


import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

__UpperCamelCase							:							Dict    							=					logging.get_logger(__name__)

def       _a	(					SCREAMING_SNAKE_CASE						:					Any     ,			SCREAMING_SNAKE_CASE						:					bool = True  ):







 """simple docstring"""



 UpperCamelCase__       :     Tuple 							= {}
 for name in model.attn_processors.keys():
  UpperCamelCase__       :     Optional[Any] 							= name.endswith('''attn2.processor'''  ) and not (
      name.startswith('''transformer_in'''  ) or '''temp_attentions''' in name.split('''.'''  )
  )
  UpperCamelCase__       :     str 							= model.config.cross_attention_dim if has_cross_attention else None
  if name.startswith('''mid_block'''  ):
   UpperCamelCase__       :     Optional[Any] 							= model.config.block_out_channels[-1]
  elif name.startswith('''up_blocks'''  ):
   UpperCamelCase__       :     int 							= int(name[len('''up_blocks.'''  )]  )
   UpperCamelCase__       :     Any 							= list(reversed(model.config.block_out_channels  )  )[block_id]
  elif name.startswith('''down_blocks'''  ):
   UpperCamelCase__       :     List[str] 							= int(name[len('''down_blocks.'''  )]  )
   UpperCamelCase__       :     Optional[Any] 							= model.config.block_out_channels[block_id]
  elif name.startswith('''transformer_in'''  ):
   # Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
   UpperCamelCase__       :     int 							= 8 * model.config.attention_head_dim

  UpperCamelCase__       :     str 							= LoRAAttnProcessor(hidden_size=SCREAMING_SNAKE_CASE     ,			cross_attention_dim=SCREAMING_SNAKE_CASE  )
  UpperCamelCase__       :     Tuple 							= lora_attn_procs[name].to(model.device  )

  if mock_weights:
   # add 1 to weights to mock trained weights
   with torch.no_grad():
    lora_attn_procs[name].to_q_lora.up.weight += 1
    lora_attn_procs[name].to_k_lora.up.weight += 1
    lora_attn_procs[name].to_v_lora.up.weight += 1
    lora_attn_procs[name].to_out_lora.up.weight += 1

 return lora_attn_procs






@skip_mps
class    __magic_name__						(  __lowerCAmelCase       ,					__lowerCAmelCase       ,					unittest.TestCase):
     A:		List[Any]								=   UNetaDConditionModel
     A:		Tuple								=   "sample"


     @property
     def   UpperCAmelCase__						(   self				:  Any      )	->   Optional[int]:





      '''simple docstring'''






      UpperCamelCase__       :     Optional[int] 							= 4
      UpperCamelCase__       :     List[str] 							= 4
      UpperCamelCase__       :     List[Any] 							= 4
      UpperCamelCase__       :     Union[str, Any] 							= (32, 32)

      UpperCamelCase__       :     Tuple 							= floats_tensor((batch_size, num_channels, num_frames) + sizes      ).to(lowerCamelCase__      )
      UpperCamelCase__       :     Optional[Any] 							= torch.tensor([10]      ).to(lowerCamelCase__      )
      UpperCamelCase__       :     List[Any] 							= floats_tensor((batch_size, 4, 32)      ).to(lowerCamelCase__      )

      return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}


     @property
     def   UpperCAmelCase__						(   self				:  Optional[int]      )	->   Dict:





      '''simple docstring'''






      return (4, 4, 32, 32)


     @property
     def   UpperCAmelCase__						(   self				:  Dict      )	->   int:





      '''simple docstring'''






      return (4, 4, 32, 32)


     def   UpperCAmelCase__						(   self				:  int      )	->   Dict:





      '''simple docstring'''






      UpperCamelCase__       :     Optional[int] 							= {
          '''block_out_channels''': (32, 64),
          '''down_block_types''': (
              '''CrossAttnDownBlock3D''',
              '''DownBlock3D''',
          ),
          '''up_block_types''': ('''UpBlock3D''', '''CrossAttnUpBlock3D'''),
          '''cross_attention_dim''': 32,
          '''attention_head_dim''': 8,
          '''out_channels''': 4,
          '''in_channels''': 4,
          '''layers_per_block''': 1,
          '''sample_size''': 32,
      }
      UpperCamelCase__       :     Union[str, Any] 							= self.dummy_input
      return init_dict, inputs_dict


