



'''simple docstring'''






import collections
import os
import re
from pathlib import Path


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


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


def 					snake_case_	(   _lowerCAmelCase    :		Dict    ) ->  Tuple:
							if _re_test_backend.search(UpperCamelCase_    ) is None:
														return None
							UpperCAmelCase					:			Dict									=						[b[0] for b in _re_backend.findall(UpperCamelCase_    )]
							backends.sort()
							return "_and_".join(UpperCamelCase_    )


def 					snake_case_	(   _lowerCAmelCase    :		int    ) ->  Union[str, Any]:
							with open(UpperCamelCase_  , '''r'''  , encoding='''utf-8'''  , newline='''\n'''    ) as f:
														UpperCAmelCase					:			Optional[Any]									=						f.readlines()

							UpperCAmelCase					:			Union[str, Any]									=						0
							while line_index < len(UpperCamelCase_    ) and not lines[line_index].startswith('''_import_structure = {'''    ):
														line_index += 1

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

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

							UpperCAmelCase					:			List[Any]									=						{'''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.
														UpperCAmelCase					:			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:
																					UpperCAmelCase					:			List[str]									=						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

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

																					UpperCAmelCase					:			Dict									=						objects
														else:
																					line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
							UpperCAmelCase					:			str									=						[]
							while (
							    line_index < len(UpperCamelCase_    )
							    and find_backend(lines[line_index]    ) is None
							    and not lines[line_index].startswith('''else'''    )
							):
														UpperCAmelCase					:			int									=						lines[line_index]
														UpperCAmelCase					:			Union[str, Any]									=						_re_import.search(UpperCamelCase_    )
														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

							UpperCAmelCase					:			Union[str, Any]									=						{'''none''': objects}
							# Let's continue with backend-specific objects
							while line_index < len(UpperCamelCase_    ):
														# If the line is an if is_backend_available, we grab all objects associated.
														UpperCAmelCase					:			Optional[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:
																					UpperCAmelCase					:			Optional[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

																					UpperCAmelCase					:			Optional[int]									=						[]
																					# Until we unindent, add backend objects to the list
																					while len(lines[line_index]    ) <= 1 or lines[line_index].startswith(''' ''' * 8    ):
																												UpperCAmelCase					:			List[str]									=						lines[line_index]
																												UpperCAmelCase					:			Dict									=						_re_import.search(UpperCamelCase_    )
																												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					:			str									=						objects
														else:
																					line_index += 1

							return import_dict_objects, type_hint_objects


def 					snake_case_	(   _lowerCAmelCase    :		int  , _lowerCAmelCase    :		Tuple    ) ->  int:

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

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


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


UpperCamelCase__:  Union[str, Any]				      =     [
    '''convert_pytorch_checkpoint_to_tf2''',
    '''modeling_flax_pytorch_utils''',
    '''models.esm.openfold_utils''',
]


def 					snake_case_	(   ) ->  Optional[Any]:
							# This is to make sure the transformers module imported is the one in the repo.
							from transformers.utils import direct_transformers_import

							UpperCAmelCase					:			Dict									=						direct_transformers_import(UpperCamelCase_    )

							UpperCAmelCase					:			Dict									=						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(UpperCamelCase_  , '''__init__.py'''    )  , '''r'''    ) as f:
														UpperCAmelCase					:			Tuple									=						f.read()
							import_structure_keys.update(set(re.findall(R'''import_structure\[\"([^\"]*)\"\]'''  , UpperCamelCase_    )    )    )

							UpperCAmelCase					:			Any									=						[
							    module
							    for module in get_transformers_submodules()
							    if module not in IGNORE_SUBMODULES and module not in import_structure_keys
							]

							if len(UpperCamelCase_    ) > 0:
														UpperCAmelCase					:			Dict									=						'''\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()









'''simple docstring'''
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
							import PIL


__SCREAMING_SNAKE_CASE					:				List[str]									=		logging.get_logger(__name__)






class 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						_UpperCAmelCase		:       Union[str, Any]   						=["pixel_values"]






						def __init__(  self							:      Optional[int] ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Dict[str, int] = None ,  lowerCAmelCase							:      PILImageResampling = PILImageResampling.BICUBIC ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Union[int, float] = 1 / 2_55 ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Optional[Union[float, List[float]]] = None ,  lowerCAmelCase							:      Optional[Union[float, List[float]]] = None ,  lowerCAmelCase							:      bool = True ,  **lowerCAmelCase							:      List[Any] ,  ):
													super().__init__(**lowerCAmelCase	)
													A_			    =		size if size is not None else {"height": 3_84, "width": 3_84}
													A_			    =		get_size_dict(lowerCAmelCase ,  default_to_square=lowerCAmelCase	)

