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


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

A     :  Tuple					     =       {
    "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   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						'''decision_transformer'''
  lowerCamelCase__     =						['''past_key_values''']
  lowerCamelCase__     =						{
      '''max_position_embeddings''': '''n_positions''',
      '''num_attention_heads''': '''n_head''',
      '''num_hidden_layers''': '''n_layer''',
  }







  def __init__(							self :       Dict  ,     __magic_name__ :       Union[str, Any]=17  ,     __magic_name__ :       int=4  ,     __magic_name__ :       List[str]=128  ,     __magic_name__ :       List[Any]=4_096  ,     __magic_name__ :       Dict=True  ,     __magic_name__ :       Any=1  ,     __magic_name__ :       Optional[int]=1_024  ,     __magic_name__ :       Optional[Any]=3  ,     __magic_name__ :       Optional[Any]=1  ,     __magic_name__ :       List[str]=None  ,     __magic_name__ :       Any="relu"  ,     __magic_name__ :       Tuple=0.1  ,     __magic_name__ :       Union[str, Any]=0.1  ,     __magic_name__ :       Any=0.1  ,     __magic_name__ :       List[Any]=1e-5  ,     __magic_name__ :       Union[str, Any]=0.02  ,     __magic_name__ :       int=True  ,     __magic_name__ :       Optional[int]=True  ,     __magic_name__ :       List[Any]=50_256  ,     __magic_name__ :       List[Any]=50_256  ,     __magic_name__ :       Tuple=False  ,     __magic_name__ :       Any=False  ,     **__magic_name__ :       Union[str, Any]  ,     )  ->	int:
        SCREAMING_SNAKE_CASE_          =  state_dim
        SCREAMING_SNAKE_CASE_          =  act_dim
        SCREAMING_SNAKE_CASE_          =  hidden_size
        SCREAMING_SNAKE_CASE_          =  max_ep_len
        SCREAMING_SNAKE_CASE_          =  action_tanh
        SCREAMING_SNAKE_CASE_          =  vocab_size
        SCREAMING_SNAKE_CASE_          =  n_positions
        SCREAMING_SNAKE_CASE_          =  n_layer
        SCREAMING_SNAKE_CASE_          =  n_head
        SCREAMING_SNAKE_CASE_          =  n_inner
        SCREAMING_SNAKE_CASE_          =  activation_function
        SCREAMING_SNAKE_CASE_          =  resid_pdrop
        SCREAMING_SNAKE_CASE_          =  embd_pdrop
        SCREAMING_SNAKE_CASE_          =  attn_pdrop
        SCREAMING_SNAKE_CASE_          =  layer_norm_epsilon
        SCREAMING_SNAKE_CASE_          =  initializer_range
        SCREAMING_SNAKE_CASE_          =  scale_attn_weights
        SCREAMING_SNAKE_CASE_          =  use_cache
        SCREAMING_SNAKE_CASE_          =  scale_attn_by_inverse_layer_idx
        SCREAMING_SNAKE_CASE_          =  reorder_and_upcast_attn

        SCREAMING_SNAKE_CASE_          =  bos_token_id
        SCREAMING_SNAKE_CASE_          =  eos_token_id

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



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
      import torch

      from transformers import XLMRobertaModel


@require_sentencepiece
@require_tokenizers
@require_torch
class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  @slow
  def    __A     (							self :       Dict   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  XLMRobertaModel.from_pretrained("xlm-roberta-base"   )
        SCREAMING_SNAKE_CASE_          =  torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]   )
        # The dog is cute and lives in the garden house

        SCREAMING_SNAKE_CASE_          =  torch.Size((1, 12, 768)   )  # batch_size, sequence_length, embedding_vector_dim
        SCREAMING_SNAKE_CASE_          =  torch.tensor(
            [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]   )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )["last_hidden_state"].detach()
        self.assertEqual(output.shape  ,     __magic_name__   )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]  ,     __magic_name__  ,     atol=1e-3   )   )







  @slow
  def    __A     (							self :       Tuple   )  ->	List[str]:
        SCREAMING_SNAKE_CASE_          =  XLMRobertaModel.from_pretrained("xlm-roberta-large"   )
        SCREAMING_SNAKE_CASE_          =  torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]   )
        # The dog is cute and lives in the garden house

        SCREAMING_SNAKE_CASE_          =  torch.Size((1, 12, 1_024)   )  # batch_size, sequence_length, embedding_vector_dim
        SCREAMING_SNAKE_CASE_          =  torch.tensor(
            [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]   )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )["last_hidden_state"].detach()
        self.assertEqual(output.shape  ,     __magic_name__   )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]  ,     __magic_name__  ,     atol=1e-3   )   )


