
import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
    import jax

    from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel




class      __A(    unittest.TestCase						):

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=13	,      SCREAMING_SNAKE_CASE_=30	,      SCREAMING_SNAKE_CASE_=2	,      SCREAMING_SNAKE_CASE_=3	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=32	,      SCREAMING_SNAKE_CASE_=5	,      SCREAMING_SNAKE_CASE_=4	,      SCREAMING_SNAKE_CASE_=37	,      SCREAMING_SNAKE_CASE_="gelu"	,      SCREAMING_SNAKE_CASE_=0.1	,      SCREAMING_SNAKE_CASE_=0.1	,      SCREAMING_SNAKE_CASE_=10	,      SCREAMING_SNAKE_CASE_=0.02	,      ):
       UpperCamelCase__				=    parent
       UpperCamelCase__				=    batch_size
       UpperCamelCase__				=    image_size
       UpperCamelCase__				=    patch_size
       UpperCamelCase__				=    num_channels
       UpperCamelCase__				=    is_training
       UpperCamelCase__				=    use_labels
       UpperCamelCase__				=    hidden_size
       UpperCamelCase__				=    num_hidden_layers
       UpperCamelCase__				=    num_attention_heads
       UpperCamelCase__				=    intermediate_size
       UpperCamelCase__				=    hidden_act
       UpperCamelCase__				=    hidden_dropout_prob
       UpperCamelCase__				=    attention_probs_dropout_prob
       UpperCamelCase__				=    type_sequence_label_size
       UpperCamelCase__				=    initializer_range

       # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
       UpperCamelCase__				=    (image_size // patch_size) ** 2
       UpperCamelCase__				=    num_patches + 1







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

       UpperCamelCase__				=    ViTConfig(
           image_size=self.image_size	,      patch_size=self.patch_size	,      num_channels=self.num_channels	,      hidden_size=self.hidden_size	,      num_hidden_layers=self.num_hidden_layers	,      num_attention_heads=self.num_attention_heads	,      intermediate_size=self.intermediate_size	,      hidden_act=self.hidden_act	,      hidden_dropout_prob=self.hidden_dropout_prob	,      attention_probs_dropout_prob=self.attention_probs_dropout_prob	,      is_decoder=SCREAMING_SNAKE_CASE_	,      initializer_range=self.initializer_range	,      )

       return config, pixel_values







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    FlaxViTModel(config=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_						)
       # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
       UpperCamelCase__				=    (self.image_size, self.image_size)
       UpperCamelCase__				=    (self.patch_size, self.patch_size)
       UpperCamelCase__				=    (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
       self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, num_patches + 1, self.hidden_size)						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    self.type_sequence_label_size
       UpperCamelCase__				=    FlaxViTForImageClassification(config=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_						)
       self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.type_sequence_label_size)						)

       # test greyscale images
       UpperCamelCase__				=    1
       UpperCamelCase__				=    FlaxViTForImageClassification(SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.prepare_config_and_inputs()
       (
           (
           UpperCamelCase__	
       )		,	(
           UpperCamelCase__	
       )		,		
       )				=    config_and_inputs
       UpperCamelCase__				=    {"""pixel_values""": pixel_values}
       return config, inputs_dict






@require_flax
class      __A(    __lowerCamelCase    ,				unittest.TestCase						):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    FlaxViTModelTester(self						)
       UpperCamelCase__				=    ConfigTester(self	,      config_class=SCREAMING_SNAKE_CASE_	,      has_text_modality=SCREAMING_SNAKE_CASE_	,      hidden_size=37						)







  def 	UpperCAmelCase_ (self						):
       self.config_tester.run_common_tests()







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__		,	UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            UpperCamelCase__				=    model_class(SCREAMING_SNAKE_CASE_						)
            UpperCamelCase__				=    inspect.signature(model.__call__						)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            UpperCamelCase__				=    [*signature.parameters.keys()]

            UpperCamelCase__				=    ["""pixel_values"""]
            self.assertListEqual(arg_names[:1]	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__		,	UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__						):
                 UpperCamelCase__				=    self._prepare_for_class(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)
                 UpperCamelCase__				=    model_class(SCREAMING_SNAKE_CASE_						)

                 @jax.jit
                 def model_jitted(SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						):
                      return model(pixel_values=SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						)

                 with self.subTest("""JIT Enabled"""						):
                      UpperCamelCase__				=    model_jitted(**SCREAMING_SNAKE_CASE_						).to_tuple()

                 with self.subTest("""JIT Disabled"""						):
                      with jax.disable_jit():
                           UpperCamelCase__				=    model_jitted(**SCREAMING_SNAKE_CASE_						).to_tuple()

                 self.assertEqual(len(SCREAMING_SNAKE_CASE_						)	,      len(SCREAMING_SNAKE_CASE_						)						)
                 for jitted_output, output in zip(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
                      self.assertEqual(jitted_output.shape	,      output.shape						)







  @slow
  def 	UpperCAmelCase_ (self						):
       for model_class_name in self.all_model_classes:
            UpperCamelCase__				=    model_class_name.from_pretrained("""google/vit-base-patch16-224"""						)
            UpperCamelCase__				=    model(np.ones((1, 3, 2_24, 2_24)						)						)
            self.assertIsNotNone(SCREAMING_SNAKE_CASE_						)







