


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   ,     __lowerCamelCase   ,     __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   ,     __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   ,     __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   ,     __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)


