





"""simple docstring"""






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 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  '''wavlm'''







					def __init__(						self	,			_UpperCAmelCase=32	,			_UpperCAmelCase=768	,			_UpperCAmelCase=12	,			_UpperCAmelCase=12	,			_UpperCAmelCase=3072	,			_UpperCAmelCase="gelu"	,			_UpperCAmelCase=0.1	,			_UpperCAmelCase=0.1	,			_UpperCAmelCase=0.1	,			_UpperCAmelCase=0.0	,			_UpperCAmelCase=0.1	,			_UpperCAmelCase=0.1	,			_UpperCAmelCase=0.0_2	,			_UpperCAmelCase=1e-5	,			_UpperCAmelCase="group"	,			_UpperCAmelCase="gelu"	,			_UpperCAmelCase=(512, 512, 512, 512, 512, 512, 512)	,			_UpperCAmelCase=(5, 2, 2, 2, 2, 2, 2)	,			_UpperCAmelCase=(10, 3, 3, 3, 3, 2, 2)	,			_UpperCAmelCase=False	,			_UpperCAmelCase=128	,			_UpperCAmelCase=16	,			_UpperCAmelCase=320	,			_UpperCAmelCase=800	,			_UpperCAmelCase=False	,			_UpperCAmelCase=True	,			_UpperCAmelCase=0.0_5	,			_UpperCAmelCase=10	,			_UpperCAmelCase=2	,			_UpperCAmelCase=0.0	,			_UpperCAmelCase=10	,			_UpperCAmelCase=320	,			_UpperCAmelCase=2	,			_UpperCAmelCase=0.1	,			_UpperCAmelCase=100	,			_UpperCAmelCase=256	,			_UpperCAmelCase=256	,			_UpperCAmelCase=0.1	,			_UpperCAmelCase="mean"	,			_UpperCAmelCase=False	,			_UpperCAmelCase=False	,			_UpperCAmelCase=256	,			_UpperCAmelCase=(512, 512, 512, 512, 1500)	,			_UpperCAmelCase=(5, 3, 3, 1, 1)	,			_UpperCAmelCase=(1, 2, 3, 1, 1)	,			_UpperCAmelCase=512	,			_UpperCAmelCase=80	,			_UpperCAmelCase=0	,			_UpperCAmelCase=1	,			_UpperCAmelCase=2	,			_UpperCAmelCase=False	,			_UpperCAmelCase=3	,			_UpperCAmelCase=2	,			_UpperCAmelCase=3	,			_UpperCAmelCase=None	,			**_UpperCAmelCase	,			):
									super().__init__(**_UpperCAmelCase	,			pad_token_id=_UpperCAmelCase	,			bos_token_id=_UpperCAmelCase	,			eos_token_id=_UpperCAmelCase     )
									__a :						Tuple			       =  hidden_size
									__a :						int			       =  feat_extract_norm
									__a :						List[Any]			       =  feat_extract_activation
									__a :						List[str]			       =  list(_UpperCAmelCase     )
									__a :						Any			       =  list(_UpperCAmelCase     )
									__a :						List[Any]			       =  list(_UpperCAmelCase     )
									__a :						Any			       =  conv_bias
									__a :						Tuple			       =  num_buckets
									__a :						Union[str, Any]			       =  max_bucket_distance
									__a :						Tuple			       =  num_conv_pos_embeddings
									__a :						Any			       =  num_conv_pos_embedding_groups
									__a :						str			       =  len(self.conv_dim     )
									__a :						Tuple			       =  num_hidden_layers
									__a :						List[str]			       =  intermediate_size
									__a :						Dict			       =  hidden_act
									__a :						Union[str, Any]			       =  num_attention_heads
									__a :						Tuple			       =  hidden_dropout
									__a :						Union[str, Any]			       =  attention_dropout
									__a :						List[str]			       =  activation_dropout
									__a :						Union[str, Any]			       =  feat_proj_dropout
									__a :						Optional[int]			       =  final_dropout
									__a :						List[str]			       =  layerdrop
									__a :						int			       =  layer_norm_eps
									__a :						List[Any]			       =  initializer_range
									__a :						Any			       =  num_ctc_classes
									__a :						Dict			       =  vocab_size
									__a :						int			       =  do_stable_layer_norm
									__a :						Dict			       =  use_weighted_layer_sum
									__a :						Dict			       =  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
									__a :						Optional[int]			       =  apply_spec_augment
									__a :						Optional[Any]			       =  mask_time_prob
									__a :						Optional[int]			       =  mask_time_length
									__a :						Union[str, Any]			       =  mask_time_min_masks
									__a :						Dict			       =  mask_feature_prob
									__a :						List[str]			       =  mask_feature_length

									# parameters for pretraining with codevector quantized representations
									__a :						Union[str, Any]			       =  num_codevectors_per_group
									__a :						str			       =  num_codevector_groups
									__a :						List[Any]			       =  contrastive_logits_temperature
									__a :						List[Any]			       =  num_negatives
									__a :						Any			       =  codevector_dim
									__a :						int			       =  proj_codevector_dim
									__a :						Any			       =  diversity_loss_weight

