


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        a					:





					def __init__(    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=3    ,	SCREAMING_SNAKE_CASE_					:				List[Any]=32    ,	SCREAMING_SNAKE_CASE_					:				int=3    ,	SCREAMING_SNAKE_CASE_					:				int=10    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=[10, 20, 30, 40]    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=[1, 1, 2, 1]    ,	SCREAMING_SNAKE_CASE_					:				str=True    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=True    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]="relu"    ,	SCREAMING_SNAKE_CASE_					:				List[Any]=3    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]=None    ,	):
								__lowerCamelCase:				Dict     =	parent
								__lowerCamelCase:				Dict     =	batch_size
								__lowerCamelCase:				Dict     =	image_size
								__lowerCamelCase:				str     =	num_channels
								__lowerCamelCase:				int     =	embeddings_size
								__lowerCamelCase:				List[Any]     =	hidden_sizes
								__lowerCamelCase:				List[str]     =	depths
								__lowerCamelCase:				int     =	is_training
								__lowerCamelCase:				Optional[int]     =	use_labels
								__lowerCamelCase:				List[str]     =	hidden_act
								__lowerCamelCase:				Optional[int]     =	num_labels
								__lowerCamelCase:				List[str]     =	scope
								__lowerCamelCase:				Optional[int]     =	len(SCREAMING_SNAKE_CASE_							)





					def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
								__lowerCamelCase:				Any     =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

								__lowerCamelCase:				Union[str, Any]     =	None
								if self.use_labels:
											__lowerCamelCase:				int     =	ids_tensor([self.batch_size]    ,	self.num_labels							)

								__lowerCamelCase:				str     =	self.get_config()

								return config, pixel_values, labels





					def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
								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  SCREAMING_SNAKE_CASE__       (    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				List[Any]							):
								__lowerCamelCase:				int     =	RegNetModel(config=SCREAMING_SNAKE_CASE_							)
								model.to(SCREAMING_SNAKE_CASE_							)
								model.eval()
								__lowerCamelCase:				Optional[Any]     =	model(SCREAMING_SNAKE_CASE_							)
								# 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  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				str							):
								__lowerCamelCase:				str     =	self.num_labels
								__lowerCamelCase:				int     =	RegNetForImageClassification(SCREAMING_SNAKE_CASE_							)
								model.to(SCREAMING_SNAKE_CASE_							)
								model.eval()
								__lowerCamelCase:				List[Any]     =	model(SCREAMING_SNAKE_CASE_    ,	labels=SCREAMING_SNAKE_CASE_							)
								self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.num_labels)							)







					def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
								__lowerCamelCase:				int     =	self.prepare_config_and_inputs()
								__lowerCamelCase:				Dict     =	config_and_inputs
								__lowerCamelCase:				Optional[Any]     =	{"""pixel_values""": pixel_values}
								return config, inputs_dict



@require_torch
class        a					(			_UpperCAmelCase     ,_UpperCAmelCase     ,unittest.TestCase   ):
					UpperCAmelCase__ :  int						      =	(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
					UpperCAmelCase__ :  Tuple						      =	(
					    {"feature-extraction": RegNetModel, "image-classification": RegNetForImageClassification}
					    if is_torch_available()
					    else {}
					)

					UpperCAmelCase__ :  List[str]						      =	False
					UpperCAmelCase__ :  Dict						      =	False
					UpperCAmelCase__ :  Dict						      =	False
					UpperCAmelCase__ :  List[str]						      =	False





					def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
								__lowerCamelCase:				Union[str, Any]     =	RegNetModelTester(self							)
								__lowerCamelCase:				List[Any]     =	ConfigTester(self    ,	config_class=SCREAMING_SNAKE_CASE_    ,	has_text_modality=SCREAMING_SNAKE_CASE_							)





					def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
								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  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
								return





					@unittest.skip(reason="""RegNet does not use inputs_embeds"""							)
					def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
								pass





					@unittest.skip(reason="""RegNet does not support input and output embeddings"""							)
					def  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
								pass





					def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
								__lowerCamelCase:				str     =	self.model_tester.prepare_config_and_inputs_for_common()

