


from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image


UpperCamelCase								=	["""text""", """image""", """audio"""]


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  A_      :				Union[str, Any]		     =					[]

  for input_type in input_types:
    if input_type == "text":
      inputs.append('''Text input'''       )
    elif input_type == "image":
      inputs.append(
          Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''       )       ) / '''000000039769.png'''       ).resize((512, 512)       )       )
    elif input_type == "audio":
      inputs.append(torch.ones(3_000       )       )
    elif isinstance(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
      inputs.append(create_inputs(SCREAMING_SNAKE_CASE       )       )
    else:
      raise ValueError(f'''Invalid type requested: {input_type}'''       )

  return inputs


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  A_      :				str		     =					[]

  for output in outputs:
    if isinstance(SCREAMING_SNAKE_CASE ,			(str, AgentText)       ):
      output_types.append('''text'''       )
    elif isinstance(SCREAMING_SNAKE_CASE ,			(Image.Image, AgentImage)       ):
      output_types.append('''image'''       )
    elif isinstance(SCREAMING_SNAKE_CASE ,			(torch.Tensor, AgentAudio)       ):
      output_types.append('''audio'''       )
    else:
      raise ValueError(f'''Invalid output: {output}'''       )

  return output_types







@is_tool_test
class   _lowerCamelCase :




       """simple docstring"""



       def        _snake_case       (      self		)->Tuple:




         '''simple docstring'''





         self.assertTrue(hasattr(self.tool				,       '''inputs'''		)		)
         self.assertTrue(hasattr(self.tool				,       '''outputs'''		)		)

         A_      :				Union[str, Any]		     =					self.tool.inputs
         for _input in inputs:
           if isinstance(_input				,       _SCREAMING_SNAKE_CASE		):
             for __input in _input:
               self.assertTrue(__input in authorized_types		)
           else:
             self.assertTrue(_input in authorized_types		)

         A_      :				Tuple		     =					self.tool.outputs
         for _output in outputs:
           self.assertTrue(_output in authorized_types		)



       def        _snake_case       (      self		)->int:




         '''simple docstring'''





         A_      :				Optional[int]		     =					create_inputs(self.tool.inputs		)
         A_      :				Tuple		     =					self.tool(*_SCREAMING_SNAKE_CASE		)

         # There is a single output
         if len(self.tool.outputs		) == 1:
           A_      :				int		     =					[outputs]

         self.assertListEqual(output_types(_SCREAMING_SNAKE_CASE		)				,       self.tool.outputs		)



       def        _snake_case       (      self		)->List[Any]:




         '''simple docstring'''





         self.assertTrue(hasattr(self.tool				,       '''description'''		)		)
         self.assertTrue(hasattr(self.tool				,       '''default_checkpoint'''		)		)
         self.assertTrue(self.tool.description.startswith('''This is a tool that'''		)		)



       def        _snake_case       (      self		)->List[str]:




         '''simple docstring'''





         A_      :				Dict		     =					create_inputs(self.tool.inputs		)
         A_      :				int		     =					self.tool(*_SCREAMING_SNAKE_CASE		)

         if not isinstance(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		):
           A_      :				Tuple		     =					[outputs]

         self.assertEqual(len(_SCREAMING_SNAKE_CASE		)				,       len(self.tool.outputs		)		)

         for output, output_type in zip(_SCREAMING_SNAKE_CASE				,       self.tool.outputs		):
           A_      :				List[Any]		     =					AGENT_TYPE_MAPPING[output_type]
           self.assertTrue(isinstance(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)		)







       def        _snake_case       (      self		)->Optional[int]:




         '''simple docstring'''





         A_      :				int		     =					create_inputs(self.tool.inputs		)

         A_      :				List[str]		     =					[]

         for _input, input_type in zip(_SCREAMING_SNAKE_CASE				,       self.tool.inputs		):
           if isinstance(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		):
             _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input		) for _input_type in input_type]		)
           else:
             _inputs.append(AGENT_TYPE_MAPPING[input_type](_input		)		)

        # Should not raise an error
         A_      :				Optional[Any]		     =					self.tool(*_SCREAMING_SNAKE_CASE		)

         if not isinstance(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		):
           A_      :				Tuple		     =					[outputs]

         self.assertEqual(len(_SCREAMING_SNAKE_CASE		)				,       len(self.tool.outputs		)		)








import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  A_      :				Any		     =					np.inf

  def set_batch_size(SCREAMING_SNAKE_CASE       ) -> None:
    nonlocal batch_size
    if isinstance(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
      A_      :				Tuple		     =					min(SCREAMING_SNAKE_CASE ,			config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS       )
    elif isinstance(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
      A_      :				str		     =					min(SCREAMING_SNAKE_CASE ,			config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS       )
    elif isinstance(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ) and feature.dtype == "binary":
      A_      :				Union[str, Any]		     =					min(SCREAMING_SNAKE_CASE ,			config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS       )

