
"""simple docstring"""
import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
lowercase_       :						Union[str, Any]     					=	{'''LayoutLMv2Config''', '''LayoutLMv3Config'''}
@is_pipeline_test
class 			UpperCamelCase      (							unittest.TestCase  ):
     A__      =			MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
     A__      =			TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

     if model_mapping is not None:
          A__      =			{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
     if tf_model_mapping is not None:
          A__      =			{
              config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
          }
     @require_torch
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = pipeline(
           task="text-classification"  ,     model="hf-internal-testing/tiny-random-distilbert"  ,     framework="pt"				)

       _SCREAMING_SNAKE_CASE   :				Dict							    = text_classifier("This is great !"				)
       self.assertEqual(nested_simplify(snake_case__				)  ,     [{"label": "LABEL_0", "score": 0.504}]				)

       _SCREAMING_SNAKE_CASE   :				List[Any]							    = text_classifier("This is great !"  ,     top_k=2				)
       self.assertEqual(
           nested_simplify(snake_case__				)  ,     [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]				)

       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = text_classifier(["This is great !", "This is bad"]  ,     top_k=2				)
       self.assertEqual(
           nested_simplify(snake_case__				)  ,     [
               [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
               [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
           ]  ,     )

       _SCREAMING_SNAKE_CASE   :				List[Any]							    = text_classifier("This is great !"  ,     top_k=1				)
       self.assertEqual(nested_simplify(snake_case__				)  ,     [{"label": "LABEL_0", "score": 0.504}]				)

       # Legacy behavior
       _SCREAMING_SNAKE_CASE   :				List[str]							    = text_classifier("This is great !"  ,     return_all_scores=snake_case__				)
       self.assertEqual(nested_simplify(snake_case__				)  ,     [{"label": "LABEL_0", "score": 0.504}]				)

       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = text_classifier("This is great !"  ,     return_all_scores=snake_case__				)
       self.assertEqual(
           nested_simplify(snake_case__				)  ,     [[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]]				)

       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = text_classifier(["This is great !", "Something else"]  ,     return_all_scores=snake_case__				)
       self.assertEqual(
           nested_simplify(snake_case__				)  ,     [
               [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
               [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
           ]  ,     )

       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = text_classifier(["This is great !", "Something else"]  ,     return_all_scores=snake_case__				)
       self.assertEqual(
           nested_simplify(snake_case__				)  ,     [
               {"label": "LABEL_0", "score": 0.504},
               {"label": "LABEL_0", "score": 0.504},
           ]  ,     )
     @require_torch
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       import torch

       _SCREAMING_SNAKE_CASE   :				Any							    = pipeline(
           task="text-classification"  ,     model="hf-internal-testing/tiny-random-distilbert"  ,     framework="pt"  ,     device=torch.device("cpu"				)  ,     )

       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = text_classifier("This is great !"				)
       self.assertEqual(nested_simplify(snake_case__				)  ,     [{"label": "LABEL_0", "score": 0.504}]				)
     @require_tf
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Any							    = pipeline(
           task="text-classification"  ,     model="hf-internal-testing/tiny-random-distilbert"  ,     framework="tf"				)

       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = text_classifier("This is great !"				)
       self.assertEqual(nested_simplify(snake_case__				)  ,     [{"label": "LABEL_0", "score": 0.504}]				)
     @slow
     @require_torch
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = pipeline("text-classification"				)

       _SCREAMING_SNAKE_CASE   :				str							    = text_classifier("This is great !"				)
       self.assertEqual(nested_simplify(snake_case__				)  ,     [{"label": "POSITIVE", "score": 1.0}]				)
       _SCREAMING_SNAKE_CASE   :				int							    = text_classifier("This is bad !"				)
       self.assertEqual(nested_simplify(snake_case__				)  ,     [{"label": "NEGATIVE", "score": 1.0}]				)
       _SCREAMING_SNAKE_CASE   :				int							    = text_classifier("Birds are a type of animal"				)
       self.assertEqual(nested_simplify(snake_case__				)  ,     [{"label": "POSITIVE", "score": 0.988}]				)
     @slow
     @require_tf
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = pipeline("text-classification"  ,     framework="tf"				)

       _SCREAMING_SNAKE_CASE   :				int							    = text_classifier("This is great !"				)
       self.assertEqual(nested_simplify(snake_case__				)  ,     [{"label": "POSITIVE", "score": 1.0}]				)
       _SCREAMING_SNAKE_CASE   :				Any							    = text_classifier("This is bad !"				)
       self.assertEqual(nested_simplify(snake_case__				)  ,     [{"label": "NEGATIVE", "score": 1.0}]				)
       _SCREAMING_SNAKE_CASE   :				int							    = text_classifier("Birds are a type of animal"				)
       self.assertEqual(nested_simplify(snake_case__				)  ,     [{"label": "POSITIVE", "score": 0.988}]				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Tuple							    = TextClassificationPipeline(model=snake_case__  ,     tokenizer=snake_case__				)
       return text_classifier, ["HuggingFace is in", "This is another test"]




