


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


SCREAMING_SNAKE_CASE       :      List[Any]												= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE       :      Optional[Any]												= {
    "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(						_a   ):
       lowercase_ :	int     =			"""decision_transformer"""
       lowercase_ :	str     =			["""past_key_values"""]
       lowercase_ :	str     =			{
           """max_position_embeddings""": """n_positions""",
           """num_attention_heads""": """n_head""",
           """num_hidden_layers""": """n_layer""",
       }
       def __init__(					self,      lowerCamelCase=17,      lowerCamelCase=4,      lowerCamelCase=1_28,      lowerCamelCase=40_96,      lowerCamelCase=True,      lowerCamelCase=1,      lowerCamelCase=10_24,      lowerCamelCase=3,      lowerCamelCase=1,      lowerCamelCase=None,      lowerCamelCase="relu",      lowerCamelCase=0.1,      lowerCamelCase=0.1,      lowerCamelCase=0.1,      lowerCamelCase=1E-5,      lowerCamelCase=0.0_2,      lowerCamelCase=True,      lowerCamelCase=True,      lowerCamelCase=5_02_56,      lowerCamelCase=5_02_56,      lowerCamelCase=False,      lowerCamelCase=False,      **lowerCamelCase,      )       ->   List[Any]:




           """simple docstring"""

           _lowercase       :							List[str]      		=		state_dim
           _lowercase       :							Any      		=		act_dim
           _lowercase       :							List[Any]      		=		hidden_size
           _lowercase       :							int      		=		max_ep_len
           _lowercase       :							Tuple      		=		action_tanh
           _lowercase       :							Any      		=		vocab_size
           _lowercase       :							int      		=		n_positions
           _lowercase       :							Dict      		=		n_layer
           _lowercase       :							Tuple      		=		n_head
           _lowercase       :							Optional[int]      		=		n_inner
           _lowercase       :							Optional[Any]      		=		activation_function
           _lowercase       :							str      		=		resid_pdrop
           _lowercase       :							List[Any]      		=		embd_pdrop
           _lowercase       :							Optional[int]      		=		attn_pdrop
           _lowercase       :							Any      		=		layer_norm_epsilon
           _lowercase       :							List[Any]      		=		initializer_range
           _lowercase       :							int      		=		scale_attn_weights
           _lowercase       :							Optional[Any]      		=		use_cache
           _lowercase       :							Union[str, Any]      		=		scale_attn_by_inverse_layer_idx
           _lowercase       :							Tuple      		=		reorder_and_upcast_attn

           _lowercase       :							Optional[int]      		=		bos_token_id
           _lowercase       :							int      		=		eos_token_id

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










"""simple docstring"""


import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType


class 					snake_case		:


   '''simple docstring'''

   def __init__(	self :     Optional[int],       _lowerCamelCase :     Optional[int]=2,       _lowerCamelCase :     Optional[int]=3,       _lowerCamelCase :     int=64,       _lowerCamelCase :     List[str]=None  ):

     '''simple docstring'''
     __A									= np.random.default_rng(_lowerCamelCase  )
     __A									= length
     __A									= rng.normal(size=(length,)  ).astype(np.floataa  )
     __A									= a * self.x + b + rng.normal(scale=0.1,       size=(length,)  ).astype(np.floataa  )


   def __len__(	self :     str  ):

     '''simple docstring'''
     return self.length






   def __getitem__(	self :     Dict,       _lowerCamelCase :     Optional[int]  ):

     '''simple docstring'''
     return {"x": self.x[i], "y": self.y[i]}





class 					snake_case		(							torch.nn.Module						):


   '''simple docstring'''

   def __init__(	self :     Optional[Any],       _lowerCamelCase :     Tuple=0,       _lowerCamelCase :     Any=0,       _lowerCamelCase :     Optional[Any]=False  ):

     '''simple docstring'''
     super().__init__()
     __A									= torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
     __A									= torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
     __A									= True






   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str],       _lowerCamelCase :     Optional[Any]=None  ):

     '''simple docstring'''
     if self.first_batch:
       print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'  )
       __A									= False
     return x * self.a[0] + self.b[0]





class 					snake_case		(							torch.nn.Module						):


   '''simple docstring'''

   def __init__(	self :     str,       _lowerCamelCase :     Optional[Any]=0,       _lowerCamelCase :     Any=0,       _lowerCamelCase :     List[Any]=False  ):

     '''simple docstring'''
     super().__init__()
     __A									= torch.nn.Parameter(torch.tensor(_lowerCamelCase  ).float()  )
     __A									= torch.nn.Parameter(torch.tensor(_lowerCamelCase  ).float()  )
     __A									= True






   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any],       _lowerCamelCase :     List[str]=None  ):

     '''simple docstring'''
     if self.first_batch:
       print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'  )
       __A									= False
     return x * self.a + self.b





def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase = 1_6 ):




  """simple docstring"""







  from datasets import load_dataset
  from transformers import AutoTokenizer

  __A									= AutoTokenizer.from_pretrained('''bert-base-cased''' )
  __A									= {'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
  __A									= load_dataset('''csv'''		,       data_files=__UpperCamelCase )
  __A									= datasets['''train'''].unique('''label''' )

  __A									= {v: i for i, v in enumerate(__UpperCamelCase )}

  def tokenize_function(__UpperCamelCase ):
    # max_length=None => use the model max length (it's actually the default)
    __A									= tokenizer(
        examples['''sentence1''']		,       examples['''sentence2''']		,       truncation=__UpperCamelCase		,       max_length=__UpperCamelCase		,       padding='''max_length''' )
    if "label" in examples:
      __A									= [label_to_id[l] for l in examples['''label''']]
    return outputs

  # Apply the method we just defined to all the examples in all the splits of the dataset
  __A									= datasets.map(
      __UpperCamelCase		,       batched=__UpperCamelCase		,       remove_columns=['''sentence1''', '''sentence2''', '''label''']		,       )

  def collate_fn(__UpperCamelCase ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    if accelerator.distributed_type == DistributedType.TPU:
      return tokenizer.pad(__UpperCamelCase		,       padding='''max_length'''		,       max_length=1_2_8		,       return_tensors='''pt''' )
    return tokenizer.pad(__UpperCamelCase		,       padding='''longest'''		,       return_tensors='''pt''' )

  # Instantiate dataloaders.
  __A									= DataLoader(tokenized_datasets['''train''']		,       shuffle=__UpperCamelCase		,       collate_fn=__UpperCamelCase		,       batch_size=2 )
  __A									= DataLoader(tokenized_datasets['''validation''']		,       shuffle=__UpperCamelCase		,       collate_fn=__UpperCamelCase		,       batch_size=1 )

  return train_dataloader, eval_dataloader




