



'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection

from diffusers import (
    AutoencoderKL,
    DPMSolverMultistepScheduler,
    PNDMScheduler,
    StableDiffusionImageVariationPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 				__lowerCAmelCase(							lowerCAmelCase__		,			lowerCAmelCase__		,			lowerCAmelCase__		,			unittest.TestCase      ):
			__snake_case  :		Tuple		=      StableDiffusionImageVariationPipeline
			__snake_case  :		Optional[int]		=      IMAGE_VARIATION_PARAMS
			__snake_case  :		List[Any]		=      IMAGE_VARIATION_BATCH_PARAMS
			__snake_case  :		Any		=      frozenset([]      )
			# TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
			__snake_case  :		List[str]		=      frozenset([]      )






			def 				_lowercase	(   self  :							str	):




					"""simple docstring"""







					torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:Dict										=      UNetaDConditionModel(
					    block_out_channels=(32, 64)      ,       layers_per_block=2      ,       sample_size=32      ,       in_channels=4      ,       out_channels=4      ,       down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')      ,       up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')      ,       cross_attention_dim=32      ,       )
					SCREAMING_SNAKE_CASE_						:Dict										=      PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE	)
					torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:int										=      AutoencoderKL(
					    block_out_channels=[32, 64]      ,       in_channels=3      ,       out_channels=3      ,       down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']      ,       up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']      ,       latent_channels=4      ,       )
					torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:Any										=      CLIPVisionConfig(
					    hidden_size=32      ,       projection_dim=32      ,       intermediate_size=37      ,       layer_norm_eps=1E-05      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       image_size=32      ,       patch_size=4      ,       )
					SCREAMING_SNAKE_CASE_						:str										=      CLIPVisionModelWithProjection(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:str										=      CLIPImageProcessor(crop_size=32      ,       size=32	)

					SCREAMING_SNAKE_CASE_						:List[str]										=      {
					    'unet': unet,
					    'scheduler': scheduler,
					    'vae': vae,
					    'image_encoder': image_encoder,
					    'feature_extractor': feature_extractor,
					    'safety_checker': None,
					}
					return components






			def 				_lowercase	(   self  :							Any      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]=0	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[str]										=      floats_tensor((1, 3, 32, 32)      ,       rng=random.Random(SCREAMING_SNAKE_CASE	)	)
					SCREAMING_SNAKE_CASE_						:str										=      image.cpu().permute(0      ,       2      ,       3      ,       1	)[0]
					SCREAMING_SNAKE_CASE_						:int										=      Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE	)	).convert('RGB'	).resize((32, 32)	)
					if str(SCREAMING_SNAKE_CASE	).startswith('mps'	):
							SCREAMING_SNAKE_CASE_						:Dict										=      torch.manual_seed(SCREAMING_SNAKE_CASE	)
					else:
							SCREAMING_SNAKE_CASE_						:Tuple										=      torch.Generator(device=SCREAMING_SNAKE_CASE	).manual_seed(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      {
					    'image': image,
					    'generator': generator,
					    'num_inference_steps': 2,
					    'guidance_scale': 6.0,
					    'output_type': 'numpy',
					}
					return inputs






			def 				_lowercase	(   self  :							Any	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Any										=      'cpu'  # ensure determinism for the device-dependent torch.Generator
					SCREAMING_SNAKE_CASE_						:Any										=      self.get_dummy_components()
					SCREAMING_SNAKE_CASE_						:Any										=      StableDiffusionImageVariationPipeline(**SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      sd_pipe.to(SCREAMING_SNAKE_CASE	)
					sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:int										=      self.get_dummy_inputs(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:str										=      sd_pipe(**SCREAMING_SNAKE_CASE	).images
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      image[0, -3:, -3:, -1]

					assert image.shape == (1, 64, 64, 3)
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      np.array([0.52_39, 0.57_23, 0.47_96, 0.50_49, 0.55_50, 0.46_85, 0.53_29, 0.48_91, 0.49_21]	)

					assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-3






			def 				_lowercase	(   self  :							int	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[str]										=      'cpu'  # ensure determinism for the device-dependent torch.Generator
					SCREAMING_SNAKE_CASE_						:List[Any]										=      self.get_dummy_components()
					SCREAMING_SNAKE_CASE_						:Tuple										=      StableDiffusionImageVariationPipeline(**SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      sd_pipe.to(SCREAMING_SNAKE_CASE	)
					sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:int										=      self.get_dummy_inputs(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:List[Any]										=      2 * [inputs['image']]
					SCREAMING_SNAKE_CASE_						:Dict										=      sd_pipe(**SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Tuple										=      output.images

