
'''simple docstring'''

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


UpperCAmelCase__      :       Dict 		=						logging.get_logger(__name__)

if is_vision_available():
		import PIL.Image
		from PIL import Image
		from PIL.Image import Image as ImageType
else:
		UpperCAmelCase__      :       Dict 		=						object

if is_torch_available():
		import torch

if is_soundfile_availble():
		import soundfile as sf






class       A   :





	def __init__(    self	:				List[Any]					,							__magic_name__	:				Union[str, Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							value





	def __str__(    self	:				Optional[int]							):


				"""simple docstring"""

				return self.to_string()





	def 							__SCREAMING_SNAKE_CASE     (    self	:				Union[str, Any]							):


				"""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 							__SCREAMING_SNAKE_CASE     (    self	:				Optional[Any]							):


				"""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       A   (		lowerCAmelCase__     ,					lowerCAmelCase__			):





	def 							__SCREAMING_SNAKE_CASE     (    self	:				List[str]							):


				"""simple docstring"""

				return self._value







	def 							__SCREAMING_SNAKE_CASE     (    self	:				int							):


				"""simple docstring"""

				return self._value






class       A   (		lowerCAmelCase__     ,					lowerCAmelCase__			):





	def __init__(    self	:				Tuple					,							__magic_name__	:				int							):


				"""simple docstring"""

				super().__init__(_lowerCamelCase							)

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

				lowerCAmelCase__									=							None
				lowerCAmelCase__									=							None
				lowerCAmelCase__									=							None

				if isinstance(_lowerCamelCase					,							_lowerCamelCase							):
							lowerCAmelCase__									=							value
				elif isinstance(_lowerCamelCase					,							(str, pathlib.Path)							):
							lowerCAmelCase__									=							value
				elif isinstance(_lowerCamelCase					,							torch.Tensor							):
							lowerCAmelCase__									=							value
				else:
							raise ValueError(f"""Unsupported type for {self.__class__.__name__}: {type(_lowerCamelCase							)}"""							)





	def 							__SCREAMING_SNAKE_CASE     (    self	:				Optional[int]					,							__magic_name__	:				int=None					,							__magic_name__	:				Union[str, Any]=None							):


				"""simple docstring"""

				from IPython.display import Image, display

				display(Image(self.to_string()							)							)





	def 							__SCREAMING_SNAKE_CASE     (    self	:				Union[str, Any]							):


				"""simple docstring"""

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

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







	def 							__SCREAMING_SNAKE_CASE     (    self	:				Dict							):


				"""simple docstring"""

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

				if self._raw is not None:
							lowerCAmelCase__									=							tempfile.mkdtemp()
							lowerCAmelCase__									=							os.path.join(_lowerCamelCase					,							str(uuid.uuida()							) + ".png"							)
							self._raw.save(self._path							)

							return self._path

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

							# There is likely simpler than load into image into save
							lowerCAmelCase__									=							Image.fromarray((array * 255).astype(np.uinta							)							)

							lowerCAmelCase__									=							tempfile.mkdtemp()
							lowerCAmelCase__									=							os.path.join(_lowerCamelCase					,							str(uuid.uuida()							) + ".png"							)

							img.save(self._path							)

							return self._path






class       A   (		lowerCAmelCase__			):





	def __init__(    self	:				Optional[Any]					,							__magic_name__	:				Dict					,							__magic_name__	:				Tuple=16000							):


				"""simple docstring"""

				super().__init__(_lowerCamelCase							)

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

				lowerCAmelCase__									=							None
				lowerCAmelCase__									=							None

				lowerCAmelCase__									=							samplerate

				if isinstance(_lowerCamelCase					,							(str, pathlib.Path)							):
							lowerCAmelCase__									=							value
				elif isinstance(_lowerCamelCase					,							torch.Tensor							):
							lowerCAmelCase__									=							value
				else:
							raise ValueError(f"""Unsupported audio type: {type(_lowerCamelCase							)}"""							)





	def 							__SCREAMING_SNAKE_CASE     (    self	:				Optional[int]					,							__magic_name__	:				int=None					,							__magic_name__	:				List[Any]=None							):


				"""simple docstring"""

				from IPython.display import Audio, display

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





	def 							__SCREAMING_SNAKE_CASE     (    self	:				Tuple							):


				"""simple docstring"""

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

				if self._path is not None:
							lowerCAmelCase__		,lowerCAmelCase__									=							sf.read(self._path							)
							lowerCAmelCase__									=							torch.tensor(_lowerCamelCase							)
							return self._tensor







	def 							__SCREAMING_SNAKE_CASE     (    self	:				Dict							):


				"""simple docstring"""

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

				if self._tensor is not None:
							lowerCAmelCase__									=							tempfile.mkdtemp()
							lowerCAmelCase__									=							os.path.join(_lowerCamelCase					,							str(uuid.uuida()							) + ".wav"							)
							sf.write(self._path					,							self._tensor					,							samplerate=self.samplerate							)
							return self._path

UpperCAmelCase__      :       Any 		=						{"text": AgentText, "image": AgentImage, "audio": AgentAudio}
UpperCAmelCase__      :       Tuple 		=						{str: AgentText}

if is_vision_available():
		UpperCAmelCase__      :       Union[str, Any] 		=						AgentImage







def      A   (					*UpperCamelCase_    :   str  ,		**UpperCamelCase_    :   List[Any]					)    ->				Tuple:







			'''simple docstring'''



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







def      A   (					UpperCamelCase_    :   str  ,		UpperCamelCase_    :   Tuple=None					)    ->				str:







			'''simple docstring'''



			if isinstance(lowerCamelCase_  ,		lowerCamelCase_					):
						lowerCAmelCase__									=							{}
						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:
															lowerCAmelCase__									=							AGENT_TYPE_MAPPING[output_types[i]](lowerCamelCase_					)
												else:
															lowerCAmelCase__									=							AgentType(lowerCamelCase_					)

									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(lowerCamelCase_  ,		_k					):
																		lowerCAmelCase__									=							_v(lowerCamelCase_					)
												if k not in decoded_outputs:
															lowerCAmelCase__									=							AgentType[v]

			elif isinstance(lowerCamelCase_  ,		(list, tuple)					):
						lowerCAmelCase__									=							type(lowerCamelCase_					)()
						for i, v in enumerate(lowerCamelCase_					):
									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]](lowerCamelCase_					)					)
												else:
															decoded_outputs.append(AgentType(lowerCamelCase_					)					)
									else:
												# If the class does not have defined output, then we map according to the type
												lowerCAmelCase__									=							False
												for _k, _v in INSTANCE_TYPE_MAPPING.items():
															if isinstance(lowerCamelCase_  ,		_k					):
																		decoded_outputs.append(_v(lowerCamelCase_					)					)
																		lowerCAmelCase__									=							True

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

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

						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(lowerCamelCase_  ,		_k					):
															return _v(lowerCamelCase_					)
									return AgentType(lowerCamelCase_					)

			return decoded_outputs












'''simple docstring'''



import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_SCREAMING_SNAKE_CASE    =			'''\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
'''

# TODO: Add description of the dataset here
# You can copy an official description
_SCREAMING_SNAKE_CASE    =			'''\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
'''

# TODO: Add a link to an official homepage for the dataset here
_SCREAMING_SNAKE_CASE    =			''''''

# TODO: Add the licence for the dataset here if you can find it
_SCREAMING_SNAKE_CASE    =			''''''

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_SCREAMING_SNAKE_CASE    =			{
    '''first_domain''': '''https://huggingface.co/great-new-dataset-first_domain.zip''',
    '''second_domain''': '''https://huggingface.co/great-new-dataset-second_domain.zip''',
}






class 							__lowercase				(					datasets.GeneratorBasedBuilder  ):







	'''simple docstring'''

	a			: Tuple      =				datasets.Version("1.1.0"  )

	# This is an example of a dataset with multiple configurations.
	# If you don't want/need to define several sub-sets in your dataset,
	# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

	# If you need to make complex sub-parts in the datasets with configurable options
	# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
	# BUILDER_CONFIG_CLASS = MyBuilderConfig

	# You will be able to load one or the other configurations in the following list with
	# data = datasets.load_dataset('my_dataset', 'first_domain')
	# data = datasets.load_dataset('my_dataset', 'second_domain')
	a			: List[Any]      =				[
	    datasets.BuilderConfig(name="first_domain"       ,					version=lowerCAmelCase__       ,					description="This part of my dataset covers a first domain"  ),
	    datasets.BuilderConfig(name="second_domain"       ,					version=lowerCAmelCase__       ,					description="This part of my dataset covers a second domain"  ),
	]

	a			: Dict      =				"first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.


	def   _UpperCAmelCase   (self       )							->							List[str]:







				'''simple docstring'''





				if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
							__lowercase   =							datasets.Features(
							    {
							        '''sentence''': datasets.Value('''string'''       ),
							        '''option1''': datasets.Value('''string'''       ),
							        '''answer''': datasets.Value('''string'''       )
							        # These are the features of your dataset like images, labels ...
							    }       )
				else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
							__lowercase   =							datasets.Features(
							    {
							        '''sentence''': datasets.Value('''string'''       ),
							        '''option2''': datasets.Value('''string'''       ),
							        '''second_domain_answer''': datasets.Value('''string'''       )
							        # These are the features of your dataset like images, labels ...
							    }       )
				return datasets.DatasetInfo(
				    # This is the description that will appear on the datasets page.
				    description=_DESCRIPTION   ,features=_lowerCamelCase   ,homepage=_HOMEPAGE   ,license=_LICENSE   ,citation=_CITATION   ,)


	def   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							Dict:







				'''simple docstring'''





				__lowercase   =							_URLS[self.config.name]
				__lowercase   =							dl_manager.download_and_extract(_lowerCamelCase       )
				return [
				    datasets.SplitGenerator(
				        name=datasets.Split.TRAIN   ,gen_kwargs={
				            '''filepath''': os.path.join(_lowerCamelCase   ,'''train.jsonl'''       ),
				            '''split''': '''train''',
				        }   ,),
				    datasets.SplitGenerator(
				        name=datasets.Split.VALIDATION   ,gen_kwargs={
				            '''filepath''': os.path.join(_lowerCamelCase   ,'''dev.jsonl'''       ),
				            '''split''': '''dev''',
				        }   ,),
				    datasets.SplitGenerator(
				        name=datasets.Split.TEST   ,gen_kwargs={
				            '''filepath''': os.path.join(_lowerCamelCase   ,'''test.jsonl'''       ),
				            '''split''': '''test'''
				        }   ,),
				]


	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase       )							->							str:







				'''simple docstring'''





				with open(_lowerCamelCase   ,encoding='''utf-8'''       ) as f:
							for key, row in enumerate(_lowerCamelCase       ):
										__lowercase   =							json.loads(_lowerCamelCase       )
										if self.config.name == "first_domain":
													# Yields examples as (key, example) tuples
													yield key, {
													    "sentence": data["sentence"],
													    "option1": data["option1"],
													    "answer": "" if split == "test" else data["answer"],
													}
										else:
													yield key, {
													    "sentence": data["sentence"],
													    "option2": data["option2"],
													    "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
													}





