




'''simple docstring'''







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


A			   ={
    'agents': ['Agent', 'AzureOpenAiAgent', 'HfAgent', 'LocalAgent', 'OpenAiAgent'],
    'base': ['PipelineTool', 'RemoteTool', 'Tool', 'launch_gradio_demo', 'load_tool'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A			   =['DocumentQuestionAnsweringTool']
   A			   =['ImageCaptioningTool']
   A			   =['ImageQuestionAnsweringTool']
   A			   =['ImageSegmentationTool']
   A			   =['SpeechToTextTool']
   A			   =['TextClassificationTool']
   A			   =['TextQuestionAnsweringTool']
   A			   =['TextSummarizationTool']
   A			   =['TextToSpeechTool']
   A			   =['TranslationTool']

if TYPE_CHECKING:
   from .agents import Agent, AzureOpenAiAgent, HfAgent, LocalAgent, OpenAiAgent
   from .base import PipelineTool, RemoteTool, Tool, launch_gradio_demo, load_tool

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .document_question_answering import DocumentQuestionAnsweringTool
      from .image_captioning import ImageCaptioningTool
      from .image_question_answering import ImageQuestionAnsweringTool
      from .image_segmentation import ImageSegmentationTool
      from .speech_to_text import SpeechToTextTool
      from .text_classification import TextClassificationTool
      from .text_question_answering import TextQuestionAnsweringTool
      from .text_summarization import TextSummarizationTool
      from .text_to_speech import TextToSpeechTool
      from .translation import TranslationTool
else:
   import sys

   A			   =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)










'''simple docstring'''







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  (     __a			):
       def __init__(     self						:     Any       ,    lowercase						:     NestedDataStructureLike[PathLike]       ,    lowercase						:     Optional[NamedSplit] = None       ,    lowercase						:     Optional[Features] = None       ,    lowercase						:     str = None       ,    lowercase						:     bool = False       ,    lowercase						:     bool = False       ,    lowercase						:     Optional[int] = None       ,    **lowercase						:     str       ,    ):



           '''simple docstring'''

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







       def    A				(     self						:     Tuple    ):



           '''simple docstring'''

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

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






class       _a  :
       def __init__(     self						:     Optional[int]       ,    lowercase						:     Dataset       ,    lowercase						:     Union[PathLike, BinaryIO]       ,    lowercase						:     Optional[int] = None       ,    lowercase						:     Optional[int] = None       ,    **lowercase						:     int       ,    ):



           '''simple docstring'''

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

           UpperCAmelCase      	=		dataset
           UpperCAmelCase      	=		path_or_buf
           UpperCAmelCase      	=		batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
           UpperCAmelCase      	=		num_proc
           UpperCAmelCase      	=		'''utf-8'''
           UpperCAmelCase      	=		to_csv_kwargs
       def    A				(     self						:     Union[str, Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.to_csv_kwargs.pop('''path_or_buf'''       ,    lowercase    )
           UpperCAmelCase      	=		self.to_csv_kwargs.pop('''header'''       ,    lowercase    )
           UpperCAmelCase      	=		self.to_csv_kwargs.pop('''index'''       ,    lowercase    )

           if isinstance(self.path_or_buf       ,    (str, bytes, os.PathLike)    ):
               with open(self.path_or_buf       ,    '''wb+'''    ) as buffer:
                   UpperCAmelCase      	=		self._write(file_obj=lowercase       ,    header=lowercase       ,    index=lowercase       ,    **self.to_csv_kwargs    )
           else:
               UpperCAmelCase      	=		self._write(file_obj=self.path_or_buf       ,    header=lowercase       ,    index=lowercase       ,    **self.to_csv_kwargs    )
           return written
       def    A				(     self						:     List[str]       ,    lowercase						:     Dict    ):



           '''simple docstring'''

           UpperCAmelCase	,					UpperCAmelCase	,					UpperCAmelCase	,					UpperCAmelCase      	=		args

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







       def    A				(     self						:     List[str]       ,    lowercase						:     BinaryIO       ,    lowercase						:     int       ,    lowercase						:     List[str]       ,    **lowercase						:     List[Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		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'''       ,    ):
                   UpperCAmelCase      	=		self._batch_csv((offset, header, index, to_csv_kwargs)    )
                   written += file_obj.write(lowercase    )

           else:
               UpperCAmelCase	,					UpperCAmelCase      	=		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       ,    lowercase       ,    lowercase    )]       ,    )       ,    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(lowercase    )

           return written




