


import argparse
import collections

import jax
import jax.numpy as jnp
import torch
import torch.nn as nn
from clip.model import CLIP
from flax.training import checkpoints
from huggingface_hub import Repository

from transformers import (
    CLIPTokenizer,
    OwlViTConfig,
    OwlViTForObjectDetection,
    OwlViTImageProcessor,
    OwlViTModel,
    OwlViTProcessor,
)


__UpperCamelCase							:							Optional[Any]    							=					{
    "vit_b32": {
        "embed_dim": 512,
        "image_resolution": 768,
        "context_length": 16,
        "vocab_size": 4_9408,
        "vision_layers": 12,
        "vision_width": 768,
        "vision_patch_size": 32,
        "transformer_width": 512,
        "transformer_heads": 8,
        "transformer_layers": 12,
    },
    "vit_b16": {
        "embed_dim": 512,
        "image_resolution": 768,
        "context_length": 16,
        "vocab_size": 4_9408,
        "vision_layers": 12,
        "vision_width": 768,
        "vision_patch_size": 16,
        "transformer_width": 512,
        "transformer_heads": 8,
        "transformer_layers": 12,
    },
    "vit_l14": {
        "embed_dim": 768,
        "image_resolution": 840,
        "context_length": 16,
        "vocab_size": 4_9408,
        "vision_layers": 24,
        "vision_width": 1024,
        "vision_patch_size": 14,
        "transformer_width": 768,
        "transformer_heads": 12,
        "transformer_layers": 12,
    },
}

def       _a	(					SCREAMING_SNAKE_CASE						:					str     ,			SCREAMING_SNAKE_CASE						:					Union[str, Any]=""     ,			SCREAMING_SNAKE_CASE						:					Optional[Any]="/"  ):







 """simple docstring"""



 UpperCamelCase__       :     int 							= []

 for k, v in params.items():
  UpperCamelCase__       :     Tuple 							= parent_key + sep + k if parent_key else k

  if isinstance(SCREAMING_SNAKE_CASE     ,			collections.MutableMapping  ):
   items.extend(flatten_nested_dict(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE     ,			sep=SCREAMING_SNAKE_CASE  ).items()  )
  else:
   items.append((new_key, v)  )
 return dict(SCREAMING_SNAKE_CASE  )

def       _a	(					SCREAMING_SNAKE_CASE						:					Optional[int]  ):







 """simple docstring"""



 return jax.tree_util.tree_map(lambda SCREAMING_SNAKE_CASE  : x.astype(jnp.floataa  ) if x.dtype == jnp.bfloataa else x     ,			SCREAMING_SNAKE_CASE  )

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







 """simple docstring"""



 UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__       :     Optional[int] 							= pt_attn_layer.in_proj_weight.chunk(3     ,			dim=0  )
 UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__       :     Dict 							= pt_attn_layer.in_proj_bias.chunk(3     ,			dim=0  )

 UpperCamelCase__       :     Optional[Any] 							= pt_attn_layer.out_proj.weight
 UpperCamelCase__       :     str 							= pt_attn_layer.out_proj.bias

 UpperCamelCase__       :     List[str] 							= q_proj
 UpperCamelCase__       :     Dict 							= q_proj_bias

 UpperCamelCase__       :     Tuple 							= k_proj
 UpperCamelCase__       :     Union[str, Any] 							= k_proj_bias

 UpperCamelCase__       :     Optional[int] 							= v_proj
 UpperCamelCase__       :     Tuple 							= v_proj_bias

 UpperCamelCase__       :     Union[str, Any] 							= out_proj_weights
 UpperCamelCase__       :     Dict 							= out_proj_bias

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







 """simple docstring"""



 copy_linear(hf_mlp.fca     ,			pt_mlp.c_fc  )
 copy_linear(hf_mlp.fca     ,			pt_mlp.c_proj  )

def       _a	(					SCREAMING_SNAKE_CASE						:					Optional[int]     ,			SCREAMING_SNAKE_CASE						:					Any  ):







