

import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import ImageGPTImageProcessor







class        a__       (   unittest.TestCase       ):







		"""simple docstring"""




		def __init__(			self    : Optional[int]				,				UpperCAmelCase__    : Optional[Any]				,				UpperCAmelCase__    : Dict=7				,				UpperCAmelCase__    : List[str]=3				,				UpperCAmelCase__    : Union[str, Any]=1_8				,				UpperCAmelCase__    : Any=3_0				,				UpperCAmelCase__    : Optional[int]=4_0_0				,				UpperCAmelCase__    : Optional[int]=True				,				UpperCAmelCase__    : List[str]=None				,				UpperCAmelCase__    : Tuple=True				,				)					->int:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      int       =   size if size is not None else {"""height""": 1_8, """width""": 1_8}
								SCREAMING_SNAKE_CASE :      List[str]       =   parent
								SCREAMING_SNAKE_CASE :      Optional[int]       =   batch_size
								SCREAMING_SNAKE_CASE :      Optional[int]       =   num_channels
								SCREAMING_SNAKE_CASE :      Optional[int]       =   image_size
								SCREAMING_SNAKE_CASE :      Optional[int]       =   min_resolution
								SCREAMING_SNAKE_CASE :      Tuple       =   max_resolution
								SCREAMING_SNAKE_CASE :      str       =   do_resize
								SCREAMING_SNAKE_CASE :      int       =   size
								SCREAMING_SNAKE_CASE :      Tuple       =   do_normalize




		def  _lowercase      (			self    : Optional[Any]		)					->Tuple:
								"""simple docstring"""







								return {
								    # here we create 2 clusters for the sake of simplicity
								    "clusters": np.asarray(
								        [
								            [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
								            [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
								        ]		),
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_normalize": self.do_normalize,
								}







@require_torch
@require_vision
class        a__       (   UpperCAmelCase	,			unittest.TestCase       ):







		"""simple docstring"""




		UpperCAmelCase__      : Optional[Any]			       =ImageGPTImageProcessor if is_vision_available() else None


		def  _lowercase      (			self    : Any		)					->str:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      Any       =   ImageGPTImageProcessingTester(self		)


		@property
		def  _lowercase      (			self    : Any		)					->Optional[int]:
								"""simple docstring"""







								return self.image_processor_tester.prepare_image_processor_dict()


		def  _lowercase      (			self    : Optional[Any]		)					->List[Any]:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      Optional[int]       =   self.image_processing_class(**self.image_processor_dict		)
								self.assertTrue(hasattr(UpperCAmelCase__				,				"""clusters"""		)		)
								self.assertTrue(hasattr(UpperCAmelCase__				,				"""do_resize"""		)		)
								self.assertTrue(hasattr(UpperCAmelCase__				,				"""size"""		)		)
								self.assertTrue(hasattr(UpperCAmelCase__				,				"""do_normalize"""		)		)


		def  _lowercase      (			self    : Union[str, Any]		)					->Union[str, Any]:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      int       =   self.image_processing_class.from_dict(self.image_processor_dict		)
								self.assertEqual(image_processor.size				,				{"""height""": 1_8, """width""": 1_8}		)

								SCREAMING_SNAKE_CASE :      int       =   self.image_processing_class.from_dict(self.image_processor_dict				,				size=4_2		)
								self.assertEqual(image_processor.size				,				{"""height""": 4_2, """width""": 4_2}		)


		def  _lowercase      (			self    : Tuple		)					->List[Any]:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      Any       =   self.image_processing_class(**self.image_processor_dict		)
								SCREAMING_SNAKE_CASE :      int       =   json.loads(image_processor.to_json_string()		)
								for key, value in self.image_processor_dict.items():
														if key == "clusters":
																				self.assertTrue(np.array_equal(UpperCAmelCase__				,				obj[key]		)		)
														else:
																				self.assertEqual(obj[key]				,				UpperCAmelCase__		)


		def  _lowercase      (			self    : str		)					->int:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      str       =   self.image_processing_class(**self.image_processor_dict		)

								with tempfile.TemporaryDirectory() as tmpdirname:
														SCREAMING_SNAKE_CASE :      Dict       =   os.path.join(UpperCAmelCase__				,				"""image_processor.json"""		)
														image_processor_first.to_json_file(UpperCAmelCase__		)
														SCREAMING_SNAKE_CASE :      Optional[int]       =   self.image_processing_class.from_json_file(UpperCAmelCase__		).to_dict()

