'''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__( lowerCAmelCase__     ):


			'''simple docstring'''






			UpperCAmelCase_      :					Optional[int]								=	['''image_processor''', '''tokenizer''']
			UpperCAmelCase_      :					Optional[Any]								=	'''LayoutLMv2ImageProcessor'''
			UpperCAmelCase_      :					str								=	('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')



			def __init__(       self	,   __lowerCAmelCase=None	,   __lowerCAmelCase=None	,   **__lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  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        =  kwargs.pop("""feature_extractor""")

					lowerCAmelCase        =  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	,   __lowerCAmelCase	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = True	,   __lowerCAmelCase = False	,   __lowerCAmelCase = False	,   __lowerCAmelCase = None	,   __lowerCAmelCase = 0	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = False	,   __lowerCAmelCase = False	,   __lowerCAmelCase = False	,   __lowerCAmelCase = False	,   __lowerCAmelCase = True	,   __lowerCAmelCase = None	,   **__lowerCAmelCase	,   ):




					"""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
					lowerCAmelCase        =  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        =  [text]  # add batch dimension (as the image processor always adds a batch dimension)
							lowerCAmelCase        =  features["""words"""]

					lowerCAmelCase        =  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        =  features.pop("""pixel_values""")
					if return_overflowing_tokens is True:
							lowerCAmelCase        =  self.get_overflowing_images(__lowerCAmelCase	,   encoded_inputs["""overflow_to_sample_mapping"""])
					lowerCAmelCase        =  images

					return encoded_inputs



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  []
					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 							a_	(       self	,   *__lowerCAmelCase	,   **__lowerCAmelCase):




					"""simple docstring"""


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



			def 							a_	(       self	,   *__lowerCAmelCase	,   **__lowerCAmelCase):




					"""simple docstring"""


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



			@property
			def 							a_	(       self):




					"""simple docstring"""


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



			@property
			def 							a_	(       self):




					"""simple docstring"""


					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 							a_	(       self):




					"""simple docstring"""


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





'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

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


__lowercase				   =  logging.get_logger(__name__)

__lowercase				   =  {
    '''flaubert/flaubert_small_cased''': '''https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json''',
    '''flaubert/flaubert_base_uncased''': '''https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json''',
    '''flaubert/flaubert_base_cased''': '''https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json''',
    '''flaubert/flaubert_large_cased''': '''https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json''',
}






class     a__( lowerCAmelCase__     ):


			'''simple docstring'''






			UpperCAmelCase_      :					int								=	'''flaubert'''
			UpperCAmelCase_      :					Optional[Any]								=	{
			    '''hidden_size''': '''emb_dim''',
			    '''num_attention_heads''': '''n_heads''',
			    '''num_hidden_layers''': '''n_layers''',
			    '''n_words''': '''vocab_size''',  # For backward compatibility
			}



			def __init__(       self	,   __lowerCAmelCase=False	,   __lowerCAmelCase=0.0	,   __lowerCAmelCase=30145	,   __lowerCAmelCase=2048	,   __lowerCAmelCase=12	,   __lowerCAmelCase=16	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=True	,   __lowerCAmelCase=False	,   __lowerCAmelCase=False	,   __lowerCAmelCase=False	,   __lowerCAmelCase=1	,   __lowerCAmelCase=True	,   __lowerCAmelCase=512	,   __lowerCAmelCase=2048**-0.5	,   __lowerCAmelCase=1E-1_2	,   __lowerCAmelCase=0.02	,   __lowerCAmelCase=0	,   __lowerCAmelCase=1	,   __lowerCAmelCase=2	,   __lowerCAmelCase=3	,   __lowerCAmelCase=5	,   __lowerCAmelCase=True	,   __lowerCAmelCase="first"	,   __lowerCAmelCase=True	,   __lowerCAmelCase=None	,   __lowerCAmelCase=True	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=5	,   __lowerCAmelCase=5	,   __lowerCAmelCase=0	,   __lowerCAmelCase=0	,   __lowerCAmelCase=2	,   __lowerCAmelCase=0	,   **__lowerCAmelCase	,   ):




					"""simple docstring"""


					lowerCAmelCase        =  pre_norm
					lowerCAmelCase        =  layerdrop
					lowerCAmelCase        =  vocab_size
					lowerCAmelCase        =  emb_dim
					lowerCAmelCase        =  n_layers
					lowerCAmelCase        =  n_heads
					lowerCAmelCase        =  dropout
					lowerCAmelCase        =  attention_dropout
					lowerCAmelCase        =  gelu_activation
					lowerCAmelCase        =  sinusoidal_embeddings
					lowerCAmelCase        =  causal
					lowerCAmelCase        =  asm
					lowerCAmelCase        =  n_langs
					lowerCAmelCase        =  use_lang_emb
					lowerCAmelCase        =  layer_norm_eps
					lowerCAmelCase        =  bos_index
					lowerCAmelCase        =  eos_index
					lowerCAmelCase        =  pad_index
					lowerCAmelCase        =  unk_index
					lowerCAmelCase        =  mask_index
					lowerCAmelCase        =  is_encoder
					lowerCAmelCase        =  max_position_embeddings
					lowerCAmelCase        =  embed_init_std
					lowerCAmelCase        =  init_std
					lowerCAmelCase        =  summary_type
					lowerCAmelCase        =  summary_use_proj
					lowerCAmelCase        =  summary_activation
					lowerCAmelCase        =  summary_proj_to_labels
					lowerCAmelCase        =  summary_first_dropout
					lowerCAmelCase        =  start_n_top
					lowerCAmelCase        =  end_n_top
					lowerCAmelCase        =  mask_token_id
					lowerCAmelCase        =  lang_id

					if "n_words" in kwargs:
							lowerCAmelCase        =  kwargs["""n_words"""]

					super().__init__(pad_token_id=__lowerCAmelCase	,   bos_token_id=__lowerCAmelCase	,   **__lowerCAmelCase)







class     a__( lowerCAmelCase__     ):


			'''simple docstring'''






			@property
			def 							a_	(       self):




					"""simple docstring"""


					if self.task == "multiple-choice":
							lowerCAmelCase        =  {0: """batch""", 1: """choice""", 2: """sequence"""}
					else:
							lowerCAmelCase        =  {0: """batch""", 1: """sequence"""}
					return OrderedDict(
					    [
					        ("""input_ids""", dynamic_axis),
					        ("""attention_mask""", dynamic_axis),
					    ])




