




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()
_snake_case										=       logging.get_logger(__name__)

_snake_case										=       torch.device("cpu")







def 				lowerCAmelCase_	(     ):
      _A							:     Optional[int]			=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      _A							:     Optional[Any]			=						Image.open(requests.get(snake_case_,stream=snake_case_     ).raw     )
      return im







def 				lowerCAmelCase_	(     snake_case_     ):
      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 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      _A							:     str			=						dct.pop(snake_case_     )
      _A							:     Optional[Any]			=						val







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







@torch.no_grad()
def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      _A							:     List[str]			=						SwiftFormerConfig()

      # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
      _A							:     str			=						1000
      _A							:     List[str]			=						"""huggingface/label-files"""
      _A							:     Union[str, Any]			=						"""imagenet-1k-id2label.json"""
      _A							:     Tuple			=						json.load(open(hf_hub_download(snake_case_,snake_case_,repo_type="""dataset"""     ),"""r"""     )     )
      _A							:     int			=						{int(snake_case_     ): v for k, v in idalabel.items()}
      _A							:     Any			=						idalabel
      _A							:     List[str]			=						{v: k for k, v in idalabel.items()}

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

      elif swiftformer_name == "swiftformer_s":
            _A							:     Dict			=						[3, 3, 9, 6]
            _A							:     Union[str, Any]			=						[48, 64, 168, 224]

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

      elif swiftformer_name == "swiftformer_l3":
            _A							:     Tuple			=						[4, 4, 12, 6]
            _A							:     List[Any]			=						[64, 128, 320, 512]

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

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

      # load HuggingFace model
      _A							:     str			=						SwiftFormerForImageClassification(snake_case_     ).eval()
      hf_model.load_state_dict(snake_case_     )

      # prepare test inputs
      _A							:     Any			=						prepare_img()
      _A							:     Optional[Any]			=						ViTImageProcessor.from_pretrained("""preprocessor_config"""     )
      _A							:     Any			=						processor(images=snake_case_,return_tensors="""pt"""     )

      # compare outputs from both models
      _A							:     Union[str, Any]			=						get_expected_output(snake_case_     )
      _A							:     List[Any]			=						hf_model(inputs["""pixel_values"""]     ).logits

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

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


if __name__ == "__main__":
  _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.")

  _snake_case										=       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 (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


_lowerCAmelCase    :Any     =					False
@skip_mps
class 							_UpperCAmelCase							( a		,a		,a		,unittest.TestCase      ):





     '''simple docstring'''



     a__									=StableDiffusionAttendAndExcitePipeline
     a__									=False
     a__									=TEXT_TO_IMAGE_PARAMS
     a__									=TEXT_TO_IMAGE_BATCH_PARAMS.union({'''token_indices'''}      )
     a__									=TEXT_TO_IMAGE_IMAGE_PARAMS
     a__									=TEXT_TO_IMAGE_IMAGE_PARAMS





     @classmethod
     def  __lowerCAmelCase	(					cls		)							->					List[str]:
         super().setUpClass()
         torch.use_deterministic_algorithms(A		)





     @classmethod
     def  __lowerCAmelCase	(					cls		)							->					Union[str, Any]:
         super().tearDownClass()
         torch.use_deterministic_algorithms(A		)





     def  __lowerCAmelCase	(					self		)							->					Tuple:
         torch.manual_seed(0		)
         _UpperCAmelCase							:  Optional[int]											=    UNetaDConditionModel(
             block_out_channels=(3_2, 6_4)       ,	layers_per_block=1       ,	sample_size=3_2       ,	in_channels=4       ,	out_channels=4       ,	down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,	up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,	cross_attention_dim=3_2       ,	attention_head_dim=(2, 4)       ,	use_linear_projection=A       ,	)
         _UpperCAmelCase							:  List[Any]											=    DDIMScheduler(
             beta_start=0.00_085       ,	beta_end=0.012       ,	beta_schedule='''scaled_linear'''       ,	clip_sample=A       ,	set_alpha_to_one=A       ,	)
         torch.manual_seed(0		)
         _UpperCAmelCase							:  int											=    AutoencoderKL(
             block_out_channels=[3_2, 6_4]       ,	in_channels=3       ,	out_channels=3       ,	down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,	up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,	latent_channels=4       ,	sample_size=1_2_8       ,	)
         torch.manual_seed(0		)
         _UpperCAmelCase							:  int											=    CLIPTextConfig(
             bos_token_id=0       ,	eos_token_id=2       ,	hidden_size=3_2       ,	intermediate_size=3_7       ,	layer_norm_eps=1E-05       ,	num_attention_heads=4       ,	num_hidden_layers=5       ,	pad_token_id=1       ,	vocab_size=1_0_0_0       ,	hidden_act='''gelu'''       ,	projection_dim=5_1_2       ,	)
         _UpperCAmelCase							:  List[str]											=    CLIPTextModel(A		)
         _UpperCAmelCase							:  str											=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

