"""simple docstring"""






import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


_A   					=    '\\n@INPROCEEDINGS{Papineni02bleu:a,\n    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n    booktitle = {},\n    year = {2002},\n    pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n    title = "{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation",\n    author = "Lin, Chin-Yew  and\n      Och, Franz Josef",\n    booktitle = "{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics",\n    month = "aug 23{--}aug 27",\n    year = "2004",\n    address = "Geneva, Switzerland",\n    publisher = "COLING",\n    url = "https://www.aclweb.org/anthology/C04-1072",\n    pages = "501--507",\n}\n'

_A   					=    '\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine\'s output and that of a human: "the closer a machine translation is to a professional human translation,\nthe better it is" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation\'s overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU\'s output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n'

_A   					=    '\nComputes BLEU score of translated segments against one or more references.\nArgs:\n    predictions: list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    max_order: Maximum n-gram order to use when computing BLEU score.\n    smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n    \'bleu\': bleu score,\n    \'precisions\': geometric mean of n-gram precisions,\n    \'brevity_penalty\': brevity penalty,\n    \'length_ratio\': ratio of lengths,\n    \'translation_length\': translation_length,\n    \'reference_length\': reference_length\nExamples:\n\n    >>> predictions = [\n    ...     ["hello", "there", "general", "kenobi"],                             # tokenized prediction of the first sample\n    ...     ["foo", "bar", "foobar"]                                             # tokenized prediction of the second sample\n    ... ]\n    >>> references = [\n    ...     [["hello", "there", "general", "kenobi"], ["hello", "there", "!"]],  # tokenized references for the first sample (2 references)\n    ...     [["foo", "bar", "foobar"]]                                           # tokenized references for the second sample (1 reference)\n    ... ]\n    >>> bleu = datasets.load_metric("bleu")\n    >>> results = bleu.compute(predictions=predictions, references=references)\n    >>> print(results["bleu"])\n    1.0\n'

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






					'''simple docstring'''


					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Optional[Any]:
									return datasets.MetricInfo(
									    description=_DESCRIPTION   , citation=_CITATION   , inputs_description=_KWARGS_DESCRIPTION   , features=datasets.Features(
									        {
									            "predictions": datasets.Sequence(datasets.Value("string"   , id="token"		)   , id="sequence"		),
									            "references": datasets.Sequence(
									                datasets.Sequence(datasets.Value("string"   , id="token"		)   , id="sequence"		)   , id="references"		),
									        }		)   , codebase_urls=["https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"]   , reference_urls=[
									        "https://en.wikipedia.org/wiki/BLEU",
									        "https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
									    ]   , )



					def 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				Optional[int]   , _snake_case			:				Optional[Any]   , _snake_case			:				Optional[Any]=4   , _snake_case			:				List[Any]=False		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				compute_bleu(
									    reference_corpus=_snake_case   , translation_corpus=_snake_case   , max_order=_snake_case   , smooth=_snake_case		)
									((SCREAMING_SNAKE_CASE__)  ,       (SCREAMING_SNAKE_CASE__)  ,       (SCREAMING_SNAKE_CASE__)  ,       (SCREAMING_SNAKE_CASE__)  ,       (SCREAMING_SNAKE_CASE__)  ,       (SCREAMING_SNAKE_CASE__))		  =				score
									return {
									    "bleu": bleu,
									    "precisions": precisions,
									    "brevity_penalty": bp,
									    "length_ratio": ratio,
									    "translation_length": translation_length,
									    "reference_length": reference_length,
									}





"""simple docstring"""






import collections
import os
import re
from pathlib import Path


_A   					=    'src/transformers'


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




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   List[str]:
				if _re_test_backend.search(__UpperCAmelCase       ) is None:
								return None
				SCREAMING_SNAKE_CASE__		  =				[b[0] for b in _re_backend.findall(__UpperCAmelCase       )]
				backends.sort()
				return "_and_".join(__UpperCAmelCase       )




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Optional[Any]:
				with open(__UpperCAmelCase  ,					"r"  ,					encoding="utf-8"  ,					newline="\n"       ) as f:
								SCREAMING_SNAKE_CASE__		  =				f.readlines()

				SCREAMING_SNAKE_CASE__		  =				0
				while line_index < len(__UpperCAmelCase       ) and not lines[line_index].startswith("_import_structure = {"       ):
								line_index += 1