     @unittest.skipIf(
         torch_device != '''cuda''' or not is_xformers_available()				,						reason='''XFormers attention is only available with CUDA and `xformers` installed'''				,						)
     def   UpperCAmelCase__						(   self				:  Optional[Any]      )	->   Dict:





      '''simple docstring'''






      UpperCamelCase__							,					UpperCamelCase__       :     List[str] 							= self.prepare_init_args_and_inputs_for_common()
      UpperCamelCase__       :     Union[str, Any] 							= self.model_class(**lowerCamelCase__      )

      model.enable_xformers_memory_efficient_attention()

      assert (
          model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
          == "XFormersAttnProcessor"
      ), "xformers is not enabled"


     def   UpperCAmelCase__						(   self				:  int      )	->   int:





      '''simple docstring'''






      UpperCamelCase__							,					UpperCamelCase__       :     Optional[int] 							= self.prepare_init_args_and_inputs_for_common()

      UpperCamelCase__       :     Any 							= 32

      UpperCamelCase__       :     Optional[Any] 							= self.model_class(**lowerCamelCase__      )
      model.to(lowerCamelCase__      )
      model.eval()

      with torch.no_grad():
       UpperCamelCase__       :     Optional[Any] 							= model(**lowerCamelCase__      )

       if isinstance(lowerCamelCase__				,						lowerCamelCase__      ):
        UpperCamelCase__       :     Any 							= output.sample

      self.assertIsNotNone(lowerCamelCase__      )
      UpperCamelCase__       :     Tuple 							= inputs_dict['''sample'''].shape
      self.assertEqual(output.shape				,						lowerCamelCase__				,						'''Input and output shapes do not match'''      )


     def   UpperCAmelCase__						(   self				:  List[Any]      )	->   List[str]:





      '''simple docstring'''






      UpperCamelCase__							,					UpperCamelCase__       :     int 							= self.prepare_init_args_and_inputs_for_common()
      UpperCamelCase__       :     List[Any] 							= self.model_class(**lowerCamelCase__      )
      model.to(lowerCamelCase__      )
      model.eval()

      with torch.no_grad():
       # Warmup pass when using mps (see #372)
       if torch_device == "mps" and isinstance(lowerCamelCase__				,						lowerCamelCase__      ):
        model(**self.dummy_input      )

       UpperCamelCase__       :     List[Any] 							= model(**lowerCamelCase__      )
       if isinstance(lowerCamelCase__				,						lowerCamelCase__      ):
        UpperCamelCase__       :     int 							= first.sample

       UpperCamelCase__       :     Union[str, Any] 							= model(**lowerCamelCase__      )
       if isinstance(lowerCamelCase__				,						lowerCamelCase__      ):
        UpperCamelCase__       :     Union[str, Any] 							= second.sample

      UpperCamelCase__       :     List[Any] 							= first.cpu().numpy()
      UpperCamelCase__       :     Optional[int] 							= second.cpu().numpy()
      UpperCamelCase__       :     int 							= out_a[~np.isnan(lowerCamelCase__      )]
      UpperCamelCase__       :     List[str] 							= out_a[~np.isnan(lowerCamelCase__      )]
      UpperCamelCase__       :     Union[str, Any] 							= np.amax(np.abs(out_a - out_a      )      )
      self.assertLessEqual(lowerCamelCase__				,						1E-5      )


     def   UpperCAmelCase__						(   self				:  Union[str, Any]      )	->   Any:





      '''simple docstring'''






      UpperCamelCase__							,					UpperCamelCase__       :     str 							= self.prepare_init_args_and_inputs_for_common()

      UpperCamelCase__       :     Dict 							= 8

      UpperCamelCase__       :     List[Any] 							= self.model_class(**lowerCamelCase__      )
      model.to(lowerCamelCase__      )
      model.eval()

      model.set_attention_slice('''auto'''      )
      with torch.no_grad():
       UpperCamelCase__       :     Optional[Any] 							= model(**lowerCamelCase__      )
      assert output is not None

      model.set_attention_slice('''max'''      )
      with torch.no_grad():
       UpperCamelCase__       :     List[Any] 							= model(**lowerCamelCase__      )
      assert output is not None

      model.set_attention_slice(2      )
      with torch.no_grad():
       UpperCamelCase__       :     Union[str, Any] 							= model(**lowerCamelCase__      )
      assert output is not None


     def   UpperCAmelCase__						(   self				:  Dict      )	->   List[str]:





