





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     :     Dict       ,      lowerCamelCase     :     Optional[int]       ,      lowerCamelCase     :     int=13       ,      lowerCamelCase     :     Union[str, Any]=64       ,      lowerCamelCase     :     int=2       ,      lowerCamelCase     :     Any=3       ,      lowerCamelCase     :     Any=True       ,      lowerCamelCase     :     Tuple=True       ,      lowerCamelCase     :     Any=32       ,      lowerCamelCase     :     Dict=5       ,      lowerCamelCase     :     Optional[int]=4       ,      lowerCamelCase     :     List[str]=37       ,      lowerCamelCase     :     Dict="gelu"       ,      lowerCamelCase     :     Dict=0.1       ,      lowerCamelCase     :     Optional[Any]=0.1       ,      lowerCamelCase     :     Optional[int]=10       ,      lowerCamelCase     :     Union[str, Any]=0.02       ,      lowerCamelCase     :     Union[str, Any]=[1, 16, 4, 4]       ,      lowerCamelCase     :     Dict=None       ,      ):
							lowerCamelCase_	:     Tuple 						=      parent
							lowerCamelCase_	:     str 						=      batch_size
							lowerCamelCase_	:     str 						=      image_size
							lowerCamelCase_	:     List[Any] 						=      patch_size
							lowerCamelCase_	:     int 						=      num_channels
							lowerCamelCase_	:     List[Any] 						=      is_training
							lowerCamelCase_	:     str 						=      use_labels
							lowerCamelCase_	:     Union[str, Any] 						=      hidden_size
							lowerCamelCase_	:     str 						=      num_hidden_layers
							lowerCamelCase_	:     Optional[int] 						=      num_attention_heads
							lowerCamelCase_	:     List[Any] 						=      intermediate_size
							lowerCamelCase_	:     Any 						=      hidden_act
							lowerCamelCase_	:     str 						=      hidden_dropout_prob
							lowerCamelCase_	:     List[str] 						=      attention_probs_dropout_prob
							lowerCamelCase_	:     List[str] 						=      type_sequence_label_size
							lowerCamelCase_	:     str 						=      initializer_range
							lowerCamelCase_	:     List[str] 						=      scope
							lowerCamelCase_	:     Optional[int] 						=      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
							lowerCamelCase_	:     Dict 						=      (self.image_size // 32) ** 2
							lowerCamelCase_	:     List[str] 						=      num_patches + 1



					def   __a     (		self     :     int						):
							lowerCamelCase_	:     List[str] 						=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

							lowerCamelCase_	:     int 						=      None
							if self.use_labels:
									lowerCamelCase_	:     List[str] 						=      ids_tensor([self.batch_size]       ,      self.type_sequence_label_size						)

							lowerCamelCase_	:     Dict 						=      self.get_config()

							return config, pixel_values, labels



					def   __a     (		self     :     Dict						):
							lowerCamelCase_	:     List[str] 						=      {
							    '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=lowerCamelCase       ,      initializer_range=self.initializer_range       ,      backbone_featmap_shape=self.backbone_featmap_shape       ,      backbone_config=lowerCamelCase       ,      )



					def   __a     (		self     :     Optional[Any]       ,      lowerCamelCase     :     Optional[Any]       ,      lowerCamelCase     :     Optional[Any]       ,      lowerCamelCase     :     List[str]						):
							lowerCamelCase_	:     Tuple 						=      ViTHybridModel(config=lowerCamelCase						)
							model.to(lowerCamelCase						)
							model.eval()
							lowerCamelCase_	:     Optional[int] 						=      model(lowerCamelCase						)
							self.parent.assertEqual(result.last_hidden_state.shape       ,      (self.batch_size, self.seq_length, self.hidden_size)						)



					def   __a     (		self     :     Optional[Any]       ,      lowerCamelCase     :     Dict       ,      lowerCamelCase     :     str       ,      lowerCamelCase     :     Dict						):
							lowerCamelCase_	:     List[str] 						=      self.type_sequence_label_size
							lowerCamelCase_	:     List[str] 						=      ViTHybridForImageClassification(lowerCamelCase						)
							model.to(lowerCamelCase						)
							model.eval()
							lowerCamelCase_	:     Any 						=      model(lowerCamelCase       ,      labels=lowerCamelCase						)
							self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.type_sequence_label_size)						)





					def   __a     (		self     :     List[str]						):
							lowerCamelCase_	:     Union[str, Any] 						=      self.prepare_config_and_inputs()
							lowerCamelCase_						,      lowerCamelCase_						,      lowerCamelCase_	:     Union[str, Any] 						=      config_and_inputs
							lowerCamelCase_	:     Any 						=      {'pixel_values': pixel_values}
							return config, inputs_dict






