





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


_lowerCAmelCase 				=     logging.get_logger(__name__)

_lowerCAmelCase 				=     {
    """edbeeching/decision-transformer-gym-hopper-medium""": (
        """https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"""
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}

class    _UpperCAmelCase							(		_lowerCamelCase     ):
      a						       =							'''decision_transformer'''
      a						       =							['''past_key_values''']
      a						       =							{
          '''max_position_embeddings''': '''n_positions''',
          '''num_attention_heads''': '''n_head''',
          '''num_hidden_layers''': '''n_layer''',
      }
      def __init__(		self      ,       a__=17      ,       a__=4      ,       a__=128      ,       a__=4096      ,       a__=True      ,       a__=1      ,       a__=1024      ,       a__=3      ,       a__=1      ,       a__=None      ,       a__="relu"      ,       a__=0.1      ,       a__=0.1      ,       a__=0.1      ,       a__=1E-5      ,       a__=0.02      ,       a__=True      ,       a__=True      ,       a__=50256      ,       a__=50256      ,       a__=False      ,       a__=False      ,       **a__      ,       ):
         A_     :				Any			      =	state_dim
         A_     :				Any			      =	act_dim
         A_     :				Dict			      =	hidden_size
         A_     :				Any			      =	max_ep_len
         A_     :				str			      =	action_tanh
         A_     :				Dict			      =	vocab_size
         A_     :				Dict			      =	n_positions
         A_     :				Optional[Any]			      =	n_layer
         A_     :				Optional[int]			      =	n_head
         A_     :				str			      =	n_inner
         A_     :				Optional[Any]			      =	activation_function
         A_     :				Optional[Any]			      =	resid_pdrop
         A_     :				Tuple			      =	embd_pdrop
         A_     :				Optional[int]			      =	attn_pdrop
         A_     :				int			      =	layer_norm_epsilon
         A_     :				str			      =	initializer_range
         A_     :				List[str]			      =	scale_attn_weights
         A_     :				str			      =	use_cache
         A_     :				int			      =	scale_attn_by_inverse_layer_idx
         A_     :				List[Any]			      =	reorder_and_upcast_attn

         A_     :				Optional[Any]			      =	bos_token_id
         A_     :				Optional[int]			      =	eos_token_id

         super().__init__(bos_token_id=a__      ,       eos_token_id=a__      ,       **a__       )













from typing import TYPE_CHECKING

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


_lowerCAmelCase 				=     {
    """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:
      _lowerCAmelCase 				=     ["""SpeechT5Tokenizer"""]

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

      _lowerCAmelCase 				=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