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_						     =					False

if is_vision_available():
    from PIL import Image

    from transformers import PixaStructImageProcessor




class      __A(    unittest.TestCase			):

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=7	,      SCREAMING_SNAKE_CASE_=3	,      SCREAMING_SNAKE_CASE_=18	,      SCREAMING_SNAKE_CASE_=30	,      SCREAMING_SNAKE_CASE_=4_00	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=None	,      ):
       UpperCamelCase__				=    size if size is not None else {"""height""": 20, """width""": 20}
       UpperCamelCase__				=    parent
       UpperCamelCase__				=    batch_size
       UpperCamelCase__				=    num_channels
       UpperCamelCase__				=    image_size
       UpperCamelCase__				=    min_resolution
       UpperCamelCase__				=    max_resolution
       UpperCamelCase__				=    size
       UpperCamelCase__				=    do_normalize
       UpperCamelCase__				=    do_convert_rgb
       UpperCamelCase__				=    [5_12, 10_24, 20_48, 40_96]
       UpperCamelCase__				=    patch_size if patch_size is not None else {"""height""": 16, """width""": 16}







  def 	UpperCAmelCase_ (self						):
       return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    """https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
       UpperCamelCase__				=    Image.open(requests.get(SCREAMING_SNAKE_CASE_	,      stream=SCREAMING_SNAKE_CASE_						).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      __A(    __lowerCamelCase    ,				unittest.TestCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      PixaStructImageProcessor if is_vision_available() else None







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    PixaStructImageProcessingTester(self						)







  @property
  def 	UpperCAmelCase_ (self						):
       return self.image_processor_tester.prepare_image_processor_dict()







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """do_normalize"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """do_convert_rgb"""						)						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.image_processor_tester.prepare_dummy_image()

       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       UpperCamelCase__				=    20_48

       UpperCamelCase__				=    image_processor(SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						)
       self.assertTrue(torch.allclose(inputs.flattened_patches.mean()	,      torch.tensor(0.0606						)	,      atol=1E-3	,      rtol=1E-3						)						)







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processor
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       UpperCamelCase__				=    prepare_image_inputs(self.image_processor_tester	,      equal_resolution=SCREAMING_SNAKE_CASE_						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_	,      Image.Image						)

       # Test not batched input
       UpperCamelCase__				=    (
           (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__				=    image_processor(
                image_inputs[0]	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (1, max_patch, expected_hidden_dim)	,      )

            # Test batched
            UpperCamelCase__				=    image_processor(
                SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,      )







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processor
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       UpperCamelCase__				=    prepare_image_inputs(self.image_processor_tester	,      equal_resolution=SCREAMING_SNAKE_CASE_						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_	,      Image.Image						)

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

       UpperCamelCase__				=    True

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

            UpperCamelCase__				=    """Hello"""

            UpperCamelCase__				=    image_processor(
                image_inputs[0]	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_	,      header_text=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (1, max_patch, expected_hidden_dim)	,      )

            # Test batched
            UpperCamelCase__				=    image_processor(
                SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_	,      header_text=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,      )







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processor
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       # create random numpy tensors
       UpperCamelCase__				=    prepare_image_inputs(self.image_processor_tester	,      equal_resolution=SCREAMING_SNAKE_CASE_	,      numpify=SCREAMING_SNAKE_CASE_						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_	,      np.ndarray						)

       UpperCamelCase__				=    (
           (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__				=    image_processor(
                image_inputs[0]	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (1, max_patch, expected_hidden_dim)	,      )

            # Test batched
            UpperCamelCase__				=    image_processor(
                SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,      )







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processor
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       # create random PyTorch tensors
       UpperCamelCase__				=    prepare_image_inputs(self.image_processor_tester	,      equal_resolution=SCREAMING_SNAKE_CASE_	,      torchify=SCREAMING_SNAKE_CASE_						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_	,      torch.Tensor						)

       # Test not batched input
       UpperCamelCase__				=    (
           (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__				=    image_processor(
                image_inputs[0]	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (1, max_patch, expected_hidden_dim)	,      )

            # Test batched
            UpperCamelCase__				=    image_processor(
                SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).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      __A(    __lowerCamelCase    ,				unittest.TestCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      PixaStructImageProcessor if is_vision_available() else None







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    PixaStructImageProcessingTester(self	,      num_channels=4						)
       UpperCamelCase__				=    3







  @property
  def 	UpperCAmelCase_ (self						):
       return self.image_processor_tester.prepare_image_processor_dict()







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """do_normalize"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """do_convert_rgb"""						)						)







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processor
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       UpperCamelCase__				=    prepare_image_inputs(self.image_processor_tester	,      equal_resolution=SCREAMING_SNAKE_CASE_						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_	,      Image.Image						)

       # Test not batched input
       UpperCamelCase__				=    (
           (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__				=    image_processor(
                image_inputs[0]	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (1, max_patch, expected_hidden_dim)	,      )

            # Test batched
            UpperCamelCase__				=    image_processor(
                SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,      )





