
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


__a  			=					False
class  lowercase__(				unittest.TestCase							):



	"""simple docstring"""




	def 						_lowercase  (       self  :			List[Any]       )       ->     int:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()
	@property
	def 						_lowercase  (       self  :			str       )       ->     str:
					return 1_2
	@property
	def 						_lowercase  (       self  :			Optional[int]       )       ->     Union[str, Any]:
					return 1_2
	@property
	def 						_lowercase  (       self  :			List[Any]       )       ->     Any:
					return 3_2
	@property
	def 						_lowercase  (       self  :			List[str]       )       ->     int:
					torch.manual_seed(0       )
					lowercase_       =					VQModel(
					    block_out_channels=[3_2, 6_4]       ,							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 						_lowercase  (       self  :			Optional[Any]       )       ->     Optional[int]:
					lowercase_       =					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )
					return tokenizer
	@property
	def 						_lowercase  (       self  :			Tuple       )       ->     Union[str, Any]:
					torch.manual_seed(0       )
					lowercase_       =					CLIPTextConfig(
					    bos_token_id=0       ,							eos_token_id=2       ,							hidden_size=self.text_embedder_hidden_size       ,							intermediate_size=3_7       ,							layer_norm_eps=1e-05       ,							num_attention_heads=4       ,							num_hidden_layers=5       ,							pad_token_id=1       ,							vocab_size=1_0_0_0       ,							)
					return CLIPTextModel(SCREAMING_SNAKE_CASE_       )
	@property
	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     int:
					torch.manual_seed(0       )

					lowercase_       =					1_2
					lowercase_       =					1_2

					lowercase_       =					{
					    '''attention_bias''': True,
					    '''cross_attention_dim''': 3_2,
					    '''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''': 3_2,
					    '''sample_size''': width,
					    '''activation_fn''': '''geglu-approximate''',
					}

					lowercase_       =					TransformeraDModel(**SCREAMING_SNAKE_CASE_       )
					return model
	def 						_lowercase  (       self  :			List[str]       )       ->     Optional[int]:
					lowercase_       =					'''cpu'''

					lowercase_       =					self.dummy_vqvae
					lowercase_       =					self.dummy_text_encoder
					lowercase_       =					self.dummy_tokenizer
					lowercase_       =					self.dummy_transformer
					lowercase_       =					VQDiffusionScheduler(self.num_embed       )
					lowercase_       =					LearnedClassifierFreeSamplingEmbeddings(learnable=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					VQDiffusionPipeline(
					    vqvae=SCREAMING_SNAKE_CASE_       ,							text_encoder=SCREAMING_SNAKE_CASE_       ,							tokenizer=SCREAMING_SNAKE_CASE_       ,							transformer=SCREAMING_SNAKE_CASE_       ,							scheduler=SCREAMING_SNAKE_CASE_       ,							learned_classifier_free_sampling_embeddings=SCREAMING_SNAKE_CASE_       ,							)
					lowercase_       =					pipe.to(SCREAMING_SNAKE_CASE_       )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					'''teddy bear playing in the pool'''

					lowercase_       =					torch.Generator(device=SCREAMING_SNAKE_CASE_       ).manual_seed(0       )
					lowercase_       =					pipe([prompt]       ,							generator=SCREAMING_SNAKE_CASE_       ,							num_inference_steps=2       ,							output_type='''np'''       )
					lowercase_       =					output.images

					lowercase_       =					torch.Generator(device=SCREAMING_SNAKE_CASE_       ).manual_seed(0       )
					lowercase_       =					pipe(
					    [prompt]       ,							generator=SCREAMING_SNAKE_CASE_       ,							output_type='''np'''       ,							return_dict=SCREAMING_SNAKE_CASE_       ,							num_inference_steps=2       )[0]

					lowercase_       =					image[0, -3:, -3:, -1]
					lowercase_       =					image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 2_4, 2_4, 3)

					lowercase_       =					np.array([0.65_51, 0.61_68, 0.50_08, 0.56_76, 0.56_59, 0.42_95, 0.60_73, 0.55_99, 0.49_92]       )

					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 						_lowercase  (       self  :			Optional[Any]       )       ->     Tuple:
					lowercase_       =					'''cpu'''

					lowercase_       =					self.dummy_vqvae
					lowercase_       =					self.dummy_text_encoder
					lowercase_       =					self.dummy_tokenizer
					lowercase_       =					self.dummy_transformer
					lowercase_       =					VQDiffusionScheduler(self.num_embed       )
					lowercase_       =					LearnedClassifierFreeSamplingEmbeddings(
					    learnable=SCREAMING_SNAKE_CASE_       ,							hidden_size=self.text_embedder_hidden_size       ,							length=tokenizer.model_max_length       )

					lowercase_       =					VQDiffusionPipeline(
					    vqvae=SCREAMING_SNAKE_CASE_       ,							text_encoder=SCREAMING_SNAKE_CASE_       ,							tokenizer=SCREAMING_SNAKE_CASE_       ,							transformer=SCREAMING_SNAKE_CASE_       ,							scheduler=SCREAMING_SNAKE_CASE_       ,							learned_classifier_free_sampling_embeddings=SCREAMING_SNAKE_CASE_       ,							)
					lowercase_       =					pipe.to(SCREAMING_SNAKE_CASE_       )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					'''teddy bear playing in the pool'''

					lowercase_       =					torch.Generator(device=SCREAMING_SNAKE_CASE_       ).manual_seed(0       )
					lowercase_       =					pipe([prompt]       ,							generator=SCREAMING_SNAKE_CASE_       ,							num_inference_steps=2       ,							output_type='''np'''       )
					lowercase_       =					output.images

					lowercase_       =					torch.Generator(device=SCREAMING_SNAKE_CASE_       ).manual_seed(0       )
					lowercase_       =					pipe(
					    [prompt]       ,							generator=SCREAMING_SNAKE_CASE_       ,							output_type='''np'''       ,							return_dict=SCREAMING_SNAKE_CASE_       ,							num_inference_steps=2       )[0]

					lowercase_       =					image[0, -3:, -3:, -1]
					lowercase_       =					image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 2_4, 2_4, 3)

					lowercase_       =					np.array([0.66_93, 0.60_75, 0.49_59, 0.57_01, 0.55_83, 0.43_33, 0.61_71, 0.56_84, 0.49_88]       )

					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  lowercase__(				unittest.TestCase							):



	"""simple docstring"""




	def 						_lowercase  (       self  :			Optional[Any]       )       ->     int:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()




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

					lowercase_       =					VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq'''       )
					lowercase_       =					pipeline.to(SCREAMING_SNAKE_CASE_       )
					pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

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

					lowercase_       =					output.images[0]

					assert image.shape == (2_5_6, 2_5_6, 3)
					assert np.abs(expected_image - image       ).max() < 2.0






import os




def 				a						(							):


				'''simple docstring'''
				lowercase_       =					os.path.join(os.path.dirname(snake_case__					)						,   '''num.txt'''					)
				with open(snake_case__					) as file_hand:
								return str(sum(int(snake_case__					) for line in file_hand					)					)[:10]


if __name__ == "__main__":
						print(solution())




