from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


SCREAMING_SNAKE_CASE :	Dict							     =		logging.get_logger(__name__)
class    A_ (       a_ ):
  _SCREAMING_SNAKE_CASE    				=			["""pixel_values"""]

  def __init__(		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      bool = True     ,							__SCREAMING_SNAKE_CASE :      Dict[str, int] = None     ,							__SCREAMING_SNAKE_CASE :      float = None     ,							__SCREAMING_SNAKE_CASE :      PILImageResampling = PILImageResampling.BILINEAR     ,							__SCREAMING_SNAKE_CASE :      bool = True     ,							__SCREAMING_SNAKE_CASE :      Union[int, float] = 1 / 2_55     ,							__SCREAMING_SNAKE_CASE :      bool = True     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[float, List[float]]] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[float, List[float]]] = None     ,							**__SCREAMING_SNAKE_CASE :      str     ,							):
   super().__init__(**__SCREAMING_SNAKE_CASE       )
   __a       =  size if size is not None else {"shortest_edge": 3_84}
   __a       =  get_size_dict(__SCREAMING_SNAKE_CASE     ,							default_to_square=__SCREAMING_SNAKE_CASE       )

   __a       =  do_resize
   __a       =  size
   # Default value set here for backwards compatibility where the value in config is None
   __a       =  crop_pct if crop_pct is not None else 2_24 / 2_56
   __a       =  resample
   __a       =  do_rescale
   __a       =  rescale_factor
   __a       =  do_normalize
   __a       =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
   __a       =  image_std if image_std is not None else IMAGENET_STANDARD_STD

  def 		_UpperCAmelCase   (		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      np.ndarray     ,							__SCREAMING_SNAKE_CASE :      Dict[str, int]     ,							__SCREAMING_SNAKE_CASE :      float     ,							__SCREAMING_SNAKE_CASE :      PILImageResampling = PILImageResampling.BICUBIC     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[str, ChannelDimension]] = None     ,							**__SCREAMING_SNAKE_CASE :      Union[str, Any]     ,							):
   __a       =  get_size_dict(__SCREAMING_SNAKE_CASE     ,							default_to_square=__SCREAMING_SNAKE_CASE       )
   if "shortest_edge" not in size:
    raise ValueError(f"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"""       )
   __a       =  size["shortest_edge"]

   if shortest_edge < 3_84:
    # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
    __a       =  int(shortest_edge / crop_pct       )
    __a       =  get_resize_output_image_size(__SCREAMING_SNAKE_CASE     ,							size=__SCREAMING_SNAKE_CASE     ,							default_to_square=__SCREAMING_SNAKE_CASE       )
    __a       =  resize(image=__SCREAMING_SNAKE_CASE     ,							size=__SCREAMING_SNAKE_CASE     ,							resample=__SCREAMING_SNAKE_CASE     ,							data_format=__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )
    # then crop to (shortest_edge, shortest_edge)
    return center_crop(image=__SCREAMING_SNAKE_CASE     ,							size=(shortest_edge, shortest_edge)     ,							data_format=__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )
   else:
    # warping (no cropping) when evaluated at 384 or larger
    return resize(
        __SCREAMING_SNAKE_CASE     ,							size=(shortest_edge, shortest_edge)     ,							resample=__SCREAMING_SNAKE_CASE     ,							data_format=__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      np.ndarray     ,							__SCREAMING_SNAKE_CASE :      Union[int, float]     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[str, ChannelDimension]] = None     ,							**__SCREAMING_SNAKE_CASE :      Union[str, Any]     ,							):
   return rescale(__SCREAMING_SNAKE_CASE     ,							scale=__SCREAMING_SNAKE_CASE     ,							data_format=__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Tuple     ,							__SCREAMING_SNAKE_CASE :      np.ndarray     ,							__SCREAMING_SNAKE_CASE :      Union[float, List[float]]     ,							__SCREAMING_SNAKE_CASE :      Union[float, List[float]]     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[str, ChannelDimension]] = None     ,							**__SCREAMING_SNAKE_CASE :      Optional[int]     ,							):
   return normalize(__SCREAMING_SNAKE_CASE     ,							mean=__SCREAMING_SNAKE_CASE     ,							std=__SCREAMING_SNAKE_CASE     ,							data_format=__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      ImageInput     ,							__SCREAMING_SNAKE_CASE :      bool = None     ,							__SCREAMING_SNAKE_CASE :      Dict[str, int] = None     ,							__SCREAMING_SNAKE_CASE :      float = None     ,							__SCREAMING_SNAKE_CASE :      PILImageResampling = None     ,							__SCREAMING_SNAKE_CASE :      bool = None     ,							__SCREAMING_SNAKE_CASE :      float = None     ,							__SCREAMING_SNAKE_CASE :      bool = None     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[float, List[float]]] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[float, List[float]]] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[str, TensorType]] = None     ,							__SCREAMING_SNAKE_CASE :      ChannelDimension = ChannelDimension.FIRST     ,							**__SCREAMING_SNAKE_CASE :      int     ,							):
   __a       =  do_resize if do_resize is not None else self.do_resize
   __a       =  crop_pct if crop_pct is not None else self.crop_pct
   __a       =  resample if resample is not None else self.resample
   __a       =  do_rescale if do_rescale is not None else self.do_rescale
   __a       =  rescale_factor if rescale_factor is not None else self.rescale_factor
   __a       =  do_normalize if do_normalize is not None else self.do_normalize
   __a       =  image_mean if image_mean is not None else self.image_mean
   __a       =  image_std if image_std is not None else self.image_std