 """simple docstring"""



 UpperCamelCase__       :     Dict 							= pt_linear.weight
 UpperCamelCase__       :     Optional[int] 							= pt_linear.bias

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







 """simple docstring"""



 copy_linear(hf_layer.layer_norma     ,			pt_layer.ln_a  )
 copy_linear(hf_layer.layer_norma     ,			pt_layer.ln_a  )

 # copy MLP
 copy_mlp(hf_layer.mlp     ,			pt_layer.mlp  )

 # copy attn
 copy_attn_layer(hf_layer.self_attn     ,			pt_layer.attn  )

def       _a	(					SCREAMING_SNAKE_CASE						:					Optional[Any]     ,			SCREAMING_SNAKE_CASE						:					Optional[int]  ):







 """simple docstring"""



 for hf_layer, pt_layer in zip(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  ):
  copy_layer(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  )

def       _a	(					SCREAMING_SNAKE_CASE						:					Optional[Any]     ,			SCREAMING_SNAKE_CASE						:					str  ):







 """simple docstring"""



 UpperCamelCase__       :     List[Any] 							= pt_model.token_embedding.weight
 UpperCamelCase__       :     Any 							= pt_model.positional_embedding

 # copy layer norm
 copy_linear(hf_encoder.final_layer_norm     ,			pt_model.ln_final  )

 # copy hidden layers
 copy_layers(hf_encoder.encoder.layers     ,			pt_model.transformer.resblocks  )

def       _a	(					SCREAMING_SNAKE_CASE						:					Any     ,			SCREAMING_SNAKE_CASE						:					int  ):







 """simple docstring"""



 UpperCamelCase__       :     List[Any] 							= pt_model.text_projection.data.T

 # copy text encoder
 copy_encoder(hf_model.text_model     ,			SCREAMING_SNAKE_CASE  )

def       _a	(					SCREAMING_SNAKE_CASE						:					str     ,			SCREAMING_SNAKE_CASE						:					int  ):







 """simple docstring"""



 UpperCamelCase__       :     int 							= pt_model.visual.proj.data.T

 # copy layer norms
 copy_linear(hf_model.vision_model.pre_layernorm     ,			pt_model.visual.ln_pre  )
 copy_linear(hf_model.vision_model.post_layernorm     ,			pt_model.visual.ln_post  )

 # copy embeds
 UpperCamelCase__       :     str 							= pt_model.visual.conva.weight.data
 UpperCamelCase__       :     Tuple 							= pt_model.visual.class_embedding
 UpperCamelCase__       :     Optional[Any] 							= pt_model.visual.positional_embedding.data

 # copy encoder
 copy_layers(hf_model.vision_model.encoder.layers     ,			pt_model.visual.transformer.resblocks  )

def       _a	(					SCREAMING_SNAKE_CASE						:					Tuple     ,			SCREAMING_SNAKE_CASE						:					Optional[Any]  ):







 """simple docstring"""



 UpperCamelCase__       :     Any 							= flatten_nested_dict(flax_params['''backbone''']['''merged_class_token''']  )

 UpperCamelCase__       :     Any 							= torch.from_numpy(flax_class_token_params['''scale''']  )
 UpperCamelCase__       :     int 							= torch.from_numpy(flax_class_token_params['''bias''']  )
 UpperCamelCase__       :     Any 							= nn.Parameter(SCREAMING_SNAKE_CASE  )
 UpperCamelCase__       :     Any 							= nn.Parameter(SCREAMING_SNAKE_CASE  )

def       _a	(					SCREAMING_SNAKE_CASE						:					Optional[Any]     ,			SCREAMING_SNAKE_CASE						:					Optional[int]  ):







 """simple docstring"""



 UpperCamelCase__       :     List[str] 							= hf_model.state_dict()
 UpperCamelCase__       :     Tuple 							= {}

