






import collections
import importlib.util
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("""^\s+\"([^\"]+)\",""")
# Catches a line with objects between brackets only:    ["foo", "bar"],
a__       =     re.compile("""^\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 _UpperCAmelCase   (	a						:	List[str] ):
							if _re_test_backend.search(a ) is None:
														return None
							snake_case__	  =       [b[0] for b in _re_backend.findall(a )]
							backends.sort()
							return "_and_".join(a )

def _UpperCAmelCase   (	a						:	List[str] ):
							with open(a  ,					"""r"""  ,					encoding="""utf-8"""  ,					newline="""\n""" ) as f:
														snake_case__	  =       f.readlines()

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

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

							# First grab the objects without a specific backend in _import_structure
							snake_case__	  =       []
							while not lines[line_index].startswith("""if TYPE_CHECKING""" ) and find_backend(lines[line_index] ) is None:
														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(a ):
																					snake_case__	  =       _re_one_line_import_struct.search(a ).groups()[0]
																					snake_case__	  =       re.findall("""\[([^\]]+)\]"""  ,					a )
																					for imp in imports:
																												objects.extend([obj[1:-1] for obj in imp.split(""", """ )] )
																					line_index += 1
																					continue
														snake_case__	  =       _re_import_struct_key_value.search(a )
														if single_line_import_search is not None:
																					snake_case__	  =       [obj[1:-1] for obj in single_line_import_search.groups()[0].split(""", """ ) if len(a ) > 0]
																					objects.extend(a )
														elif line.startswith(""" """ * 8 + """\"""" ):
																					objects.append(line[9:-3] )
														line_index += 1

							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.
														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:
																					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

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

																					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
							snake_case__	  =       []
							while (
							    line_index < len(a )
							    and find_backend(lines[line_index] ) is None
							    and not lines[line_index].startswith("""else""" )
							):
														snake_case__	  =       lines[line_index]
														snake_case__	  =       _re_import.search(a )
														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

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

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

																					snake_case__	  =       objects
														else:
																					line_index += 1

							return import_dict_objects, type_hint_objects

def _UpperCAmelCase   (	a						:	int  ,					a						:	int ):

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

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

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


a__       =     [
    """convert_pytorch_checkpoint_to_tf2""",
    """modeling_flax_pytorch_utils""",
]

def _UpperCAmelCase   (	):
							# This is to make sure the transformers module imported is the one in the repo.
							snake_case__	  =       importlib.util.spec_from_file_location(
							    """transformers"""  ,					os.path.join(a  ,					"""__init__.py""" )  ,					submodule_search_locations=[PATH_TO_TRANSFORMERS]  ,					)
							snake_case__	  =       spec.loader.load_module()

							snake_case__	  =       [
							    module
							    for module in get_transformers_submodules()
							    if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
							]
							if len(a ) > 0:
														snake_case__	  =       """\n""".join(F'''- {module}''' for module in module_not_registered )
														raise ValueError(
														    """The following submodules are not properly registered 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()








import comet  # From: unbabel-comet
import torch

import datasets


a__       =     datasets.logging.get_logger(__name__)

a__       =     """\
@inproceedings{rei-EtAl:2020:WMT,
   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},
   title     = {Unbabel's Participation in the WMT20 Metrics Shared Task},
   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},
   month          = {November},
   year           = {2020},
   address        = {Online},
   publisher      = {Association for Computational Linguistics},
   pages     = {909--918},
}
@inproceedings{rei-etal-2020-comet,
   title = \"{COMET}: A Neural Framework for {MT} Evaluation\",
   author = \"Rei, Ricardo  and
      Stewart, Craig  and
      Farinha, Ana C  and
      Lavie, Alon\",
   booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",
   month = nov,
   year = \"2020\",
   address = \"Online\",
   publisher = \"Association for Computational Linguistics\",
   url = \"https://www.aclweb.org/anthology/2020.emnlp-main.213\",
   pages = \"2685--2702\",
}
"""

a__       =     """\
Crosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA's or MQM).
With the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.

See the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.
"""

a__       =     """
COMET score.

Args:

`sources` (list of str): Source sentences
`predictions` (list of str): candidate translations
`references` (list of str): reference translations
`cuda` (bool): If set to True, runs COMET using GPU
`show_progress` (bool): Shows progress
`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.

Returns:
    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.
    `scores`: List of scores.

Examples:

    >>> comet_metric = datasets.load_metric('comet')
    >>> # comet_metric = load_metric('comet', 'wmt20-comet-da')  # you can also choose which model to use
    >>> source = [\"Dem Feuer konnte Einhalt geboten werden\", \"Schulen und Kindergärten wurden eröffnet.\"]
    >>> hypothesis = [\"The fire could be stopped\", \"Schools and kindergartens were open\"]
    >>> reference = [\"They were able to control the fire.\", \"Schools and kindergartens opened\"]
    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)
    >>> print([round(v, 2) for v in results[\"scores\"]])
    [0.19, 0.92]
"""


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



				"""simple docstring"""



				def 					__magic_name__							(      self    :    Optional[Any]):






											'''simple docstring'''







											return datasets.MetricInfo(
											    description=_DESCRIPTION			,		citation=_CITATION			,		homepage="""https://unbabel.github.io/COMET/html/index.html"""			,		inputs_description=_KWARGS_DESCRIPTION			,		features=datasets.Features(
											        {
											            """sources""": datasets.Value("""string"""			,		id="""sequence"""),
											            """predictions""": datasets.Value("""string"""			,		id="""sequence"""),
											            """references""": datasets.Value("""string"""			,		id="""sequence"""),
											        })			,		codebase_urls=["""https://github.com/Unbabel/COMET"""]			,		reference_urls=[
											        """https://github.com/Unbabel/COMET""",
											        """https://www.aclweb.org/anthology/2020.emnlp-main.213/""",
											        """http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6""",
											    ]			,		)



				def 					__magic_name__							(      self    :    str			,		UpperCamelCase__    :    Dict):






											'''simple docstring'''







											if self.config_name == "default":
																		snake_case__	  =       comet.load_from_checkpoint(comet.download_model("""wmt20-comet-da"""))
											else:
																		snake_case__	  =       comet.load_from_checkpoint(comet.download_model(self.config_name))



				def 					__magic_name__							(      self    :    List[str]			,		UpperCamelCase__    :    str			,		UpperCamelCase__    :    Tuple			,		UpperCamelCase__    :    List[Any]			,		UpperCamelCase__    :    int=None			,		UpperCamelCase__    :    List[Any]=False):






											'''simple docstring'''







											if gpus is None:
																		snake_case__	  =       1 if torch.cuda.is_available() else 0
											snake_case__	  =       {"""src""": sources, """mt""": predictions, """ref""": references}
											snake_case__	  =       [dict(zip(UpperCamelCase__			,		UpperCamelCase__)) for t in zip(*data.values())]
											snake_case__  , snake_case__	  =       self.scorer.predict(UpperCamelCase__			,		gpus=UpperCamelCase__			,		progress_bar=UpperCamelCase__)
											return {"mean_score": mean_score, "scores": scores}
