





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 				__a (			SCREAMING_SNAKE_CASE__					):


     """simple docstring"""


     def __init__(     self      :			List[str]    ,_UpperCamelCase      :			int    ,_UpperCamelCase      :			Union[str, Any] = None    ,_UpperCamelCase      :			List[str] = None    ,_UpperCamelCase      :			int = None    ,_UpperCamelCase      :			str = False    ,_UpperCamelCase      :			str = False    ,_UpperCamelCase      :			Optional[Any] = None    ,**_UpperCamelCase      :			Optional[int]    ,)		->       str:


         '''simple docstring'''





         super().__init__(
             _UpperCAmelCase    ,split=_UpperCAmelCase    ,features=_UpperCAmelCase    ,cache_dir=_UpperCAmelCase    ,keep_in_memory=_UpperCAmelCase    ,streaming=_UpperCAmelCase    ,num_proc=_UpperCAmelCase    ,**_UpperCAmelCase    ,)
         SCREAMING_SNAKE_CASE__       =path_or_paths if isinstance(_UpperCAmelCase    ,_UpperCAmelCase						) else {self.split: path_or_paths}
         SCREAMING_SNAKE_CASE__       =Csv(
             cache_dir=_UpperCAmelCase    ,data_files=_UpperCAmelCase    ,features=_UpperCAmelCase    ,**_UpperCAmelCase    ,)




     def   __A    (     self      :			List[str]						)		->       Optional[int]:


         '''simple docstring'''





         if self.streaming:
             SCREAMING_SNAKE_CASE__       =self.builder.as_streaming_dataset(split=self.split						)
         # Build regular (map-style) dataset
         else:
             SCREAMING_SNAKE_CASE__       =None
             SCREAMING_SNAKE_CASE__       =None
             SCREAMING_SNAKE_CASE__       =None
             SCREAMING_SNAKE_CASE__       =None

             self.builder.download_and_prepare(
                 download_config=_UpperCAmelCase    ,download_mode=_UpperCAmelCase    ,verification_mode=_UpperCAmelCase    ,base_path=_UpperCAmelCase    ,num_proc=self.num_proc    ,)
             SCREAMING_SNAKE_CASE__       =self.builder.as_dataset(
                 split=self.split    ,verification_mode=_UpperCAmelCase    ,in_memory=self.keep_in_memory						)
         return dataset






class 				__a :


     """simple docstring"""


     def __init__(     self      :			int    ,_UpperCamelCase      :			str    ,_UpperCamelCase      :			int    ,_UpperCamelCase      :			Any = None    ,_UpperCamelCase      :			Dict = None    ,**_UpperCamelCase      :			Tuple    ,)		->       Dict:


         '''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__       =path_or_buf
         SCREAMING_SNAKE_CASE__       =batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
         SCREAMING_SNAKE_CASE__       =num_proc
         SCREAMING_SNAKE_CASE__       ="utf-8"
         SCREAMING_SNAKE_CASE__       =to_csv_kwargs




     def   __A    (     self      :			List[str]						)		->       int:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =self.to_csv_kwargs.pop("""path_or_buf"""    ,_UpperCAmelCase						)
         SCREAMING_SNAKE_CASE__       =self.to_csv_kwargs.pop("""header"""    ,_UpperCAmelCase						)
         SCREAMING_SNAKE_CASE__       =self.to_csv_kwargs.pop("""index"""    ,_UpperCAmelCase						)

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




     def   __A    (     self      :			List[str]    ,_UpperCamelCase      :			Any						)		->       List[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =args

         SCREAMING_SNAKE_CASE__       =query_table(
             table=self.dataset.data    ,key=slice(_UpperCAmelCase    ,offset + self.batch_size						)    ,indices=self.dataset._indices    ,)
         SCREAMING_SNAKE_CASE__       =batch.to_pandas().to_csv(
             path_or_buf=_UpperCAmelCase    ,header=header if (offset == 0) else False    ,index=_UpperCAmelCase    ,**_UpperCAmelCase						)
         return csv_str.encode(self.encoding						)




     def   __A    (     self      :			str    ,_UpperCamelCase      :			Optional[Any]    ,_UpperCamelCase      :			Optional[Any]    ,_UpperCamelCase      :			str    ,**_UpperCamelCase      :			int						)		->       int:


         '''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 CSV from Arrow format"""    ,):
                 SCREAMING_SNAKE_CASE__       =self._batch_csv((offset, header, index, to_csv_kwargs)						)
                 written += file_obj.write(_UpperCAmelCase						)

         else:
             SCREAMING_SNAKE_CASE__       =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    ,_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 CSV from Arrow format"""    ,):
                     written += file_obj.write(_UpperCAmelCase						)

         return written












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_  						=       {
    "configuration_marian": ["MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarianConfig", "MarianOnnxConfig"],
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_  						=       ["MarianTokenizer"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_  						=       [
        "MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST",
        "MarianForCausalLM",
        "MarianModel",
        "MarianMTModel",
        "MarianPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_  						=       ["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_  						=       ["FlaxMarianModel", "FlaxMarianMTModel", "FlaxMarianPreTrainedModel"]

if TYPE_CHECKING:
    from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_marian import MarianTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_marian import (
            MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
            MarianForCausalLM,
            MarianModel,
            MarianMTModel,
            MarianPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
    import sys

    lowerCamelCase_  						=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