 # Rename class prediction head flax params to pytorch HF
 UpperCamelCase__       :     Dict 							= flatten_nested_dict(flax_params['''class_head''']  )

 for flax_key, v in flax_class_params.items():
  UpperCamelCase__       :     List[Any] 							= flax_key.replace('''/'''     ,			'''.'''  )
  UpperCamelCase__       :     str 							= torch_key.replace('''.kernel'''     ,			'''.weight'''  )
  UpperCamelCase__       :     List[str] 							= torch_key.replace('''Dense_0'''     ,			'''dense0'''  )
  UpperCamelCase__       :     Optional[int] 							= '''class_head.''' + torch_key

  if "weight" in torch_key and v.ndim == 2:
   UpperCamelCase__       :     List[str] 							= v.T

  UpperCamelCase__       :     int 							= nn.Parameter(torch.from_numpy(SCREAMING_SNAKE_CASE  )  )

 # Rename box prediction box flax params to pytorch HF
 UpperCamelCase__       :     Optional[int] 							= flatten_nested_dict(flax_params['''obj_box_head''']  )

 for flax_key, v in flax_box_params.items():
  UpperCamelCase__       :     int 							= flax_key.replace('''/'''     ,			'''.'''  )
  UpperCamelCase__       :     Dict 							= torch_key.replace('''.kernel'''     ,			'''.weight'''  )
  UpperCamelCase__       :     Optional[int] 							= torch_key.replace('''_'''     ,			''''''  ).lower()
  UpperCamelCase__       :     Optional[Any] 							= '''box_head.''' + torch_key

  if "weight" in torch_key and v.ndim == 2:
   UpperCamelCase__       :     str 							= v.T

  UpperCamelCase__       :     List[Any] 							= nn.Parameter(torch.from_numpy(SCREAMING_SNAKE_CASE  )  )

 # Copy flax params to PyTorch params
 for name, param in new_params.items():
  if name in pt_params.keys():
   pt_params[name].copy_(SCREAMING_SNAKE_CASE  )

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







 """simple docstring"""



 for k, v in flax_attn_params.items():
  if k.startswith('''transformer'''  ):
   UpperCamelCase__       :     Tuple 							= k.replace('''transformer.resblocks'''     ,			'''text_model.encoder.layers'''  )
  else:
   UpperCamelCase__       :     Dict 							= k.replace('''visual.transformer.resblocks'''     ,			'''vision_model.encoder.layers'''  )

  UpperCamelCase__       :     List[str] 							= torch_key.replace('''attn'''     ,			'''self_attn'''  )
  UpperCamelCase__       :     Any 							= torch_key.replace('''key'''     ,			'''k_proj'''  )
  UpperCamelCase__       :     Optional[int] 							= torch_key.replace('''value'''     ,			'''v_proj'''  )
  UpperCamelCase__       :     List[str] 							= torch_key.replace('''query'''     ,			'''q_proj'''  )
  UpperCamelCase__       :     Tuple 							= torch_key.replace('''out'''     ,			'''out_proj'''  )

  if "bias" in torch_key and v.ndim == 2:
   UpperCamelCase__       :     Tuple 							= v.shape[0] * v.shape[1]
   UpperCamelCase__       :     Optional[Any] 							= v.reshape(SCREAMING_SNAKE_CASE  )

  if "weight" in torch_key and "out" in torch_key:
   UpperCamelCase__       :     Optional[Any] 							= (v.shape[0] * v.shape[1], v.shape[2])
   UpperCamelCase__       :     Optional[int] 							= v.reshape(SCREAMING_SNAKE_CASE  ).T

  if "weight" in torch_key and "out" not in torch_key:
   UpperCamelCase__       :     List[str] 							= (v.shape[0], v.shape[1] * v.shape[2])
   UpperCamelCase__       :     Dict 							= v.reshape(SCREAMING_SNAKE_CASE  ).T