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = text_classifier.model
       # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
       _SCREAMING_SNAKE_CASE   :				Any							    = "HuggingFace is in"
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = text_classifier(snake_case__				)

       self.assertEqual(nested_simplify(snake_case__				)  ,     [{"label": ANY(snake_case__				), "score": ANY(snake_case__				)}]				)
       self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()				)

       _SCREAMING_SNAKE_CASE   :				int							    = ["HuggingFace is in ", "Paris is in France"]
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = text_classifier(snake_case__				)
       self.assertEqual(
           nested_simplify(snake_case__				)  ,     [{"label": ANY(snake_case__				), "score": ANY(snake_case__				)}, {"label": ANY(snake_case__				), "score": ANY(snake_case__				)}]  ,     )
       self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()				)
       self.assertTrue(outputs[1]["label"] in model.config.idalabel.values()				)

       # Forcing to get all results with `top_k=None`
       # This is NOT the legacy format
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = text_classifier(snake_case__  ,     top_k=snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = len(model.config.idalabel.values()				)
       self.assertEqual(
           nested_simplify(snake_case__				)  ,     [[{"label": ANY(snake_case__				), "score": ANY(snake_case__				)}] * N, [{"label": ANY(snake_case__				), "score": ANY(snake_case__				)}] * N]  ,     )

       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = {"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = text_classifier(snake_case__				)
       self.assertEqual(
           nested_simplify(snake_case__				)  ,     {"label": ANY(snake_case__				), "score": ANY(snake_case__				)}  ,     )
       self.assertTrue(outputs["label"] in model.config.idalabel.values()				)

       # This might be used a text pair, but tokenizer + pipe interaction
       # makes it hard to understand that it's not using the pair properly
       # https://github.com/huggingface/transformers/issues/17305
       # We disabled this usage instead as it was outputting wrong outputs.
       _SCREAMING_SNAKE_CASE   :				Tuple							    = [["HuggingFace is in ", "Paris is in France"]]
       with self.assertRaises(snake_case__				):
         text_classifier(snake_case__				)

       # This used to be valid for doing text pairs
       # We're keeping it working because of backward compatibility
       _SCREAMING_SNAKE_CASE   :				Dict							    = text_classifier([[["HuggingFace is in ", "Paris is in France"]]]				)
       self.assertEqual(
           nested_simplify(snake_case__				)  ,     [{"label": ANY(snake_case__				), "score": ANY(snake_case__				)}]  ,     )
       self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()				)





"""simple docstring"""
import collections
import os
import re
from pathlib import Path


lowercase_       :						Union[str, Any]     					=	'''src/transformers'''


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





def    _lowerCAmelCase    (     lowerCamelCase__							:							str				)  ->    Dict:
  if _re_test_backend.search(lowerCamelCase__				) is None:
    return None
  _SCREAMING_SNAKE_CASE   :				Any							    = [b[0] for b in _re_backend.findall(lowerCamelCase__				)]
  backends.sort()
  return "_and_".join(lowerCamelCase__				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							List[str]				)  ->    Any:
  with open(lowerCamelCase__,						"r",						encoding="utf-8",						newline="\n"				) as f:
    _SCREAMING_SNAKE_CASE   :				str							    = f.readlines()

  _SCREAMING_SNAKE_CASE   :				List[str]							    = 0
  while line_index < len(lowerCamelCase__				) and not lines[line_index].startswith("_import_structure = {"				):
    line_index += 1

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

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

  _SCREAMING_SNAKE_CASE   :				Dict							    = {"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   :				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:
      _SCREAMING_SNAKE_CASE   :				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

      _SCREAMING_SNAKE_CASE   :				Optional[int]							    = []
      # Until we unindent, add backend objects to the list
      while len(lines[line_index]				) <= 1 or lines[line_index].startswith(" " * 4				):
        _SCREAMING_SNAKE_CASE   :				Any							    = lines[line_index]
        if _re_import_struct_add_one.search(lowerCamelCase__				) is not None:
          objects.append(_re_import_struct_add_one.search(lowerCamelCase__				).groups()[0]				)
        elif _re_import_struct_add_many.search(lowerCamelCase__				) is not None:
          _SCREAMING_SNAKE_CASE   :				List[Any]							    = _re_import_struct_add_many.search(lowerCamelCase__				).groups()[0].split(", "				)
          _SCREAMING_SNAKE_CASE   :				Any							    = [obj[1:-1] for obj in imports if len(lowerCamelCase__				) > 0]
          objects.extend(lowerCamelCase__				)
        elif _re_between_brackets.search(lowerCamelCase__				) is not None:
          _SCREAMING_SNAKE_CASE   :				List[str]							    = _re_between_brackets.search(lowerCamelCase__				).groups()[0].split(", "				)
          _SCREAMING_SNAKE_CASE   :				Any							    = [obj[1:-1] for obj in imports if len(lowerCamelCase__				) > 0]
          objects.extend(lowerCamelCase__				)
        elif _re_quote_object.search(lowerCamelCase__				) is not None:
          objects.append(_re_quote_object.search(lowerCamelCase__				).groups()[0]				)
        elif line.startswith(" " * 8 + "\""				):
          objects.append(line[9:-3]				)
        elif line.startswith(" " * 1_2 + "\""				):
          objects.append(line[1_3:-3]				)
        line_index += 1

