






"""simple docstring"""



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    lowercase__			(							snake_case__   ):
			_UpperCAmelCase		:Union[str, Any]   			= ["image_processor", "tokenizer"]
			_UpperCAmelCase		:str   			= "LayoutLMv3ImageProcessor"
			_UpperCAmelCase		:Optional[int]   			= ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")


			def __init__(      self     :  Union[str, Any]   ,       snake_case__     :  List[Any]=None   ,       snake_case__     :  Optional[Any]=None   ,       **snake_case__     :  int					):
										lowerCamelCase_       :							Optional[Any]									=None
										if "feature_extractor" in kwargs:
																	warnings.warn(
																	    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
																	    " instead."   ,       __lowerCamelCase   ,       )
																	lowerCamelCase_       :							List[str]									=kwargs.pop("feature_extractor"					)

										lowerCamelCase_       :							Union[str, Any]									=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__(__lowerCamelCase   ,       __lowerCamelCase					)


			def __call__(      self     :  Optional[int]   ,       snake_case__     :  Any   ,       snake_case__     :  Optional[Any] = None   ,       snake_case__     :  Any = None   ,       snake_case__     :  Optional[Any] = None   ,       snake_case__     :  Union[str, Any] = None   ,       snake_case__     :  Tuple = True   ,       snake_case__     :  List[Any] = False   ,       snake_case__     :  Optional[int] = None   ,       snake_case__     :  Dict = None   ,       snake_case__     :  List[str] = 0   ,       snake_case__     :  Any = None   ,       snake_case__     :  Optional[int] = None   ,       snake_case__     :  Union[str, Any] = None   ,       snake_case__     :  List[Any] = False   ,       snake_case__     :  Tuple = False   ,       snake_case__     :  str = False   ,       snake_case__     :  Optional[int] = False   ,       snake_case__     :  str = True   ,       snake_case__     :  List[Any] = None   ,       **snake_case__     :  Dict   ,       ):
										# verify input
										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
										lowerCamelCase_       :							List[Any]									=self.image_processor(images=__lowerCamelCase   ,       return_tensors=__lowerCamelCase					)

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

										lowerCamelCase_       :							List[str]									=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=__lowerCamelCase   ,       add_special_tokens=__lowerCamelCase   ,       padding=__lowerCamelCase   ,       truncation=__lowerCamelCase   ,       max_length=__lowerCamelCase   ,       stride=__lowerCamelCase   ,       pad_to_multiple_of=__lowerCamelCase   ,       return_token_type_ids=__lowerCamelCase   ,       return_attention_mask=__lowerCamelCase   ,       return_overflowing_tokens=__lowerCamelCase   ,       return_special_tokens_mask=__lowerCamelCase   ,       return_offsets_mapping=__lowerCamelCase   ,       return_length=__lowerCamelCase   ,       verbose=__lowerCamelCase   ,       return_tensors=__lowerCamelCase   ,       **__lowerCamelCase   ,       )

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

										return encoded_inputs


			def 	UpperCAmelCase__						(      self     :  List[Any]   ,       snake_case__     :  Optional[Any]   ,       snake_case__     :  List[str]					):
										# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
										lowerCamelCase_       :							Optional[Any]									=[]
										for sample_idx in overflow_to_sample_mapping:
																	images_with_overflow.append(images[sample_idx]					)

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

										return images_with_overflow


			def 	UpperCAmelCase__						(      self     :  Dict   ,       *snake_case__     :  List[Any]   ,       **snake_case__     :  List[Any]					):
										return self.tokenizer.batch_decode(*__lowerCamelCase   ,       **__lowerCamelCase					)


			def 	UpperCAmelCase__						(      self     :  Optional[Any]   ,       *snake_case__     :  Optional[Any]   ,       **snake_case__     :  Optional[int]					):
										return self.tokenizer.decode(*__lowerCamelCase   ,       **__lowerCamelCase					)


			@property
			def 	UpperCAmelCase__						(      self     :  List[str]					):
										return ["input_ids", "bbox", "attention_mask", "pixel_values"]


			@property
			def 	UpperCAmelCase__						(      self     :  Union[str, Any]					):
										warnings.warn(
										    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."   ,       __lowerCamelCase   ,       )
										return self.image_processor_class


			@property
			def 	UpperCAmelCase__						(      self     :  Tuple					):
										warnings.warn(
										    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."   ,       __lowerCamelCase   ,       )
										return self.image_processor







import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
lowerCAmelCase__      		= object()

# For specifying empty leaf dict `{}`
lowerCAmelCase__      		= object()
def 							_UpperCAmelCase				(UpperCamelCase__     :  str				,							UpperCamelCase__     :  Union[str, Any]						):
       _A      :       str				    = tuple((re.compile(x + "$"						) for x in qs)						)
       for i in range(len(UpperCamelCase__						) - len(UpperCamelCase__						) + 1						):
              _A      :       Tuple				    = [x.match(UpperCamelCase__						) for x, y in zip(UpperCamelCase__				,							ks[i:]						)]
              if matches and all(UpperCamelCase__						):
                     return True
       return False
def 							_UpperCAmelCase				(UpperCamelCase__     :  str						):
       def replace(UpperCamelCase__     :  List[str]				,							UpperCamelCase__     :  Optional[int]						):
              for rule, replacement in rules:
                     if _match(UpperCamelCase__				,							UpperCamelCase__						):
                            return replacement
              return val

       return replace
def 							_UpperCAmelCase				():
       return [
           # embeddings
           (("transformer", "wpe", "embedding"), P("mp"				,							UpperCamelCase__						)),
           (("transformer", "wte", "embedding"), P("mp"				,							UpperCamelCase__						)),
           # atention
           (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(UpperCamelCase__				,							"mp"						)),
           (("attention", "out_proj", "kernel"), P("mp"				,							UpperCamelCase__						)),
           (("attention", "out_proj", "bias"), None),
           # mlp
           (("mlp", "c_fc", "kernel"), P(UpperCamelCase__				,							"mp"						)),
           (("mlp", "c_fc", "bias"), P("mp"						)),
           (("mlp", "c_proj", "kernel"), P("mp"				,							UpperCamelCase__						)),
           (("mlp", "c_proj", "bias"), None),
           # layer norms
           ((r"ln_\d+", "bias"), None),
           ((r"\d+", r"ln_\d+", "scale"), None),
           (("ln_f", "bias"), None),
           (("ln_f", "scale"), None),
       ]






def 							_UpperCAmelCase				(UpperCamelCase__     :  List[str]						):
       _A      :       int				    = _get_partition_rules()
       _A      :       Optional[int]				    = _replacement_rules(UpperCamelCase__						)
       _A      :       Optional[int]				    = {k: _unmatched for k in flatten_dict(UpperCamelCase__						)}
       _A      :       List[str]				    = {k: replace(UpperCamelCase__				,							UpperCamelCase__						) for k, v in initd.items()}
       assert _unmatched not in result.values(), "Incomplete partition spec."
       return freeze(unflatten_dict(UpperCamelCase__						)						)
