





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()
_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      	=		torch.device("""cpu""")


def 							SCREAMING_SNAKE_CASE__						(    ):
 snake_case_		:			Union[str, Any]											=	'http://images.cocodataset.org/val2017/000000039769.jpg'
 snake_case_		:			str											=	Image.open(requests.get(__a   , stream=__a   ).raw   )
 return im


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
 if swiftformer_name == "swiftformer_xs":
  return torch.tensor([-2.1_703E00, 2.1_107E00, -2.0_811E00, 8.8_685E-01, 2.4_360E-01]   )

 elif swiftformer_name == "swiftformer_s":
  return torch.tensor([3.9_636E-01, 2.3_478E-01, -1.6_963E00, -1.7_381E00, -8.6_337E-01]   )

 elif swiftformer_name == "swiftformer_l1":
  return torch.tensor([-4.2_768E-01, -4.7_429E-01, -1.0_897E00, -1.0_248E00, 3.5_523E-02]   )

 elif swiftformer_name == "swiftformer_l3":
  return torch.tensor([-2.5_330E-01, 2.4_211E-01, -6.0_185E-01, -8.2_789E-01, -6.0_446E-02]   )


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):
 snake_case_		:			Tuple											=	dct.pop(__a   )
 snake_case_		:			List[str]											=	val


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
 snake_case_		:			int											=	[]
 for k in state_dict.keys():
  snake_case_		:			Optional[int]											=	k
  if ".pwconv" in k:
   snake_case_		:			Tuple											=	k_new.replace('.pwconv'   , '.point_wise_conv'   )
  if ".dwconv" in k:
   snake_case_		:			Optional[int]											=	k_new.replace('.dwconv'   , '.depth_wise_conv'   )
  if ".Proj." in k:
   snake_case_		:			Optional[Any]											=	k_new.replace('.Proj.'   , '.proj.'   )
  if "patch_embed" in k_new:
   snake_case_		:			List[Any]											=	k_new.replace('patch_embed'   , 'swiftformer.patch_embed.patch_embedding'   )
  if "network" in k_new:
   snake_case_		:			List[Any]											=	k_new.split('.'   )
   if ls[2].isdigit():
    snake_case_		:			str											=	'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]   )
   else:
    snake_case_		:			int											=	k_new.replace('network'   , 'swiftformer.encoder.network'   )
  rename_keys.append((k, k_new)   )
 return rename_keys


@torch.no_grad()
def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):
 snake_case_		:			Optional[int]											=	SwiftFormerConfig()

 # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
 snake_case_		:			List[Any]											=	10_00
 snake_case_		:			Optional[int]											=	'huggingface/label-files'
 snake_case_		:			Tuple											=	'imagenet-1k-id2label.json'
 snake_case_		:			Optional[int]											=	json.load(open(hf_hub_download(__a   , __a   , repo_type='dataset'   )   , 'r'   )   )
 snake_case_		:			List[Any]											=	{int(__a   ): v for k, v in idalabel.items()}
 snake_case_		:			Tuple											=	idalabel
 snake_case_		:			str											=	{v: k for k, v in idalabel.items()}

 # size of the architecture
 if swiftformer_name == "swiftformer_xs":
  snake_case_		:			List[str]											=	[3, 3, 6, 4]
  snake_case_		:			str											=	[48, 56, 1_12, 2_20]

 elif swiftformer_name == "swiftformer_s":
  snake_case_		:			Optional[Any]											=	[3, 3, 9, 6]
  snake_case_		:			Union[str, Any]											=	[48, 64, 1_68, 2_24]

 elif swiftformer_name == "swiftformer_l1":
  snake_case_		:			List[str]											=	[4, 3, 10, 5]
  snake_case_		:			Optional[int]											=	[48, 96, 1_92, 3_84]

 elif swiftformer_name == "swiftformer_l3":
  snake_case_		:			str											=	[4, 4, 12, 6]
  snake_case_		:			List[Any]											=	[64, 1_28, 3_20, 5_12]

