



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        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     Optional[Any]								=     ['image_processor', 'tokenizer']
				A_   :     int								=     'LayoutLMv2ImageProcessor'
				A_   :     Optional[Any]								=     ('LayoutXLMTokenizer', 'LayoutXLMTokenizerFast')


				def __init__(							self      :  str						, __UpperCamelCase      :  Tuple=None						, __UpperCamelCase      :  Union[str, Any]=None						, **__UpperCamelCase      :  Optional[Any]       )		->     Any:
											if "feature_extractor" in kwargs:
																		warnings.warn(
																		    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																		    ' instead.'						, __UpperCamelCase						, )
																		A      						=    kwargs.pop('feature_extractor'       )

											A      						=    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      :  List[Any]						, __UpperCamelCase      :  List[Any]						, __UpperCamelCase      :  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						, __UpperCamelCase      :  Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None						, __UpperCamelCase      :  Union[List[List[int]], List[List[List[int]]]] = None						, __UpperCamelCase      :  Optional[Union[List[int], List[List[int]]]] = None						, __UpperCamelCase      :  bool = True						, __UpperCamelCase      :  Union[bool, str, PaddingStrategy] = False						, __UpperCamelCase      :  Union[bool, str, TruncationStrategy] = None						, __UpperCamelCase      :  Optional[int] = None						, __UpperCamelCase      :  int = 0						, __UpperCamelCase      :  Optional[int] = None						, __UpperCamelCase      :  Optional[bool] = None						, __UpperCamelCase      :  Optional[bool] = None						, __UpperCamelCase      :  bool = False						, __UpperCamelCase      :  bool = False						, __UpperCamelCase      :  bool = False						, __UpperCamelCase      :  bool = False						, __UpperCamelCase      :  bool = True						, __UpperCamelCase      :  Optional[Union[str, TensorType]] = None						, **__UpperCamelCase      :  List[str]						, )		->     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
											A      						=    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       ):
																									A      						=    [text]  # add batch dimension (as the image processor always adds a batch dimension)
																		A      						=    features['words']

											A      						=    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
											A      						=    features.pop('pixel_values'       )
											if return_overflowing_tokens is True:
																		A      						=    self.get_overflowing_images(__UpperCamelCase						, encoded_inputs['overflow_to_sample_mapping']       )
											A      						=    images

											return encoded_inputs


				def __UpperCamelCase						(							self      :  List[str]						, __UpperCamelCase      :  Optional[Any]						, __UpperCamelCase      :  int       )		->     str:
											# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
											A      						=    []
											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 __UpperCamelCase						(							self      :  Optional[int]						, *__UpperCamelCase      :  int						, **__UpperCamelCase      :  List[str]       )		->     Union[str, Any]:
											return self.tokenizer.batch_decode(*__UpperCamelCase						, **__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  List[str]						, *__UpperCamelCase      :  List[Any]						, **__UpperCamelCase      :  Optional[int]       )		->     Dict:
											return self.tokenizer.decode(*__UpperCamelCase						, **__UpperCamelCase       )


				@property
				def __UpperCamelCase						(							self      :  List[str]       )		->     List[Any]:
											return ["input_ids", "bbox", "attention_mask", "image"]


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



'''simple docstring'''
import functools
import operator

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


__SCREAMING_SNAKE_CASE					:				Dict									=		logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE					:				int									=		{
    '''facebook/wav2vec2-base-960h''': '''https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json''',
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}






class 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						_UpperCAmelCase		:       Dict   						="wav2vec2"






