




from typing import TYPE_CHECKING

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


lowercase__    :					Optional[int]					=						{
    "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__    :					Optional[int]					=						["CamembertTokenizer"]

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__    :					str					=						["CamembertTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__    :					Tuple					=						[
		    "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "CamembertForCausalLM",
		    "CamembertForMaskedLM",
		    "CamembertForMultipleChoice",
		    "CamembertForQuestionAnswering",
		    "CamembertForSequenceClassification",
		    "CamembertForTokenClassification",
		    "CamembertModel",
		    "CamembertPreTrainedModel",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__    :					Union[str, Any]					=						[
		    "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFCamembertForCausalLM",
		    "TFCamembertForMaskedLM",
		    "TFCamembertForMultipleChoice",
		    "TFCamembertForQuestionAnswering",
		    "TFCamembertForSequenceClassification",
		    "TFCamembertForTokenClassification",
		    "TFCamembertModel",
		    "TFCamembertPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_camembert import CamembertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_camembert_fast import CamembertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_camembert import (
				    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CamembertForCausalLM,
				    CamembertForMaskedLM,
				    CamembertForMultipleChoice,
				    CamembertForQuestionAnswering,
				    CamembertForSequenceClassification,
				    CamembertForTokenClassification,
				    CamembertModel,
				    CamembertPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_camembert import (
				    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFCamembertForCausalLM,
				    TFCamembertForMaskedLM,
				    TFCamembertForMultipleChoice,
				    TFCamembertForQuestionAnswering,
				    TFCamembertForSequenceClassification,
				    TFCamembertForTokenClassification,
				    TFCamembertModel,
				    TFCamembertPreTrainedModel,
				)

else:
		import sys

		lowercase__    :					str					=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


lowercase__    :					str					=						datasets.utils.logging.get_logger(__name__)
@dataclass
class 		UpperCAmelCase       (    datasets.BuilderConfig      ):







						'''simple docstring'''





						lowerCAmelCase_							=       None







class 		UpperCAmelCase       (    datasets.ArrowBasedBuilder      ):







						'''simple docstring'''





						lowerCAmelCase_							=       ArrowConfig






						def 	snake_case__						(						self							:					Dict						):






							"""simple docstring"""






							return datasets.DatasetInfo(features=self.config.features						)






						def 	snake_case__						(						self							:					Tuple	,     __lowercase							:					Union[str, Any]						):






							"""simple docstring"""






							if not self.config.data_files:
								raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}"						)
							snake_case_		   =					dl_manager.download_and_extract(self.config.data_files						)
							if isinstance(__lowercase	,     (str, list, tuple)						):
								snake_case_		   =					data_files
								if isinstance(__lowercase	,     __lowercase						):
									snake_case_		   =					[files]
								# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
								snake_case_		   =					[dl_manager.iter_files(__lowercase						) for file in files]
								return [datasets.SplitGenerator(name=datasets.Split.TRAIN	,     gen_kwargs={"files": files}						)]
							snake_case_		   =					[]
							for split_name, files in data_files.items():
								if isinstance(__lowercase	,     __lowercase						):
									snake_case_		   =					[files]
								# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
								snake_case_		   =					[dl_manager.iter_files(__lowercase						) for file in files]
								# Infer features is they are stoed in the arrow schema
								if self.info.features is None:
									for file in itertools.chain.from_iterable(__lowercase						):
										with open(__lowercase	,     "rb"						) as f:
											snake_case_		   =					datasets.Features.from_arrow_schema(pa.ipc.open_stream(__lowercase						).schema						)
										break
								splits.append(datasets.SplitGenerator(name=__lowercase	,     gen_kwargs={"files": files}						)						)
							return splits






						def 	snake_case__						(						self							:					str	,     __lowercase							:					pa.Table						):






							"""simple docstring"""






							if self.info.features is not None:
								# more expensive cast to support nested features with keys in a different order
								# allows str <-> int/float or str to Audio for example
								snake_case_		   =					table_cast(__lowercase	,     self.info.features.arrow_schema						)
							return pa_table






						def 	snake_case__						(						self							:					Tuple	,     __lowercase							:					Optional[Any]						):






							"""simple docstring"""






							for file_idx, file in enumerate(itertools.chain.from_iterable(__lowercase						)						):
								with open(__lowercase	,     "rb"						) as f:
									try:
										for batch_idx, record_batch in enumerate(pa.ipc.open_stream(__lowercase						)						):
											snake_case_		   =					pa.Table.from_batches([record_batch]						)
											# Uncomment for debugging (will print the Arrow table size and elements)
											# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
											# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
											yield f"{file_idx}_{batch_idx}", self._cast_table(__lowercase						)
									except ValueError as e:
										logger.error(f"Failed to read file '{file}' with error {type(__lowercase						)}: {e}"						)
										raise
