





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:
     _snake_case    :       Dict		       =    False

if is_vision_available():
     from PIL import Image

     from transformers import PixaStructImageProcessor



class 							_UpperCAmelCase       (       unittest.TestCase       ):

  """simple docstring"""


  def __init__(							self					:						Optional[int]   ,    lowerCAmelCase_					:						List[Any]   ,    lowerCAmelCase_					:						List[str]=7   ,    lowerCAmelCase_					:						Dict=3   ,    lowerCAmelCase_					:						Optional[Any]=1_8   ,    lowerCAmelCase_					:						Optional[Any]=3_0   ,    lowerCAmelCase_					:						str=4_0_0   ,    lowerCAmelCase_					:						Union[str, Any]=None   ,    lowerCAmelCase_					:						Optional[int]=True   ,    lowerCAmelCase_					:						str=True   ,    lowerCAmelCase_					:						str=None   ,    )				->		Tuple:
     __lowerCAmelCase				      =      size if size is not None else {'height': 2_0, 'width': 2_0}
     __lowerCAmelCase				      =      parent
     __lowerCAmelCase				      =      batch_size
     __lowerCAmelCase				      =      num_channels
     __lowerCAmelCase				      =      image_size
     __lowerCAmelCase				      =      min_resolution
     __lowerCAmelCase				      =      max_resolution
     __lowerCAmelCase				      =      size
     __lowerCAmelCase				      =      do_normalize
     __lowerCAmelCase				      =      do_convert_rgb
     __lowerCAmelCase				      =      [5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
     __lowerCAmelCase				      =      patch_size if patch_size is not None else {'height': 1_6, 'width': 1_6}






  def 							lowercase						(							self					:						List[Any]	)				->		Tuple:
     return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}






  def 							lowercase						(							self					:						Union[str, Any]	)				->		Union[str, Any]:
     __lowerCAmelCase				      =      'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
     __lowerCAmelCase				      =      Image.open(requests.get(lowerCAmelCase_   ,    stream=lowerCAmelCase_	).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 							_UpperCAmelCase       (       _UpperCamelCase						,       unittest.TestCase       ):

  """simple docstring"""


  a_              =					PixaStructImageProcessor if is_vision_available() else None






  def 							lowercase						(							self					:						List[Any]	)				->		Dict:
     __lowerCAmelCase				      =      PixaStructImageProcessingTester(self	)






  @property
  def 							lowercase						(							self					:						List[str]	)				->		Any:
     return self.image_processor_tester.prepare_image_processor_dict()






  def 							lowercase						(							self					:						Union[str, Any]	)				->		Union[str, Any]:
     __lowerCAmelCase				      =      self.image_processing_class(**self.image_processor_dict	)
     self.assertTrue(hasattr(lowerCAmelCase_   ,    'do_normalize'	)	)
     self.assertTrue(hasattr(lowerCAmelCase_   ,    'do_convert_rgb'	)	)






  def 							lowercase						(							self					:						str	)				->		Dict:
     __lowerCAmelCase				      =      self.image_processor_tester.prepare_dummy_image()

     __lowerCAmelCase				      =      self.image_processing_class(**self.image_processor_dict	)
     __lowerCAmelCase				      =      2_0_4_8

     __lowerCAmelCase				      =      image_processor(lowerCAmelCase_   ,    return_tensors='pt'   ,    max_patches=lowerCAmelCase_	)
     self.assertTrue(torch.allclose(inputs.flattened_patches.mean()   ,    torch.tensor(0.06_06	)   ,    atol=1e-3   ,    rtol=1e-3	)	)






  def 							lowercase						(							self					:						Any	)				->		Dict:
     # Initialize image_processor
     __lowerCAmelCase				      =      self.image_processing_class(**self.image_processor_dict	)
     # create random PIL images
     __lowerCAmelCase				      =      prepare_image_inputs(self.image_processor_tester   ,    equal_resolution=lowerCAmelCase_	)
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase_   ,    Image.Image	)

     # Test not batched input
     __lowerCAmelCase				      =      (
         (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
        __lowerCAmelCase				      =      image_processor(
            image_inputs[0]   ,    return_tensors='pt'   ,    max_patches=lowerCAmelCase_	).flattened_patches
        self.assertEqual(
            encoded_images.shape   ,    (1, max_patch, expected_hidden_dim)   ,    )

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






  def 							lowercase						(							self					:						List[str]	)				->		Union[str, Any]:
     # Initialize image_processor
     __lowerCAmelCase				      =      self.image_processing_class(**self.image_processor_dict	)
     # create random PIL images
     __lowerCAmelCase				      =      prepare_image_inputs(self.image_processor_tester   ,    equal_resolution=lowerCAmelCase_	)
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase_   ,    Image.Image	)

