import inspect
import unittest

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

from ...test_backbone_common import BackboneTesterMixin
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 transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
					from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor
class     lowerCAmelCase_     :







							def __init__(   self      :     int ,			_A      :     int ,			_A      :     Dict=13 ,			_A      :     Optional[Any]=32 ,			_A      :     List[str]=3 ,			_A      :     Tuple=4 ,			_A      :     Union[str, Any]=[10, 20, 30, 40] ,			_A      :     Optional[int]=[2, 2, 3, 2] ,			_A      :     Union[str, Any]=True ,			_A      :     str=True ,			_A      :     List[Any]=37 ,			_A      :     List[str]="gelu" ,			_A      :     str=10 ,			_A      :     Optional[Any]=0.02 ,			_A      :     Optional[Any]=["stage2", "stage3", "stage4"] ,			_A      :     Any=[2, 3, 4] ,			_A      :     Union[str, Any]=None ,			):
												_UpperCamelCase			     =     parent
												_UpperCamelCase			     =     batch_size
												_UpperCamelCase			     =     image_size
												_UpperCamelCase			     =     num_channels
												_UpperCamelCase			     =     num_stages
												_UpperCamelCase			     =     hidden_sizes
												_UpperCamelCase			     =     depths
												_UpperCamelCase			     =     is_training
												_UpperCamelCase			     =     use_labels
												_UpperCamelCase			     =     intermediate_size
												_UpperCamelCase			     =     hidden_act
												_UpperCamelCase			     =     num_labels
												_UpperCamelCase			     =     initializer_range
												_UpperCamelCase			     =     out_features
												_UpperCamelCase			     =     out_indices
												_UpperCamelCase			     =     scope







							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												_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.num_labels		)

												_UpperCamelCase			     =     self.get_config()
												return config, pixel_values, labels







							def  UpperCamelCase_						(   self      :     int		):
												return ConvNextConfig(
												    num_channels=self.num_channels ,			hidden_sizes=self.hidden_sizes ,			depths=self.depths ,			num_stages=self.num_stages ,			hidden_act=self.hidden_act ,			is_decoder=_A ,			initializer_range=self.initializer_range ,			out_features=self.out_features ,			out_indices=self.out_indices ,			num_labels=self.num_labels ,			)







							def  UpperCamelCase_						(   self      :     Optional[Any] ,			_A      :     Optional[int] ,			_A      :     Union[str, Any] ,			_A      :     Dict		):
												_UpperCamelCase			     =     ConvNextModel(config=_A		)
												model.to(_A		)
												model.eval()
												_UpperCamelCase			     =     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 // 32, self.image_size // 32) ,			)







							def  UpperCamelCase_						(   self      :     Optional[int] ,			_A      :     Optional[Any] ,			_A      :     Tuple ,			_A      :     int		):
												_UpperCamelCase			     =     ConvNextForImageClassification(_A		)
												model.to(_A		)
												model.eval()
												_UpperCamelCase			     =     model(_A ,			labels=_A		)
												self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.num_labels)		)







							def  UpperCamelCase_						(   self      :     List[str] ,			_A      :     Union[str, Any] ,			_A      :     Dict ,			_A      :     Any		):
												_UpperCamelCase			     =     ConvNextBackbone(config=_A		)
												model.to(_A		)
												model.eval()
												_UpperCamelCase			     =     model(_A		)

												# verify hidden states
												self.parent.assertEqual(len(result.feature_maps		) ,			len(config.out_features		)		)
												self.parent.assertListEqual(list(result.feature_maps[0].shape		) ,			[self.batch_size, self.hidden_sizes[1], 4, 4]		)

												# verify channels
												self.parent.assertEqual(len(model.channels		) ,			len(config.out_features		)		)
												self.parent.assertListEqual(model.channels ,			config.hidden_sizes[1:]		)

												# verify backbone works with out_features=None
												_UpperCamelCase			     =     None
												_UpperCamelCase			     =     ConvNextBackbone(config=_A		)
												model.to(_A		)
												model.eval()
												_UpperCamelCase			     =     model(_A		)

												# verify feature maps
												self.parent.assertEqual(len(result.feature_maps		) ,			1		)
												self.parent.assertListEqual(list(result.feature_maps[0].shape		) ,			[self.batch_size, self.hidden_sizes[-1], 1, 1]		)

												# verify channels
												self.parent.assertEqual(len(model.channels		) ,			1		)
												self.parent.assertListEqual(model.channels ,			[config.hidden_sizes[-1]]		)







							def  UpperCamelCase_						(   self      :     Tuple		):
												_UpperCamelCase			     =     self.prepare_config_and_inputs()
												_UpperCamelCase     ,    _UpperCamelCase     ,    _UpperCamelCase			     =     config_and_inputs
												_UpperCamelCase			     =     {'''pixel_values''': pixel_values}
												return config, inputs_dict




