




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 		__A      (			_snake_case			):
						UpperCamelCase     						=					["""image_processor""", """tokenizer"""]
						UpperCamelCase     						=					"""LayoutLMv2ImageProcessor"""
						UpperCamelCase     						=					("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")

						def __init__(		self   :List[Any]    , __snake_case   :Tuple=None    , __snake_case   :Tuple=None    , **__snake_case   :int ):
										'''simple docstring'''

										if "feature_extractor" in kwargs:
														warnings.warn(
														    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
														    """ instead."""    , snake_case_    , )
														__magic_name__	:	List[str]								=kwargs.pop("""feature_extractor""" )

										__magic_name__	:	Tuple								=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__(snake_case_    , snake_case_ )

						def __call__(		self   :Optional[Any]    , __snake_case   :Optional[int]    , __snake_case   :Optional[int] = None    , __snake_case   :str = None    , __snake_case   :Optional[Any] = None    , __snake_case   :Optional[Any] = None    , __snake_case   :Dict = True    , __snake_case   :Any = False    , __snake_case   :List[Any] = None    , __snake_case   :Optional[int] = None    , __snake_case   :int = 0    , __snake_case   :int = None    , __snake_case   :Union[str, Any] = None    , __snake_case   :Any = None    , __snake_case   :List[Any] = False    , __snake_case   :List[str] = False    , __snake_case   :List[Any] = False    , __snake_case   :Tuple = False    , __snake_case   :List[Any] = True    , __snake_case   :Optional[int] = None    , **__snake_case   :List[str]    , ):
										'''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.""" )

										if return_overflowing_tokens is True and return_offsets_mapping is False:
														raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping.""" )

										# first, apply the image processor
										__magic_name__	:	List[Any]								=self.image_processor(images=snake_case_    , return_tensors=snake_case_ )

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

										__magic_name__	:	List[Any]								=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=snake_case_    , add_special_tokens=snake_case_    , padding=snake_case_    , truncation=snake_case_    , max_length=snake_case_    , stride=snake_case_    , pad_to_multiple_of=snake_case_    , return_token_type_ids=snake_case_    , return_attention_mask=snake_case_    , return_overflowing_tokens=snake_case_    , return_special_tokens_mask=snake_case_    , return_offsets_mapping=snake_case_    , return_length=snake_case_    , verbose=snake_case_    , return_tensors=snake_case_    , **snake_case_    , )

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

										return encoded_inputs

						def     A__						(		self   :int    , __snake_case   :List[str]    , __snake_case   :Union[str, Any] ):
										'''simple docstring'''

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

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

										return images_with_overflow

						def     A__						(		self   :int    , *__snake_case   :Tuple    , **__snake_case   :Dict ):
										'''simple docstring'''

										return self.tokenizer.batch_decode(*snake_case_    , **snake_case_ )

						def     A__						(		self   :List[str]    , *__snake_case   :Tuple    , **__snake_case   :List[Any] ):
										'''simple docstring'''

										return self.tokenizer.decode(*snake_case_    , **snake_case_ )

						@property
						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

										return ["input_ids", "bbox", "attention_mask", "image"]

						@property
						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

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


						@property
						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

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










from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


UpperCAmelCase_		:				str									=     logging.get_logger(__name__)

UpperCAmelCase_		:				Tuple									=     {
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					"""t5"""
						UpperCamelCase     						=					["""past_key_values"""]
						UpperCamelCase     						=					{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}

						def __init__(		self   :Optional[Any]    , __snake_case   :Optional[Any]=3_21_28    , __snake_case   :Optional[int]=5_12    , __snake_case   :str=64    , __snake_case   :int=20_48    , __snake_case   :Union[str, Any]=6    , __snake_case   :Union[str, Any]=None    , __snake_case   :Union[str, Any]=8    , __snake_case   :List[str]=32    , __snake_case   :Dict=1_28    , __snake_case   :List[Any]=0.1    , __snake_case   :int=1E-6    , __snake_case   :List[str]=1.0    , __snake_case   :Optional[int]="relu"    , __snake_case   :List[str]=True    , __snake_case   :Union[str, Any]=True    , __snake_case   :Union[str, Any]=0    , __snake_case   :str=1    , **__snake_case   :int    , ):
										'''simple docstring'''

										__magic_name__	:	str								=vocab_size
										__magic_name__	:	str								=d_model
										__magic_name__	:	Optional[Any]								=d_kv
										__magic_name__	:	int								=d_ff
										__magic_name__	:	Union[str, Any]								=num_layers
										__magic_name__	:	Tuple								=(
										    num_decoder_layers if num_decoder_layers is not None else self.num_layers
										)  # default = symmetry
										__magic_name__	:	List[str]								=num_heads
										__magic_name__	:	Optional[Any]								=relative_attention_num_buckets
										__magic_name__	:	Union[str, Any]								=relative_attention_max_distance
										__magic_name__	:	Dict								=dropout_rate
										__magic_name__	:	Dict								=layer_norm_epsilon
										__magic_name__	:	List[str]								=initializer_factor
										__magic_name__	:	Any								=feed_forward_proj
										__magic_name__	:	Optional[int]								=use_cache

										__magic_name__	:	Tuple								=self.feed_forward_proj.split("""-""" )
										__magic_name__	:	int								=act_info[-1]
										__magic_name__	:	int								=act_info[0] == """gated"""

										if len(__snake_case ) > 1 and act_info[0] != "gated" or len(__snake_case ) > 2:
														raise ValueError(
														    f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
														    """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
														    """'gated-gelu' or 'relu'""" )

										# for backwards compatibility
										if feed_forward_proj == "gated-gelu":
														__magic_name__	:	str								="""gelu_new"""

										super().__init__(
										    pad_token_id=__snake_case    , eos_token_id=__snake_case    , is_encoder_decoder=__snake_case    , **__snake_case    , )




class 		__A      (			UpperCamelCase__			):

						@property
						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	int								={
										    """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
										    """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
										}
										if self.use_past:
														__magic_name__	:	List[str]								="""past_encoder_sequence + sequence"""
														__magic_name__	:	List[Any]								={0: """batch"""}
														__magic_name__	:	Union[str, Any]								={0: """batch""", 1: """past_decoder_sequence + sequence"""}
										else:
														__magic_name__	:	int								={0: """batch""", 1: """decoder_sequence"""}
														__magic_name__	:	Tuple								={0: """batch""", 1: """decoder_sequence"""}

										if self.use_past:
														self.fill_with_past_key_values_(__snake_case    , direction="""inputs""" )

										return common_inputs


						@property
						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

										return 13




