

from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse('3.8'):
	import importlib_metadata
else:
	import importlib.metadata as importlib_metadata


A_			:					str    			=     ''


if version.parse(importlib_metadata.version('jiwer')) < version.parse('2.3.0'):



	class        _a		(tr.AbstractTransform							):


						'''simple docstring'''






						def __init__(					self       ,			A__ = " "     ):
							A__				:       Optional[Any]	  =						sentence_delimiter






						def     __A     (					self       ,			A__     ):
							return list(A__     )






						def     __A     (					self       ,			A__     ):
							A__				:       Optional[Any]	  =						[]
							for sent_idx, sentence in enumerate(A__     ):
								chars.extend(self.process_string(A__     )     )
								if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(A__     ) - 1:
									chars.append(self.sentence_delimiter     )
							return chars



	A_			:					Optional[Any]    			=     tr.Compose(
	    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
	)
else:
	A_			:					List[str]    			=     tr.Compose(
	    [
	        tr.RemoveMultipleSpaces(),
	        tr.Strip(),
	        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
	        tr.ReduceToListOfListOfChars(),
	    ]
	)


A_			:					Any    			=     '\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n'

A_			:					int    			=     '\\nCharacter error rate (CER) is a common metric of the performance of an automatic speech recognition system.\n\nCER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.\n\nCharacter error rate can be computed as:\n\nCER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct characters,\nN is the number of characters in the reference (N=S+D+C).\n\nCER\'s output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the\nperformance of the ASR system with a CER of 0 being a perfect score.\n'

A_			:					Any    			=     '\nComputes CER score of transcribed segments against references.\nArgs:\n    references: list of references for each speech input.\n    predictions: list of transcribtions to score.\n    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.\nReturns:\n    (float): the character error rate\n\nExamples:\n\n    >>> predictions = ["this is the prediction", "there is an other sample"]\n    >>> references = ["this is the reference", "there is another one"]\n    >>> cer = datasets.load_metric("cer")\n    >>> cer_score = cer.compute(predictions=predictions, references=references)\n    >>> print(cer_score)\n    0.34146341463414637\n'



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


					'''simple docstring'''






					def     __A     (					self     ):
						return datasets.MetricInfo(
						    description=_DESCRIPTION       ,			citation=_CITATION       ,			inputs_description=_KWARGS_DESCRIPTION       ,			features=datasets.Features(
						        {
						            """predictions""": datasets.Value("""string"""       ,			id="""sequence"""     ),
						            """references""": datasets.Value("""string"""       ,			id="""sequence"""     ),
						        }     )       ,			codebase_urls=["""https://github.com/jitsi/jiwer/"""]       ,			reference_urls=[
						        """https://en.wikipedia.org/wiki/Word_error_rate""",
						        """https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates""",
						    ]       ,			)






					def     __A     (					self       ,			A__       ,			A__       ,			A__=False     ):
						if concatenate_texts:
							return jiwer.compute_measures(
							    A__       ,			A__       ,			truth_transform=A__       ,			hypothesis_transform=A__       ,			)["wer"]

						A__				:       List[str]	  =						0
						A__				:       Any	  =						0
						for prediction, reference in zip(A__       ,			A__     ):
							A__				:       Union[str, Any]	  =						jiwer.compute_measures(
							    A__       ,			A__       ,			truth_transform=A__       ,			hypothesis_transform=A__       ,			)
							incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
							total += measures["substitutions"] + measures["deletions"] + measures["hits"]

						return incorrect / total






import collections
import os
import re
from pathlib import Path


A_			:					List[str]    			=     'src/transformers'