								SCREAMING_SNAKE_CASE :      Tuple       =   image_processor_first.to_dict()
								for key, value in image_processor_first.items():
														if key == "clusters":
																				self.assertTrue(np.array_equal(UpperCAmelCase__				,				image_processor_second[key]		)		)
														else:
																				self.assertEqual(image_processor_first[key]				,				UpperCAmelCase__		)


		def  _lowercase      (			self    : List[str]		)					->List[Any]:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      int       =   self.image_processing_class(**self.image_processor_dict		)

								with tempfile.TemporaryDirectory() as tmpdirname:
														image_processor_first.save_pretrained(UpperCAmelCase__		)
														SCREAMING_SNAKE_CASE :      str       =   self.image_processing_class.from_pretrained(UpperCAmelCase__		).to_dict()

								SCREAMING_SNAKE_CASE :      str       =   image_processor_first.to_dict()
								for key, value in image_processor_first.items():
														if key == "clusters":
																				self.assertTrue(np.array_equal(UpperCAmelCase__				,				image_processor_second[key]		)		)
														else:
																				self.assertEqual(image_processor_first[key]				,				UpperCAmelCase__		)




		@unittest.skip("""ImageGPT requires clusters at initialization"""		)
		def  _lowercase      (			self    : Optional[int]		)					->Optional[int]:
								"""simple docstring"""







								pass






def 					__lowercase      (     )     ->						Optional[Any]:
						SCREAMING_SNAKE_CASE :      str       =   load_dataset("""hf-internal-testing/fixtures_image_utils"""						,    split="""test"""			)

						SCREAMING_SNAKE_CASE :      Union[str, Any]       =   Image.open(dataset[4]["""file"""]			)
						SCREAMING_SNAKE_CASE :      int       =   Image.open(dataset[5]["""file"""]			)

						SCREAMING_SNAKE_CASE :      List[str]       =   [imagea, imagea]

						return images







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







		"""simple docstring"""




		@slow
		def  _lowercase      (			self    : str		)					->int:
								"""simple docstring"""







								SCREAMING_SNAKE_CASE :      int       =   ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""		)

								SCREAMING_SNAKE_CASE :      str       =   prepare_images()

								# test non-batched
								SCREAMING_SNAKE_CASE :      Optional[Any]       =   image_processing(images[0]				,				return_tensors="""pt"""		)

								self.assertIsInstance(encoding.input_ids				,				torch.LongTensor		)
								self.assertEqual(encoding.input_ids.shape				,				(1, 1_0_2_4)		)

								SCREAMING_SNAKE_CASE :      str       =   [3_0_6, 1_9_1, 1_9_1]
								self.assertEqual(encoding.input_ids[0, :3].tolist()				,				UpperCAmelCase__		)

								# test batched
								SCREAMING_SNAKE_CASE :      Optional[int]       =   image_processing(UpperCAmelCase__				,				return_tensors="""pt"""		)

								self.assertIsInstance(encoding.input_ids				,				torch.LongTensor		)
								self.assertEqual(encoding.input_ids.shape				,				(2, 1_0_2_4)		)

								SCREAMING_SNAKE_CASE :      Optional[Any]       =   [3_0_3, 1_3, 1_3]
								self.assertEqual(encoding.input_ids[1, -3:].tolist()				,				UpperCAmelCase__		)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase__ :     List[Any]				    =    {"""configuration_vit""": ["""VIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTConfig""", """ViTOnnxConfig"""]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     Union[str, Any]				    =    ["""ViTFeatureExtractor"""]
  UpperCAmelCase__ :     List[Any]				    =    ["""ViTImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     List[Any]				    =    [
      """VIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """ViTForImageClassification""",
      """ViTForMaskedImageModeling""",
      """ViTModel""",
      """ViTPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     Tuple				    =    [
      """TFViTForImageClassification""",
      """TFViTModel""",
      """TFViTPreTrainedModel""",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     str				    =    [
      """FlaxViTForImageClassification""",
      """FlaxViTModel""",
      """FlaxViTPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_vit import ViTFeatureExtractor
    from .image_processing_vit import ViTImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vit import (
        VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        ViTForImageClassification,
        ViTForMaskedImageModeling,
        ViTModel,
        ViTPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
  import sys

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




