





import functools
import operator

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


_UpperCAmelCase        =				logging.get_logger(__name__)

_UpperCAmelCase        =				{
    '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 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Optional[Any]         =   '''sew-d'''

       def __init__(		self: List[Any]					,   _SCREAMING_SNAKE_CASE: Tuple=32					,   _SCREAMING_SNAKE_CASE: Optional[Any]=768					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=12					,   _SCREAMING_SNAKE_CASE: Any=12					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=3072					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=2					,   _SCREAMING_SNAKE_CASE: Optional[Any]=512					,   _SCREAMING_SNAKE_CASE: Dict=256					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=True					,   _SCREAMING_SNAKE_CASE: List[Any]=True					,   _SCREAMING_SNAKE_CASE: List[str]=("p2c", "c2p")					,   _SCREAMING_SNAKE_CASE: List[Any]="layer_norm"					,   _SCREAMING_SNAKE_CASE: Union[str, Any]="gelu_python"					,   _SCREAMING_SNAKE_CASE: List[Any]=0.1					,   _SCREAMING_SNAKE_CASE: Tuple=0.1					,   _SCREAMING_SNAKE_CASE: List[Any]=0.1					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=0.0					,   _SCREAMING_SNAKE_CASE: int=0.1					,   _SCREAMING_SNAKE_CASE: str=0.02					,   _SCREAMING_SNAKE_CASE: int=1e-7					,   _SCREAMING_SNAKE_CASE: Optional[int]=1e-5					,   _SCREAMING_SNAKE_CASE: Any="group"					,   _SCREAMING_SNAKE_CASE: Any="gelu"					,   _SCREAMING_SNAKE_CASE: Any=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)					,   _SCREAMING_SNAKE_CASE: List[str]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)					,   _SCREAMING_SNAKE_CASE: Dict=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)					,   _SCREAMING_SNAKE_CASE: int=False					,   _SCREAMING_SNAKE_CASE: List[Any]=128					,   _SCREAMING_SNAKE_CASE: Optional[int]=16					,   _SCREAMING_SNAKE_CASE: Dict=True					,   _SCREAMING_SNAKE_CASE: Optional[Any]=0.05					,   _SCREAMING_SNAKE_CASE: List[str]=10					,   _SCREAMING_SNAKE_CASE: List[str]=2					,   _SCREAMING_SNAKE_CASE: Any=0.0					,   _SCREAMING_SNAKE_CASE: Optional[int]=10					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=0					,   _SCREAMING_SNAKE_CASE: Dict="mean"					,   _SCREAMING_SNAKE_CASE: List[str]=False					,   _SCREAMING_SNAKE_CASE: List[str]=False					,   _SCREAMING_SNAKE_CASE: Dict=256					,   _SCREAMING_SNAKE_CASE: Optional[Any]=0					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=1					,   _SCREAMING_SNAKE_CASE: Dict=2					,   **_SCREAMING_SNAKE_CASE: int					,   )       -> Tuple:




              """simple docstring"""


              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_          =  squeeze_factor
              UpperCamelCase_          =  max_position_embeddings
              UpperCamelCase_          =  position_buckets
              UpperCamelCase_          =  share_att_key
              UpperCamelCase_          =  relative_attention
              UpperCamelCase_          =  norm_rel_ebd
              UpperCamelCase_          =  list(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  hidden_act
              UpperCamelCase_          =  num_attention_heads
              UpperCamelCase_          =  hidden_dropout
              UpperCamelCase_          =  attention_dropout
              UpperCamelCase_          =  activation_dropout
              UpperCamelCase_          =  feat_proj_dropout
              UpperCamelCase_          =  final_dropout
              UpperCamelCase_          =  layer_norm_eps
              UpperCamelCase_          =  feature_layer_norm_eps
              UpperCamelCase_          =  initializer_range
              UpperCamelCase_          =  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
              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

              # ctc loss
              UpperCamelCase_          =  ctc_loss_reduction
              UpperCamelCase_          =  ctc_zero_infinity

              # sequence classification
              UpperCamelCase_          =  use_weighted_layer_sum
              UpperCamelCase_          =  classifier_proj_size



       @property
       def lowercase (		self: Optional[Any]		)       -> Tuple:




              """simple docstring"""


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








from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


_UpperCAmelCase        =				logging.get_logger(__name__)

_UpperCAmelCase        =				{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

_UpperCAmelCase        =				{
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

_UpperCAmelCase        =				{
    'facebook/blenderbot_small-90M': 5_1_2,
}



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Optional[Any]         =   VOCAB_FILES_NAMES
       _UpperCamelCase				:					str         =   PRETRAINED_VOCAB_FILES_MAP
       _UpperCamelCase				:					List[Any]         =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _UpperCamelCase				:					str         =   BlenderbotSmallTokenizer

       def __init__(		self: Dict					,   _SCREAMING_SNAKE_CASE: Optional[int]=None					,   _SCREAMING_SNAKE_CASE: List[str]=None					,   _SCREAMING_SNAKE_CASE: Optional[int]="<|endoftext|>"					,   _SCREAMING_SNAKE_CASE: List[Any]="<|endoftext|>"					,   _SCREAMING_SNAKE_CASE: List[str]="<|endoftext|>"					,   _SCREAMING_SNAKE_CASE: Optional[Any]=False					,   _SCREAMING_SNAKE_CASE: int=True					,   **_SCREAMING_SNAKE_CASE: Optional[int]					,   )       -> int:




              """simple docstring"""


              super().__init__(
                  ByteLevelBPETokenizer(
                      vocab=_SCREAMING_SNAKE_CASE					,   merges=_SCREAMING_SNAKE_CASE					,   add_prefix_space=_SCREAMING_SNAKE_CASE					,   trim_offsets=_SCREAMING_SNAKE_CASE					,   )					,   bos_token=_SCREAMING_SNAKE_CASE					,   eos_token=_SCREAMING_SNAKE_CASE					,   unk_token=_SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE					,   )
              UpperCamelCase_          =  add_prefix_space

       def lowercase (		self: Any					,   _SCREAMING_SNAKE_CASE: Any					,   _SCREAMING_SNAKE_CASE: Any=None		)       -> List[str]:




              """simple docstring"""


              UpperCamelCase_          =  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
              if token_ids_a is None:
                     return output

              return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



       def lowercase (		self: int					,   _SCREAMING_SNAKE_CASE: List[int]					,   _SCREAMING_SNAKE_CASE: Optional[List[int]] = None		)       -> List[int]:




              """simple docstring"""


              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 + sep + token_ids_a + sep		) * [0]

