



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__		:					Optional[Any]							={
    '''configuration_xlm_roberta''': [
        '''XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XLMRobertaConfig''',
        '''XLMRobertaOnnxConfig''',
    ],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					List[str]							=['''XLMRobertaTokenizer''']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Dict							=['''XLMRobertaTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					List[str]							=[
			    '''XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''XLMRobertaForCausalLM''',
			    '''XLMRobertaForMaskedLM''',
			    '''XLMRobertaForMultipleChoice''',
			    '''XLMRobertaForQuestionAnswering''',
			    '''XLMRobertaForSequenceClassification''',
			    '''XLMRobertaForTokenClassification''',
			    '''XLMRobertaModel''',
			    '''XLMRobertaPreTrainedModel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					List[str]							=[
			    '''TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFXLMRobertaForCausalLM''',
			    '''TFXLMRobertaForMaskedLM''',
			    '''TFXLMRobertaForMultipleChoice''',
			    '''TFXLMRobertaForQuestionAnswering''',
			    '''TFXLMRobertaForSequenceClassification''',
			    '''TFXLMRobertaForTokenClassification''',
			    '''TFXLMRobertaModel''',
			    '''TFXLMRobertaPreTrainedModel''',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Union[str, Any]							=[
			    '''FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''FlaxXLMRobertaForMaskedLM''',
			    '''FlaxXLMRobertaForCausalLM''',
			    '''FlaxXLMRobertaForMultipleChoice''',
			    '''FlaxXLMRobertaForQuestionAnswering''',
			    '''FlaxXLMRobertaForSequenceClassification''',
			    '''FlaxXLMRobertaForTokenClassification''',
			    '''FlaxXLMRobertaModel''',
			    '''FlaxXLMRobertaPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_xlm_roberta import (
			    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    XLMRobertaConfig,
			    XLMRobertaOnnxConfig,
			)

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlm_roberta import XLMRobertaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlm_roberta import (
						    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLMRobertaForCausalLM,
						    XLMRobertaForMaskedLM,
						    XLMRobertaForMultipleChoice,
						    XLMRobertaForQuestionAnswering,
						    XLMRobertaForSequenceClassification,
						    XLMRobertaForTokenClassification,
						    XLMRobertaModel,
						    XLMRobertaPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xlm_roberta import (
						    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXLMRobertaForCausalLM,
						    TFXLMRobertaForMaskedLM,
						    TFXLMRobertaForMultipleChoice,
						    TFXLMRobertaForQuestionAnswering,
						    TFXLMRobertaForSequenceClassification,
						    TFXLMRobertaForTokenClassification,
						    TFXLMRobertaModel,
						    TFXLMRobertaPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_xlm_roberta import (
						    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FlaxXLMRobertaForCausalLM,
						    FlaxXLMRobertaForMaskedLM,
						    FlaxXLMRobertaForMultipleChoice,
						    FlaxXLMRobertaForQuestionAnswering,
						    FlaxXLMRobertaForSequenceClassification,
						    FlaxXLMRobertaForTokenClassification,
						    FlaxXLMRobertaModel,
						    FlaxXLMRobertaPreTrainedModel,
						)

else:
			import sys

			lowerCAmelCase__		:					Tuple							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)











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_  (       UpperCamelCase_	):

						'''simple docstring'''




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







													'''simple docstring'''

													super().__init__(features=_A				,							cache_dir=_A				,							keep_in_memory=_A				,							**_A		)
													__SCREAMING_SNAKE_CASE									= Sql(
													    cache_dir=_A				,							features=_A				,							sql=_A				,							con=_A				,							**_A				,							)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= None
													__SCREAMING_SNAKE_CASE									= None
													__SCREAMING_SNAKE_CASE									= None
													__SCREAMING_SNAKE_CASE									= None

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

													# Build dataset for splits
													__SCREAMING_SNAKE_CASE									= self.builder.as_dataset(
													    split='train'				,							verification_mode=_A				,							in_memory=self.keep_in_memory		)
													return dataset







class        UpperCAmelCase_  :

						'''simple docstring'''




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







													'''simple docstring'''

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

													__SCREAMING_SNAKE_CASE									= dataset
													__SCREAMING_SNAKE_CASE									= name
													__SCREAMING_SNAKE_CASE									= con
													__SCREAMING_SNAKE_CASE									= batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
													__SCREAMING_SNAKE_CASE									= num_proc
													__SCREAMING_SNAKE_CASE									= to_sql_kwargs






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.to_sql_kwargs.pop('sql'				,							_A		)
													__SCREAMING_SNAKE_CASE									= self.to_sql_kwargs.pop('con'				,							_A		)
													__SCREAMING_SNAKE_CASE									= self.to_sql_kwargs.pop('index'				,							_A		)

													__SCREAMING_SNAKE_CASE									= self._write(index=_A				,							**self.to_sql_kwargs		)
													return written






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= args
													__SCREAMING_SNAKE_CASE									= {**to_sql_kwargs, 'if_exists': 'append'} if offset > 0 else to_sql_kwargs
													__SCREAMING_SNAKE_CASE									= query_table(
													    table=self.dataset.data				,							key=slice(_A				,							offset + self.batch_size		)				,							indices=self.dataset._indices				,							)
													__SCREAMING_SNAKE_CASE									= batch.to_pandas()
													__SCREAMING_SNAKE_CASE									= df.to_sql(self.name				,							self.con				,							index=_A				,							**_A		)
													return num_rows or len(_A		)






						def 	_A     (   self				,							_A				,							**_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 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:
																				__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= 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				,							_A				,							_A		)]				,							)				,							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