   __a       =  size if size is not None else self.size
   __a       =  get_size_dict(__SCREAMING_SNAKE_CASE     ,							default_to_square=__SCREAMING_SNAKE_CASE       )

   __a       =  make_list_of_images(__SCREAMING_SNAKE_CASE       )

   if not valid_images(__SCREAMING_SNAKE_CASE       ):
    raise ValueError(
        "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
        "torch.Tensor, tf.Tensor or jax.ndarray."       )

   if do_resize and size is None or resample is None:
    raise ValueError("Size and resample must be specified if do_resize is True."       )

   if do_resize and size["shortest_edge"] < 3_84 and crop_pct is None:
    raise ValueError("crop_pct must be specified if size < 384."       )

   if do_rescale and rescale_factor is None:
    raise ValueError("Rescale factor must be specified if do_rescale is True."       )

   if do_normalize and (image_mean is None or image_std is None):
    raise ValueError("Image mean and std must be specified if do_normalize is True."       )

   # All transformations expect numpy arrays.
   __a       =  [to_numpy_array(__SCREAMING_SNAKE_CASE       ) for image in images]

   if do_resize:
    __a       =  [self.resize(image=__SCREAMING_SNAKE_CASE     ,							size=__SCREAMING_SNAKE_CASE     ,							crop_pct=__SCREAMING_SNAKE_CASE     ,							resample=__SCREAMING_SNAKE_CASE       ) for image in images]

   if do_rescale:
    __a       =  [self.rescale(image=__SCREAMING_SNAKE_CASE     ,							scale=__SCREAMING_SNAKE_CASE       ) for image in images]

   if do_normalize:
    __a       =  [self.normalize(image=__SCREAMING_SNAKE_CASE     ,							mean=__SCREAMING_SNAKE_CASE     ,							std=__SCREAMING_SNAKE_CASE       ) for image in images]

   __a       =  [to_channel_dimension_format(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ) for image in images]

   __a       =  {"pixel_values": images}
   return BatchFeature(data=__SCREAMING_SNAKE_CASE     ,							tensor_type=__SCREAMING_SNAKE_CASE       )



import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
  from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import ViTImageProcessor
class    A_ :

  def __init__(		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      List[Any]=13     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=30     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=2     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=3     ,							__SCREAMING_SNAKE_CASE :      List[Any]=True     ,							__SCREAMING_SNAKE_CASE :      Dict=True     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=32     ,							__SCREAMING_SNAKE_CASE :      str=5     ,							__SCREAMING_SNAKE_CASE :      List[str]=4     ,							__SCREAMING_SNAKE_CASE :      List[Any]=37     ,							__SCREAMING_SNAKE_CASE :      int="gelu"     ,							__SCREAMING_SNAKE_CASE :      List[Any]=0.1     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=0.1     ,							__SCREAMING_SNAKE_CASE :      List[Any]=10     ,							__SCREAMING_SNAKE_CASE :      int=0.02     ,							__SCREAMING_SNAKE_CASE :      Dict=None     ,							__SCREAMING_SNAKE_CASE :      str=2     ,							):
   __a       =  parent
   __a       =  batch_size
   __a       =  image_size
   __a       =  patch_size
   __a       =  num_channels
   __a       =  is_training
   __a       =  use_labels
   __a       =  hidden_size
   __a       =  num_hidden_layers
   __a       =  num_attention_heads
   __a       =  intermediate_size
   __a       =  hidden_act
   __a       =  hidden_dropout_prob
   __a       =  attention_probs_dropout_prob
   __a       =  type_sequence_label_size
   __a       =  initializer_range
   __a       =  scope
   __a       =  encoder_stride