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

											__lowerCamelCase:				str     =	["""pixel_values"""]
											self.assertListEqual(arg_names[:1]    ,	SCREAMING_SNAKE_CASE_							)





					def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
								__lowerCamelCase:				Union[str, Any]     =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_							)





					def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
								__lowerCamelCase:				List[Any]     =	self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											__lowerCamelCase:				str     =	model_class(config=SCREAMING_SNAKE_CASE_							)
											for name, module in model.named_modules():
														if isinstance(SCREAMING_SNAKE_CASE_    ,	(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  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
								def check_hidden_states_output(SCREAMING_SNAKE_CASE_					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				List[str]							):
											__lowerCamelCase:				Tuple     =	model_class(SCREAMING_SNAKE_CASE_							)
											model.to(SCREAMING_SNAKE_CASE_							)
											model.eval()

											with torch.no_grad():
														__lowerCamelCase:				Dict     =	model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)							)

											__lowerCamelCase:				List[str]     =	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

											__lowerCamelCase:				Optional[Any]     =	self.model_tester.num_stages
											self.assertEqual(len(SCREAMING_SNAKE_CASE_							)    ,	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]    ,	)

								__lowerCamelCase:				int     =	self.model_tester.prepare_config_and_inputs_for_common()
								__lowerCamelCase:				Any     =	["""basic""", """bottleneck"""]
								for model_class in self.all_model_classes:
											for layer_type in layers_type:
														__lowerCamelCase:				List[Any]     =	layer_type
														__lowerCamelCase:				Tuple     =	True
														check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														__lowerCamelCase:				Optional[Any]     =	True

														check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)





					def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
								__lowerCamelCase:				Dict     =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_							)







					@slow
					def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
								for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__lowerCamelCase:				List[Any]     =	RegNetModel.from_pretrained(SCREAMING_SNAKE_CASE_							)
											self.assertIsNotNone(SCREAMING_SNAKE_CASE_							)



def     __lowerCAmelCase						(							)       ->							Optional[Any]:
			__lowerCamelCase:				Optional[int]     =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
			return image







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





					@cached_property
					def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
								return (
								    AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)
								    if is_vision_available()
								    else None
								)







					@slow
					def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
								__lowerCamelCase:				List[Any]     =	RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(SCREAMING_SNAKE_CASE_							)

								__lowerCamelCase:				List[str]     =	self.default_image_processor
								__lowerCamelCase:				List[Any]     =	prepare_img()
								__lowerCamelCase:				Tuple     =	image_processor(images=SCREAMING_SNAKE_CASE_    ,	return_tensors="""pt"""							).to(SCREAMING_SNAKE_CASE_							)

								# forward pass
								with torch.no_grad():
											__lowerCamelCase:				List[str]     =	model(**SCREAMING_SNAKE_CASE_							)

								# verify the logits
								__lowerCamelCase:				int     =	torch.Size((1, 1000)							)
								self.assertEqual(outputs.logits.shape    ,	SCREAMING_SNAKE_CASE_							)

								__lowerCamelCase:				Any     =	torch.tensor([-0.4180, -1.5051, -3.4836]							).to(SCREAMING_SNAKE_CASE_							)

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






import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
   _A		:				str									=       argparse.ArgumentParser()

   parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

   parser.add_argument(
       '''--txt2img_unclip''',
       default='''kakaobrain/karlo-v1-alpha''',
       type=str,
       required=False,
       help='''The pretrained txt2img unclip.''',
   )

   _A		:				Optional[Any]									=       parser.parse_args()

   _A		:				str									=       UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

   _A		:				Optional[Any]									=       CLIPImageProcessor()
   _A		:				List[Any]									=       CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

   _A		:				str									=       UnCLIPImageVariationPipeline(
       decoder=txtaimg.decoder,
       text_encoder=txtaimg.text_encoder,
       tokenizer=txtaimg.tokenizer,
       text_proj=txtaimg.text_proj,
       feature_extractor=feature_extractor,
       image_encoder=image_encoder,
       super_res_first=txtaimg.super_res_first,
       super_res_last=txtaimg.super_res_last,
       decoder_scheduler=txtaimg.decoder_scheduler,
       super_res_scheduler=txtaimg.super_res_scheduler,
   )

   imgaimg.save_pretrained(args.dump_path)


