



'''simple docstring'''


import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class 					SCREAMING_SNAKE_CASE					(    _a						):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE          =    ["""image_processor""", """tokenizer"""]
    _SCREAMING_SNAKE_CASE          =    """AutoImageProcessor"""
    _SCREAMING_SNAKE_CASE          =    """AutoTokenizer"""






    def __init__(    self :   int    ,		UpperCamelCase__ :   List[Any]=None    ,		UpperCamelCase__ :   int=None    ,		**UpperCamelCase__ :   Dict					):


       """simple docstring"""







       UpperCamelCase      							=    None
       if "feature_extractor" in kwargs:
          warnings.warn(
              'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
              ' instead.'    ,		a_    ,		)
          UpperCamelCase      							=    kwargs.pop('feature_extractor'					)

       UpperCamelCase      							=    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__(a_    ,		a_					)
       UpperCamelCase      							=    self.image_processor
       UpperCamelCase      							=    False






    def __call__(    self :   Optional[int]    ,		*UpperCamelCase__ :   Any    ,		**UpperCamelCase__ :   Optional[int]					):


       """simple docstring"""







       if self._in_target_context_manager:
          return self.current_processor(*a_    ,		**a_					)

       UpperCamelCase      							=    kwargs.pop('images'    ,		a_					)
       UpperCamelCase      							=    kwargs.pop('text'    ,		a_					)
       if len(a_					) > 0:
          UpperCamelCase      							=    args[0]
          UpperCamelCase      							=    args[1:]

       if images is None and text is None:
          raise ValueError('You need to specify either an `images` or `text` input to process.'					)

       if images is not None:
          UpperCamelCase      							=    self.image_processor(a_    ,		*a_    ,		**a_					)
       if text is not None:
          UpperCamelCase      							=    self.tokenizer(a_    ,		**a_					)

       if text is None:
          return inputs
       elif images is None:
          return encodings
       else:
          UpperCamelCase      							=    encodings['input_ids']
          return inputs






    def 					A			(    self :   List[Any]    ,		*UpperCamelCase__ :   List[Any]    ,		**UpperCamelCase__ :   List[Any]					):


       """simple docstring"""







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






    def 					A			(    self :   str    ,		*UpperCamelCase__ :   Optional[int]    ,		**UpperCamelCase__ :   str					):


       """simple docstring"""







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






    @contextmanager
    def 					A			(    self :   str					):


       """simple docstring"""







       warnings.warn(
           '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
           'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
           'your images inputs, or in a separate call.'					)
       UpperCamelCase      							=    True
       UpperCamelCase      							=    self.tokenizer
       yield
       UpperCamelCase      							=    self.image_processor
       UpperCamelCase      							=    False






    def 					A			(    self :   Dict    ,		UpperCamelCase__ :   List[Any]    ,		UpperCamelCase__ :   Optional[Any]=False    ,		UpperCamelCase__ :   Optional[Any]=None					):


       """simple docstring"""







       if added_vocab is None:
          UpperCamelCase      							=    self.tokenizer.get_added_vocab()

       UpperCamelCase      							=    {}

       while tokens:
          UpperCamelCase      							=    re.search(R'<s_(.*?)>'    ,		a_    ,		re.IGNORECASE					)
          if start_token is None:
             break
          UpperCamelCase      							=    start_token.group(1					)
          UpperCamelCase      							=    re.search(Rf"""</s_{key}>"""    ,		a_    ,		re.IGNORECASE					)
          UpperCamelCase      							=    start_token.group()
          if end_token is None:
             UpperCamelCase      							=    tokens.replace(a_    ,		''					)
          else:
             UpperCamelCase      							=    end_token.group()
             UpperCamelCase      							=    re.escape(a_					)
             UpperCamelCase      							=    re.escape(a_					)
             UpperCamelCase      							=    re.search(f"""{start_token_escaped}(.*?){end_token_escaped}"""    ,		a_    ,		re.IGNORECASE					)
             if content is not None:
                UpperCamelCase      							=    content.group(1					).strip()
                if r"<s_" in content and r"</s_" in content:  # non-leaf node
                   UpperCamelCase      							=    self.tokenajson(a_    ,		is_inner_value=a_    ,		added_vocab=a_					)
                   if value:
                      if len(a_					) == 1:
                         UpperCamelCase      							=    value[0]
                      UpperCamelCase      							=    value
                else:  # leaf nodes
                   UpperCamelCase      							=    []
                   for leaf in content.split(R'<sep/>'					):
                      UpperCamelCase      							=    leaf.strip()
                      if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                         UpperCamelCase      							=    leaf[1:-2]  # for categorical special tokens
                      output[key].append(a_					)
                   if len(output[key]					) == 1:
                      UpperCamelCase      							=    output[key][0]

