
"""simple docstring"""




import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


A__   :						Dict 							=     """\
@inproceedings{Rajpurkar2016SQuAD10,
  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
  booktitle={EMNLP},
  year={2016}
}
"""

A__   :						Tuple 							=     """
This metric wrap the official scoring script for version 2 of the Stanford Question
Answering Dataset (SQuAD).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.

SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions
written adversarially by crowdworkers to look similar to answerable ones.
To do well on SQuAD2.0, systems must not only answer questions when possible, but also
determine when no answer is supported by the paragraph and abstain from answering.
"""

A__   :						int 							=     """
Computes SQuAD v2 scores (F1 and EM).
Args:
    predictions: List of triple for question-answers to score with the following elements:
        - the question-answer 'id' field as given in the references (see below)
        - the text of the answer
        - the probability that the question has no answer
    references: List of question-answers dictionaries with the following key-values:
            - 'id': id of the question-answer pair (see above),
            - 'answers': a list of Dict {'text': text of the answer as a string}
    no_answer_threshold: float
        Probability threshold to decide that a question has no answer.
Returns:
    'exact': Exact match (the normalized answer exactly match the gold answer)
    'f1': The F-score of predicted tokens versus the gold answer
    'total': Number of score considered
    'HasAns_exact': Exact match (the normalized answer exactly match the gold answer)
    'HasAns_f1': The F-score of predicted tokens versus the gold answer
    'HasAns_total': Number of score considered
    'NoAns_exact': Exact match (the normalized answer exactly match the gold answer)
    'NoAns_f1': The F-score of predicted tokens versus the gold answer
    'NoAns_total': Number of score considered
    'best_exact': Best exact match (with varying threshold)
    'best_exact_thresh': No-answer probability threshold associated to the best exact match
    'best_f1': Best F1 (with varying threshold)
    'best_f1_thresh': No-answer probability threshold associated to the best F1
Examples:

    >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22', 'no_answer_probability': 0.}]
    >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]
    >>> squad_v2_metric = datasets.load_metric(\"squad_v2\")
    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact': 100.0, 'f1': 100.0, 'total': 1, 'HasAns_exact': 100.0, 'HasAns_f1': 100.0, 'HasAns_total': 1, 'best_exact': 100.0, 'best_exact_thresh': 0.0, 'best_f1': 100.0, 'best_f1_thresh': 0.0}
"""







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


     '''simple docstring'''





     def 			lowerCAmelCase__							(   self	)->       int:
          return datasets.MetricInfo(
              description=_DESCRIPTION			,      citation=_CITATION			,      inputs_description=_KWARGS_DESCRIPTION			,      features=datasets.Features(
                  {
                      "predictions": {
                          "id": datasets.Value("string"	),
                          "prediction_text": datasets.Value("string"	),
                          "no_answer_probability": datasets.Value("float32"	),
                      },
                      "references": {
                          "id": datasets.Value("string"	),
                          "answers": datasets.features.Sequence(
                              {"text": datasets.Value("string"	), "answer_start": datasets.Value("int32"	)}	),
                      },
                  }	)			,      codebase_urls=["https://rajpurkar.github.io/SQuAD-explorer/"]			,      reference_urls=["https://rajpurkar.github.io/SQuAD-explorer/"]			,      )



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase=1.0	)->       str:
          UpperCAmelCase__				:			Tuple							=    {p["id"]: p["no_answer_probability"] for p in predictions}
          UpperCAmelCase__				:			Any							=    [{"paragraphs": [{"qas": references}]}]
          UpperCAmelCase__				:			Union[str, Any]							=    {p["id"]: p["prediction_text"] for p in predictions}

          UpperCAmelCase__				:			int							=    make_qid_to_has_ans(__UpperCamelCase	)  # maps qid to True/False
          UpperCAmelCase__				:			str							=    [k for k, v in qid_to_has_ans.items() if v]
          UpperCAmelCase__				:			str							=    [k for k, v in qid_to_has_ans.items() if not v]