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

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

				SCREAMING_SNAKE_CASE__		  =				{"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.
								SCREAMING_SNAKE_CASE__		  =				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:
												SCREAMING_SNAKE_CASE__		  =				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

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

												SCREAMING_SNAKE_CASE__		  =				objects
								else:
												line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
				SCREAMING_SNAKE_CASE__		  =				[]
				while (
				    line_index < len(__UpperCAmelCase       )
				    and find_backend(lines[line_index]       ) is None
				    and not lines[line_index].startswith("else"       )
				):
								SCREAMING_SNAKE_CASE__		  =				lines[line_index]
								SCREAMING_SNAKE_CASE__		  =				_re_import.search(__UpperCAmelCase       )
								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

				SCREAMING_SNAKE_CASE__		  =				{"none": objects}
				# Let's continue with backend-specific objects
				while line_index < len(__UpperCAmelCase       ):
								# If the line is an if is_backend_available, we grab all objects associated.
								SCREAMING_SNAKE_CASE__		  =				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:
												SCREAMING_SNAKE_CASE__		  =				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

												SCREAMING_SNAKE_CASE__		  =				[]
												# Until we unindent, add backend objects to the list
												while len(lines[line_index]       ) <= 1 or lines[line_index].startswith(" " * 8       ):
																SCREAMING_SNAKE_CASE__		  =				lines[line_index]
																SCREAMING_SNAKE_CASE__		  =				_re_import.search(__UpperCAmelCase       )
																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

												SCREAMING_SNAKE_CASE__		  =				objects
								else:
												line_index += 1

				return import_dict_objects, type_hint_objects




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   Any:

				def find_duplicates(__UpperCAmelCase       ):
								return [k for k, v in collections.Counter(__UpperCAmelCase       ).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!"]

				SCREAMING_SNAKE_CASE__		  =				[]
				for key in import_dict_objects.keys():
								SCREAMING_SNAKE_CASE__		  =				find_duplicates(import_dict_objects[key]       )
								if duplicate_imports:
												errors.append(F"""Duplicate _import_structure definitions for: {duplicate_imports}"""       )
								SCREAMING_SNAKE_CASE__		  =				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]       )       ):
												SCREAMING_SNAKE_CASE__		  =				"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 						SCREAMING_SNAKE_CASE					(  )							->   List[Any]:
				SCREAMING_SNAKE_CASE__		  =				[]
				for root, _, files in os.walk(__UpperCAmelCase       ):
								if "__init__.py" in files:
												SCREAMING_SNAKE_CASE__		  =				os.path.join(__UpperCAmelCase  ,					"__init__.py"       )
												SCREAMING_SNAKE_CASE__		  =				parse_init(__UpperCAmelCase       )
												if objects is not None:
																SCREAMING_SNAKE_CASE__		  =				analyze_results(*__UpperCAmelCase       )
																if len(__UpperCAmelCase       ) > 0:
																				SCREAMING_SNAKE_CASE__		  =				F"""Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"""
																				failures.append("\n".join(__UpperCAmelCase       )       )
				if len(__UpperCAmelCase       ) > 0:
								raise ValueError("\n\n".join(__UpperCAmelCase       )       )




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


_A   					=    [
    'convert_pytorch_checkpoint_to_tf2',
    'modeling_flax_pytorch_utils',
    'models.esm.openfold_utils',
]




def 						SCREAMING_SNAKE_CASE					(  )							->   Union[str, Any]:
				# This is to make sure the transformers module imported is the one in the repo.
				from transformers.utils import direct_transformers_import

				SCREAMING_SNAKE_CASE__		  =				direct_transformers_import(__UpperCAmelCase       )

				SCREAMING_SNAKE_CASE__		  =				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(__UpperCAmelCase  ,					"__init__.py"       )  ,					"r"       ) as f:
								SCREAMING_SNAKE_CASE__		  =				f.read()
				import_structure_keys.update(set(re.findall(R"import_structure\[\"([^\"]*)\"\]"  ,					__UpperCAmelCase       )       )       )

				SCREAMING_SNAKE_CASE__		  =				[
				    module
				    for module in get_transformers_submodules()
				    if module not in IGNORE_SUBMODULES and module not in import_structure_keys
				]

				if len(__UpperCAmelCase       ) > 0:
								SCREAMING_SNAKE_CASE__		  =				"\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()




