

     __A    :						Dict						 =     tf.convert_to_tensor(
         [[0, 158, 735, 2592, 1424, 6727, 82, 1]]					,						dtype=tf.intaa					,						)  # "J'aime flaubert !"

     __A    :						Any						 =     model(__lowerCamelCase  )[0]
     __A    :						str						 =     tf.TensorShape((1, 8, 512)  )
     self.assertEqual(output.shape					,						__lowerCamelCase  )
     # compare the actual values for a slice.
     __A    :						Dict						 =     tf.convert_to_tensor(
         [
             [
                 [-1.8_7_6_8_7_7_3, -1.5_6_6_5_5_5, 0.2_7_0_7_2_4_1_8],
                 [-1.6_9_2_0_0_3_8, -0.5_8_7_3_5_0_5, 1.9_3_2_9_5_9_9],
                 [-2.9_5_6_3_9_8_5, -1.6_9_9_3_8_3_5, 1.7_9_7_2_0_5_2],
             ]
         ]					,						dtype=tf.floataa					,						)

     self.assertTrue(np.allclose(output[:, :3, :3].numpy()					,						expected_slice.numpy()					,						atol=1e-4  )  )











"""simple docstring"""



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






class 		__snake_case					(		unittest.TestCase     ):





 """simple docstring"""


 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Union[str, Any]						 =     1
     __A    :						Any						 =     3
     __A    :						List[str]						 =     (32, 32)

     __A    :						List[str]						 =     floats_tensor((batch_size, num_channels) + sizes					,						rng=random.Random(0  )  ).to(__lowerCamelCase  )
     return image

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     torch.manual_seed(0  )
     __A    :						List[Any]						 =     UNetaDConditionModel(
         block_out_channels=(32, 32, 64)					,						layers_per_block=2					,						sample_size=32					,						in_channels=7					,						out_channels=4					,						down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''', '''CrossAttnDownBlock2D''')					,						up_block_types=('''CrossAttnUpBlock2D''', '''CrossAttnUpBlock2D''', '''UpBlock2D''')					,						cross_attention_dim=32					,						attention_head_dim=8					,						use_linear_projection=__lowerCamelCase					,						only_cross_attention=(True, True, False)					,						num_class_embeds=100					,						)
     return model

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     torch.manual_seed(0  )
     __A    :						Any						 =     AutoencoderKL(
         block_out_channels=[32, 32, 64]					,						in_channels=3					,						out_channels=3					,						down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''', '''DownEncoderBlock2D''']					,						up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']					,						latent_channels=4					,						)
     return model

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     torch.manual_seed(0  )
     __A    :						Tuple						 =     CLIPTextConfig(
         bos_token_id=0					,						eos_token_id=2					,						hidden_size=32					,						intermediate_size=37					,						layer_norm_eps=1e-0_5					,						num_attention_heads=4					,						num_hidden_layers=5					,						pad_token_id=1					,						vocab_size=1000					,						hidden_act='''gelu'''					,						projection_dim=512					,						)
     return CLIPTextModel(__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						List[Any]						 =     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
     __A    :						int						 =     self.dummy_cond_unet_upscale
     __A    :						Union[str, Any]						 =     DDPMScheduler()
     __A    :						Dict						 =     DDIMScheduler(prediction_type='''v_prediction'''  )
     __A    :						int						 =     self.dummy_vae
     __A    :						int						 =     self.dummy_text_encoder
     __A    :						Union[str, Any]						 =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

     __A    :						Tuple						 =     self.dummy_image.cpu().permute(0					,						2					,						3					,						1  )[0]
     __A    :						Any						 =     Image.fromarray(np.uinta(__lowerCamelCase  )  ).convert('''RGB'''  ).resize((64, 64)  )

     # make sure here that pndm scheduler skips prk
     __A    :						Dict						 =     StableDiffusionUpscalePipeline(
         unet=__lowerCamelCase					,						low_res_scheduler=__lowerCamelCase					,						scheduler=__lowerCamelCase					,						vae=__lowerCamelCase					,						text_encoder=__lowerCamelCase					,						tokenizer=__lowerCamelCase					,						max_noise_level=350					,						)
     __A    :						str						 =     sd_pipe.to(__lowerCamelCase  )
     sd_pipe.set_progress_bar_config(disable=__lowerCamelCase  )

