


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       lowerCamelCase__   (       lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      			=			['''image_processor''', '''tokenizer''']
     SCREAMING_SNAKE_CASE__      			=			'''BlipImageProcessor'''
     SCREAMING_SNAKE_CASE__      			=			'''AutoTokenizer'''





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Dict:
      super().__init__(UpperCAmelCase		,     UpperCAmelCase       )

      # add QFormer tokenizer
      _lowercase							      =qformer_tokenizer





     def __call__(self		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     UpperCAmelCase = True		,     UpperCAmelCase = False		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     UpperCAmelCase = 0		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     UpperCAmelCase = False		,     UpperCAmelCase = False		,     UpperCAmelCase = False		,     UpperCAmelCase = False		,     UpperCAmelCase = False		,     UpperCAmelCase = True		,     UpperCAmelCase = None		,     **UpperCAmelCase		,     )     ->   BatchFeature:
      if images is None and text is None:
       raise ValueError('''You have to specify at least images or text.'''       )

      _lowercase							      =BatchFeature()

      if text is not None:
       _lowercase							      =self.tokenizer(
           text=UpperCAmelCase		,     add_special_tokens=UpperCAmelCase		,     padding=UpperCAmelCase		,     truncation=UpperCAmelCase		,     max_length=UpperCAmelCase		,     stride=UpperCAmelCase		,     pad_to_multiple_of=UpperCAmelCase		,     return_attention_mask=UpperCAmelCase		,     return_overflowing_tokens=UpperCAmelCase		,     return_special_tokens_mask=UpperCAmelCase		,     return_offsets_mapping=UpperCAmelCase		,     return_token_type_ids=UpperCAmelCase		,     return_length=UpperCAmelCase		,     verbose=UpperCAmelCase		,     return_tensors=UpperCAmelCase		,     **UpperCAmelCase		,     )
       encoding.update(UpperCAmelCase       )
       _lowercase							      =self.qformer_tokenizer(
           text=UpperCAmelCase		,     add_special_tokens=UpperCAmelCase		,     padding=UpperCAmelCase		,     truncation=UpperCAmelCase		,     max_length=UpperCAmelCase		,     stride=UpperCAmelCase		,     pad_to_multiple_of=UpperCAmelCase		,     return_attention_mask=UpperCAmelCase		,     return_overflowing_tokens=UpperCAmelCase		,     return_special_tokens_mask=UpperCAmelCase		,     return_offsets_mapping=UpperCAmelCase		,     return_token_type_ids=UpperCAmelCase		,     return_length=UpperCAmelCase		,     verbose=UpperCAmelCase		,     return_tensors=UpperCAmelCase		,     **UpperCAmelCase		,     )
       _lowercase							      =qformer_text_encoding.pop('''input_ids'''       )
       _lowercase							      =qformer_text_encoding.pop('''attention_mask'''       )

      if images is not None:
       _lowercase							      =self.image_processor(UpperCAmelCase		,     return_tensors=UpperCAmelCase       )
       encoding.update(UpperCAmelCase       )

      return encoding





     def      __A					(self		,     *UpperCAmelCase		,     **UpperCAmelCase       )     ->   Any:
      return self.tokenizer.batch_decode(*UpperCAmelCase		,     **UpperCAmelCase       )





     def      __A					(self		,     *UpperCAmelCase		,     **UpperCAmelCase       )     ->   int:
      return self.tokenizer.decode(*UpperCAmelCase		,     **UpperCAmelCase       )





     @property
     # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
     def      __A					(self       )     ->   List[Any]:
      _lowercase							      =self.tokenizer.model_input_names
      _lowercase							      =self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )





     def      __A					(self		,     UpperCAmelCase		,     **UpperCAmelCase       )     ->   Optional[Any]:
      if os.path.isfile(UpperCAmelCase       ):
       raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file"       )
      os.makedirs(UpperCAmelCase		,     exist_ok=UpperCAmelCase       )
      _lowercase							      =os.path.join(UpperCAmelCase		,     '''qformer_tokenizer'''       )
      self.qformer_tokenizer.save_pretrained(UpperCAmelCase       )
      return super().save_pretrained(UpperCAmelCase		,     **UpperCAmelCase       )





     @classmethod
     def      __A					(cls		,     UpperCAmelCase		,     **UpperCAmelCase       )     ->   Dict:
      _lowercase							      =AutoTokenizer.from_pretrained(UpperCAmelCase		,     subfolder='''qformer_tokenizer'''       )
      _lowercase							      =cls._get_arguments_from_pretrained(UpperCAmelCase		,     **UpperCAmelCase       )
      args.append(UpperCAmelCase       )
      return cls(*UpperCAmelCase       )





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       )

