

import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
_a:      Optional[Any]								=     """src/transformers"""

# Matches is_xxx_available()
_a:      Tuple								=     re.compile(r"""is\_([a-z_]*)_available()""")
# Matches from xxx import bla
_a:      Dict								=     re.compile(r"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")
_a:      str								=     re.compile(r"""^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)""")


_a:      List[str]								=     """
{0} = None
"""

_a:      Tuple								=     """
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
"""


_a:      Optional[int]								=     """
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""



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



def  __lowerCAmelCase (  ):
	with open(os.path.join(A    ,       "__init__.py"      )    ,       "r"    ,       encoding="utf-8"    ,       newline="\n"      ) as f:
		UpperCAmelCase_    			=     f.readlines()

	# Get to the point we do the actual imports for type checking
	UpperCAmelCase_    			=     0
	while not lines[line_index].startswith("if TYPE_CHECKING"      ):
		line_index += 1

	UpperCAmelCase_    			=     {}
	# Go through the end of the file
	while line_index < len(A      ):
		# If the line is an if is_backend_available, we grab all objects associated.
		UpperCAmelCase_    			=     find_backend(lines[line_index]      )
		if backend is not None:
			while not lines[line_index].startswith("    else:"      ):
				line_index += 1
			line_index += 1

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

			UpperCAmelCase_    			=     objects
		else:
			line_index += 1

	return backend_specific_objects



def  __lowerCAmelCase (  A    ,       A      ):
	if name.isupper():
		return DUMMY_CONSTANT.format(A      )
	elif name.islower():
		return DUMMY_FUNCTION.format(A    ,       A      )
	else:
		return DUMMY_CLASS.format(A    ,       A      )



def  __lowerCAmelCase (  A=None      ):
	if backend_specific_objects is None:
		UpperCAmelCase_    			=     read_init()
	# For special correspondence backend to module name as used in the function requires_modulename
	UpperCAmelCase_    			=     {}

	for backend, objects in backend_specific_objects.items():
		UpperCAmelCase_    			=     "[" + ", ".join(F"\"{b}\"" for b in backend.split("_and_"      )      ) + "]"
		UpperCAmelCase_    			=     "# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
		dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
		dummy_file += "\n".join([create_dummy_object(A    ,       A      ) for o in objects]      )
		UpperCAmelCase_    			=     dummy_file

	return dummy_files



def  __lowerCAmelCase (  A=False      ):
	UpperCAmelCase_    			=     create_dummy_files()
	# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
	UpperCAmelCase_    			=     {"torch": "pt"}

	# Locate actual dummy modules and read their content.
	UpperCAmelCase_    			=     os.path.join(A    ,       "utils"      )
	UpperCAmelCase_    			=     {
	    backend: os.path.join(A    ,       F"dummy_{short_names.get(A    ,       A      )}_objects.py"      )
	    for backend in dummy_files.keys()
	}

	UpperCAmelCase_    			=     {}
	for backend, file_path in dummy_file_paths.items():
		if os.path.isfile(A      ):
			with open(A    ,       "r"    ,       encoding="utf-8"    ,       newline="\n"      ) as f:
				UpperCAmelCase_    			=     f.read()
		else:
			UpperCAmelCase_    			=     ""

	for backend in dummy_files.keys():
		if dummy_files[backend] != actual_dummies[backend]:
			if overwrite:
				print(
				    F"Updating transformers.utils.dummy_{short_names.get(A    ,       A      )}_objects.py as the main "
				    "__init__ has new objects."      )
				with open(dummy_file_paths[backend]    ,       "w"    ,       encoding="utf-8"    ,       newline="\n"      ) as f:
					f.write(dummy_files[backend]      )
			else:
				raise ValueError(
				    "The main __init__ has objects that are not present in "
				    F"transformers.utils.dummy_{short_names.get(A    ,       A      )}_objects.py. Run `make fix-copies` "
				    "to fix this."      )


if __name__ == "__main__":
				_a:      Tuple								=     argparse.ArgumentParser()
				parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
				_a:      Optional[int]								=     parser.parse_args()

				check_dummies(args.fix_and_overwrite)


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_a:      Tuple								=     {
    """configuration_segformer""": ["""SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SegformerConfig""", """SegformerOnnxConfig"""]
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:      List[str]								=     ["""SegformerFeatureExtractor"""]
				_a:      Union[str, Any]								=     ["""SegformerImageProcessor"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:      Any								=     [
				    """SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """SegformerDecodeHead""",
				    """SegformerForImageClassification""",
				    """SegformerForSemanticSegmentation""",
				    """SegformerLayer""",
				    """SegformerModel""",
				    """SegformerPreTrainedModel""",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:      Optional[Any]								=     [
				    """TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFSegformerDecodeHead""",
				    """TFSegformerForImageClassification""",
				    """TFSegformerForSemanticSegmentation""",
				    """TFSegformerModel""",
				    """TFSegformerPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_segformer import SegformerFeatureExtractor
								from .image_processing_segformer import SegformerImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_segformer import (
								    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SegformerDecodeHead,
								    SegformerForImageClassification,
								    SegformerForSemanticSegmentation,
								    SegformerLayer,
								    SegformerModel,
								    SegformerPreTrainedModel,
								)
				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_segformer import (
								    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFSegformerDecodeHead,
								    TFSegformerForImageClassification,
								    TFSegformerForSemanticSegmentation,
								    TFSegformerModel,
								    TFSegformerPreTrainedModel,
								)

else:
				import sys

				_a:      Optional[int]								=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)