@require_torch
class 						UpperCamelCase_  (  snake_case__  ,     snake_case__  ,     unittest.TestCase							):
					_a      :						str						    =					(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
					_a      :						Dict						    =					(
					    {'feature-extraction': ViTHybridModel, 'image-classification': ViTHybridForImageClassification}
					    if is_torch_available()
					    else {}
					)
					_a      :						int						    =					False
					_a      :						Any						    =					False
					_a      :						Any						    =					False



					def   __a     (		self     :     Dict						):
							lowerCamelCase_	:     Optional[int] 						=      ViTHybridModelTester(self						)
							lowerCamelCase_	:     str 						=      ConfigTester(self       ,      config_class=lowerCamelCase       ,      has_text_modality=lowerCamelCase       ,      hidden_size=37						)



					def   __a     (		self     :     Any						):
							self.config_tester.run_common_tests()



					@unittest.skip(reason='ViT does not use inputs_embeds'						)
					def   __a     (		self     :     Union[str, Any]						):
							pass



					def   __a     (		self     :     Dict						):
							lowerCamelCase_						,      lowerCamelCase_	:     Union[str, Any] 						=      self.model_tester.prepare_config_and_inputs_for_common()

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



					def   __a     (		self     :     int						):
							lowerCamelCase_						,      lowerCamelCase_	:     List[str] 						=      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									lowerCamelCase_	:     str 						=      model_class(lowerCamelCase						)
									lowerCamelCase_	:     Optional[int] 						=      inspect.signature(model.forward						)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									lowerCamelCase_	:     Union[str, Any] 						=      [*signature.parameters.keys()]

									lowerCamelCase_	:     Any 						=      ['pixel_values']
									self.assertListEqual(arg_names[:1]       ,      lowerCamelCase						)



					def   __a     (		self     :     str						):
							lowerCamelCase_	:     Optional[int] 						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCamelCase						)



					def   __a     (		self     :     Union[str, Any]						):
							lowerCamelCase_	:     List[str] 						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*lowerCamelCase						)



					def   __a     (		self     :     Dict						):
							lowerCamelCase_						,      lowerCamelCase_	:     Any 						=      self.model_tester.prepare_config_and_inputs_for_common()

							lowerCamelCase_	:     Tuple 						=      _config_zero_init(lowerCamelCase						)
							for model_class in self.all_model_classes:
									lowerCamelCase_	:     Dict 						=      model_class(config=lowerCamelCase						)
									# Skip the check for the backbone
									for name, module in model.named_modules():
											if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
													lowerCamelCase_	:     Optional[int] 						=      [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   __a     (		self     :     Optional[int]						):
							for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCamelCase_	:     Optional[int] 						=      ViTHybridModel.from_pretrained(lowerCamelCase						)
									self.assertIsNotNone(lowerCamelCase						)


def 		_SCREAMING_SNAKE_CASE     ( ):
		lowerCamelCase_	:     List[str] 						=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
		return image






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



					@cached_property
					def   __a     (		self     :     Union[str, Any]						):
							return (
							    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
							    if is_vision_available()
							    else None
							)



					@slow
					def   __a     (		self     :     str						):
							lowerCamelCase_	:     str 						=      ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(
							    lowerCamelCase						)

							lowerCamelCase_	:     Tuple 						=      self.default_image_processor
							lowerCamelCase_	:     Tuple 						=      prepare_img()
							lowerCamelCase_	:     Tuple 						=      image_processor(images=lowerCamelCase       ,      return_tensors='pt'						).to(lowerCamelCase						)

							# forward pass
							with torch.no_grad():
									lowerCamelCase_	:     Dict 						=      model(**lowerCamelCase						)

							# verify the logits
							lowerCamelCase_	:     List[Any] 						=      torch.Size((1, 10_00)						)
							self.assertEqual(outputs.logits.shape       ,      lowerCamelCase						)

							lowerCamelCase_	:     Any 						=      torch.tensor([-1.9_090, -0.4_993, -0.2_389]						).to(lowerCamelCase						)

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





					@slow
					@require_accelerate
					def   __a     (		self     :     List[str]						):
							lowerCamelCase_	:     str 						=      ViTHybridImageProcessor.from_pretrained('google/vit-hybrid-base-bit-384'						)
							lowerCamelCase_	:     Union[str, Any] 						=      ViTHybridForImageClassification.from_pretrained('google/vit-hybrid-base-bit-384'       ,      device_map='auto'						)

							lowerCamelCase_	:     List[Any] 						=      prepare_img()

							lowerCamelCase_	:     List[Any] 						=      image_processor(images=lowerCamelCase       ,      return_tensors='pt'						)
							lowerCamelCase_	:     Dict 						=      model(**lowerCamelCase						)
							lowerCamelCase_	:     Any 						=      outputs.logits
							# model predicts one of the 1000 ImageNet classes
							lowerCamelCase_	:     Dict 						=      logits.argmax(-1						).item()

