


"""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
__lowercase    					=      """src/transformers"""
__lowercase    					=      """docs/source/en"""
__lowercase    					=      """."""


def      lowercase			(				A_  ,   A_  ,   A_       )->      List[Any]:
  '''simple docstring'''







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

  a						:		List[Any]      							=			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 |
__lowercase    					=      """Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
__lowercase    					=      re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
__lowercase    					=      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.
__lowercase    					=      re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


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


def      lowercase			(				A_       )->      List[Any]:
  '''simple docstring'''







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


def      lowercase			(				A_  ,   A_       )->      List[str]:
  '''simple docstring'''







  a						:		Dict      							=			2 if text == "✅" or text == "❌" else len(A_       )
  a						:		List[Any]      							=			(width - text_length) // 2
  a						:		Optional[Any]      							=			width - text_length - left_indent
  return " " * left_indent + text + " " * right_indent


def      lowercase			(				)->      Optional[Any]:
  '''simple docstring'''







  a						:		Dict      							=			transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
  a						:		Any      							=			{
      name: config_maping_names[code]
      for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
      if code in config_maping_names
  }
  a						:		Tuple      							=			{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.
  a						:		Optional[int]      							=			collections.defaultdict(A_       )
  a						:		Optional[int]      							=			collections.defaultdict(A_       )
  a						:		List[str]      							=			collections.defaultdict(A_       )
  a						:		List[str]      							=			collections.defaultdict(A_       )
  a						:		Dict      							=			collections.defaultdict(A_       )

  # Let's lookup through all transformers object (once).
  for attr_name in dir(A_       ):
    a						:		Optional[Any]      							=			None
    if attr_name.endswith("Tokenizer"       ):
      a						:		Optional[Any]      							=			slow_tokenizers
      a						:		int      							=			attr_name[:-9]
    elif attr_name.endswith("TokenizerFast"       ):
      a						:		Dict      							=			fast_tokenizers
      a						:		Optional[Any]      							=			attr_name[:-13]
    elif _re_tf_models.match(A_       ) is not None:
      a						:		Union[str, Any]      							=			tf_models
      a						:		List[Any]      							=			_re_tf_models.match(A_       ).groups()[0]
    elif _re_flax_models.match(A_       ) is not None:
      a						:		Union[str, Any]      							=			flax_models
      a						:		str      							=			_re_flax_models.match(A_       ).groups()[0]
    elif _re_pt_models.match(A_       ) is not None:
      a						:		Optional[Any]      							=			pt_models
      a						:		Any      							=			_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():
          a						:		Optional[Any]      							=			True
          break
        # Try again after removing the last word in the name
        a						:		Dict      							=			"".join(camel_case_split(A_       )[:-1]       )

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

  # Build the table per se
  a						:		Union[str, Any]      							=			"|" + "|".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"

  a						:		Optional[int]      							=			{True: "✅", False: "❌"}
  for name in model_names:
    a						:		List[Any]      							=			model_name_to_prefix[name]
    a						:		Union[str, 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      lowercase			(				A_=False       )->      Optional[int]:
  '''simple docstring'''







  a							,		a							,		a							,		a						:		Any      							=			_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-->"  ,   )
  a						:		str      							=			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__":
 __lowercase    					=      argparse.ArgumentParser()
 parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
 __lowercase    					=      parser.parse_args()

 check_model_table(args.fix_and_overwrite)










"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase    					=      logging.get_logger(__name__)

__lowercase    					=      {
    """facebook/dpr-ctx_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-ctx_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-multiset-base""": (
        """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
    ),
}






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase       :							int	=       """dpr"""






    def __init__(					self				:    List[Any]				, __UpperCAmelCase				:    int=30522				, __UpperCAmelCase				:    Union[str, Any]=768				, __UpperCAmelCase				:    Dict=12				, __UpperCAmelCase				:    List[str]=12				, __UpperCAmelCase				:    Any=3072				, __UpperCAmelCase				:    Optional[int]="gelu"				, __UpperCAmelCase				:    Any=0.1				, __UpperCAmelCase				:    Union[str, Any]=0.1				, __UpperCAmelCase				:    str=512				, __UpperCAmelCase				:    List[str]=2				, __UpperCAmelCase				:    Tuple=0.02				, __UpperCAmelCase				:    List[str]=1e-12				, __UpperCAmelCase				:    List[str]=0				, __UpperCAmelCase				:    str="absolute"				, __UpperCAmelCase				:    int = 0				, **__UpperCAmelCase				:    Tuple				, ):
      super().__init__(pad_token_id=__UpperCAmelCase				, **__UpperCAmelCase)

      a						:		List[Any]      							=			vocab_size
      a						:		Optional[Any]      							=			hidden_size
      a						:		Union[str, Any]      							=			num_hidden_layers
      a						:		Dict      							=			num_attention_heads
      a						:		int      							=			hidden_act
      a						:		Any      							=			intermediate_size
      a						:		Any      							=			hidden_dropout_prob
      a						:		Dict      							=			attention_probs_dropout_prob
      a						:		Any      							=			max_position_embeddings
      a						:		Union[str, Any]      							=			type_vocab_size
      a						:		Optional[Any]      							=			initializer_range
      a						:		Dict      							=			layer_norm_eps
      a						:		int      							=			projection_dim
      a						:		str      							=			position_embedding_type






