




"""simple docstring"""



from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__:str			=	logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__:str			=	{
    "nielsr/canine-s": 2048,
}

# Unicode defines 1,114,112 total “codepoints”
SCREAMING_SNAKE_CASE__:List[str]			=	1114112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
SCREAMING_SNAKE_CASE__:Any			=	0

SCREAMING_SNAKE_CASE__:Tuple			=	0xE_0_0_0
SCREAMING_SNAKE_CASE__:Optional[Any]			=	0xE_0_0_1
SCREAMING_SNAKE_CASE__:Tuple			=	0xE_0_0_2
SCREAMING_SNAKE_CASE__:Union[str, Any]			=	0xE_0_0_3
SCREAMING_SNAKE_CASE__:Dict			=	0xE_0_0_4

# Maps special codepoints to human-readable names.
SCREAMING_SNAKE_CASE__:Dict[int, str]			=	{
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
SCREAMING_SNAKE_CASE__:Dict[str, int]			=	{name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}






class 				snake_case__     (   snake_case__   ):
 _snake_case   :			List[str]           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


 def __init__(				self  ,						lowerCamelCase=chr(UpperCAmelCase_		)  ,						lowerCamelCase=chr(UpperCAmelCase_		)  ,						lowerCamelCase=chr(UpperCAmelCase_		)  ,						lowerCamelCase=chr(UpperCAmelCase_		)  ,						lowerCamelCase=chr(UpperCAmelCase_		)  ,						lowerCamelCase=chr(UpperCAmelCase_		)  ,						lowerCamelCase=False  ,						lowerCamelCase=2048  ,						**lowerCamelCase  ,						):
        __a     				=  AddedToken(UpperCAmelCase_  ,						lstrip=UpperCAmelCase_  ,						rstrip=UpperCAmelCase_		) if isinstance(UpperCAmelCase_  ,						UpperCAmelCase_		) else bos_token
        __a     				=  AddedToken(UpperCAmelCase_  ,						lstrip=UpperCAmelCase_  ,						rstrip=UpperCAmelCase_		) if isinstance(UpperCAmelCase_  ,						UpperCAmelCase_		) else eos_token
        __a     				=  AddedToken(UpperCAmelCase_  ,						lstrip=UpperCAmelCase_  ,						rstrip=UpperCAmelCase_		) if isinstance(UpperCAmelCase_  ,						UpperCAmelCase_		) else sep_token
        __a     				=  AddedToken(UpperCAmelCase_  ,						lstrip=UpperCAmelCase_  ,						rstrip=UpperCAmelCase_		) if isinstance(UpperCAmelCase_  ,						UpperCAmelCase_		) else cls_token
        __a     				=  AddedToken(UpperCAmelCase_  ,						lstrip=UpperCAmelCase_  ,						rstrip=UpperCAmelCase_		) if isinstance(UpperCAmelCase_  ,						UpperCAmelCase_		) else pad_token

        # Mask token behave like a normal word, i.e. include the space before it
        __a     				=  AddedToken(UpperCAmelCase_  ,						lstrip=UpperCAmelCase_  ,						rstrip=UpperCAmelCase_		) if isinstance(UpperCAmelCase_  ,						UpperCAmelCase_		) else mask_token

        super().__init__(
            bos_token=UpperCAmelCase_  ,						eos_token=UpperCAmelCase_  ,						sep_token=UpperCAmelCase_  ,						cls_token=UpperCAmelCase_  ,						pad_token=UpperCAmelCase_  ,						mask_token=UpperCAmelCase_  ,						add_prefix_space=UpperCAmelCase_  ,						model_max_length=UpperCAmelCase_  ,						**UpperCAmelCase_  ,						)

        # Creates a mapping for looking up the IDs of special symbols.
        __a     				=  {}
        for codepoint, name in SPECIAL_CODEPOINTS.items():
               __a     				=  codepoint

        # Creates a mapping for looking up the string forms of special symbol IDs.
        __a     				=  {
            codepoint: name for name, codepoint in self._special_codepoints.items()
        }

        __a     				=  UNICODE_VOCAB_SIZE
        __a     				=  len(self._special_codepoints		)


 @property
 def 		a__ (				self		):
        return self._unicode_vocab_size


 def 		a__ (				self  ,						lowerCamelCase		):
        return list(UpperCAmelCase_		)


 def 		a__ (				self  ,						lowerCamelCase		):
        try:
               return ord(UpperCAmelCase_		)
        except TypeError:
               raise ValueError(F"invalid token: '{token}'"		)


 def 		a__ (				self  ,						lowerCamelCase		):
        try:
               if index in SPECIAL_CODEPOINTS:
                      return SPECIAL_CODEPOINTS[index]
               return chr(UpperCAmelCase_		)
        except TypeError:
               raise ValueError(F"invalid id: {index}"		)


 def 		a__ (				self  ,						lowerCamelCase		):
        return "".join(UpperCAmelCase_		)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        __a     				=  [self.sep_token_id]
        __a     				=  [self.cls_token_id]

