






"""simple docstring"""
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
			import sqlitea

			import sqlalchemy







class     UpperCamelCase_ (snake_case__  ):





	def __init__(   self      :				Optional[int]			,			lowerCAmelCase_      :				Union[str, "sqlalchemy.sql.Selectable"]			,			lowerCAmelCase_      :				Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]			,			lowerCAmelCase_      :				Optional[Features] = None			,			lowerCAmelCase_      :				str = None			,			lowerCAmelCase_      :				bool = False			,			**lowerCAmelCase_      :				Optional[Any]			,			)		->    int:
				super().__init__(features=UpperCAmelCase_			,			cache_dir=UpperCAmelCase_			,			keep_in_memory=UpperCAmelCase_			,			**UpperCAmelCase_		)
				UpperCAmelCase_  :				Union[str, Any]      =      Sql(
				    cache_dir=UpperCAmelCase_			,			features=UpperCAmelCase_			,			sql=UpperCAmelCase_			,			con=UpperCAmelCase_			,			**UpperCAmelCase_			,			)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    str:
				UpperCAmelCase_  :				str      =      None
				UpperCAmelCase_  :				Tuple      =      None
				UpperCAmelCase_  :				Tuple      =      None
				UpperCAmelCase_  :				Optional[Any]      =      None

				self.builder.download_and_prepare(
				    download_config=UpperCAmelCase_			,			download_mode=UpperCAmelCase_			,			verification_mode=UpperCAmelCase_			,			base_path=UpperCAmelCase_			,			)

				# Build dataset for splits
				UpperCAmelCase_  :				int      =      self.builder.as_dataset(
				    split="train"			,			verification_mode=UpperCAmelCase_			,			in_memory=self.keep_in_memory		)
				return dataset







class     UpperCamelCase_ :





	def __init__(   self      :				Any			,			lowerCAmelCase_      :				Dataset			,			lowerCAmelCase_      :				str			,			lowerCAmelCase_      :				Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]			,			lowerCAmelCase_      :				Optional[int] = None			,			lowerCAmelCase_      :				Optional[int] = None			,			**lowerCAmelCase_      :				Tuple			,			)		->    Dict:



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

				UpperCAmelCase_  :				Optional[int]      =      dataset
				UpperCAmelCase_  :				Optional[int]      =      name
				UpperCAmelCase_  :				str      =      con
				UpperCAmelCase_  :				List[str]      =      batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
				UpperCAmelCase_  :				Dict      =      num_proc
				UpperCAmelCase_  :				List[Any]      =      to_sql_kwargs





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    int:
				UpperCAmelCase_  :				List[str]      =      self.to_sql_kwargs.pop("sql"			,			UpperCAmelCase_		)
				UpperCAmelCase_  :				List[Any]      =      self.to_sql_kwargs.pop("con"			,			UpperCAmelCase_		)
				UpperCAmelCase_  :				Tuple      =      self.to_sql_kwargs.pop("index"			,			UpperCAmelCase_		)

				UpperCAmelCase_  :				Dict      =      self._write(index=UpperCAmelCase_			,			**self.to_sql_kwargs		)
				return written





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]			,			lowerCAmelCase_      :				Dict		)		->    Union[str, Any]:
				UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  :				List[Any]      =      args
				UpperCAmelCase_  :				int      =      {**to_sql_kwargs, "if_exists": "append"} if offset > 0 else to_sql_kwargs
				UpperCAmelCase_  :				Dict      =      query_table(
				    table=self.dataset.data			,			key=slice(UpperCAmelCase_			,			offset + self.batch_size		)			,			indices=self.dataset._indices			,			)
				UpperCAmelCase_  :				List[str]      =      batch.to_pandas()
				UpperCAmelCase_  :				List[str]      =      df.to_sql(self.name			,			self.con			,			index=UpperCAmelCase_			,			**UpperCAmelCase_		)
				return num_rows or len(UpperCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict			,			lowerCAmelCase_      :				List[Any]			,			**lowerCAmelCase_      :				Optional[int]		)		->    int:
				UpperCAmelCase_  :				List[Any]      =      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 SQL from Arrow format"			,			):
										written += self._batch_sql((offset, index, to_sql_kwargs)		)
				else:
							UpperCAmelCase_  ,			UpperCAmelCase_  :				int      =      len(self.dataset		), self.batch_size
							with multiprocessing.Pool(self.num_proc		) as pool:
										for num_rows in logging.tqdm(
										    pool.imap(
										        self._batch_sql			,			[(offset, index, to_sql_kwargs) for offset in range(0			,			UpperCAmelCase_			,			UpperCAmelCase_		)]			,			)			,			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 SQL from Arrow format"			,			):
													written += num_rows

				return written











"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

lowerCamelCase_       			=   '''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
lowerCamelCase_       			=   [
    '''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>''',
]
lowerCamelCase_       			=   dict(zip(vocab, range(len(vocab))))
lowerCamelCase_       			=   ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
			lowerCamelCase_       			=   Path(tmpdirname)
			lowerCamelCase_       			=   build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
			lowerCamelCase_       			=   build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
			lowerCamelCase_       			=   build_dir / VOCAB_FILES_NAMES['''merges_file''']
			with open(src_vocab_file, '''w''') as fp:
						fp.write(json.dumps(vocab_tokens))
			with open(tgt_vocab_file, '''w''') as fp:
						fp.write(json.dumps(vocab_tokens))
			with open(merges_file, '''w''') as fp:
						fp.write('''\n'''.join(merges))

			lowerCamelCase_       			=   FSMTTokenizer(
			    langs=['''en''', '''ru'''],
			    src_vocab_size=len(vocab),
			    tgt_vocab_size=len(vocab),
			    src_vocab_file=src_vocab_file,
			    tgt_vocab_file=tgt_vocab_file,
			    merges_file=merges_file,
			)

lowerCamelCase_       			=   FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1000,
    tgt_vocab_size=1000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

lowerCamelCase_       			=   FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

# Test
lowerCamelCase_       			=   tokenizer(['''Making tiny model'''], return_tensors='''pt''')
lowerCamelCase_       			=   tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f'Generated {mname_tiny}')

# Upload
# transformers-cli upload tiny-wmt19-en-ru



