





"""simple docstring"""



from __future__ import annotations

import os
import tempfile
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow

from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import (
           AutoTokenizer,
           TFAutoModelForCausalLM,
           TFAutoModelForSeqaSeqLM,
           TFAutoModelForSpeechSeqaSeq,
           TFAutoModelForVisionaSeq,
           TFBartForConditionalGeneration,
           TFLogitsProcessorList,
           TFMinLengthLogitsProcessor,
           tf_top_k_top_p_filtering,
       )

if is_tensorflow_text_available():
       import tensorflow_text as text






@require_tf
class 		__snake_case					(		unittest.TestCase     ):





 """simple docstring"""


 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						List[Any]						 =     tf.convert_to_tensor(
         [
             [
                 8.2_2_2_0_9_9_1,  # 3rd highest value; idx. 0
                 -0.5_6_2_0_0_4_4,
                 5.2_3_2_2_9_7_5_2,
                 4.0_3_8_6_3_9_3,
                 -6.8_7_9_8_3_7_8,
                 -0.5_4_7_8_5_8_0_2,
                 -3.2_0_1_2_1_5_3,
                 2.9_2_7_7_7_1_7_6,
                 1.8_8_1_7_1_9_5_3,
                 7.3_5_3_4_1_2_7_6,  # 5th highest value; idx. 9
                 8.4_3_2_0_7_8_3_3,  # 2nd highest value; idx. 10
                 -9.8_5_7_1_1_8_3_6,
                 -5.9_6_2_0_9_2_3_6,
                 -1.1_3_0_3_9_1_6_1,
                 -7.1_1_1_5_2_9_4,
                 -0.8_3_6_9_6_3_3,
                 -5.3_1_8_6_4_0_8,
                 7.0_6_4_2_7_4_0_7,
                 0.8_1_3_6_9_3_4_4,
                 -0.8_2_0_2_3_8_1_7,
                 -5.9_1_7_9_7_9_6,
                 0.5_8_8_1_3_4_4_3,
                 -6.9_9_7_7_8_4_3_8,
                 4.7_1_5_5_1_1_8_9,
                 -0.1_8_7_7_1_6_3_7,
                 7.4_4_0_2_0_7_5_9,  # 4th highest value; idx. 25
                 9.3_8_4_5_0_9_8_7,  # 1st highest value; idx. 26
                 2.1_2_6_6_2_9_4_1,
                 -9.3_2_5_6_2_0_3_8,
                 2.3_5_6_5_2_5_2_2,
             ],  # cummulative prob of 5 highest values <= 0.6
             [
                 0.5_8_4_2_5_5_1_8,
                 4.5_3_1_3_9_2_3_8,
                 -5.5_7_5_1_0_4_6_4,
                 -6.2_8_0_3_0_6_9_9,
                 -7.1_9_5_2_9_5_0_3,
                 -4.0_2_1_2_2_5_5_1,
                 1.3_9_3_3_7_0_3_7,
                 -6.0_6_7_0_7_0_5_7,
                 1.5_9_4_8_0_5_1_7,
                 -9.6_4_3_1_1_9,
                 0.0_3_9_0_7_7_9_9,
                 0.6_7_2_3_1_7_6_2,
                 -8.8_8_2_0_6_7_2_6,
                 6.2_7_1_1_5_9_2_2,  # 4th highest value; idx. 13
                 2.2_8_5_2_0_7_2_3,
                 4.8_2_7_6_7_5_0_6,
                 4.3_0_4_2_1_3_6_8,
                 8.8_2_7_5_3_1_3,  # 2nd highest value; idx. 17
                 5.4_4_0_2_9_9_5_8,  # 5th highest value; idx. 18
                 -4.4_7_3_5_7_9_4,
                 7.3_8_5_7_9_5_3_6,  # 3rd highest value; idx. 20
                 -2.9_1_0_5_1_6_6_3,
                 2.6_1_9_4_6_0_7_7,
                 -2.5_6_7_4_7_6_2,
                 -9.4_8_9_5_9_3_0_2,
                 -4.0_2_9_2_2_6_4_5,
                 -1.3_5_4_1_6_9_1_8,
                 9.6_7_7_0_2_3_2_3,  # 1st highest value; idx. 27
                 -5.8_9_4_7_8_5_5_3,
                 1.8_5_3_7_0_4_6_7,
             ],  # cummulative prob of 5 highest values <= 0.6
         ]					,						dtype=tf.floataa					,						)