             UpperCamelCase      							=    tokens[tokens.find(a_					) + len(a_					) :].strip()
             if tokens[:6] == r"<sep/>":  # non-leaf nodes
                return [output] + self.tokenajson(tokens[6:]    ,		is_inner_value=a_    ,		added_vocab=a_					)

       if len(a_					):
          return [output] if is_inner_value else output
       else:
          return [] if is_inner_value else {"text_sequence": tokens}






    @property
    def 					A			(    self :   Optional[Any]					):


       """simple docstring"""







       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'    ,		a_    ,		)
       return self.image_processor_class






    @property
    def 					A			(    self :   int					):


       """simple docstring"""







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






import functools
import operator

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


_lowerCAmelCase	:   List[str]    =   logging.get_logger(__name__)

_lowerCAmelCase	:   Tuple    =   {
    "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 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		SCREAMING_SNAKE_CASE__								=	'wavlm'
		def __init__(							self    ,a_=32    ,a_=768    ,a_=12    ,a_=12    ,a_=3072    ,a_="gelu"    ,a_=0.1    ,a_=0.1    ,a_=0.1    ,a_=0.0    ,a_=0.1    ,a_=0.1    ,a_=0.02    ,a_=1e-5    ,a_="group"    ,a_="gelu"    ,a_=(512, 512, 512, 512, 512, 512, 512)    ,a_=(5, 2, 2, 2, 2, 2, 2)    ,a_=(10, 3, 3, 3, 3, 2, 2)    ,a_=False    ,a_=128    ,a_=16    ,a_=320    ,a_=800    ,a_=False    ,a_=True    ,a_=0.05    ,a_=10    ,a_=2    ,a_=0.0    ,a_=10    ,a_=320    ,a_=2    ,a_=0.1    ,a_=100    ,a_=256    ,a_=256    ,a_=0.1    ,a_="mean"    ,a_=False    ,a_=False    ,a_=256    ,a_=(512, 512, 512, 512, 1500)    ,a_=(5, 3, 3, 1, 1)    ,a_=(1, 2, 3, 1, 1)    ,a_=512    ,a_=80    ,a_=0    ,a_=1    ,a_=2    ,a_=False    ,a_=3    ,a_=2    ,a_=3    ,a_=None    ,**a_    ,):







						"""simple docstring"""
						super().__init__(**a_    ,pad_token_id=a_    ,bos_token_id=a_    ,eos_token_id=a_						)
						lowerCAmelCase__		  =  hidden_size
						lowerCAmelCase__		  =  feat_extract_norm
						lowerCAmelCase__		  =  feat_extract_activation
						lowerCAmelCase__		  =  list(a_						)
						lowerCAmelCase__		  =  list(a_						)
						lowerCAmelCase__		  =  list(a_						)
						lowerCAmelCase__		  =  conv_bias
						lowerCAmelCase__		  =  num_buckets
						lowerCAmelCase__		  =  max_bucket_distance
						lowerCAmelCase__		  =  num_conv_pos_embeddings
						lowerCAmelCase__		  =  num_conv_pos_embedding_groups
						lowerCAmelCase__		  =  len(self.conv_dim						)
						lowerCAmelCase__		  =  num_hidden_layers
						lowerCAmelCase__		  =  intermediate_size
						lowerCAmelCase__		  =  hidden_act
						lowerCAmelCase__		  =  num_attention_heads
						lowerCAmelCase__		  =  hidden_dropout
						lowerCAmelCase__		  =  attention_dropout
						lowerCAmelCase__		  =  activation_dropout
						lowerCAmelCase__		  =  feat_proj_dropout
						lowerCAmelCase__		  =  final_dropout
						lowerCAmelCase__		  =  layerdrop
						lowerCAmelCase__		  =  layer_norm_eps
						lowerCAmelCase__		  =  initializer_range
						lowerCAmelCase__		  =  num_ctc_classes
						lowerCAmelCase__		  =  vocab_size
						lowerCAmelCase__		  =  do_stable_layer_norm
						lowerCAmelCase__		  =  use_weighted_layer_sum
						lowerCAmelCase__		  =  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
						lowerCAmelCase__		  =  apply_spec_augment
						lowerCAmelCase__		  =  mask_time_prob
						lowerCAmelCase__		  =  mask_time_length
						lowerCAmelCase__		  =  mask_time_min_masks
						lowerCAmelCase__		  =  mask_feature_prob
						lowerCAmelCase__		  =  mask_feature_length

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

						# ctc loss
						lowerCAmelCase__		  =  ctc_loss_reduction
						lowerCAmelCase__		  =  ctc_zero_infinity

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

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

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



		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return functools.reduce(operator.mul    ,self.conv_stride    ,1						)