# Matches is_xxx_available()
A_			:					Any    			=     re.compile(r'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
A_			:					Optional[int]    			=     re.compile(r'^_import_structure\s+=\s+\{([^\}]+)\}')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
A_			:					Dict    			=     re.compile(r'\s+"\S*":\s+\[([^\]]*)\]')
# Catches a line if not is_foo_available
A_			:					Dict    			=     re.compile(r'^\s*if\s+not\s+is\_[a-z_]*\_available\(\)')
# Catches a line _import_struct["bla"].append("foo")
A_			:					Tuple    			=     re.compile(r'^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
A_			:					List[Any]    			=     re.compile(r'^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]')
# Catches a line with an object between quotes and a comma:     "MyModel",
A_			:					Dict    			=     re.compile(r'^\s+"([^"]+)",')
# Catches a line with objects between brackets only:    ["foo", "bar"],
A_			:					Tuple    			=     re.compile(r'^\s+\[([^\]]+)\]')
# Catches a line with from foo import bar, bla, boo
A_			:					Union[str, Any]    			=     re.compile(r'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
# Catches a line with try:
A_			:					Any    			=     re.compile(r'^\s*try:')
# Catches a line with else:
A_			:					Optional[Any]    			=     re.compile(r'^\s*else:')

def 		UpperCamelCase   (lowercase_:					Optional[Any]   )	-> Any:
	if _re_test_backend.search(lowercase_   ) is None:
		return None
	A__				:       Optional[int]	  =						[b[0] for b in _re_backend.findall(lowercase_   )]
	backends.sort()
	return "_and_".join(lowercase_   )

def 		UpperCamelCase   (lowercase_:					Any   )	-> Dict:
	with open(lowercase_       , """r"""       , encoding="""utf-8"""       , newline="""\n"""   ) as f:
		A__				:       Optional[Any]	  =						f.readlines()

	A__				:       Optional[Any]	  =						0
	while line_index < len(lowercase_   ) and not lines[line_index].startswith("""_import_structure = {"""   ):
		line_index += 1

	# If this is a traditional init, just return.
	if line_index >= len(lowercase_   ):
		return None

	# First grab the objects without a specific backend in _import_structure
	A__				:       List[Any]	  =						[]
	while not lines[line_index].startswith("""if TYPE_CHECKING"""   ) and find_backend(lines[line_index]   ) is None:
		A__				:       Tuple	  =						lines[line_index]
		# If we have everything on a single line, let's deal with it.
		if _re_one_line_import_struct.search(lowercase_   ):
			A__				:       str	  =						_re_one_line_import_struct.search(lowercase_   ).groups()[0]
			A__				:       Union[str, Any]	  =						re.findall(r"""\[([^\]]+)\]"""       , lowercase_   )
			for imp in imports:
				objects.extend([obj[1:-1] for obj in imp.split(""", """   )]   )
			line_index += 1
			continue
		A__				:       int	  =						_re_import_struct_key_value.search(lowercase_   )
		if single_line_import_search is not None:
			A__				:       int	  =						[obj[1:-1] for obj in single_line_import_search.groups()[0].split(""", """   ) if len(lowercase_   ) > 0]
			objects.extend(lowercase_   )
		elif line.startswith(""" """ * 8 + """\""""   ):
			objects.append(line[9:-3]   )
		line_index += 1

	A__				:       str	  =						{"""none""": objects}
	# Let's continue with backend-specific objects in _import_structure
	while not lines[line_index].startswith("""if TYPE_CHECKING"""   ):
		# If the line is an if not is_backend_available, we grab all objects associated.
		A__				:       Optional[int]	  =						find_backend(lines[line_index]   )
		# Check if the backend declaration is inside a try block:
		if _re_try.search(lines[line_index - 1]   ) is None:
			A__				:       List[str]	  =						None

		if backend is not None:
			line_index += 1

			# Scroll until we hit the else block of try-except-else
			while _re_else.search(lines[line_index]   ) is None:
				line_index += 1