     __A    :						Optional[int]						 =     tf.convert_to_tensor(
         [[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]]					,						dtype=tf.intaa					,						)  # expected non filtered idx as noted above

     __A    :						str						 =     tf.convert_to_tensor(
         [8.2_2_2_0_9_9, 7.3_5_3_4_1_2_6, 8.4_3_2_0_7_8, 7.4_4_0_2_0_7_5, 9.3_8_4_5_1, 6.2_7_1_1_5_9, 8.8_2_7_5_3_1, 5.4_4_0_2_9_9_5, 7.3_8_5_7_9_5_6, 9.6_7_7_0_2_3]					,						dtype=tf.floataa					,						)  # expected non filtered values as noted above

     __A    :						List[Any]						 =     tf_top_k_top_p_filtering(__lowerCamelCase					,						top_k=10					,						top_p=0.6					,						min_tokens_to_keep=4  )

     __A    :						int						 =     output[output != -float('''inf'''  )]
     __A    :						Any						 =     tf.cast(
         tf.where(tf.not_equal(__lowerCamelCase					,						tf.constant(-float('''inf'''  )					,						dtype=tf.floataa  )  )  )					,						dtype=tf.intaa					,						)

     tf.debugging.assert_near(__lowerCamelCase					,						__lowerCamelCase					,						rtol=1e-1_2  )
     tf.debugging.assert_equal(__lowerCamelCase					,						__lowerCamelCase  )






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





 """simple docstring"""


 if is_tf_available():
  _lowerCamelCase        =     {
      """AutoModelForCausalLM""": TFAutoModelForCausalLM,
      """AutoModelForSpeechSeq2Seq""": TFAutoModelForSpeechSeqaSeq,
      """AutoModelForSeq2SeqLM""": TFAutoModelForSeqaSeqLM,
      """AutoModelForVision2Seq""": TFAutoModelForVisionaSeq,
      """LogitsProcessorList""": TFLogitsProcessorList,
      """MinLengthLogitsProcessor""": TFMinLengthLogitsProcessor,
      """create_tensor_fn""": tf.convert_to_tensor,
      """floats_tensor""": floats_tensor,
      """return_tensors""": """tf""",
  }

 @slow
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Any						 =     TFAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''  )
     __A    :						int						 =     2
     __A    :						Dict						 =     2






     class 		__snake_case					(		tf.Module     ):





      """simple docstring"""


      def __init__(    self					,						__lowerCamelCase  ):



          '''simple docstring'''




          super(__lowerCamelCase					,						self  ).__init__()
          __A    :						Dict						 =     model







      @tf.function(
          input_signature=(
              tf.TensorSpec((None, input_length)					,						tf.intaa					,						name='''input_ids'''  ),
              tf.TensorSpec((None, input_length)					,						tf.intaa					,						name='''attention_mask'''  ),
          )					,						jit_compile=__lowerCamelCase					,						)
      def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase  ):



          '''simple docstring'''




          __A    :						Optional[Any]						 =     self.model.generate(
              input_ids=__lowerCamelCase					,						attention_mask=__lowerCamelCase					,						max_new_tokens=__lowerCamelCase					,						return_dict_in_generate=__lowerCamelCase					,						)
          return {"sequences": outputs["sequences"]}






