import argparse
from collections import defaultdict
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Any      ,							UpperCAmelCase_   :    Union[str, Any]      ,							UpperCAmelCase_   :    List[str]      ,							UpperCAmelCase_   :    List[Any]      ,							UpperCAmelCase_   :    Union[str, Any]  )							->      int:

	'''simple docstring'''





	__snake_case      :							Optional[Any]						=			F"{file}_{class_name}_{test_name}"
	done_test[_id] += 1

	with open(UpperCAmelCase_      ,							'r'  ) as f:
		__snake_case      :							Union[str, Any]						=			f.readlines()

	__snake_case      :							List[str]						=			F"class {class_name}("
	__snake_case      :							str						=			F"{4 * ' '}def {test_name}("
	__snake_case      :							str						=			F"{8 * ' '}{correct_line.split()[0]}"
	__snake_case      :							List[str]						=			F"{16 * ' '}{correct_line.split()[0]}"
	__snake_case      :							Dict						=			False
	__snake_case      :							Tuple						=			False
	__snake_case      :							int						=			False
	__snake_case      :							List[Any]						=			False
	__snake_case      :							Tuple						=			0
	__snake_case      :							Any						=			0

	__snake_case      :							Tuple						=			[]
	for line in lines:
		if line.startswith(UpperCAmelCase_  ):
			__snake_case      :							Any						=			True
		elif in_class and line.startswith(UpperCAmelCase_  ):
			__snake_case      :							List[str]						=			True
		elif in_class and in_func and (line.startswith(UpperCAmelCase_  ) or line.startswith(UpperCAmelCase_  )):
			__snake_case      :							Union[str, Any]						=			len(line.split(correct_line.split()[0]  )[0]  )
			count += 1

			if count == done_test[_id]:
				__snake_case      :							str						=			True

		if in_class and in_func and in_line:
			if ")" not in line:
				continue
			else:
				__snake_case      :							Tuple						=			True

		if in_class and in_func and in_line and insert_line:
			new_lines.append(F"{spaces * ' '}{correct_line}"  )
			__snake_case      :							Optional[int]						=			False
		else:
			new_lines.append(UpperCAmelCase_  )

	with open(UpperCAmelCase_      ,							'w'  ) as f:
		for line in new_lines:
			f.write(UpperCAmelCase_  )
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Optional[Any]      ,							UpperCAmelCase_   :    int=None  )							->      str:

	'''simple docstring'''





	if fail is not None:
		with open(UpperCAmelCase_      ,							'r'  ) as f:
			__snake_case      :							Dict						=			{l.strip() for l in f.readlines()}
	else:
		__snake_case      :							int						=			None

	with open(UpperCAmelCase_      ,							'r'  ) as f:
		__snake_case      :							int						=			f.readlines()

	__snake_case      :							Tuple						=			defaultdict(UpperCAmelCase_  )
	for line in correct_lines:
		__snake_case      :							int						=			line.split(';'  )
		if test_failures is None or "::".join([file, class_name, test_name]  ) in test_failures:
			overwrite_file(UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_  )


if __name__ == "__main__":
	_a					:					Dict=			argparse.ArgumentParser()
	parser.add_argument("--correct_filename", help="filename of tests with expected result")
	parser.add_argument("--fail_filename", help="filename of test failures", type=str, default=None)
	_a					:					Any=			parser.parse_args()

	main(args.correct_filename, args.fail_filename)






"""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


_a					:					List[str]=			False

class 		UpperCamelCase      (   unittest.TestCase						):



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



				@property
				def  _lowercase					(self    :				Optional[Any])   ->     Union[str, Any]:
					return 12



				@property
				def  _lowercase					(self    :				Dict)   ->     Union[str, Any]:
					return 12



				@property
				def  _lowercase					(self    :				int)   ->     Tuple:
					return 32



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



				@property
				def  _lowercase					(self    :				Union[str, Any])   ->     Optional[int]:
					torch.manual_seed(0)
					__snake_case      :							Any						=			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=10_00     ,  )
					return CLIPTextModel(_A)



