






import unittest

import numpy as np
import requests

from transformers.testing_utils import 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

     from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
     UpperCAmelCase__							  =						False

if is_vision_available():
     from PIL import Image

     from transformers import PixaStructImageProcessor






class 						lowercase_   (      unittest.TestCase  ):






      '''simple docstring'''




      def __init__( self :		List[Any]					,   __UpperCAmelCase :		int					,   __UpperCAmelCase :		Union[str, Any]=7					,   __UpperCAmelCase :		int=3					,   __UpperCAmelCase :		Optional[Any]=18					,   __UpperCAmelCase :		Union[str, Any]=30					,   __UpperCAmelCase :		Optional[Any]=400					,   __UpperCAmelCase :		Optional[int]=None					,   __UpperCAmelCase :		Union[str, Any]=True					,   __UpperCAmelCase :		List[Any]=True					,   __UpperCAmelCase :		int=None					,   )       ->Any:




       """simple docstring"""


       a						    = size if size is not None else {'''height''': 20, '''width''': 20}
       a						    = parent
       a						    = batch_size
       a						    = num_channels
       a						    = image_size
       a						    = min_resolution
       a						    = max_resolution
       a						    = size
       a						    = do_normalize
       a						    = do_convert_rgb
       a						    = [512, 1_024, 2_048, 4_096]
       a						    = patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}







      def  __lowerCAmelCase   ( self :		Optional[Any]    )       ->Tuple:




       """simple docstring"""


       return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







      def  __lowerCAmelCase   ( self :		str    )       ->Dict:




       """simple docstring"""


       a						    = '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
       a						    = Image.open(requests.get(__UpperCAmelCase					,   stream=__UpperCAmelCase    ).raw    ).convert('''RGB'''    )
       return raw_image






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11					,    reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''					,    )
@require_torch
@require_vision
class 						lowercase_   (      lowercase					,    unittest.TestCase  ):






      '''simple docstring'''




      __snake_case	    =    PixaStructImageProcessor if is_vision_available() else None







      def  __lowerCAmelCase   ( self :		Optional[Any]    )       ->Union[str, Any]:




       """simple docstring"""


       a						    = PixaStructImageProcessingTester(self    )







      @property
      def  __lowerCAmelCase   ( self :		List[str]    )       ->Union[str, Any]:




       """simple docstring"""


       return self.image_processor_tester.prepare_image_processor_dict()







      def  __lowerCAmelCase   ( self :		Dict    )       ->Any:




       """simple docstring"""


       a						    = self.image_processing_class(**self.image_processor_dict    )
       self.assertTrue(hasattr(__UpperCAmelCase					,   '''do_normalize'''    )    )
       self.assertTrue(hasattr(__UpperCAmelCase					,   '''do_convert_rgb'''    )    )







      def  __lowerCAmelCase   ( self :		List[Any]    )       ->Optional[Any]:




       """simple docstring"""


       a						    = self.image_processor_tester.prepare_dummy_image()

       a						    = self.image_processing_class(**self.image_processor_dict    )
       a						    = 2_048

       a						    = image_processor(__UpperCAmelCase					,   return_tensors='''pt'''					,   max_patches=__UpperCAmelCase    )
       self.assertTrue(torch.allclose(inputs.flattened_patches.mean()					,   torch.tensor(0.0606    )					,   atol=1e-3					,   rtol=1e-3    )    )







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




       """simple docstring"""


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

       # Test not batched input
       a						    = (
           (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
        # Test not batched input
        a						    = image_processor(
            image_inputs[0]					,   return_tensors='''pt'''					,   max_patches=__UpperCAmelCase    ).flattened_patches
        self.assertEqual(
            encoded_images.shape					,   (1, max_patch, expected_hidden_dim)					,   )

        # Test batched
        a						    = image_processor(
            __UpperCAmelCase					,   return_tensors='''pt'''					,   max_patches=__UpperCAmelCase    ).flattened_patches
        self.assertEqual(
            encoded_images.shape					,   (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)					,   )







      def  __lowerCAmelCase   ( self :		Dict    )       ->List[str]:




       """simple docstring"""


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

       # Test not batched input
       a						    = (
           (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
           * self.image_processor_tester.num_channels
       ) + 2

       a						    = True

       for max_patch in self.image_processor_tester.max_patches:
        # Test not batched input
        with self.assertRaises(__UpperCAmelCase    ):
         a						    = image_processor(
             image_inputs[0]					,   return_tensors='''pt'''					,   max_patches=__UpperCAmelCase    ).flattened_patches

        a						    = '''Hello'''

        a						    = image_processor(
            image_inputs[0]					,   return_tensors='''pt'''					,   max_patches=__UpperCAmelCase					,   header_text=__UpperCAmelCase    ).flattened_patches
        self.assertEqual(
            encoded_images.shape					,   (1, max_patch, expected_hidden_dim)					,   )

