





import os
import pathlib
import tempfile
import uuid

import numpy as np

from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging


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

if is_vision_available():
		import PIL.Image
		from PIL import Image
		from PIL.Image import Image as ImageType
else:
		_lowerCamelCase					:				List[Any]  							=					object

if is_torch_available():
		import torch

if is_soundfile_availble():
		import soundfile as sf


class 						lowerCamelCase   :







							"""simple docstring"""




							def __init__(					self	:     Union[str, Any], _UpperCAmelCase	:     str						)      -> Tuple:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   :			List[Any]      =						value






							def __str__(					self	:     Tuple						)      -> Union[str, Any]:





														"""simple docstring"""
														return self.to_string()






							def 					A_	(					self	:     Tuple						)      -> str:





														"""simple docstring"""
														logger.error(
														    "This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"						)
														return self._value






							def 					A_	(					self	:     Dict						)      -> Optional[int]:





														"""simple docstring"""
														logger.error(
														    "This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"						)
														return str(self._value						)


class 						lowerCamelCase   (__lowerCamelCase							,	__lowerCamelCase			):







							"""simple docstring"""




							def 					A_	(					self	:     Union[str, Any]						)      -> List[Any]:





														"""simple docstring"""
														return self._value






							def 					A_	(					self	:     List[Any]						)      -> List[Any]:





														"""simple docstring"""
														return self._value


class 						lowerCamelCase   (__lowerCamelCase							,	__lowerCamelCase			):







							"""simple docstring"""




							def __init__(					self	:     Tuple, _UpperCAmelCase	:     str						)      -> Optional[Any]:





														"""simple docstring"""
														super().__init__(__lowercase						)

														if not is_vision_available():
																					raise ImportError("PIL must be installed in order to handle images."						)

														SCREAMING_SNAKE_CASE__   :			List[Any]      =						None
														SCREAMING_SNAKE_CASE__   :			Optional[int]      =						None
														SCREAMING_SNAKE_CASE__   :			Tuple      =						None

														if isinstance(__lowercase, __lowercase						):
																					SCREAMING_SNAKE_CASE__   :			Any      =						value
														elif isinstance(__lowercase, (str, pathlib.Path)						):
																					SCREAMING_SNAKE_CASE__   :			int      =						value
														elif isinstance(__lowercase, torch.Tensor						):
																					SCREAMING_SNAKE_CASE__   :			Dict      =						value
														else:
																					raise ValueError(F'''Unsupported type for {self.__class__.__name__}: {type(__lowercase						)}'''						)






							def 					A_	(					self	:     Any, _UpperCAmelCase	:     Optional[int]=None, _UpperCAmelCase	:     Tuple=None						)      -> int:





														"""simple docstring"""
														from IPython.display import Image, display

														display(Image(self.to_string()						)						)






							def 					A_	(					self	:     List[Any]						)      -> Optional[int]:





														"""simple docstring"""




														if self._raw is not None:
																					return self._raw

														if self._path is not None:
																					SCREAMING_SNAKE_CASE__   :			Tuple      =						Image.open(self._path						)
																					return self._raw






							def 					A_	(					self	:     Any						)      -> Optional[int]:





														"""simple docstring"""




														if self._path is not None:
																					return self._path

														if self._raw is not None:
																					SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						tempfile.mkdtemp()
																					SCREAMING_SNAKE_CASE__   :			List[Any]      =						os.path.join(__lowercase, str(uuid.uuida()						) + ".png"						)
																					self._raw.save(self._path						)

																					return self._path

														if self._tensor is not None:
																					SCREAMING_SNAKE_CASE__   :			int      =						self._tensor.cpu().detach().numpy()

																					# There is likely simpler than load into image into save
																					SCREAMING_SNAKE_CASE__   :			Any      =						Image.fromarray((array * 2_5_5).astype(np.uinta						)						)

																					SCREAMING_SNAKE_CASE__   :			str      =						tempfile.mkdtemp()
																					SCREAMING_SNAKE_CASE__   :			Tuple      =						os.path.join(__lowercase, str(uuid.uuida()						) + ".png"						)

																					img.save(self._path						)

																					return self._path


class 						lowerCamelCase   (__lowerCamelCase			):







							"""simple docstring"""




							def __init__(					self	:     str, _UpperCAmelCase	:     Optional[int], _UpperCAmelCase	:     str=1_6_0_0_0						)      -> int:





