
'''simple docstring'''







import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard






class 				lowerCAmelCase__     (						unittest.TestCase  ):






					def 			_snake_case					(     self	):


											"""simple docstring"""





											lowercase_			:  Optional[Any]									= {
											    '''model_details''': {
											        '''Organization''': '''testing''',
											        '''Model date''': '''today''',
											        '''Model version''': '''v2.1, Developed by Test Corp in 2019.''',
											        '''Architecture''': '''Convolutional Neural Network.''',
											    },
											    '''metrics''': '''BLEU and ROUGE-1''',
											    '''evaluation_data''': {
											        '''Datasets''': {'''BLEU''': '''My-great-dataset-v1''', '''ROUGE-1''': '''My-short-dataset-v2.1'''},
											        '''Preprocessing''': '''See details on https://arxiv.org/pdf/1810.03993.pdf''',
											    },
											    '''training_data''': {
											        '''Dataset''': '''English Wikipedia dump dated 2018-12-01''',
											        '''Preprocessing''': (
											            '''Using SentencePiece vocabulary of size 52k tokens. See details on'''
											            ''' https://arxiv.org/pdf/1810.03993.pdf'''
											        ),
											    },
											    '''quantitative_analyses''': {'''BLEU''': 55.1, '''ROUGE-1''': 76},
											}






					def 			_snake_case					(     self	):


											"""simple docstring"""





											lowercase_			:  Tuple									= ModelCard.from_dict(self.inputs_dict	)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,							'''model_details'''	)	)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,							'''intended_use'''	)	)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,							'''factors'''	)	)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,							'''metrics'''	)	)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,							'''evaluation_data'''	)	)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,							'''training_data'''	)	)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,							'''quantitative_analyses'''	)	)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,							'''ethical_considerations'''	)	)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,							'''caveats_and_recommendations'''	)	)






					def 			_snake_case					(     self	):


											"""simple docstring"""





											lowercase_			:  List[Any]									= ModelCard.from_dict(self.inputs_dict	)
											lowercase_			:  Any									= json.loads(modelcard.to_json_string()	)
											for key, value in self.inputs_dict.items():
																	self.assertEqual(obj[key]       ,							__SCREAMING_SNAKE_CASE	)






					def 			_snake_case					(     self	):


											"""simple docstring"""





											lowercase_			:  Dict									= ModelCard.from_dict(self.inputs_dict	)

											with tempfile.TemporaryDirectory() as tmpdirname:
																	lowercase_			:  List[str]									= os.path.join(__SCREAMING_SNAKE_CASE       ,							'''modelcard.json'''	)
																	model_card_first.to_json_file(__SCREAMING_SNAKE_CASE	)
																	lowercase_			:  List[Any]									= ModelCard.from_json_file(__SCREAMING_SNAKE_CASE	)

											self.assertEqual(model_card_second.to_dict()       ,							model_card_first.to_dict()	)






					def 			_snake_case					(     self	):


											"""simple docstring"""





											lowercase_			:  Dict									= ModelCard.from_dict(self.inputs_dict	)

											with tempfile.TemporaryDirectory() as tmpdirname:
																	model_card_first.save_pretrained(__SCREAMING_SNAKE_CASE	)
																	lowercase_			:  Dict									= ModelCard.from_pretrained(__SCREAMING_SNAKE_CASE	)

											self.assertEqual(model_card_second.to_dict()       ,							model_card_first.to_dict()	)





'''simple docstring'''







import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






class 				lowerCAmelCase__     (						lowerCamelCase_  ):






					def __init__(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE = None       ,							__SCREAMING_SNAKE_CASE = None       ,							__SCREAMING_SNAKE_CASE = None       ,							__SCREAMING_SNAKE_CASE = False       ,							__SCREAMING_SNAKE_CASE = False       ,							__SCREAMING_SNAKE_CASE = None       ,							**__SCREAMING_SNAKE_CASE       ,							):


											"""simple docstring"""





											super().__init__(
											    __SCREAMING_SNAKE_CASE       ,							split=__SCREAMING_SNAKE_CASE       ,							features=__SCREAMING_SNAKE_CASE       ,							cache_dir=__SCREAMING_SNAKE_CASE       ,							keep_in_memory=__SCREAMING_SNAKE_CASE       ,							streaming=__SCREAMING_SNAKE_CASE       ,							num_proc=__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE       ,							)
											lowercase_			:  int									= path_or_paths if isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	) else {self.split: path_or_paths}
											lowercase_			:  List[Any]									= Csv(
											    cache_dir=__SCREAMING_SNAKE_CASE       ,							data_files=__SCREAMING_SNAKE_CASE       ,							features=__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE       ,							)






					def 			_snake_case					(     self	):