				@property
				def  _lowercase					(self    :				Union[str, Any])   ->     Dict:
					torch.manual_seed(0)

					__snake_case      :							Any						=			12
					__snake_case      :							int						=			12

					__snake_case      :							List[Any]						=			{
					    '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',
					}

					__snake_case      :							Union[str, Any]						=			TransformeraDModel(**_A)
					return model



				def  _lowercase					(self    :				Union[str, Any])   ->     Dict:
					__snake_case      :							Tuple						=			'cpu'

					__snake_case      :							List[str]						=			self.dummy_vqvae
					__snake_case      :							str						=			self.dummy_text_encoder
					__snake_case      :							Optional[Any]						=			self.dummy_tokenizer
					__snake_case      :							Dict						=			self.dummy_transformer
					__snake_case      :							Optional[int]						=			VQDiffusionScheduler(self.num_embed)
					__snake_case      :							List[Any]						=			LearnedClassifierFreeSamplingEmbeddings(learnable=_A)

					__snake_case      :							List[Any]						=			VQDiffusionPipeline(
					    vqvae=_A     ,  text_encoder=_A     ,  tokenizer=_A     ,  transformer=_A     ,  scheduler=_A     ,  learned_classifier_free_sampling_embeddings=_A     ,  )
					__snake_case      :							List[Any]						=			pipe.to(_A)
					pipe.set_progress_bar_config(disable=_A)

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

					__snake_case      :							str						=			torch.Generator(device=_A).manual_seed(0)
					__snake_case      :							Union[str, Any]						=			pipe([prompt]     ,  generator=_A     ,  num_inference_steps=2     ,  output_type='np')
					__snake_case      :							Optional[int]						=			output.images

					__snake_case      :							int						=			torch.Generator(device=_A).manual_seed(0)
					__snake_case      :							Tuple						=			pipe(
					    [prompt]     ,  generator=_A     ,  output_type='np'     ,  return_dict=_A     ,  num_inference_steps=2)[0]

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

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

					__snake_case      :							str						=			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  _lowercase					(self    :				Tuple)   ->     Optional[int]:
					__snake_case      :							Optional[Any]						=			'cpu'

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

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

					__snake_case      :							Union[str, Any]						=			'teddy bear playing in the pool'

					__snake_case      :							Optional[int]						=			torch.Generator(device=_A).manual_seed(0)
					__snake_case      :							Tuple						=			pipe([prompt]     ,  generator=_A     ,  num_inference_steps=2     ,  output_type='np')
					__snake_case      :							Optional[Any]						=			output.images

					__snake_case      :							str						=			torch.Generator(device=_A).manual_seed(0)
					__snake_case      :							Dict						=			pipe(
					    [prompt]     ,  generator=_A     ,  output_type='np'     ,  return_dict=_A     ,  num_inference_steps=2)[0]

					__snake_case      :							int						=			image[0, -3:, -3:, -1]
					__snake_case      :							Tuple						=			image_from_tuple[0, -3:, -3:, -1]

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

					__snake_case      :							Optional[Any]						=			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 		UpperCamelCase      (   unittest.TestCase						):



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






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

					__snake_case      :							Union[str, Any]						=			VQDiffusionPipeline.from_pretrained('microsoft/vq-diffusion-ithq')
					__snake_case      :							Tuple						=			pipeline.to(_A)
					pipeline.set_progress_bar_config(disable=_A)

					# requires GPU generator for gumbel softmax
					# don't use GPU generator in tests though
					__snake_case      :							Optional[int]						=			torch.Generator(device=_A).manual_seed(0)
					__snake_case      :							int						=			pipeline(
					    'teddy bear playing in the pool'     ,  num_images_per_prompt=1     ,  generator=_A     ,  output_type='np'     ,  )

					__snake_case      :							int						=			output.images[0]

					assert image.shape == (2_56, 2_56, 3)
					assert np.abs(expected_image - image).max() < 2.0





