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        lowerCamelCase__				(   unittest.TestCase				):





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	Union[str, Any]						):
										torch.manual_seed(0						)
										_lowerCAmelCase							=							UNetaDModel(
										    block_out_channels=(32, 64)  ,  layers_per_block=2  ,  sample_size=32  ,  in_channels=3  ,  out_channels=3  ,  down_block_types=('DownBlock2D', 'AttnDownBlock2D')  ,  up_block_types=('AttnUpBlock2D', 'UpBlock2D')  ,  )
										return model





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										torch.manual_seed(0						)
										_lowerCAmelCase							=							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  ,  )
										return model





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]						):
										torch.manual_seed(0						)
										_lowerCAmelCase							=							CLIPTextConfig(
										    bos_token_id=0  ,  eos_token_id=2  ,  hidden_size=32  ,  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(lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										_lowerCAmelCase							=							self.dummy_uncond_unet
										_lowerCAmelCase							=							DDIMScheduler()
										_lowerCAmelCase							=							self.dummy_vq_model

										_lowerCAmelCase							=							LDMPipeline(unet=lowercase__  ,  vqvae=lowercase__  ,  scheduler=lowercase__						)
										ldm.to(lowercase__						)
										ldm.set_progress_bar_config(disable=lowercase__						)

										_lowerCAmelCase							=							torch.manual_seed(0						)
										_lowerCAmelCase							=							ldm(generator=lowercase__  ,  num_inference_steps=2  ,  output_type='numpy'						).images

										_lowerCAmelCase							=							torch.manual_seed(0						)
										_lowerCAmelCase							=							ldm(generator=lowercase__  ,  num_inference_steps=2  ,  output_type='numpy'  ,  return_dict=lowercase__						)[0]

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

										assert image.shape == (1, 64, 64, 3)
										_lowerCAmelCase							=							np.array([0.8_5_1_2, 0.8_1_8, 0.6_4_1_1, 0.6_8_0_8, 0.4_4_6_5, 0.5_6_1_8, 0.4_6, 0.6_2_3_1, 0.5_1_7_2]						)
										_lowerCAmelCase							=							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        lowerCamelCase__				(   unittest.TestCase				):





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]						):
										_lowerCAmelCase							=							LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256'						)
										ldm.to(lowercase__						)
										ldm.set_progress_bar_config(disable=lowercase__						)

										_lowerCAmelCase							=							torch.manual_seed(0						)
										_lowerCAmelCase							=							ldm(generator=lowercase__  ,  num_inference_steps=5  ,  output_type='numpy'						).images

										_lowerCAmelCase							=							image[0, -3:, -3:, -1]

										assert image.shape == (1, 2_56, 2_56, 3)
										_lowerCAmelCase							=							np.array([0.4_3_9_9, 0.4_4_9_7_5, 0.4_6_8_2_5, 0.4_7_4, 0.4_3_5_9, 0.4_5_8_1, 0.4_5_0_9_5, 0.4_3_4_1, 0.4_4_4_7]						)
										_lowerCAmelCase							=							1e-2 if torch_device != 'mps' else 3e-2

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







import argparse
import os
import re


_lowercase:	Optional[Any]       			=		'''src/transformers/models/auto'''


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
_lowercase:	Union[str, Any]       			=		re.compile(R'''[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict''')
# re pattern that matches identifiers in mappings
_lowercase:	Optional[Any]       			=		re.compile(R'''\s*\(\s*"(\S[^"]+)"''')






def _lowerCamelCase  (		snake_case     , snake_case = False  ):
							with open(snake_case     , 'r'     , encoding='utf-8'  ) as f:
														_lowerCAmelCase							=							f.read()

							_lowerCAmelCase							=							content.split('\n'  )
							_lowerCAmelCase							=							[]
							_lowerCAmelCase							=							0
							while line_idx < len(snake_case  ):
														if _re_intro_mapping.search(lines[line_idx]  ) is not None:
																					_lowerCAmelCase							=							len(re.search(R'^(\s*)\S'     , lines[line_idx]  ).groups()[0]  ) + 8
																					# Start of a new mapping!
																					while not lines[line_idx].startswith(' ' * indent + '('  ):
																												new_lines.append(lines[line_idx]  )
																												line_idx += 1

																					_lowerCAmelCase							=							[]
																					while lines[line_idx].strip() != "]":
																												# Blocks either fit in one line or not
																												if lines[line_idx].strip() == "(":
																																			_lowerCAmelCase							=							line_idx
																																			while not lines[line_idx].startswith(' ' * indent + ')'  ):
																																										line_idx += 1
																																			blocks.append('\n'.join(lines[start_idx : line_idx + 1]  )  )
																												else:
																																			blocks.append(lines[line_idx]  )
																												line_idx += 1

																					# Sort blocks by their identifiers
																					_lowerCAmelCase							=							sorted(snake_case     , key=lambda snake_case  : _re_identifier.search(snake_case  ).groups()[0]  )
																					new_lines += blocks
														else:
																					new_lines.append(lines[line_idx]  )
																					line_idx += 1

							if overwrite:
														with open(snake_case     , 'w'     , encoding='utf-8'  ) as f:
																					f.write('\n'.join(snake_case  )  )
							elif "\n".join(snake_case  ) != content:
														return True






def _lowerCamelCase  (		snake_case = False  ):
							_lowerCAmelCase							=							[os.path.join(snake_case     , snake_case  ) for f in os.listdir(snake_case  ) if f.endswith('.py'  )]
							_lowerCAmelCase							=							[sort_auto_mapping(snake_case     , overwrite=snake_case  ) for fname in fnames]

							if not overwrite and any(snake_case  ):
														_lowerCAmelCase							=							[f for f, d in zip(snake_case     , snake_case  ) if d]
														raise ValueError(
														    F'The following files have auto mappings that need sorting: {", ".join(snake_case  )}. Run `make style` to fix'
														    ' this.'  )


if __name__ == "__main__":
				_lowercase:	Any       			=		argparse.ArgumentParser()
				parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
				_lowercase:	Tuple       			=		parser.parse_args()

				sort_all_auto_mappings(not args.check_only)






