


'''simple docstring'''


import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_						:  List[str]				  =						logging.get_logger()







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__ = True	):







				"""simple docstring"""





				print(f"""Converting {name}..."""	)

				with torch.no_grad():
								if hidden_sizes == 128:
												if name[-1] == "S":
																_SCREAMING_SNAKE_CASE					:			List[str]							= timm.create_model("""levit_128s"""				, pretrained=__a	)
												else:
																_SCREAMING_SNAKE_CASE					:			str							= timm.create_model("""levit_128"""				, pretrained=__a	)
								if hidden_sizes == 192:
												_SCREAMING_SNAKE_CASE					:			int							= timm.create_model("""levit_192"""				, pretrained=__a	)
								if hidden_sizes == 256:
												_SCREAMING_SNAKE_CASE					:			Optional[Any]							= timm.create_model("""levit_256"""				, pretrained=__a	)
								if hidden_sizes == 384:
												_SCREAMING_SNAKE_CASE					:			List[str]							= timm.create_model("""levit_384"""				, pretrained=__a	)

								from_model.eval()
								_SCREAMING_SNAKE_CASE					:			List[str]							= LevitForImageClassificationWithTeacher(__a	).eval()
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= OrderedDict()

								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= from_model.state_dict()
								_SCREAMING_SNAKE_CASE					:			Any							= list(from_model.state_dict().keys()	)
								_SCREAMING_SNAKE_CASE					:			List[Any]							= list(our_model.state_dict().keys()	)
								print(len(__a	)				, len(__a	)	)
								for i in range(len(__a	)	):
												_SCREAMING_SNAKE_CASE					:			int							= weights[og_keys[i]]
								our_model.load_state_dict(__a	)

								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= torch.randn((2, 3, 224, 224)	)
								_SCREAMING_SNAKE_CASE					:			int							= from_model(__a	)
								_SCREAMING_SNAKE_CASE					:			List[str]							= our_model(__a	).logits

				assert torch.allclose(__a				, __a	), "The model logits don't match the original one."

				_SCREAMING_SNAKE_CASE					:			List[Any]							= name
				print(__a	)

				if push_to_hub:
								our_model.save_pretrained(save_directory / checkpoint_name	)
								_SCREAMING_SNAKE_CASE					:			List[str]							= LevitImageProcessor()
								image_processor.save_pretrained(save_directory / checkpoint_name	)

								print(f"""Pushed {checkpoint_name}"""	)







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__ = None				, SCREAMING_SNAKE_CASE__ = True	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			List[Any]							= """imagenet-1k-id2label.json"""
				_SCREAMING_SNAKE_CASE					:			Optional[int]							= 1000
				_SCREAMING_SNAKE_CASE					:			List[Any]							= (1, num_labels)

				_SCREAMING_SNAKE_CASE					:			Dict							= """huggingface/label-files"""
				_SCREAMING_SNAKE_CASE					:			Any							= num_labels
				_SCREAMING_SNAKE_CASE					:			Dict							= json.load(open(hf_hub_download(__a				, __a				, repo_type="""dataset"""	)				, """r"""	)	)
				_SCREAMING_SNAKE_CASE					:			List[Any]							= {int(__a	): v for k, v in idalabel.items()}

				_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= idalabel
				_SCREAMING_SNAKE_CASE					:			List[Any]							= {v: k for k, v in idalabel.items()}

				_SCREAMING_SNAKE_CASE					:			str							= partial(__a				, num_labels=__a				, idalabel=__a				, labelaid=__a	)

				_SCREAMING_SNAKE_CASE					:			str							= {
				    """levit-128S""": 128,
				    """levit-128""": 128,
				    """levit-192""": 192,
				    """levit-256""": 256,
				    """levit-384""": 384,
				}

