'''simple docstring'''

import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
    import torch


class  _UpperCAmelCase ( TensorFormatter[Mapping, "torch.Tensor", Mapping]    ):

     """simple docstring"""

     def __init__(      self							,		lowerCAmelCase_=None							,		**lowerCAmelCase_ ):

       '''simple docstring'''

       super().__init__(features=lowerCAmelCase_ )
       a_      :	Dict      =							torch_tensor_kwargs
       import torch  # noqa import torch at initialization


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       import torch

       if isinstance(lowerCAmelCase_							,		lowerCAmelCase_ ) and column:
         if all(
             isinstance(lowerCAmelCase_							,		torch.Tensor ) and x.shape == column[0].shape and x.dtype == column[0].dtype
             for x in column ):
           return torch.stack(lowerCAmelCase_ )
       return column


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       import torch

       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.tolist()

       a_      :	List[str]      =							{}

       if isinstance(lowerCAmelCase_							,		(np.number, np.ndarray) ) and np.issubdtype(value.dtype							,		np.integer ):
         a_      :	str      =							{"""dtype""": torch.intaa}
       elif isinstance(lowerCAmelCase_							,		(np.number, np.ndarray) ) and np.issubdtype(value.dtype							,		np.floating ):
         a_      :	Dict      =							{"""dtype""": torch.floataa}
       elif config.PIL_AVAILABLE and "PIL" in sys.modules:
         import PIL.Image

         if isinstance(lowerCAmelCase_							,		PIL.Image.Image ):
           a_      :	int      =							np.asarray(lowerCAmelCase_ )
       return torch.tensor(lowerCAmelCase_							,		**{**default_dtype, **self.torch_tensor_kwargs} )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       import torch

       # support for torch, tf, jax etc.
       if hasattr(lowerCAmelCase_							,		"""__array__""" ) and not isinstance(lowerCAmelCase_							,		torch.Tensor ):
         a_      :	str      =							data_struct.__array__()
       # support for nested types like struct of list of struct
       if isinstance(lowerCAmelCase_							,		np.ndarray ):
         if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
           return self._consolidate([self.recursive_tensorize(lowerCAmelCase_ ) for substruct in data_struct] )
       elif isinstance(lowerCAmelCase_							,		(list, tuple) ):
         return self._consolidate([self.recursive_tensorize(lowerCAmelCase_ ) for substruct in data_struct] )
       return self._tensorize(lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

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


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	str      =							self.numpy_arrow_extractor().extract_row(lowerCAmelCase_ )
       a_      :	Any      =							self.python_features_decoder.decode_row(lowerCAmelCase_ )
       return self.recursive_tensorize(lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	List[Any]      =							self.numpy_arrow_extractor().extract_column(lowerCAmelCase_ )
       a_      :	Optional[Any]      =							self.python_features_decoder.decode_column(lowerCAmelCase_							,		pa_table.column_names[0] )
       a_      :	str      =							self.recursive_tensorize(lowerCAmelCase_ )
       a_      :	Optional[Any]      =							self._consolidate(lowerCAmelCase_ )
       return column






     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Optional[int]      =							self.numpy_arrow_extractor().extract_batch(lowerCAmelCase_ )
       a_      :	Dict      =							self.python_features_decoder.decode_batch(lowerCAmelCase_ )
       a_      :	Optional[Any]      =							self.recursive_tensorize(lowerCAmelCase_ )
       for column_name in batch:
         a_      :	str      =							self._consolidate(batch[column_name] )
       return batch




'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import DonutImageProcessor


class  _UpperCAmelCase ( unittest.TestCase    ):

     """simple docstring"""

     def __init__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_=7							,		lowerCAmelCase_=3							,		lowerCAmelCase_=18							,		lowerCAmelCase_=30							,		lowerCAmelCase_=4_00							,		lowerCAmelCase_=True							,		lowerCAmelCase_=None							,		lowerCAmelCase_=True							,		lowerCAmelCase_=False							,		lowerCAmelCase_=True							,		lowerCAmelCase_=True							,		lowerCAmelCase_=[0.5, 0.5, 0.5]							,		lowerCAmelCase_=[0.5, 0.5, 0.5]							,		):

