import argparse
import os
import re


_UpperCAmelCase            =		"""src/transformers/models/auto"""


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
_UpperCAmelCase            =		re.compile(r"""[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict""")
# re pattern that matches identifiers in mappings
_UpperCAmelCase            =		re.compile(r"""\s*\(\s*\"(\S[^\"]+)\"""")




def     UpperCamelCase						(	__lowercase						:			Tuple						,__lowercase						:			bool = False     ):
		'''simple docstring'''




		with open(__lowercase						,'r'						,encoding='utf-8'     ) as f:
				A_		:    int         =				f.read()

		A_		:    Tuple         =				content.split('\n'     )
		A_		:    Any         =				[]
		A_		:    int         =				0
		while line_idx < len(__lowercase     ):
				if _re_intro_mapping.search(lines[line_idx]     ) is not None:
						A_		:    Dict         =				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

						A_		:    int         =				[]
						while lines[line_idx].strip() != "]":
								# Blocks either fit in one line or not
								if lines[line_idx].strip() == "(":
										A_		:    str         =				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
						A_		:    List[Any]         =				sorted(__lowercase						,key=lambda __lowercase     : _re_identifier.search(__lowercase     ).groups()[0]     )
						new_lines += blocks
				else:
						new_lines.append(lines[line_idx]     )
						line_idx += 1

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




def     UpperCamelCase						(	__lowercase						:			bool = False     ):
		'''simple docstring'''




		A_		:    Dict         =				[os.path.join(__lowercase						,__lowercase     ) for f in os.listdir(__lowercase     ) if f.endswith('.py'     )]
		A_		:    Any         =				[sort_auto_mapping(__lowercase						,overwrite=__lowercase     ) for fname in fnames]

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


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

	sort_all_auto_mappings(not args.check_only)







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 UpperCAmelCase						( unittest.TestCase  ):


				'''simple docstring'''






				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						torch.manual_seed(0 )
						A_		:    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   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						torch.manual_seed(0 )
						A_		:    Optional[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   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						torch.manual_seed(0 )
						A_		:    List[Any]         =				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(lowercase )







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[int]         =				self.dummy_uncond_unet
						A_		:    List[Any]         =				DDIMScheduler()
						A_		:    Any         =				self.dummy_vq_model

						A_		:    int         =				LDMPipeline(unet=lowercase		,     vqvae=lowercase		,     scheduler=lowercase )
						ldm.to(lowercase )
						ldm.set_progress_bar_config(disable=lowercase )

						A_		:    Any         =				torch.manual_seed(0 )
						A_		:    Dict         =				ldm(generator=lowercase		,     num_inference_steps=2		,     output_type='numpy' ).images

						A_		:    Any         =				torch.manual_seed(0 )
						A_		:    List[str]         =				ldm(generator=lowercase		,     num_inference_steps=2		,     output_type='numpy'		,     return_dict=lowercase )[0]

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

						assert image.shape == (1, 6_4, 6_4, 3)
						A_		:    List[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 UpperCAmelCase						( unittest.TestCase  ):


				'''simple docstring'''






				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[Any]         =				LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256' )
						ldm.to(lowercase )
						ldm.set_progress_bar_config(disable=lowercase )

						A_		:    Any         =				torch.manual_seed(0 )
						A_		:    List[str]         =				ldm(generator=lowercase		,     num_inference_steps=5		,     output_type='numpy' ).images

						A_		:    List[Any]         =				image[0, -3:, -3:, -1]

						assert image.shape == (1, 2_5_6, 2_5_6, 3)
						A_		:    Tuple         =				np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447] )
						A_		:    Dict         =				1E-2 if torch_device != 'mps' else 3E-2

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






