


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






# 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.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser




def       _snake_case	(   ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		int				      =    ArgumentParser("Accelerate CLI tool"   , usage="accelerate <command> [<args>]"   , allow_abbrev=lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    parser.add_subparsers(help="accelerate command helpers"	)

		# Register commands
		get_config_parser(subparsers=lowerCAmelCase	)
		env_command_parser(subparsers=lowerCAmelCase	)
		launch_command_parser(subparsers=lowerCAmelCase	)
		tpu_command_parser(subparsers=lowerCAmelCase	)
		test_command_parser(subparsers=lowerCAmelCase	)

		# Let's go
		SCREAMING_SNAKE_CASE_  :		Dict				      =    parser.parse_args()

		if not hasattr(lowerCAmelCase   , "func"	):
				parser.print_help()
				exit(1	)

		# Run
		args.func(lowerCAmelCase	)


if __name__ == "__main__":
		main()
