
"""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
snake_case			       =		'src/diffusers'
snake_case			       =		'docs/source/en'
snake_case			       =		'.'

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):






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

					SCREAMING_SNAKE_CASE           =     start_index
					while not lines[end_index].startswith(SCREAMING_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 |
snake_case			       =		'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
snake_case			       =		re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
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.
snake_case			       =		re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


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

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

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     2 if text == '✅' or text == '❌' else len(SCREAMING_SNAKE_CASE_      )
					SCREAMING_SNAKE_CASE           =     (width - text_length) // 2
					SCREAMING_SNAKE_CASE           =     width - text_length - left_indent
					return " " * left_indent + text + " " * right_indent

def   UpperCamelCase_  (		):
					SCREAMING_SNAKE_CASE           =     diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
					SCREAMING_SNAKE_CASE           =     {
					    name: config_mapping_names[code]
					    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
					    if code in config_mapping_names
					}
					SCREAMING_SNAKE_CASE           =     {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.
					SCREAMING_SNAKE_CASE           =     collections.defaultdict(SCREAMING_SNAKE_CASE_      )
					SCREAMING_SNAKE_CASE           =     collections.defaultdict(SCREAMING_SNAKE_CASE_      )
					SCREAMING_SNAKE_CASE           =     collections.defaultdict(SCREAMING_SNAKE_CASE_      )
					SCREAMING_SNAKE_CASE           =     collections.defaultdict(SCREAMING_SNAKE_CASE_      )
					SCREAMING_SNAKE_CASE           =     collections.defaultdict(SCREAMING_SNAKE_CASE_      )

					# Let's lookup through all diffusers object (once).
					for attr_name in dir(SCREAMING_SNAKE_CASE_      ):
										SCREAMING_SNAKE_CASE           =     None
										if attr_name.endswith('Tokenizer'      ):
															SCREAMING_SNAKE_CASE           =     slow_tokenizers
															SCREAMING_SNAKE_CASE           =     attr_name[:-9]
										elif attr_name.endswith('TokenizerFast'      ):
															SCREAMING_SNAKE_CASE           =     fast_tokenizers
															SCREAMING_SNAKE_CASE           =     attr_name[:-1_3]
										elif _re_tf_models.match(SCREAMING_SNAKE_CASE_      ) is not None:
															SCREAMING_SNAKE_CASE           =     tf_models
															SCREAMING_SNAKE_CASE           =     _re_tf_models.match(SCREAMING_SNAKE_CASE_      ).groups()[0]
										elif _re_flax_models.match(SCREAMING_SNAKE_CASE_      ) is not None:
															SCREAMING_SNAKE_CASE           =     flax_models
															SCREAMING_SNAKE_CASE           =     _re_flax_models.match(SCREAMING_SNAKE_CASE_      ).groups()[0]
										elif _re_pt_models.match(SCREAMING_SNAKE_CASE_      ) is not None:
															SCREAMING_SNAKE_CASE           =     pt_models
															SCREAMING_SNAKE_CASE           =     _re_pt_models.match(SCREAMING_SNAKE_CASE_      ).groups()[0]

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

																				# Let's build that table!
					SCREAMING_SNAKE_CASE           =     list(model_name_to_config.keys()      )
					model_names.sort(key=str.lower      )
					SCREAMING_SNAKE_CASE           =     ['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).
					SCREAMING_SNAKE_CASE           =     [len(SCREAMING_SNAKE_CASE_      ) + 2 for c in columns]
					SCREAMING_SNAKE_CASE           =     max([len(SCREAMING_SNAKE_CASE_      ) for name in model_names]      ) + 2

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

					SCREAMING_SNAKE_CASE           =     {True: '✅', False: '❌'}
					for name in model_names:
										SCREAMING_SNAKE_CASE           =     model_name_to_prefix[name]
										SCREAMING_SNAKE_CASE           =     [
										    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(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ) for l, w in zip(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      )]      ) + "|\n"
					return table