      '''simple docstring'''






      UpperCamelCase__							,					UpperCamelCase__       :     Dict 							= self.prepare_init_args_and_inputs_for_common()

      UpperCamelCase__       :     List[str] 							= 8

      UpperCamelCase__       :     int 							= self.model_class(**lowerCamelCase__      )
      model.to(lowerCamelCase__      )

      with torch.no_grad():
       UpperCamelCase__       :     List[str] 							= model(**lowerCamelCase__      ).sample

      UpperCamelCase__       :     Optional[Any] 							= create_lora_layers(lowerCamelCase__      )

      # make sure we can set a list of attention processors
      model.set_attn_processor(lowerCamelCase__      )
      model.to(lowerCamelCase__      )

      # test that attn processors can be set to itself
      model.set_attn_processor(model.attn_processors      )

      with torch.no_grad():
       UpperCamelCase__       :     Tuple 							= model(**lowerCamelCase__				,						cross_attention_kwargs={'''scale''': 0.0}      ).sample
       UpperCamelCase__       :     str 							= model(**lowerCamelCase__				,						cross_attention_kwargs={'''scale''': 0.5}      ).sample
       UpperCamelCase__       :     Union[str, Any] 							= model(**lowerCamelCase__				,						cross_attention_kwargs={'''scale''': 0.5}      ).sample

      assert (samplea - samplea).abs().max() < 3E-3
      assert (samplea - samplea).abs().max() < 3E-3

      # sample 2 and sample 3 should be different
      assert (samplea - samplea).abs().max() > 3E-3


     def   UpperCAmelCase__						(   self				:  int      )	->   Optional[int]:





      '''simple docstring'''






      UpperCamelCase__							,					UpperCamelCase__       :     str 							= self.prepare_init_args_and_inputs_for_common()

      UpperCamelCase__       :     int 							= 8

      torch.manual_seed(0      )
      UpperCamelCase__       :     Tuple 							= self.model_class(**lowerCamelCase__      )
      model.to(lowerCamelCase__      )

      with torch.no_grad():
       UpperCamelCase__       :     Optional[Any] 							= model(**lowerCamelCase__      ).sample

      UpperCamelCase__       :     List[Any] 							= create_lora_layers(lowerCamelCase__      )
      model.set_attn_processor(lowerCamelCase__      )

      with torch.no_grad():
       UpperCamelCase__       :     Tuple 							= model(**lowerCamelCase__				,						cross_attention_kwargs={'''scale''': 0.5}      ).sample

      with tempfile.TemporaryDirectory() as tmpdirname:
       model.save_attn_procs(lowerCamelCase__      )
       self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__				,						'''pytorch_lora_weights.bin'''      )      )      )
       torch.manual_seed(0      )
       UpperCamelCase__       :     Union[str, Any] 							= self.model_class(**lowerCamelCase__      )
       new_model.to(lowerCamelCase__      )
       new_model.load_attn_procs(lowerCamelCase__      )

      with torch.no_grad():
       UpperCamelCase__       :     Union[str, Any] 							= new_model(**lowerCamelCase__				,						cross_attention_kwargs={'''scale''': 0.5}      ).sample

      assert (sample - new_sample).abs().max() < 1E-3

      # LoRA and no LoRA should NOT be the same
      assert (sample - old_sample).abs().max() > 1E-4


     def   UpperCAmelCase__						(   self				:  str      )	->   List[str]:





      '''simple docstring'''






      UpperCamelCase__							,					UpperCamelCase__       :     Any 							= self.prepare_init_args_and_inputs_for_common()

      UpperCamelCase__       :     Dict 							= 8

      torch.manual_seed(0      )
      UpperCamelCase__       :     Any 							= self.model_class(**lowerCamelCase__      )
      model.to(lowerCamelCase__      )

      with torch.no_grad():
       UpperCamelCase__       :     int 							= model(**lowerCamelCase__      ).sample