														"""simple docstring"""
														super().__init__(__lowercase						)

														if not is_soundfile_availble():
																					raise ImportError("soundfile must be installed in order to handle audio."						)

														SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						None
														SCREAMING_SNAKE_CASE__   :			Tuple      =						None

														SCREAMING_SNAKE_CASE__   :			Tuple      =						samplerate

														if isinstance(__lowercase, (str, pathlib.Path)						):
																					SCREAMING_SNAKE_CASE__   :			List[Any]      =						value
														elif isinstance(__lowercase, torch.Tensor						):
																					SCREAMING_SNAKE_CASE__   :			Union[str, Any]      =						value
														else:
																					raise ValueError(F'''Unsupported audio type: {type(__lowercase						)}'''						)






							def 					A_	(					self	:     Optional[int], _UpperCAmelCase	:     List[Any]=None, _UpperCAmelCase	:     List[Any]=None						)      -> Any:





														"""simple docstring"""
														from IPython.display import Audio, display

														display(Audio(self.to_string(), rate=self.samplerate						)						)






							def 					A_	(					self	:     List[Any]						)      -> int:





														"""simple docstring"""




														if self._tensor is not None:
																					return self._tensor

														if self._path is not None:
																					SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						sf.read(self._path						)
																					SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						torch.tensor(__lowercase						)
																					return self._tensor






							def 					A_	(					self	:     str						)      -> int:





														"""simple docstring"""




														if self._path is not None:
																					return self._path

														if self._tensor is not None:
																					SCREAMING_SNAKE_CASE__   :			Any      =						tempfile.mkdtemp()
																					SCREAMING_SNAKE_CASE__   :			List[str]      =						os.path.join(__lowercase, str(uuid.uuida()						) + ".wav"						)
																					sf.write(self._path, self._tensor, samplerate=self.samplerate						)
																					return self._path


_lowerCamelCase					:				Dict  							=					{'''text''': AgentText, '''image''': AgentImage, '''audio''': AgentAudio}
_lowerCamelCase					:				List[str]  							=					{str: AgentText}

if is_vision_available():
		_lowerCamelCase					:				Optional[Any]  							=					AgentImage




def _a					(  *SCREAMING_SNAKE_CASE__			:  List[Any] ,      **SCREAMING_SNAKE_CASE__			:  int   )       ->				Dict:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__   :			int      =						[(arg.to_raw() if isinstance(SCREAMING_SNAKE_CASE__ ,      SCREAMING_SNAKE_CASE__   ) else arg) for arg in args]
							SCREAMING_SNAKE_CASE__   :			int      =						{k: (v.to_raw() if isinstance(SCREAMING_SNAKE_CASE__ ,      SCREAMING_SNAKE_CASE__   ) else v) for k, v in kwargs.items()}
							return args, kwargs







def _a					(  SCREAMING_SNAKE_CASE__			:  int ,      SCREAMING_SNAKE_CASE__			:  str=None   )       ->				str:

							'''simple docstring'''






							if isinstance(SCREAMING_SNAKE_CASE__ ,      SCREAMING_SNAKE_CASE__   ):
														SCREAMING_SNAKE_CASE__   :			int      =						{}
														for i, (k, v) in enumerate(outputs.items()   ):
																					if output_types is not None:
																												# If the class has defined outputs, we can map directly according to the class definition
																												if output_types[i] in AGENT_TYPE_MAPPING:
																																			SCREAMING_SNAKE_CASE__   :			Optional[Any]      =						AGENT_TYPE_MAPPING[output_types[i]](SCREAMING_SNAKE_CASE__   )
																												else:
																																			SCREAMING_SNAKE_CASE__   :			List[str]      =						AgentType(SCREAMING_SNAKE_CASE__   )

																					else:
																												# If the class does not have defined output, then we map according to the type
																												for _k, _v in INSTANCE_TYPE_MAPPING.items():
																																			if isinstance(SCREAMING_SNAKE_CASE__ ,      _k   ):
																																										SCREAMING_SNAKE_CASE__   :			List[str]      =						_v(SCREAMING_SNAKE_CASE__   )
																												if k not in decoded_outputs:
																																			SCREAMING_SNAKE_CASE__   :			Tuple      =						AgentType[v]

