

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


a_						     =    {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

a_						     =    {
    'vocab_file': {
        'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt',
    },
    'tokenizer_file': {
        'unc-nlp/lxmert-base-uncased': (
            'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json'
        ),
    },
}

a_						     =    {
    'unc-nlp/lxmert-base-uncased': 512,
}

a_						     =    {
    'unc-nlp/lxmert-base-uncased': {'do_lower_case': True},
}






class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			lowerCamelCase__           =VOCAB_FILES_NAMES
			lowerCamelCase__           =PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__           =PRETRAINED_INIT_CONFIGURATION
			lowerCamelCase__           =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__           =LxmertTokenizer
			def __init__(			self							: int      ,      a							: List[str]=None      ,      a							: List[Any]=None      ,      a							: str=True      ,      a							: str="[UNK]"      ,      a							: Optional[Any]="[SEP]"      ,      a							: Any="[PAD]"      ,      a							: int="[CLS]"      ,      a							: Tuple="[MASK]"      ,      a							: Optional[int]=True      ,      a							: str=None      ,      **a							: int      ,      )							->       List[Any]:

				"""simple docstring"""

				super().__init__(
				    a      ,      tokenizer_file=a      ,      do_lower_case=a      ,      unk_token=a      ,      sep_token=a      ,      pad_token=a      ,      cls_token=a      ,      mask_token=a      ,      tokenize_chinese_chars=a      ,      strip_accents=a      ,      **a      ,      )

				SCREAMING_SNAKE_CASE   :      List[str]				     =       json.loads(self.backend_tokenizer.normalizer.__getstate__()	)
				if (
				    normalizer_state.get("lowercase"      ,      a	) != do_lower_case
				    or normalizer_state.get("strip_accents"      ,      a	) != strip_accents
				    or normalizer_state.get("handle_chinese_chars"      ,      a	) != tokenize_chinese_chars
				):
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       getattr(a      ,      normalizer_state.pop("type"	)	)
					SCREAMING_SNAKE_CASE   :      Tuple				     =       do_lower_case
					SCREAMING_SNAKE_CASE   :      List[str]				     =       strip_accents
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       tokenize_chinese_chars
					SCREAMING_SNAKE_CASE   :      str				     =       normalizer_class(**a	)

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       do_lower_case
			def 							__UpperCamelCase (			self							: Tuple      ,      a							: Union[str, Any]      ,      a							: Union[str, Any]=None	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [self.cls_token_id] + token_ids_a + [self.sep_token_id]

				if token_ids_a:
					output += token_ids_a + [self.sep_token_id]

				return output
			def 							__UpperCamelCase (			self							: Tuple      ,      a							: List[int]      ,      a							: Optional[List[int]] = None	)							->       List[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       [self.sep_token_id]
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       [self.cls_token_id]
				if token_ids_a is None:
					return len(cls + token_ids_a + sep	) * [0]
				return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]






			def 							__UpperCamelCase (			self							: Optional[Any]      ,      a							: str      ,      a							: Optional[str] = None	)							->       Tuple[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       self._tokenizer.model.save(a      ,      name=a	)
				return tuple(a	)


import functools
import operator

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


a_						     =    logging.get_logger(__name__)

a_						     =    {
    '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 _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			lowerCamelCase__           ='wavlm'
			def __init__(			self							: Optional[int]      ,      a							: Optional[Any]=32      ,      a							: int=768      ,      a							: Tuple=12      ,      a							: List[str]=12      ,      a							: str=3072      ,      a							: Any="gelu"      ,      a							: Dict=0.1      ,      a							: int=0.1      ,      a							: str=0.1      ,      a							: Optional[Any]=0.0      ,      a							: Any=0.1      ,      a							: Any=0.1      ,      a							: List[str]=0.02      ,      a							: List[Any]=1e-5      ,      a							: Any="group"      ,      a							: Optional[int]="gelu"      ,      a							: List[str]=(512, 512, 512, 512, 512, 512, 512)      ,      a							: Any=(5, 2, 2, 2, 2, 2, 2)      ,      a							: Union[str, Any]=(10, 3, 3, 3, 3, 2, 2)      ,      a							: Optional[Any]=False      ,      a							: Dict=128      ,      a							: Optional[Any]=16      ,      a							: Optional[Any]=320      ,      a							: str=800      ,      a							: Optional[int]=False      ,      a							: Tuple=True      ,      a							: Optional[Any]=0.05      ,      a							: Any=10      ,      a							: Optional[int]=2      ,      a							: Dict=0.0      ,      a							: str=10      ,      a							: Tuple=320      ,      a							: Optional[int]=2      ,      a							: int=0.1      ,      a							: List[str]=100      ,      a							: Tuple=256      ,      a							: str=256      ,      a							: Tuple=0.1      ,      a							: str="mean"      ,      a							: int=False      ,      a							: int=False      ,      a							: Optional[Any]=256      ,      a							: Any=(512, 512, 512, 512, 1500)      ,      a							: Tuple=(5, 3, 3, 1, 1)      ,      a							: str=(1, 2, 3, 1, 1)      ,      a							: Optional[Any]=512      ,      a							: Optional[Any]=80      ,      a							: Tuple=0      ,      a							: Any=1      ,      a							: Optional[Any]=2      ,      a							: int=False      ,      a							: Dict=3      ,      a							: Any=2      ,      a							: List[Any]=3      ,      a							: int=None      ,      **a							: Any      ,      )							->       Dict:

				"""simple docstring"""

				super().__init__(**a      ,      pad_token_id=a      ,      bos_token_id=a      ,      eos_token_id=a	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       hidden_size
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       feat_extract_norm
				SCREAMING_SNAKE_CASE   :      Any				     =       feat_extract_activation
				SCREAMING_SNAKE_CASE   :      Any				     =       list(a	)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       list(a	)
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       list(a	)
				SCREAMING_SNAKE_CASE   :      Any				     =       conv_bias
				SCREAMING_SNAKE_CASE   :      str				     =       num_buckets
				SCREAMING_SNAKE_CASE   :      str				     =       max_bucket_distance
				SCREAMING_SNAKE_CASE   :      List[str]				     =       num_conv_pos_embeddings
				SCREAMING_SNAKE_CASE   :      Any				     =       num_conv_pos_embedding_groups
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       len(self.conv_dim	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       num_hidden_layers
				SCREAMING_SNAKE_CASE   :      List[str]				     =       intermediate_size
				SCREAMING_SNAKE_CASE   :      Dict				     =       hidden_act
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       num_attention_heads
				SCREAMING_SNAKE_CASE   :      int				     =       hidden_dropout
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       attention_dropout
				SCREAMING_SNAKE_CASE   :      List[str]				     =       activation_dropout
				SCREAMING_SNAKE_CASE   :      int				     =       feat_proj_dropout
				SCREAMING_SNAKE_CASE   :      Any				     =       final_dropout
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       layerdrop
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       layer_norm_eps
				SCREAMING_SNAKE_CASE   :      int				     =       initializer_range
				SCREAMING_SNAKE_CASE   :      Dict				     =       num_ctc_classes
				SCREAMING_SNAKE_CASE   :      Tuple				     =       vocab_size
				SCREAMING_SNAKE_CASE   :      int				     =       do_stable_layer_norm
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       use_weighted_layer_sum
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       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
				SCREAMING_SNAKE_CASE   :      int				     =       apply_spec_augment
				SCREAMING_SNAKE_CASE   :      int				     =       mask_time_prob
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       mask_time_length
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       mask_time_min_masks
				SCREAMING_SNAKE_CASE   :      Tuple				     =       mask_feature_prob
				SCREAMING_SNAKE_CASE   :      List[str]				     =       mask_feature_length

				# parameters for pretraining with codevector quantized representations
				SCREAMING_SNAKE_CASE   :      str				     =       num_codevectors_per_group
				SCREAMING_SNAKE_CASE   :      Dict				     =       num_codevector_groups
				SCREAMING_SNAKE_CASE   :      Tuple				     =       contrastive_logits_temperature
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       num_negatives
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       codevector_dim
				SCREAMING_SNAKE_CASE   :      int				     =       proj_codevector_dim
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       diversity_loss_weight

				# ctc loss
				SCREAMING_SNAKE_CASE   :      Any				     =       ctc_loss_reduction
				SCREAMING_SNAKE_CASE   :      Dict				     =       ctc_zero_infinity

				# adapter
				SCREAMING_SNAKE_CASE   :      Any				     =       add_adapter
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       adapter_kernel_size
				SCREAMING_SNAKE_CASE   :      Any				     =       adapter_stride
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       num_adapter_layers
				SCREAMING_SNAKE_CASE   :      int				     =       output_hidden_size or hidden_size

				# SequenceClassification-specific parameter. Feel free to ignore for other classes.
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       classifier_proj_size

				# XVector-specific parameters. Feel free to ignore for other classes.
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       list(a	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       list(a	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       list(a	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       xvector_output_dim






			@property
			def 							__UpperCamelCase (			self							: int	)							->       Union[str, Any]:

				"""simple docstring"""

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