






'''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
__A    	=   '''src/transformers'''
__A    	=   '''docs/source/en'''
__A    	=   '''.'''




def        _SCREAMING_SNAKE_CASE  (				A  :       Any  ,		A  :       str  ,		A  :       int  )    -> Tuple:







   """simple docstring"""
   with open(A  ,		'r'  ,		encoding='utf-8'  ,		newline='\n'  ) as f:
      __snake_case				:      Union[str, Any]     							= f.readlines()
   # Find the start prompt.
   __snake_case				:      Optional[Any]     							= 0
   while not lines[start_index].startswith(A  ):
      start_index += 1
   start_index += 1

   __snake_case				:      Tuple     							= start_index
   while not lines[end_index].startswith(A  ):
      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 |
__A    	=   '''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
__A    	=   re.compile(r'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
__A    	=   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.
__A    	=   re.compile(r'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


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




def        _SCREAMING_SNAKE_CASE  (				A  :       Optional[Any]  )    -> Optional[int]:







   """simple docstring"""
   __snake_case				:      List[Any]     							= re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'  ,		A  )
   return [m.group(0  ) for m in matches]




def        _SCREAMING_SNAKE_CASE  (				A  :       Tuple  ,		A  :       List[str]  )    -> Union[str, Any]:







   """simple docstring"""
   __snake_case				:      int     							= 2 if text == '✅' or text == '❌' else len(A  )
   __snake_case				:      str     							= (width - text_length) // 2
   __snake_case				:      Dict     							= width - text_length - left_indent
   return " " * left_indent + text + " " * right_indent




def        _SCREAMING_SNAKE_CASE  (				)    -> List[str]:







   """simple docstring"""
   __snake_case				:      List[Any]     							= transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
   __snake_case				:      Optional[int]     							= {
       name: config_maping_names[code]
       for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
       if code in config_maping_names
   }
   __snake_case				:      List[Any]     							= {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.
   __snake_case				:      Optional[Any]     							= collections.defaultdict(A  )
   __snake_case				:      Any     							= collections.defaultdict(A  )
   __snake_case				:      List[Any]     							= collections.defaultdict(A  )
   __snake_case				:      Tuple     							= collections.defaultdict(A  )
   __snake_case				:      int     							= collections.defaultdict(A  )

   # Let's lookup through all transformers object (once).
   for attr_name in dir(A  ):
      __snake_case				:      List[str]     							= None
      if attr_name.endswith('Tokenizer'  ):
         __snake_case				:      Tuple     							= slow_tokenizers
         __snake_case				:      int     							= attr_name[:-9]
      elif attr_name.endswith('TokenizerFast'  ):
         __snake_case				:      Optional[Any]     							= fast_tokenizers
         __snake_case				:      str     							= attr_name[:-13]
      elif _re_tf_models.match(A  ) is not None:
         __snake_case				:      Optional[Any]     							= tf_models
         __snake_case				:      Optional[int]     							= _re_tf_models.match(A  ).groups()[0]
      elif _re_flax_models.match(A  ) is not None:
         __snake_case				:      Dict     							= flax_models
         __snake_case				:      Dict     							= _re_flax_models.match(A  ).groups()[0]
      elif _re_pt_models.match(A  ) is not None:
         __snake_case				:      Union[str, Any]     							= pt_models
         __snake_case				:      List[str]     							= _re_pt_models.match(A  ).groups()[0]

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

    # Let's build that table!
   __snake_case				:      Optional[Any]     							= list(model_name_to_config.keys()  )
   model_names.sort(key=str.lower  )
   __snake_case				:      Union[str, 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).
   __snake_case				:      Optional[int]     							= [len(A  ) + 2 for c in columns]
   __snake_case				:      List[str]     							= max([len(A  ) for name in model_names]  ) + 2

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

   __snake_case				:      Union[str, Any]     							= {True: '✅', False: '❌'}
   for name in model_names:
      __snake_case				:      Dict     							= model_name_to_prefix[name]
      __snake_case				:      Optional[Any]     							= [
          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(A  ,		A  ) for l, w in zip(A  ,		A  )]  ) + "|\n"
   return table




def        _SCREAMING_SNAKE_CASE  (				A  :       Dict=False  )    -> Dict:







   """simple docstring"""
   __snake_case							,__snake_case							,__snake_case							,__snake_case				:      Dict     							= _find_text_in_file(
       filename=os.path.join(A  ,		'index.md'  )  ,		start_prompt='<!--This table is updated automatically from the auto modules'  ,		end_prompt='<!-- End table-->'  ,		)
   __snake_case				:      Any     							= get_model_table_from_auto_modules()

   if current_table != new_table:
      if overwrite:
         with open(os.path.join(A  ,		'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__":
      __A    	=   argparse.ArgumentParser()
      parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
      __A    	=   parser.parse_args()

      check_model_table(args.fix_and_overwrite)







'''simple docstring'''






import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
      import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin




@require_torch
class a_    (    unittest.TestCase		,      UpperCamelCase_							):







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      __snake_case				:      List[str]     							= load_tool('text-to-speech')
      self.tool.setup()







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[Any]:
      """simple docstring"""






      torch.manual_seed(0)
      __snake_case				:      Dict     							= self.tool('hey')
      __snake_case				:      List[Any]     							= result.to_raw()
      self.assertTrue(
          torch.allclose(
              resulting_tensor[:3]					,   torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485])					,   ))







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      torch.manual_seed(0)
      __snake_case				:      Any     							= self.tool('hey')
      __snake_case				:      Any     							= result.to_raw()
      self.assertTrue(
          torch.allclose(
              resulting_tensor[:3]					,   torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485])					,   ))