  # Copy flax CLIP attn params to HF PyTorch params
  UpperCamelCase__       :     Optional[int] 							= torch.from_numpy(SCREAMING_SNAKE_CASE  )
  hf_backbone.state_dict()[torch_key].copy_(SCREAMING_SNAKE_CASE  )

def       _a	(					SCREAMING_SNAKE_CASE						:					Optional[int]  ):







 """simple docstring"""



 UpperCamelCase__       :     Union[str, Any] 							= {}
 UpperCamelCase__       :     List[str] 							= []

 for k, v in params.items():
  if "attn." in k:
   UpperCamelCase__       :     int 							= k[: k.rindex('''attn.'''  ) + 5]
   if base in processed_attn_layers:
    continue

   processed_attn_layers.append(SCREAMING_SNAKE_CASE  )
   UpperCamelCase__       :     Optional[int] 							= params[base + '''out.weight'''].shape[-1]
   UpperCamelCase__       :     Dict 							= params[base + '''out.weight'''].reshape(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  ).T
   UpperCamelCase__       :     Union[str, Any] 							= params[base + '''out.bias''']
  else:
   UpperCamelCase__       :     List[Any] 							= v
 return new_params

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







 """simple docstring"""



 UpperCamelCase__       :     Union[str, Any] 							= CLIP(**SCREAMING_SNAKE_CASE  )
 torch_model.eval()
 UpperCamelCase__       :     Optional[int] 							= torch_model.state_dict()

 UpperCamelCase__       :     int 							= flatten_nested_dict(flax_params['''backbone''']['''clip''']  )
 UpperCamelCase__       :     Tuple 							= {}

 for flax_key, v in flax_clip_params.items():
  UpperCamelCase__       :     Dict 							= flax_key.replace('''/'''     ,			'''.'''  )
  UpperCamelCase__       :     Union[str, Any] 							= torch_key.replace('''text.token_embedding.embedding'''     ,			'''token_embedding.kernel'''  )

  if (
      torch_key.startswith('''text.transformer'''  )
      or torch_key.startswith('''text.text_projection'''  )
      or torch_key.startswith('''text.ln_final'''  )
      or torch_key.startswith('''text.positional_embedding'''  )
  ):
   UpperCamelCase__       :     Any 							= torch_key[5:]

  UpperCamelCase__       :     int 							= torch_key.replace('''text_projection.kernel'''     ,			'''text_projection'''  )
  UpperCamelCase__       :     str 							= torch_key.replace('''visual.proj.kernel'''     ,			'''visual.proj'''  )
  UpperCamelCase__       :     List[Any] 							= torch_key.replace('''.scale'''     ,			'''.weight'''  )
  UpperCamelCase__       :     List[Any] 							= torch_key.replace('''.kernel'''     ,			'''.weight'''  )

  if "conv" in torch_key or "downsample.0.weight" in torch_key:
   UpperCamelCase__       :     List[Any] 							= v.transpose(3     ,			2     ,			0     ,			1  )

  elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
   # Fully connected layers are transposed, embeddings are not
   UpperCamelCase__       :     Tuple 							= v.T

  UpperCamelCase__       :     List[Any] 							= v

 UpperCamelCase__       :     int 							= _convert_attn_layers(SCREAMING_SNAKE_CASE  )
 new_torch_params.update(SCREAMING_SNAKE_CASE  )
 UpperCamelCase__       :     str 							= {}

 # Copy flax CLIP backbone params to PyTorch params
 for name, param in new_torch_params.items():
  if name in torch_clip_params.keys():
   UpperCamelCase__       :     Optional[int] 							= torch.from_numpy(new_torch_params[name]  )
   torch_clip_params[name].copy_(SCREAMING_SNAKE_CASE  )
  else:
   UpperCamelCase__       :     int 							= param

 return torch_clip_params, torch_model, attn_params

@torch.no_grad()
def       _a	(					SCREAMING_SNAKE_CASE						:					Optional[int]     ,			SCREAMING_SNAKE_CASE						:					Any     ,			SCREAMING_SNAKE_CASE						:					str     ,			SCREAMING_SNAKE_CASE						:					List[str]     ,			SCREAMING_SNAKE_CASE						:					Tuple=None  ):







