




from typing import TYPE_CHECKING

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


_lowerCAmelCase						:   Union[str, Any]		=   {
    "configuration_bert": ["BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BertConfig", "BertOnnxConfig"],
    "tokenization_bert": ["BasicTokenizer", "BertTokenizer", "WordpieceTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   str		=   ["BertTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   int		=   [
      "BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "BertForMaskedLM",
      "BertForMultipleChoice",
      "BertForNextSentencePrediction",
      "BertForPreTraining",
      "BertForQuestionAnswering",
      "BertForSequenceClassification",
      "BertForTokenClassification",
      "BertLayer",
      "BertLMHeadModel",
      "BertModel",
      "BertPreTrainedModel",
      "load_tf_weights_in_bert",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   Dict		=   [
      "TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFBertEmbeddings",
      "TFBertForMaskedLM",
      "TFBertForMultipleChoice",
      "TFBertForNextSentencePrediction",
      "TFBertForPreTraining",
      "TFBertForQuestionAnswering",
      "TFBertForSequenceClassification",
      "TFBertForTokenClassification",
      "TFBertLMHeadModel",
      "TFBertMainLayer",
      "TFBertModel",
      "TFBertPreTrainedModel",
  ]
try:
  if not is_tensorflow_text_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   str		=   ["TFBertTokenizer"]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   Optional[Any]		=   [
      "FlaxBertForCausalLM",
      "FlaxBertForMaskedLM",
      "FlaxBertForMultipleChoice",
      "FlaxBertForNextSentencePrediction",
      "FlaxBertForPreTraining",
      "FlaxBertForQuestionAnswering",
      "FlaxBertForSequenceClassification",
      "FlaxBertForTokenClassification",
      "FlaxBertModel",
      "FlaxBertPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
  from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_bert_fast import BertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bert import (
        BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BertForMaskedLM,
        BertForMultipleChoice,
        BertForNextSentencePrediction,
        BertForPreTraining,
        BertForQuestionAnswering,
        BertForSequenceClassification,
        BertForTokenClassification,
        BertLayer,
        BertLMHeadModel,
        BertModel,
        BertPreTrainedModel,
        load_tf_weights_in_bert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_bert import (
        TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFBertEmbeddings,
        TFBertForMaskedLM,
        TFBertForMultipleChoice,
        TFBertForNextSentencePrediction,
        TFBertForPreTraining,
        TFBertForQuestionAnswering,
        TFBertForSequenceClassification,
        TFBertForTokenClassification,
        TFBertLMHeadModel,
        TFBertMainLayer,
        TFBertModel,
        TFBertPreTrainedModel,
    )

  try:
    if not is_tensorflow_text_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_bert_tf import TFBertTokenizer

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_bert import (
        FlaxBertForCausalLM,
        FlaxBertForMaskedLM,
        FlaxBertForMultipleChoice,
        FlaxBertForNextSentencePrediction,
        FlaxBertForPreTraining,
        FlaxBertForQuestionAnswering,
        FlaxBertForSequenceClassification,
        FlaxBertForTokenClassification,
        FlaxBertModel,
        FlaxBertPreTrainedModel,
    )

else:
  import sys

  _lowerCAmelCase						:   Union[str, Any]		=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)











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


_lowerCAmelCase						:   Union[str, Any]		=   logging.get_logger(__name__)

_lowerCAmelCase						:   Tuple		=   {
    "CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
        "https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}
class 			_UpperCamelCase      (				lowerCAmelCase		):
      UpperCAmelCase_        =		"""trajectory_transformer"""
      UpperCAmelCase_        =		["""past_key_values"""]
      UpperCAmelCase_        =		{
          """hidden_size""": """n_embd""",
          """num_attention_heads""": """n_head""",
          """num_hidden_layers""": """n_layer""",
      }
      def __init__(			self   :Union[str, Any] ,      lowerCamelCase   :Optional[int]=100 ,      lowerCamelCase   :Optional[int]=5 ,      lowerCamelCase   :Optional[Any]=1 ,      lowerCamelCase   :Any=1 ,      lowerCamelCase   :int=249 ,      lowerCamelCase   :Optional[Any]=6 ,      lowerCamelCase   :Optional[Any]=17 ,      lowerCamelCase   :Optional[int]=25 ,      lowerCamelCase   :Union[str, Any]=4 ,      lowerCamelCase   :Union[str, Any]=4 ,      lowerCamelCase   :Optional[int]=128 ,      lowerCamelCase   :List[Any]=0.1 ,      lowerCamelCase   :List[str]=0.1 ,      lowerCamelCase   :Tuple=0.1 ,      lowerCamelCase   :Dict=0.00_06 ,      lowerCamelCase   :Dict=512 ,      lowerCamelCase   :Optional[Any]=0.02 ,      lowerCamelCase   :Any=1e-12 ,      lowerCamelCase   :Optional[Any]=1 ,      lowerCamelCase   :List[Any]=True ,      lowerCamelCase   :int=1 ,      lowerCamelCase   :Dict=5_0256 ,      lowerCamelCase   :Union[str, Any]=5_0256 ,      **lowerCamelCase   :int ,      )     ->			Optional[Any]:
       UpperCAmelCase__							 =						vocab_size
       UpperCAmelCase__							 =						action_weight
       UpperCAmelCase__							 =						reward_weight
       UpperCAmelCase__							 =						value_weight
       UpperCAmelCase__							 =						max_position_embeddings
       UpperCAmelCase__							 =						block_size
       UpperCAmelCase__							 =						action_dim
       UpperCAmelCase__							 =						observation_dim
       UpperCAmelCase__							 =						transition_dim
       UpperCAmelCase__							 =						learning_rate
       UpperCAmelCase__							 =						n_layer
       UpperCAmelCase__							 =						n_head
       UpperCAmelCase__							 =						n_embd
       UpperCAmelCase__							 =						embd_pdrop
       UpperCAmelCase__							 =						attn_pdrop
       UpperCAmelCase__							 =						resid_pdrop
       UpperCAmelCase__							 =						initializer_range
       UpperCAmelCase__							 =						layer_norm_eps
       UpperCAmelCase__							 =						kaiming_initializer_range
       UpperCAmelCase__							 =						use_cache
       super().__init__(pad_token_id=lowerCamelCase ,      bos_token_id=lowerCamelCase ,      eos_token_id=lowerCamelCase ,      **lowerCamelCase      )





