


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      :    Union[str, Any]		=					"src/transformers"
A      :    Optional[int]		=					"docs/source/en"
A      :    Any		=					"."

def       lowercase_		(		_A			:    Any    ,					_A			:    Optional[Any]    ,					_A			:    Optional[Any]						):

      """simple docstring"""




      with open(_A    ,					"r"    ,					encoding="utf-8"    ,					newline="\n"						) as f:
            lowerCamelCase__		:				Optional[Any]    =  f.readlines()
      # Find the start prompt.
      lowerCamelCase__		:				Dict    =  0
      while not lines[start_index].startswith(_A						):
            start_index += 1
      start_index += 1

      lowerCamelCase__		:				Optional[int]    =  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      :    List[Any]		=					"Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
A      :    Union[str, Any]		=					re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
A      :    Dict		=					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      :    int		=					re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# This is to make sure the transformers module imported is the one in the repo.
A      :    List[str]		=					direct_transformers_import(TRANSFORMERS_PATH)

def       lowercase_		(		_A			:    Tuple						):

      """simple docstring"""




      lowerCamelCase__		:				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			:    Union[str, Any]    ,					_A			:    int						):

      """simple docstring"""




      lowerCamelCase__		:				Tuple    =  2 if text == "✅" or text == "❌" else len(_A						)
      lowerCamelCase__		:				Tuple    =  (width - text_length) // 2
      lowerCamelCase__		:				int    =  width - text_length - left_indent
      return " " * left_indent + text + " " * right_indent

def       lowercase_		(		):

      """simple docstring"""




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

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

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

      # Build the table per se
      lowerCamelCase__		:				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"

      lowerCamelCase__		:				int    =  {True: "✅", False: "❌"}
      for name in model_names:
            lowerCamelCase__		:				Union[str, Any]    =  model_name_to_prefix[name]
            lowerCamelCase__		:				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(_A    ,					_A						) for l, w in zip(_A    ,					_A						)]						) + "|\n"
      return table

def       lowercase_		(		_A			:    int=False						):

      """simple docstring"""




      lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__		:				Union[str, 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-->"    ,					)
      lowerCamelCase__		:				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__":
    A      :    Union[str, Any]		=					argparse.ArgumentParser()
    parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
    A      :    List[Any]		=					parser.parse_args()

    check_model_table(args.fix_and_overwrite)






import datasets

from .evaluate import evaluate


A      :    Dict		=					"\\n@article{hendrycks2021cuad,\n      title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n      author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n      journal={arXiv preprint arXiv:2103.06268},\n      year={2021}\n}\n"

A      :    Any		=					"\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n"

A      :    str		=					"\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair as given in the references (see below)\n        - 'prediction_text': list of possible texts for the answer, as a list of strings\n        depending on a threshold on the confidence probability of each prediction.\n    references: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair (see above),\n        - 'answers': a Dict in the CUAD dataset format\n            {\n                'text': list of possible texts for the answer, as a list of strings\n                'answer_start': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    'exact_match': Exact match (the normalized answer exactly match the gold answer)\n    'f1': The F-score of predicted tokens versus the gold answer\n    'aupr': Area Under the Precision-Recall curve\n    'prec_at_80_recall': Precision at 80% recall\n    'prec_at_90_recall': Precision at 90% recall\nExamples:\n    >>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n    >>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n    >>> cuad_metric = datasets.load_metric(\"cuad\")\n    >>> results = cuad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}\n"







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,  _KWARGS_DESCRIPTION)
class 			_lowercase (							datasets.Metric):


       """simple docstring"""




       def 						lowerCAmelCase     (   self							:			Optional[Any]       ):


             '''simple docstring'''



             return datasets.MetricInfo(
                 description=_DESCRIPTION					,      citation=_CITATION					,      inputs_description=_KWARGS_DESCRIPTION					,      features=datasets.Features(
                     {
                         "predictions": {
                             "id": datasets.Value("string"       ),
                             "prediction_text": datasets.features.Sequence(datasets.Value("string"       )       ),
                         },
                         "references": {
                             "id": datasets.Value("string"       ),
                             "answers": datasets.features.Sequence(
                                 {
                                     "text": datasets.Value("string"       ),
                                     "answer_start": datasets.Value("int32"       ),
                                 }       ),
                         },
                     }       )					,      codebase_urls=["https://www.atticusprojectai.org/cuad"]					,      reference_urls=["https://www.atticusprojectai.org/cuad"]					,      )





       def 						lowerCAmelCase     (   self							:			Union[str, Any]					,      __lowerCamelCase							:			List[str]					,      __lowerCamelCase							:			Dict       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
             lowerCamelCase__		:				Any    =  [
                 {
                     "paragraphs": [
                         {
                             "qas": [
                                 {
                                     "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
                                     "id": ref["id"],
                                 }
                                 for ref in references
                             ]
                         }
                     ]
                 }
             ]
             lowerCamelCase__		:				Optional[int]    =  evaluate(dataset=__lowerCamelCase					,      predictions=__lowerCamelCase       )
             return score