 """simple docstring"""



 UpperCamelCase__       :     Dict 							= Repository(SCREAMING_SNAKE_CASE     ,			clone_from=F"google/{pytorch_dump_folder_path}"  )
 repo.git_pull()

 if config_path is not None:
  UpperCamelCase__       :     Union[str, Any] 							= OwlViTConfig.from_pretrained(SCREAMING_SNAKE_CASE  )
 else:
  UpperCamelCase__       :     Any 							= OwlViTConfig()

 UpperCamelCase__       :     Any 							= OwlViTModel(SCREAMING_SNAKE_CASE  ).eval()
 UpperCamelCase__       :     Union[str, Any] 							= OwlViTForObjectDetection(SCREAMING_SNAKE_CASE  ).eval()

 copy_text_model_and_projection(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  )
 copy_vision_model_and_projection(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  )
 UpperCamelCase__       :     Dict 							= pt_backbone.logit_scale
 copy_flax_attn_params(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  )

 UpperCamelCase__       :     List[str] 							= hf_backbone
 copy_class_merge_token(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  )
 copy_class_box_heads(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  )

 # Save HF model
 hf_model.save_pretrained(repo.local_dir  )

 # Initialize image processor
 UpperCamelCase__       :     Optional[Any] 							= OwlViTImageProcessor(
     size=config.vision_config.image_size     ,			crop_size=config.vision_config.image_size  )
 # Initialize tokenizer
 UpperCamelCase__       :     str 							= CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''     ,			pad_token='''!'''     ,			model_max_length=16  )

 # Initialize processor
 UpperCamelCase__       :     Any 							= OwlViTProcessor(image_processor=SCREAMING_SNAKE_CASE     ,			tokenizer=SCREAMING_SNAKE_CASE  )
 image_processor.save_pretrained(repo.local_dir  )
 processor.save_pretrained(repo.local_dir  )

 repo.git_add()
 repo.git_commit('''Upload model and processor'''  )
 repo.git_push()


if __name__ == "__main__":
       __UpperCamelCase							:							str    							=					argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--owlvit_version",
           default=None,
           type=str,
           required=True,
           help="OWL-ViT model name [clip_b16, clip_b32, clip_l14].",
       )
       parser.add_argument(
           "--owlvit_checkpoint", default=None, type=str, required=True, help="Path to flax model checkpoint."
       )
       parser.add_argument("--hf_config", default=None, type=str, required=True, help="Path to HF model config.")
       parser.add_argument(
           "--pytorch_dump_folder_path", default="hf_model", type=str, help="Path to the output PyTorch model."
       )
       __UpperCamelCase							:							List[str]    							=					parser.parse_args()

       # Initialize PyToch clip model
       __UpperCamelCase							:							Union[str, Any]    							=					args.owlvit_version
       if model_name == "clip_b16":
              __UpperCamelCase							:							Any    							=					CONFIGS["vit_b16"]
       elif model_name == "clip_b32":
              __UpperCamelCase							:							int    							=					CONFIGS["vit_b32"]
       elif model_name == "clip_l14":
              __UpperCamelCase							:							List[Any]    							=					CONFIGS["vit_l14"]

       # Load from checkpoint and convert params to float-32
       __UpperCamelCase							:							Tuple    							=					checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)["optimizer"]["target"]
       __UpperCamelCase							:							Dict    							=					jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
       del variables

       # Convert CLIP backbone
       __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							:							Optional[int]    							=					convert_clip_backbone(flax_params, torch_config)

       convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)






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)


