



"""simple docstring"""





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowercase					=       logging.get_logger(__name__)

lowercase					=       torch.device('''cpu''')




def 						UpperCAmelCase  (		):

		'''simple docstring'''


		_UpperCAmelCase							=		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
		_UpperCAmelCase							=		Image.open(requests.get(a_  ,					stream=a_							).raw							)
		return im




def 						UpperCAmelCase  (		A						:    List[str]							):

		'''simple docstring'''


		if swiftformer_name == "swiftformer_xs":
				return torch.tensor([-2.17_03e00, 2.11_07e00, -2.08_11e00, 8.86_85e-01, 2.43_60e-01]							)

		elif swiftformer_name == "swiftformer_s":
				return torch.tensor([3.96_36e-01, 2.34_78e-01, -1.69_63e00, -1.73_81e00, -8.63_37e-01]							)

		elif swiftformer_name == "swiftformer_l1":
				return torch.tensor([-4.27_68e-01, -4.74_29e-01, -1.08_97e00, -1.02_48e00, 3.55_23e-02]							)

		elif swiftformer_name == "swiftformer_l3":
				return torch.tensor([-2.53_30e-01, 2.42_11e-01, -6.01_85e-01, -8.27_89e-01, -6.04_46e-02]							)




def 						UpperCAmelCase  (		A						:    List[str]  ,					A						:    Optional[Any]  ,					A						:    Dict							):

		'''simple docstring'''


		_UpperCAmelCase							=		dct.pop(a_							)
		_UpperCAmelCase							=		val




def 						UpperCAmelCase  (		A						:    Union[str, Any]							):

		'''simple docstring'''


		_UpperCAmelCase							=		[]
		for k in state_dict.keys():
				_UpperCAmelCase							=		k
				if ".pwconv" in k:
						_UpperCAmelCase							=		k_new.replace('.pwconv'  ,					'.point_wise_conv'							)
				if ".dwconv" in k:
						_UpperCAmelCase							=		k_new.replace('.dwconv'  ,					'.depth_wise_conv'							)
				if ".Proj." in k:
						_UpperCAmelCase							=		k_new.replace('.Proj.'  ,					'.proj.'							)
				if "patch_embed" in k_new:
						_UpperCAmelCase							=		k_new.replace('patch_embed'  ,					'swiftformer.patch_embed.patch_embedding'							)
				if "network" in k_new:
						_UpperCAmelCase							=		k_new.split('.'							)
						if ls[2].isdigit():
								_UpperCAmelCase							=		"""swiftformer.encoder.network.""" + ls[1] + """.blocks.""" + ls[2] + """.""" + """.""".join(ls[3:]							)
						else:
								_UpperCAmelCase							=		k_new.replace('network'  ,					'swiftformer.encoder.network'							)
				rename_keys.append((k, k_new)							)
		return rename_keys




@torch.no_grad()
def 						UpperCAmelCase  (		A						:    Any  ,					A						:    List[str]  ,					A						:    Optional[Any]							):

		'''simple docstring'''


		_UpperCAmelCase							=		SwiftFormerConfig()

		# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
		_UpperCAmelCase							=		1000
		_UpperCAmelCase							=		"""huggingface/label-files"""
		_UpperCAmelCase							=		"""imagenet-1k-id2label.json"""
		_UpperCAmelCase							=		json.load(open(hf_hub_download(a_  ,					a_  ,					repo_type='dataset'							)  ,					'r'							)							)
		_UpperCAmelCase							=		{int(a_							): v for k, v in idalabel.items()}
		_UpperCAmelCase							=		idalabel
		_UpperCAmelCase							=		{v: k for k, v in idalabel.items()}

		# size of the architecture
		if swiftformer_name == "swiftformer_xs":
				_UpperCAmelCase							=		[3, 3, 6, 4]
				_UpperCAmelCase							=		[48, 56, 112, 220]

		elif swiftformer_name == "swiftformer_s":
				_UpperCAmelCase							=		[3, 3, 9, 6]
				_UpperCAmelCase							=		[48, 64, 168, 224]

		elif swiftformer_name == "swiftformer_l1":
				_UpperCAmelCase							=		[4, 3, 10, 5]
				_UpperCAmelCase							=		[48, 96, 192, 384]

		elif swiftformer_name == "swiftformer_l3":
				_UpperCAmelCase							=		[4, 4, 12, 6]
				_UpperCAmelCase							=		[64, 128, 320, 512]

		# load state_dict of original model, remove and rename some keys
		if original_ckpt:
				if original_ckpt.startswith('https'							):
						_UpperCAmelCase							=		torch.hub.load_state_dict_from_url(a_  ,					map_location='cpu'  ,					check_hash=a_							)
				else:
						_UpperCAmelCase							=		torch.load(a_  ,					map_location='cpu'							)
		_UpperCAmelCase							=		checkpoint

