


import gc
import unittest

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

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


snake_case				:    List[str]       				=     False







class 		_snake_case  (      unittest.TestCase							):




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       return 12




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       return 12




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       return 32




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       torch.manual_seed(0    )
       a						:Optional[Any] 							=       VQModel(
           block_out_channels=[32, 64]					,  in_channels=3					,  out_channels=3					,  down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']					,  up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']					,  latent_channels=3					,  num_vq_embeddings=self.num_embed					,  vq_embed_dim=3					,  )
       return model




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Optional[Any] 							=       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )
       return tokenizer




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       torch.manual_seed(0    )
       a						:int 							=       CLIPTextConfig(
           bos_token_id=0					,  eos_token_id=2					,  hidden_size=self.text_embedder_hidden_size					,  intermediate_size=37					,  layer_norm_eps=1e-05					,  num_attention_heads=4					,  num_hidden_layers=5					,  pad_token_id=1					,  vocab_size=1000					,  )
       return CLIPTextModel(_lowerCamelCase    )




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       torch.manual_seed(0    )

       a						:str 							=       12
       a						:Optional[Any] 							=       12

       a						:Dict 							=       {
           '''attention_bias''': True,
           '''cross_attention_dim''': 32,
           '''attention_head_dim''': height * width,
           '''num_attention_heads''': 1,
           '''num_vector_embeds''': self.num_embed,
           '''num_embeds_ada_norm''': self.num_embeds_ada_norm,
           '''norm_num_groups''': 32,
           '''sample_size''': width,
           '''activation_fn''': '''geglu-approximate''',
       }

       a						:Dict 							=       TransformeraDModel(**_lowerCamelCase    )
       return model




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Union[str, Any] 							=       '''cpu'''

       a						:Optional[int] 							=       self.dummy_vqvae
       a						:Any 							=       self.dummy_text_encoder
       a						:Tuple 							=       self.dummy_tokenizer
       a						:List[str] 							=       self.dummy_transformer
       a						:str 							=       VQDiffusionScheduler(self.num_embed    )
       a						:Optional[int] 							=       LearnedClassifierFreeSamplingEmbeddings(learnable=_lowerCamelCase    )

       a						:Union[str, Any] 							=       VQDiffusionPipeline(
           vqvae=_lowerCamelCase					,  text_encoder=_lowerCamelCase					,  tokenizer=_lowerCamelCase					,  transformer=_lowerCamelCase					,  scheduler=_lowerCamelCase					,  learned_classifier_free_sampling_embeddings=_lowerCamelCase					,  )
       a						:List[Any] 							=       pipe.to(_lowerCamelCase    )
       pipe.set_progress_bar_config(disable=_lowerCamelCase    )

       a						:List[str] 							=       '''teddy bear playing in the pool'''

       a						:Union[str, Any] 							=       torch.Generator(device=_lowerCamelCase    ).manual_seed(0    )
       a						:List[str] 							=       pipe([prompt]					,  generator=_lowerCamelCase					,  num_inference_steps=2					,  output_type='''np'''    )
       a						:List[str] 							=       output.images

       a						:List[Any] 							=       torch.Generator(device=_lowerCamelCase    ).manual_seed(0    )
       a						:Any 							=       pipe(
           [prompt]					,  generator=_lowerCamelCase					,  output_type='''np'''					,  return_dict=_lowerCamelCase					,  num_inference_steps=2    )[0]

       a						:List[Any] 							=       image[0, -3:, -3:, -1]
       a						:Tuple 							=       image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 24, 24, 3)

       a						:int 							=       np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]    )