 # load state_dict of original model, remove and rename some keys
 if original_ckpt:
  if original_ckpt.startswith('https'   ):
   snake_case_		:			List[Any]											=	torch.hub.load_state_dict_from_url(__a   , map_location='cpu'   , check_hash=__a   )
  else:
   snake_case_		:			Any											=	torch.load(__a   , map_location='cpu'   )
 snake_case_		:			List[Any]											=	checkpoint

 snake_case_		:			Dict											=	create_rename_keys(__a   )
 for rename_key_src, rename_key_dest in rename_keys:
  rename_key(__a   , __a   , __a   )

 # load HuggingFace model
 snake_case_		:			List[str]											=	SwiftFormerForImageClassification(__a   ).eval()
 hf_model.load_state_dict(__a   )

 # prepare test inputs
 snake_case_		:			Optional[Any]											=	prepare_img()
 snake_case_		:			List[str]											=	ViTImageProcessor.from_pretrained('preprocessor_config'   )
 snake_case_		:			str											=	processor(images=__a   , return_tensors='pt'   )

 # compare outputs from both models
 snake_case_		:			Optional[int]											=	get_expected_output(__a   )
 snake_case_		:			Optional[Any]											=	hf_model(inputs['pixel_values']   ).logits

 assert hf_logits.shape == torch.Size([1, 10_00]   )
 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__":
      _SCREAMING_SNAKE_CASE      	=		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.""")

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








import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()
class SCREAMING_SNAKE_CASE_						(					snake_case_       ,				unittest.TestCase					):
						__magic_name__:			Dict       =       PriorTransformer
						__magic_name__:			str       =       "hidden_states"






						@property
						def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			Any											=	4
							snake_case_		:			int											=	8
							snake_case_		:			Dict											=	7

							snake_case_		:			Union[str, Any]											=	floats_tensor((batch_size, embedding_dim)	).to(_A	)

							snake_case_		:			int											=	floats_tensor((batch_size, embedding_dim)	).to(_A	)
							snake_case_		:			str											=	floats_tensor((batch_size, num_embeddings, embedding_dim)	).to(_A	)

							return {
							    "hidden_states": hidden_states,
							    "timestep": 2,
							    "proj_embedding": proj_embedding,
							    "encoder_hidden_states": encoder_hidden_states,
							}






						def UpperCAmelCase_				(      self      :      List[Any]		,   _A      :      List[Any]=0	)							->    str:







							"""simple docstring"""


							torch.manual_seed(_A	)
							snake_case_		:			List[Any]											=	4
							snake_case_		:			str											=	8
							snake_case_		:			Any											=	7

							snake_case_		:			List[Any]											=	torch.randn((batch_size, embedding_dim)	).to(_A	)

							snake_case_		:			Optional[int]											=	torch.randn((batch_size, embedding_dim)	).to(_A	)
							snake_case_		:			List[Any]											=	torch.randn((batch_size, num_embeddings, embedding_dim)	).to(_A	)

							return {
							    "hidden_states": hidden_states,
							    "timestep": 2,
							    "proj_embedding": proj_embedding,
							    "encoder_hidden_states": encoder_hidden_states,
							}






						@property
						def UpperCAmelCase_				(      self      :      str	)							->    Optional[Any]:







							"""simple docstring"""


							return (4, 8)






						@property
						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    List[Any]:







							"""simple docstring"""


							return (4, 8)






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    Union[str, Any]:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	{
							    'num_attention_heads': 2,
							    'attention_head_dim': 4,
							    'num_layers': 2,
							    'embedding_dim': 8,
							    'num_embeddings': 7,
							    'additional_embeddings': 4,
							}
							snake_case_		:			Tuple											=	self.dummy_input
							return init_dict, inputs_dict






						def UpperCAmelCase_				(      self      :      List[Any]	)							->    Dict:







							"""simple docstring"""


							snake_case_						,snake_case_		:			str											=	PriorTransformer.from_pretrained(
							    'hf-internal-testing/prior-dummy'		,   output_loading_info=_A	)
							self.assertIsNotNone(_A	)
							self.assertEqual(len(loading_info['missing_keys']	)		,   0	)

							model.to(_A	)
							snake_case_		:			Optional[Any]											=	model(**self.dummy_input	)[0]

							assert hidden_states is not None, "Make sure output is not None"






						def UpperCAmelCase_				(      self      :      Tuple	)							->    List[Any]:







