





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     ):

       @property
       def lowercase (		self: List[Any]		)       -> int:




              """simple docstring"""


              torch.manual_seed(0		)
              UpperCamelCase_          =  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 lowercase (		self: Optional[Any]		)       -> Optional[Any]:




              """simple docstring"""


              torch.manual_seed(0		)
              UpperCamelCase_          =  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 lowercase (		self: Union[str, Any]		)       -> List[str]:




              """simple docstring"""


              torch.manual_seed(0		)
              UpperCamelCase_          =  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=1000					,   )
              return CLIPTextModel(_SCREAMING_SNAKE_CASE		)



       def lowercase (		self: List[str]		)       -> int:




              """simple docstring"""


              UpperCamelCase_          =  self.dummy_uncond_unet
              UpperCamelCase_          =  DDIMScheduler()
              UpperCamelCase_          =  self.dummy_vq_model

              UpperCamelCase_          =  LDMPipeline(unet=_SCREAMING_SNAKE_CASE					,   vqvae=_SCREAMING_SNAKE_CASE					,   scheduler=_SCREAMING_SNAKE_CASE		)
              ldm.to(_SCREAMING_SNAKE_CASE		)
              ldm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  torch.manual_seed(0		)
              UpperCamelCase_          =  ldm(generator=_SCREAMING_SNAKE_CASE					,   num_inference_steps=2					,   output_type="numpy"		).images

              UpperCamelCase_          =  torch.manual_seed(0		)
              UpperCamelCase_          =  ldm(generator=_SCREAMING_SNAKE_CASE					,   num_inference_steps=2					,   output_type="numpy"					,   return_dict=_SCREAMING_SNAKE_CASE		)[0]

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

              assert image.shape == (1, 64, 64, 3)
              UpperCamelCase_          =  np.array([0.85_12, 0.8_18, 0.64_11, 0.68_08, 0.44_65, 0.56_18, 0.46, 0.62_31, 0.51_72]		)
              UpperCamelCase_          =  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     ):

       def lowercase (		self: Optional[int]		)       -> Dict:




              """simple docstring"""


              UpperCamelCase_          =  LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256"		)
              ldm.to(_SCREAMING_SNAKE_CASE		)
              ldm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  torch.manual_seed(0		)
              UpperCamelCase_          =  ldm(generator=_SCREAMING_SNAKE_CASE					,   num_inference_steps=5					,   output_type="numpy"		).images

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

              assert image.shape == (1, 256, 256, 3)
              UpperCamelCase_          =  np.array([0.43_99, 0.4_49_75, 0.4_68_25, 0.4_74, 0.43_59, 0.45_81, 0.4_50_95, 0.43_41, 0.44_47]		)
              UpperCamelCase_          =  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


_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  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_ = False  )     ->		Union[str, Any]:
       with open(UpperCamelCase_					,		"r"					,		encoding="utf-8"  ) as f:
              UpperCamelCase_          =  f.read()

       UpperCamelCase_          =  content.split("\n"  )
       UpperCamelCase_          =  []
       UpperCamelCase_          =  0
       while line_idx < len(UpperCamelCase_  ):
              if _re_intro_mapping.search(lines[line_idx]  ) is not None:
                     UpperCamelCase_          =  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

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

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




def  lowerCAmelCase_ (							UpperCamelCase_ = False  )     ->		List[Any]:
       UpperCamelCase_          =  [os.path.join(UpperCamelCase_					,		UpperCamelCase_  ) for f in os.listdir(UpperCamelCase_  ) if f.endswith(".py"  )]
       UpperCamelCase_          =  [sort_auto_mapping(UpperCamelCase_					,		overwrite=UpperCamelCase_  ) for fname in fnames]

       if not overwrite and any(UpperCamelCase_  ):
              UpperCamelCase_          =  [f for f, d in zip(UpperCamelCase_					,		UpperCamelCase_  ) if d]
              raise ValueError(
                  F'''The following files have auto mappings that need sorting: {", ".join(UpperCamelCase_  )}. 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)

