






"""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 						_a  (		lowercase_       ):





   '''simple docstring'''






   UpperCamelCase__     	=							["""image_processor""", """tokenizer"""]
   UpperCamelCase__     	=							"""LayoutLMv2ImageProcessor"""
   UpperCamelCase__     	=							("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")





   def __init__(       self					, UpperCAmelCase_=None					, UpperCAmelCase_=None					, **UpperCAmelCase_)			->   str:




          '''simple docstring'''




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

          lowercase__:							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__(UpperCAmelCase_					, UpperCAmelCase_)





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




          '''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
          lowercase__:							Optional[int]         =     self.image_processor(images=UpperCAmelCase_					, return_tensors=UpperCAmelCase_)

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

          lowercase__:							Union[str, 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=UpperCAmelCase_					, add_special_tokens=UpperCAmelCase_					, padding=UpperCAmelCase_					, truncation=UpperCAmelCase_					, max_length=UpperCAmelCase_					, stride=UpperCAmelCase_					, pad_to_multiple_of=UpperCAmelCase_					, return_token_type_ids=UpperCAmelCase_					, return_attention_mask=UpperCAmelCase_					, return_overflowing_tokens=UpperCAmelCase_					, return_special_tokens_mask=UpperCAmelCase_					, return_offsets_mapping=UpperCAmelCase_					, return_length=UpperCAmelCase_					, verbose=UpperCAmelCase_					, return_tensors=UpperCAmelCase_					, **UpperCAmelCase_					, )

          # add pixel values
          lowercase__:							List[str]         =     features.pop("pixel_values")
          if return_overflowing_tokens is True:
                 lowercase__:							Dict         =     self.get_overflowing_images(UpperCAmelCase_					, encoded_inputs["overflow_to_sample_mapping"])
          lowercase__:							Optional[Any]         =     images

          return encoded_inputs





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_)			->   Dict:




          '''simple docstring'''




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

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

          return images_with_overflow





   def __lowercase							(       self					, *UpperCAmelCase_					, **UpperCAmelCase_)			->   str:




          '''simple docstring'''




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





   def __lowercase							(       self					, *UpperCAmelCase_					, **UpperCAmelCase_)			->   int:




          '''simple docstring'''




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





   @property
   def __lowercase							(       self)			->   Optional[Any]:




          '''simple docstring'''




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





   @property
   def __lowercase							(       self)			->   str:




          '''simple docstring'''




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





   @property
   def __lowercase							(       self)			->   List[Any]:




          '''simple docstring'''




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













"""simple docstring"""
import copy

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


_snake_case	:						int					=					logging.get_logger(__name__)

_snake_case	:						Optional[int]					=					{}






class    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				'''fsmt_decoder'''


					def __init__(   self     :Optional[int]	,			__UpperCamelCase     :Tuple=0	,			__UpperCamelCase     :List[str]=0						):
												super().__init__()
												A						=    vocab_size
												A						=    bos_token_id







class    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				'''fsmt'''
					UpperCamelCase						=				{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


					def __init__(   self     :Dict	,			__UpperCamelCase     :List[str]=["en", "de"]	,			__UpperCamelCase     :List[str]=4_20_24	,			__UpperCamelCase     :Dict=4_20_24	,			__UpperCamelCase     :Tuple="relu"	,			__UpperCamelCase     :Optional[Any]=10_24	,			__UpperCamelCase     :Dict=2_00	,			__UpperCamelCase     :Optional[Any]=10_24	,			__UpperCamelCase     :Optional[Any]=40_96	,			__UpperCamelCase     :Union[str, Any]=12	,			__UpperCamelCase     :Dict=16	,			__UpperCamelCase     :Optional[Any]=0.0	,			__UpperCamelCase     :Dict=40_96	,			__UpperCamelCase     :Dict=12	,			__UpperCamelCase     :str=16	,			__UpperCamelCase     :Any=0.0	,			__UpperCamelCase     :str=0.0	,			__UpperCamelCase     :List[Any]=0.1	,			__UpperCamelCase     :List[Any]=0.0	,			__UpperCamelCase     :str=0.02	,			__UpperCamelCase     :List[str]=2	,			__UpperCamelCase     :Union[str, Any]=True	,			__UpperCamelCase     :Optional[Any]=True	,			__UpperCamelCase     :int=False	,			__UpperCamelCase     :List[str]=5	,			__UpperCamelCase     :Optional[int]=1.0	,			__UpperCamelCase     :List[str]=False	,			__UpperCamelCase     :Any=True	,			__UpperCamelCase     :List[str]=1	,			__UpperCamelCase     :int=0	,			__UpperCamelCase     :str=2	,			__UpperCamelCase     :Dict=2	,			**__UpperCamelCase     :Optional[int]	,			):
												A						=    langs
												A						=    src_vocab_size
												A						=    tgt_vocab_size
												A						=    d_model  # encoder_embed_dim and decoder_embed_dim

												A						=    encoder_ffn_dim
												A						=    A						=    encoder_layers
												A						=    encoder_attention_heads
												A						=    encoder_layerdrop
												A						=    decoder_layerdrop
												A						=    decoder_ffn_dim
												A						=    decoder_layers
												A						=    decoder_attention_heads
												A						=    max_position_embeddings
												A						=    init_std  # Normal(0, this parameter)
												A						=    activation_function

												A						=    DecoderConfig(vocab_size=__UpperCamelCase	,			bos_token_id=__UpperCamelCase						)
												if "decoder" in common_kwargs:
																			del common_kwargs["decoder"]

												A						=    scale_embedding  # scale factor will be sqrt(d_model) if True

												# 3 Types of Dropout
												A						=    attention_dropout
												A						=    activation_dropout
												A						=    dropout

												A						=    use_cache
												super().__init__(
												    pad_token_id=__UpperCamelCase	,			bos_token_id=__UpperCamelCase	,			eos_token_id=__UpperCamelCase	,			decoder_start_token_id=__UpperCamelCase	,			is_encoder_decoder=__UpperCamelCase	,			tie_word_embeddings=__UpperCamelCase	,			forced_eos_token_id=__UpperCamelCase	,			max_length=__UpperCamelCase	,			num_beams=__UpperCamelCase	,			length_penalty=__UpperCamelCase	,			early_stopping=__UpperCamelCase	,			**__UpperCamelCase	,			)


					def       lowerCamelCase			(   self     :List[str]						):
												A						=    copy.deepcopy(self.__dict__						)
												A						=    self.decoder.to_dict()
												A						=    self.__class__.model_type
												return output