		_UpperCAmelCase							=		create_rename_keys(a_							)
		for rename_key_src, rename_key_dest in rename_keys:
				rename_key(a_  ,					a_  ,					a_							)

		# load HuggingFace model
		_UpperCAmelCase							=		SwiftFormerForImageClassification(a_							).eval()
		hf_model.load_state_dict(a_							)

		# prepare test inputs
		_UpperCAmelCase							=		prepare_img()
		_UpperCAmelCase							=		ViTImageProcessor.from_pretrained('preprocessor_config'							)
		_UpperCAmelCase							=		processor(images=a_  ,					return_tensors='pt'							)

		# compare outputs from both models
		_UpperCAmelCase							=		get_expected_output(a_							)
		_UpperCAmelCase							=		hf_model(inputs['pixel_values']							).logits

		assert hf_logits.shape == torch.Size([1, 1000]							)
		assert torch.allclose(hf_logits[0, 0:5]  ,					a_  ,					atol=1e-3							)

		Path(a_							).mkdir(exist_ok=a_							)
		print(f'Saving model {swiftformer_name} to {pytorch_dump_folder_path}'							)
		hf_model.save_pretrained(a_							)


if __name__ == "__main__":
					lowercase					=       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '''--swiftformer_name''',
					    default='''swiftformer_xs''',
					    choices=['''swiftformer_xs''', '''swiftformer_s''', '''swiftformer_l1''', '''swiftformer_l3'''],
					    type=str,
					    help='''Name of the SwiftFormer model you\'d like to convert.''',
					)
					parser.add_argument(
					    '''--pytorch_dump_folder_path''',
					    default='''./converted_outputs/''',
					    type=str,
					    help='''Path to the output PyTorch model directory.''',
					)
					parser.add_argument('''--original_ckpt''', default=None, type=str, help='''Path to the original model checkpoint.''')

					lowercase					=       parser.parse_args()
					convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)










'''simple docstring'''

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


UpperCamelCase      					=False




class      A     (   unittest.TestCase    ):






 """simple docstring"""





 def 	_UpperCAmelCase							(				self			):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()
 @property
 def 	_UpperCAmelCase							(				self			):
       return 12
 @property
 def 	_UpperCAmelCase							(				self			):
       return 12
 @property
 def 	_UpperCAmelCase							(				self			):
       return 32
 @property
 def 	_UpperCAmelCase							(				self			):
       torch.manual_seed(0			)
       UpperCamelCase_					: Union[str, Any]            =				VQModel(
           block_out_channels=[32, 64]      ,  in_channels=3      ,  out_channels=3      ,  down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]      ,  up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]      ,  latent_channels=3      ,  num_vq_embeddings=self.num_embed      ,  vq_embed_dim=3      ,  )
       return model
 @property
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: str            =				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)
       return tokenizer
 @property
 def 	_UpperCAmelCase							(				self			):
       torch.manual_seed(0			)
       UpperCamelCase_					: Any            =				CLIPTextConfig(
           bos_token_id=0      ,  eos_token_id=2      ,  hidden_size=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=10_00      ,  )
       return CLIPTextModel(__lowerCAmelCase			)
 @property
 def 	_UpperCAmelCase							(				self			):
       torch.manual_seed(0			)

       UpperCamelCase_					: Optional[Any]            =				12
       UpperCamelCase_					: Optional[int]            =				12

       UpperCamelCase_					: Optional[Any]            =				{
           """attention_bias""": True,
           """cross_attention_dim""": 32,
           """attention_head_dim""": height * width,
           """num_attention_heads""": 1,
           """num_vector_embeds""": self.num_embed,
           """num_embeds_ada_norm""": self.num_embeds_ada_norm,
           """norm_num_groups""": 32,
           """sample_size""": width,
           """activation_fn""": """geglu-approximate""",
       }

       UpperCamelCase_					: Optional[Any]            =				TransformeraDModel(**__lowerCAmelCase			)
       return model
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Union[str, Any]            =				"""cpu"""

       UpperCamelCase_					: List[str]            =				self.dummy_vqvae
       UpperCamelCase_					: str            =				self.dummy_text_encoder
       UpperCamelCase_					: Optional[Any]            =				self.dummy_tokenizer
       UpperCamelCase_					: Optional[int]            =				self.dummy_transformer
       UpperCamelCase_					: Optional[Any]            =				VQDiffusionScheduler(self.num_embed			)
       UpperCamelCase_					: Union[str, Any]            =				LearnedClassifierFreeSamplingEmbeddings(learnable=__lowerCAmelCase			)

       UpperCamelCase_					: Tuple            =				VQDiffusionPipeline(
           vqvae=__lowerCAmelCase      ,  text_encoder=__lowerCAmelCase      ,  tokenizer=__lowerCAmelCase      ,  transformer=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase      ,  learned_classifier_free_sampling_embeddings=__lowerCAmelCase      ,  )
       UpperCamelCase_					: Union[str, Any]            =				pipe.to(__lowerCAmelCase			)
       pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

