

'''simple docstring'''






import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch






@require_torch
class      lowercase_      :




  """simple docstring"""




  @property
  def    SCREAMING_SNAKE_CASE ( self  :	List[str]     ):
   return self.get_dummy_input()






  @property
  def    SCREAMING_SNAKE_CASE ( self  :	int     ):
   if self.block_type == "down":
    return (4, 3_2, 1_6, 1_6)
   elif self.block_type == "mid":
    return (4, 3_2, 3_2, 3_2)
   elif self.block_type == "up":
    return (4, 3_2, 6_4, 6_4)

   raise ValueError(F"'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."     )






  def    SCREAMING_SNAKE_CASE ( self  :	str   ,lowercase__  :	Any=True   ,lowercase__  :	Dict=False   ,lowercase__  :	Dict=False   ,lowercase__  :	Tuple=False   ,):
   __lowercase				    =					4
   __lowercase				    =					3_2
   __lowercase				    =					(3_2, 3_2)

   __lowercase				    =					torch.manual_seed(0     )
   __lowercase				    =					torch.device(lowercase__     )
   __lowercase				    =					(batch_size, num_channels) + sizes
   __lowercase				    =					randn_tensor(lowercase__   ,generator=lowercase__   ,device=lowercase__     )
   __lowercase				    =					{'''hidden_states''': hidden_states}

   if include_temb:
    __lowercase				    =					1_2_8
    __lowercase				    =					randn_tensor((batch_size, temb_channels)   ,generator=lowercase__   ,device=lowercase__     )

   if include_res_hidden_states_tuple:
    __lowercase				    =					torch.manual_seed(1     )
    __lowercase				    =					(randn_tensor(lowercase__   ,generator=lowercase__   ,device=lowercase__     ),)

   if include_encoder_hidden_states:
    __lowercase				    =					floats_tensor((batch_size, 3_2, 3_2)     ).to(lowercase__     )

   if include_skip_sample:
    __lowercase				    =					randn_tensor(((batch_size, 3) + sizes)   ,generator=lowercase__   ,device=lowercase__     )

   return dummy_input






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]     ):
   __lowercase				    =					{
       '''in_channels''': 3_2,
       '''out_channels''': 3_2,
       '''temb_channels''': 1_2_8,
   }
   if self.block_type == "up":
    __lowercase				    =					3_2

   if self.block_type == "mid":
    init_dict.pop('''out_channels'''     )

   __lowercase				    =					self.dummy_input
   return init_dict, inputs_dict






  def    SCREAMING_SNAKE_CASE ( self  :	int   ,lowercase__  :	Union[str, Any]     ):
   __lowercase    ,      __lowercase				    =					self.prepare_init_args_and_inputs_for_common()
   __lowercase				    =					self.block_class(**lowercase__     )
   unet_block.to(lowercase__     )
   unet_block.eval()

   with torch.no_grad():
    __lowercase				    =					unet_block(**lowercase__     )

   if isinstance(lowercase__   ,lowercase__     ):
    __lowercase				    =					output[0]

   self.assertEqual(output.shape   ,self.output_shape     )

   __lowercase				    =					output[0, -1, -3:, -3:]
   __lowercase				    =					torch.tensor(lowercase__     ).to(lowercase__     )
   assert torch_all_close(output_slice.flatten()   ,lowercase__   ,atol=5e-3     )






  @unittest.skipIf(torch_device == '''mps'''   ,'''Training is not supported in mps'''     )
  def    SCREAMING_SNAKE_CASE ( self  :	int     ):
   __lowercase    ,      __lowercase				    =					self.prepare_init_args_and_inputs_for_common()
   __lowercase				    =					self.block_class(**lowercase__     )
   model.to(lowercase__     )
   model.train()
   __lowercase				    =					model(**lowercase__     )

   if isinstance(lowercase__   ,lowercase__     ):
    __lowercase				    =					output[0]

   __lowercase				    =					torch.device(lowercase__     )
   __lowercase				    =					randn_tensor(output.shape   ,device=lowercase__     )
   __lowercase				    =					torch.nn.functional.mse_loss(lowercase__   ,lowercase__     )
   loss.backward()




import argparse
import os
import re


a_     :    List[str]     		=   "src/transformers/models/auto"


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


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Tuple		,				_UpperCamelCase		:	bool = False						)		->				Optional[int]:
 '''simple docstring'''


 with open(_UpperCamelCase		,				'r'		,				encoding='utf-8'						) as f:
  SCREAMING_SNAKE_CASE		    =      f.read()

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

   SCREAMING_SNAKE_CASE		    =      []
   while lines[line_idx].strip() != "]":
    # Blocks either fit in one line or not
    if lines[line_idx].strip() == "(":
     SCREAMING_SNAKE_CASE		    =      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
   SCREAMING_SNAKE_CASE		    =      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		:	bool = False						)		->				Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      [os.path.join(_UpperCamelCase		,				_UpperCamelCase						) for f in os.listdir(_UpperCamelCase						) if f.endswith('.py'						)]
 SCREAMING_SNAKE_CASE		    =      [sort_auto_mapping(_UpperCamelCase		,				overwrite=_UpperCamelCase						) for fname in fnames]

 if not overwrite and any(_UpperCamelCase						):
  SCREAMING_SNAKE_CASE		    =      [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__":
      a_     :    Union[str, Any]     		=   argparse.ArgumentParser()
      parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
      a_     :    List[str]     		=   parser.parse_args()

      sort_all_auto_mappings(not args.check_only)