@require_torch
class     lowerCAmelCase_     (       __lowercase,  __lowercase,  unittest.TestCase ):
							UpperCAmelCase				   =     (
							    (
							        ConvNextModel,
							        ConvNextForImageClassification,
							        ConvNextBackbone,
							    )
							    if is_torch_available()
							    else ()
							)
							UpperCAmelCase				   =     (
							    {"feature-extraction": ConvNextModel, "image-classification": ConvNextForImageClassification}
							    if is_torch_available()
							    else {}
							)

							UpperCAmelCase				   =     True
							UpperCAmelCase				   =     False
							UpperCAmelCase				   =     False
							UpperCAmelCase				   =     False
							UpperCAmelCase				   =     False







							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												_UpperCamelCase			     =     ConvNextModelTester(self		)
												_UpperCamelCase			     =     ConfigTester(self ,			config_class=_A ,			has_text_modality=_A ,			hidden_size=37		)







							def  UpperCamelCase_						(   self      :     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      :     str		):
												return







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







							@unittest.skip(reason='''ConvNext does not support input and output embeddings'''		)
							def  UpperCamelCase_						(   self      :     Optional[Any]		):
												pass







							@unittest.skip(reason='''ConvNext does not use feedforward chunking'''		)
							def  UpperCamelCase_						(   self      :     Optional[int]		):
												pass







							def  UpperCamelCase_						(   self      :     List[str]		):
												_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      :     Optional[Any]		):
												_UpperCamelCase			     =     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_A		)







							def  UpperCamelCase_						(   self      :     int		):
												_UpperCamelCase			     =     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_backbone(*_A		)







							def  UpperCamelCase_						(   self      :     List[str]		):
												def check_hidden_states_output(_A      :     int ,			_A      :     Optional[int] ,			_A      :     Dict		):
																	_UpperCamelCase			     =     model_class(_A		)
																	model.to(_A		)
																	model.eval()

																	with torch.no_grad():
																						_UpperCamelCase			     =     model(**self._prepare_for_class(_A ,			_A		)		)

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

																	_UpperCamelCase			     =     self.model_tester.num_stages
																	self.assertEqual(len(_A		) ,			expected_num_stages + 1		)

																	# ConvNext'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 // 4, self.model_tester.image_size // 4] ,			)

												_UpperCamelCase     ,    _UpperCamelCase			     =     self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	_UpperCamelCase			     =     True
																	check_hidden_states_output(_A ,			_A ,			_A		)

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

																	check_hidden_states_output(_A ,			_A ,			_A		)







							def  UpperCamelCase_						(   self      :     Dict		):
												_UpperCamelCase			     =     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*_A		)







							@slow
							def  UpperCamelCase_						(   self      :     List[str]		):
												for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_UpperCamelCase			     =     ConvNextModel.from_pretrained(_A		)
																	self.assertIsNotNone(_A		)




def 					_snake_case	(  ):
					_UpperCamelCase			     =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
					return image
@require_torch
@require_vision
class     lowerCAmelCase_     (       unittest.TestCase ):







							@cached_property
							def  UpperCamelCase_						(   self      :     int		):
												return AutoImageProcessor.from_pretrained('''facebook/convnext-tiny-224'''		) if is_vision_available() else None







							@slow
							def  UpperCamelCase_						(   self      :     List[str]		):
												_UpperCamelCase			     =     ConvNextForImageClassification.from_pretrained('''facebook/convnext-tiny-224'''		).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, 1000)		)
												self.assertEqual(outputs.logits.shape ,			_A		)

												_UpperCamelCase			     =     torch.tensor([-0.0260, -0.4739, 0.1911]		).to(_A		)

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




@require_torch
class     lowerCAmelCase_     (       unittest.TestCase,  __lowercase ):
							UpperCAmelCase				   =     (ConvNextBackbone,) if is_torch_available() else ()
							UpperCAmelCase				   =     ConvNextConfig

							UpperCAmelCase				   =     False







							def  UpperCamelCase_						(   self      :     Dict		):
												_UpperCamelCase			     =     ConvNextModelTester(self		)

import random

import torch
from huggingface_hub import HfApi

from diffusers import UNetaDModel


_lowerCAmelCase       =		HfApi()

_lowerCAmelCase       =		{}
# fmt: off
_lowerCAmelCase       =		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       =		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       =		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       =		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       =		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       =		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       =		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       =		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       =		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       =		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       =		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       =		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       =		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       =		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       =		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       =		api.list_models(filter="diffusers")
for mod in models:
					if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
										_lowerCAmelCase       =		"/home/patrick/google_checkpoints/" + mod.modelId.split("/")[-1]

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

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

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

										_lowerCAmelCase       =		torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
										_lowerCAmelCase       =		torch.tensor([10] * noise.shape[0])
										with torch.no_grad():
															_lowerCAmelCase       =		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!!!')
