






"""simple docstring"""

import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


lowercase__          = datasets.utils.logging.get_logger(__name__)


@dataclass
class 		__lowerCamelCase				( datasets.BuilderConfig			):





		'''simple docstring'''






		a_	:				Optional[datasets.Features]            =							None
		a_	:				str            =							"utf-8"
		a_	:				Optional[str]            =							None
		a_	:				Optional[str]            =							None
		a_	:				bool            =							True  # deprecated
		a_	:				Optional[int]            =							None  # deprecated
		a_	:				int            =							10 << 20  # 10MB
		a_	:				Optional[bool]            =							None







class 		__lowerCamelCase				( datasets.ArrowBasedBuilder			):





		'''simple docstring'''






		a_	:				int            =							JsonConfig





		def    lowerCamelCase       (   self							:	List[Any]	):
				if self.config.block_size is not None:
						logger.warning("The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"	)
						lowerCAmelCase_   :							Union[str, Any]									=			self.config.block_size
				if self.config.use_threads is not True:
						logger.warning(
						    "The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore."	)
				if self.config.newlines_in_values is not None:
						raise ValueError("The JSON loader parameter `newlines_in_values` is no longer supported"	)
				return datasets.DatasetInfo(features=self.config.features	)





		def    lowerCamelCase       (   self							:	int						,			a_							:	Tuple	):
				if not self.config.data_files:
						raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}'''	)
				lowerCAmelCase_   :							List[Any]									=			dl_manager.download_and_extract(self.config.data_files	)
				if isinstance(__lowerCamelCase						,			(str, list, tuple)	):
						lowerCAmelCase_   :							Optional[int]									=			data_files
						if isinstance(__lowerCamelCase						,			__lowerCamelCase	):
								lowerCAmelCase_   :							Optional[int]									=			[files]
						lowerCAmelCase_   :							Optional[Any]									=			[dl_manager.iter_files(__lowerCamelCase	) for file in files]
						return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,			gen_kwargs={"files": files}	)]
				lowerCAmelCase_   :							Tuple									=			[]
				for split_name, files in data_files.items():
						if isinstance(__lowerCamelCase						,			__lowerCamelCase	):
								lowerCAmelCase_   :							Dict									=			[files]
						lowerCAmelCase_   :							List[Any]									=			[dl_manager.iter_files(__lowerCamelCase	) for file in files]
						splits.append(datasets.SplitGenerator(name=__lowerCamelCase						,			gen_kwargs={"files": files}	)	)
				return splits





		def    lowerCamelCase       (   self							:	List[str]						,			a_							:	pa.Table	):
				if self.config.features is not None:
						# adding missing columns
						for column_name in set(self.config.features	) - set(pa_table.column_names	):
								lowerCAmelCase_   :							List[str]									=			self.config.features.arrow_schema.field(__lowerCamelCase	).type
								lowerCAmelCase_   :							Union[str, Any]									=			pa_table.append_column(__lowerCamelCase						,			pa.array([None] * len(__lowerCamelCase	)						,			type=__lowerCamelCase	)	)
						# more expensive cast to support nested structures with keys in a different order
						# allows str <-> int/float or str to Audio for example
						lowerCAmelCase_   :							Any									=			table_cast(__lowerCamelCase						,			self.config.features.arrow_schema	)
				return pa_table





		def    lowerCamelCase       (   self							:	Tuple						,			a_							:	int	):
				for file_idx, file in enumerate(itertools.chain.from_iterable(__lowerCamelCase	)	):
						# If the file is one json object and if we need to look at the list of items in one specific field
						if self.config.field is not None:
								with open(__lowerCamelCase						,			encoding=self.config.encoding						,			errors=self.config.encoding_errors	) as f:
										lowerCAmelCase_   :							Dict									=			json.load(__lowerCamelCase	)

								# We keep only the field we are interested in
								lowerCAmelCase_   :							int									=			dataset[self.config.field]

								# We accept two format: a list of dicts or a dict of lists
								if isinstance(__lowerCamelCase						,			(list, tuple)	):
										lowerCAmelCase_   :							Optional[int]									=			set().union(*[row.keys() for row in dataset]	)
										lowerCAmelCase_   :							Any									=			{col: [row.get(__lowerCamelCase	) for row in dataset] for col in keys}
								else:
										lowerCAmelCase_   :							Optional[int]									=			dataset
								lowerCAmelCase_   :							Optional[Any]									=			pa.Table.from_pydict(__lowerCamelCase	)
								yield file_idx, self._cast_table(__lowerCamelCase	)

