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


lowercase__         =logging.get_logger(__name__)

lowercase__         ={
    '''MIT/ast-finetuned-audioset-10-10-0.4593''': (
        '''https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json'''
    ),
}



class 						UpperCamelCase__    (			a_       ):
   _SCREAMING_SNAKE_CASE :				int      			= "audio-spectrogram-transformer"
   def __init__(self					:   List[Any]   ,							snake_case_					:   int=7_6_8   ,							snake_case_					:   Optional[Any]=1_2   ,							snake_case_					:   Tuple=1_2   ,							snake_case_					:   Union[str, Any]=3_0_7_2   ,							snake_case_					:   Tuple="gelu"   ,							snake_case_					:   Optional[Any]=0.0   ,							snake_case_					:   List[Any]=0.0   ,							snake_case_					:   Optional[int]=0.02   ,							snake_case_					:   str=1E-12   ,							snake_case_					:   Tuple=1_6   ,							snake_case_					:   Optional[Any]=True   ,							snake_case_					:   Dict=1_0   ,							snake_case_					:   int=1_0   ,							snake_case_					:   Any=1_0_2_4   ,							snake_case_					:   str=1_2_8   ,							**snake_case_					:   List[str]   ,							):
    super().__init__(**lowercase_  )

    __a					:    str													=    hidden_size
    __a					:    List[str]													=    num_hidden_layers
    __a					:    List[Any]													=    num_attention_heads
    __a					:    Optional[Any]													=    intermediate_size
    __a					:    List[Any]													=    hidden_act
    __a					:    Union[str, Any]													=    hidden_dropout_prob
    __a					:    Optional[int]													=    attention_probs_dropout_prob
    __a					:    Optional[int]													=    initializer_range
    __a					:    Optional[int]													=    layer_norm_eps
    __a					:    List[str]													=    patch_size
    __a					:    Tuple													=    qkv_bias
    __a					:    Dict													=    frequency_stride
    __a					:    List[str]													=    time_stride
    __a					:    str													=    max_length
    __a					:    str													=    num_mel_bins



"""simple docstring"""





from typing import TYPE_CHECKING

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


__UpperCamelCase    : List[str]				 =						{'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : Union[str, Any]				 =						['''XGLMTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : str				 =						['''XGLMTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : Optional[int]				 =						[
							    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''XGLMForCausalLM''',
							    '''XGLMModel''',
							    '''XGLMPreTrainedModel''',
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : str				 =						[
							    '''FlaxXGLMForCausalLM''',
							    '''FlaxXGLMModel''',
							    '''FlaxXGLMPreTrainedModel''',
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : str				 =						[
							    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFXGLMForCausalLM''',
							    '''TFXGLMModel''',
							    '''TFXGLMPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm import XGLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm_fast import XGLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xglm import (
														    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXGLMForCausalLM,
														    TFXGLMModel,
														    TFXGLMPreTrainedModel,
														)


else:
							import sys

							__UpperCamelCase    : Optional[int]				 =						_LazyModule(__name__, globals()['''__file__'''], _import_structure)




