


from collections import OrderedDict
from typing import Mapping

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


__a        = logging.get_logger(__name__)

__a        = {
    'mnaylor/mega-base-wikitext': 'https://huggingface.co/mnaylor/mega-base-wikitext/resolve/main/config.json',
}
class 			lowercase__(    UpperCAmelCase							):



	"""simple docstring"""




	a      :Optional[Any]   					=     'mega'
	def __init__(	self  :      List[str]	,							SCREAMING_SNAKE_CASE_  :      str=3_0_5_2_2	,							SCREAMING_SNAKE_CASE_  :      Tuple=1_2_8	,							SCREAMING_SNAKE_CASE_  :      str=4	,							SCREAMING_SNAKE_CASE_  :      Any=2_5_6	,							SCREAMING_SNAKE_CASE_  :      List[str]=1_6	,							SCREAMING_SNAKE_CASE_  :      Tuple=True	,							SCREAMING_SNAKE_CASE_  :      List[Any]=6_4	,							SCREAMING_SNAKE_CASE_  :      Any=False	,							SCREAMING_SNAKE_CASE_  :      List[str]=-1	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=None	,							SCREAMING_SNAKE_CASE_  :      Any=True	,							SCREAMING_SNAKE_CASE_  :      Tuple="scalenorm"	,							SCREAMING_SNAKE_CASE_  :      Any=True	,							SCREAMING_SNAKE_CASE_  :      int="silu"	,							SCREAMING_SNAKE_CASE_  :      List[Any]="softmax"	,							SCREAMING_SNAKE_CASE_  :      Tuple=0.1	,							SCREAMING_SNAKE_CASE_  :      List[Any]=0.1	,							SCREAMING_SNAKE_CASE_  :      Tuple=0.1	,							SCREAMING_SNAKE_CASE_  :      int=False	,							SCREAMING_SNAKE_CASE_  :      Union[str, Any]=True	,							SCREAMING_SNAKE_CASE_  :      List[str]=2_5_6	,							SCREAMING_SNAKE_CASE_  :      int=True	,							SCREAMING_SNAKE_CASE_  :      str=0.1	,							SCREAMING_SNAKE_CASE_  :      Tuple=2_0_4_8	,							SCREAMING_SNAKE_CASE_  :      int=False	,							SCREAMING_SNAKE_CASE_  :      Dict=2	,							SCREAMING_SNAKE_CASE_  :      List[Any]=0.02	,							SCREAMING_SNAKE_CASE_  :      Any=0.2	,							SCREAMING_SNAKE_CASE_  :      Union[str, Any]=0.02	,							SCREAMING_SNAKE_CASE_  :      int=1.0	,							SCREAMING_SNAKE_CASE_  :      Union[str, Any]=1	,							SCREAMING_SNAKE_CASE_  :      List[str]=0	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=2	,							SCREAMING_SNAKE_CASE_  :      Union[str, Any]="rotary"	,							SCREAMING_SNAKE_CASE_  :      List[Any]=None	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=True	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=True	,							**SCREAMING_SNAKE_CASE_  :      int	,							)	->					Optional[int]:
					super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_	,							bos_token_id=SCREAMING_SNAKE_CASE_	,							eos_token_id=SCREAMING_SNAKE_CASE_	,							**SCREAMING_SNAKE_CASE_	)

					lowercase_					  = vocab_size
					lowercase_					  = hidden_size
					lowercase_					  = num_hidden_layers
					lowercase_					  = activation
					lowercase_					  = attention_activation
					lowercase_					  = intermediate_size
					lowercase_					  = ema_projection_size
					lowercase_					  = bidirectional
					lowercase_					  = shared_representation_size
					lowercase_					  = use_chunking
					lowercase_					  = chunk_size
					lowercase_					  = truncation
					lowercase_					  = normalize_before_mega
					lowercase_					  = normalization_type
					lowercase_					  = norm_affine
					lowercase_					  = dropout_prob
					lowercase_					  = hidden_dropout_prob
					lowercase_					  = attention_probs_dropout_prob
					lowercase_					  = use_feature_dropout
					lowercase_					  = use_normalized_ffn
					lowercase_					  = nffn_hidden_size
					lowercase_					  = normalize_before_ffn
					lowercase_					  = nffn_activation_dropout_prob
					lowercase_					  = max_positions
					lowercase_					  = add_token_type_embeddings
					lowercase_					  = type_vocab_size
					lowercase_					  = initializer_range
					lowercase_					  = ema_delta_alpha_range
					lowercase_					  = ema_beta_range
					lowercase_					  = ema_gamma_omega_range
					lowercase_					  = relative_positional_bias
					lowercase_					  = use_cache
					lowercase_					  = classifier_dropout
					lowercase_					  = add_lm_hidden_dense_layer
					lowercase_					  = 1  # not used but required by Hugging Face

class 			lowercase__(    UpperCAmelCase							):