      UpperCamelCase__       :     str 							= create_lora_layers(lowerCamelCase__      )
      model.set_attn_processor(lowerCamelCase__      )

      with torch.no_grad():
       UpperCamelCase__       :     Dict 							= model(**lowerCamelCase__				,						cross_attention_kwargs={'''scale''': 0.5}      ).sample

      with tempfile.TemporaryDirectory() as tmpdirname:
       model.save_attn_procs(lowerCamelCase__				,						safe_serialization=lowerCamelCase__      )
       self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__				,						'''pytorch_lora_weights.safetensors'''      )      )      )
       torch.manual_seed(0      )
       UpperCamelCase__       :     str 							= self.model_class(**lowerCamelCase__      )
       new_model.to(lowerCamelCase__      )
       new_model.load_attn_procs(lowerCamelCase__      )

      with torch.no_grad():
       UpperCamelCase__       :     Optional[Any] 							= new_model(**lowerCamelCase__				,						cross_attention_kwargs={'''scale''': 0.5}      ).sample

      assert (sample - new_sample).abs().max() < 3E-3

      # LoRA and no LoRA should NOT be the same
      assert (sample - old_sample).abs().max() > 1E-4


     def   UpperCAmelCase__						(   self				:  Tuple      )	->   int:





      '''simple docstring'''






      UpperCamelCase__							,					UpperCamelCase__       :     Tuple 							= self.prepare_init_args_and_inputs_for_common()

      UpperCamelCase__       :     Dict 							= 8

      torch.manual_seed(0      )
      UpperCamelCase__       :     Dict 							= self.model_class(**lowerCamelCase__      )
      model.to(lowerCamelCase__      )

      UpperCamelCase__       :     Tuple 							= create_lora_layers(lowerCamelCase__				,						mock_weights=lowerCamelCase__      )
      model.set_attn_processor(lowerCamelCase__      )
      # Saving as torch, properly reloads with directly filename
      with tempfile.TemporaryDirectory() as tmpdirname:
       model.save_attn_procs(lowerCamelCase__      )
       self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__				,						'''pytorch_lora_weights.bin'''      )      )      )
       torch.manual_seed(0      )
       UpperCamelCase__       :     Any 							= self.model_class(**lowerCamelCase__      )
       new_model.to(lowerCamelCase__      )
       new_model.load_attn_procs(lowerCamelCase__				,						weight_name='''pytorch_lora_weights.bin'''      )


     def   UpperCAmelCase__						(   self				:  Optional[int]      )	->   Tuple:





      '''simple docstring'''






      UpperCamelCase__							,					UpperCamelCase__       :     Union[str, Any] 							= self.prepare_init_args_and_inputs_for_common()

      UpperCamelCase__       :     Union[str, Any] 							= 8

      torch.manual_seed(0      )
      UpperCamelCase__       :     List[str] 							= self.model_class(**lowerCamelCase__      )
      model.to(lowerCamelCase__      )

      UpperCamelCase__       :     Dict 							= create_lora_layers(lowerCamelCase__				,						mock_weights=lowerCamelCase__      )
      model.set_attn_processor(lowerCamelCase__      )
      # Saving as torch, properly reloads with directly filename
      with tempfile.TemporaryDirectory() as tmpdirname:
       model.save_attn_procs(lowerCamelCase__      )
       self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__				,						'''pytorch_lora_weights.bin'''      )      )      )
       torch.manual_seed(0      )
       UpperCamelCase__       :     List[str] 							= self.model_class(**lowerCamelCase__      )
       new_model.to(lowerCamelCase__      )
       with self.assertRaises(lowerCamelCase__      ) as e:
        new_model.load_attn_procs(lowerCamelCase__				,						use_safetensors=lowerCamelCase__      )
       self.assertIn('''Error no file named pytorch_lora_weights.safetensors'''				,						str(e.exception      )      )


     def   UpperCAmelCase__						(   self				:  str      )	->   int:





      '''simple docstring'''






      UpperCamelCase__							,					UpperCamelCase__       :     Optional[int] 							= self.prepare_init_args_and_inputs_for_common()

      UpperCamelCase__       :     str 							= 8

      torch.manual_seed(0      )
      UpperCamelCase__       :     List[str] 							= self.model_class(**lowerCamelCase__      )
      model.to(lowerCamelCase__      )

      with torch.no_grad():
       UpperCamelCase__       :     str 							= model(**lowerCamelCase__      ).sample

      UpperCamelCase__       :     Any 							= create_lora_layers(lowerCamelCase__      )
      model.set_attn_processor(lowerCamelCase__      )

      with torch.no_grad():
       UpperCamelCase__       :     Any 							= model(**lowerCamelCase__				,						cross_attention_kwargs={'''scale''': 0.0}      ).sample

      model.set_attn_processor(AttnProcessor()      )

      with torch.no_grad():
       UpperCamelCase__       :     Union[str, Any] 							= model(**lowerCamelCase__      ).sample

      assert (sample - new_sample).abs().max() < 1E-4
      assert (sample - old_sample).abs().max() < 3E-3


