




from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


a_ 		=  logging.get_logger(__name__)

a_ 		=  {
    "layoutlmv2-base-uncased": "https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json",
    "layoutlmv2-large-uncased": "https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json",
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
 import detectrona

class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      lowerCAmelCase__      :			str     =       "layoutlmv2"







      def __init__(  self       :			Optional[int]			,							snake_case       :			Optional[Any]=30522			,							snake_case       :			int=768			,							snake_case       :			int=12			,							snake_case       :			Any=12			,							snake_case       :			List[Any]=3072			,							snake_case       :			List[Any]="gelu"			,							snake_case       :			Dict=0.1			,							snake_case       :			Dict=0.1			,							snake_case       :			List[str]=512			,							snake_case       :			Tuple=2			,							snake_case       :			Union[str, Any]=0.02			,							snake_case       :			Any=1E-12			,							snake_case       :			Tuple=0			,							snake_case       :			List[str]=1024			,							snake_case       :			int=128			,							snake_case       :			Dict=32			,							snake_case       :			Dict=True			,							snake_case       :			Optional[Any]=256			,							snake_case       :			List[str]=64			,							snake_case       :			Any=True			,							snake_case       :			Any=[7, 7, 256]			,							snake_case       :			List[Any]=128			,							snake_case       :			List[Any]=128			,							snake_case       :			List[Any]=True			,							snake_case       :			Optional[Any]=True			,							snake_case       :			Optional[int]=False			,							snake_case       :			List[Any]=None			,							**snake_case       :			Dict			,							):
          super().__init__(
              vocab_size=snake_case			,							hidden_size=snake_case			,							num_hidden_layers=snake_case			,							num_attention_heads=snake_case			,							intermediate_size=snake_case			,							hidden_act=snake_case			,							hidden_dropout_prob=snake_case			,							attention_probs_dropout_prob=snake_case			,							max_position_embeddings=snake_case			,							type_vocab_size=snake_case			,							initializer_range=snake_case			,							layer_norm_eps=snake_case			,							pad_token_id=snake_case			,							**snake_case			,							)
          __UpperCamelCase			=    max_ad_position_embeddings
          __UpperCamelCase			=    max_rel_pos
          __UpperCamelCase			=    rel_pos_bins
          __UpperCamelCase			=    fast_qkv
          __UpperCamelCase			=    max_rel_ad_pos
          __UpperCamelCase			=    rel_ad_pos_bins
          __UpperCamelCase			=    convert_sync_batchnorm
          __UpperCamelCase			=    image_feature_pool_shape
          __UpperCamelCase			=    coordinate_size
          __UpperCamelCase			=    shape_size
          __UpperCamelCase			=    has_relative_attention_bias
          __UpperCamelCase			=    has_spatial_attention_bias
          __UpperCamelCase			=    has_visual_segment_embedding
          __UpperCamelCase			=    (
              detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
          )







      @classmethod
      def    snake_case						(  self       :			Dict					):
          return {
              "MODEL.MASK_ON": True,
              "MODEL.PIXEL_STD": [57.3_75, 57.1_20, 58.3_95],
              "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
              "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
              "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
              "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
              "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000,
              "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
              "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
              "MODEL.POST_NMS_TOPK_TEST": 1000,
              "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
              "MODEL.ROI_HEADS.NUM_CLASSES": 5,
              "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
              "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
              "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
              "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
              "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
              "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
              "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
              "MODEL.RESNETS.DEPTH": 101,
              "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
              "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
              "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
              "MODEL.RESNETS.NUM_GROUPS": 32,
              "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
              "MODEL.RESNETS.STRIDE_IN_1X1": False,
          }







      def    snake_case						(  self       :			Optional[Any]					):
          __UpperCamelCase			=    detectrona.config.get_cfg()
          for k, v in self.detectrona_config_args.items():
              __UpperCamelCase			=    k.split('''.'''					)
              __UpperCamelCase			=    detectrona_config
              for attribute in attributes[:-1]:
                  __UpperCamelCase			=    getattr(snake_case			,							snake_case					)
              setattr(snake_case			,							attributes[-1]			,							snake_case					)

          return detectrona_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)