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







import random

import torch
from huggingface_hub import HfApi

from diffusers import UNetaDModel


_lowercase		:  Dict			       =HfApi()

_lowercase		:  str			       ={}
# fmt: off
_lowercase		:  Union[str, Any]			       =torch.tensor([
    -0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,
    1.2342, -2.2485, 0.4636, 0.8076, -0.7991, 0.3969, 0.8498, 0.9189,
    -1.8887, -3.3522, 0.7639, 0.2040, 0.6271, -2.7148, -1.6316, 3.0839,
    0.3186, 0.2721, -0.9759, -1.2461, 2.6257, 1.3557
])
_lowercase		:  Optional[Any]			       =torch.tensor([
    -2.3639, -2.5344, 0.0054, -0.6674, 1.5990, 1.0158, 0.3124, -2.1436,
    1.8795, -2.5429, -0.1566, -0.3973, 1.2490, 2.6447, 1.2283, -0.5208,
    -2.8154, -3.5119, 2.3838, 1.2033, 1.7201, -2.1256, -1.4576, 2.7948,
    2.4204, -0.9752, -1.2546, 0.8027, 3.2758, 3.1365
])
_lowercase		:  Union[str, Any]			       =torch.tensor([
    -0.6531, -0.6891, -0.3172, -0.5375, -0.9140, -0.5367, -0.1175, -0.7869,
    -0.3808, -0.4513, -0.2098, -0.0083, 0.3183, 0.5140, 0.2247, -0.1304,
    -0.1302, -0.2802, -0.2084, -0.2025, -0.4967, -0.4873, -0.0861, 0.6925,
    0.0250, 0.1290, -0.1543, 0.6316, 1.0460, 1.4943
])
_lowercase		:  Optional[Any]			       =torch.tensor([
    0.0911, 0.1107, 0.0182, 0.0435, -0.0805, -0.0608, 0.0381, 0.2172,
    -0.0280, 0.1327, -0.0299, -0.0255, -0.0050, -0.1170, -0.1046, 0.0309,
    0.1367, 0.1728, -0.0533, -0.0748, -0.0534, 0.1624, 0.0384, -0.1805,
    -0.0707, 0.0642, 0.0220, -0.0134, -0.1333, -0.1505
])
_lowercase		:  Tuple			       =torch.tensor([
    0.1321, 0.1337, 0.0440, 0.0622, -0.0591, -0.0370, 0.0503, 0.2133,
    -0.0177, 0.1415, -0.0116, -0.0112, 0.0044, -0.0980, -0.0789, 0.0395,
    0.1502, 0.1785, -0.0488, -0.0514, -0.0404, 0.1539, 0.0454, -0.1559,
    -0.0665, 0.0659, 0.0383, -0.0005, -0.1266, -0.1386
])
_lowercase		:  int			       =torch.tensor([
    0.1154, 0.1218, 0.0307, 0.0526, -0.0711, -0.0541, 0.0366, 0.2078,
    -0.0267, 0.1317, -0.0226, -0.0193, -0.0014, -0.1055, -0.0902, 0.0330,
    0.1391, 0.1709, -0.0562, -0.0693, -0.0560, 0.1482, 0.0381, -0.1683,
    -0.0681, 0.0661, 0.0331, -0.0046, -0.1268, -0.1431
])
_lowercase		:  Tuple			       =torch.tensor([
    0.1192, 0.1240, 0.0414, 0.0606, -0.0557, -0.0412, 0.0430, 0.2042,
    -0.0200, 0.1385, -0.0115, -0.0132, 0.0017, -0.0965, -0.0802, 0.0398,
    0.1433, 0.1747, -0.0458, -0.0533, -0.0407, 0.1545, 0.0419, -0.1574,
    -0.0645, 0.0626, 0.0341, -0.0010, -0.1199, -0.1390
])
_lowercase		:  Optional[int]			       =torch.tensor([
    0.1075, 0.1074, 0.0205, 0.0431, -0.0774, -0.0607, 0.0298, 0.2042,
    -0.0320, 0.1267, -0.0281, -0.0250, -0.0064, -0.1091, -0.0946, 0.0290,
    0.1328, 0.1650, -0.0580, -0.0738, -0.0586, 0.1440, 0.0337, -0.1746,
    -0.0712, 0.0605, 0.0250, -0.0099, -0.1316, -0.1473
])
_lowercase		:  List[Any]			       =torch.tensor([
    -1.4572, -2.0481, -0.0414, -0.6005, 1.4136, 0.5848, 0.4028, -2.7330,
    1.2212, -2.1228, 0.2155, 0.4039, 0.7662, 2.0535, 0.7477, -0.3243,
    -2.1758, -2.7648, 1.6947, 0.7026, 1.2338, -1.6078, -0.8682, 2.2810,