     __A    :						Dict						 =     [[2, 0], [102, 103]]
     __A    :						str						 =     [[1, 0], [1, 1]]
     __A    :						Optional[int]						 =     DummyModel(model=__lowerCamelCase  )
     with tempfile.TemporaryDirectory() as tmp_dir:
         tf.saved_model.save(__lowerCamelCase					,						__lowerCamelCase					,						signatures={'''serving_default''': dummy_model.serving}  )
         __A    :						Union[str, Any]						 =     tf.saved_model.load(__lowerCamelCase  ).signatures['''serving_default''']
         for batch_size in range(1					,						len(__lowerCamelCase  ) + 1  ):
             __A    :						Tuple						 =     {
                 '''input_ids''': tf.constant(dummy_input_ids[:batch_size]  ),
                 '''attention_mask''': tf.constant(dummy_attention_masks[:batch_size]  ),
             }
             __A    :						str						 =     serving_func(**__lowerCamelCase  )['''sequences''']
             __A    :						Union[str, Any]						 =     test_model.generate(**__lowerCamelCase					,						max_new_tokens=__lowerCamelCase  )
             tf.debugging.assert_equal(__lowerCamelCase					,						__lowerCamelCase  )

 @slow
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						int						 =     TFAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''  )
     __A    :						Dict						 =     1
     __A    :						Optional[Any]						 =     2






     class 		__snake_case					(		tf.Module     ):





      """simple docstring"""


      def __init__(    self					,						__lowerCamelCase  ):



          '''simple docstring'''




          super(__lowerCamelCase					,						self  ).__init__()
          __A    :						Union[str, Any]						 =     model







      @tf.function(
          input_signature=(
              tf.TensorSpec((batch_size, None)					,						tf.intaa					,						name='''input_ids'''  ),
              tf.TensorSpec((batch_size, None)					,						tf.intaa					,						name='''attention_mask'''  ),
          )					,						jit_compile=__lowerCamelCase					,						)
      def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase  ):



          '''simple docstring'''




          __A    :						Optional[Any]						 =     self.model.generate(
              input_ids=__lowerCamelCase					,						attention_mask=__lowerCamelCase					,						max_new_tokens=__lowerCamelCase					,						return_dict_in_generate=__lowerCamelCase					,						)
          return {"sequences": outputs["sequences"]}






     __A    :						Optional[int]						 =     [[2], [102, 103]]
     __A    :						Any						 =     [[1], [1, 1]]
     __A    :						Tuple						 =     DummyModel(model=__lowerCamelCase  )
     with tempfile.TemporaryDirectory() as tmp_dir:
         tf.saved_model.save(__lowerCamelCase					,						__lowerCamelCase					,						signatures={'''serving_default''': dummy_model.serving}  )
         __A    :						Dict						 =     tf.saved_model.load(__lowerCamelCase  ).signatures['''serving_default''']
         for input_row in range(len(__lowerCamelCase  )  ):
             __A    :						Union[str, Any]						 =     {
                 '''input_ids''': tf.constant([dummy_input_ids[input_row]]  ),
                 '''attention_mask''': tf.constant([dummy_attention_masks[input_row]]  ),
             }
             __A    :						Optional[int]						 =     serving_func(**__lowerCamelCase  )['''sequences''']
             __A    :						str						 =     test_model.generate(**__lowerCamelCase					,						max_new_tokens=__lowerCamelCase  )
             tf.debugging.assert_equal(__lowerCamelCase					,						__lowerCamelCase  )

 @slow
 @require_tensorflow_text
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     with tempfile.TemporaryDirectory() as tmp_dir:
         # file needed to load the TF tokenizer
         hf_hub_download(repo_id='''google/flan-t5-small'''					,						filename='''spiece.model'''					,						local_dir=__lowerCamelCase  )






         class 		__snake_case					(		tf.keras.layers.Layer     ):





          """simple docstring"""


          def __init__(    self  ):



              '''simple docstring'''




              super().__init__()
              __A    :						List[Any]						 =     text.SentencepieceTokenizer(
                  model=tf.io.gfile.GFile(os.path.join(__lowerCamelCase					,						'''spiece.model'''  )					,						'''rb'''  ).read()  )
              __A    :						Union[str, Any]						 =     TFAutoModelForSeqaSeqLM.from_pretrained('''hf-internal-testing/tiny-random-t5'''  )







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



              '''simple docstring'''




