





'''simple docstring'''




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


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

_A:					List[Any]												=				{
    """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							(		UpperCAmelCase_							):
    _A :			str						   =       "trajectory_transformer"
    _A :			int						   =       ["past_key_values"]
    _A :			Optional[int]						   =       {
        "hidden_size": "n_embd",
        "num_attention_heads": "n_head",
        "num_hidden_layers": "n_layer",
    }



    def __init__(  self     ,			__A=100     ,			__A=5     ,			__A=1     ,			__A=1     ,			__A=249     ,			__A=6     ,			__A=17     ,			__A=25     ,			__A=4     ,			__A=4     ,			__A=128     ,			__A=0.1     ,			__A=0.1     ,			__A=0.1     ,			__A=0.0_0_0_6     ,			__A=512     ,			__A=0.0_2     ,			__A=1E-12     ,			__A=1     ,			__A=True     ,			__A=1     ,			__A=50_256     ,			__A=50_256     ,			**__A     ,			):
     __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=UpperCamelCase__     ,			bos_token_id=UpperCamelCase__     ,			eos_token_id=UpperCamelCase__     ,			**UpperCamelCase__					)












'''simple docstring'''




import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
    import sqlitea

    import sqlalchemy






class UpperCAmelCase							(		UpperCAmelCase_							):



    def __init__(  self     ,			__A     ,			__A     ,			__A = None     ,			__A = None     ,			__A = False     ,			**__A     ,			):
     super().__init__(features=__A     ,			cache_dir=__A     ,			keep_in_memory=__A     ,			**__A					)
     __UpperCAmelCase					=   Sql(
         cache_dir=__A     ,			features=__A     ,			sql=__A     ,			con=__A     ,			**__A     ,			)




    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   None
     __UpperCAmelCase					=   None
     __UpperCAmelCase					=   None
     __UpperCAmelCase					=   None

     self.builder.download_and_prepare(
         download_config=__A     ,			download_mode=__A     ,			verification_mode=__A     ,			base_path=__A     ,			)

     # Build dataset for splits
     __UpperCAmelCase					=   self.builder.as_dataset(
         split='train'     ,			verification_mode=__A     ,			in_memory=self.keep_in_memory					)
     return dataset






class UpperCAmelCase							:



    def __init__(  self     ,			__A     ,			__A     ,			__A     ,			__A = None     ,			__A = None     ,			**__A     ,			):
     if num_proc is not None and num_proc <= 0:
      raise ValueError(f'num_proc {num_proc} must be an integer > 0.'					)

     __UpperCAmelCase					=   dataset
     __UpperCAmelCase					=   name
     __UpperCAmelCase					=   con
     __UpperCAmelCase					=   batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
     __UpperCAmelCase					=   num_proc
     __UpperCAmelCase					=   to_sql_kwargs



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.to_sql_kwargs.pop('sql'     ,			__A					)
     __UpperCAmelCase					=   self.to_sql_kwargs.pop('con'     ,			__A					)
     __UpperCAmelCase					=   self.to_sql_kwargs.pop('index'     ,			__A					)

     __UpperCAmelCase					=   self._write(index=__A     ,			**self.to_sql_kwargs					)
     return written



    def 					__lowerCamelCase						(  self     ,			__A					):
     __UpperCAmelCase					,    __UpperCAmelCase					,    __UpperCAmelCase					=   args
     __UpperCAmelCase					=   {**to_sql_kwargs, 'if_exists': 'append'} if offset > 0 else to_sql_kwargs
     __UpperCAmelCase					=   query_table(
         table=self.dataset.data     ,			key=slice(__A     ,			offset + self.batch_size					)     ,			indices=self.dataset._indices     ,			)
     __UpperCAmelCase					=   batch.to_pandas()
     __UpperCAmelCase					=   df.to_sql(self.name     ,			self.con     ,			index=__A     ,			**__A					)
     return num_rows or len(__A					)




    def 					__lowerCamelCase						(  self     ,			__A     ,			**__A					):
     __UpperCAmelCase					=   0

     if self.num_proc is None or self.num_proc == 1:
      for offset in logging.tqdm(
          range(0     ,			len(self.dataset					)     ,			self.batch_size					)     ,			unit='ba'     ,			disable=not logging.is_progress_bar_enabled()     ,			desc='Creating SQL from Arrow format'     ,			):
       written += self._batch_sql((offset, index, to_sql_kwargs)					)
     else:
      __UpperCAmelCase					,    __UpperCAmelCase					=   len(self.dataset					), self.batch_size
      with multiprocessing.Pool(self.num_proc					) as pool:
       for num_rows in logging.tqdm(
           pool.imap(
               self._batch_sql     ,			[(offset, index, to_sql_kwargs) for offset in range(0     ,			__A     ,			__A					)]     ,			)     ,			total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size     ,			unit='ba'     ,			disable=not logging.is_progress_bar_enabled()     ,			desc='Creating SQL from Arrow format'     ,			):
        written += num_rows

     return written





