'''simple docstring'''






import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer





class     A__							(       UpperCamelCase   ):





   """simple docstring"""





   UpperCamelCase_       :						Optional[int]    				=     ['''image_processor''', '''tokenizer''']
   UpperCamelCase_       :						Optional[int]    				=     '''BlipImageProcessor'''
   UpperCamelCase_       :						Tuple    				=     '''AutoTokenizer'''




   def __init__( self     :		Dict						,     lowerCAmelCase__     :		Union[str, Any]						,     lowerCAmelCase__     :		int						,     lowerCAmelCase__     :		Optional[Any]			)       ->    Dict:







      """simple docstring"""




      super().__init__(lowerCAmelCase__						,     lowerCAmelCase__			)

      # add QFormer tokenizer
      _UpperCAmelCase							:	Union[str, Any]							=   qformer_tokenizer




   def __call__( self     :		Optional[Any]						,     lowerCAmelCase__     :		Tuple = None						,     lowerCAmelCase__     :		Union[str, Any] = None						,     lowerCAmelCase__     :		Union[str, Any] = True						,     lowerCAmelCase__     :		List[str] = False						,     lowerCAmelCase__     :		str = None						,     lowerCAmelCase__     :		List[str] = None						,     lowerCAmelCase__     :		List[str] = 0						,     lowerCAmelCase__     :		Optional[int] = None						,     lowerCAmelCase__     :		str = None						,     lowerCAmelCase__     :		Optional[int] = False						,     lowerCAmelCase__     :		Tuple = False						,     lowerCAmelCase__     :		Union[str, Any] = False						,     lowerCAmelCase__     :		Tuple = False						,     lowerCAmelCase__     :		Optional[int] = False						,     lowerCAmelCase__     :		Dict = True						,     lowerCAmelCase__     :		List[str] = None						,     **lowerCAmelCase__     :		Dict						,     )       ->    BatchFeature:







      """simple docstring"""




      if images is None and text is None:
         raise ValueError("You have to specify at least images or text."			)

      _UpperCAmelCase							:	int							=   BatchFeature()

      if text is not None:
         _UpperCAmelCase							:	Optional[Any]							=   self.tokenizer(
             text=lowerCAmelCase__						,     add_special_tokens=lowerCAmelCase__						,     padding=lowerCAmelCase__						,     truncation=lowerCAmelCase__						,     max_length=lowerCAmelCase__						,     stride=lowerCAmelCase__						,     pad_to_multiple_of=lowerCAmelCase__						,     return_attention_mask=lowerCAmelCase__						,     return_overflowing_tokens=lowerCAmelCase__						,     return_special_tokens_mask=lowerCAmelCase__						,     return_offsets_mapping=lowerCAmelCase__						,     return_token_type_ids=lowerCAmelCase__						,     return_length=lowerCAmelCase__						,     verbose=lowerCAmelCase__						,     return_tensors=lowerCAmelCase__						,     **lowerCAmelCase__						,     )
         encoding.update(lowerCAmelCase__			)
         _UpperCAmelCase							:	List[Any]							=   self.qformer_tokenizer(
             text=lowerCAmelCase__						,     add_special_tokens=lowerCAmelCase__						,     padding=lowerCAmelCase__						,     truncation=lowerCAmelCase__						,     max_length=lowerCAmelCase__						,     stride=lowerCAmelCase__						,     pad_to_multiple_of=lowerCAmelCase__						,     return_attention_mask=lowerCAmelCase__						,     return_overflowing_tokens=lowerCAmelCase__						,     return_special_tokens_mask=lowerCAmelCase__						,     return_offsets_mapping=lowerCAmelCase__						,     return_token_type_ids=lowerCAmelCase__						,     return_length=lowerCAmelCase__						,     verbose=lowerCAmelCase__						,     return_tensors=lowerCAmelCase__						,     **lowerCAmelCase__						,     )
         _UpperCAmelCase							:	int							=   qformer_text_encoding.pop("input_ids"			)
         _UpperCAmelCase							:	Union[str, Any]							=   qformer_text_encoding.pop("attention_mask"			)

      if images is not None:
         _UpperCAmelCase							:	int							=   self.image_processor(lowerCAmelCase__						,     return_tensors=lowerCAmelCase__			)
         encoding.update(lowerCAmelCase__			)

      return encoding




   def 			_lowerCAmelCase		( self     :		Any						,     *lowerCAmelCase__     :		Optional[int]						,     **lowerCAmelCase__     :		int			)       ->    Any:







      """simple docstring"""




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




   def 			_lowerCAmelCase		( self     :		int						,     *lowerCAmelCase__     :		Any						,     **lowerCAmelCase__     :		Union[str, Any]			)       ->    int:







      """simple docstring"""




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




   @property
   # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
   def 			_lowerCAmelCase		( self     :		int			)       ->    List[Any]:







      """simple docstring"""




      _UpperCAmelCase							:	Any							=   self.tokenizer.model_input_names
      _UpperCAmelCase							:	Dict							=   self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)




   def 			_lowerCAmelCase		( self     :		Union[str, Any]						,     lowerCAmelCase__     :		Tuple						,     **lowerCAmelCase__     :		str			)       ->    Optional[Any]:







      """simple docstring"""




      if os.path.isfile(lowerCAmelCase__			):
         raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file"""			)
      os.makedirs(lowerCAmelCase__						,     exist_ok=lowerCAmelCase__			)
      _UpperCAmelCase							:	Optional[Any]							=   os.path.join(lowerCAmelCase__						,     "qformer_tokenizer"			)
      self.qformer_tokenizer.save_pretrained(lowerCAmelCase__			)
      return super().save_pretrained(lowerCAmelCase__						,     **lowerCAmelCase__			)