       '''simple docstring'''

       a_      :	Optional[int]      =							parent
       a_      :	Union[str, Any]      =							batch_size
       a_      :	Any      =							num_channels
       a_      :	Tuple      =							image_size
       a_      :	Any      =							min_resolution
       a_      :	Dict      =							max_resolution
       a_      :	Optional[Any]      =							do_resize
       a_      :	Tuple      =							size if size is not None else {"""height""": 18, """width""": 20}
       a_      :	int      =							do_thumbnail
       a_      :	Optional[int]      =							do_align_axis
       a_      :	Tuple      =							do_pad
       a_      :	int      =							do_normalize
       a_      :	Tuple      =							image_mean
       a_      :	Optional[Any]      =							image_std






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_thumbnail": self.do_thumbnail,
           "do_align_long_axis": self.do_align_axis,
           "do_pad": self.do_pad,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
       }







@require_torch
@require_vision
class  _UpperCAmelCase ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					DonutImageProcessor if is_vision_available() else None


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							DonutImageProcessingTester(self )


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return self.image_processor_tester.prepare_image_processor_dict()


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Any      =							self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""do_resize""" ) )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""size""" ) )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""do_thumbnail""" ) )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""do_align_long_axis""" ) )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""do_pad""" ) )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""do_normalize""" ) )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""image_mean""" ) )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""image_std""" ) )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							self.image_processing_class.from_dict(self.image_processor_dict )
       self.assertEqual(image_processor.size							,		{"""height""": 18, """width""": 20} )

       a_      :	str      =							self.image_processing_class.from_dict(self.image_processor_dict							,		size=42 )
       self.assertEqual(image_processor.size							,		{"""height""": 42, """width""": 42} )

       # Previous config had dimensions in (width, height) order
       a_      :	Any      =							self.image_processing_class.from_dict(self.image_processor_dict							,		size=(42, 84) )
       self.assertEqual(image_processor.size							,		{"""height""": 84, """width""": 42} )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       pass


     @is_flaky()
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							self.image_processing_class(**self.image_processor_dict )
       # create random PIL images
       a_      :	str      =							prepare_image_inputs(self.image_processor_tester							,		equal_resolution=lowerCAmelCase_ )
       for image in image_inputs:
         self.assertIsInstance(lowerCAmelCase_							,		Image.Image )

       # Test not batched input
       a_      :	Dict      =							image_processing(image_inputs[0]							,		return_tensors="""pt""" ).pixel_values
       self.assertEqual(
           encoded_images.shape							,		(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )							,		)

       # Test batched
       a_      :	int      =							image_processing(lowerCAmelCase_							,		return_tensors="""pt""" ).pixel_values
       self.assertEqual(
           encoded_images.shape							,		(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )							,		)


     @is_flaky()
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							self.image_processing_class(**self.image_processor_dict )
       # create random numpy tensors
       a_      :	int      =							prepare_image_inputs(self.image_processor_tester							,		equal_resolution=lowerCAmelCase_							,		numpify=lowerCAmelCase_ )
       for image in image_inputs:
         self.assertIsInstance(lowerCAmelCase_							,		np.ndarray )

       # Test not batched input
       a_      :	Optional[Any]      =							image_processing(image_inputs[0]							,		return_tensors="""pt""" ).pixel_values
       self.assertEqual(
           encoded_images.shape							,		(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )							,		)

       # Test batched
       a_      :	Dict      =							image_processing(lowerCAmelCase_							,		return_tensors="""pt""" ).pixel_values
       self.assertEqual(
           encoded_images.shape							,		(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )							,		)






     @is_flaky()
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							self.image_processing_class(**self.image_processor_dict )
       # create random PyTorch tensors
       a_      :	Optional[Any]      =							prepare_image_inputs(self.image_processor_tester							,		equal_resolution=lowerCAmelCase_							,		torchify=lowerCAmelCase_ )
       for image in image_inputs:
         self.assertIsInstance(lowerCAmelCase_							,		torch.Tensor )

       # Test not batched input
       a_      :	Optional[int]      =							image_processing(image_inputs[0]							,		return_tensors="""pt""" ).pixel_values
       self.assertEqual(
           encoded_images.shape							,		(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )							,		)

       # Test batched
       a_      :	Any      =							image_processing(lowerCAmelCase_							,		return_tensors="""pt""" ).pixel_values
       self.assertEqual(
           encoded_images.shape							,		(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )							,		)


