






import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


A__			:    Optional[Any]         =				'\\n@inproceedings{snover-etal-2006-study,\n    title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n    author = "Snover, Matthew  and\n      Dorr, Bonnie  and\n      Schwartz, Rich  and\n      Micciulla, Linnea  and\n      Makhoul, John",\n    booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",\n    month = aug # " 8-12",\n    year = "2006",\n    address = "Cambridge, Massachusetts, USA",\n    publisher = "Association for Machine Translation in the Americas",\n    url = "https://aclanthology.org/2006.amta-papers.25",\n    pages = "223--231",\n}\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

A__			:    Optional[int]         =				'\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n'

A__			:    int         =				'\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n    predictions (list of str): The system stream (a sequence of segments).\n    references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n                                    Only applies if `normalized = True`. Defaults to `False`.\n    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n    \'score\' (float): TER score (num_edits / sum_ref_lengths * 100)\n    \'num_edits\' (int): The cumulative number of edits\n    \'ref_length\' (float): The cumulative average reference length\n\nExamples:\n    Example 1:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?",\n        ...                     "What did the TER metric user say to the developer?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n        ...             ["Your jokes are...", "...TERrible"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}\n\n    Example 2:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}\n\n    Example 3:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         normalized=True,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}\n\n    Example 4:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}\n\n    Example 5:\n        >>> predictions = ["does this sentence match??",\n        ...                    "what about this sentence?",\n        ...                    "What did the TER metric user say to the developer?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n        ...             ["Your jokes are...", "...TERrible"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,_KWARGS_DESCRIPTION			)
class    _UpperCAmelCase      ( datasets.Metric			):

				"""simple docstring"""




				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								if version.parse(scb.__version__						) < version.parse('''1.4.12'''						):
												raise ImportWarning(
												    '''To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'''
												    '''You can install it with `pip install "sacrebleu>=1.4.12"`.'''						)
								return datasets.MetricInfo(
								    description=_DESCRIPTION,						citation=_CITATION,						homepage='''http://www.cs.umd.edu/~snover/tercom/''',						inputs_description=_KWARGS_DESCRIPTION,						features=datasets.Features(
								        {
								            '''predictions''': datasets.Value('''string''',						id='''sequence'''						),
								            '''references''': datasets.Sequence(datasets.Value('''string''',						id='''sequence'''						),						id='''references'''						),
								        }						),						codebase_urls=['''https://github.com/mjpost/sacreBLEU#ter'''],						reference_urls=[
								        '''https://github.com/jhclark/tercom''',
								    ],						)




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					Optional[Any],						lowerCamelCase		:					Optional[int],						lowerCamelCase		:					bool = False,						lowerCamelCase		:					bool = False,						lowerCamelCase		:					bool = False,						lowerCamelCase		:					bool = False,						):

								'''simple docstring'''
								lowercase__     				=    len(references[0]						)
								if any(len(lowerCamelCase						) != references_per_prediction for refs in references						):
												raise ValueError('''Sacrebleu requires the same number of references for each prediction'''						)
								lowercase__     				=    [[refs[i] for refs in references] for i in range(lowerCamelCase						)]

								lowercase__     				=    TER(
								    normalized=lowerCamelCase,						no_punct=lowerCamelCase,						asian_support=lowerCamelCase,						case_sensitive=lowerCamelCase,						)
								lowercase__     				=    sb_ter.corpus_score(lowerCamelCase,						lowerCamelCase						)

								return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}












import argparse
import os
import re


A__			:    Optional[int]         =				'src/transformers'

# Pattern that looks at the indentation in a line.
A__			:    Union[str, Any]         =				re.compile(r'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
A__			:    List[str]         =				re.compile(r'^\s*"([^"]+)":')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
A__			:    List[Any]         =				re.compile(r'^\s*_import_structure\["([^"]+)"\]')
# Pattern that matches `"key",` and puts `key` in group 0.
A__			:    int         =				re.compile(r'^\s*"([^"]+)",\s*$')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
A__			:    Tuple         =				re.compile(r'\[([^\]]+)\]')






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    _re_indent.search(lowerCamelCase_       )
				return "" if search is None else search.groups()[0]






def 						a    (		lowerCamelCase_	,     lowerCamelCase_=""	,     lowerCamelCase_=None	,     lowerCamelCase_=None       ):






				'''simple docstring'''
				lowercase__     				=    0
				lowercase__     				=    code.split('''\n'''       )
				if start_prompt is not None:
								while not lines[index].startswith(lowerCamelCase_       ):
												index += 1
								lowercase__     				=    ['''\n'''.join(lines[:index]       )]
				else:
								lowercase__     				=    []

