



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 			__magic_name__ (				unittest.TestCase    ):






		def __init__(    self   :  int					,		UpperCamelCase__   :  Dict					,		UpperCamelCase__   :  int=7					,		UpperCamelCase__   :  Optional[int]=3					,		UpperCamelCase__   :  Optional[int]=18					,		UpperCamelCase__   :  List[Any]=30					,		UpperCamelCase__   :  Dict=4_00					,		UpperCamelCase__   :  Optional[Any]=True					,		UpperCamelCase__   :  Any=None					,		UpperCamelCase__   :  Optional[Any]=True					,		)					->       Any:
								'''simple docstring'''







								UpperCAmelCase								=  size if size is not None else {"height": 18, "width": 18}
								UpperCAmelCase								=  parent
								UpperCAmelCase								=  batch_size
								UpperCAmelCase								=  num_channels
								UpperCAmelCase								=  image_size
								UpperCAmelCase								=  min_resolution
								UpperCAmelCase								=  max_resolution
								UpperCAmelCase								=  do_resize
								UpperCAmelCase								=  size
								UpperCAmelCase								=  do_normalize







		def SCREAMING_SNAKE_CASE_      (    self   :  int   )					->       int:
								'''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 			__magic_name__ (				A__,	unittest.TestCase    ):
		lowercase   :							str        =ImageGPTImageProcessor if is_vision_available() else None






		def SCREAMING_SNAKE_CASE_      (    self   :  List[str]   )					->       Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase								=  ImageGPTImageProcessingTester(self   )






		@property
		def SCREAMING_SNAKE_CASE_      (    self   :  List[str]   )					->       Dict:
								'''simple docstring'''







								return self.image_processor_tester.prepare_image_processor_dict()






		def SCREAMING_SNAKE_CASE_      (    self   :  Union[str, Any]   )					->       Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase								=  self.image_processing_class(**self.image_processor_dict   )
								self.assertTrue(hasattr(__a					,		"clusters"   )   )
								self.assertTrue(hasattr(__a					,		"do_resize"   )   )
								self.assertTrue(hasattr(__a					,		"size"   )   )
								self.assertTrue(hasattr(__a					,		"do_normalize"   )   )






		def SCREAMING_SNAKE_CASE_      (    self   :  List[str]   )					->       Dict:
								'''simple docstring'''







								UpperCAmelCase								=  self.image_processing_class.from_dict(self.image_processor_dict   )
								self.assertEqual(image_processor.size					,		{"height": 18, "width": 18}   )

								UpperCAmelCase								=  self.image_processing_class.from_dict(self.image_processor_dict					,		size=42   )
								self.assertEqual(image_processor.size					,		{"height": 42, "width": 42}   )






		def SCREAMING_SNAKE_CASE_      (    self   :  Dict   )					->       List[Any]:
								'''simple docstring'''







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






		def SCREAMING_SNAKE_CASE_      (    self   :  Optional[Any]   )					->       List[Any]:
								'''simple docstring'''







								UpperCAmelCase								=  self.image_processing_class(**self.image_processor_dict   )

								with tempfile.TemporaryDirectory() as tmpdirname:
														UpperCAmelCase								=  os.path.join(__a					,		"image_processor.json"   )
														image_processor_first.to_json_file(__a   )
														UpperCAmelCase								=  self.image_processing_class.from_json_file(__a   ).to_dict()

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






		def SCREAMING_SNAKE_CASE_      (    self   :  str   )					->       Tuple:
								'''simple docstring'''







								UpperCAmelCase								=  self.image_processing_class(**self.image_processor_dict   )

								with tempfile.TemporaryDirectory() as tmpdirname:
														image_processor_first.save_pretrained(__a   )
														UpperCAmelCase								=  self.image_processing_class.from_pretrained(__a   ).to_dict()

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







		@unittest.skip("ImageGPT requires clusters at initialization"   )
		def SCREAMING_SNAKE_CASE_      (    self   :  Tuple   )					->       List[Any]:
								'''simple docstring'''







								pass

def  lowerCamelCase_()			->							Any:
						UpperCAmelCase								=  load_dataset("hf-internal-testing/fixtures_image_utils"     , split="test"     )

						UpperCAmelCase								=  Image.open(dataset[4]["file"]     )
						UpperCAmelCase								=  Image.open(dataset[5]["file"]     )

						UpperCAmelCase								=  [imagea, imagea]

						return images





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






		@slow
		def SCREAMING_SNAKE_CASE_      (    self   :  List[str]   )					->       Tuple:
								'''simple docstring'''







								UpperCAmelCase								=  ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small"   )

								UpperCAmelCase								=  prepare_images()

								# test non-batched
								UpperCAmelCase								=  image_processing(images[0]					,		return_tensors="pt"   )

								self.assertIsInstance(encoding.input_ids					,		torch.LongTensor   )
								self.assertEqual(encoding.input_ids.shape					,		(1, 10_24)   )

								UpperCAmelCase								=  [3_06, 1_91, 1_91]
								self.assertEqual(encoding.input_ids[0, :3].tolist()					,		__a   )

								# test batched
								UpperCAmelCase								=  image_processing(__a					,		return_tensors="pt"   )

								self.assertIsInstance(encoding.input_ids					,		torch.LongTensor   )
								self.assertEqual(encoding.input_ids.shape					,		(2, 10_24)   )