         _UpperCAmelCase							:  Union[str, Any]											=    {
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''vae''': vae,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''safety_checker''': None,
             '''feature_extractor''': None,
         }

         return components





     def  __lowerCAmelCase	(					self       ,	A       ,	A=0		)							->					List[Any]:
         if str(A		).startswith('''mps'''		):
             _UpperCAmelCase							:  Optional[int]											=    torch.manual_seed(A		)
         else:
             _UpperCAmelCase							:  Union[str, Any]											=    torch.Generator(device=A		).manual_seed(A		)
         _UpperCAmelCase							:  List[str]											=    {
             '''prompt''': '''a cat and a frog''',
             '''token_indices''': [2, 5],
             '''generator''': generator,
             '''num_inference_steps''': 1,
             '''guidance_scale''': 6.0,
             '''output_type''': '''numpy''',
             '''max_iter_to_alter''': 2,
             '''thresholds''': {0: 0.7},
         }
         return inputs





     def  __lowerCAmelCase	(					self		)							->					int:
         _UpperCAmelCase							:  List[str]											=    '''cpu'''

         _UpperCAmelCase							:  Tuple											=    self.get_dummy_components()
         _UpperCAmelCase							:  int											=    self.pipeline_class(**A		)
         pipe.to(A		)
         pipe.set_progress_bar_config(disable=A		)

         _UpperCAmelCase							:  Dict											=    self.get_dummy_inputs(A		)
         _UpperCAmelCase							:  Union[str, Any]											=    pipe(**A		).images
         _UpperCAmelCase							:  Tuple											=    image[0, -3:, -3:, -1]

         self.assertEqual(image.shape       ,	(1, 6_4, 6_4, 3)		)
         _UpperCAmelCase							:  int											=    np.array(
             [0.63_905_364, 0.62_897_307, 0.48_599_017, 0.5_133_624, 0.5_550_048, 0.45_769_516, 0.50_326_973, 0.5_023_139, 0.45_384_496]		)
         _UpperCAmelCase							:  Tuple											=    np.abs(image_slice.flatten() - expected_slice		).max()
         self.assertLessEqual(A       ,	1E-3		)





     def  __lowerCAmelCase	(					self		)							->					Dict:
         super().test_cpu_offload_forward_pass(expected_max_diff=5E-4		)





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
         self._test_inference_batch_consistent(batch_sizes=[1, 2]		)





     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         self._test_inference_batch_single_identical(batch_size=2       ,	expected_max_diff=7E-4		)





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3		)





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4		)





     def  __lowerCAmelCase	(					self		)							->					str:
         super().test_save_load_local(expected_max_difference=5E-4		)





     def  __lowerCAmelCase	(					self		)							->					Optional[int]:
         super().test_save_load_optional_components(expected_max_difference=4E-4		)



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





     '''simple docstring'''



     @classmethod
     def  __lowerCAmelCase	(					cls		)							->					Union[str, Any]:
         super().setUpClass()
         torch.use_deterministic_algorithms(A		)





     @classmethod
     def  __lowerCAmelCase	(					cls		)							->					Optional[int]:
         super().tearDownClass()
         torch.use_deterministic_algorithms(A		)





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





     def  __lowerCAmelCase	(					self		)							->					str:
         _UpperCAmelCase							:  Any											=    torch.manual_seed(5_1		)

         _UpperCAmelCase							:  Optional[Any]											=    StableDiffusionAttendAndExcitePipeline.from_pretrained(
             '''CompVis/stable-diffusion-v1-4'''       ,	safety_checker=A       ,	torch_dtype=torch.floataa		)
         pipe.to('''cuda'''		)

         _UpperCAmelCase							:  Optional[int]											=    '''a painting of an elephant with glasses'''
         _UpperCAmelCase							:  int											=    [5, 7]

         _UpperCAmelCase							:  Dict											=    pipe(
             prompt=A       ,	token_indices=A       ,	guidance_scale=7.5       ,	generator=A       ,	num_inference_steps=5       ,	max_iter_to_alter=5       ,	output_type='''numpy'''       ,	).images[0]

         _UpperCAmelCase							:  List[Any]											=    load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy'''		)
         assert np.abs((expected_image - image).max()		) < 5E-1