				# We split into blocks until we get to the `end_prompt` (or the end of the block).
				lowercase__     				=    [lines[index]]
				index += 1
				while index < len(lowerCamelCase_       ) and (end_prompt is None or not lines[index].startswith(lowerCamelCase_       )):
								if len(lines[index]       ) > 0 and get_indent(lines[index]       ) == indent_level:
												if len(lowerCamelCase_       ) > 0 and get_indent(current_block[-1]       ).startswith(indent_level + ''' '''       ):
																current_block.append(lines[index]       )
																blocks.append('''\n'''.join(lowerCamelCase_       )       )
																if index < len(lowerCamelCase_       ) - 1:
																				lowercase__     				=    [lines[index + 1]]
																				index += 1
																else:
																				lowercase__     				=    []
												else:
																blocks.append('''\n'''.join(lowerCamelCase_       )       )
																lowercase__     				=    [lines[index]]
								else:
												current_block.append(lines[index]       )
								index += 1

				# Adds current block if it's nonempty.
				if len(lowerCamelCase_       ) > 0:
								blocks.append('''\n'''.join(lowerCamelCase_       )       )

				# Add final block after end_prompt if provided.
				if end_prompt is not None and index < len(lowerCamelCase_       ):
								blocks.append('''\n'''.join(lines[index:]       )       )

				return blocks






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''

				def _inner(lowerCamelCase_       ):
								return key(lowerCamelCase_       ).lower().replace('''_'''	,     ''''''       )

				return _inner






def 						a    (		lowerCamelCase_	,     lowerCamelCase_=None       ):






				'''simple docstring'''

				# If no key is provided, we use a noop.
				def noop(lowerCamelCase_       ):
								return x

				if key is None:
								lowercase__     				=    noop
				# Constants are all uppercase, they go first.
				lowercase__     				=    [obj for obj in objects if key(lowerCamelCase_       ).isupper()]
				# Classes are not all uppercase but start with a capital, they go second.
				lowercase__     				=    [obj for obj in objects if key(lowerCamelCase_       )[0].isupper() and not key(lowerCamelCase_       ).isupper()]
				# Functions begin with a lowercase, they go last.
				lowercase__     				=    [obj for obj in objects if not key(lowerCamelCase_       )[0].isupper()]

				lowercase__     				=    ignore_underscore(lowerCamelCase_       )
				return sorted(lowerCamelCase_	,     key=lowerCamelCase_       ) + sorted(lowerCamelCase_	,     key=lowerCamelCase_       ) + sorted(lowerCamelCase_	,     key=lowerCamelCase_       )






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''

				# This inner function sort imports between [ ].
				def _replace(lowerCamelCase_       ):
								lowercase__     				=    match.groups()[0]
								if "," not in imports:
												return F"""[{imports}]"""
								lowercase__     				=    [part.strip().replace('''"'''	,     ''''''       ) for part in imports.split(''','''       )]
								# We will have a final empty element if the line finished with a comma.
								if len(keys[-1]       ) == 0:
												lowercase__     				=    keys[:-1]
								return "[" + ", ".join([F"""\"{k}\"""" for k in sort_objects(lowerCamelCase_       )]       ) + "]"

				lowercase__     				=    import_statement.split('''\n'''       )
				if len(lowerCamelCase_       ) > 3:
								# Here we have to sort internal imports that are on several lines (one per name):
								# key: [
								#     "object1",
								#     "object2",
								#     ...
								# ]

								# We may have to ignore one or two lines on each side.
								lowercase__     				=    2 if lines[1].strip() == '''[''' else 1
								lowercase__     				=    [(i, _re_strip_line.search(lowerCamelCase_       ).groups()[0]) for i, line in enumerate(lines[idx:-idx]       )]
								lowercase__     				=    sort_objects(lowerCamelCase_	,     key=lambda lowerCamelCase_       : x[1]       )
								lowercase__     				=    [lines[x[0] + idx] for x in sorted_indices]
								return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]       )
				elif len(lowerCamelCase_       ) == 3:
								# Here we have to sort internal imports that are on one separate line:
								# key: [
								#     "object1", "object2", ...
								# ]
								if _re_bracket_content.search(lines[1]       ) is not None:
												lowercase__     				=    _re_bracket_content.sub(_replace	,     lines[1]       )
								else:
												lowercase__     				=    [part.strip().replace('''"'''	,     ''''''       ) for part in lines[1].split(''','''       )]
												# We will have a final empty element if the line finished with a comma.
												if len(keys[-1]       ) == 0:
																lowercase__     				=    keys[:-1]
												lowercase__     				=    get_indent(lines[1]       ) + ''', '''.join([F"""\"{k}\"""" for k in sort_objects(lowerCamelCase_       )]       )
								return "\n".join(lowerCamelCase_       )
				else:
								# Finally we have to deal with imports fitting on one line
								lowercase__     				=    _re_bracket_content.sub(_replace	,     lowerCamelCase_       )
								return import_statement






def 						a    (		lowerCamelCase_	,     lowerCamelCase_=True       ):