        # Test batched
        a						    = image_processor(
            __UpperCAmelCase					,   return_tensors='''pt'''					,   max_patches=__UpperCAmelCase					,   header_text=__UpperCAmelCase    ).flattened_patches
        self.assertEqual(
            encoded_images.shape					,   (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)					,   )







      def  __lowerCAmelCase   ( self :		List[Any]    )       ->str:




       """simple docstring"""


       a						    = self.image_processing_class(**self.image_processor_dict    )
       # create random numpy tensors
       a						    = prepare_image_inputs(self.image_processor_tester					,   equal_resolution=__UpperCAmelCase					,   numpify=__UpperCAmelCase    )
       for image in image_inputs:
        self.assertIsInstance(__UpperCAmelCase					,   np.ndarray    )

       a						    = (
           (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
        # Test not batched input
        a						    = image_processor(
            image_inputs[0]					,   return_tensors='''pt'''					,   max_patches=__UpperCAmelCase    ).flattened_patches
        self.assertEqual(
            encoded_images.shape					,   (1, max_patch, expected_hidden_dim)					,   )

        # Test batched
        a						    = image_processor(
            __UpperCAmelCase					,   return_tensors='''pt'''					,   max_patches=__UpperCAmelCase    ).flattened_patches
        self.assertEqual(
            encoded_images.shape					,   (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)					,   )







      def  __lowerCAmelCase   ( self :		Dict    )       ->int:




       """simple docstring"""


       a						    = self.image_processing_class(**self.image_processor_dict    )
       # create random PyTorch tensors
       a						    = prepare_image_inputs(self.image_processor_tester					,   equal_resolution=__UpperCAmelCase					,   torchify=__UpperCAmelCase    )
       for image in image_inputs:
        self.assertIsInstance(__UpperCAmelCase					,   torch.Tensor    )

       # Test not batched input
       a						    = (
           (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
        # Test not batched input
        a						    = image_processor(
            image_inputs[0]					,   return_tensors='''pt'''					,   max_patches=__UpperCAmelCase    ).flattened_patches
        self.assertEqual(
            encoded_images.shape					,   (1, max_patch, expected_hidden_dim)					,   )

        # Test batched
        a						    = image_processor(
            __UpperCAmelCase					,   return_tensors='''pt'''					,   max_patches=__UpperCAmelCase    ).flattened_patches
        self.assertEqual(
            encoded_images.shape					,   (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)					,   )






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11					,    reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''					,    )
@require_torch
@require_vision
class 						lowercase_   (      lowercase					,    unittest.TestCase  ):






      '''simple docstring'''




      __snake_case	    =    PixaStructImageProcessor if is_vision_available() else None







      def  __lowerCAmelCase   ( self :		Union[str, Any]    )       ->Dict:




       """simple docstring"""


       a						    = PixaStructImageProcessingTester(self					,   num_channels=4    )
       a						    = 3







      @property
      def  __lowerCAmelCase   ( self :		List[str]    )       ->List[str]:




       """simple docstring"""


       return self.image_processor_tester.prepare_image_processor_dict()







      def  __lowerCAmelCase   ( self :		int    )       ->Dict:




       """simple docstring"""


       a						    = self.image_processing_class(**self.image_processor_dict    )
       self.assertTrue(hasattr(__UpperCAmelCase					,   '''do_normalize'''    )    )
       self.assertTrue(hasattr(__UpperCAmelCase					,   '''do_convert_rgb'''    )    )







      def  __lowerCAmelCase   ( self :		Union[str, Any]    )       ->Optional[Any]:




       """simple docstring"""


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

       # Test not batched input
       a						    = (
           (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
           * (self.image_processor_tester.num_channels - 1)
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
        # Test not batched input
        a						    = image_processor(
            image_inputs[0]					,   return_tensors='''pt'''					,   max_patches=__UpperCAmelCase    ).flattened_patches
        self.assertEqual(
            encoded_images.shape					,   (1, max_patch, expected_hidden_dim)					,   )

        # Test batched
        a						    = image_processor(
            __UpperCAmelCase					,   return_tensors='''pt'''					,   max_patches=__UpperCAmelCase    ).flattened_patches
        self.assertEqual(
            encoded_images.shape					,   (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)					,   )










from __future__ import annotations






class 						lowercase_   :






      '''simple docstring'''




      def __init__( self :		List[str]					,   __UpperCAmelCase :		int    )       ->None:




       """simple docstring"""


       a						    = order

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

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







      def  __lowerCAmelCase   ( self :		Union[str, Any]					,   __UpperCAmelCase :		list[float]					,   __UpperCAmelCase :		list[float]    )       ->None:




       """simple docstring"""


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

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

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

       a						    = a_coeffs
       a						    = b_coeffs







      def  __lowerCAmelCase   ( self :		Any					,   __UpperCAmelCase :		float    )       ->float:




       """simple docstring"""


       a						    = 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]
        )

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

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

       a						    = sample
       a						    = result

       return result


