





'''simple docstring'''







import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
snake_case_  :							str            =	"stas/tiny-wmt19-en-ru"


class        __a   (lowerCamelCase							,		unittest.TestCase  ):
							__a       : Any          =							FSMTTokenizer
							__a       : List[Any]          =							False

							def      UpperCAmelCase__						(				self				:  str )       ->							Dict:







													"""simple docstring"""
													super().setUp()

													# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
													UpperCAmelCase_      :   Optional[int]      =	[
													    '''l''',
													    '''o''',
													    '''w''',
													    '''e''',
													    '''r''',
													    '''s''',
													    '''t''',
													    '''i''',
													    '''d''',
													    '''n''',
													    '''w</w>''',
													    '''r</w>''',
													    '''t</w>''',
													    '''lo''',
													    '''low''',
													    '''er</w>''',
													    '''low</w>''',
													    '''lowest</w>''',
													    '''newer</w>''',
													    '''wider</w>''',
													    '''<unk>''',
													]
													UpperCAmelCase_      :   Optional[int]      =	dict(zip(__magic_name__							,  range(len(__magic_name__ ) ) ) )
													UpperCAmelCase_      :   Any      =	['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

													UpperCAmelCase_      :   str      =	['''en''', '''ru''']
													UpperCAmelCase_      :   Tuple      =	{
													    '''langs''': self.langs,
													    '''src_vocab_size''': 10,
													    '''tgt_vocab_size''': 20,
													}

													UpperCAmelCase_      :   Tuple      =	os.path.join(self.tmpdirname							,  VOCAB_FILES_NAMES['''src_vocab_file'''] )
													UpperCAmelCase_      :   Any      =	os.path.join(self.tmpdirname							,  VOCAB_FILES_NAMES['''tgt_vocab_file'''] )
													UpperCAmelCase_      :   Optional[Any]      =	os.path.join(self.tmpdirname							,  '''tokenizer_config.json''' )
													UpperCAmelCase_      :   Union[str, Any]      =	os.path.join(self.tmpdirname							,  VOCAB_FILES_NAMES['''merges_file'''] )
													with open(self.src_vocab_file							,  '''w''' ) as fp:
																			fp.write(json.dumps(__magic_name__ ) )
													with open(self.tgt_vocab_file							,  '''w''' ) as fp:
																			fp.write(json.dumps(__magic_name__ ) )
													with open(self.merges_file							,  '''w''' ) as fp:
																			fp.write('''\n'''.join(__magic_name__ ) )
													with open(__magic_name__							,  '''w''' ) as fp:
																			fp.write(json.dumps(__magic_name__ ) )

							@cached_property
							def      UpperCAmelCase__						(				self				:  Union[str, Any] )       ->							int:







													"""simple docstring"""
													return FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en''' )

							@cached_property
							def      UpperCAmelCase__						(				self				:  str )       ->							str:







													"""simple docstring"""
													return FSMTTokenizer.from_pretrained('''facebook/wmt19-en-ru''' )

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







													"""simple docstring"""
													UpperCAmelCase_      :   Optional[Any]      =	FSMTTokenizer.from_pretrained(__magic_name__ )
													self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang]							,  ['''en''', '''ru'''] )
													self.assertEqual(tokenizer.src_vocab_size							,  21 )
													self.assertEqual(tokenizer.tgt_vocab_size							,  21 )

							def      UpperCAmelCase__						(				self				:  Dict )       ->							Any:







													"""simple docstring"""
													UpperCAmelCase_      :   List[Any]      =	FSMTTokenizer(self.langs							,  self.src_vocab_file							,  self.tgt_vocab_file							,  self.merges_file )

													UpperCAmelCase_      :   Tuple      =	'''lower'''
													UpperCAmelCase_      :   str      =	['''low''', '''er</w>''']
													UpperCAmelCase_      :   str      =	tokenizer.tokenize(__magic_name__ )
													self.assertListEqual(__magic_name__							,  __magic_name__ )