	"""simple docstring"""




	@property
	def 						_lowercase  (	self  :      Dict	)	->					Mapping[str, Mapping[int, str]]:
					if self.task == "multiple-choice":
									lowercase_					  = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
					else:
									lowercase_					  = {0: '''batch''', 1: '''sequence'''}
					return OrderedDict(
					    [
					        ('''input_ids''', dynamic_axis),
					        ('''attention_mask''', dynamic_axis),
					    ]	)






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__(    UpperCAmelCase							):



	"""simple docstring"""




	a      :Any   					=     ['image_processor', 'tokenizer']
	a      :Tuple   					=     'LayoutLMv2ImageProcessor'
	a      :List[Any]   					=     ('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')
	def __init__(	self  :      Tuple	,							SCREAMING_SNAKE_CASE_  :      Any=None	,							SCREAMING_SNAKE_CASE_  :      Any=None	,							**SCREAMING_SNAKE_CASE_  :      List[str]	)	->					Optional[Any]:
					lowercase_					  = None
					if "feature_extractor" in kwargs:
									warnings.warn(
									    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
									    ''' instead.'''	,							SCREAMING_SNAKE_CASE_	,							)
									lowercase_					  = kwargs.pop('''feature_extractor'''	)

					lowercase_					  = 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__(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	)
	def __call__(	self  :      Any	,							SCREAMING_SNAKE_CASE_  :      int	,							SCREAMING_SNAKE_CASE_  :      Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,							SCREAMING_SNAKE_CASE_  :      Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None	,							SCREAMING_SNAKE_CASE_  :      Union[List[List[int]], List[List[List[int]]]] = None	,							SCREAMING_SNAKE_CASE_  :      Optional[Union[List[int], List[List[int]]]] = None	,							SCREAMING_SNAKE_CASE_  :      bool = True	,							SCREAMING_SNAKE_CASE_  :      Union[bool, str, PaddingStrategy] = False	,							SCREAMING_SNAKE_CASE_  :      Union[bool, str, TruncationStrategy] = False	,							SCREAMING_SNAKE_CASE_  :      Optional[int] = None	,							SCREAMING_SNAKE_CASE_  :      int = 0	,							SCREAMING_SNAKE_CASE_  :      Optional[int] = None	,							SCREAMING_SNAKE_CASE_  :      Optional[bool] = None	,							SCREAMING_SNAKE_CASE_  :      Optional[bool] = None	,							SCREAMING_SNAKE_CASE_  :      bool = False	,							SCREAMING_SNAKE_CASE_  :      bool = False	,							SCREAMING_SNAKE_CASE_  :      bool = False	,							SCREAMING_SNAKE_CASE_  :      bool = False	,							SCREAMING_SNAKE_CASE_  :      bool = True	,							SCREAMING_SNAKE_CASE_  :      Optional[Union[str, TensorType]] = None	,							**SCREAMING_SNAKE_CASE_  :      List[Any]	,							)	->					BatchEncoding:
					# 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
					lowercase_					  = self.image_processor(images=SCREAMING_SNAKE_CASE_	,							return_tensors=SCREAMING_SNAKE_CASE_	)

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

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

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

					return encoded_inputs
	def 						_lowercase  (	self  :      Tuple	,							SCREAMING_SNAKE_CASE_  :      str	,							SCREAMING_SNAKE_CASE_  :      Optional[int]	)	->					List[str]:
					# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
					lowercase_					  = []
					for sample_idx in overflow_to_sample_mapping:
									images_with_overflow.append(images[sample_idx]	)

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

					return images_with_overflow
	def 						_lowercase  (	self  :      List[Any]	,							*SCREAMING_SNAKE_CASE_  :      Union[str, Any]	,							**SCREAMING_SNAKE_CASE_  :      Tuple	)	->					List[str]:
					return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_	,							**SCREAMING_SNAKE_CASE_	)
	def 						_lowercase  (	self  :      Optional[Any]	,							*SCREAMING_SNAKE_CASE_  :      Dict	,							**SCREAMING_SNAKE_CASE_  :      List[str]	)	->					List[Any]:
					return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_	,							**SCREAMING_SNAKE_CASE_	)
	@property
	def 						_lowercase  (	self  :      List[Any]	)	->					List[Any]:
					return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]
	@property
	def 						_lowercase  (	self  :      Dict	)	->					str:
					warnings.warn(
					    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''	,							SCREAMING_SNAKE_CASE_	,							)
					return self.image_processor_class




	@property
	def 						_lowercase  (	self  :      int	)	->					List[str]:
					warnings.warn(
					    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''	,							SCREAMING_SNAKE_CASE_	,							)
					return self.image_processor