			line_index += 1

			A__				:       Tuple	  =						[]
			# Until we unindent, add backend objects to the list
			while len(lines[line_index]   ) <= 1 or lines[line_index].startswith(""" """ * 4   ):
				A__				:       Any	  =						lines[line_index]
				if _re_import_struct_add_one.search(lowercase_   ) is not None:
					objects.append(_re_import_struct_add_one.search(lowercase_   ).groups()[0]   )
				elif _re_import_struct_add_many.search(lowercase_   ) is not None:
					A__				:       Any	  =						_re_import_struct_add_many.search(lowercase_   ).groups()[0].split(""", """   )
					A__				:       int	  =						[obj[1:-1] for obj in imports if len(lowercase_   ) > 0]
					objects.extend(lowercase_   )
				elif _re_between_brackets.search(lowercase_   ) is not None:
					A__				:       Any	  =						_re_between_brackets.search(lowercase_   ).groups()[0].split(""", """   )
					A__				:       Any	  =						[obj[1:-1] for obj in imports if len(lowercase_   ) > 0]
					objects.extend(lowercase_   )
				elif _re_quote_object.search(lowercase_   ) is not None:
					objects.append(_re_quote_object.search(lowercase_   ).groups()[0]   )
				elif line.startswith(""" """ * 8 + """\""""   ):
					objects.append(line[9:-3]   )
				elif line.startswith(""" """ * 12 + """\""""   ):
					objects.append(line[13:-3]   )
				line_index += 1

			A__				:       List[str]	  =						objects
		else:
			line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
	A__				:       Any	  =						[]
	while (
	    line_index < len(lowercase_   )
	    and find_backend(lines[line_index]   ) is None
	    and not lines[line_index].startswith("""else"""   )
	):
		A__				:       Dict	  =						lines[line_index]
		A__				:       Any	  =						_re_import.search(lowercase_   )
		if single_line_import_search is not None:
			objects.extend(single_line_import_search.groups()[0].split(""", """   )   )
		elif line.startswith(""" """ * 8   ):
			objects.append(line[8:-2]   )
		line_index += 1

	A__				:       List[str]	  =						{"""none""": objects}
	# Let's continue with backend-specific objects
	while line_index < len(lowercase_   ):
		# If the line is an if is_backend_available, we grab all objects associated.
		A__				:       Optional[Any]	  =						find_backend(lines[line_index]   )
		# Check if the backend declaration is inside a try block:
		if _re_try.search(lines[line_index - 1]   ) is None:
			A__				:       Any	  =						None

		if backend is not None:
			line_index += 1

			# Scroll until we hit the else block of try-except-else
			while _re_else.search(lines[line_index]   ) is None:
				line_index += 1

			line_index += 1

			A__				:       List[str]	  =						[]
			# Until we unindent, add backend objects to the list
			while len(lines[line_index]   ) <= 1 or lines[line_index].startswith(""" """ * 8   ):
				A__				:       Union[str, Any]	  =						lines[line_index]
				A__				:       List[str]	  =						_re_import.search(lowercase_   )
				if single_line_import_search is not None:
					objects.extend(single_line_import_search.groups()[0].split(""", """   )   )
				elif line.startswith(""" """ * 12   ):
					objects.append(line[12:-2]   )
				line_index += 1

			A__				:       Tuple	  =						objects
		else:
			line_index += 1

	return import_dict_objects, type_hint_objects

def 		UpperCamelCase   (lowercase_:					Optional[Any]       , lowercase_:					Union[str, Any]   )	-> List[Any]:

	def find_duplicates(lowercase_:					Tuple   ):
		return [k for k, v in collections.Counter(lowercase_   ).items() if v > 1]

	if list(import_dict_objects.keys()   ) != list(type_hint_objects.keys()   ):
		return ["Both sides of the init do not have the same backends!"]

