




import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class 						__lowerCAmelCase      (      __magic_name__					):
							UpperCamelCase__   	=    ['''image_processor''', '''tokenizer''']
							UpperCamelCase__   	=    '''LayoutLMv3ImageProcessor'''
							UpperCamelCase__   	=    ('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')
							def __init__(		self		:int						, __magic_name__		:Any=None						, __magic_name__		:Union[str, Any]=None						, **__magic_name__		:Any							):







								'''simple docstring'''







								a									=				None
								if "feature_extractor" in kwargs:
									warnings.warn(
									    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
									    """ instead."""						, __magic_name__						, )
									a									=				kwargs.pop("""feature_extractor"""							)

								a									=				image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
									raise ValueError("""You need to specify an `image_processor`."""							)
								if tokenizer is None:
									raise ValueError("""You need to specify a `tokenizer`."""							)

								super().__init__(__magic_name__						, __magic_name__							)
							def __call__(		self		:Union[str, Any]						, __magic_name__		:int						, __magic_name__		:Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						, __magic_name__		:Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None						, __magic_name__		:Union[List[List[int]], List[List[List[int]]]] = None						, __magic_name__		:Optional[Union[List[int], List[List[int]]]] = None						, __magic_name__		:bool = True						, __magic_name__		:Union[bool, str, PaddingStrategy] = False						, __magic_name__		:Union[bool, str, TruncationStrategy] = None						, __magic_name__		:Optional[int] = None						, __magic_name__		:int = 0						, __magic_name__		:Optional[int] = None						, __magic_name__		:Optional[bool] = None						, __magic_name__		:Optional[bool] = None						, __magic_name__		:bool = False						, __magic_name__		:bool = False						, __magic_name__		:bool = False						, __magic_name__		:bool = False						, __magic_name__		:bool = True						, __magic_name__		:Optional[Union[str, TensorType]] = None						, **__magic_name__		:int						, ):







								'''simple docstring'''







								if self.image_processor.apply_ocr and (boxes is not None):
									raise ValueError(
									    """You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."""							)

								if self.image_processor.apply_ocr and (word_labels is not None):
									raise ValueError(
									    """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""							)

								# first, apply the image processor
								a									=				self.image_processor(images=__magic_name__						, return_tensors=__magic_name__							)

								# second, apply the tokenizer
								if text is not None and self.image_processor.apply_ocr and text_pair is None:
									if isinstance(__magic_name__						, __magic_name__							):
										a									=				[text]  # add batch dimension (as the image processor always adds a batch dimension)
									a									=				features["""words"""]

								a									=				self.tokenizer(
								    text=text if text is not None else features["""words"""]						, text_pair=text_pair if text_pair is not None else None						, boxes=boxes if boxes is not None else features["""boxes"""]						, word_labels=__magic_name__						, add_special_tokens=__magic_name__						, padding=__magic_name__						, truncation=__magic_name__						, max_length=__magic_name__						, stride=__magic_name__						, pad_to_multiple_of=__magic_name__						, return_token_type_ids=__magic_name__						, return_attention_mask=__magic_name__						, return_overflowing_tokens=__magic_name__						, return_special_tokens_mask=__magic_name__						, return_offsets_mapping=__magic_name__						, return_length=__magic_name__						, verbose=__magic_name__						, return_tensors=__magic_name__						, **__magic_name__						, )

								# add pixel values
								a									=				features.pop("""pixel_values"""							)
								if return_overflowing_tokens is True:
									a									=				self.get_overflowing_images(__magic_name__						, encoded_inputs["""overflow_to_sample_mapping"""]							)
								a									=				images

								return encoded_inputs
							def  lowerCamelCase__				(		self		:Union[str, Any]						, __magic_name__		:str						, __magic_name__		:Dict							):







								'''simple docstring'''







								a									=				[]
								for sample_idx in overflow_to_sample_mapping:
									images_with_overflow.append(images[sample_idx]							)

								if len(__magic_name__							) != len(__magic_name__							):
									raise ValueError(
									    """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
									    F' {len(__magic_name__							)} and {len(__magic_name__							)}'							)

								return images_with_overflow
							def  lowerCamelCase__				(		self		:Optional[int]						, *__magic_name__		:List[Any]						, **__magic_name__		:Dict							):







