






'''simple docstring'''



import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
SCREAMING_SNAKE_CASE							=						'src/transformers'
SCREAMING_SNAKE_CASE							=						'docs/source/en'
SCREAMING_SNAKE_CASE							=						'.'


def 				lowercase_					(     __A			:							Tuple       ,   __A			:							Dict       ,   __A			:							Tuple      )							->   Optional[Any]:



 """simple docstring"""




 with open(_snake_case       ,   '''r'''       ,   encoding='''utf-8'''       ,   newline='''\n'''      ) as f:
  lowercase       : List[str]						=f.readlines()
 # Find the start prompt.
 lowercase       : int						=0
 while not lines[start_index].startswith(_snake_case      ):
  start_index += 1
 start_index += 1

 lowercase       : List[str]						=start_index
 while not lines[end_index].startswith(_snake_case      ):
  end_index += 1
 end_index -= 1

 while len(lines[start_index]      ) <= 1:
  start_index += 1
 while len(lines[end_index]      ) <= 1:
  end_index -= 1
 end_index += 1
 return "".join(lines[start_index:end_index]      ), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
SCREAMING_SNAKE_CASE							=						'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
SCREAMING_SNAKE_CASE							=						re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
SCREAMING_SNAKE_CASE							=						re.compile(r'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
SCREAMING_SNAKE_CASE							=						re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the transformers module imported is the one in the repo.
SCREAMING_SNAKE_CASE							=						direct_transformers_import(TRANSFORMERS_PATH)


def 				lowercase_					(     __A			:							Dict      )							->   List[str]:



 """simple docstring"""




 lowercase       : Dict						=re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''       ,   _snake_case      )
 return [m.group(0      ) for m in matches]


def 				lowercase_					(     __A			:							str       ,   __A			:							Any      )							->   int:



 """simple docstring"""




 lowercase       : List[Any]						=2 if text == '''✅''' or text == '''❌''' else len(_snake_case      )
 lowercase       : str						=(width - text_length) // 2
 lowercase       : Any						=width - text_length - left_indent
 return " " * left_indent + text + " " * right_indent


def 				lowercase_					(     )							->   Optional[Any]:



 """simple docstring"""




 lowercase       : str						=transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
 lowercase       : Optional[Any]						={
     name: config_maping_names[code]
     for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
     if code in config_maping_names
 }
 lowercase       : str						={name: config.replace('''Config'''       ,   ''''''      ) for name, config in model_name_to_config.items()}

 # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
 lowercase       : Union[str, Any]						=collections.defaultdict(_snake_case      )
 lowercase       : List[str]						=collections.defaultdict(_snake_case      )
 lowercase       : Union[str, Any]						=collections.defaultdict(_snake_case      )
 lowercase       : Union[str, Any]						=collections.defaultdict(_snake_case      )
 lowercase       : Union[str, Any]						=collections.defaultdict(_snake_case      )

 # Let's lookup through all transformers object (once).
 for attr_name in dir(_snake_case      ):
  lowercase       : str						=None
  if attr_name.endswith('''Tokenizer'''      ):
   lowercase       : int						=slow_tokenizers
   lowercase       : List[Any]						=attr_name[:-9]
  elif attr_name.endswith('''TokenizerFast'''      ):
   lowercase       : Dict						=fast_tokenizers
   lowercase       : Tuple						=attr_name[:-1_3]
  elif _re_tf_models.match(_snake_case      ) is not None:
   lowercase       : Optional[int]						=tf_models
   lowercase       : Optional[int]						=_re_tf_models.match(_snake_case      ).groups()[0]
  elif _re_flax_models.match(_snake_case      ) is not None:
   lowercase       : Optional[Any]						=flax_models
   lowercase       : str						=_re_flax_models.match(_snake_case      ).groups()[0]
  elif _re_pt_models.match(_snake_case      ) is not None:
   lowercase       : Union[str, Any]						=pt_models
   lowercase       : Dict						=_re_pt_models.match(_snake_case      ).groups()[0]

  if lookup_dict is not None:
   while len(_snake_case      ) > 0:
    if attr_name in model_name_to_prefix.values():
     lowercase       : int						=True
     break
    # Try again after removing the last word in the name
    lowercase       : Optional[int]						=''''''.join(camel_case_split(_snake_case      )[:-1]      )

