import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
class SCREAMING_SNAKE_CASE_		(			TensorFormatter[Mapping, np.ndarray, Mapping]		):







   def __init__(    self	:	Optional[Any]      ,      lowerCamelCase_	:	List[Any]=None      ,      **lowerCamelCase_	:	str							):





    """simple docstring"""


    super().__init__(features=lowerCamelCase_							)
    UpperCamelCase      =			np_array_kwargs







   def 	lowerCamelCase_  (    self	:	Tuple      ,      lowerCamelCase_	:	List[Any]							):





    """simple docstring"""


    if isinstance(lowerCamelCase_      ,      lowerCamelCase_							):
     if column and all(
         isinstance(lowerCamelCase_      ,      np.ndarray							) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column							):
      return np.stack(lowerCamelCase_							)
     else:
      # don't use np.array(column, dtype=object)
      # since it fails in certain cases
      # see https://stackoverflow.com/q/51005699
      UpperCamelCase      =			np.empty(len(lowerCamelCase_							)      ,      dtype=lowerCamelCase_							)
      UpperCamelCase      =			column
      return out
    return column







   def 	lowerCamelCase_  (    self	:	List[str]      ,      lowerCamelCase_	:	List[Any]							):





    """simple docstring"""


    if isinstance(lowerCamelCase_      ,      (str, bytes, type(lowerCamelCase_							))							):
     return value
    elif isinstance(lowerCamelCase_      ,      (np.character, np.ndarray)							) and np.issubdtype(value.dtype      ,      np.character							):
     return value
    elif isinstance(lowerCamelCase_      ,      np.number							):
     return value

    UpperCamelCase      =			{}

    if isinstance(lowerCamelCase_      ,      np.ndarray							) and np.issubdtype(value.dtype      ,      np.integer							):
     UpperCamelCase      =			{"""dtype""": np.intaa}
    elif isinstance(lowerCamelCase_      ,      np.ndarray							) and np.issubdtype(value.dtype      ,      np.floating							):
     UpperCamelCase      =			{"""dtype""": np.floataa}
    elif config.PIL_AVAILABLE and "PIL" in sys.modules:
     import PIL.Image

     if isinstance(lowerCamelCase_      ,      PIL.Image.Image							):
      return np.asarray(lowerCamelCase_      ,      **self.np_array_kwargs							)

    return np.asarray(lowerCamelCase_      ,      **{**default_dtype, **self.np_array_kwargs}							)







   def 	lowerCamelCase_  (    self	:	Any      ,      lowerCamelCase_	:	List[str]							):





    """simple docstring"""


    if config.TORCH_AVAILABLE and "torch" in sys.modules:
     import torch

     if isinstance(lowerCamelCase_      ,      torch.Tensor							):
      return self._tensorize(data_struct.detach().cpu().numpy()[()]							)
    if hasattr(lowerCamelCase_      ,      """__array__"""							) and not isinstance(lowerCamelCase_      ,      (np.ndarray, np.character, np.number)							):
     UpperCamelCase      =			data_struct.__array__()
    # support for nested types like struct of list of struct
    if isinstance(lowerCamelCase_      ,      np.ndarray							):
     if data_struct.dtype == object:
      return self._consolidate([self.recursive_tensorize(lowerCamelCase_							) for substruct in data_struct]							)
    if isinstance(lowerCamelCase_      ,      (list, tuple)							):
     return self._consolidate([self.recursive_tensorize(lowerCamelCase_							) for substruct in data_struct]							)
    return self._tensorize(lowerCamelCase_							)







   def 	lowerCamelCase_  (    self	:	Optional[Any]      ,      lowerCamelCase_	:	dict							):





    """simple docstring"""


    return map_nested(self._recursive_tensorize      ,      lowerCamelCase_      ,      map_list=lowerCamelCase_							)







   def 	lowerCamelCase_  (    self	:	Union[str, Any]      ,      lowerCamelCase_	:	pa.Table							):