     @unittest.skipIf(
         torch_device != '''cuda''' or not is_xformers_available()				,						reason='''XFormers attention is only available with CUDA and `xformers` installed'''				,						)
     def   UpperCAmelCase__						(   self				:  Tuple      )	->   Optional[Any]:





      '''simple docstring'''






      UpperCamelCase__							,					UpperCamelCase__       :     Union[str, Any] 							= self.prepare_init_args_and_inputs_for_common()

      UpperCamelCase__       :     Optional[int] 							= 4

      torch.manual_seed(0      )
      UpperCamelCase__       :     Dict 							= self.model_class(**lowerCamelCase__      )
      model.to(lowerCamelCase__      )
      UpperCamelCase__       :     Union[str, Any] 							= create_lora_layers(lowerCamelCase__      )
      model.set_attn_processor(lowerCamelCase__      )

      # default
      with torch.no_grad():
       UpperCamelCase__       :     Tuple 							= model(**lowerCamelCase__      ).sample

       model.enable_xformers_memory_efficient_attention()
       UpperCamelCase__       :     Optional[Any] 							= model(**lowerCamelCase__      ).sample

       model.disable_xformers_memory_efficient_attention()
       UpperCamelCase__       :     Dict 							= model(**lowerCamelCase__      ).sample

      assert (sample - on_sample).abs().max() < 1E-4
      assert (sample - off_sample).abs().max() < 1E-4


     def   UpperCAmelCase__						(   self				:  int      )	->   Any:





      '''simple docstring'''






      UpperCamelCase__							,					UpperCamelCase__       :     List[str] 							= self.prepare_init_args_and_inputs_for_common()
      UpperCamelCase__       :     int 							= 32

      UpperCamelCase__       :     Dict 							= self.model_class(**lowerCamelCase__      )
      model.to(lowerCamelCase__      )
      model.eval()

      with torch.no_grad():
       UpperCamelCase__       :     List[Any] 							= model(**lowerCamelCase__      )[0]

      model.enable_forward_chunking()
      with torch.no_grad():
       UpperCamelCase__       :     Any 							= model(**lowerCamelCase__      )[0]

      self.assertEqual(output.shape				,						output_a.shape				,						'''Shape doesn\'t match'''      )
      assert np.abs(output.cpu() - output_a.cpu()      ).max() < 1E-2


# (todo: sayakpaul) implement SLOW tests.






import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
__UpperCamelCase							:							Optional[Any]    							=					"src/transformers"

# Matches is_xxx_available()
__UpperCamelCase							:							str    							=					re.compile(r"is\_([a-z_]*)_available()")
# Matches from xxx import bla
__UpperCamelCase							:							Union[str, Any]    							=					re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
__UpperCamelCase							:							Dict    							=					re.compile(r"^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)")


__UpperCamelCase							:							Any    							=					"\n{0} = None\n"

__UpperCamelCase							:							Tuple    							=					"\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"


__UpperCamelCase							:							Optional[Any]    							=					"\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"

def       _a	(					SCREAMING_SNAKE_CASE						:					str  ):







 """simple docstring"""



 if _re_test_backend.search(SCREAMING_SNAKE_CASE  ) is None:
  return None
 UpperCamelCase__       :     List[str] 							= [b[0] for b in _re_backend.findall(SCREAMING_SNAKE_CASE  )]
 backends.sort()
 return "_and_".join(SCREAMING_SNAKE_CASE  )

def       _a	(					):







 """simple docstring"""



 with open(os.path.join(SCREAMING_SNAKE_CASE     ,			'''__init__.py'''  )     ,			'''r'''     ,			encoding='''utf-8'''     ,			newline='''\n'''  ) as f:
  UpperCamelCase__       :     Union[str, Any] 							= f.readlines()

 # Get to the point we do the actual imports for type checking
 UpperCamelCase__       :     int 							= 0
 while not lines[line_index].startswith('''if TYPE_CHECKING'''  ):
  line_index += 1