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

  def 		_UpperCAmelCase   (		self :      Any       ):
   __a       =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

   __a       =  None
   if self.use_labels:
    __a       =  ids_tensor([self.batch_size]     ,							self.type_sequence_label_size       )

   __a       =  self.get_config()

   return config, pixel_values, labels

  def 		_UpperCAmelCase   (		self :      str       ):
   return 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     ,							encoder_stride=self.encoder_stride     ,							)

  def 		_UpperCAmelCase   (		self :      Dict     ,							__SCREAMING_SNAKE_CASE :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      Dict     ,							__SCREAMING_SNAKE_CASE :      Any       ):
   __a       =  ViTModel(config=__SCREAMING_SNAKE_CASE       )
   model.to(__SCREAMING_SNAKE_CASE       )
   model.eval()
   __a       =  model(__SCREAMING_SNAKE_CASE       )
   self.parent.assertEqual(result.last_hidden_state.shape     ,							(self.batch_size, self.seq_length, self.hidden_size)       )

  def 		_UpperCAmelCase   (		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Tuple     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      Dict       ):
   __a       =  ViTForMaskedImageModeling(config=__SCREAMING_SNAKE_CASE       )
   model.to(__SCREAMING_SNAKE_CASE       )
   model.eval()
   __a       =  model(__SCREAMING_SNAKE_CASE       )
   self.parent.assertEqual(
       result.reconstruction.shape     ,							(self.batch_size, self.num_channels, self.image_size, self.image_size)       )

   # test greyscale images
   __a       =  1
   __a       =  ViTForMaskedImageModeling(__SCREAMING_SNAKE_CASE       )
   model.to(__SCREAMING_SNAKE_CASE       )
   model.eval()

   __a       =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
   __a       =  model(__SCREAMING_SNAKE_CASE       )
   self.parent.assertEqual(result.reconstruction.shape     ,							(self.batch_size, 1, self.image_size, self.image_size)       )

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      Tuple     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      List[str]       ):
   __a       =  self.type_sequence_label_size
   __a       =  ViTForImageClassification(__SCREAMING_SNAKE_CASE       )
   model.to(__SCREAMING_SNAKE_CASE       )
   model.eval()
   __a       =  model(__SCREAMING_SNAKE_CASE     ,							labels=__SCREAMING_SNAKE_CASE       )
   self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.type_sequence_label_size)       )

   # test greyscale images
   __a       =  1
   __a       =  ViTForImageClassification(__SCREAMING_SNAKE_CASE       )
   model.to(__SCREAMING_SNAKE_CASE       )
   model.eval()

   __a       =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
   __a       =  model(__SCREAMING_SNAKE_CASE       )
   self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.type_sequence_label_size)       )

  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   __a       =  self.prepare_config_and_inputs()
   (
       (
       __a
   )       ,     (
       __a
   )       ,     (
       __a
   )       ,     
   )       =  config_and_inputs
   __a       =  {"pixel_values": pixel_values}
   return config, inputs_dict
@require_torch
class    A_ (       a_			,       a_			,       unittest.TestCase ):
  _SCREAMING_SNAKE_CASE    				=			(
      (
          ViTModel,
          ViTForImageClassification,
          ViTForMaskedImageModeling,
      )
      if is_torch_available()
      else ()
  )
  _SCREAMING_SNAKE_CASE    				=			(
      {"""feature-extraction""": ViTModel, """image-classification""": ViTForImageClassification}
      if is_torch_available()
      else {}
  )
  _SCREAMING_SNAKE_CASE    				=			True

  _SCREAMING_SNAKE_CASE    				=			False
  _SCREAMING_SNAKE_CASE    				=			False
  _SCREAMING_SNAKE_CASE    				=			False

  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   __a       =  ViTModelTester(self       )
   __a       =  ConfigTester(self     ,							config_class=__SCREAMING_SNAKE_CASE     ,							has_text_modality=__SCREAMING_SNAKE_CASE     ,							hidden_size=37       )

  def 		_UpperCAmelCase   (		self :      List[str]       ):
   self.config_tester.run_common_tests()

