


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 A__     ( unittest.TestCase  ):



  """simple docstring"""



  def __init__( self			:  Optional[Any]				,				lowerCamelCase__			:  Union[str, Any]				,				lowerCamelCase__			:  List[Any]=7				,				lowerCamelCase__			:  Any=3				,				lowerCamelCase__			:  Dict=18				,				lowerCamelCase__			:  str=30				,				lowerCamelCase__			:  Tuple=400				,				lowerCamelCase__			:  int=True				,				lowerCamelCase__			:  Optional[Any]=None				,				lowerCamelCase__			:  int=True				,				lowerCamelCase__			:  Optional[int]=False				,				lowerCamelCase__			:  List[str]=True				,				lowerCamelCase__			:  Optional[int]=True				,				lowerCamelCase__			:  Optional[Any]=[0.5, 0.5, 0.5]				,				lowerCamelCase__			:  Optional[Any]=[0.5, 0.5, 0.5]				,				):
        a__	:  Any     =   parent
        a__	:  Any     =   batch_size
        a__	:  Any     =   num_channels
        a__	:  Union[str, Any]     =   image_size
        a__	:  Optional[Any]     =   min_resolution
        a__	:  str     =   max_resolution
        a__	:  List[str]     =   do_resize
        a__	:  Any     =   size if size is not None else {"height": 18, "width": 20}
        a__	:  Union[str, Any]     =   do_thumbnail
        a__	:  Any     =   do_align_axis
        a__	:  List[Any]     =   do_pad
        a__	:  List[str]     =   do_normalize
        a__	:  Any     =   image_mean
        a__	:  List[Any]     =   image_std

  def    _UpperCamelCase( self			:  Union[str, Any]       ):
        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 A__     ( A__				,		unittest.TestCase  ):



  """simple docstring"""



  _lowercase									=		DonutImageProcessor if is_vision_available() else None

  def    _UpperCamelCase( self			:  Tuple       ):
        a__	:  int     =   DonutImageProcessingTester(self       )

  @property
  def    _UpperCamelCase( self			:  List[Any]       ):
        return self.image_processor_tester.prepare_image_processor_dict()

  def    _UpperCamelCase( self			:  List[Any]       ):
        a__	:  int     =   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    _UpperCamelCase( self			:  Dict       ):
        a__	:  Optional[Any]     =   self.image_processing_class.from_dict(self.image_processor_dict       )
        self.assertEqual(image_processor.size				,				{"height": 18, "width": 20}       )

        a__	:  Any     =   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__	:  Tuple     =   self.image_processing_class.from_dict(self.image_processor_dict				,				size=(42, 84)       )
        self.assertEqual(image_processor.size				,				{"height": 84, "width": 42}       )

  def    _UpperCamelCase( self			:  Any       ):
        pass

  @is_flaky()
  def    _UpperCamelCase( self			:  List[Any]       ):
        # Initialize image_processing
        a__	:  Optional[int]     =   self.image_processing_class(**self.image_processor_dict       )
        # create random PIL images
        a__	:  Any     =   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__	:  List[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__	:  Tuple     =   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    _UpperCamelCase( self			:  Optional[int]       ):
        # Initialize image_processing
        a__	:  str     =   self.image_processing_class(**self.image_processor_dict       )
        # create random numpy tensors
        a__	:  List[str]     =   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__	:  Optional[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"],
            )				,				)

  @is_flaky()
  def    _UpperCamelCase( self			:  str       ):
        # Initialize image_processing
        a__	:  Any     =   self.image_processing_class(**self.image_processor_dict       )
        # create random PyTorch tensors
        a__	:  Tuple     =   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__	:  List[str]     =   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__	:  str     =   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"],
            )				,				)





from __future__ import annotations
class 						lowerCamelCase_							:



					'''simple docstring'''







					def __init__(					self			,							snake_case_    ) ->      None:







								'''simple docstring'''

								__lowercase			       = order

								# a_{0} ... a_{k}
								__lowercase			       = [1.0] + [0.0] * order
								# b_{0} ... b_{k}
								__lowercase			       = [1.0] + [0.0] * order

								# x[n-1] ... x[n-k]
								__lowercase			       = [0.0] * self.order
								# y[n-1] ... y[n-k]
								__lowercase			       = [0.0] * self.order






					def A   (					self			,							snake_case_			,							snake_case_    ) ->      None:







								'''simple docstring'''

								if len(snake_case_    ) < self.order:
											__lowercase			       = [1.0, *a_coeffs]

								if len(snake_case_    ) != self.order + 1:
											__lowercase			       = (
											    F'Expected a_coeffs to have {self.order + 1} elements '
											    F'for {self.order}-order filter, got {len(snake_case_    )}'
											)
											raise ValueError(snake_case_    )

								if len(snake_case_    ) != self.order + 1:
											__lowercase			       = (
											    F'Expected b_coeffs to have {self.order + 1} elements '
											    F'for {self.order}-order filter, got {len(snake_case_    )}'
											)
											raise ValueError(snake_case_    )

								__lowercase			       = a_coeffs
								__lowercase			       = b_coeffs






					def A   (					self			,							snake_case_    ) ->      float:







								'''simple docstring'''

								__lowercase			       = 0.0

								# Start at index 1 and do index 0 at the end.
								for i in range(1			,							self.order + 1    ):
											result += (
											    self.b_coeffs[i] * self.input_history[i - 1]
											    - self.a_coeffs[i] * self.output_history[i - 1]
											)

								__lowercase			       = (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

								__lowercase			       = self.input_history[:-1]
								__lowercase			       = self.output_history[:-1]

								__lowercase			       = sample
								__lowercase			       = result

								return result