              __A    :						Optional[Any]						 =     self.tokenizer.tokenize(__lowerCamelCase  )
              __A ,       __A    :						List[str]						 =     text.pad_model_inputs(
                  __lowerCamelCase					,						max_seq_length=64					,						pad_value=self.model.config.pad_token_id  )
              __A    :						List[str]						 =     self.model.generate(input_ids=__lowerCamelCase					,						attention_mask=__lowerCamelCase  )
              return self.tokenizer.detokenize(__lowerCamelCase  )






         __A    :						List[Any]						 =     CompleteSentenceTransformer()
         __A    :						int						 =     tf.keras.layers.Input(shape=(1,)					,						dtype=tf.string					,						name='''inputs'''  )
         __A    :						Union[str, Any]						 =     complete_model(__lowerCamelCase  )
         __A    :						int						 =     tf.keras.Model(__lowerCamelCase					,						__lowerCamelCase  )
         keras_model.save(__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Any						 =     {
         '''do_sample''': True,
         '''num_beams''': 1,
         '''top_p''': 0.7,
         '''top_k''': 10,
         '''temperature''': 0.7,
     }
     __A    :						Union[str, Any]						 =     14

     __A    :						Optional[int]						 =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''  )
     __A    :						str						 =     '''Hello, my dog is cute and'''
     __A    :						List[Any]						 =     tokenizer(__lowerCamelCase					,						return_tensors='''tf'''  )
     __A    :						Optional[int]						 =     TFAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''  )

     __A    :						List[Any]						 =     638
     # forces the generation to happen on CPU, to avoid GPU-related quirks
     with tf.device(''':/CPU:0'''  ):
         tf.random.set_seed(0  )
         __A    :						Union[str, Any]						 =     model.generate(**__lowerCamelCase					,						eos_token_id=__lowerCamelCase					,						**__lowerCamelCase  )
     self.assertTrue(expectation == len(generated_tokens[0]  )  )

     __A    :						Union[str, Any]						 =     [638, 198]
     with tf.device(''':/CPU:0'''  ):
         tf.random.set_seed(0  )
         __A    :						List[Any]						 =     model.generate(**__lowerCamelCase					,						eos_token_id=__lowerCamelCase					,						**__lowerCamelCase  )
     self.assertTrue(expectation == len(generated_tokens[0]  )  )







 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Optional[int]						 =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bart'''  )
     __A    :						Union[str, Any]						 =     '''Hugging Face is a technology company based in New York and Paris.'''
     __A    :						List[Any]						 =     bart_tokenizer(__lowerCamelCase					,						return_tensors='''tf'''  ).input_ids
     __A    :						int						 =     TFBartForConditionalGeneration.from_pretrained('''hf-internal-testing/tiny-random-bart'''  )
     __A    :						Any						 =     bart_model.generate(__lowerCamelCase  ).numpy()






     class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





      """simple docstring"""


      def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase=None					,						**__lowerCamelCase  ):



          '''simple docstring'''




          return super().call(__lowerCamelCase					,						**__lowerCamelCase  )






     __A    :						Union[str, Any]						 =     FakeBart.from_pretrained('''hf-internal-testing/tiny-random-bart'''  )
     __A    :						Union[str, Any]						 =     bart_model.generate(__lowerCamelCase					,						foo='''bar'''  ).numpy()
     self.assertTrue(np.array_equal(__lowerCamelCase					,						__lowerCamelCase  )  )






     class 		__snake_case					(		bart_model.model.encoder.__class__     ):





      """simple docstring"""


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



          '''simple docstring'''




          return super().call(__lowerCamelCase					,						**__lowerCamelCase  )






     __A    :						Union[str, Any]						 =     FakeEncoder(bart_model.config					,						bart_model.model.shared  )
     __A    :						Optional[int]						 =     fake_encoder

     # Normal generation still works (the output will be different because the encoder weights are different)
     __A    :						Optional[Any]						 =     bart_model.generate(__lowerCamelCase  ).numpy()
     with self.assertRaises(__lowerCamelCase  ):
         # FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
         bart_model.generate(__lowerCamelCase					,						foo='''bar'''  )











"""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




