






"""simple docstring"""





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__					: Any=	"""src/diffusers"""

# Matches is_xxx_available()
A__					: Tuple=	re.compile(r"""is\_([a-z_]*)_available\(\)""")
# Matches from xxx import bla
A__					: Any=	re.compile(r"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")


A__					: Optional[Any]=	"""
{0} = None
"""

A__					: List[Any]=	"""
class {0}(metaclass=DummyObject):
    _backends = {1}

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

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, {1})

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, {1})
"""


A__					: Dict=	"""
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Union[str, Any]:
    """simple docstring"""

    UpperCamelCase__         =    _re_backend.findall(SCREAMING_SNAKE_CASE					)
    if len(SCREAMING_SNAKE_CASE					) == 0:
        return None

    return "_and_".join(SCREAMING_SNAKE_CASE					)







def   lowerCAmelCase_( )					->	str:
    """simple docstring"""

    with open(os.path.join(SCREAMING_SNAKE_CASE ,	'__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
    UpperCamelCase__         =    {}
    # Go through the end of the file
    while line_index < len(SCREAMING_SNAKE_CASE					):
        # If the line contains is_backend_available, we grab all objects associated with the `else` block
        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 line_index < len(SCREAMING_SNAKE_CASE					) and len(lines[line_index]					) > 1:
                UpperCamelCase__         =    lines[line_index]
                UpperCamelCase__         =    _re_single_line_import.search(SCREAMING_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

            if len(SCREAMING_SNAKE_CASE					) > 0:
                UpperCamelCase__         =    objects
        else:
            line_index += 1

    return backend_specific_objects







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	List[Any]:
    """simple docstring"""

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







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE=None					)					->	int:
    """simple docstring"""

    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(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					) for o in objects]					)
        UpperCamelCase__         =    dummy_file

    return dummy_files







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE=False					)					->	int:
    """simple docstring"""

    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(SCREAMING_SNAKE_CASE ,	'utils'					)
    UpperCamelCase__         =    {
        backend: os.path.join(SCREAMING_SNAKE_CASE ,	F'dummy_{short_names.get(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)}_objects.py'					)
        for backend in dummy_files.keys()
    }

    UpperCamelCase__         =    {}
    for backend, file_path in dummy_file_paths.items():
        if os.path.isfile(SCREAMING_SNAKE_CASE					):
            with open(SCREAMING_SNAKE_CASE ,	'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 diffusers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)}_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'diffusers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)}_objects.py. Run `make fix-copies` '
                    'to fix this.'					)


if __name__ == "__main__":
  A__					: Any=	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)












"""simple docstring"""





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
  from ...processing_utils import ProcessorMixin
  from ...utils import TensorType


A__					: Any=	logging.get_logger(__name__)

A__					: str=	{
    """microsoft/layoutlmv3-base""": """https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json""",
}
class    __lowerCamelCase    (  _a  ):
   a      :				List[str] 						="""layoutlmv3"""