								UpperCAmelCase								=  [3_03, 13, 13]
								self.assertEqual(encoding.input_ids[1, -3:].tolist()					,		__a   )





"""simple docstring"""





import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
				from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image






class        _UpperCAmelCase:



		def __init__(   self							,						__a							,						__a=13							,						__a=64							,						__a=2							,						__a=3							,						__a=True							,						__a=True							,						__a=32							,						__a=5							,						__a=4							,						__a=37							,						__a="gelu"							,						__a=0.1							,						__a=0.1							,						__a=10							,						__a=0.02							,						__a=[1, 16, 4, 4]							,						__a=None							,						)			->			Optional[Any]:






							'''simple docstring'''
							_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
							_UpperCamelCase									=    scope
							_UpperCamelCase									=    backbone_featmap_shape

							# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
							# the number of patches is based on the feature map of the backbone, which by default uses an output stride
							# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
							_UpperCamelCase									=    (self.image_size // 32) ** 2
							_UpperCamelCase									=    num_patches + 1



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							_UpperCamelCase									=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

							_UpperCamelCase									=    self.get_config()

							return config, pixel_values, labels



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							_UpperCamelCase									=    {
							    '''global_padding''': '''same''',
							    '''layer_type''': '''bottleneck''',
							    '''depths''': [3, 4, 9],
							    '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
							    '''embedding_dynamic_padding''': True,
							    '''hidden_sizes''': [4, 8, 16, 32],
							    '''num_groups''': 2,
							}

							return ViTHybridConfig(
							    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=__a							,						initializer_range=self.initializer_range							,						backbone_featmap_shape=self.backbone_featmap_shape							,						backbone_config=__a							,						)



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    ViTHybridModel(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    model(__a)
							self.parent.assertEqual(result.last_hidden_state.shape							,						(self.batch_size, self.seq_length, self.hidden_size))



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.type_sequence_label_size
							_UpperCamelCase									=    ViTHybridForImageClassification(__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    model(__a							,						labels=__a)
							self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.type_sequence_label_size))




		def 		UpperCAmelCase				(   self)			->			List[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.prepare_config_and_inputs()
							_UpperCamelCase					,    _UpperCamelCase					,    _UpperCamelCase									=    config_and_inputs
							_UpperCamelCase									=    {'''pixel_values''': pixel_values}
							return config, inputs_dict






@require_torch
class        _UpperCAmelCase(       lowerCamelCase ,	lowerCamelCase ,	unittest.TestCase      ):
		lowercase__         = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
		lowercase__         = (
		    {'feature-extraction': ViTHybridModel, 'image-classification': ViTHybridForImageClassification}
		    if is_torch_available()
		    else {}
		)
		lowercase__         = False
		lowercase__         = False
		lowercase__         = False



		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    ViTHybridModelTester(self)
							_UpperCamelCase									=    ConfigTester(self							,						config_class=__a							,						has_text_modality=__a							,						hidden_size=37)



		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							self.config_tester.run_common_tests()



		@unittest.skip(reason='''ViT does not use inputs_embeds''')
		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							pass



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()

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



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()

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

												_UpperCamelCase									=    ['''pixel_values''']
												self.assertListEqual(arg_names[:1]							,						__a)



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__a)



		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__a)



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()

							_UpperCamelCase									=    _config_zero_init(__a)
							for model_class in self.all_model_classes:
												_UpperCamelCase									=    model_class(config=__a)
												# Skip the check for the backbone
												for name, module in model.named_modules():
																	if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
																						_UpperCamelCase									=    [F'''{name}.{key}''' for key in module.state_dict().keys()]
																						break

												for name, param in model.named_parameters():
																	if param.requires_grad:
																						if name in backbone_params:
																											continue
																						self.assertIn(
																						    ((param.data.mean() * 1e9).round() / 1e9).item()							,						[0.0, 1.0]							,						msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''							,						)




		@slow
		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_UpperCamelCase									=    ViTHybridModel.from_pretrained(__a)
												self.assertIsNotNone(__a)





def  lowerCamelCase__						(     )				->						Tuple:


					"""simple docstring"""





					_UpperCamelCase									=    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
					return image






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



		@cached_property
		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							return (
							    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0])
							    if is_vision_available()
							    else None
							)



		@slow
		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
							    __a)

							_UpperCamelCase									=    self.default_image_processor
							_UpperCamelCase									=    prepare_img()
							_UpperCamelCase									=    image_processor(images=__a							,						return_tensors='''pt''').to(__a)

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

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

							_UpperCamelCase									=    torch.tensor([-1.9090, -0.4993, -0.2389]).to(__a)

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




		@slow
		@require_accelerate
		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384''')
							_UpperCamelCase									=    ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384'''							,						device_map='''auto''')

							_UpperCamelCase									=    prepare_img()

							_UpperCamelCase									=    image_processor(images=__a							,						return_tensors='''pt''')
							_UpperCamelCase									=    model(**__a)
							_UpperCamelCase									=    outputs.logits
							# model predicts one of the 1000 ImageNet classes
							_UpperCamelCase									=    logits.argmax(-1).item()

							self.assertTrue(model.config.idalabel[predicted_class_idx]							,						'''tabby, tabby cat''')






