

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_        =			{
    '''configuration_rembert''': ['''REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RemBertConfig''', '''RemBertOnnxConfig''']
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case_        =			['''RemBertTokenizer''']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case_        =			['''RemBertTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case_        =			[
         '''REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''RemBertForCausalLM''',
         '''RemBertForMaskedLM''',
         '''RemBertForMultipleChoice''',
         '''RemBertForQuestionAnswering''',
         '''RemBertForSequenceClassification''',
         '''RemBertForTokenClassification''',
         '''RemBertLayer''',
         '''RemBertModel''',
         '''RemBertPreTrainedModel''',
         '''load_tf_weights_in_rembert''',
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case_        =			[
         '''TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFRemBertForCausalLM''',
         '''TFRemBertForMaskedLM''',
         '''TFRemBertForMultipleChoice''',
         '''TFRemBertForQuestionAnswering''',
         '''TFRemBertForSequenceClassification''',
         '''TFRemBertForTokenClassification''',
         '''TFRemBertLayer''',
         '''TFRemBertModel''',
         '''TFRemBertPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_rembert import RemBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_rembert_fast import RemBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_rembert import (
              REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              RemBertForCausalLM,
              RemBertForMaskedLM,
              RemBertForMultipleChoice,
              RemBertForQuestionAnswering,
              RemBertForSequenceClassification,
              RemBertForTokenClassification,
              RemBertLayer,
              RemBertModel,
              RemBertPreTrainedModel,
              load_tf_weights_in_rembert,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_rembert import (
              TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFRemBertForCausalLM,
              TFRemBertForMaskedLM,
              TFRemBertForMultipleChoice,
              TFRemBertForQuestionAnswering,
              TFRemBertForSequenceClassification,
              TFRemBertForTokenClassification,
              TFRemBertLayer,
              TFRemBertModel,
              TFRemBertPreTrainedModel,
          )


else:
     import sys

     snake_case_        =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




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


snake_case_        =			logging.get_logger(__name__)

snake_case_        =			{
    '''facebook/s2t-wav2vec2-large-en-de''': (
        '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	List[Any]														=					"""speech_to_text_2"""
    __lowerCamelCase						:	str														=					["""past_key_values"""]
    __lowerCamelCase						:	List[Any]														=					{"""num_attention_heads""": """decoder_attention_heads""", """hidden_size""": """d_model"""}





    def __init__(				self						,       a=1_0000						,       a=6						,       a=2048						,       a=4						,       a=0.0						,       a=True						,       a="relu"						,       a=256						,       a=0.1						,       a=0.0						,       a=0.0						,       a=0.02						,       a=2						,       a=True						,       a=1						,       a=0						,       a=2						,       a=1024						,       **a						,       ):
         lowercase__   :       Optional[int]				=	vocab_size
         lowercase__   :       List[str]				=	d_model
         lowercase__   :       int				=	decoder_ffn_dim
         lowercase__   :       Optional[Any]				=	decoder_layers
         lowercase__   :       int				=	decoder_attention_heads
         lowercase__   :       Dict				=	dropout
         lowercase__   :       Optional[int]				=	attention_dropout
         lowercase__   :       Optional[int]				=	activation_dropout
         lowercase__   :       Optional[int]				=	activation_function
         lowercase__   :       Dict				=	init_std
         lowercase__   :       List[Any]				=	decoder_layerdrop
         lowercase__   :       int				=	use_cache
         lowercase__   :       Any				=	decoder_layers
         lowercase__   :       Optional[int]				=	scale_embedding  # scale factor will be sqrt(d_model) if True
         lowercase__   :       Tuple				=	max_target_positions

         super().__init__(
             pad_token_id=a						,       bos_token_id=a						,       eos_token_id=a						,       decoder_start_token_id=a						,       **a						,       )