				_SCREAMING_SNAKE_CASE					:			Dict							= {
				    """levit-128S""": ImageNetPreTrainedConfig(
				        hidden_sizes=[128, 256, 384]				, num_attention_heads=[4, 6, 8]				, depths=[2, 3, 4]				, key_dim=[16, 16, 16]				, drop_path_rate=0				, ),
				    """levit-128""": ImageNetPreTrainedConfig(
				        hidden_sizes=[128, 256, 384]				, num_attention_heads=[4, 8, 12]				, depths=[4, 4, 4]				, key_dim=[16, 16, 16]				, drop_path_rate=0				, ),
				    """levit-192""": ImageNetPreTrainedConfig(
				        hidden_sizes=[192, 288, 384]				, num_attention_heads=[3, 5, 6]				, depths=[4, 4, 4]				, key_dim=[32, 32, 32]				, drop_path_rate=0				, ),
				    """levit-256""": ImageNetPreTrainedConfig(
				        hidden_sizes=[256, 384, 512]				, num_attention_heads=[4, 6, 8]				, depths=[4, 4, 4]				, key_dim=[32, 32, 32]				, drop_path_rate=0				, ),
				    """levit-384""": ImageNetPreTrainedConfig(
				        hidden_sizes=[384, 512, 768]				, num_attention_heads=[6, 9, 12]				, depths=[4, 4, 4]				, key_dim=[32, 32, 32]				, drop_path_rate=0.1				, ),
				}

				if model_name:
								convert_weight_and_push(
								    names_to_hidden_sizes[model_name]				, __a				, names_to_config[model_name]				, __a				, __a	)
				else:
								for model_name, config in names_to_config.items():
												convert_weight_and_push(names_to_hidden_sizes[model_name]				, __a				, __a				, __a				, __a	)
				return config, expected_shape


if __name__ == "__main__":
				UpperCAmelCase_						:  Any				  =						argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--model_name',
				    default=None,
				    type=str,
				    help='The name of the model you wish to convert, it must be one of the supported Levit* architecture,',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path',
				    default='levit-dump-folder/',
				    type=Path,
				    required=False,
				    help='Path to the output PyTorch model directory.',
				)
				parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
				parser.add_argument(
				    '--no-push_to_hub',
				    dest='push_to_hub',
				    action='store_false',
				    help='Do not push model and image processor to the hub',
				)

				UpperCAmelCase_						:  Union[str, Any]				  =						parser.parse_args()
				UpperCAmelCase_						:  Path				  =						args.pytorch_dump_folder_path
				pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
				convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)








import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class       lowercase_					(lowercase__      ,					unittest.TestCase   ):
				snake_case	    =KandinskyVaaPriorPipeline
				snake_case	    =['prompt']
				snake_case	    =['prompt', 'negative_prompt']
				snake_case	    =[
				    'num_images_per_prompt',
				    'generator',
				    'num_inference_steps',
				    'latents',
				    'negative_prompt',
				    'guidance_scale',
				    'output_type',
				    'return_dict',
				]
				snake_case	    =False





				@property
				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											return 32





				@property
				def 						__UpperCamelCase    (				self)  -> Tuple:
											return 32





				@property
				def 						__UpperCamelCase    (				self)  -> int:
											return self.time_input_dim





				@property
				def 						__UpperCamelCase    (				self)  -> str:
											return self.time_input_dim * 4





				@property
				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											return 100





				@property
				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											a__      =CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')
											return tokenizer





				@property
				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											torch.manual_seed(0)
											a__      =CLIPTextConfig(
											    bos_token_id=0       ,  eos_token_id=2       ,  hidden_size=self.text_embedder_hidden_size       ,  projection_dim=self.text_embedder_hidden_size       ,  intermediate_size=37       ,  layer_norm_eps=1e-05       ,  num_attention_heads=4       ,  num_hidden_layers=5       ,  pad_token_id=1       ,  vocab_size=1000       ,  )
											return CLIPTextModelWithProjection(lowercase_)