					SCREAMING_SNAKE_CASE_						:int										=      image[-1, -3:, -3:, -1]

					assert image.shape == (2, 64, 64, 3)
					SCREAMING_SNAKE_CASE_						:List[Any]										=      np.array([0.68_92, 0.56_37, 0.58_36, 0.57_71, 0.62_54, 0.64_09, 0.55_80, 0.55_69, 0.52_89]	)

					assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-3






			def 				_lowercase	(   self  :							str	):




					"""simple docstring"""







					super().test_inference_batch_single_identical(expected_max_diff=3E-3	)




@slow
@require_torch_gpu
class 				__lowerCAmelCase(							unittest.TestCase      ):






			def 				_lowercase	(   self  :							str	):




					"""simple docstring"""







					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()






			def 				_lowercase	(   self  :							Optional[int]      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							Dict="cpu"      ,       SCREAMING_SNAKE_CASE  :							Any=torch.floataa      ,       SCREAMING_SNAKE_CASE  :							Tuple=0	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      torch.Generator(device=SCREAMING_SNAKE_CASE	).manual_seed(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      load_image(
					    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
					    '/stable_diffusion_imgvar/input_image_vermeer.png'	)
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      np.random.RandomState(SCREAMING_SNAKE_CASE	).standard_normal((1, 4, 64, 64)	)
					SCREAMING_SNAKE_CASE_						:str										=      torch.from_numpy(SCREAMING_SNAKE_CASE	).to(device=SCREAMING_SNAKE_CASE      ,       dtype=SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:int										=      {
					    'image': init_image,
					    'latents': latents,
					    'generator': generator,
					    'num_inference_steps': 3,
					    'guidance_scale': 7.5,
					    'output_type': 'numpy',
					}
					return inputs






			def 				_lowercase	(   self  :							Dict	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Dict										=      StableDiffusionImageVariationPipeline.from_pretrained(
					    'lambdalabs/sd-image-variations-diffusers'      ,       safety_checker=SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      sd_pipe.to(SCREAMING_SNAKE_CASE	)
					sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:str										=      self.get_inputs(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      sd_pipe(**SCREAMING_SNAKE_CASE	).images
					SCREAMING_SNAKE_CASE_						:int										=      image[0, -3:, -3:, -1].flatten()

					assert image.shape == (1, 512, 512, 3)
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      np.array([0.8_44_91, 0.9_07_89, 0.7_57_08, 0.7_87_34, 0.8_34_85, 0.7_00_99, 0.6_69_38, 0.6_87_27, 0.6_13_79]	)
					assert np.abs(image_slice - expected_slice	).max() < 6E-3






			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Dict										=      0

					def callback_fn(SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							torch.FloatTensor	) -> None:
							SCREAMING_SNAKE_CASE_						:List[Any]										=      True
							nonlocal number_of_steps
							number_of_steps += 1
							if step == 1:
									SCREAMING_SNAKE_CASE_						:List[Any]										=      latents.detach().cpu().numpy()
									assert latents.shape == (1, 4, 64, 64)
									SCREAMING_SNAKE_CASE_						:Tuple										=      latents[0, -3:, -3:, -1]
									SCREAMING_SNAKE_CASE_						:Tuple										=      np.array(
									    [-0.16_21, 0.28_37, -0.79_79, -0.12_21, -1.30_57, 0.76_81, -2.11_91, 0.04_64, 1.63_09]	)

									assert np.abs(latents_slice.flatten() - expected_slice	).max() < 5E-2
							elif step == 2:
									SCREAMING_SNAKE_CASE_						:List[Any]										=      latents.detach().cpu().numpy()
									assert latents.shape == (1, 4, 64, 64)
									SCREAMING_SNAKE_CASE_						:Dict										=      latents[0, -3:, -3:, -1]
									SCREAMING_SNAKE_CASE_						:Dict										=      np.array([0.62_99, 1.75_00, 1.19_92, -2.15_82, -1.89_94, 0.73_34, -0.70_90, 1.01_37, 1.52_73]	)

									assert np.abs(latents_slice.flatten() - expected_slice	).max() < 5E-2

					SCREAMING_SNAKE_CASE_						:List[Any]										=      False

					SCREAMING_SNAKE_CASE_						:Any										=      StableDiffusionImageVariationPipeline.from_pretrained(
					    'fusing/sd-image-variations-diffusers'      ,       safety_checker=SCREAMING_SNAKE_CASE      ,       torch_dtype=torch.floataa      ,       )
					pipe.to(SCREAMING_SNAKE_CASE	)
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)
					pipe.enable_attention_slicing()

