
"""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    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				['image_processor', 'tokenizer']
		lowercase__      =				'LayoutLMv2ImageProcessor'
		lowercase__      =				('LayoutXLMTokenizer', 'LayoutXLMTokenizerFast')



		def __init__( self    ,		__a=None    ,		__a=None    ,		**__a)	->	Union[str, Any]:






							'''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.'''    ,		__a    ,		)
												_UpperCamelCase										=  kwargs.pop('''feature_extractor''')

							_UpperCamelCase										=  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__(__a    ,		__a)



		def __call__( self    ,		__a    ,		__a = None    ,		__a = None    ,		__a = None    ,		__a = None    ,		__a = True    ,		__a = False    ,		__a = None    ,		__a = None    ,		__a = 0    ,		__a = None    ,		__a = None    ,		__a = None    ,		__a = False    ,		__a = False    ,		__a = False    ,		__a = False    ,		__a = True    ,		__a = None    ,		**__a    ,		)	->	BatchEncoding:






							'''simple docstring'''
							# 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.''')

							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
							_UpperCamelCase										=  self.image_processor(images=__a    ,		return_tensors=__a)

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

							_UpperCamelCase										=  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=__a    ,		add_special_tokens=__a    ,		padding=__a    ,		truncation=__a    ,		max_length=__a    ,		stride=__a    ,		pad_to_multiple_of=__a    ,		return_token_type_ids=__a    ,		return_attention_mask=__a    ,		return_overflowing_tokens=__a    ,		return_special_tokens_mask=__a    ,		return_offsets_mapping=__a    ,		return_length=__a    ,		verbose=__a    ,		return_tensors=__a    ,		**__a    ,		)

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

							return encoded_inputs



		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	List[str]:






							'''simple docstring'''
							# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
							_UpperCamelCase										=  []
							for sample_idx in overflow_to_sample_mapping:
												images_with_overflow.append(images[sample_idx])

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

							return images_with_overflow



		def 						UpperCAmelCase							( self    ,		*__a    ,		**__a)	->	int:






							'''simple docstring'''
							return self.tokenizer.batch_decode(*__a    ,		**__a)



		def 						UpperCAmelCase							( self    ,		*__a    ,		**__a)	->	Dict:






							'''simple docstring'''
							return self.tokenizer.decode(*__a    ,		**__a)



		@property
		def 						UpperCAmelCase							( self)	->	Union[str, Any]:






							'''simple docstring'''
							return ["input_ids", "bbox", "attention_mask", "image"]



		@property
		def 						UpperCAmelCase							( self)	->	Any:






							'''simple docstring'''
							warnings.warn(
							    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''    ,		__a    ,		)
							return self.image_processor_class




		@property
		def 						UpperCAmelCase							( self)	->	List[str]:






							'''simple docstring'''
							warnings.warn(
							    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''    ,		__a    ,		)
							return self.image_processor








"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a														=		logging.get_logger(__name__)

_a														=		{
    """microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/config.json""",
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'biogpt'



		def __init__( self    ,		__a=4_23_84    ,		__a=10_24    ,		__a=24    ,		__a=16    ,		__a=40_96    ,		__a="gelu"    ,		__a=0.1    ,		__a=0.1    ,		__a=10_24    ,		__a=0.02    ,		__a=1e-12    ,		__a=True    ,		__a=True    ,		__a=0.0    ,		__a=0.0    ,		__a=1    ,		__a=0    ,		__a=2    ,		**__a    ,		)	->	Dict:






							'''simple docstring'''
							_UpperCamelCase										=  vocab_size
							_UpperCamelCase										=  max_position_embeddings
							_UpperCamelCase										=  hidden_size
							_UpperCamelCase										=  num_hidden_layers
							_UpperCamelCase										=  num_attention_heads
							_UpperCamelCase										=  intermediate_size
							_UpperCamelCase										=  hidden_act
							_UpperCamelCase										=  hidden_dropout_prob
							_UpperCamelCase										=  attention_probs_dropout_prob
							_UpperCamelCase										=  initializer_range
							_UpperCamelCase										=  layer_norm_eps
							_UpperCamelCase										=  scale_embedding
							_UpperCamelCase										=  use_cache
							_UpperCamelCase										=  layerdrop
							_UpperCamelCase										=  activation_dropout
							super().__init__(pad_token_id=__a    ,		bos_token_id=__a    ,		eos_token_id=__a    ,		**__a)