       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        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[str] 							=       '''cpu'''

       a						:Union[str, Any] 							=       self.dummy_vqvae
       a						:Optional[Any] 							=       self.dummy_text_encoder
       a						:int 							=       self.dummy_tokenizer
       a						:Optional[int] 							=       self.dummy_transformer
       a						:Optional[int] 							=       VQDiffusionScheduler(self.num_embed    )
       a						:str 							=       LearnedClassifierFreeSamplingEmbeddings(
           learnable=_lowerCamelCase					,  hidden_size=self.text_embedder_hidden_size					,  length=tokenizer.model_max_length    )

       a						:Tuple 							=       VQDiffusionPipeline(
           vqvae=_lowerCamelCase					,  text_encoder=_lowerCamelCase					,  tokenizer=_lowerCamelCase					,  transformer=_lowerCamelCase					,  scheduler=_lowerCamelCase					,  learned_classifier_free_sampling_embeddings=_lowerCamelCase					,  )
       a						:int 							=       pipe.to(_lowerCamelCase    )
       pipe.set_progress_bar_config(disable=_lowerCamelCase    )

       a						:Optional[Any] 							=       '''teddy bear playing in the pool'''

       a						:List[str] 							=       torch.Generator(device=_lowerCamelCase    ).manual_seed(0    )
       a						:Tuple 							=       pipe([prompt]					,  generator=_lowerCamelCase					,  num_inference_steps=2					,  output_type='''np'''    )
       a						:Union[str, Any] 							=       output.images

       a						:Union[str, Any] 							=       torch.Generator(device=_lowerCamelCase    ).manual_seed(0    )
       a						:int 							=       pipe(
           [prompt]					,  generator=_lowerCamelCase					,  output_type='''np'''					,  return_dict=_lowerCamelCase					,  num_inference_steps=2    )[0]

       a						:Union[str, Any] 							=       image[0, -3:, -3:, -1]
       a						:List[str] 							=       image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 24, 24, 3)

       a						:Optional[int] 							=       np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]    )

       assert np.abs(image_slice.flatten() - expected_slice    ).max() < 2.0
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1e-2







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




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Optional[int] 							=       load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'''    )

       a						:Any 							=       VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq'''    )
       a						:List[Any] 							=       pipeline.to(_lowerCamelCase    )
       pipeline.set_progress_bar_config(disable=_lowerCamelCase    )

       # requires GPU generator for gumbel softmax
       # don't use GPU generator in tests though
       a						:Dict 							=       torch.Generator(device=_lowerCamelCase    ).manual_seed(0    )
       a						:Any 							=       pipeline(
           '''teddy bear playing in the pool'''					,  num_images_per_prompt=1					,  generator=_lowerCamelCase					,  output_type='''np'''					,  )

       a						:Union[str, Any] 							=       output.images[0]

       assert image.shape == (256, 256, 3)
       assert np.abs(expected_image - image    ).max() < 2.0









def 				__lowerCamelCase						( UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		int						):




      """simple docstring"""





      while b:
            a							,				a						:Optional[Any] 							=       b, a % b
      return a
def 				__lowerCamelCase						( UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		int						):




      """simple docstring"""





      return a if b == 0 else euclidean_gcd_recursive(UpperCAmelCase_ ,  a % b						)
def 				__lowerCamelCase						( ):




      """simple docstring"""





      print(F'''euclidean_gcd(3, 5) = {euclidean_gcd(3 ,  5						)}'''						)
      print(F'''euclidean_gcd(5, 3) = {euclidean_gcd(5 ,  3						)}'''						)
      print(F'''euclidean_gcd(1, 3) = {euclidean_gcd(1 ,  3						)}'''						)
      print(F'''euclidean_gcd(3, 6) = {euclidean_gcd(3 ,  6						)}'''						)
      print(F'''euclidean_gcd(6, 3) = {euclidean_gcd(6 ,  3						)}'''						)

      print(F'''euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 ,  5						)}'''						)
      print(F'''euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 ,  3						)}'''						)
      print(F'''euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 ,  3						)}'''						)
      print(F'''euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 ,  6						)}'''						)
      print(F'''euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 ,  3						)}'''						)


if __name__ == "__main__":
     main()