				'''simple docstring'''
				with open(lowerCamelCase_	,     encoding='''utf-8'''       ) as f:
								lowercase__     				=    f.read()

				if "_import_structure" not in code:
								return

				# Blocks of indent level 0
				lowercase__     				=    split_code_in_indented_blocks(
				    lowerCamelCase_	,     start_prompt='''_import_structure = {'''	,     end_prompt='''if TYPE_CHECKING:'''       )

				# We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
				for block_idx in range(1	,     len(lowerCamelCase_       ) - 1       ):
								# Check if the block contains some `_import_structure`s thingy to sort.
								lowercase__     				=    main_blocks[block_idx]
								lowercase__     				=    block.split('''\n'''       )

								# Get to the start of the imports.
								lowercase__     				=    0
								while line_idx < len(lowerCamelCase_       ) and "_import_structure" not in block_lines[line_idx]:
												# Skip dummy import blocks
												if "import dummy" in block_lines[line_idx]:
																lowercase__     				=    len(lowerCamelCase_       )
												else:
																line_idx += 1
								if line_idx >= len(lowerCamelCase_       ):
												continue

								# Ignore beginning and last line: they don't contain anything.
								lowercase__     				=    '''\n'''.join(block_lines[line_idx:-1]       )
								lowercase__     				=    get_indent(block_lines[1]       )
								# Slit the internal block into blocks of indent level 1.
								lowercase__     				=    split_code_in_indented_blocks(lowerCamelCase_	,     indent_level=lowerCamelCase_       )
								# We have two categories of import key: list or _import_structure[key].append/extend
								lowercase__     				=    _re_direct_key if '''_import_structure = {''' in block_lines[0] else _re_indirect_key
								# Grab the keys, but there is a trap: some lines are empty or just comments.
								lowercase__     				=    [(pattern.search(lowerCamelCase_       ).groups()[0] if pattern.search(lowerCamelCase_       ) is not None else None) for b in internal_blocks]
								# We only sort the lines with a key.
								lowercase__     				=    [(i, key) for i, key in enumerate(lowerCamelCase_       ) if key is not None]
								lowercase__     				=    [x[0] for x in sorted(lowerCamelCase_	,     key=lambda lowerCamelCase_       : x[1]       )]

								# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
								lowercase__     				=    0
								lowercase__     				=    []
								for i in range(len(lowerCamelCase_       )       ):
												if keys[i] is None:
																reorderded_blocks.append(internal_blocks[i]       )
												else:
																lowercase__     				=    sort_objects_in_import(internal_blocks[sorted_indices[count]]       )
																reorderded_blocks.append(lowerCamelCase_       )
																count += 1

        # And we put our main block back together with its first and last line.
								lowercase__     				=    '''\n'''.join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]]       )

				if code != "\n".join(lowerCamelCase_       ):
								if check_only:
												return True
								else:
												print(F"""Overwriting {file}."""       )
												with open(lowerCamelCase_	,     '''w'''	,     encoding='''utf-8'''       ) as f:
																f.write('''\n'''.join(lowerCamelCase_       )       )






def 						a    (		lowerCamelCase_=True       ):






				'''simple docstring'''
				lowercase__     				=    []
				for root, _, files in os.walk(lowerCamelCase_       ):
								if "__init__.py" in files:
												lowercase__     				=    sort_imports(os.path.join(lowerCamelCase_	,     '''__init__.py'''       )	,     check_only=lowerCamelCase_       )
												if result:
																lowercase__     				=    [os.path.join(lowerCamelCase_	,     '''__init__.py'''       )]
				if len(lowerCamelCase_       ) > 0:
								raise ValueError(F"""Would overwrite {len(lowerCamelCase_       )} files, run `make style`."""       )


if __name__ == "__main__":
							A__			:    str         =				argparse.ArgumentParser()
							parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
							A__			:    int         =				parser.parse_args()

							sort_imports_in_all_inits(check_only=args.check_only)




