





'''simple docstring'''

import random

import torch
from huggingface_hub import HfApi

from diffusers import UNetaDModel


__lowerCamelCase : Union[str, Any]											=			HfApi()

__lowerCamelCase : Tuple											=			{}
# fmt: off
__lowerCamelCase : 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
])
__lowerCamelCase : Tuple											=			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
])
__lowerCamelCase : 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
])
__lowerCamelCase : List[str]											=			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
])
__lowerCamelCase : Any											=			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
])
__lowerCamelCase : List[Any]											=			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
])
__lowerCamelCase : Optional[int]											=			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
])
__lowerCamelCase : 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
])
__lowerCamelCase : Tuple											=			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])
__lowerCamelCase : Optional[int]											=			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
])
__lowerCamelCase : 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
])
__lowerCamelCase : 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
])
__lowerCamelCase : List[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
])
__lowerCamelCase : Optional[Any]											=			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
])
__lowerCamelCase : str											=			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

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

						print(f"Started running {mod.modelId}!!!")

						if mod.modelId.startswith("CompVis"):
									__lowerCamelCase : Optional[Any]											=			UNetaDModel.from_pretrained(local_checkpoint, subfolder="unet")
						else:
									__lowerCamelCase : Any											=			UNetaDModel.from_pretrained(local_checkpoint)

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

						__lowerCamelCase : Optional[int]											=			torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
						__lowerCamelCase : int											=			torch.tensor([10] * noise.shape[0])
						with torch.no_grad():
									__lowerCamelCase : List[str]											=			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!!!")











'''simple docstring'''

import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 RegNetForImageClassification, RegNetModel
			from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor

class       UpperCAmelCase :




		def __init__(self      :	Dict							,			A__      :	str							,			A__      :	int=3							,			A__      :	Dict=3_2							,			A__      :	str=3							,			A__      :	str=1_0							,			A__      :	Optional[int]=[1_0, 2_0, 3_0, 4_0]							,			A__      :	Tuple=[1, 1, 2, 1]							,			A__      :	int=True							,			A__      :	List[Any]=True							,			A__      :	List[Any]="relu"							,			A__      :	Any=3							,			A__      :	Any=None							,			)					-> Tuple:
						lowercase         = parent
						lowercase         = batch_size
						lowercase         = image_size
						lowercase         = num_channels
						lowercase         = embeddings_size
						lowercase         = hidden_sizes
						lowercase         = depths
						lowercase         = is_training
						lowercase         = use_labels
						lowercase         = hidden_act
						lowercase         = num_labels
						lowercase         = scope
						lowercase         = len(A__				)




		def 				UpperCAmelCase__   (self      :	str				)					-> List[str]:
						lowercase         = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

						lowercase         = None
						if self.use_labels:
										lowercase         = ids_tensor([self.batch_size]							,			self.num_labels				)

						lowercase         = self.get_config()

						return config, pixel_values, labels




		def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> List[str]:
						return RegNetConfig(
						    num_channels=self.num_channels							,			embeddings_size=self.embeddings_size							,			hidden_sizes=self.hidden_sizes							,			depths=self.depths							,			hidden_act=self.hidden_act							,			num_labels=self.num_labels							,			)




		def 				UpperCAmelCase__   (self      :	Dict							,			A__      :	Union[str, Any]							,			A__      :	Optional[int]							,			A__      :	Dict				)					-> str:
						lowercase         = RegNetModel(config=A__				)
						model.to(A__				)
						model.eval()
						lowercase         = model(A__				)
						# expected last hidden states: B, C, H // 32, W // 32
						self.parent.assertEqual(
						    result.last_hidden_state.shape							,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)							,			)




		def 				UpperCAmelCase__   (self      :	List[str]							,			A__      :	List[str]							,			A__      :	Union[str, Any]							,			A__      :	str				)					-> Dict:
						lowercase         = self.num_labels
						lowercase         = RegNetForImageClassification(A__				)
						model.to(A__				)
						model.eval()
						lowercase         = model(A__							,			labels=A__				)
						self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.num_labels)				)





		def 				UpperCAmelCase__   (self      :	Any				)					-> Union[str, Any]:
						lowercase         = self.prepare_config_and_inputs()
						lowercase   ,							lowercase   ,							lowercase         = config_and_inputs
						lowercase         = {"pixel_values": pixel_values}
						return config, inputs_dict