          UpperCAmelCase__				,						UpperCAmelCase__				:			Dict							=    get_raw_scores(__UpperCamelCase			,      __UpperCamelCase	)
          UpperCAmelCase__				:			Tuple							=    apply_no_ans_threshold(__UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase	)
          UpperCAmelCase__				:			Any							=    apply_no_ans_threshold(__UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase	)
          UpperCAmelCase__				:			Union[str, Any]							=    make_eval_dict(__UpperCamelCase			,      __UpperCamelCase	)

          if has_ans_qids:
               UpperCAmelCase__				:			Dict							=    make_eval_dict(__UpperCamelCase			,      __UpperCamelCase			,      qid_list=__UpperCamelCase	)
               merge_eval(__UpperCamelCase			,      __UpperCamelCase			,      "HasAns"	)
          if no_ans_qids:
               UpperCAmelCase__				:			Dict							=    make_eval_dict(__UpperCamelCase			,      __UpperCamelCase			,      qid_list=__UpperCamelCase	)
               merge_eval(__UpperCamelCase			,      __UpperCamelCase			,      "NoAns"	)
          find_all_best_thresh(__UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase	)
          return dict(__UpperCamelCase	)





"""simple docstring"""




import argparse
import collections
import importlib.util
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_table.py
A__   :						List[str] 							=     """src/diffusers"""
A__   :						List[str] 							=     """docs/source/en"""
A__   :						List[Any] 							=     """."""



def 				a__			(					lowerCAmelCase						:							List[str]							,     lowerCAmelCase						:							Any							,     lowerCAmelCase						:							List[Any]		):






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

     UpperCAmelCase__				:			List[Any]							=    start_index
     while not lines[end_index].startswith(lowerCAmelCase		):
          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__   :						Tuple 							=     """Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
A__   :						Any 							=     re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
A__   :						Tuple 							=     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__   :						List[Any] 							=     re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the diffusers module imported is the one in the repo.
A__   :						List[Any] 							=     importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(TRANSFORMERS_PATH, """__init__.py"""),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
A__   :						Dict 							=     spec.loader.load_module()



def 				a__			(					lowerCAmelCase						:							List[str]		):






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



def 				a__			(					lowerCAmelCase						:							Tuple							,     lowerCAmelCase						:							Union[str, Any]		):






     '''simple docstring'''
     UpperCAmelCase__				:			List[Any]							=    2 if text == "✅" or text == "❌" else len(lowerCAmelCase		)
     UpperCAmelCase__				:			Any							=    (width - text_length) // 2
     UpperCAmelCase__				:			List[Any]							=    width - text_length - left_indent
     return " " * left_indent + text + " " * right_indent



def 				a__			(					):






     '''simple docstring'''
     UpperCAmelCase__				:			int							=    diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
     UpperCAmelCase__				:			Optional[int]							=    {
         name: config_mapping_names[code]
         for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
         if code in config_mapping_names
     }
     UpperCAmelCase__				:			Optional[int]							=    {name: config.replace("ConfigMixin"							,     ""		) 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.
     UpperCAmelCase__				:			str							=    collections.defaultdict(lowerCAmelCase		)
     UpperCAmelCase__				:			Optional[int]							=    collections.defaultdict(lowerCAmelCase		)
     UpperCAmelCase__				:			str							=    collections.defaultdict(lowerCAmelCase		)
     UpperCAmelCase__				:			str							=    collections.defaultdict(lowerCAmelCase		)
     UpperCAmelCase__				:			Union[str, Any]							=    collections.defaultdict(lowerCAmelCase		)

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

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

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

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

     UpperCAmelCase__				:			List[Any]							=    {True: "✅", False: "❌"}
     for name in model_names:
          UpperCAmelCase__				:			Optional[Any]							=    model_name_to_prefix[name]
          UpperCAmelCase__				:			List[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(lowerCAmelCase							,     lowerCAmelCase		) for l, w in zip(lowerCAmelCase							,     lowerCAmelCase		)]		) + "|\n"
     return table



def 				a__			(					lowerCAmelCase						:							Union[str, Any]=False		):






     '''simple docstring'''
     UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__				:			int							=    _find_text_in_file(
         filename=os.path.join(lowerCAmelCase							,     "index.mdx"		)							,     start_prompt="<!--This table is updated automatically from the auto modules"							,     end_prompt="<!-- End table-->"							,     )
     UpperCAmelCase__				:			Optional[Any]							=    get_model_table_from_auto_modules()

     if current_table != new_table:
          if overwrite:
               with open(os.path.join(lowerCAmelCase							,     "index.mdx"		)							,     "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.mdx` 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__   :						str 							=     parser.parse_args()

    check_model_table(args.fix_and_overwrite)



