



"""simple docstring"""
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:
  _lowerCAmelCase    :Any     =					False

if is_vision_available():
  from PIL import Image

  from transformers import PixaStructImageProcessor
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



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





     def  __lowerCAmelCase	(					self		)							->					Any:
         return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}





     def  __lowerCAmelCase	(					self		)							->					int:
         _UpperCAmelCase							:  str											=    '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
         _UpperCAmelCase							:  Any											=    Image.open(requests.get(A       ,	stream=A		).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							( a		,unittest.TestCase      ):





     '''simple docstring'''



     a__									=PixaStructImageProcessor if is_vision_available() else None





     def  __lowerCAmelCase	(					self		)							->					Optional[int]:
         _UpperCAmelCase							:  Optional[Any]											=    PixaStructImageProcessingTester(self		)





     @property
     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         return self.image_processor_tester.prepare_image_processor_dict()





     def  __lowerCAmelCase	(					self		)							->					Optional[Any]:
         _UpperCAmelCase							:  Dict											=    self.image_processing_class(**self.image_processor_dict		)
         self.assertTrue(hasattr(A       ,	'''do_normalize'''		)		)
         self.assertTrue(hasattr(A       ,	'''do_convert_rgb'''		)		)





     def  __lowerCAmelCase	(					self		)							->					Any:
         _UpperCAmelCase							:  int											=    self.image_processor_tester.prepare_dummy_image()

         _UpperCAmelCase							:  Union[str, Any]											=    self.image_processing_class(**self.image_processor_dict		)
         _UpperCAmelCase							:  Any											=    2_0_4_8

         _UpperCAmelCase							:  Optional[Any]											=    image_processor(A       ,	return_tensors='''pt'''       ,	max_patches=A		)
         self.assertTrue(torch.allclose(inputs.flattened_patches.mean()       ,	torch.tensor(0.0_606		)       ,	atol=1E-3       ,	rtol=1E-3		)		)





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

         # Test not batched input
         _UpperCAmelCase							:  Dict											=    (
             (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
             _UpperCAmelCase							:  Optional[Any]											=    image_processor(
                 image_inputs[0]       ,	return_tensors='''pt'''       ,	max_patches=A		).flattened_patches
             self.assertEqual(
                 encoded_images.shape       ,	(1, max_patch, expected_hidden_dim)       ,	)

             # Test batched
             _UpperCAmelCase							:  List[Any]											=    image_processor(
                 A       ,	return_tensors='''pt'''       ,	max_patches=A		).flattened_patches
             self.assertEqual(
                 encoded_images.shape       ,	(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,	)





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

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

         _UpperCAmelCase							:  Optional[Any]											=    True

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

             _UpperCAmelCase							:  Union[str, Any]											=    '''Hello'''

             _UpperCAmelCase							:  Optional[int]											=    image_processor(
                 image_inputs[0]       ,	return_tensors='''pt'''       ,	max_patches=A       ,	header_text=A		).flattened_patches
             self.assertEqual(
                 encoded_images.shape       ,	(1, max_patch, expected_hidden_dim)       ,	)

             # Test batched
             _UpperCAmelCase							:  int											=    image_processor(
                 A       ,	return_tensors='''pt'''       ,	max_patches=A       ,	header_text=A		).flattened_patches
             self.assertEqual(
                 encoded_images.shape       ,	(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,	)





     def  __lowerCAmelCase	(					self		)							->					str:
         # Initialize image_processor
         _UpperCAmelCase							:  Union[str, Any]											=    self.image_processing_class(**self.image_processor_dict		)
         # create random numpy tensors
         _UpperCAmelCase							:  List[Any]											=    prepare_image_inputs(self.image_processor_tester       ,	equal_resolution=A       ,	numpify=A		)
         for image in image_inputs:
             self.assertIsInstance(A       ,	np.ndarray		)

         _UpperCAmelCase							:  Tuple											=    (
             (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
             _UpperCAmelCase							:  Optional[Any]											=    image_processor(
                 image_inputs[0]       ,	return_tensors='''pt'''       ,	max_patches=A		).flattened_patches
             self.assertEqual(
                 encoded_images.shape       ,	(1, max_patch, expected_hidden_dim)       ,	)