													A_			    =		do_resize
													A_			    =		size
													A_			    =		resample
													A_			    =		do_rescale
													A_			    =		rescale_factor
													A_			    =		do_normalize
													A_			    =		image_mean if image_mean is not None else OPENAI_CLIP_MEAN
													A_			    =		image_std if image_std is not None else OPENAI_CLIP_STD
													A_			    =		do_convert_rgb






						def 				_UpperCAmelCase       (  self							:      Optional[Any] ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Dict[str, int] ,  lowerCAmelCase							:      PILImageResampling = PILImageResampling.BICUBIC ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      Tuple ,  ):
													A_			    =		get_size_dict(lowerCAmelCase ,  default_to_square=lowerCAmelCase	)
													if "height" not in size or "width" not in size:
																				raise ValueError(F"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"	)
													A_			    =		(size["height"], size["width"])
													return resize(lowerCAmelCase ,  size=lowerCAmelCase ,  resample=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      str ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Union[int, float] ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      str ,  ):
													return rescale(lowerCAmelCase ,  scale=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Optional[int] ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Union[float, List[float]] ,  lowerCAmelCase							:      Union[float, List[float]] ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      Dict ,  ):
													return normalize(lowerCAmelCase ,  mean=lowerCAmelCase ,  std=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Tuple ,  lowerCAmelCase							:      ImageInput ,  lowerCAmelCase							:      Optional[bool] = None ,  lowerCAmelCase							:      Optional[Dict[str, int]] = None ,  lowerCAmelCase							:      PILImageResampling = None ,  lowerCAmelCase							:      Optional[bool] = None ,  lowerCAmelCase							:      Optional[float] = None ,  lowerCAmelCase							:      Optional[bool] = None ,  lowerCAmelCase							:      Optional[Union[float, List[float]]] = None ,  lowerCAmelCase							:      Optional[Union[float, List[float]]] = None ,  lowerCAmelCase							:      Optional[Union[str, TensorType]] = None ,  lowerCAmelCase							:      bool = None ,  lowerCAmelCase							:      ChannelDimension = ChannelDimension.FIRST ,  **lowerCAmelCase							:      List[str] ,  ):
													A_			    =		do_resize if do_resize is not None else self.do_resize
													A_			    =		resample if resample is not None else self.resample
													A_			    =		do_rescale if do_rescale is not None else self.do_rescale
													A_			    =		rescale_factor if rescale_factor is not None else self.rescale_factor
													A_			    =		do_normalize if do_normalize is not None else self.do_normalize
													A_			    =		image_mean if image_mean is not None else self.image_mean
													A_			    =		image_std if image_std is not None else self.image_std
													A_			    =		do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

													A_			    =		size if size is not None else self.size
													A_			    =		get_size_dict(lowerCAmelCase ,  default_to_square=lowerCAmelCase	)

													A_			    =		make_list_of_images(lowerCAmelCase	)

													if not valid_images(lowerCAmelCase	):
																				raise ValueError(
																				    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
																				    "torch.Tensor, tf.Tensor or jax.ndarray."	)

													if do_resize and size is None or resample is None:
																				raise ValueError("Size and resample must be specified if do_resize is True."	)

													if do_rescale and rescale_factor is None:
																				raise ValueError("Rescale factor must be specified if do_rescale is True."	)

													if do_normalize and (image_mean is None or image_std is None):
																				raise ValueError("Image mean and std must be specified if do_normalize is True."	)

													# PIL RGBA images are converted to RGB
													if do_convert_rgb:
																				A_			    =		[convert_to_rgb(lowerCAmelCase	) for image in images]

													# All transformations expect numpy arrays.
													A_			    =		[to_numpy_array(lowerCAmelCase	) for image in images]

													if do_resize:
																				A_			    =		[self.resize(image=lowerCAmelCase ,  size=lowerCAmelCase ,  resample=lowerCAmelCase	) for image in images]

													if do_rescale:
																				A_			    =		[self.rescale(image=lowerCAmelCase ,  scale=lowerCAmelCase	) for image in images]

													if do_normalize:
																				A_			    =		[self.normalize(image=lowerCAmelCase ,  mean=lowerCAmelCase ,  std=lowerCAmelCase	) for image in images]

													A_			    =		[to_channel_dimension_format(lowerCAmelCase ,  lowerCAmelCase	) for image in images]

													A_			    =		BatchFeature(data={"pixel_values": images} ,  tensor_type=lowerCAmelCase	)

													return encoded_outputs