							elif isinstance(SCREAMING_SNAKE_CASE__ ,      (list, tuple)   ):
														SCREAMING_SNAKE_CASE__   :			List[Any]      =						type(SCREAMING_SNAKE_CASE__   )()
														for i, v in enumerate(SCREAMING_SNAKE_CASE__   ):
																					if output_types is not None:
																												# If the class has defined outputs, we can map directly according to the class definition
																												if output_types[i] in AGENT_TYPE_MAPPING:
																																			decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](SCREAMING_SNAKE_CASE__   )   )
																												else:
																																			decoded_outputs.append(AgentType(SCREAMING_SNAKE_CASE__   )   )
																					else:
																												# If the class does not have defined output, then we map according to the type
																												SCREAMING_SNAKE_CASE__   :			List[Any]      =						False
																												for _k, _v in INSTANCE_TYPE_MAPPING.items():
																																			if isinstance(SCREAMING_SNAKE_CASE__ ,      _k   ):
																																										decoded_outputs.append(_v(SCREAMING_SNAKE_CASE__   )   )
																																										SCREAMING_SNAKE_CASE__   :			Tuple      =						True

																												if not found:
																																			decoded_outputs.append(AgentType(SCREAMING_SNAKE_CASE__   )   )

							else:
														if output_types[0] in AGENT_TYPE_MAPPING:
																					# If the class has defined outputs, we can map directly according to the class definition
																					SCREAMING_SNAKE_CASE__   :			Dict      =						AGENT_TYPE_MAPPING[output_types[0]](SCREAMING_SNAKE_CASE__   )

														else:
																					# If the class does not have defined output, then we map according to the type
																					for _k, _v in INSTANCE_TYPE_MAPPING.items():
																												if isinstance(SCREAMING_SNAKE_CASE__ ,      _k   ):
																																			return _v(SCREAMING_SNAKE_CASE__   )
																					return AgentType(SCREAMING_SNAKE_CASE__   )

							return decoded_outputs












import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


UpperCamelCase__              =						1_0_0_0_0_0_0_0_0_0_0_0
UpperCamelCase__              =						1_0_0_0_0

UpperCamelCase__       , UpperCamelCase__              =						os.path.split(__file__)
UpperCamelCase__              =						os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))






def 					snake_case					(							A						,   A			)							->							datasets.Dataset:




	'''simple docstring'''


	lowerCamelCase_							:		int							   =							pa.Table.from_pydict({'col': [0] * chunk_size}			)
	lowerCamelCase_							:		List[str]							   =							pa.concat_tables([table] * (num_examples // chunk_size)			)
	return datasets.Dataset(A						,   fingerprint='table_100B'			)


@dataclass
class 				__SCREAMING_SNAKE_CASE							:
					_lowerCamelCase		  = 42
					_lowerCamelCase		  = 42
					_lowerCamelCase		  = 42
					_lowerCamelCase		  = 42

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							   =							np.random.default_rng(self.seed					)
						lowerCamelCase_							:		Any							   =							rng.integers(low=self.low ,       high=self.high ,       size=self.size					).tolist()

					def __iter__(				self					):







						'''simple docstring'''




						return iter(self._sampled_values					)





					def __len__(				self					):







						'''simple docstring'''




						return self.size






@get_duration
def 					snake_case					(							A			)							->							Optional[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Union[str, Any]							   =							dataset[0]






@get_duration
def 					snake_case					(							A			)							->							Any:




	'''simple docstring'''


	lowerCamelCase_							:		List[Any]							   =							dataset[-1]






@get_duration
def 					snake_case					(							A			)							->							Optional[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Tuple							   =							dataset[range(len(A			) // 2						,   len(A			) // 2 + 1024			)]






@get_duration
def 					snake_case					(							A			)							->							str:




	'''simple docstring'''


	lowerCamelCase_							:		List[str]							   =							dataset[RandIter(0						,   len(A			)						,   1024						,   seed=42			)]






def 					snake_case					(							)							->							List[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Tuple							   =							{'num examples': SPEED_TEST_N_EXAMPLES}
	lowerCamelCase_							:		Optional[int]							   =							(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
	print('generating dataset'			)
	lowerCamelCase_							:		List[Any]							   =							generate_100B_dataset(num_examples=A						,   chunk_size=A			)
	print('Functions'			)
	for func in functions:
		print(func.__name__			)
		lowerCamelCase_							:		str							   =							func(A			)

	with open(A						,   'wb'			) as f:
		f.write(json.dumps(A			).encode('utf-8'			)			)


if __name__ == "__main__":  # useful to run the profiler
							benchmark_table_100B()
