






import collections
import os
import re
from pathlib import Path


lowerCamelCase      :       Any    =						'''src/transformers'''


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





def    __lowerCAmelCase   (    __snake_case							):
     if _re_test_backend.search(__snake_case							) is None:
          return None
     __lowerCAmelCase             =       [b[0] for b in _re_backend.findall(__snake_case							)]
     backends.sort()
     return "_and_".join(__snake_case							)





def    __lowerCAmelCase   (    __snake_case							):
     with open(__snake_case       ,			"r"       ,			encoding="utf-8"       ,			newline="\n"							) as f:
          __lowerCAmelCase             =       f.readlines()

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

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

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

     __lowerCAmelCase             =       {"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.
          __lowerCAmelCase             =       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:
               __lowerCAmelCase             =       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

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

               __lowerCAmelCase             =       objects
          else:
               line_index += 1

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

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

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

               __lowerCAmelCase             =       objects
          else:
               line_index += 1

     return import_dict_objects, type_hint_objects





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case							):

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

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





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


lowerCamelCase      :       Any    =						[
    '''convert_pytorch_checkpoint_to_tf2''',
    '''modeling_flax_pytorch_utils''',
    '''models.esm.openfold_utils''',
]





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

     __lowerCAmelCase             =       direct_transformers_import(__snake_case							)

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

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

     if len(__snake_case							) > 0:
          __lowerCAmelCase             =       "\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()














from sklearn.metrics import fa_score

import datasets


lowerCamelCase      :       Union[str, Any]    =						'''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''


lowerCamelCase      :       Optional[Any]    =						'''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.

        - \'binary\': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
        - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.
        - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
        - \'weighted\': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
        - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.

Examples:

    Example 1-A simple binary example
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {\'f1\': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results[\'f1\'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
        >>> print(round(results[\'f1\'], 2))
        0.35

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")
        >>> print(round(results[\'f1\'], 2))
        0.33
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {\'f1\': array([0.8, 0. , 0. ])}
'''


lowerCamelCase      :       Optional[Any]    =						'''
@article{scikit-learn,
    title={Scikit-learn: Machine Learning in {P}ython},
    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
    journal={Journal of Machine Learning Research},
    volume={12},
    pages={2825--2830},
    year={2011}
}
'''



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


      def 			__UpperCAmelCase		(						self						)->						Tuple:
           return datasets.MetricInfo(
               description=_DESCRIPTION       ,    citation=_CITATION       ,    inputs_description=_KWARGS_DESCRIPTION       ,    features=datasets.Features(
                   {
                       "predictions": datasets.Sequence(datasets.Value("int32"						)						),
                       "references": datasets.Sequence(datasets.Value("int32"						)						),
                   }
                   if self.config_name == "multilabel"
                   else {
                       "predictions": datasets.Value("int32"						),
                       "references": datasets.Value("int32"						),
                   }						)       ,    reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"]       ,    )


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase=None       ,    __UpperCamelCase=1       ,    __UpperCamelCase="binary"       ,    __UpperCamelCase=None						)->						Optional[int]:
           __lowerCAmelCase             =       fa_score(
               __UpperCamelCase       ,    __UpperCamelCase       ,    labels=__UpperCamelCase       ,    pos_label=__UpperCamelCase       ,    average=__UpperCamelCase       ,    sample_weight=__UpperCamelCase						)
           return {"f1": float(__UpperCamelCase						) if score.size == 1 else score}






