


'''simple docstring'''





import unittest

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

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class 				SCREAMING_SNAKE_CASE__						(       unittest.TestCase    ):
      @property
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    Optional[int]:
             torch.manual_seed(0  )
             a_      :	Optional[int]							=						UNetaDModel(
                 block_out_channels=(3_2, 6_4)  ,							layers_per_block=2  ,							sample_size=3_2  ,							in_channels=3  ,							out_channels=3  ,							down_block_types=('DownBlock2D', 'AttnDownBlock2D')  ,							up_block_types=('AttnUpBlock2D', 'UpBlock2D')  ,							)
             return model
      @property
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    str:
             torch.manual_seed(0  )
             a_      :	Any							=						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  ,							)
             return model
      @property
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    List[Any]:
             torch.manual_seed(0  )
             a_      :	int							=						CLIPTextConfig(
                 bos_token_id=0  ,							eos_token_id=2  ,							hidden_size=3_2  ,							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__  )

      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Optional[Any]:
             a_      :	int							=						self.dummy_uncond_unet
             a_      :	List[str]							=						DDIMScheduler()
             a_      :	List[Any]							=						self.dummy_vq_model

             a_      :	List[Any]							=						LDMPipeline(unet=SCREAMING_SNAKE_CASE__  ,							vqvae=SCREAMING_SNAKE_CASE__  ,							scheduler=SCREAMING_SNAKE_CASE__  )
             ldm.to(SCREAMING_SNAKE_CASE__  )
             ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__  )

             a_      :	List[Any]							=						torch.manual_seed(0  )
             a_      :	Any							=						ldm(generator=SCREAMING_SNAKE_CASE__  ,							num_inference_steps=2  ,							output_type='numpy'  ).images

             a_      :	Dict							=						torch.manual_seed(0  )
             a_      :	str							=						ldm(generator=SCREAMING_SNAKE_CASE__  ,							num_inference_steps=2  ,							output_type='numpy'  ,							return_dict=SCREAMING_SNAKE_CASE__  )[0]

             a_      :	Optional[int]							=						image[0, -3:, -3:, -1]
             a_      :	Union[str, Any]							=						image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (1, 6_4, 6_4, 3)
             a_      :	str							=						np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]  )
             a_      :	str							=						1E-2 if torch_device != 'mps' else 3E-2

             assert np.abs(image_slice.flatten() - expected_slice  ).max() < tolerance
             assert np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < tolerance
@slow
@require_torch
class 				SCREAMING_SNAKE_CASE__						(       unittest.TestCase    ):
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    Any:
             a_      :	Dict							=						LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256'  )
             ldm.to(SCREAMING_SNAKE_CASE__  )
             ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__  )

             a_      :	Tuple							=						torch.manual_seed(0  )
             a_      :	Any							=						ldm(generator=SCREAMING_SNAKE_CASE__  ,							num_inference_steps=5  ,							output_type='numpy'  ).images

             a_      :	int							=						image[0, -3:, -3:, -1]

             assert image.shape == (1, 2_5_6, 2_5_6, 3)
             a_      :	Optional[Any]							=						np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447]  )
             a_      :	str							=						1E-2 if torch_device != 'mps' else 3E-2

             assert np.abs(image_slice.flatten() - expected_slice  ).max() < tolerance






from argparse import ArgumentParser

from .env import EnvironmentCommand







def  SCREAMING_SNAKE_CASE_		(    )			->	str:

       """simple docstring"""

       a_      :	str							=						ArgumentParser('Diffusers CLI tool'						,					usage='diffusers-cli <command> [<args>]'       )
       a_      :	Any							=						parser.add_subparsers(help='diffusers-cli command helpers'       )

       # Register commands
       EnvironmentCommand.register_subcommand(__A       )

       # Let's go
       a_      :	int							=						parser.parse_args()

       if not hasattr(__A						,					'func'       ):
              parser.print_help()
              exit(1       )

       # Run
       a_      :	Union[str, Any]							=						args.func(__A       )
       service.run()


if __name__ == "__main__":
   main()