        __a     				=  cls + token_ids_a + sep
        if token_ids_a is not None:
               result += token_ids_a + sep
        return result


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						lowerCamelCase = False		):
        if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=UpperCAmelCase_  ,						token_ids_a=UpperCAmelCase_  ,						already_has_special_tokens=UpperCAmelCase_		)

        __a     				=  [1] + ([0] * len(UpperCAmelCase_		)) + [1]
        if token_ids_a is not None:
               result += ([0] * len(UpperCAmelCase_		)) + [1]
        return result


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        __a     				=  [self.sep_token_id]
        __a     				=  [self.cls_token_id]

        __a     				=  len(cls + token_ids_a + sep		) * [0]
        if token_ids_a is not None:
               result += len(token_ids_a + sep		) * [1]
        return result






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        return ()

"""simple docstring"""



import functools
import operator

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


SCREAMING_SNAKE_CASE__:Optional[Any]			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:str			=	{
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			str           =		"""sew-d"""


 def __init__(				self  ,						lowerCamelCase=32  ,						lowerCamelCase=768  ,						lowerCamelCase=12  ,						lowerCamelCase=12  ,						lowerCamelCase=3072  ,						lowerCamelCase=2  ,						lowerCamelCase=512  ,						lowerCamelCase=256  ,						lowerCamelCase=True  ,						lowerCamelCase=True  ,						lowerCamelCase=("p2c", "c2p")  ,						lowerCamelCase="layer_norm"  ,						lowerCamelCase="gelu_python"  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.0  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.02  ,						lowerCamelCase=1E-7  ,						lowerCamelCase=1E-5  ,						lowerCamelCase="group"  ,						lowerCamelCase="gelu"  ,						lowerCamelCase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)  ,						lowerCamelCase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)  ,						lowerCamelCase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)  ,						lowerCamelCase=False  ,						lowerCamelCase=128  ,						lowerCamelCase=16  ,						lowerCamelCase=True  ,						lowerCamelCase=0.05  ,						lowerCamelCase=10  ,						lowerCamelCase=2  ,						lowerCamelCase=0.0  ,						lowerCamelCase=10  ,						lowerCamelCase=0  ,						lowerCamelCase="mean"  ,						lowerCamelCase=False  ,						lowerCamelCase=False  ,						lowerCamelCase=256  ,						lowerCamelCase=0  ,						lowerCamelCase=1  ,						lowerCamelCase=2  ,						**lowerCamelCase  ,						):
        super().__init__(**lowerCamelCase  ,						pad_token_id=lowerCamelCase  ,						bos_token_id=lowerCamelCase  ,						eos_token_id=lowerCamelCase		)
        __a     				=  hidden_size
        __a     				=  feat_extract_norm
        __a     				=  feat_extract_activation
        __a     				=  list(lowerCamelCase		)
        __a     				=  list(lowerCamelCase		)
        __a     				=  list(lowerCamelCase		)
        __a     				=  conv_bias
        __a     				=  num_conv_pos_embeddings
        __a     				=  num_conv_pos_embedding_groups
        __a     				=  len(self.conv_dim		)
        __a     				=  num_hidden_layers
        __a     				=  intermediate_size
        __a     				=  squeeze_factor
        __a     				=  max_position_embeddings
        __a     				=  position_buckets
        __a     				=  share_att_key
        __a     				=  relative_attention
        __a     				=  norm_rel_ebd
        __a     				=  list(lowerCamelCase		)
        __a     				=  hidden_act
        __a     				=  num_attention_heads
        __a     				=  hidden_dropout
        __a     				=  attention_dropout
        __a     				=  activation_dropout
        __a     				=  feat_proj_dropout
        __a     				=  final_dropout
        __a     				=  layer_norm_eps
        __a     				=  feature_layer_norm_eps
        __a     				=  initializer_range
        __a     				=  vocab_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)`,"
                   F"but is `len(config.conv_dim) = {len(self.conv_dim		)}`, `len(config.conv_stride)"
                   F"= {len(self.conv_stride		)}`, `len(config.conv_kernel) = {len(self.conv_kernel		)}`."		)

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        __a     				=  apply_spec_augment
        __a     				=  mask_time_prob
        __a     				=  mask_time_length
        __a     				=  mask_time_min_masks
        __a     				=  mask_feature_prob
        __a     				=  mask_feature_length
        __a     				=  mask_feature_min_masks

        # ctc loss
        __a     				=  ctc_loss_reduction
        __a     				=  ctc_zero_infinity

        # sequence classification
        __a     				=  use_weighted_layer_sum
        __a     				=  classifier_proj_size






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