    # Let's build that table!
 lowercase       : Dict						=list(model_name_to_config.keys()      )
 model_names.sort(key=str.lower      )
 lowercase       : List[Any]						=['''Model''', '''Tokenizer slow''', '''Tokenizer fast''', '''PyTorch support''', '''TensorFlow support''', '''Flax Support''']
 # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
 lowercase       : List[str]						=[len(_snake_case      ) + 2 for c in columns]
 lowercase       : int						=max([len(_snake_case      ) for name in model_names]      ) + 2

 # Build the table per se
 lowercase       : Dict						='''|''' + '''|'''.join([_center_text(_snake_case       ,   _snake_case      ) for c, w in zip(_snake_case       ,   _snake_case      )]      ) + '''|\n'''
 # Use ":-----:" format to center-aligned table cell texts
 table += "|" + "|".join([''':''' + '''-''' * (w - 2) + ''':''' for w in widths]      ) + "|\n"

 lowercase       : Dict						={True: '''✅''', False: '''❌'''}
 for name in model_names:
  lowercase       : str						=model_name_to_prefix[name]
  lowercase       : Optional[int]						=[
      name,
      check[slow_tokenizers[prefix]],
      check[fast_tokenizers[prefix]],
      check[pt_models[prefix]],
      check[tf_models[prefix]],
      check[flax_models[prefix]],
  ]
  table += "|" + "|".join([_center_text(_snake_case       ,   _snake_case      ) for l, w in zip(_snake_case       ,   _snake_case      )]      ) + "|\n"
 return table


def 				lowercase_					(     __A			:							List[str]=False      )							->   Union[str, Any]:



 """simple docstring"""




 lowercase	,		lowercase	,		lowercase	,		lowercase       : List[Any]						=_find_text_in_file(
     filename=os.path.join(_snake_case       ,   '''index.md'''      )       ,   start_prompt='''<!--This table is updated automatically from the auto modules'''       ,   end_prompt='''<!-- End table-->'''       ,   )
 lowercase       : Union[str, Any]						=get_model_table_from_auto_modules()

 if current_table != new_table:
  if overwrite:
   with open(os.path.join(_snake_case       ,   '''index.md'''      )       ,   '''w'''       ,   encoding='''utf-8'''       ,   newline='''\n'''      ) as f:
    f.writelines(lines[:start_index] + [new_table] + lines[end_index:]      )
  else:
   raise ValueError(
       '''The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this.'''      )


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE							=						argparse.ArgumentParser()
      parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
      SCREAMING_SNAKE_CASE							=						parser.parse_args()

      check_model_table(args.fix_and_overwrite)









"""simple docstring"""

class 							lowercase_			:



 '''simple docstring'''

 def __init__(					self			:			List[Any]       ,				_UpperCAmelCase			:			Optional[Any]       ,				_UpperCAmelCase			:			int       ,				_UpperCAmelCase			:			int						):
       _A     =  None
       _A     =  None
       _A     =  graph

       self._normalize_graph(_UpperCAmelCase       ,				_UpperCAmelCase						)
       _A     =  len(_UpperCAmelCase						)
       _A     =  None

 def      lowerCAmelCase_				(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			Union[str, Any]       ,				_UpperCAmelCase			:			Dict						):
       if sources is int:
             _A     =  [sources]
       if sinks is int:
             _A     =  [sinks]

       if len(_UpperCAmelCase						) == 0 or len(_UpperCAmelCase						) == 0:
             return

       _A     =  sources[0]
       _A     =  sinks[0]

       # make fake vertex if there are more
       # than one source or sink
       if len(_UpperCAmelCase						) > 1 or len(_UpperCAmelCase						) > 1:
             _A     =  0
             for i in sources:
                   max_input_flow += sum(self.graph[i]						)

             _A     =  len(self.graph						) + 1
             for room in self.graph:
                   room.insert(0       ,				0						)
             self.graph.insert(0       ,				[0] * size						)
             for i in sources:
                   _A     =  max_input_flow
             _A     =  0

             _A     =  len(self.graph						) + 1
             for room in self.graph:
                   room.append(0						)
             self.graph.append([0] * size						)
             for i in sinks:
                   _A     =  max_input_flow
             _A     =  size - 1

 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       if self.maximum_flow_algorithm is None:
             raise Exception('You need to set maximum flow algorithm before.'						)
       if self.source_index is None or self.sink_index is None:
             return 0

       self.maximum_flow_algorithm.execute()
       return self.maximum_flow_algorithm.getMaximumFlow()



 def      lowerCAmelCase_				(					self			:			List[str]       ,				_UpperCAmelCase			:			Union[str, Any]						):
       _A     =  algorithm(self						)





class 							lowercase_			:



 '''simple docstring'''

 def __init__(					self			:			List[Any]       ,				_UpperCAmelCase			:			Union[str, Any]						):
       _A     =  flow_network
       _A     =  flow_network.verticesCount
       _A     =  flow_network.sourceIndex
       _A     =  flow_network.sinkIndex
       # it's just a reference, so you shouldn't change
       # it in your algorithms, use deep copy before doing that
       _A     =  flow_network.graph
       _A     =  False

 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       if not self.executed:
             self._algorithm()
             _A     =  True



 def      lowerCAmelCase_				(					self			:			int						):
       pass





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''

 def __init__(					self			:			int       ,				_UpperCAmelCase			:			Any						):
       super().__init__(_UpperCAmelCase						)
       # use this to save your result
       _A     =  -1



 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       if not self.executed:
             raise Exception('You should execute algorithm before using its result!'						)

       return self.maximum_flow





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''

 def __init__(					self			:			Dict       ,				_UpperCAmelCase			:			List[Any]						):
       super().__init__(_UpperCAmelCase						)

       _A     =  [[0] * self.verticies_count for i in range(self.verticies_count						)]

       _A     =  [0] * self.verticies_count
       _A     =  [0] * self.verticies_count

 def      lowerCAmelCase_				(					self			:			Dict						):
       _A     =  self.verticies_count

       # push some substance to graph
       for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index]						):
             self.preflow[self.source_index][nextvertex_index] += bandwidth
             self.preflow[nextvertex_index][self.source_index] -= bandwidth
             self.excesses[nextvertex_index] += bandwidth

       # Relabel-to-front selection rule
       _A     =  [
           i
           for i in range(self.verticies_count						)
           if i != self.source_index and i != self.sink_index
       ]

       # move through list
       _A     =  0
       while i < len(_UpperCAmelCase						):
             _A     =  vertices_list[i]
             _A     =  self.heights[vertex_index]
             self.process_vertex(_UpperCAmelCase						)
             if self.heights[vertex_index] > previous_height:
                   # if it was relabeled, swap elements
                   # and start from 0 index
                   vertices_list.insert(0       ,				vertices_list.pop(_UpperCAmelCase						)						)
                   _A     =  0
             else:
                   i += 1

       _A     =  sum(self.preflow[self.source_index]						)

 def      lowerCAmelCase_				(					self			:			int       ,				_UpperCAmelCase			:			Any						):
       while self.excesses[vertex_index] > 0:
             for neighbour_index in range(self.verticies_count						):
                   # if it's neighbour and current vertex is higher
                   if (
                       self.graph[vertex_index][neighbour_index]
                       - self.preflow[vertex_index][neighbour_index]
                       > 0
                       and self.heights[vertex_index] > self.heights[neighbour_index]
                   ):
                         self.push(_UpperCAmelCase       ,				_UpperCAmelCase						)

             self.relabel(_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Dict       ,				_UpperCAmelCase			:			Tuple       ,				_UpperCAmelCase			:			Tuple						):
       _A     =  min(
           self.excesses[from_index]       ,				self.graph[from_index][to_index] - self.preflow[from_index][to_index]       ,				)
       self.preflow[from_index][to_index] += preflow_delta
       self.preflow[to_index][from_index] -= preflow_delta
       self.excesses[from_index] -= preflow_delta
       self.excesses[to_index] += preflow_delta



 def      lowerCAmelCase_				(					self			:			Union[str, Any]       ,				_UpperCAmelCase			:			int						):
       _A     =  None
       for to_index in range(self.verticies_count						):
             if (
                 self.graph[vertex_index][to_index]
                 - self.preflow[vertex_index][to_index]
                 > 0
             ) and (min_height is None or self.heights[to_index] < min_height):
                   _A     =  self.heights[to_index]

       if min_height is not None:
             _A     =  min_height + 1


if __name__ == "__main__":
       a							      =     [0]
       a							      =     [3]
       # graph = [
       #     [0, 0, 4, 6, 0, 0],
       #     [0, 0, 5, 2, 0, 0],
       #     [0, 0, 0, 0, 4, 4],
       #     [0, 0, 0, 0, 6, 6],
       #     [0, 0, 0, 0, 0, 0],
       #     [0, 0, 0, 0, 0, 0],
       # ]
       a							      =     [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

       # prepare our network
       a							      =     FlowNetwork(graph, entrances, exits)
       # set algorithm
       flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
       # and calculate
       a							      =     flow_network.find_maximum_flow()

       print(F'''maximum flow is {maximum_flow}''')