   def __init__(					self ,				snake_case_=5_0265 ,				snake_case_=768 ,				snake_case_=12 ,				snake_case_=12 ,				snake_case_=3072 ,				snake_case_="gelu" ,				snake_case_=0.1 ,				snake_case_=0.1 ,				snake_case_=512 ,				snake_case_=2 ,				snake_case_=0.02 ,				snake_case_=1E-5 ,				snake_case_=1 ,				snake_case_=0 ,				snake_case_=2 ,				snake_case_=1024 ,				snake_case_=128 ,				snake_case_=128 ,				snake_case_=True ,				snake_case_=32 ,				snake_case_=128 ,				snake_case_=64 ,				snake_case_=256 ,				snake_case_=True ,				snake_case_=True ,				snake_case_=True ,				snake_case_=224 ,				snake_case_=3 ,				snake_case_=16 ,				snake_case_=None ,				**snake_case_ ,				)		->       Union[str, Any]:
       super().__init__(
           vocab_size=snake_case_ ,				hidden_size=snake_case_ ,				num_hidden_layers=snake_case_ ,				num_attention_heads=snake_case_ ,				intermediate_size=snake_case_ ,				hidden_act=snake_case_ ,				hidden_dropout_prob=snake_case_ ,				attention_probs_dropout_prob=snake_case_ ,				max_position_embeddings=snake_case_ ,				type_vocab_size=snake_case_ ,				initializer_range=snake_case_ ,				layer_norm_eps=snake_case_ ,				pad_token_id=snake_case_ ,				bos_token_id=snake_case_ ,				eos_token_id=snake_case_ ,				**snake_case_ ,				)
       UpperCamelCase__         =    max_ad_position_embeddings
       UpperCamelCase__         =    coordinate_size
       UpperCamelCase__         =    shape_size
       UpperCamelCase__         =    has_relative_attention_bias
       UpperCamelCase__         =    rel_pos_bins
       UpperCamelCase__         =    max_rel_pos
       UpperCamelCase__         =    has_spatial_attention_bias
       UpperCamelCase__         =    rel_ad_pos_bins
       UpperCamelCase__         =    max_rel_ad_pos
       UpperCamelCase__         =    text_embed
       UpperCamelCase__         =    visual_embed
       UpperCamelCase__         =    input_size
       UpperCamelCase__         =    num_channels
       UpperCamelCase__         =    patch_size
       UpperCamelCase__         =    classifier_dropout







class    __lowerCamelCase    (  _a  ):
   a      :				Tuple 						=version.parse("""1.12"""  )

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Mapping[str, Mapping[int, str]]:
       # The order of inputs is different for question answering and sequence classification
       if self.task in ["question-answering", "sequence-classification"]:
           return OrderedDict(
               [
                   ('input_ids', {0: 'batch', 1: 'sequence'}),
                   ('attention_mask', {0: 'batch', 1: 'sequence'}),
                   ('bbox', {0: 'batch', 1: 'sequence'}),
                   ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
               ]      )
       else:
           return OrderedDict(
               [
                   ('input_ids', {0: 'batch', 1: 'sequence'}),
                   ('bbox', {0: 'batch', 1: 'sequence'}),
                   ('attention_mask', {0: 'batch', 1: 'sequence'}),
                   ('pixel_values', {0: 'batch', 1: 'num_channels'}),
               ]      )

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       float:
       return 1E-5

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       return 12







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = -1 ,				snake_case_ = -1 ,				snake_case_ = False ,				snake_case_ = None ,				snake_case_ = 3 ,				snake_case_ = 40 ,				snake_case_ = 40 ,				)		->       Mapping[str, Any]:
       setattr(processor.image_processor ,				'apply_ocr' ,				snake_case_      )

       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       UpperCamelCase__         =    compute_effective_axis_dimension(
           snake_case_ ,				fixed_dimension=OnnxConfig.default_fixed_batch ,				num_token_to_add=0      )
       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       UpperCamelCase__         =    processor.tokenizer.num_special_tokens_to_add(snake_case_      )
       UpperCamelCase__         =    compute_effective_axis_dimension(
           snake_case_ ,				fixed_dimension=OnnxConfig.default_fixed_sequence ,				num_token_to_add=snake_case_      )
       # Generate dummy inputs according to compute batch and sequence
       UpperCamelCase__         =    [[' '.join([processor.tokenizer.unk_token]      ) * seq_length]] * batch_size

       # Generate dummy bounding boxes
       UpperCamelCase__         =    [[[48, 84, 73, 128]]] * batch_size

       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       # batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
       UpperCamelCase__         =    self._generate_dummy_images(snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )

       UpperCamelCase__         =    dict(
           processor(
               snake_case_ ,				text=snake_case_ ,				boxes=snake_case_ ,				return_tensors=snake_case_ ,				)      )

       return inputs