								'''simple docstring'''







								return self.tokenizer.batch_decode(*__magic_name__						, **__magic_name__							)
							def  lowerCamelCase__				(		self		:Any						, *__magic_name__		:List[Any]						, **__magic_name__		:Union[str, Any]							):







								'''simple docstring'''







								return self.tokenizer.decode(*__magic_name__						, **__magic_name__							)
							@property
							def  lowerCamelCase__				(		self		:Dict							):







								'''simple docstring'''







								return ["input_ids", "bbox", "attention_mask", "pixel_values"]
							@property
							def  lowerCamelCase__				(		self		:int							):







								'''simple docstring'''







								warnings.warn(
								    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""						, __magic_name__						, )
								return self.image_processor_class





							@property
							def  lowerCamelCase__				(		self		:List[Any]							):







								'''simple docstring'''







								warnings.warn(
								    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""						, __magic_name__						, )
								return self.image_processor







import operator as op


__UpperCamelCase   :			Optional[Any]          = "scaler.pt"
__UpperCamelCase   :			Optional[Any]          = "pytorch_model"
__UpperCamelCase   :			str          = "random_states"
__UpperCamelCase   :			Optional[int]          = "optimizer"
__UpperCamelCase   :			Optional[int]          = "scheduler"
__UpperCamelCase   :			str          = "pytorch_model.bin"
__UpperCamelCase   :			List[str]          = "pytorch_model.bin.index.json"
__UpperCamelCase   :			List[str]          = "model.safetensors"
__UpperCamelCase   :			Optional[int]          = "model.safetensors.index.json"
__UpperCamelCase   :			List[str]          = "1.10.2"
__UpperCamelCase   :			Dict          = "py38"
__UpperCamelCase   :			List[str]          = "4.17.0"
__UpperCamelCase   :			Any          = ["ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.p4dn.24xlarge"]
__UpperCamelCase   :			Any          = ["FULL_SHARD", "SHARD_GRAD_OP", "NO_SHARD", "HYBRID_SHARD", "HYBRID_SHARD_ZERO2"]
__UpperCamelCase   :			int          = ["TRANSFORMER_BASED_WRAP", "SIZE_BASED_WRAP", "NO_WRAP"]
__UpperCamelCase   :			Dict          = ["BACKWARD_PRE", "BACKWARD_POST", "NO_PREFETCH"]
__UpperCamelCase   :			str          = ["FULL_STATE_DICT", "LOCAL_STATE_DICT", "SHARDED_STATE_DICT"]
__UpperCamelCase   :			List[Any]          = "2.0.1"
__UpperCamelCase   :			int          = ["pdsh", "standard", "openmpi", "mvapich"]
__UpperCamelCase   :			List[str]          = ["default", "reduce-overhead", "max-autotune"]

__UpperCamelCase   :			List[Any]          = {">": op.gt, ">=": op.ge, "==": op.eq, "!=": op.ne, "<=": op.le, "<": op.lt}

# These are the args for `torch.distributed.launch` for pytorch < 1.9
__UpperCamelCase   :			List[Any]          = [
    "nnodes",
    "nproc_per_node",
    "rdzv_backend",
    "rdzv_endpoint",
    "rdzv_id",
    "rdzv_conf",
    "standalone",
    "max_restarts",
    "monitor_interval",
    "start_method",
    "role",
    "module",
    "m",
    "no_python",
    "run_path",
    "log_dir",
    "r",
    "redirects",
    "t",
    "tee",
    "node_rank",
    "master_addr",
    "master_port",
]

__UpperCamelCase   :			List[str]          = ["DEEPSPEED", "MULTI_GPU", "FSDP", "MEGATRON_LM"]
__UpperCamelCase   :			int          = ["DEEPSPEED", "MULTI_XPU", "FSDP"]

