'''simple docstring'''


# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async


def 						SCREAMING_SNAKE_CASE       (      a_	:							Optional[int]=None			):
  if subparsers is not None:
    __a			=					subparsers.add_parser('test'			)
  else:
    __a			=					argparse.ArgumentParser('Accelerate test command'			)

  parser.add_argument(
      '--config_file'				,					default=UpperCAmelCase__				,					help=(
          'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
          'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
          'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
          'with \'huggingface\'.'
      )				,					)

  if subparsers is not None:
    parser.set_defaults(func=UpperCAmelCase__			)
  return parser


def 						SCREAMING_SNAKE_CASE       (      a_	:							Dict			):
  __a			=					os.path.sep.join(__file__.split(os.path.sep			)[:-2] + ['test_utils', 'scripts', 'test_script.py']			)

  if args.config_file is None:
    __a			=					script_name
  else:
    __a			=					f"--config_file={args.config_file} {script_name}"

  __a			=					['accelerate-launch'] + test_args.split()
  __a			=					execute_subprocess_async(UpperCAmelCase__				,					env=os.environ.copy()			)
  if result.returncode == 0:
    print('Test is a success! You are ready for your distributed training!'			)


def 						SCREAMING_SNAKE_CASE       (      ):
  __a			=					test_command_parser()
  __a			=					parser.parse_args()
  test_command(UpperCAmelCase__			)


if __name__ == "__main__":
  main()









'''simple docstring'''


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


UpperCAmelCase_ 		=      False





class    __lowercase						(  unittest.TestCase ):







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







   @property
   def 		UpperCamelCase__			(	self      )				->    Tuple:
     return 12







   @property
   def 		UpperCamelCase__			(	self      )				->    Optional[int]:
     return 12







   @property
   def 		UpperCamelCase__			(	self      )				->    List[str]:
     return 32







   @property
   def 		UpperCamelCase__			(	self      )				->    Optional[int]:
     torch.manual_seed(0      )
     __a			=					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 		UpperCamelCase__			(	self      )				->    Optional[int]:
     __a			=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )
     return tokenizer







   @property
   def 		UpperCamelCase__			(	self      )				->    Tuple:
     torch.manual_seed(0      )
     __a			=					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(UpperCamelCase      )







   @property
   def 		UpperCamelCase__			(	self      )				->    Tuple:
     torch.manual_seed(0      )

     __a			=					12
     __a			=					12

     __a			=					{
         '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			=					TransformeraDModel(**UpperCamelCase      )
     return model







   def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
     __a			=					'cpu'

     __a			=					self.dummy_vqvae
     __a			=					self.dummy_text_encoder
     __a			=					self.dummy_tokenizer
     __a			=					self.dummy_transformer
     __a			=					VQDiffusionScheduler(self.num_embed      )
     __a			=					LearnedClassifierFreeSamplingEmbeddings(learnable=UpperCamelCase      )

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

     __a			=					'teddy bear playing in the pool'

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

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

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

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

     __a			=					np.array([0.6_551, 0.6_168, 0.5_008, 0.5_676, 0.5_659, 0.4_295, 0.6_073, 0.5_599, 0.4_992]      )

     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      )				->    int:
     __a			=					'cpu'

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

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

     __a			=					'teddy bear playing in the pool'

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

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

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

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

     __a			=					np.array([0.6_693, 0.6_075, 0.4_959, 0.5_701, 0.5_583, 0.4_333, 0.6_171, 0.5_684, 0.4_988]      )

     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 ):







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







   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     __a			=					load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'      )

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

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

     __a			=					output.images[0]

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



