




import functools
import operator

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


a													=			logging.get_logger(__name__)

a													=			{
    "facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json",
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}




class       _A				(		__lowercase     ):
		__a					    =       """wav2vec2"""






		def __init__(    self						,		_SCREAMING_SNAKE_CASE=32						,		_SCREAMING_SNAKE_CASE=768						,		_SCREAMING_SNAKE_CASE=12						,		_SCREAMING_SNAKE_CASE=12						,		_SCREAMING_SNAKE_CASE=3072						,		_SCREAMING_SNAKE_CASE="gelu"						,		_SCREAMING_SNAKE_CASE=0.1						,		_SCREAMING_SNAKE_CASE=0.1						,		_SCREAMING_SNAKE_CASE=0.1						,		_SCREAMING_SNAKE_CASE=0.0						,		_SCREAMING_SNAKE_CASE=0.0						,		_SCREAMING_SNAKE_CASE=0.1						,		_SCREAMING_SNAKE_CASE=0.1						,		_SCREAMING_SNAKE_CASE=0.02						,		_SCREAMING_SNAKE_CASE=1e-5						,		_SCREAMING_SNAKE_CASE="group"						,		_SCREAMING_SNAKE_CASE="gelu"						,		_SCREAMING_SNAKE_CASE=(512, 512, 512, 512, 512, 512, 512)						,		_SCREAMING_SNAKE_CASE=(5, 2, 2, 2, 2, 2, 2)						,		_SCREAMING_SNAKE_CASE=(10, 3, 3, 3, 3, 2, 2)						,		_SCREAMING_SNAKE_CASE=False						,		_SCREAMING_SNAKE_CASE=128						,		_SCREAMING_SNAKE_CASE=16						,		_SCREAMING_SNAKE_CASE=False						,		_SCREAMING_SNAKE_CASE=True						,		_SCREAMING_SNAKE_CASE=0.05						,		_SCREAMING_SNAKE_CASE=10						,		_SCREAMING_SNAKE_CASE=2						,		_SCREAMING_SNAKE_CASE=0.0						,		_SCREAMING_SNAKE_CASE=10						,		_SCREAMING_SNAKE_CASE=0						,		_SCREAMING_SNAKE_CASE=320						,		_SCREAMING_SNAKE_CASE=2						,		_SCREAMING_SNAKE_CASE=0.1						,		_SCREAMING_SNAKE_CASE=100						,		_SCREAMING_SNAKE_CASE=256						,		_SCREAMING_SNAKE_CASE=256						,		_SCREAMING_SNAKE_CASE=0.1						,		_SCREAMING_SNAKE_CASE="sum"						,		_SCREAMING_SNAKE_CASE=False						,		_SCREAMING_SNAKE_CASE=False						,		_SCREAMING_SNAKE_CASE=256						,		_SCREAMING_SNAKE_CASE=(512, 512, 512, 512, 1500)						,		_SCREAMING_SNAKE_CASE=(5, 3, 3, 1, 1)						,		_SCREAMING_SNAKE_CASE=(1, 2, 3, 1, 1)						,		_SCREAMING_SNAKE_CASE=512						,		_SCREAMING_SNAKE_CASE=0						,		_SCREAMING_SNAKE_CASE=1						,		_SCREAMING_SNAKE_CASE=2						,		_SCREAMING_SNAKE_CASE=False						,		_SCREAMING_SNAKE_CASE=3						,		_SCREAMING_SNAKE_CASE=2						,		_SCREAMING_SNAKE_CASE=3						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		**_SCREAMING_SNAKE_CASE						,		):
								super().__init__(**_SCREAMING_SNAKE_CASE						,		pad_token_id=_SCREAMING_SNAKE_CASE						,		bos_token_id=_SCREAMING_SNAKE_CASE						,		eos_token_id=_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			hidden_size
								_UpperCAmelCase      =			feat_extract_norm
								_UpperCAmelCase      =			feat_extract_activation
								_UpperCAmelCase      =			list(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			list(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			list(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			conv_bias
								_UpperCAmelCase      =			num_conv_pos_embeddings
								_UpperCAmelCase      =			num_conv_pos_embedding_groups
								_UpperCAmelCase      =			len(self.conv_dim			)
								_UpperCAmelCase      =			num_hidden_layers
								_UpperCAmelCase      =			intermediate_size
								_UpperCAmelCase      =			hidden_act
								_UpperCAmelCase      =			num_attention_heads
								_UpperCAmelCase      =			hidden_dropout
								_UpperCAmelCase      =			attention_dropout
								_UpperCAmelCase      =			activation_dropout
								_UpperCAmelCase      =			feat_proj_dropout
								_UpperCAmelCase      =			final_dropout
								_UpperCAmelCase      =			layerdrop
								_UpperCAmelCase      =			layer_norm_eps
								_UpperCAmelCase      =			initializer_range
								_UpperCAmelCase      =			vocab_size
								_UpperCAmelCase      =			do_stable_layer_norm
								_UpperCAmelCase      =			use_weighted_layer_sum

								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
								_UpperCAmelCase      =			apply_spec_augment
								_UpperCAmelCase      =			mask_time_prob
								_UpperCAmelCase      =			mask_time_length
								_UpperCAmelCase      =			mask_time_min_masks
								_UpperCAmelCase      =			mask_feature_prob
								_UpperCAmelCase      =			mask_feature_length
								_UpperCAmelCase      =			mask_feature_min_masks