						# If the file has one json object per line
						else:
								with open(__lowerCamelCase						,			"rb"	) as f:
										lowerCAmelCase_   :							List[str]									=			0
										# Use block_size equal to the chunk size divided by 32 to leverage multithreading
										# Set a default minimum value of 16kB if the chunk size is really small
										lowerCAmelCase_   :							str									=			max(self.config.chunksize // 32						,			16 << 10	)
										lowerCAmelCase_   :							Optional[int]									=			(
										    self.config.encoding_errors if self.config.encoding_errors is not None else """strict"""
										)
										while True:
												lowerCAmelCase_   :							int									=			f.read(self.config.chunksize	)
												if not batch:
														break
												# Finish current line
												try:
														batch += f.readline()
												except (AttributeError, io.UnsupportedOperation):
														batch += readline(__lowerCamelCase	)
												# PyArrow only accepts utf-8 encoded bytes
												if self.config.encoding != "utf-8":
														lowerCAmelCase_   :							List[str]									=			batch.decode(self.config.encoding						,			errors=__lowerCamelCase	).encode("utf-8"	)
												try:
														while True:
																try:
																		lowerCAmelCase_   :							int									=			paj.read_json(
																		    io.BytesIO(__lowerCamelCase	)						,			read_options=paj.ReadOptions(block_size=__lowerCamelCase	)	)
																		break
																except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
																		if (
																		    isinstance(__lowerCamelCase						,			pa.ArrowInvalid	)
																		    and "straddling" not in str(__lowerCamelCase	)
																		    or block_size > len(__lowerCamelCase	)
																		):
																				raise
																		else:
																				# Increase the block size in case it was too small.
																				# The block size will be reset for the next file.
																				logger.debug(
																				    f'''Batch of {len(__lowerCamelCase	)} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.'''	)
																				block_size *= 2
												except pa.ArrowInvalid as e:
														try:
																with open(
																    __lowerCamelCase						,			encoding=self.config.encoding						,			errors=self.config.encoding_errors	) as f:
																		lowerCAmelCase_   :							Optional[Any]									=			json.load(__lowerCamelCase	)
														except json.JSONDecodeError:
																logger.error(f'''Failed to read file \'{file}\' with error {type(__lowerCamelCase	)}: {e}'''	)
																raise e
														# If possible, parse the file as a list of json objects and exit the loop
														if isinstance(__lowerCamelCase						,			__lowerCamelCase	):  # list is the only sequence type supported in JSON
																try:
																		lowerCAmelCase_   :							Tuple									=			set().union(*[row.keys() for row in dataset]	)
																		lowerCAmelCase_   :							Dict									=			{col: [row.get(__lowerCamelCase	) for row in dataset] for col in keys}
																		lowerCAmelCase_   :							Any									=			pa.Table.from_pydict(__lowerCamelCase	)
																except (pa.ArrowInvalid, AttributeError) as e:
																		logger.error(f'''Failed to read file \'{file}\' with error {type(__lowerCamelCase	)}: {e}'''	)
																		raise ValueError(f'''Not able to read records in the JSON file at {file}.'''	) from None
																yield file_idx, self._cast_table(__lowerCamelCase	)
																break
														else:
																logger.error(f'''Failed to read file \'{file}\' with error {type(__lowerCamelCase	)}: {e}'''	)
																raise ValueError(
																    f'''Not able to read records in the JSON file at {file}. '''
																    f'''You should probably indicate the field of the JSON file containing your records. '''
																    f'''This JSON file contain the following fields: {str(list(dataset.keys()	)	)}. '''
																    f'''Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. '''	) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
												yield (file_idx, batch_idx), self._cast_table(__lowerCamelCase	)
												batch_idx += 1








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


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

UpperCAmelCase_ :						int      =      {
    '''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     _SCREAMING_SNAKE_CASE       (			_a      ):
	snake_case__							:     Any     	=  """trajectory_transformer"""
	snake_case__							:     Optional[Any]     	=  ["""past_key_values"""]
	snake_case__							:     Tuple     	=  {
	    """hidden_size""": """n_embd""",
	    """num_attention_heads""": """n_head""",
	    """num_hidden_layers""": """n_layer""",
	}





	def __init__(		self				:					Union[str, Any] ,  __lowerCamelCase				:					Any=100 ,  __lowerCamelCase				:					str=5 ,  __lowerCamelCase				:					str=1 ,  __lowerCamelCase				:					Optional[int]=1 ,  __lowerCamelCase				:					int=249 ,  __lowerCamelCase				:					str=6 ,  __lowerCamelCase				:					Dict=17 ,  __lowerCamelCase				:					Optional[Any]=25 ,  __lowerCamelCase				:					List[str]=4 ,  __lowerCamelCase				:					str=4 ,  __lowerCamelCase				:					Tuple=128 ,  __lowerCamelCase				:					Dict=0.1 ,  __lowerCamelCase				:					str=0.1 ,  __lowerCamelCase				:					Any=0.1 ,  __lowerCamelCase				:					int=0.0006 ,  __lowerCamelCase				:					List[str]=512 ,  __lowerCamelCase				:					str=0.02 ,  __lowerCamelCase				:					Any=1E-12 ,  __lowerCamelCase				:					int=1 ,  __lowerCamelCase				:					Optional[Any]=True ,  __lowerCamelCase				:					Tuple=1 ,  __lowerCamelCase				:					int=50_256 ,  __lowerCamelCase				:					Union[str, Any]=50_256 ,  **__lowerCamelCase				:					Dict ,  ):
		UpperCamelCase     :Dict          =	vocab_size
		UpperCamelCase     :int          =	action_weight
		UpperCamelCase     :Tuple          =	reward_weight
		UpperCamelCase     :str          =	value_weight
		UpperCamelCase     :Tuple          =	max_position_embeddings
		UpperCamelCase     :Tuple          =	block_size
		UpperCamelCase     :Optional[int]          =	action_dim
		UpperCamelCase     :int          =	observation_dim
		UpperCamelCase     :List[str]          =	transition_dim
		UpperCamelCase     :List[Any]          =	learning_rate
		UpperCamelCase     :Optional[Any]          =	n_layer
		UpperCamelCase     :Any          =	n_head
		UpperCamelCase     :List[str]          =	n_embd
		UpperCamelCase     :Any          =	embd_pdrop
		UpperCamelCase     :str          =	attn_pdrop
		UpperCamelCase     :Union[str, Any]          =	resid_pdrop
		UpperCamelCase     :Optional[Any]          =	initializer_range
		UpperCamelCase     :List[Any]          =	layer_norm_eps
		UpperCamelCase     :Optional[int]          =	kaiming_initializer_range
		UpperCamelCase     :Tuple          =	use_cache
		super().__init__(pad_token_id=__lowerCamelCase ,  bos_token_id=__lowerCamelCase ,  eos_token_id=__lowerCamelCase ,  **__lowerCamelCase	)