     __A    :						List[str]						 =     '''A painting of a squirrel eating a burger'''
     __A    :						Any						 =     torch.Generator(device=__lowerCamelCase  ).manual_seed(0  )
     __A    :						List[str]						 =     sd_pipe(
         [prompt]					,						image=__lowerCamelCase					,						generator=__lowerCamelCase					,						guidance_scale=6.0					,						noise_level=20					,						num_inference_steps=2					,						output_type='''np'''					,						)

     __A    :						Union[str, Any]						 =     output.images

     __A    :						List[str]						 =     torch.Generator(device=__lowerCamelCase  ).manual_seed(0  )
     __A    :						str						 =     sd_pipe(
         [prompt]					,						image=__lowerCamelCase					,						generator=__lowerCamelCase					,						guidance_scale=6.0					,						noise_level=20					,						num_inference_steps=2					,						output_type='''np'''					,						return_dict=__lowerCamelCase					,						)[0]

     __A    :						Tuple						 =     image[0, -3:, -3:, -1]
     __A    :						int						 =     image_from_tuple[0, -3:, -3:, -1]

     __A    :						Dict						 =     low_res_image.size[0] * 4
     assert image.shape == (1, expected_height_width, expected_height_width, 3)
     __A    :						str						 =     np.array([0.3_1_1_3, 0.3_9_1_0, 0.4_2_7_2, 0.4_8_5_9, 0.5_0_6_1, 0.4_6_5_2, 0.5_3_6_2, 0.5_7_1_5, 0.5_6_6_1]  )

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

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Tuple						 =     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
     __A    :						Dict						 =     self.dummy_cond_unet_upscale
     __A    :						List[str]						 =     DDPMScheduler()
     __A    :						str						 =     DDIMScheduler(prediction_type='''v_prediction'''  )
     __A    :						Optional[int]						 =     self.dummy_vae
     __A    :						Optional[Any]						 =     self.dummy_text_encoder
     __A    :						Optional[Any]						 =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

     __A    :						List[Any]						 =     self.dummy_image.cpu().permute(0					,						2					,						3					,						1  )[0]
     __A    :						int						 =     Image.fromarray(np.uinta(__lowerCamelCase  )  ).convert('''RGB'''  ).resize((64, 64)  )

     # make sure here that pndm scheduler skips prk
     __A    :						Any						 =     StableDiffusionUpscalePipeline(
         unet=__lowerCamelCase					,						low_res_scheduler=__lowerCamelCase					,						scheduler=__lowerCamelCase					,						vae=__lowerCamelCase					,						text_encoder=__lowerCamelCase					,						tokenizer=__lowerCamelCase					,						max_noise_level=350					,						)
     __A    :						Any						 =     sd_pipe.to(__lowerCamelCase  )
     sd_pipe.set_progress_bar_config(disable=__lowerCamelCase  )

     __A    :						Any						 =     '''A painting of a squirrel eating a burger'''
     __A    :						Any						 =     sd_pipe(
         2 * [prompt]					,						image=2 * [low_res_image]					,						guidance_scale=6.0					,						noise_level=20					,						num_inference_steps=2					,						output_type='''np'''					,						)
     __A    :						Union[str, Any]						 =     output.images
     assert image.shape[0] == 2

     __A    :						Optional[Any]						 =     torch.Generator(device=__lowerCamelCase  ).manual_seed(0  )
     __A    :						Any						 =     sd_pipe(
         [prompt]					,						image=__lowerCamelCase					,						generator=__lowerCamelCase					,						num_images_per_prompt=2					,						guidance_scale=6.0					,						noise_level=20					,						num_inference_steps=2					,						output_type='''np'''					,						)
     __A    :						Union[str, Any]						 =     output.images
     assert image.shape[0] == 2







 @unittest.skipIf(torch_device != '''cuda'''					,						'''This test requires a GPU'''  )
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						List[Any]						 =     self.dummy_cond_unet_upscale
     __A    :						int						 =     DDPMScheduler()
     __A    :						List[Any]						 =     DDIMScheduler(prediction_type='''v_prediction'''  )
     __A    :						Optional[Any]						 =     self.dummy_vae
     __A    :						List[str]						 =     self.dummy_text_encoder
     __A    :						Dict						 =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

     __A    :						Union[str, Any]						 =     self.dummy_image.cpu().permute(0					,						2					,						3					,						1  )[0]
     __A    :						int						 =     Image.fromarray(np.uinta(__lowerCamelCase  )  ).convert('''RGB'''  ).resize((64, 64)  )

     # put models in fp16, except vae as it overflows in fp16
     __A    :						Union[str, Any]						 =     unet.half()
     __A    :						Optional[int]						 =     text_encoder.half()

