
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
					from .tokenization_fnet import FNetTokenizer
else:
					UpperCamelCase_   	=	None

UpperCamelCase_   	=	logging.get_logger(__name__)
UpperCamelCase_   	=	{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

UpperCamelCase_   	=	{
    "vocab_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json",
    },
}

UpperCamelCase_   	=	{
    "google/fnet-base": 5_1_2,
    "google/fnet-large": 5_1_2,
}

UpperCamelCase_   	=	"▁"

class       a		(   __UpperCAmelCase					):
							lowercase_		:						List[Any]   							=  VOCAB_FILES_NAMES
							lowercase_		:						Dict   							=  PRETRAINED_VOCAB_FILES_MAP
							lowercase_		:						str   							=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_		:						Dict   							=  ['input_ids', 'token_type_ids']
							lowercase_		:						Optional[int]   							=  FNetTokenizer







							def __init__(						self  :  List[Any]      ,				snake_case__  :  Dict=None      ,				snake_case__  :  Any=None      ,				snake_case__  :  Tuple=False      ,				snake_case__  :  Union[str, Any]=True      ,				snake_case__  :  Dict=True      ,				snake_case__  :  Optional[Any]="<unk>"      ,				snake_case__  :  Union[str, Any]="[SEP]"      ,				snake_case__  :  Union[str, Any]="<pad>"      ,				snake_case__  :  List[Any]="[CLS]"      ,				snake_case__  :  int="[MASK]"      ,				**snake_case__  :  List[Any]      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							(
								    AddedToken(snake_case__      ,				lstrip=snake_case__      ,				rstrip=snake_case__      ,				normalized=snake_case__ )
								    if isinstance(snake_case__      ,				snake_case__ )
								    else mask_token
								)

								super().__init__(
								    snake_case__      ,				tokenizer_file=snake_case__      ,				do_lower_case=snake_case__      ,				remove_space=snake_case__      ,				keep_accents=snake_case__      ,				unk_token=snake_case__      ,				sep_token=snake_case__      ,				pad_token=snake_case__      ,				cls_token=snake_case__      ,				mask_token=snake_case__      ,				**snake_case__      ,				)

								__lowerCAmelCase         =							do_lower_case
								__lowerCAmelCase         =							remove_space
								__lowerCAmelCase         =							keep_accents
								__lowerCAmelCase         =							vocab_file
								__lowerCAmelCase         =							False if not self.vocab_file else True







							def       UpperCAmelCase__					(						self  :  int      ,				snake_case__  :  List[int]      ,				snake_case__  :  Optional[List[int]] = None ):
								"""simple docstring"""







								__lowerCAmelCase         =							[self.sep_token_id]
								__lowerCAmelCase         =							[self.cls_token_id]
								if token_ids_a is None:
									return cls + token_ids_a + sep
								return cls + token_ids_a + sep + token_ids_a + sep







							def       UpperCAmelCase__					(						self  :  int      ,				snake_case__  :  List[int]      ,				snake_case__  :  Optional[List[int]] = None ):
								"""simple docstring"""







								__lowerCAmelCase         =							[self.sep_token_id]
								__lowerCAmelCase         =							[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  :  Union[str, Any]      ,				snake_case__  :  str      ,				snake_case__  :  Optional[str] = None ):
								"""simple docstring"""







								if not os.path.isdir(snake_case__ ):
									logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
									return
								__lowerCAmelCase         =							os.path.join(
								    snake_case__      ,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

								if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case__ ):
									copyfile(self.vocab_file      ,				snake_case__ )

								return (out_vocab_file,)








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


UpperCamelCase_   	=	logging.get_logger(__name__)

UpperCamelCase_   	=	{
    "speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json",
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}

class       a		(   __UpperCAmelCase					):
							lowercase_		:						Union[str, Any]   							=  'mctct'







							def __init__(						self  :  Dict      ,				snake_case__  :  Any=8_065      ,				snake_case__  :  Tuple=1_536      ,				snake_case__  :  Union[str, Any]=36      ,				snake_case__  :  List[Any]=6_144      ,				snake_case__  :  Tuple=4      ,				snake_case__  :  List[str]=384      ,				snake_case__  :  List[Any]=920      ,				snake_case__  :  Any=1E-5      ,				snake_case__  :  List[Any]=0.3      ,				snake_case__  :  List[str]="relu"      ,				snake_case__  :  List[Any]=0.0_2      ,				snake_case__  :  Optional[Any]=0.3      ,				snake_case__  :  List[str]=0.3      ,				snake_case__  :  List[Any]=1      ,				snake_case__  :  Optional[int]=0      ,				snake_case__  :  Any=2      ,				snake_case__  :  Any=1      ,				snake_case__  :  List[str]=0.3      ,				snake_case__  :  Tuple=1      ,				snake_case__  :  Union[str, Any]=(7,)      ,				snake_case__  :  List[Any]=(3,)      ,				snake_case__  :  List[Any]=80      ,				snake_case__  :  Optional[int]=1      ,				snake_case__  :  List[str]=None      ,				snake_case__  :  int="sum"      ,				snake_case__  :  Dict=False      ,				**snake_case__  :  List[str]      ,				):
								"""simple docstring"""







								super().__init__(**snake_case__      ,				pad_token_id=snake_case__      ,				bos_token_id=snake_case__      ,				eos_token_id=snake_case__ )
								__lowerCAmelCase         =							vocab_size
								__lowerCAmelCase         =							hidden_size
								__lowerCAmelCase         =							num_hidden_layers
								__lowerCAmelCase         =							intermediate_size
								__lowerCAmelCase         =							num_attention_heads
								__lowerCAmelCase         =							attention_head_dim
								__lowerCAmelCase         =							max_position_embeddings
								__lowerCAmelCase         =							layer_norm_eps
								__lowerCAmelCase         =							layerdrop
								__lowerCAmelCase         =							hidden_act
								__lowerCAmelCase         =							initializer_range
								__lowerCAmelCase         =							hidden_dropout_prob
								__lowerCAmelCase         =							attention_probs_dropout_prob
								__lowerCAmelCase         =							pad_token_id
								__lowerCAmelCase         =							bos_token_id
								__lowerCAmelCase         =							eos_token_id
								__lowerCAmelCase         =							conv_glu_dim
								__lowerCAmelCase         =							conv_dropout
								__lowerCAmelCase         =							num_conv_layers
								__lowerCAmelCase         =							input_feat_per_channel
								__lowerCAmelCase         =							input_channels
								__lowerCAmelCase         =							conv_channels
								__lowerCAmelCase         =							ctc_loss_reduction
								__lowerCAmelCase         =							ctc_zero_infinity

								# prevents config testing fail with exporting to json
								__lowerCAmelCase         =							list(snake_case__ )
								__lowerCAmelCase         =							list(snake_case__ )

								if len(self.conv_kernel ) != self.num_conv_layers:
									raise ValueError(
									    "Configuration for convolutional module is incorrect. "
									    "It is required that `len(config.conv_kernel)` == `config.num_conv_layers` "
									    F"but is `len(config.conv_kernel) = {len(self.conv_kernel )}`, "
									    F"`config.num_conv_layers = {self.num_conv_layers}`." )