 UpperCamelCase__       :     Optional[Any] 							= {}
 # Go through the end of the file
 while line_index < len(SCREAMING_SNAKE_CASE  ):
  # If the line is an if is_backend_available, we grab all objects associated.
  UpperCamelCase__       :     Any 							= find_backend(lines[line_index]  )
  if backend is not None:
   while not lines[line_index].startswith('''    else:'''  ):
    line_index += 1
   line_index += 1

   UpperCamelCase__       :     List[str] 							= []
   # Until we unindent, add backend objects to the list
   while len(lines[line_index]  ) <= 1 or lines[line_index].startswith(''' ''' * 8  ):
    UpperCamelCase__       :     str 							= lines[line_index]
    UpperCamelCase__       :     Union[str, Any] 							= _re_single_line_import.search(SCREAMING_SNAKE_CASE  )
    if single_line_import_search is not None:
     objects.extend(single_line_import_search.groups()[0].split(''', '''  )  )
    elif line.startswith(''' ''' * 12  ):
     objects.append(line[12:-2]  )
    line_index += 1

   UpperCamelCase__       :     Optional[int] 							= objects
  else:
   line_index += 1

 return backend_specific_objects

def       _a	(					SCREAMING_SNAKE_CASE						:					str     ,			SCREAMING_SNAKE_CASE						:					List[str]  ):







 """simple docstring"""



 if name.isupper():
  return DUMMY_CONSTANT.format(SCREAMING_SNAKE_CASE  )
 elif name.islower():
  return DUMMY_FUNCTION.format(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  )
 else:
  return DUMMY_CLASS.format(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  )

def       _a	(					SCREAMING_SNAKE_CASE						:					Dict=None  ):







 """simple docstring"""



 if backend_specific_objects is None:
  UpperCamelCase__       :     Any 							= read_init()
 # For special correspondence backend to module name as used in the function requires_modulename
 UpperCamelCase__       :     Optional[Any] 							= {}

 for backend, objects in backend_specific_objects.items():
  UpperCamelCase__       :     int 							= '''[''' + ''', '''.join(F"\"{b}\"" for b in backend.split('''_and_'''  )  ) + ''']'''
  UpperCamelCase__       :     Any 							= '''# This file is autogenerated by the command `make fix-copies`, do not edit.\n'''
  dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
  dummy_file += "\n".join([create_dummy_object(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  ) for o in objects]  )
  UpperCamelCase__       :     List[str] 							= dummy_file

 return dummy_files

def       _a	(					SCREAMING_SNAKE_CASE						:					Union[str, Any]=False  ):







 """simple docstring"""



 UpperCamelCase__       :     Dict 							= create_dummy_files()
 # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
 UpperCamelCase__       :     Tuple 							= {'''torch''': '''pt'''}

 # Locate actual dummy modules and read their content.
 UpperCamelCase__       :     Optional[Any] 							= os.path.join(SCREAMING_SNAKE_CASE     ,			'''utils'''  )
 UpperCamelCase__       :     int 							= {
     backend: os.path.join(SCREAMING_SNAKE_CASE     ,			F"dummy_{short_names.get(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  )}_objects.py"  )
     for backend in dummy_files.keys()
 }

 UpperCamelCase__       :     Optional[Any] 							= {}
 for backend, file_path in dummy_file_paths.items():
  if os.path.isfile(SCREAMING_SNAKE_CASE  ):
   with open(SCREAMING_SNAKE_CASE     ,			'''r'''     ,			encoding='''utf-8'''     ,			newline='''\n'''  ) as f:
    UpperCamelCase__       :     List[Any] 							= f.read()
  else:
   UpperCamelCase__       :     List[Any] 							= ''''''

 for backend in dummy_files.keys():
  if dummy_files[backend] != actual_dummies[backend]:
   if overwrite:
    print(
        F"Updating transformers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  )}_objects.py as the main "
        '''__init__ has new objects.'''  )
    with open(dummy_file_paths[backend]     ,			'''w'''     ,			encoding='''utf-8'''     ,			newline='''\n'''  ) as f:
     f.write(dummy_files[backend]  )
   else:
    raise ValueError(
        '''The main __init__ has objects that are not present in '''
        F"transformers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  )}_objects.py. Run `make fix-copies` "
        '''to fix this.'''  )


if __name__ == "__main__":
       __UpperCamelCase							:							Optional[int]    							=					argparse.ArgumentParser()
       parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
       __UpperCamelCase							:							str    							=					parser.parse_args()

       check_dummies(args.fix_and_overwrite)