											"""simple docstring"""





											if self.streaming:
																	lowercase_			:  List[str]									= self.builder.as_streaming_dataset(split=self.split	)
											# Build regular (map-style) dataset
											else:
																	lowercase_			:  Any									= None
																	lowercase_			:  Optional[Any]									= None
																	lowercase_			:  str									= None
																	lowercase_			:  Optional[int]									= None

																	self.builder.download_and_prepare(
																	    download_config=__SCREAMING_SNAKE_CASE       ,							download_mode=__SCREAMING_SNAKE_CASE       ,							verification_mode=__SCREAMING_SNAKE_CASE       ,							base_path=__SCREAMING_SNAKE_CASE       ,							num_proc=self.num_proc       ,							)
																	lowercase_			:  Tuple									= self.builder.as_dataset(
																	    split=self.split       ,							verification_mode=__SCREAMING_SNAKE_CASE       ,							in_memory=self.keep_in_memory	)
											return dataset







class 				lowerCAmelCase__     :






					def __init__(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE = None       ,							__SCREAMING_SNAKE_CASE = None       ,							**__SCREAMING_SNAKE_CASE       ,							):


											"""simple docstring"""





											if num_proc is not None and num_proc <= 0:
																	raise ValueError(F'''num_proc {num_proc} must be an integer > 0.'''	)

											lowercase_			:  List[Any]									= dataset
											lowercase_			:  Dict									= path_or_buf
											lowercase_			:  int									= batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
											lowercase_			:  int									= num_proc
											lowercase_			:  Optional[int]									= '''utf-8'''
											lowercase_			:  Optional[Any]									= to_csv_kwargs






					def 			_snake_case					(     self	):


											"""simple docstring"""





											lowercase_			:  List[str]									= self.to_csv_kwargs.pop('''path_or_buf'''       ,							__SCREAMING_SNAKE_CASE	)
											lowercase_			:  Optional[Any]									= self.to_csv_kwargs.pop('''header'''       ,							__SCREAMING_SNAKE_CASE	)
											lowercase_			:  Dict									= self.to_csv_kwargs.pop('''index'''       ,							__SCREAMING_SNAKE_CASE	)

											if isinstance(self.path_or_buf       ,							(str, bytes, os.PathLike)	):
																	with open(self.path_or_buf       ,							'''wb+'''	) as buffer:
																							lowercase_			:  Any									= self._write(file_obj=__SCREAMING_SNAKE_CASE       ,							header=__SCREAMING_SNAKE_CASE       ,							index=__SCREAMING_SNAKE_CASE       ,							**self.to_csv_kwargs	)
											else:
																	lowercase_			:  str									= self._write(file_obj=self.path_or_buf       ,							header=__SCREAMING_SNAKE_CASE       ,							index=__SCREAMING_SNAKE_CASE       ,							**self.to_csv_kwargs	)
											return written






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											lowercase_		,				lowercase_		,				lowercase_		,				lowercase_			:  int									= args

											lowercase_			:  List[str]									= query_table(
											    table=self.dataset.data       ,							key=slice(__SCREAMING_SNAKE_CASE       ,							offset + self.batch_size	)       ,							indices=self.dataset._indices       ,							)
											lowercase_			:  Dict									= batch.to_pandas().to_csv(
											    path_or_buf=__SCREAMING_SNAKE_CASE       ,							header=header if (offset == 0) else False       ,							index=__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)
											return csv_str.encode(self.encoding	)






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											lowercase_			:  Dict									= 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 CSV from Arrow format'''       ,							):
																							lowercase_			:  Any									= self._batch_csv((offset, header, index, to_csv_kwargs)	)
																							written += file_obj.write(__SCREAMING_SNAKE_CASE	)

											else:
																	lowercase_		,				lowercase_			:  Union[str, Any]									= len(self.dataset	), self.batch_size
																	with multiprocessing.Pool(self.num_proc	) as pool:
																							for csv_str in logging.tqdm(
																							    pool.imap(
																							        self._batch_csv       ,							[(offset, header, index, to_csv_kwargs) for offset in range(0       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	)]       ,							)       ,							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 CSV from Arrow format'''       ,							):
																													written += file_obj.write(__SCREAMING_SNAKE_CASE	)

											return written



