


from typing import List

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


SCREAMING_SNAKE_CASE       :      List[str]												= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE       :      int												= {
    "snap-research/efficientformer-l1-300": (
        "https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"
    ),
}






class _lowerCamelCase(						_a   ):
       lowercase_ :	List[Any]     =			"""efficientformer"""
       def __init__(					self,      lowerCamelCase = [3, 2, 6, 4],      lowerCamelCase = [48, 96, 2_24, 4_48],      lowerCamelCase = [True, True, True, True],      lowerCamelCase = 4_48,      lowerCamelCase = 32,      lowerCamelCase = 4,      lowerCamelCase = 7,      lowerCamelCase = 5,      lowerCamelCase = 8,      lowerCamelCase = 4,      lowerCamelCase = 0.0,      lowerCamelCase = 16,      lowerCamelCase = 3,      lowerCamelCase = 3,      lowerCamelCase = 3,      lowerCamelCase = 2,      lowerCamelCase = 1,      lowerCamelCase = 0.0,      lowerCamelCase = 1,      lowerCamelCase = True,      lowerCamelCase = True,      lowerCamelCase = 1E-5,      lowerCamelCase = "gelu",      lowerCamelCase = 0.0_2,      lowerCamelCase = 1E-12,      lowerCamelCase = 2_24,      lowerCamelCase = 1E-05,      **lowerCamelCase,      )       ->   None:




           """simple docstring"""

           super().__init__(**lowerCamelCase)

           _lowercase       :							Dict      		=		hidden_act
           _lowercase       :							Union[str, Any]      		=		hidden_dropout_prob
           _lowercase       :							int      		=		hidden_sizes
           _lowercase       :							Union[str, Any]      		=		num_hidden_layers
           _lowercase       :							List[Any]      		=		num_attention_heads
           _lowercase       :							Dict      		=		initializer_range
           _lowercase       :							int      		=		layer_norm_eps
           _lowercase       :							Optional[Any]      		=		patch_size
           _lowercase       :							List[str]      		=		num_channels
           _lowercase       :							Tuple      		=		depths
           _lowercase       :							Tuple      		=		mlp_expansion_ratio
           _lowercase       :							str      		=		downsamples
           _lowercase       :							Tuple      		=		dim
           _lowercase       :							Tuple      		=		key_dim
           _lowercase       :							Union[str, Any]      		=		attention_ratio
           _lowercase       :							int      		=		resolution
           _lowercase       :							Optional[Any]      		=		pool_size
           _lowercase       :							Optional[int]      		=		downsample_patch_size
           _lowercase       :							Dict      		=		downsample_stride
           _lowercase       :							Optional[Any]      		=		downsample_pad
           _lowercase       :							Optional[int]      		=		drop_path_rate
           _lowercase       :							List[str]      		=		num_metaad_blocks
           _lowercase       :							List[Any]      		=		distillation
           _lowercase       :							List[str]      		=		use_layer_scale
           _lowercase       :							Dict      		=		layer_scale_init_value
           _lowercase       :							Union[str, Any]      		=		image_size
           _lowercase       :							Dict      		=		batch_norm_eps






from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE       :      List[str]												= {
    "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:
 SCREAMING_SNAKE_CASE       :      Union[str, Any]												= ["Speech2TextTokenizer"]

try:
 if not is_speech_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      int												= ["Speech2TextFeatureExtractor"]

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      str												= [
     "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

 SCREAMING_SNAKE_CASE       :      Optional[Any]												= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