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_=False      ):
					SCREAMING_SNAKE_CASE           =     _find_text_in_file(
					    filename=os.path.join(SCREAMING_SNAKE_CASE_,					'index.mdx'      ),					start_prompt='<!--This table is updated automatically from the auto modules',					end_prompt='<!-- End table-->',					)
					SCREAMING_SNAKE_CASE           =     get_model_table_from_auto_modules()

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

	check_model_table(args.fix_and_overwrite)


"""simple docstring"""




import argparse
import json
import re
import string
import sys
from collections import Counter

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_      ):

					def remove_articles(SCREAMING_SNAKE_CASE_      ):
										return re.sub(R'\b(a|an|the)\b',					' ',					SCREAMING_SNAKE_CASE_      )

					def white_space_fix(SCREAMING_SNAKE_CASE_      ):
										return " ".join(text.split()      )

					def remove_punc(SCREAMING_SNAKE_CASE_      ):
										SCREAMING_SNAKE_CASE           =     set(string.punctuation      )
										return "".join(ch for ch in text if ch not in exclude      )

					def lower(SCREAMING_SNAKE_CASE_      ):
										return text.lower()

					return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE_      )      )      )      )

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     normalize_answer(SCREAMING_SNAKE_CASE_      ).split()
					SCREAMING_SNAKE_CASE           =     normalize_answer(SCREAMING_SNAKE_CASE_      ).split()
					SCREAMING_SNAKE_CASE           =     Counter(SCREAMING_SNAKE_CASE_      ) & Counter(SCREAMING_SNAKE_CASE_      )
					SCREAMING_SNAKE_CASE           =     sum(common.values()      )
					if num_same == 0:
										return 0
					SCREAMING_SNAKE_CASE           =     1.0 * num_same / len(SCREAMING_SNAKE_CASE_      )
					SCREAMING_SNAKE_CASE           =     1.0 * num_same / len(SCREAMING_SNAKE_CASE_      )
					SCREAMING_SNAKE_CASE           =     (2 * precision * recall) / (precision + recall)
					return fa

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					return normalize_answer(SCREAMING_SNAKE_CASE_      ) == normalize_answer(SCREAMING_SNAKE_CASE_      )

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     []
					for ground_truth in ground_truths:
										SCREAMING_SNAKE_CASE           =     metric_fn(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      )
										scores_for_ground_truths.append(SCREAMING_SNAKE_CASE_      )
					return max(SCREAMING_SNAKE_CASE_      )

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     SCREAMING_SNAKE_CASE           =     SCREAMING_SNAKE_CASE           =     0
					for article in dataset:
										for paragraph in article["paragraphs"]:
															for qa in paragraph["qas"]:
																				total += 1
																				if qa["id"] not in predictions:
																									SCREAMING_SNAKE_CASE           =     'Unanswered question ' + qa['id'] + ' will receive score 0.'
																									print(SCREAMING_SNAKE_CASE_,					file=sys.stderr      )
																									continue
																				SCREAMING_SNAKE_CASE           =     [x['text'] for x in qa['answers']]
																				SCREAMING_SNAKE_CASE           =     predictions[qa['id']]
																				exact_match += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      )
																				fa += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      )

					SCREAMING_SNAKE_CASE           =     100.0 * exact_match / total
					SCREAMING_SNAKE_CASE           =     100.0 * fa / total

					return {"exact_match": exact_match, "f1": fa}


if __name__ == "__main__":
	snake_case			       =		'1.1'
	snake_case			       =		argparse.ArgumentParser(description='Evaluation for SQuAD ' + expected_version)
	parser.add_argument('dataset_file', help='Dataset file')
	parser.add_argument('prediction_file', help='Prediction File')
	snake_case			       =		parser.parse_args()
	with open(args.dataset_file) as dataset_file:
		snake_case			       =		json.load(dataset_file)
		if dataset_json["version"] != expected_version:
			print(
			    'Evaluation expects v-' + expected_version + ', but got dataset with v-' + dataset_json['version'],
			    file=sys.stderr,
			)
		snake_case			       =		dataset_json['data']
	with open(args.prediction_file) as prediction_file:
		snake_case			       =		json.load(prediction_file)
	print(json.dumps(evaluate(dataset, predictions)))