   @classmethod
   def 			_lowerCAmelCase		( cls     :		Optional[int]						,     lowerCAmelCase__     :		Dict						,     **lowerCAmelCase__     :		int			)       ->    Dict:







      """simple docstring"""




      _UpperCAmelCase							:	str							=   AutoTokenizer.from_pretrained(lowerCAmelCase__						,     subfolder="qformer_tokenizer"			)
      _UpperCAmelCase							:	int							=   cls._get_arguments_from_pretrained(lowerCAmelCase__						,     **lowerCAmelCase__			)
      args.append(lowerCAmelCase__			)
      return cls(*lowerCAmelCase__			)



import functools
import operator

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


UpperCAmelCase__   	=							logging.get_logger(__name__)

UpperCAmelCase__   	=							{
    '''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json''',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}







class       lowerCamelCase__   (       lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      			=			'''wavlm'''





     def __init__(self		,     UpperCAmelCase=3_2		,     UpperCAmelCase=7_6_8		,     UpperCAmelCase=1_2		,     UpperCAmelCase=1_2		,     UpperCAmelCase=3_0_7_2		,     UpperCAmelCase="gelu"		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.0		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.02		,     UpperCAmelCase=1e-5		,     UpperCAmelCase="group"		,     UpperCAmelCase="gelu"		,     UpperCAmelCase=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)		,     UpperCAmelCase=(5, 2, 2, 2, 2, 2, 2)		,     UpperCAmelCase=(1_0, 3, 3, 3, 3, 2, 2)		,     UpperCAmelCase=False		,     UpperCAmelCase=1_2_8		,     UpperCAmelCase=1_6		,     UpperCAmelCase=3_2_0		,     UpperCAmelCase=8_0_0		,     UpperCAmelCase=False		,     UpperCAmelCase=True		,     UpperCAmelCase=0.05		,     UpperCAmelCase=1_0		,     UpperCAmelCase=2		,     UpperCAmelCase=0.0		,     UpperCAmelCase=1_0		,     UpperCAmelCase=3_2_0		,     UpperCAmelCase=2		,     UpperCAmelCase=0.1		,     UpperCAmelCase=1_0_0		,     UpperCAmelCase=2_5_6		,     UpperCAmelCase=2_5_6		,     UpperCAmelCase=0.1		,     UpperCAmelCase="mean"		,     UpperCAmelCase=False		,     UpperCAmelCase=False		,     UpperCAmelCase=2_5_6		,     UpperCAmelCase=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)		,     UpperCAmelCase=(5, 3, 3, 1, 1)		,     UpperCAmelCase=(1, 2, 3, 1, 1)		,     UpperCAmelCase=5_1_2		,     UpperCAmelCase=8_0		,     UpperCAmelCase=0		,     UpperCAmelCase=1		,     UpperCAmelCase=2		,     UpperCAmelCase=False		,     UpperCAmelCase=3		,     UpperCAmelCase=2		,     UpperCAmelCase=3		,     UpperCAmelCase=None		,     **UpperCAmelCase		,     )     ->   Optional[Any]:
      super().__init__(**UpperCAmelCase		,     pad_token_id=UpperCAmelCase		,     bos_token_id=UpperCAmelCase		,     eos_token_id=UpperCAmelCase       )
      _lowercase							      =hidden_size
      _lowercase							      =feat_extract_norm
      _lowercase							      =feat_extract_activation
      _lowercase							      =list(UpperCAmelCase       )
      _lowercase							      =list(UpperCAmelCase       )
      _lowercase							      =list(UpperCAmelCase       )
      _lowercase							      =conv_bias
      _lowercase							      =num_buckets
      _lowercase							      =max_bucket_distance
      _lowercase							      =num_conv_pos_embeddings
      _lowercase							      =num_conv_pos_embedding_groups
      _lowercase							      =len(self.conv_dim       )
      _lowercase							      =num_hidden_layers
      _lowercase							      =intermediate_size
      _lowercase							      =hidden_act
      _lowercase							      =num_attention_heads
      _lowercase							      =hidden_dropout
      _lowercase							      =attention_dropout
      _lowercase							      =activation_dropout
      _lowercase							      =feat_proj_dropout
      _lowercase							      =final_dropout
      _lowercase							      =layerdrop
      _lowercase							      =layer_norm_eps
      _lowercase							      =initializer_range
      _lowercase							      =num_ctc_classes
      _lowercase							      =vocab_size
      _lowercase							      =do_stable_layer_norm
      _lowercase							      =use_weighted_layer_sum
      _lowercase							      =classifier_proj_size

      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
      _lowercase							      =apply_spec_augment
      _lowercase							      =mask_time_prob
      _lowercase							      =mask_time_length
      _lowercase							      =mask_time_min_masks
      _lowercase							      =mask_feature_prob
      _lowercase							      =mask_feature_length

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

      # ctc loss
      _lowercase							      =ctc_loss_reduction
      _lowercase							      =ctc_zero_infinity

      # adapter
      _lowercase							      =add_adapter
      _lowercase							      =adapter_kernel_size
      _lowercase							      =adapter_stride
      _lowercase							      =num_adapter_layers
      _lowercase							      =output_hidden_size or hidden_size

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

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





     @property
     def      __A					(self       )     ->   int:
      return functools.reduce(operator.mul		,     self.conv_stride		,     1       )