     # make sure here that pndm scheduler skips prk
     __A    :						Optional[int]						 =     StableDiffusionUpscalePipeline(
         unet=__lowerCamelCase					,						low_res_scheduler=__lowerCamelCase					,						scheduler=__lowerCamelCase					,						vae=__lowerCamelCase					,						text_encoder=__lowerCamelCase					,						tokenizer=__lowerCamelCase					,						max_noise_level=350					,						)
     __A    :						Union[str, Any]						 =     sd_pipe.to(__lowerCamelCase  )
     sd_pipe.set_progress_bar_config(disable=__lowerCamelCase  )

     __A    :						Union[str, Any]						 =     '''A painting of a squirrel eating a burger'''
     __A    :						Optional[Any]						 =     torch.manual_seed(0  )
     __A    :						Tuple						 =     sd_pipe(
         [prompt]					,						image=__lowerCamelCase					,						generator=__lowerCamelCase					,						num_inference_steps=2					,						output_type='''np'''					,						).images

     __A    :						str						 =     low_res_image.size[0] * 4
     assert image.shape == (1, expected_height_width, expected_height_width, 3)






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





 """simple docstring"""


 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						List[Any]						 =     load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/sd2-upscale/low_res_cat.png'''  )
     __A    :						Dict						 =     load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
         '''/upsampled_cat.npy'''  )

     __A    :						str						 =     '''stabilityai/stable-diffusion-x4-upscaler'''
     __A    :						Optional[Any]						 =     StableDiffusionUpscalePipeline.from_pretrained(__lowerCamelCase  )
     pipe.to(__lowerCamelCase  )
     pipe.set_progress_bar_config(disable=__lowerCamelCase  )
     pipe.enable_attention_slicing()

     __A    :						Union[str, Any]						 =     '''a cat sitting on a park bench'''

     __A    :						Union[str, Any]						 =     torch.manual_seed(0  )
     __A    :						Optional[Any]						 =     pipe(
         prompt=__lowerCamelCase					,						image=__lowerCamelCase					,						generator=__lowerCamelCase					,						output_type='''np'''					,						)
     __A    :						List[str]						 =     output.images[0]

     assert image.shape == (512, 512, 3)
     assert np.abs(expected_image - image  ).max() < 1e-3

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Dict						 =     load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/sd2-upscale/low_res_cat.png'''  )
     __A    :						List[Any]						 =     load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
         '''/upsampled_cat_fp16.npy'''  )

     __A    :						Optional[int]						 =     '''stabilityai/stable-diffusion-x4-upscaler'''
     __A    :						Optional[int]						 =     StableDiffusionUpscalePipeline.from_pretrained(
         __lowerCamelCase					,						torch_dtype=torch.floataa					,						)
     pipe.to(__lowerCamelCase  )
     pipe.set_progress_bar_config(disable=__lowerCamelCase  )
     pipe.enable_attention_slicing()

     __A    :						Dict						 =     '''a cat sitting on a park bench'''

     __A    :						Any						 =     torch.manual_seed(0  )
     __A    :						Optional[int]						 =     pipe(
         prompt=__lowerCamelCase					,						image=__lowerCamelCase					,						generator=__lowerCamelCase					,						output_type='''np'''					,						)
     __A    :						Any						 =     output.images[0]

     assert image.shape == (512, 512, 3)
     assert np.abs(expected_image - image  ).max() < 5e-1







 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     __A    :						Union[str, Any]						 =     load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/sd2-upscale/low_res_cat.png'''  )

     __A    :						List[str]						 =     '''stabilityai/stable-diffusion-x4-upscaler'''
     __A    :						Dict						 =     StableDiffusionUpscalePipeline.from_pretrained(
         __lowerCamelCase					,						torch_dtype=torch.floataa					,						)
     pipe.to(__lowerCamelCase  )
     pipe.set_progress_bar_config(disable=__lowerCamelCase  )
     pipe.enable_attention_slicing(1  )
     pipe.enable_sequential_cpu_offload()

     __A    :						Tuple						 =     '''a cat sitting on a park bench'''

     __A    :						Tuple						 =     torch.manual_seed(0  )
     __A    :						List[str]						 =     pipe(
         prompt=__lowerCamelCase					,						image=__lowerCamelCase					,						generator=__lowerCamelCase					,						num_inference_steps=5					,						output_type='''np'''					,						)

     __A    :						Any						 =     torch.cuda.max_memory_allocated()
     # make sure that less than 2.9 GB is allocated
     assert mem_bytes < 2.9 * 10**9