													UpperCAmelCase_      :   List[Any]      =	tokens + ['''<unk>''']
													UpperCAmelCase_      :   int      =	[14, 15, 20]
													self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__ )							,  __magic_name__ )

							@slow
							def      UpperCAmelCase__						(				self				:  Tuple )       ->							Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_      :   Any      =	self.tokenizer_ru_en

													UpperCAmelCase_      :   List[Any]      =	tokenizer.encode('''sequence builders'''							,  add_special_tokens=__magic_name__ )
													UpperCAmelCase_      :   Dict      =	tokenizer.encode('''multi-sequence build'''							,  add_special_tokens=__magic_name__ )

													UpperCAmelCase_      :   Union[str, Any]      =	tokenizer.build_inputs_with_special_tokens(__magic_name__ )
													UpperCAmelCase_      :   List[Any]      =	tokenizer.build_inputs_with_special_tokens(__magic_name__							,  __magic_name__ )

													assert encoded_sentence == text + [2]
													assert encoded_pair == text + [2] + text_a + [2]

							@slow
							def      UpperCAmelCase__						(				self				:  Tuple )       ->							Any:







													"""simple docstring"""
													UpperCAmelCase_      :   Dict      =	self.tokenizer_en_ru
													UpperCAmelCase_      :   Tuple      =	self.tokenizer_ru_en

													UpperCAmelCase_      :   Dict      =	[
													    [
													        '''Here\'s a little song I wrote. Don\'t worry, be happy.''',
													        [24_70, 39, 11, 23_49, 72_22, 70, 59_79, 7, 84_50, 10_50, 1_31_60, 5, 26, 64_45, 7, 2],
													    ],
													    ['''This is it. No more. I\'m done!''', [1_32, 21, 37, 7, 14_34, 86, 7, 70, 64_76, 13_05, 4_27, 2]],
													]

													# if data needs to be recreated or added, run:
													# import torch
													# model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
													# for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

													for src_text, tgt_input_ids in targets:
																			UpperCAmelCase_      :   int      =	tokenizer_enc.encode(__magic_name__							,  return_tensors=__magic_name__ )
																			self.assertListEqual(__magic_name__							,  __magic_name__ )

																			# and decode backward, using the reversed languages model
																			UpperCAmelCase_      :   List[str]      =	tokenizer_dec.decode(__magic_name__							,  skip_special_tokens=__magic_name__ )
																			self.assertEqual(__magic_name__							,  __magic_name__ )

							@slow
							def      UpperCAmelCase__						(				self				:  Tuple )       ->							Tuple:







													"""simple docstring"""
													UpperCAmelCase_      :   List[Any]      =	FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''							,  do_lower_case=__magic_name__ )
													UpperCAmelCase_      :   Union[str, Any]      =	tokenizer.tokenize('''USA is United States of America''' )
													UpperCAmelCase_      :   Union[str, Any]      =	['''us''', '''a</w>''', '''is</w>''', '''un''', '''i''', '''ted</w>''', '''st''', '''ates</w>''', '''of</w>''', '''am''', '''er''', '''ica</w>''']
													self.assertListEqual(__magic_name__							,  __magic_name__ )

							@unittest.skip('''FSMTConfig.__init__  requires non-optional args''' )
							def      UpperCAmelCase__						(				self				:  int )       ->							Optional[Any]:







													"""simple docstring"""
													pass



							@unittest.skip('''FSMTConfig.__init__  requires non-optional args''' )
							def      UpperCAmelCase__						(				self				:  Optional[Any] )       ->							Union[str, Any]:







													"""simple docstring"""
													pass









import sys
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


if TYPE_CHECKING:
			import sqlitea

			import sqlalchemy


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



@dataclass
class      snake_case__	(   datasets.BuilderConfig	):
				__lowerCAmelCase		=   None
				__lowerCAmelCase		=   None
				__lowerCAmelCase		=   None
				__lowerCAmelCase		=   True
				__lowerCAmelCase		=   None
				__lowerCAmelCase		=   None
				__lowerCAmelCase		=   None
				__lowerCAmelCase		=   10_000
				__lowerCAmelCase		=   None
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										if self.sql is None:
																raise ValueError('sql must be specified'   )
										if self.con is None:
																raise ValueError('con must be specified'   )
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase = None  ,   ):