						def __init__(  self							:      str ,  lowerCAmelCase							:      Dict=32 ,  lowerCAmelCase							:      List[Any]=7_68 ,  lowerCAmelCase							:      List[str]=12 ,  lowerCAmelCase							:      List[str]=12 ,  lowerCAmelCase							:      List[Any]=30_72 ,  lowerCAmelCase							:      Optional[Any]="gelu" ,  lowerCAmelCase							:      Optional[int]=0.1 ,  lowerCAmelCase							:      Optional[int]=0.1 ,  lowerCAmelCase							:      List[Any]=0.1 ,  lowerCAmelCase							:      Dict=0.0 ,  lowerCAmelCase							:      Union[str, Any]=0.0 ,  lowerCAmelCase							:      str=0.1 ,  lowerCAmelCase							:      List[str]=0.1 ,  lowerCAmelCase							:      Dict=0.0_2 ,  lowerCAmelCase							:      List[str]=1e-5 ,  lowerCAmelCase							:      Optional[Any]="group" ,  lowerCAmelCase							:      List[Any]="gelu" ,  lowerCAmelCase							:      int=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) ,  lowerCAmelCase							:      Union[str, Any]=(5, 2, 2, 2, 2, 2, 2) ,  lowerCAmelCase							:      Any=(10, 3, 3, 3, 3, 2, 2) ,  lowerCAmelCase							:      str=False ,  lowerCAmelCase							:      Tuple=1_28 ,  lowerCAmelCase							:      Union[str, Any]=16 ,  lowerCAmelCase							:      List[str]=False ,  lowerCAmelCase							:      Union[str, Any]=True ,  lowerCAmelCase							:      Any=0.0_5 ,  lowerCAmelCase							:      Any=10 ,  lowerCAmelCase							:      Dict=2 ,  lowerCAmelCase							:      List[Any]=0.0 ,  lowerCAmelCase							:      int=10 ,  lowerCAmelCase							:      List[str]=0 ,  lowerCAmelCase							:      Tuple=3_20 ,  lowerCAmelCase							:      List[str]=2 ,  lowerCAmelCase							:      int=0.1 ,  lowerCAmelCase							:      List[Any]=1_00 ,  lowerCAmelCase							:      List[str]=2_56 ,  lowerCAmelCase							:      int=2_56 ,  lowerCAmelCase							:      Optional[Any]=0.1 ,  lowerCAmelCase							:      str="sum" ,  lowerCAmelCase							:      List[Any]=False ,  lowerCAmelCase							:      List[str]=False ,  lowerCAmelCase							:      Tuple=2_56 ,  lowerCAmelCase							:      str=(5_12, 5_12, 5_12, 5_12, 15_00) ,  lowerCAmelCase							:      List[str]=(5, 3, 3, 1, 1) ,  lowerCAmelCase							:      Tuple=(1, 2, 3, 1, 1) ,  lowerCAmelCase							:      Optional[Any]=5_12 ,  lowerCAmelCase							:      Any=0 ,  lowerCAmelCase							:      str=1 ,  lowerCAmelCase							:      Any=2 ,  lowerCAmelCase							:      Any=False ,  lowerCAmelCase							:      List[Any]=3 ,  lowerCAmelCase							:      Any=2 ,  lowerCAmelCase							:      List[str]=3 ,  lowerCAmelCase							:      int=None ,  lowerCAmelCase							:      Optional[int]=None ,  **lowerCAmelCase							:      Union[str, Any] ,  ):
													super().__init__(**lowerCAmelCase ,  pad_token_id=lowerCAmelCase ,  bos_token_id=lowerCAmelCase ,  eos_token_id=lowerCAmelCase	)
													A_			    =		hidden_size
													A_			    =		feat_extract_norm
													A_			    =		feat_extract_activation
													A_			    =		list(lowerCAmelCase	)
													A_			    =		list(lowerCAmelCase	)
													A_			    =		list(lowerCAmelCase	)
													A_			    =		conv_bias
													A_			    =		num_conv_pos_embeddings
													A_			    =		num_conv_pos_embedding_groups
													A_			    =		len(self.conv_dim	)
													A_			    =		num_hidden_layers
													A_			    =		intermediate_size
													A_			    =		hidden_act
													A_			    =		num_attention_heads
													A_			    =		hidden_dropout
													A_			    =		attention_dropout
													A_			    =		activation_dropout
													A_			    =		feat_proj_dropout
													A_			    =		final_dropout
													A_			    =		layerdrop
													A_			    =		layer_norm_eps
													A_			    =		initializer_range
													A_			    =		vocab_size
													A_			    =		do_stable_layer_norm
													A_			    =		use_weighted_layer_sum

													if (
													    (len(self.conv_stride	) != self.num_feat_extract_layers)
													    or (len(self.conv_kernel	) != self.num_feat_extract_layers)
													    or (len(self.conv_dim	) != self.num_feat_extract_layers)
													):
																				raise ValueError(
																				    "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
																				    " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
																				    F" {len(self.conv_dim	)}`, `len(config.conv_stride) = {len(self.conv_stride	)}`,"
																				    F" `len(config.conv_kernel) = {len(self.conv_kernel	)}`."	)

													# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
													A_			    =		apply_spec_augment
													A_			    =		mask_time_prob
													A_			    =		mask_time_length
													A_			    =		mask_time_min_masks
													A_			    =		mask_feature_prob
													A_			    =		mask_feature_length
													A_			    =		mask_feature_min_masks

													# parameters for pretraining with codevector quantized representations
													A_			    =		num_codevectors_per_group
													A_			    =		num_codevector_groups
													A_			    =		contrastive_logits_temperature
													A_			    =		feat_quantizer_dropout
													A_			    =		num_negatives
													A_			    =		codevector_dim
													A_			    =		proj_codevector_dim
													A_			    =		diversity_loss_weight

													# ctc loss
													A_			    =		ctc_loss_reduction
													A_			    =		ctc_zero_infinity

													# adapter
													A_			    =		add_adapter
													A_			    =		adapter_kernel_size
													A_			    =		adapter_stride
													A_			    =		num_adapter_layers
													A_			    =		output_hidden_size or hidden_size
													A_			    =		adapter_attn_dim

													# SequenceClassification-specific parameter. Feel free to ignore for other classes.
													A_			    =		classifier_proj_size

													# XVector-specific parameters. Feel free to ignore for other classes.
													A_			    =		list(lowerCAmelCase	)
													A_			    =		list(lowerCAmelCase	)
													A_			    =		list(lowerCAmelCase	)
													A_			    =		xvector_output_dim






						@property
						def 				_UpperCAmelCase       (  self							:      Tuple	):
													return functools.reduce(operator.mul ,  self.conv_stride ,  1	)