							"""simple docstring"""


							snake_case_						,snake_case_		:			Optional[int]											=	self.prepare_init_args_and_inputs_for_common()

							snake_case_		:			Tuple											=	self.model_class(**_A	)
							snake_case_		:			Any											=	inspect.signature(model.forward	)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							snake_case_		:			List[str]											=	[*signature.parameters.keys()]

							snake_case_		:			int											=	['hidden_states', 'timestep']
							self.assertListEqual(arg_names[:2]		,   _A	)






						def UpperCAmelCase_				(      self      :      Union[str, Any]	)							->    List[Any]:







							"""simple docstring"""


							snake_case_		:			str											=	PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'	)
							snake_case_		:			str											=	model.to(_A	)

							if hasattr(_A		,   'set_default_attn_processor'	):
								model.set_default_attn_processor()

							snake_case_		:			Optional[int]											=	self.get_dummy_seed_input()

							with torch.no_grad():
								snake_case_		:			Any											=	model(**_A	)[0]

							snake_case_		:			Any											=	output[0, :5].flatten().cpu()
							print(_A	)

							# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
							# the expected output slices are not the same for CPU and GPU.
							snake_case_		:			str											=	torch.tensor([-1.3_4_3_6, -0.2_8_7_0, 0.7_5_3_8, 0.4_3_6_8, -0.0_2_3_9]	)
							self.assertTrue(torch_all_close(_A		,   _A		,   rtol=1E-2	)	)
@slow
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						def UpperCAmelCase_				(      self      :      Optional[Any]		,   _A      :      int=1		,   _A      :      int=768		,   _A      :      str=77		,   _A      :      List[str]=0	)							->    Tuple:







							"""simple docstring"""


							torch.manual_seed(_A	)
							snake_case_		:			Dict											=	batch_size
							snake_case_		:			Any											=	embedding_dim
							snake_case_		:			int											=	num_embeddings

							snake_case_		:			Dict											=	torch.randn((batch_size, embedding_dim)	).to(_A	)

							snake_case_		:			List[Any]											=	torch.randn((batch_size, embedding_dim)	).to(_A	)
							snake_case_		:			Optional[int]											=	torch.randn((batch_size, num_embeddings, embedding_dim)	).to(_A	)

							return {
							    "hidden_states": hidden_states,
							    "timestep": 2,
							    "proj_embedding": proj_embedding,
							    "encoder_hidden_states": encoder_hidden_states,
							}






						def UpperCAmelCase_				(      self      :      List[Any]	)							->    Optional[int]:







							"""simple docstring"""


							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






						@parameterized.expand(
						    [
						        # fmt: off
						        [13, [-0.5_8_6_1,  0.1_2_8_3, -0.0_9_3_1,  0.0_8_8_2,  0.4_4_7_6,  0.1_3_2_9, -0.0_4_9_8,  0.0_6_4_0]],
						        [37, [-0.4_9_1_3,  0.0_1_1_0, -0.0_4_8_3,  0.0_5_4_1,  0.4_9_5_4, -0.0_1_7_0,  0.0_3_5_4,  0.1_6_5_1]],
						        # fmt: on
						    ]	)
						def UpperCAmelCase_				(      self      :      Tuple		,   _A      :      List[Any]		,   _A      :      List[str]	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			str											=	PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'		,   subfolder='prior'	)
							model.to(_A	)
							snake_case_		:			Optional[Any]											=	self.get_dummy_seed_input(seed=_A	)

							with torch.no_grad():
								snake_case_		:			str											=	model(**_A	)[0]

							assert list(sample.shape	) == [1, 768]

							snake_case_		:			Optional[Any]											=	sample[0, :8].flatten().cpu()
							print(_A	)
							snake_case_		:			int											=	torch.tensor(_A	)

							assert torch_all_close(_A		,   _A		,   atol=1E-3	)

