





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




def _a					(  SCREAMING_SNAKE_CASE__			:  Dict   )       ->				int:

							'''simple docstring'''






							def remove_articles(SCREAMING_SNAKE_CASE__			:  Union[str, Any]   ):
														return re.sub(R"\b(a|an|the)\b" ,      " " ,      SCREAMING_SNAKE_CASE__   )

							def white_space_fix(SCREAMING_SNAKE_CASE__			:  Optional[int]   ):
														return " ".join(text.split()   )

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

							def lower(SCREAMING_SNAKE_CASE__			:  str   ):
														return text.lower()

							return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE__   )   )   )   )




def _a					(  SCREAMING_SNAKE_CASE__			:  Dict ,      SCREAMING_SNAKE_CASE__			:  List[str]   )       ->				int:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__   :			Any      =						normalize_answer(SCREAMING_SNAKE_CASE__   ).split()
							SCREAMING_SNAKE_CASE__   :			Any      =						normalize_answer(SCREAMING_SNAKE_CASE__   ).split()
							SCREAMING_SNAKE_CASE__   :			str      =						Counter(SCREAMING_SNAKE_CASE__   ) & Counter(SCREAMING_SNAKE_CASE__   )
							SCREAMING_SNAKE_CASE__   :			Optional[int]      =						sum(common.values()   )
							if num_same == 0:
														return 0
							SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						1.0 * num_same / len(SCREAMING_SNAKE_CASE__   )
							SCREAMING_SNAKE_CASE__   :			Optional[int]      =						1.0 * num_same / len(SCREAMING_SNAKE_CASE__   )
							SCREAMING_SNAKE_CASE__   :			int      =						(2 * precision * recall) / (precision + recall)
							return fa




def _a					(  SCREAMING_SNAKE_CASE__			:  Optional[Any] ,      SCREAMING_SNAKE_CASE__			:  Union[str, Any]   )       ->				int:

							'''simple docstring'''






							return normalize_answer(SCREAMING_SNAKE_CASE__   ) == normalize_answer(SCREAMING_SNAKE_CASE__   )




def _a					(  SCREAMING_SNAKE_CASE__			:  Dict ,      SCREAMING_SNAKE_CASE__			:  Any ,      SCREAMING_SNAKE_CASE__			:  Tuple   )       ->				int:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__   :			List[Any]      =						[]
							for ground_truth in ground_truths:
														SCREAMING_SNAKE_CASE__   :			str      =						metric_fn(SCREAMING_SNAKE_CASE__ ,      SCREAMING_SNAKE_CASE__   )
														scores_for_ground_truths.append(SCREAMING_SNAKE_CASE__   )
							return max(SCREAMING_SNAKE_CASE__   )




def _a					(  SCREAMING_SNAKE_CASE__			:  Union[str, Any] ,      SCREAMING_SNAKE_CASE__			:  Any   )       ->				Dict:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__   :			List[Any]      =						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__   :			int      =						'Unanswered question ' + qa['id'] + ' will receive score 0.'
																																			print(SCREAMING_SNAKE_CASE__ ,      file=sys.stderr   )
																																			continue
																												SCREAMING_SNAKE_CASE__   :			Dict      =						[x['text'] for x in qa['answers']]
																												SCREAMING_SNAKE_CASE__   :			str      =						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__   :			str      =						1_0_0.0 * exact_match / total
							SCREAMING_SNAKE_CASE__   :			int      =						1_0_0.0 * fa / total

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


if __name__ == "__main__":
		_lowerCamelCase					:				Any  							=					'''1.1'''
		_lowerCamelCase					:				Optional[int]  							=					argparse.ArgumentParser(description='''Evaluation for SQuAD ''' + expected_version)
		parser.add_argument('''dataset_file''', help='''Dataset file''')
		parser.add_argument('''prediction_file''', help='''Prediction File''')
		_lowerCamelCase					:				Tuple  							=					parser.parse_args()
		with open(args.dataset_file) as dataset_file:
				_lowerCamelCase					:				Optional[int]  							=					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,
						)
				_lowerCamelCase					:				Any  							=					dataset_json['''data''']
		with open(args.prediction_file) as prediction_file:
				_lowerCamelCase					:				str  							=					json.load(prediction_file)
		print(json.dumps(evaluate(dataset, predictions)))












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
UpperCamelCase__              =						'src/diffusers'
UpperCamelCase__              =						'docs/source/en'
UpperCamelCase__              =						'.'






def 					snake_case					(							A						,   A						,   A			)							->							int:




	'''simple docstring'''


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

	lowerCamelCase_							:		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 |
UpperCamelCase__              =						'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
UpperCamelCase__              =						re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
UpperCamelCase__              =						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.
UpperCamelCase__              =						re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


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






def 					snake_case					(							A			)							->							str:




	'''simple docstring'''


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






def 					snake_case					(							A						,   A			)							->							List[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		str							   =							2 if text == '✅' or text == '❌' else len(A			)
	lowerCamelCase_							:		List[str]							   =							(width - text_length) // 2
	lowerCamelCase_							:		int							   =							width - text_length - left_indent
	return " " * left_indent + text + " " * right_indent






def 					snake_case					(							)							->							Dict:




	'''simple docstring'''


	lowerCamelCase_							:		Union[str, Any]							   =							diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
	lowerCamelCase_							:		Optional[Any]							   =							{
	    name: config_mapping_names[code]
	    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
	    if code in config_mapping_names
	}
	lowerCamelCase_							:		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.
	lowerCamelCase_							:		Tuple							   =							collections.defaultdict(A			)
	lowerCamelCase_							:		Any							   =							collections.defaultdict(A			)
	lowerCamelCase_							:		int							   =							collections.defaultdict(A			)
	lowerCamelCase_							:		Optional[int]							   =							collections.defaultdict(A			)
	lowerCamelCase_							:		Dict							   =							collections.defaultdict(A			)

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

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

	# Build the table per se
	lowerCamelCase_							:		Optional[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_							:		str							   =							{True: '✅', False: '❌'}
	for name in model_names:
		lowerCamelCase_							:		List[str]							   =							model_name_to_prefix[name]
		lowerCamelCase_							:		Tuple							   =							[
		    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 					snake_case					(							A=False			)							->							List[Any]:




	'''simple docstring'''


	lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_							:		Tuple							   =							_find_text_in_file(
	    filename=os.path.join(A						,   'index.mdx'			)						,   start_prompt='<!--This table is updated automatically from the auto modules'						,   end_prompt='<!-- End table-->'						,   )
	lowerCamelCase_							:		Any							   =							get_model_table_from_auto_modules()

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

							check_model_table(args.fix_and_overwrite)