									# ctc loss
									__a :						Optional[int]			       =  ctc_loss_reduction
									__a :						Optional[int]			       =  ctc_zero_infinity

									# adapter
									__a :						Optional[Any]			       =  add_adapter
									__a :						Tuple			       =  adapter_kernel_size
									__a :						str			       =  adapter_stride
									__a :						Union[str, Any]			       =  num_adapter_layers
									__a :						Any			       =  output_hidden_size or hidden_size

									# SequenceClassification-specific parameter. Feel free to ignore for other classes.
									__a :						Optional[int]			       =  classifier_proj_size

									# XVector-specific parameters. Feel free to ignore for other classes.
									__a :						Any			       =  list(_UpperCAmelCase     )
									__a :						List[Any]			       =  list(_UpperCAmelCase     )
									__a :						Optional[Any]			       =  list(_UpperCAmelCase     )
									__a :						Optional[Any]			       =  xvector_output_dim







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






"""simple docstring"""






import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 				__lowercase			(			_UpperCamelCase   ,      unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  OpenAIGPTTokenizer
					__lowerCAmelCase  =  OpenAIGPTTokenizerFast
					__lowerCAmelCase  =  True
					__lowerCAmelCase  =  False







					def  _lowerCamelCase		(						self     ):
									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									__a :						List[str]			       =  [
									    '''l''',
									    '''o''',
									    '''w''',
									    '''e''',
									    '''r''',
									    '''s''',
									    '''t''',
									    '''i''',
									    '''d''',
									    '''n''',
									    '''w</w>''',
									    '''r</w>''',
									    '''t</w>''',
									    '''lo''',
									    '''low''',
									    '''er</w>''',
									    '''low</w>''',
									    '''lowest</w>''',
									    '''newer</w>''',
									    '''wider</w>''',
									    '''<unk>''',
									]
									__a :						str			       =  dict(zip(_UpperCAmelCase	,			range(len(_UpperCAmelCase     )     )     )     )
									__a :						List[Any]			       =  ['''#version: 0.2''', '''l o''', '''lo w''', '''e r</w>''', '''''']

									__a :						Union[str, Any]			       =  os.path.join(self.tmpdirname	,			VOCAB_FILES_NAMES['''vocab_file''']     )
									__a :						Dict			       =  os.path.join(self.tmpdirname	,			VOCAB_FILES_NAMES['''merges_file''']     )
									with open(self.vocab_file	,			'''w'''     ) as fp:
													fp.write(json.dumps(_UpperCAmelCase     )     )
									with open(self.merges_file	,			'''w'''     ) as fp:
													fp.write('''\n'''.join(_UpperCAmelCase     )     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									return "lower newer", "lower newer"







					def  _lowerCamelCase		(						self     ):
									__a :						Tuple			       =  OpenAIGPTTokenizer(self.vocab_file	,			self.merges_file     )

									__a :						Tuple			       =  '''lower'''
									__a :						Union[str, Any]			       =  ['''low''', '''er</w>''']
									__a :						str			       =  tokenizer.tokenize(_UpperCAmelCase     )
									self.assertListEqual(_UpperCAmelCase	,			_UpperCAmelCase     )

									__a :						List[Any]			       =  tokens + ['''<unk>''']
									__a :						Tuple			       =  [14, 15, 20]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase     )	,			_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase=15     ):
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
																	__a :						Any			       =  self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase	,			**_UpperCAmelCase     )

																	# Simple input
																	__a :						str			       =  '''This is a simple input'''
																	__a :						Dict			       =  ['''This is a simple input 1''', '''This is a simple input 2''']
																	__a :						List[Any]			       =  ('''This is a simple input''', '''This is a pair''')
																	__a :						Optional[Any]			       =  [
																	    ('''This is a simple input 1''', '''This is a simple input 2'''),
																	    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
																	]

																	# Simple input tests
																	self.assertRaises(_UpperCAmelCase	,			tokenizer_r.encode	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''     )

																	# Simple input
																	self.assertRaises(_UpperCAmelCase	,			tokenizer_r.encode_plus	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''     )

																	# Simple input
																	self.assertRaises(
																	    _UpperCAmelCase	,			tokenizer_r.batch_encode_plus	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''	,			)

																	# Pair input
																	self.assertRaises(_UpperCAmelCase	,			tokenizer_r.encode	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''     )

																	# Pair input
																	self.assertRaises(_UpperCAmelCase	,			tokenizer_r.encode_plus	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''     )

																	# Pair input
																	self.assertRaises(
																	    _UpperCAmelCase	,			tokenizer_r.batch_encode_plus	,			_UpperCAmelCase	,			max_length=_UpperCAmelCase	,			padding='''max_length'''	,			)







					def  _lowerCamelCase		(						self     ):
									pass






@require_ftfy
@require_spacy
@require_tokenizers
class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					pass