  _visit(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )

  return None if batch_size is np.inf else batch_size






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""



       def __init__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = False				,       _SCREAMING_SNAKE_CASE = False				,       _SCREAMING_SNAKE_CASE = None				,       **_SCREAMING_SNAKE_CASE				,       )->str:




         '''simple docstring'''





         super().__init__(
             _SCREAMING_SNAKE_CASE				,       split=_SCREAMING_SNAKE_CASE				,       features=_SCREAMING_SNAKE_CASE				,       cache_dir=_SCREAMING_SNAKE_CASE				,       keep_in_memory=_SCREAMING_SNAKE_CASE				,       streaming=_SCREAMING_SNAKE_CASE				,       num_proc=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE				,       )
         A_      :				str		     =					path_or_paths if isinstance(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		) else {self.split: path_or_paths}
         A_      :				Optional[int]		     =					_PACKAGED_DATASETS_MODULES['''parquet'''][1]
         A_      :				Union[str, Any]		     =					Parquet(
             cache_dir=_SCREAMING_SNAKE_CASE				,       data_files=_SCREAMING_SNAKE_CASE				,       features=_SCREAMING_SNAKE_CASE				,       hash=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE				,       )







       def        _snake_case       (      self		)->Optional[int]:




         '''simple docstring'''





         if self.streaming:
           A_      :				List[str]		     =					self.builder.as_streaming_dataset(split=self.split		)
         # Build regular (map-style) dataset
         else:
           A_      :				List[str]		     =					None
           A_      :				List[str]		     =					None
           A_      :				List[Any]		     =					None
           A_      :				Dict		     =					None

           self.builder.download_and_prepare(
               download_config=_SCREAMING_SNAKE_CASE				,       download_mode=_SCREAMING_SNAKE_CASE				,       verification_mode=_SCREAMING_SNAKE_CASE				,       base_path=_SCREAMING_SNAKE_CASE				,       num_proc=self.num_proc				,       )
           A_      :				Union[str, Any]		     =					self.builder.as_dataset(
               split=self.split				,       verification_mode=_SCREAMING_SNAKE_CASE				,       in_memory=self.keep_in_memory		)
         return dataset






class   _lowerCamelCase :




       """simple docstring"""



       def __init__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None				,       **_SCREAMING_SNAKE_CASE				,       )->List[Any]:




         '''simple docstring'''





         A_      :				Union[str, Any]		     =					dataset
         A_      :				Union[str, Any]		     =					path_or_buf
         A_      :				Any		     =					batch_size or get_writer_batch_size(dataset.features		)
         A_      :				Optional[int]		     =					parquet_writer_kwargs



       def        _snake_case       (      self		)->int:




         '''simple docstring'''





         A_      :				Union[str, Any]		     =					self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

         if isinstance(self.path_or_buf				,       (str, bytes, os.PathLike)		):
           with open(self.path_or_buf				,       '''wb+'''		) as buffer:
             A_      :				str		     =					self._write(file_obj=_SCREAMING_SNAKE_CASE				,       batch_size=_SCREAMING_SNAKE_CASE				,       **self.parquet_writer_kwargs		)
         else:
           A_      :				Tuple		     =					self._write(file_obj=self.path_or_buf				,       batch_size=_SCREAMING_SNAKE_CASE				,       **self.parquet_writer_kwargs		)
         return written







       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)->int:




         '''simple docstring'''





         A_      :				List[Any]		     =					0
         A_      :				int		     =					parquet_writer_kwargs.pop('''path_or_buf'''				,       _SCREAMING_SNAKE_CASE		)
         A_      :				List[Any]		     =					self.dataset.features.arrow_schema

         A_      :				List[str]		     =					pq.ParquetWriter(_SCREAMING_SNAKE_CASE				,       schema=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)

         for offset in logging.tqdm(
             range(0				,       len(self.dataset		)				,       _SCREAMING_SNAKE_CASE		)				,       unit='''ba'''				,       disable=not logging.is_progress_bar_enabled()				,       desc='''Creating parquet from Arrow format'''				,       ):
           A_      :				List[Any]		     =					query_table(
               table=self.dataset._data				,       key=slice(_SCREAMING_SNAKE_CASE				,       offset + batch_size		)				,       indices=self.dataset._indices if self.dataset._indices is not None else None				,       )
           writer.write_table(_SCREAMING_SNAKE_CASE		)
           written += batch.nbytes
         writer.close()
         return written