    """simple docstring"""


    UpperCamelCase      =			self.numpy_arrow_extractor().extract_row(lowerCamelCase_							)
    UpperCamelCase      =			self.python_features_decoder.decode_row(lowerCamelCase_							)
    return self.recursive_tensorize(lowerCamelCase_							)







   def 	lowerCamelCase_  (    self	:	List[str]      ,      lowerCamelCase_	:	pa.Table							):





    """simple docstring"""


    UpperCamelCase      =			self.numpy_arrow_extractor().extract_column(lowerCamelCase_							)
    UpperCamelCase      =			self.python_features_decoder.decode_column(lowerCamelCase_      ,      pa_table.column_names[0]							)
    UpperCamelCase      =			self.recursive_tensorize(lowerCamelCase_							)
    UpperCamelCase      =			self._consolidate(lowerCamelCase_							)
    return column







   def 	lowerCamelCase_  (    self	:	Any      ,      lowerCamelCase_	:	pa.Table							):





    """simple docstring"""


    UpperCamelCase      =			self.numpy_arrow_extractor().extract_batch(lowerCamelCase_							)
    UpperCamelCase      =			self.python_features_decoder.decode_batch(lowerCamelCase_							)
    UpperCamelCase      =			self.recursive_tensorize(lowerCamelCase_							)
    for column_name in batch:
     UpperCamelCase      =			self._consolidate(batch[column_name]							)
    return batch

import unittest

from datasets import load_dataset

from transformers import load_tool

from .test_tools_common import ToolTesterMixin
class SCREAMING_SNAKE_CASE_		(			unittest.TestCase     ,    __lowerCAmelCase		):







   def 	lowerCamelCase_  (    self	:	Optional[Any]							):





    """simple docstring"""


    UpperCamelCase      =			load_tool("""document-question-answering"""							)
    self.tool.setup()
    UpperCamelCase      =			load_tool("""document-question-answering"""      ,      remote=lowerCamelCase_							)







   def 	lowerCamelCase_  (    self	:	Optional[Any]							):





    """simple docstring"""


    UpperCamelCase      =			load_dataset("""hf-internal-testing/example-documents"""      ,      split="""test"""							)
    UpperCamelCase      =			dataset[0]["""image"""]

    UpperCamelCase      =			self.tool(lowerCamelCase_      ,      """When is the coffee break?"""							)
    self.assertEqual(lowerCamelCase_      ,      """11-14 to 11:39 a.m."""							)







   def 	lowerCamelCase_  (    self	:	Union[str, Any]							):





    """simple docstring"""


    UpperCamelCase      =			load_dataset("""hf-internal-testing/example-documents"""      ,      split="""test"""							)
    UpperCamelCase      =			dataset[0]["""image"""]

    UpperCamelCase      =			self.remote_tool(lowerCamelCase_      ,      """When is the coffee break?"""							)
    self.assertEqual(lowerCamelCase_      ,      """11-14 to 11:39 a.m."""							)







   def 	lowerCamelCase_  (    self	:	int							):





    """simple docstring"""


    UpperCamelCase      =			load_dataset("""hf-internal-testing/example-documents"""      ,      split="""test"""							)
    UpperCamelCase      =			dataset[0]["""image"""]

    self.tool(document=lowerCamelCase_      ,      question="""When is the coffee break?"""							)







   def 	lowerCamelCase_  (    self	:	Any							):





    """simple docstring"""


    UpperCamelCase      =			load_dataset("""hf-internal-testing/example-documents"""      ,      split="""test"""							)
    UpperCamelCase      =			dataset[0]["""image"""]

    UpperCamelCase      =			self.remote_tool(document=lowerCamelCase_      ,      question="""When is the coffee break?"""							)
    self.assertEqual(lowerCamelCase_      ,      """11-14 to 11:39 a.m."""							)