					SCREAMING_SNAKE_CASE_						:str										=      self.get_inputs(SCREAMING_SNAKE_CASE      ,       dtype=torch.floataa	)
					pipe(**SCREAMING_SNAKE_CASE      ,       callback=SCREAMING_SNAKE_CASE      ,       callback_steps=1	)
					assert callback_fn.has_been_called
					assert number_of_steps == inputs["num_inference_steps"]






			def 				_lowercase	(   self  :							Any	):




					"""simple docstring"""







					torch.cuda.empty_cache()
					torch.cuda.reset_max_memory_allocated()
					torch.cuda.reset_peak_memory_stats()

					SCREAMING_SNAKE_CASE_						:List[Any]										=      'fusing/sd-image-variations-diffusers'
					SCREAMING_SNAKE_CASE_						:Tuple										=      StableDiffusionImageVariationPipeline.from_pretrained(
					    SCREAMING_SNAKE_CASE      ,       safety_checker=SCREAMING_SNAKE_CASE      ,       torch_dtype=torch.floataa	)
					SCREAMING_SNAKE_CASE_						:Dict										=      pipe.to(SCREAMING_SNAKE_CASE	)
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)
					pipe.enable_attention_slicing(1	)
					pipe.enable_sequential_cpu_offload()

					SCREAMING_SNAKE_CASE_						:List[str]										=      self.get_inputs(SCREAMING_SNAKE_CASE      ,       dtype=torch.floataa	)
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      pipe(**SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Any										=      torch.cuda.max_memory_allocated()
					# make sure that less than 2.6 GB is allocated
					assert mem_bytes < 2.6 * 10**9




@nightly
@require_torch_gpu
class 				__lowerCAmelCase(							unittest.TestCase      ):






			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()






			def 				_lowercase	(   self  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							int      ,       SCREAMING_SNAKE_CASE  :							Any="cpu"      ,       SCREAMING_SNAKE_CASE  :							List[Any]=torch.floataa      ,       SCREAMING_SNAKE_CASE  :							List[str]=0	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:int										=      torch.Generator(device=SCREAMING_SNAKE_CASE	).manual_seed(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      load_image(
					    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
					    '/stable_diffusion_imgvar/input_image_vermeer.png'	)
					SCREAMING_SNAKE_CASE_						:int										=      np.random.RandomState(SCREAMING_SNAKE_CASE	).standard_normal((1, 4, 64, 64)	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      torch.from_numpy(SCREAMING_SNAKE_CASE	).to(device=SCREAMING_SNAKE_CASE      ,       dtype=SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Any										=      {
					    'image': init_image,
					    'latents': latents,
					    'generator': generator,
					    'num_inference_steps': 50,
					    'guidance_scale': 7.5,
					    'output_type': 'numpy',
					}
					return inputs






			def 				_lowercase	(   self  :							int	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[Any]										=      StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'	)
					sd_pipe.to(SCREAMING_SNAKE_CASE	)
					sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:List[str]										=      self.get_inputs(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      sd_pipe(**SCREAMING_SNAKE_CASE	).images[0]

					SCREAMING_SNAKE_CASE_						:str										=      load_numpy(
					    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
					    '/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy'	)
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      np.abs(expected_image - image	).max()
					assert max_diff < 1E-3






			def 				_lowercase	(   self  :							Optional[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[Any]										=      StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'	)
					SCREAMING_SNAKE_CASE_						:int										=      DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config	)
					sd_pipe.to(SCREAMING_SNAKE_CASE	)
					sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Any										=      self.get_inputs(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:List[Any]										=      25
					SCREAMING_SNAKE_CASE_						:List[str]										=      sd_pipe(**SCREAMING_SNAKE_CASE	).images[0]

					SCREAMING_SNAKE_CASE_						:str										=      load_numpy(
					    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
					    '/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy'	)
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      np.abs(expected_image - image	).max()
					assert max_diff < 1E-3




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)