										"""simple docstring"""




										__magic_name__					:				Optional[int]				=   config_kwargs.copy()
										# We need to stringify the Selectable object to make its hash deterministic

										# The process of stringifying is explained here: http://docs.sqlalchemy.org/en/latest/faq/sqlexpressions.html
										__magic_name__					:				Union[str, Any]				=   config_kwargs['sql']
										if not isinstance(_UpperCAmelCase  ,   _UpperCAmelCase   ):
																if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
																						import sqlalchemy

																						if isinstance(_UpperCAmelCase  ,   sqlalchemy.sql.Selectable   ):
																												__magic_name__					:				List[Any]				=   sqlalchemy.create_engine(config_kwargs['con'].split('://'   )[0] + '://'   )
																												__magic_name__					:				Any				=   str(sql.compile(dialect=engine.dialect   )   )
																												__magic_name__					:				str				=   sql_str
																						else:
																												raise TypeError(
																												    F"Supported types for 'sql' are string and sqlalchemy.sql.Selectable but got {type(_UpperCAmelCase   )}: {sql}"   )
																else:
																						raise TypeError(
																						    F"Supported types for 'sql' are string and sqlalchemy.sql.Selectable but got {type(_UpperCAmelCase   )}: {sql}"   )
										__magic_name__					:				List[Any]				=   config_kwargs['con']
										if not isinstance(_UpperCAmelCase  ,   _UpperCAmelCase   ):
																__magic_name__					:				Dict				=   id(_UpperCAmelCase   )
																logger.info(
																    F"SQL connection 'con' of type {type(_UpperCAmelCase   )} couldn't be hashed properly. To enable hashing, specify 'con' as URI string instead."   )

										return super().create_config_id(_UpperCAmelCase  ,   custom_features=_UpperCAmelCase   )





				@property
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				str				=   {
										    'index_col': self.index_col,
										    'columns': self.columns,
										    'params': self.params,
										    'coerce_float': self.coerce_float,
										    'parse_dates': self.parse_dates,
										}
										return pd_read_sql_kwargs






class      snake_case__	(   datasets.ArrowBasedBuilder	):
				__lowerCAmelCase		=   SqlConfig
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										return datasets.DatasetInfo(features=self.config.features   )
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										return [datasets.SplitGenerator(name=datasets.Split.TRAIN  ,   gen_kwargs={}   )]
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										if self.config.features is not None:
																__magic_name__					:				List[Any]				=   self.config.features.arrow_schema
																if all(not require_storage_cast(_UpperCAmelCase   ) for feature in self.config.features.values()   ):
																						# cheaper cast
																						__magic_name__					:				Dict				=   pa.Table.from_arrays([pa_table[field.name] for field in schema]  ,   schema=_UpperCAmelCase   )
																else:
																						# more expensive cast; allows str <-> int/float or str to Audio for example
																						__magic_name__					:				List[Any]				=   table_cast(_UpperCAmelCase  ,   _UpperCAmelCase   )
										return pa_table





				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				int				=   self.config.chunksize
										__magic_name__					:				Tuple				=   pd.read_sql(
										    self.config.sql  ,   self.config.con  ,   chunksize=_UpperCAmelCase  ,   **self.config.pd_read_sql_kwargs   )
										__magic_name__					:				Dict				=   [sql_reader] if chunksize is None else sql_reader
										for chunk_idx, df in enumerate(_UpperCAmelCase   ):
																__magic_name__					:				Optional[Any]				=   pa.Table.from_pandas(_UpperCAmelCase   )
																yield chunk_idx, self._cast_table(_UpperCAmelCase   )