             # Test batched
             _UpperCAmelCase							:  Tuple											=    image_processor(
                 A       ,	return_tensors='''pt'''       ,	max_patches=A		).flattened_patches
             self.assertEqual(
                 encoded_images.shape       ,	(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,	)





     def  __lowerCAmelCase	(					self		)							->					List[Any]:
         # Initialize image_processor
         _UpperCAmelCase							:  Union[str, Any]											=    self.image_processing_class(**self.image_processor_dict		)
         # create random PyTorch tensors
         _UpperCAmelCase							:  Any											=    prepare_image_inputs(self.image_processor_tester       ,	equal_resolution=A       ,	torchify=A		)
         for image in image_inputs:
             self.assertIsInstance(A       ,	torch.Tensor		)

         # Test not batched input
         _UpperCAmelCase							:  str											=    (
             (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
             _UpperCAmelCase							:  Optional[int]											=    image_processor(
                 image_inputs[0]       ,	return_tensors='''pt'''       ,	max_patches=A		).flattened_patches
             self.assertEqual(
                 encoded_images.shape       ,	(1, max_patch, expected_hidden_dim)       ,	)

             # Test batched
             _UpperCAmelCase							:  Union[str, Any]											=    image_processor(
                 A       ,	return_tensors='''pt'''       ,	max_patches=A		).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							( a		,unittest.TestCase      ):





     '''simple docstring'''



     a__									=PixaStructImageProcessor if is_vision_available() else None





     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         _UpperCAmelCase							:  Any											=    PixaStructImageProcessingTester(self       ,	num_channels=4		)
         _UpperCAmelCase							:  Optional[Any]											=    3





     @property
     def  __lowerCAmelCase	(					self		)							->					str:
         return self.image_processor_tester.prepare_image_processor_dict()





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         _UpperCAmelCase							:  str											=    self.image_processing_class(**self.image_processor_dict		)
         self.assertTrue(hasattr(A       ,	'''do_normalize'''		)		)
         self.assertTrue(hasattr(A       ,	'''do_convert_rgb'''		)		)





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

         # Test not batched input
         _UpperCAmelCase							:  Optional[Any]											=    (
             (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
             _UpperCAmelCase							:  Optional[Any]											=    image_processor(
                 image_inputs[0]       ,	return_tensors='''pt'''       ,	max_patches=A		).flattened_patches
             self.assertEqual(
                 encoded_images.shape       ,	(1, max_patch, expected_hidden_dim)       ,	)

             # Test batched
             _UpperCAmelCase							:  Any											=    image_processor(
                 A       ,	return_tensors='''pt'''       ,	max_patches=A		).flattened_patches
             self.assertEqual(
                 encoded_images.shape       ,	(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,	)







"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowerCAmelCase    :str     =					{
    'configuration_squeezebert': [
        'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SqueezeBertConfig',
        'SqueezeBertOnnxConfig',
    ],
    'tokenization_squeezebert': ['SqueezeBertTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Optional[int]     =					['SqueezeBertTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :str     =					[
      'SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'SqueezeBertForMaskedLM',
      'SqueezeBertForMultipleChoice',
      'SqueezeBertForQuestionAnswering',
      'SqueezeBertForSequenceClassification',
      'SqueezeBertForTokenClassification',
      'SqueezeBertModel',
      'SqueezeBertModule',
      'SqueezeBertPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_squeezebert import (
      SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      SqueezeBertConfig,
      SqueezeBertOnnxConfig,
  )
  from .tokenization_squeezebert import SqueezeBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_squeezebert import (
        SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        SqueezeBertForMaskedLM,
        SqueezeBertForMultipleChoice,
        SqueezeBertForQuestionAnswering,
        SqueezeBertForSequenceClassification,
        SqueezeBertForTokenClassification,
        SqueezeBertModel,
        SqueezeBertModule,
        SqueezeBertPreTrainedModel,
    )

else:
  import sys

  _lowerCAmelCase    :Optional[Any]     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