      _SCREAMING_SNAKE_CASE   :				str							    = 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   :				Any							    = []
  while (
      line_index < len(lowerCamelCase__				)
      and find_backend(lines[line_index]				) is None
      and not lines[line_index].startswith("else"				)
  ):
    _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = lines[line_index]
    _SCREAMING_SNAKE_CASE   :				int							    = _re_import.search(lowerCamelCase__				)
    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   :				Optional[int]							    = {"none": objects}
  # Let's continue with backend-specific objects
  while line_index < len(lowerCamelCase__				):
    # If the line is an if is_backend_available, we grab all objects associated.
    _SCREAMING_SNAKE_CASE   :				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:
      _SCREAMING_SNAKE_CASE   :				Tuple							    = 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   :				Dict							    = []
      # Until we unindent, add backend objects to the list
      while len(lines[line_index]				) <= 1 or lines[line_index].startswith(" " * 8				):
        _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = lines[line_index]
        _SCREAMING_SNAKE_CASE   :				List[Any]							    = _re_import.search(lowerCamelCase__				)
        if single_line_import_search is not None:
          objects.extend(single_line_import_search.groups()[0].split(", "				)				)
        elif line.startswith(" " * 1_2				):
          objects.append(line[1_2:-2]				)
        line_index += 1

      _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = objects
    else:
      line_index += 1

  return import_dict_objects, type_hint_objects





def    _lowerCAmelCase    (     lowerCamelCase__							:							Tuple,						lowerCamelCase__							:							Dict				)  ->    Tuple:

  def find_duplicates(lowerCamelCase__							:							List[str]				):
    return [k for k, v in collections.Counter(lowerCamelCase__				).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   :				Any							    = []
  for key in import_dict_objects.keys():
    _SCREAMING_SNAKE_CASE   :				Dict							    = find_duplicates(import_dict_objects[key]				)
    if duplicate_imports:
      errors.append(f'''Duplicate _import_structure definitions for: {duplicate_imports}'''				)
    _SCREAMING_SNAKE_CASE   :				List[Any]							    = 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   :				Dict							    = "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    (     )  ->    Union[str, Any]:
  _SCREAMING_SNAKE_CASE   :				int							    = []
  for root, _, files in os.walk(lowerCamelCase__				):
    if "__init__.py" in files:
      _SCREAMING_SNAKE_CASE   :				Tuple							    = os.path.join(lowerCamelCase__,						"__init__.py"				)
      _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = parse_init(lowerCamelCase__				)
      if objects is not None:
        _SCREAMING_SNAKE_CASE   :				List[str]							    = analyze_results(*lowerCamelCase__				)
        if len(lowerCamelCase__				) > 0:
          _SCREAMING_SNAKE_CASE   :				Optional[int]							    = f'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
          failures.append("\n".join(lowerCamelCase__				)				)
  if len(lowerCamelCase__				) > 0:
    raise ValueError("\n\n".join(lowerCamelCase__				)				)





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


lowercase_       :						Optional[int]     					=	[
    '''convert_pytorch_checkpoint_to_tf2''',
    '''modeling_flax_pytorch_utils''',
    '''models.esm.openfold_utils''',
]





def    _lowerCAmelCase    (     )  ->    List[str]:
  # 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   :				Optional[Any]							    = direct_transformers_import(lowerCamelCase__				)

  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = 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(lowerCamelCase__,						"__init__.py"				),						"r"				) as f:
    _SCREAMING_SNAKE_CASE   :				Any							    = f.read()
  import_structure_keys.update(set(re.findall(R"import_structure\[\"([^\"]*)\"\]",						lowerCamelCase__				)				)				)

  _SCREAMING_SNAKE_CASE   :				int							    = [
      module
      for module in get_transformers_submodules()
      if module not in IGNORE_SUBMODULES and module not in import_structure_keys
  ]

  if len(lowerCamelCase__				) > 0:
    _SCREAMING_SNAKE_CASE   :				List[Any]							    = "\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()



