






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


a_		:    Optional[int]   							=     logging.get_logger(__name__)

a_		:    int   							=     {
    'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config.json',
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}

class 		_snake_case	(	A__       ):
			_lowercase		:     Dict        =				'''cvt'''






			def __init__(							self		,       a=3		,       a=[7, 3, 3]		,       a=[4, 2, 2]		,       a=[2, 1, 1]		,       a=[64, 192, 384]		,       a=[1, 3, 6]		,       a=[1, 2, 10]		,       a=[4.0, 4.0, 4.0]		,       a=[0.0, 0.0, 0.0]		,       a=[0.0, 0.0, 0.0]		,       a=[0.0, 0.0, 0.1]		,       a=[True, True, True]		,       a=[False, False, True]		,       a=["dw_bn", "dw_bn", "dw_bn"]		,       a=[3, 3, 3]		,       a=[1, 1, 1]		,       a=[2, 2, 2]		,       a=[1, 1, 1]		,       a=[1, 1, 1]		,       a=0.02		,       a=1E-12		,       **a		,       )   ->							List[Any]:
						super().__init__(**a)
						SCREAMING_SNAKE_CASE							=			num_channels
						SCREAMING_SNAKE_CASE							=			patch_sizes
						SCREAMING_SNAKE_CASE							=			patch_stride
						SCREAMING_SNAKE_CASE							=			patch_padding
						SCREAMING_SNAKE_CASE							=			embed_dim
						SCREAMING_SNAKE_CASE							=			num_heads
						SCREAMING_SNAKE_CASE							=			depth
						SCREAMING_SNAKE_CASE							=			mlp_ratio
						SCREAMING_SNAKE_CASE							=			attention_drop_rate
						SCREAMING_SNAKE_CASE							=			drop_rate
						SCREAMING_SNAKE_CASE							=			drop_path_rate
						SCREAMING_SNAKE_CASE							=			qkv_bias
						SCREAMING_SNAKE_CASE							=			cls_token
						SCREAMING_SNAKE_CASE							=			qkv_projection_method
						SCREAMING_SNAKE_CASE							=			kernel_qkv
						SCREAMING_SNAKE_CASE							=			padding_kv
						SCREAMING_SNAKE_CASE							=			stride_kv
						SCREAMING_SNAKE_CASE							=			padding_q
						SCREAMING_SNAKE_CASE							=			stride_q
						SCREAMING_SNAKE_CASE							=			initializer_range
						SCREAMING_SNAKE_CASE							=			layer_norm_eps











'''simple docstring'''






from typing import TYPE_CHECKING

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


A__							:  Any    ={
    '''configuration_speecht5''': [
        '''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP''',
        '''SpeechT5Config''',
        '''SpeechT5HifiGanConfig''',
    ],
    '''feature_extraction_speecht5''': ['''SpeechT5FeatureExtractor'''],
    '''processing_speecht5''': ['''SpeechT5Processor'''],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  Union[str, Any]    =['''SpeechT5Tokenizer''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  List[Any]    =[
          '''SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''SpeechT5ForSpeechToText''',
          '''SpeechT5ForSpeechToSpeech''',
          '''SpeechT5ForTextToSpeech''',
          '''SpeechT5Model''',
          '''SpeechT5PreTrainedModel''',
          '''SpeechT5HifiGan''',
      ]

if TYPE_CHECKING:
      from .configuration_speechta import (
          SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
          SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
          SpeechTaConfig,
          SpeechTaHifiGanConfig,
      )
      from .feature_extraction_speechta import SpeechTaFeatureExtractor
      from .processing_speechta import SpeechTaProcessor

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_speechta import SpeechTaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_speechta import (
                SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
                SpeechTaForSpeechToSpeech,
                SpeechTaForSpeechToText,
                SpeechTaForTextToSpeech,
                SpeechTaHifiGan,
                SpeechTaModel,
                SpeechTaPreTrainedModel,
            )

else:
      import sys

      A__							:  Tuple    =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






