


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


__snake_case									=		logging.get_logger(__name__)

__snake_case									=		{
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}



class      __lowerCamelCase       (a_		):
       _lowercase											=			"""megatron-bert"""







       def __init__(       self:      List[str],A_:      Dict=2_9056,A_:      str=1024,A_:      Dict=24,A_:      List[str]=16,A_:      Optional[Any]=4096,A_:      str="gelu",A_:      Optional[Any]=0.1,A_:      Optional[Any]=0.1,A_:      Optional[Any]=512,A_:      int=2,A_:      Tuple=0.0_2,A_:      Tuple=1E-12,A_:      str=0,A_:      Tuple="absolute",A_:      int=True,**A_:      List[str],):






            '''simple docstring'''




            super().__init__(pad_token_id=_A,**_A )

            __UpperCamelCase      =       vocab_size
            __UpperCamelCase      =       hidden_size
            __UpperCamelCase      =       num_hidden_layers
            __UpperCamelCase      =       num_attention_heads
            __UpperCamelCase      =       hidden_act
            __UpperCamelCase      =       intermediate_size
            __UpperCamelCase      =       hidden_dropout_prob
            __UpperCamelCase      =       attention_probs_dropout_prob
            __UpperCamelCase      =       max_position_embeddings
            __UpperCamelCase      =       type_vocab_size
            __UpperCamelCase      =       initializer_range
            __UpperCamelCase      =       layer_norm_eps
            __UpperCamelCase      =       position_embedding_type
            __UpperCamelCase      =       use_cache









from typing import TYPE_CHECKING

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


__A				:    Union[str, Any]       =   {
    "configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2TextConfig"],
    "processing_speech_to_text": ["Speech2TextProcessor"],
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    List[Any]       =   ["Speech2TextTokenizer"]

try:
				if not is_speech_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    List[Any]       =   ["Speech2TextFeatureExtractor"]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    int       =   [
				    "TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFSpeech2TextForConditionalGeneration",
				    "TFSpeech2TextModel",
				    "TFSpeech2TextPreTrainedModel",
				]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    Tuple       =   [
				    "SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "Speech2TextForConditionalGeneration",
				    "Speech2TextModel",
				    "Speech2TextPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
				from .processing_speech_to_text import SpeechaTextProcessor

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_speech_to_text import SpeechaTextTokenizer

				try:
								if not is_speech_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_speech_to_text import (
								    TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFSpeechaTextForConditionalGeneration,
								    TFSpeechaTextModel,
								    TFSpeechaTextPreTrainedModel,
								)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_speech_to_text import (
								    SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SpeechaTextForConditionalGeneration,
								    SpeechaTextModel,
								    SpeechaTextPreTrainedModel,
								)

else:
				import sys

				__A				:    Dict       =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