    1.8574, -0.5718, -0.5586, -0.0186, 2.3415, 2.1251])
_lowercase		:  List[Any]			       =torch.tensor([
    -1.3690, -1.9720, -0.4090, -0.6966, 1.4660, 0.9938, -0.1385, -2.7324,
    0.7736, -1.8917, 0.2923, 0.4293, 0.1693, 1.4112, 1.1887, -0.3181,
    -2.2160, -2.6381, 1.3170, 0.8163, 0.9240, -1.6544, -0.6099, 2.5259,
    1.6430, -0.9090, -0.9392, -0.0126, 2.4268, 2.3266
])
_lowercase		:  int			       =torch.tensor([
    -1.3525, -1.9628, -0.3956, -0.6860, 1.4664, 1.0014, -0.1259, -2.7212,
    0.7772, -1.8811, 0.2996, 0.4388, 0.1704, 1.4029, 1.1701, -0.3027,
    -2.2053, -2.6287, 1.3350, 0.8131, 0.9274, -1.6292, -0.6098, 2.5131,
    1.6505, -0.8958, -0.9298, -0.0151, 2.4257, 2.3355
])
_lowercase		:  List[Any]			       =torch.tensor([
    -2.0585, -2.7897, -0.2850, -0.8940, 1.9052, 0.5702, 0.6345, -3.8959,
    1.5932, -3.2319, 0.1974, 0.0287, 1.7566, 2.6543, 0.8387, -0.5351,
    -3.2736, -4.3375, 2.9029, 1.6390, 1.4640, -2.1701, -1.9013, 2.9341,
    3.4981, -0.6255, -1.1644, -0.1591, 3.7097, 3.2066
])
_lowercase		:  Any			       =torch.tensor([
    -2.3139, -2.5594, -0.0197, -0.6785, 1.7001, 1.1606, 0.3075, -2.1740,
    1.8071, -2.5630, -0.0926, -0.3811, 1.2116, 2.6246, 1.2731, -0.5398,
    -2.8153, -3.6140, 2.3893, 1.3262, 1.6258, -2.1856, -1.3267, 2.8395,
    2.3779, -1.0623, -1.2468, 0.8959, 3.3367, 3.2243
])
_lowercase		:  List[str]			       =torch.tensor([
    -2.0628, -2.7667, -0.2089, -0.8263, 2.0539, 0.5992, 0.6495, -3.8336,
    1.6025, -3.2817, 0.1721, -0.0633, 1.7516, 2.7039, 0.8100, -0.5908,
    -3.2113, -4.4343, 2.9257, 1.3632, 1.5562, -2.1489, -1.9894, 3.0560,
    3.3396, -0.7328, -1.0417, 0.0383, 3.7093, 3.2343
])
_lowercase		:  Tuple			       =torch.tensor([
    -1.4574, -2.0569, -0.0473, -0.6117, 1.4018, 0.5769, 0.4129, -2.7344,
    1.2241, -2.1397, 0.2000, 0.3937, 0.7616, 2.0453, 0.7324, -0.3391,
    -2.1746, -2.7744, 1.6963, 0.6921, 1.2187, -1.6172, -0.8877, 2.2439,
    1.8471, -0.5839, -0.5605, -0.0464, 2.3250, 2.1219
])
# fmt: on

_lowercase		:  Optional[int]			       =api.list_models(filter="""diffusers""")
for mod in models:
		if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
				_lowercase		:  str			       ="""/home/patrick/google_checkpoints/""" + mod.modelId.split("""/""")[-1]

				print(F'''Started running {mod.modelId}!!!''')

				if mod.modelId.startswith("""CompVis"""):
						_lowercase		:  str			       =UNetaDModel.from_pretrained(local_checkpoint, subfolder="""unet""")
				else:
						_lowercase		:  Dict			       =UNetaDModel.from_pretrained(local_checkpoint)

				torch.manual_seed(0)
				random.seed(0)

				_lowercase		:  Optional[Any]			       =torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
				_lowercase		:  Dict			       =torch.tensor([10] * noise.shape[0])
				with torch.no_grad():
						_lowercase		:  Union[str, Any]			       =model(noise, time_step).sample

				assert torch.allclose(
				    logits[0, 0, 0, :30], results["""_""".join("""_""".join(mod.modelId.split("""/""")).split("""-"""))], atol=1e-3
				)
				print(F'''{mod.modelId} has passed successfully!!!''')