@require_torch
class       UpperCAmelCase (						_lowercase			,       _lowercase			,       unittest.TestCase  ):
		UpperCAmelCase					:	str 			=		(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
		UpperCAmelCase					:	Dict 			=		(
		    {'''feature-extraction''': RegNetModel, '''image-classification''': RegNetForImageClassification}
		    if is_torch_available()
		    else {}
		)

		UpperCAmelCase					:	Dict 			=		False
		UpperCAmelCase					:	int 			=		False
		UpperCAmelCase					:	Tuple 			=		False
		UpperCAmelCase					:	Tuple 			=		False




		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> Tuple:
						lowercase         = RegNetModelTester(self				)
						lowercase         = ConfigTester(self							,			config_class=A__							,			has_text_modality=A__				)




		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> str:
						self.create_and_test_config_common_properties()
						self.config_tester.create_and_test_config_to_json_string()
						self.config_tester.create_and_test_config_to_json_file()
						self.config_tester.create_and_test_config_from_and_save_pretrained()
						self.config_tester.create_and_test_config_with_num_labels()
						self.config_tester.check_config_can_be_init_without_params()
						self.config_tester.check_config_arguments_init()




		def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> int:
						return




		@unittest.skip(reason="RegNet does not use inputs_embeds"				)
		def 				UpperCAmelCase__   (self      :	int				)					-> Optional[int]:
						pass




		@unittest.skip(reason="RegNet does not support input and output embeddings"				)
		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Optional[Any]:
						pass




		def 				UpperCAmelCase__   (self      :	Any				)					-> str:
						lowercase   ,							lowercase         = self.model_tester.prepare_config_and_inputs_for_common()

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

										lowercase         = ["pixel_values"]
										self.assertListEqual(arg_names[:1]							,			A__				)




		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Union[str, Any]:
						lowercase         = self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A__				)




		def 				UpperCAmelCase__   (self      :	List[str]				)					-> Tuple:
						lowercase   ,							lowercase         = self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										lowercase         = model_class(config=A__				)
										for name, module in model.named_modules():
														if isinstance(A__							,			(nn.BatchNormad, nn.GroupNorm)				):
																		self.assertTrue(
																		    torch.all(module.weight == 1				)							,			msg=f'Parameter {name} of model {model_class} seems not properly initialized'							,			)
																		self.assertTrue(
																		    torch.all(module.bias == 0				)							,			msg=f'Parameter {name} of model {model_class} seems not properly initialized'							,			)




		def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> List[str]:
						def check_hidden_states_output(A__      :	Optional[Any]							,			A__      :	List[str]							,			A__      :	Tuple				):
										lowercase         = model_class(A__				)
										model.to(A__				)
										model.eval()

										with torch.no_grad():
														lowercase         = model(**self._prepare_for_class(A__							,			A__				)				)

										lowercase         = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

										lowercase         = self.model_tester.num_stages
										self.assertEqual(len(A__				)							,			expected_num_stages + 1				)

										# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
										self.assertListEqual(
										    list(hidden_states[0].shape[-2:]				)							,			[self.model_tester.image_size // 2, self.model_tester.image_size // 2]							,			)

						lowercase   ,							lowercase         = self.model_tester.prepare_config_and_inputs_for_common()
						lowercase         = ["basic", "bottleneck"]
						for model_class in self.all_model_classes:
										for layer_type in layers_type:
														lowercase         = layer_type
														lowercase         = True
														check_hidden_states_output(A__							,			A__							,			A__				)

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														lowercase         = True

														check_hidden_states_output(A__							,			A__							,			A__				)




		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Tuple:
						lowercase         = self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*A__				)





		@slow
		def 				UpperCAmelCase__   (self      :	Tuple				)					-> Any:
						for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowercase         = RegNetModel.from_pretrained(A__				)
										self.assertIsNotNone(A__				)







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






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


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




		@cached_property
		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> Union[str, Any]:
						return (
						    AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)
						    if is_vision_available()
						    else None
						)





		@slow
		def 				UpperCAmelCase__   (self      :	List[str]				)					-> int:
						lowercase         = RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(A__				)

						lowercase         = self.default_image_processor
						lowercase         = prepare_img()
						lowercase         = image_processor(images=A__							,			return_tensors="pt"				).to(A__				)

						# forward pass
						with torch.no_grad():
										lowercase         = model(**A__				)

						# verify the logits
						lowercase         = torch.Size((1, 1_0_0_0)				)
						self.assertEqual(outputs.logits.shape							,			A__				)

						lowercase         = torch.tensor([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]				).to(A__				)

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