	A__				:       str	  =						[]
	for key in import_dict_objects.keys():
		A__				:       Optional[int]	  =						find_duplicates(import_dict_objects[key]   )
		if duplicate_imports:
			errors.append(f"""Duplicate _import_structure definitions for: {duplicate_imports}"""   )
		A__				:       Tuple	  =						find_duplicates(type_hint_objects[key]   )
		if duplicate_type_hints:
			errors.append(f"""Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}"""   )

		if sorted(set(import_dict_objects[key]   )   ) != sorted(set(type_hint_objects[key]   )   ):
			A__				:       Tuple	  =						"""base imports""" if key == """none""" else f"""{key} backend"""
			errors.append(f"""Differences for {name}:"""   )
			for a in type_hint_objects[key]:
				if a not in import_dict_objects[key]:
					errors.append(f"""  {a} in TYPE_HINT but not in _import_structure."""   )
			for a in import_dict_objects[key]:
				if a not in type_hint_objects[key]:
					errors.append(f"""  {a} in _import_structure but not in TYPE_HINT."""   )
	return errors

def 		UpperCamelCase   ()	-> str:
	A__				:       str	  =						[]
	for root, _, files in os.walk(lowercase_   ):
		if "__init__.py" in files:
			A__				:       Tuple	  =						os.path.join(lowercase_       , """__init__.py"""   )
			A__				:       Union[str, Any]	  =						parse_init(lowercase_   )
			if objects is not None:
				A__				:       List[Any]	  =						analyze_results(*lowercase_   )
				if len(lowercase_   ) > 0:
					A__				:       int	  =						f"""Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"""
					failures.append("""\n""".join(lowercase_   )   )
	if len(lowercase_   ) > 0:
		raise ValueError("""\n\n""".join(lowercase_   )   )

def 		UpperCamelCase   ()	-> Dict:
	A__				:       int	  =						[]
	for path, directories, files in os.walk(lowercase_   ):
		for folder in directories:
			# Ignore private modules
			if folder.startswith("""_"""   ):
				directories.remove(lowercase_   )
				continue
			# Ignore leftovers from branches (empty folders apart from pycache)
			if len(list((Path(lowercase_   ) / folder).glob("""*.py"""   )   )   ) == 0:
				continue
			A__				:       Union[str, Any]	  =						str((Path(lowercase_   ) / folder).relative_to(lowercase_   )   )
			A__				:       List[str]	  =						short_path.replace(os.path.sep       , """."""   )
			submodules.append(lowercase_   )
		for fname in files:
			if fname == "__init__.py":
				continue
			A__				:       Any	  =						str((Path(lowercase_   ) / fname).relative_to(lowercase_   )   )
			A__				:       Union[str, Any]	  =						short_path.replace(""".py"""       , """"""   ).replace(os.path.sep       , """."""   )
			if len(submodule.split("""."""   )   ) == 1:
				submodules.append(lowercase_   )
	return submodules


A_			:					str    			=     [
    'convert_pytorch_checkpoint_to_tf2',
    'modeling_flax_pytorch_utils',
    'models.esm.openfold_utils',
]

def 		UpperCamelCase   ()	-> str:
	# This is to make sure the transformers module imported is the one in the repo.
	from transformers.utils import direct_transformers_import

	A__				:       Any	  =						direct_transformers_import(lowercase_   )

	A__				:       List[str]	  =						set(transformers._import_structure.keys()   )
	# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
	# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
	# (potentiall re-) add them.
	with open(os.path.join(lowercase_       , """__init__.py"""   )       , """r"""   ) as f:
		A__				:       str	  =						f.read()
	import_structure_keys.update(set(re.findall(r"""import_structure\[\"([^\"]*)\"\]"""       , lowercase_   )   )   )

	A__				:       Union[str, Any]	  =						[
	    module
	    for module in get_transformers_submodules()
	    if module not in IGNORE_SUBMODULES and module not in import_structure_keys
	]

	if len(lowercase_   ) > 0:
		A__				:       Dict	  =						"""\n""".join(f"""- {module}""" for module in module_not_registered   )
		raise ValueError(
		    """The following submodules are not properly registed in the main init of Transformers:\n"""
		    f"""{list_of_modules}\n"""
		    """Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value."""   )


if __name__ == "__main__":
	check_all_inits()
	check_submodules()