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

     __lowerCAmelCase				      =      True

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

        __lowerCAmelCase				      =      'Hello'

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

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






  def 							lowercase						(							self					:						Any	)				->		Tuple:
     # Initialize image_processor
     __lowerCAmelCase				      =      self.image_processing_class(**self.image_processor_dict	)
     # create random numpy tensors
     __lowerCAmelCase				      =      prepare_image_inputs(self.image_processor_tester   ,    equal_resolution=lowerCAmelCase_   ,    numpify=lowerCAmelCase_	)
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase_   ,    np.ndarray	)

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

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






  def 							lowercase						(							self					:						Optional[Any]	)				->		List[Any]:
     # Initialize image_processor
     __lowerCAmelCase				      =      self.image_processing_class(**self.image_processor_dict	)
     # create random PyTorch tensors
     __lowerCAmelCase				      =      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
     __lowerCAmelCase				      =      (
         (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
        __lowerCAmelCase				      =      image_processor(
            image_inputs[0]   ,    return_tensors='pt'   ,    max_patches=lowerCAmelCase_	).flattened_patches
        self.assertEqual(
            encoded_images.shape   ,    (1, max_patch, expected_hidden_dim)   ,    )

        # Test batched
        __lowerCAmelCase				      =      image_processor(
            lowerCAmelCase_   ,    return_tensors='pt'   ,    max_patches=lowerCAmelCase_	).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 							_UpperCAmelCase       (       _UpperCamelCase						,       unittest.TestCase       ):

  """simple docstring"""


  a_              =					PixaStructImageProcessor if is_vision_available() else None






  def 							lowercase						(							self					:						Any	)				->		int:
     __lowerCAmelCase				      =      PixaStructImageProcessingTester(self   ,    num_channels=4	)
     __lowerCAmelCase				      =      3






  @property
  def 							lowercase						(							self					:						Any	)				->		Optional[int]:
     return self.image_processor_tester.prepare_image_processor_dict()






  def 							lowercase						(							self					:						Dict	)				->		Tuple:
     __lowerCAmelCase				      =      self.image_processing_class(**self.image_processor_dict	)
     self.assertTrue(hasattr(lowerCAmelCase_   ,    'do_normalize'	)	)
     self.assertTrue(hasattr(lowerCAmelCase_   ,    'do_convert_rgb'	)	)






  def 							lowercase						(							self					:						List[Any]	)				->		Any:
     # Initialize image_processor
     __lowerCAmelCase				      =      self.image_processing_class(**self.image_processor_dict	)
     # create random PIL images
     __lowerCAmelCase				      =      prepare_image_inputs(self.image_processor_tester   ,    equal_resolution=lowerCAmelCase_	)
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase_   ,    Image.Image	)

     # Test not batched input
     __lowerCAmelCase				      =      (
         (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
        __lowerCAmelCase				      =      image_processor(
            image_inputs[0]   ,    return_tensors='pt'   ,    max_patches=lowerCAmelCase_	).flattened_patches
        self.assertEqual(
            encoded_images.shape   ,    (1, max_patch, expected_hidden_dim)   ,    )

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









import re
from pathlib import Path
from unittest import TestCase

import pytest


@pytest.mark.integration
class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			Dict:

     with open(_UpperCAmelCase		,		encoding='utf-8'			) as input_file:
        UpperCamelCase_						=  re.compile(R'(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)'			)
        UpperCamelCase_						=  input_file.read()
        UpperCamelCase_						=  regexp.search(_UpperCAmelCase			)

     return match




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			Dict:

     with open(_UpperCAmelCase		,		encoding='utf-8'			) as input_file:
        UpperCamelCase_						=  re.compile(R'#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()'		,		re.DOTALL			)
        UpperCamelCase_						=  input_file.read()
        # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
        UpperCamelCase_						=  regexp.finditer(_UpperCAmelCase			)

     UpperCamelCase_						=  [match for match in matches if match is not None and match.group(1			) is not None]
     return matches[0] if matches else None




  def  _UpperCAmelCase  (  self			)      ->			List[str]:
     UpperCamelCase_						=  Path('./datasets'			)
     UpperCamelCase_						=  list(dataset_paths.absolute().glob('**/*.py'			)			)

     for dataset in dataset_files:
        if self._no_encoding_on_file_open(str(_UpperCAmelCase			)			):
           raise AssertionError(f"""open(...) must use utf-8 encoding in {dataset}"""			)




  def  _UpperCAmelCase  (  self			)      ->			Optional[int]:
     UpperCamelCase_						=  Path('./datasets'			)
     UpperCamelCase_						=  list(dataset_paths.absolute().glob('**/*.py'			)			)

     for dataset in dataset_files:
        if self._no_print_statements(str(_UpperCAmelCase			)			):
           raise AssertionError(f"""print statement found in {dataset}. Use datasets.logger/logging instead."""			)