  @unittest.skip(reason="ViT does not use inputs_embeds"       )
  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   pass

  def 		_UpperCAmelCase   (		self :      int       ):
   __a       ,     __a       =  self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __a       =  model_class(__SCREAMING_SNAKE_CASE       )
    self.assertIsInstance(model.get_input_embeddings()     ,							(nn.Module)       )
    __a       =  model.get_output_embeddings()
    self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE     ,							nn.Linear       )       )

  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   __a       ,     __a       =  self.model_tester.prepare_config_and_inputs_for_common()

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

    __a       =  ["pixel_values"]
    self.assertListEqual(arg_names[:1]     ,							__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      List[str]       ):
   __a       =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Any       ):
   __a       =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_image_modeling(*__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Tuple       ):
   __a       =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE       )

  @slow
  def 		_UpperCAmelCase   (		self :      Dict       ):
   for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __a       =  ViTModel.from_pretrained(__SCREAMING_SNAKE_CASE       )
    self.assertIsNotNone(__SCREAMING_SNAKE_CASE       )



def   __A		(			):



 """simple docstring"""


 __a       =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
 return image






@require_torch
@require_vision
class    A_ (       unittest.TestCase ):

  @cached_property
  def 		_UpperCAmelCase   (		self :      List[str]       ):
   return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224"       ) if is_vision_available() else None

  @slow
  def 		_UpperCAmelCase   (		self :      str       ):
   __a       =  ViTForImageClassification.from_pretrained("google/vit-base-patch16-224"       ).to(__SCREAMING_SNAKE_CASE       )

   __a       =  self.default_image_processor
   __a       =  prepare_img()
   __a       =  image_processor(images=__SCREAMING_SNAKE_CASE     ,							return_tensors="pt"       ).to(__SCREAMING_SNAKE_CASE       )

   # forward pass
   with torch.no_grad():
    __a       =  model(**__SCREAMING_SNAKE_CASE       )

   # verify the logits
   __a       =  torch.Size((1, 10_00)       )
   self.assertEqual(outputs.logits.shape     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  torch.tensor([-0.27_44, 0.82_15, -0.08_36]       ).to(__SCREAMING_SNAKE_CASE       )

   self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,							__SCREAMING_SNAKE_CASE     ,							atol=1E-4       )       )

  @slow
  def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
   # ViT models have an `interpolate_pos_encoding` argument in their forward method,
   # allowing to interpolate the pre-trained position embeddings in order to use
   # the model on higher resolutions. The DINO model by Facebook AI leverages this
   # to visualize self-attention on higher resolution images.
   __a       =  ViTModel.from_pretrained("facebook/dino-vits8"       ).to(__SCREAMING_SNAKE_CASE       )

   __a       =  ViTImageProcessor.from_pretrained("facebook/dino-vits8"     ,							size=4_80       )
   __a       =  prepare_img()
   __a       =  image_processor(images=__SCREAMING_SNAKE_CASE     ,							return_tensors="pt"       )
   __a       =  inputs.pixel_values.to(__SCREAMING_SNAKE_CASE       )

   # forward pass
   with torch.no_grad():
    __a       =  model(__SCREAMING_SNAKE_CASE     ,							interpolate_pos_encoding=__SCREAMING_SNAKE_CASE       )

   # verify the logits
   __a       =  torch.Size((1, 36_01, 3_84)       )
   self.assertEqual(outputs.last_hidden_state.shape     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  torch.tensor(
       [[4.23_40, 4.39_06, -6.66_92], [4.54_63, 1.89_28, -6.72_57], [4.44_29, 0.84_96, -5.85_85]]       ).to(__SCREAMING_SNAKE_CASE       )

   self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]     ,							__SCREAMING_SNAKE_CASE     ,							atol=1E-4       )       )

  @slow
  @require_accelerate
  @require_torch_gpu
  def 		_UpperCAmelCase   (		self :      Any       ):
   __a       =  ViTModel.from_pretrained("facebook/dino-vits8"     ,							torch_dtype=torch.floataa     ,							device_map="auto"       )
   __a       =  self.default_image_processor

   __a       =  prepare_img()
   __a       =  image_processor(images=__SCREAMING_SNAKE_CASE     ,							return_tensors="pt"       )
   __a       =  inputs.pixel_values.to(__SCREAMING_SNAKE_CASE       )

   # forward pass to make sure inference works in fp16
   with torch.no_grad():
    __a       =  model(__SCREAMING_SNAKE_CASE       )