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

								# ctc loss
								_UpperCAmelCase      =			ctc_loss_reduction
								_UpperCAmelCase      =			ctc_zero_infinity

								# adapter
								_UpperCAmelCase      =			add_adapter
								_UpperCAmelCase      =			adapter_kernel_size
								_UpperCAmelCase      =			adapter_stride
								_UpperCAmelCase      =			num_adapter_layers
								_UpperCAmelCase      =			output_hidden_size or hidden_size
								_UpperCAmelCase      =			adapter_attn_dim

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

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






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





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer


a													=			logging.get_logger(__name__)

a													=			{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

a													=			{
    "vocab_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/vocab.txt",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/vocab.txt",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt"
        ),
        "bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt",
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt"
        ),
        "bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt",
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json"
        ),
        "bert-base-multilingual-cased": (
            "https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-cased": (
            "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json"
        ),
    },
}

a													=			{
    "bert-base-uncased": 512,
    "bert-large-uncased": 512,
    "bert-base-cased": 512,
    "bert-large-cased": 512,
    "bert-base-multilingual-uncased": 512,
    "bert-base-multilingual-cased": 512,
    "bert-base-chinese": 512,
    "bert-base-german-cased": 512,
    "bert-large-uncased-whole-word-masking": 512,
    "bert-large-cased-whole-word-masking": 512,
    "bert-large-uncased-whole-word-masking-finetuned-squad": 512,
    "bert-large-cased-whole-word-masking-finetuned-squad": 512,
    "bert-base-cased-finetuned-mrpc": 512,
    "bert-base-german-dbmdz-cased": 512,
    "bert-base-german-dbmdz-uncased": 512,
    "TurkuNLP/bert-base-finnish-cased-v1": 512,
    "TurkuNLP/bert-base-finnish-uncased-v1": 512,
    "wietsedv/bert-base-dutch-cased": 512,
}

a													=			{
    "bert-base-uncased": {"do_lower_case": True},
    "bert-large-uncased": {"do_lower_case": True},
    "bert-base-cased": {"do_lower_case": False},
    "bert-large-cased": {"do_lower_case": False},
    "bert-base-multilingual-uncased": {"do_lower_case": True},
    "bert-base-multilingual-cased": {"do_lower_case": False},
    "bert-base-chinese": {"do_lower_case": False},
    "bert-base-german-cased": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking-finetuned-squad": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking-finetuned-squad": {"do_lower_case": False},
    "bert-base-cased-finetuned-mrpc": {"do_lower_case": False},
    "bert-base-german-dbmdz-cased": {"do_lower_case": False},
    "bert-base-german-dbmdz-uncased": {"do_lower_case": True},
    "TurkuNLP/bert-base-finnish-cased-v1": {"do_lower_case": False},
    "TurkuNLP/bert-base-finnish-uncased-v1": {"do_lower_case": True},
    "wietsedv/bert-base-dutch-cased": {"do_lower_case": False},
}




class       _A				(		__lowercase     ):
		__a					    =       VOCAB_FILES_NAMES
		__a					    =       PRETRAINED_VOCAB_FILES_MAP
		__a					    =       PRETRAINED_INIT_CONFIGURATION
		__a					    =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__a					    =       BertTokenizer






		def __init__(    self						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=True						,		_SCREAMING_SNAKE_CASE="[UNK]"						,		_SCREAMING_SNAKE_CASE="[SEP]"						,		_SCREAMING_SNAKE_CASE="[PAD]"						,		_SCREAMING_SNAKE_CASE="[CLS]"						,		_SCREAMING_SNAKE_CASE="[MASK]"						,		_SCREAMING_SNAKE_CASE=True						,		_SCREAMING_SNAKE_CASE=None						,		**_SCREAMING_SNAKE_CASE						,		):
								super().__init__(
								    _SCREAMING_SNAKE_CASE						,		tokenizer_file=_SCREAMING_SNAKE_CASE						,		do_lower_case=_SCREAMING_SNAKE_CASE						,		unk_token=_SCREAMING_SNAKE_CASE						,		sep_token=_SCREAMING_SNAKE_CASE						,		pad_token=_SCREAMING_SNAKE_CASE						,		cls_token=_SCREAMING_SNAKE_CASE						,		mask_token=_SCREAMING_SNAKE_CASE						,		tokenize_chinese_chars=_SCREAMING_SNAKE_CASE						,		strip_accents=_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE						,		)

								_UpperCAmelCase      =			json.loads(self.backend_tokenizer.normalizer.__getstate__()			)
								if (
								    normalizer_state.get("""lowercase"""						,		_SCREAMING_SNAKE_CASE			) != do_lower_case
								    or normalizer_state.get("""strip_accents"""						,		_SCREAMING_SNAKE_CASE			) != strip_accents
								    or normalizer_state.get("""handle_chinese_chars"""						,		_SCREAMING_SNAKE_CASE			) != tokenize_chinese_chars
								):
														_UpperCAmelCase      =			getattr(_SCREAMING_SNAKE_CASE						,		normalizer_state.pop("""type"""			)			)
														_UpperCAmelCase      =			do_lower_case
														_UpperCAmelCase      =			strip_accents
														_UpperCAmelCase      =			tokenize_chinese_chars
														_UpperCAmelCase      =			normalizer_class(**_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			do_lower_case






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=None			):
								_UpperCAmelCase      =			[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						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = None			):
								_UpperCAmelCase      =			[self.sep_token_id]
								_UpperCAmelCase      =			[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						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = None			):
								_UpperCAmelCase      =			self._tokenizer.model.save(_SCREAMING_SNAKE_CASE						,		name=_SCREAMING_SNAKE_CASE			)
								return tuple(_SCREAMING_SNAKE_CASE			)