       UpperCamelCase_					: Optional[int]            =				"""teddy bear playing in the pool"""

       UpperCamelCase_					: Union[str, Any]            =				torch.Generator(device=__lowerCAmelCase			).manual_seed(0			)
       UpperCamelCase_					: List[Any]            =				pipe([prompt]      ,  generator=__lowerCAmelCase      ,  num_inference_steps=2      ,  output_type="""np"""			)
       UpperCamelCase_					: Dict            =				output.images

       UpperCamelCase_					: List[str]            =				torch.Generator(device=__lowerCAmelCase			).manual_seed(0			)
       UpperCamelCase_					: Dict            =				pipe(
           [prompt]      ,  generator=__lowerCAmelCase      ,  output_type="""np"""      ,  return_dict=__lowerCAmelCase      ,  num_inference_steps=2			)[0]

       UpperCamelCase_					: Union[str, Any]            =				image[0, -3:, -3:, -1]
       UpperCamelCase_					: Any            =				image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 24, 24, 3)

       UpperCamelCase_					: List[str]            =				np.array([0.65_51, 0.61_68, 0.50_08, 0.56_76, 0.56_59, 0.42_95, 0.60_73, 0.55_99, 0.49_92]			)

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





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: int            =				"""cpu"""

       UpperCamelCase_					: List[Any]            =				self.dummy_vqvae
       UpperCamelCase_					: Union[str, Any]            =				self.dummy_text_encoder
       UpperCamelCase_					: int            =				self.dummy_tokenizer
       UpperCamelCase_					: Tuple            =				self.dummy_transformer
       UpperCamelCase_					: Tuple            =				VQDiffusionScheduler(self.num_embed			)
       UpperCamelCase_					: List[Any]            =				LearnedClassifierFreeSamplingEmbeddings(
           learnable=__lowerCAmelCase      ,  hidden_size=self.text_embedder_hidden_size      ,  length=tokenizer.model_max_length			)

       UpperCamelCase_					: Optional[Any]            =				VQDiffusionPipeline(
           vqvae=__lowerCAmelCase      ,  text_encoder=__lowerCAmelCase      ,  tokenizer=__lowerCAmelCase      ,  transformer=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase      ,  learned_classifier_free_sampling_embeddings=__lowerCAmelCase      ,  )
       UpperCamelCase_					: List[Any]            =				pipe.to(__lowerCAmelCase			)
       pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

       UpperCamelCase_					: Tuple            =				"""teddy bear playing in the pool"""

       UpperCamelCase_					: List[str]            =				torch.Generator(device=__lowerCAmelCase			).manual_seed(0			)
       UpperCamelCase_					: Tuple            =				pipe([prompt]      ,  generator=__lowerCAmelCase      ,  num_inference_steps=2      ,  output_type="""np"""			)
       UpperCamelCase_					: List[str]            =				output.images

       UpperCamelCase_					: Optional[int]            =				torch.Generator(device=__lowerCAmelCase			).manual_seed(0			)
       UpperCamelCase_					: List[str]            =				pipe(
           [prompt]      ,  generator=__lowerCAmelCase      ,  output_type="""np"""      ,  return_dict=__lowerCAmelCase      ,  num_inference_steps=2			)[0]

       UpperCamelCase_					: Dict            =				image[0, -3:, -3:, -1]
       UpperCamelCase_					: Optional[int]            =				image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 24, 24, 3)

       UpperCamelCase_					: List[Any]            =				np.array([0.66_93, 0.60_75, 0.49_59, 0.57_01, 0.55_83, 0.43_33, 0.61_71, 0.56_84, 0.49_88]			)

       assert np.abs(image_slice.flatten() - expected_slice			).max() < 2.0
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1E-2






@slow
@require_torch_gpu
class      A     (   unittest.TestCase    ):






 """simple docstring"""





 def 	_UpperCAmelCase							(				self			):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: List[Any]            =				load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"""			)

       UpperCamelCase_					: Optional[int]            =				VQDiffusionPipeline.from_pretrained("""microsoft/vq-diffusion-ithq"""			)
       UpperCamelCase_					: str            =				pipeline.to(__lowerCAmelCase			)
       pipeline.set_progress_bar_config(disable=__lowerCAmelCase			)

       # requires GPU generator for gumbel softmax
       # don't use GPU generator in tests though
       UpperCamelCase_					: List[Any]            =				torch.Generator(device=__lowerCAmelCase			).manual_seed(0			)
       UpperCamelCase_					: str            =				pipeline(
           """teddy bear playing in the pool"""      ,  num_images_per_prompt=1      ,  generator=__lowerCAmelCase      ,  output_type="""np"""      ,  )

       UpperCamelCase_					: Union[str, Any]            =				output.images[0]

       assert image.shape == (2_56, 2_56, 3)
       assert np.abs(expected_image - image			).max() < 2.0