				@property
				def 						__UpperCamelCase    (				self)  -> Tuple:
											torch.manual_seed(0)

											a__      ={
											    'num_attention_heads': 2,
											    'attention_head_dim': 12,
											    'embedding_dim': self.text_embedder_hidden_size,
											    'num_layers': 1,
											}

											a__      =PriorTransformer(**lowercase_)
											# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
											a__      =nn.Parameter(torch.ones(model.clip_std.shape))
											return model





				@property
				def 						__UpperCamelCase    (				self)  -> Any:
											torch.manual_seed(0)
											a__      =CLIPVisionConfig(
											    hidden_size=self.text_embedder_hidden_size       ,  image_size=224       ,  projection_dim=self.text_embedder_hidden_size       ,  intermediate_size=37       ,  num_attention_heads=4       ,  num_channels=3       ,  num_hidden_layers=5       ,  patch_size=14       ,  )

											a__      =CLIPVisionModelWithProjection(lowercase_)
											return model





				@property
				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											a__      =CLIPImageProcessor(
											    crop_size=224       ,  do_center_crop=lowercase_       ,  do_normalize=lowercase_       ,  do_resize=lowercase_       ,  image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]       ,  image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]       ,  resample=3       ,  size=224       ,  )

											return image_processor





				def 						__UpperCamelCase    (				self)  -> Any:
											a__      =self.dummy_prior
											a__      =self.dummy_image_encoder
											a__      =self.dummy_text_encoder
											a__      =self.dummy_tokenizer
											a__      =self.dummy_image_processor

											a__      =UnCLIPScheduler(
											    variance_type='fixed_small_log'       ,  prediction_type='sample'       ,  num_train_timesteps=1000       ,  clip_sample=lowercase_       ,  clip_sample_range=10.0       ,  )

											a__      ={
											    'prior': prior,
											    'image_encoder': image_encoder,
											    'text_encoder': text_encoder,
											    'tokenizer': tokenizer,
											    'scheduler': scheduler,
											    'image_processor': image_processor,
											}

											return components





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_=0)  -> Tuple:
											if str(lowercase_).startswith('mps'):
																		a__      =torch.manual_seed(lowercase_)
											else:
																		a__      =torch.Generator(device=lowercase_).manual_seed(lowercase_)
											a__      ={
											    'prompt': 'horse',
											    'generator': generator,
											    'guidance_scale': 4.0,
											    'num_inference_steps': 2,
											    'output_type': 'np',
											}
											return inputs





				def 						__UpperCamelCase    (				self)  -> int:
											a__      ='cpu'

											a__      =self.get_dummy_components()

											a__      =self.pipeline_class(**lowercase_)
											a__      =pipe.to(lowercase_)

											pipe.set_progress_bar_config(disable=lowercase_)

											a__      =pipe(**self.get_dummy_inputs(lowercase_))
											a__      =output.image_embeds

											a__      =pipe(
											    **self.get_dummy_inputs(lowercase_)       ,  return_dict=lowercase_       ,  )[0]

											a__      =image[0, -10:]
											a__      =image_from_tuple[0, -10:]

											assert image.shape == (1, 32)

											a__      =np.array(
											    [-0.05_32, 1.71_20, 0.36_56, -1.08_52, -0.89_46, -1.17_56, 0.43_48, 0.24_82, 0.51_46, -0.11_56])

											assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
											assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2





				@skip_mps
				def 						__UpperCamelCase    (				self)  -> List[Any]:
											a__      =torch_device == 'cpu'
											a__      =True
											a__      =False

											self._test_inference_batch_single_identical(
											    test_max_difference=lowercase_       ,  relax_max_difference=lowercase_       ,  test_mean_pixel_difference=lowercase_       ,  )





				@skip_mps
				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											a__      =torch_device == 'cpu'
											a__      =False

											self._test_attention_slicing_forward_pass(
											    test_max_difference=lowercase_       ,  test_mean_pixel_difference=lowercase_       ,  )

