import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class 		_lowercase						(					lowerCAmelCase_						,      lowerCAmelCase_						,      unittest.TestCase       ):
 lowercase				     =   StableDiffusionDiffEditPipeline
 lowercase				     =   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'height', 'width', 'image'} | {'image_latents'}
 lowercase				     =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'image'} | {'image_latents'}
 lowercase				     =   frozenset(
     []       )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 lowercase				     =   frozenset([]       )

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple			)   ->						List[str]:

  """simple docstring"""





  torch.manual_seed(0			)
  UpperCamelCase_					:						List[str] =					UNetaDConditionModel(
      block_out_channels=(3_2, 6_4)   ,      layers_per_block=2   ,      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_					:						Tuple =					DDIMScheduler(
      beta_start=0.00085   ,      beta_end=0.012   ,      beta_schedule='scaled_linear'   ,      clip_sample=__a   ,      set_alpha_to_one=__a   ,      )
  UpperCamelCase_					:						Any =					DDIMInverseScheduler(
      beta_start=0.00085   ,      beta_end=0.012   ,      beta_schedule='scaled_linear'   ,      clip_sample=__a   ,      set_alpha_to_zero=__a   ,      )
  torch.manual_seed(0			)
  UpperCamelCase_					:						Union[str, Any] =					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_					:						Union[str, Any] =					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[Any] =					CLIPTextModel(__a			)
  UpperCamelCase_					:						Any =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

  UpperCamelCase_					:						List[Any] =					{
      'unet': unet,
      'scheduler': scheduler,
      'inverse_scheduler': inverse_scheduler,
      'vae': vae,
      'text_encoder': text_encoder,
      'tokenizer': tokenizer,
      'safety_checker': None,
      'feature_extractor': None,
  }

  return components

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]   ,      snake_case	:							Dict   ,      snake_case	:							List[Any]=0			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						int =					floats_tensor((1, 1_6, 1_6)   ,      rng=random.Random(__a			)			).to(__a			)
  UpperCamelCase_					:						Union[str, Any] =					floats_tensor((1, 2, 4, 1_6, 1_6)   ,      rng=random.Random(__a			)			).to(__a			)
  if str(__a			).startswith('mps'			):
   UpperCamelCase_					:						Any =					torch.manual_seed(__a			)
  else:
   UpperCamelCase_					:						Optional[int] =					torch.Generator(device=__a			).manual_seed(__a			)
  UpperCamelCase_					:						Any =					{
      'prompt': 'a dog and a newt',
      'mask_image': mask,
      'image_latents': latents,
      'generator': generator,
      'num_inference_steps': 2,
      'inpaint_strength': 1.0,
      'guidance_scale': 6.0,
      'output_type': 'numpy',
  }

  return inputs

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int   ,      snake_case	:							List[Any]   ,      snake_case	:							List[str]=0			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					floats_tensor((1, 3, 3_2, 3_2)   ,      rng=random.Random(__a			)			).to(__a			)
  UpperCamelCase_					:						Optional[Any] =					image.cpu().permute(0   ,      2   ,      3   ,      1			)[0]
  UpperCamelCase_					:						List[str] =					Image.fromarray(np.uinta(__a			)			).convert('RGB'			)
  if str(__a			).startswith('mps'			):
   UpperCamelCase_					:						Optional[int] =					torch.manual_seed(__a			)
  else:
   UpperCamelCase_					:						Optional[int] =					torch.Generator(device=__a			).manual_seed(__a			)
  UpperCamelCase_					:						str =					{
      'image': image,
      'source_prompt': 'a cat and a frog',
      'target_prompt': 'a dog and a newt',
      'generator': generator,
      'num_inference_steps': 2,
      'num_maps_per_mask': 2,
      'mask_encode_strength': 1.0,
      'guidance_scale': 6.0,
      'output_type': 'numpy',
  }

  return inputs

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							Optional[Any]   ,      snake_case	:							Any=0			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						Union[str, Any] =					floats_tensor((1, 3, 3_2, 3_2)   ,      rng=random.Random(__a			)			).to(__a			)
  UpperCamelCase_					:						int =					image.cpu().permute(0   ,      2   ,      3   ,      1			)[0]
  UpperCamelCase_					:						str =					Image.fromarray(np.uinta(__a			)			).convert('RGB'			)
  if str(__a			).startswith('mps'			):
   UpperCamelCase_					:						Optional[Any] =					torch.manual_seed(__a			)
  else:
   UpperCamelCase_					:						List[str] =					torch.Generator(device=__a			).manual_seed(__a			)
  UpperCamelCase_					:						str =					{
      'image': image,
      'prompt': 'a cat and a frog',
      'generator': generator,
      'num_inference_steps': 2,
      'inpaint_strength': 1.0,
      'guidance_scale': 6.0,
      'decode_latents': True,
      'output_type': 'numpy',
  }
  return inputs

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict			)   ->						Optional[Any]:

  """simple docstring"""





  if not hasattr(self.pipeline_class   ,      '_optional_components'			):
   return

  UpperCamelCase_					:						Tuple =					self.get_dummy_components()
  UpperCamelCase_					:						List[Any] =					self.pipeline_class(**__a			)
  pipe.to(__a			)
  pipe.set_progress_bar_config(disable=__a			)

  # set all optional components to None and update pipeline config accordingly
  for optional_component in pipe._optional_components:
   setattr(__a   ,      __a   ,      __a			)
  pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}			)

  UpperCamelCase_					:						Optional[Any] =					self.get_dummy_inputs(__a			)
  UpperCamelCase_					:						str =					pipe(**__a			)[0]

  with tempfile.TemporaryDirectory() as tmpdir:
   pipe.save_pretrained(__a			)
   UpperCamelCase_					:						Any =					self.pipeline_class.from_pretrained(__a			)
   pipe_loaded.to(__a			)
   pipe_loaded.set_progress_bar_config(disable=__a			)

  for optional_component in pipe._optional_components:
   self.assertTrue(
       getattr(__a   ,      __a			) is None   ,      f"`{optional_component}` did not stay set to None after loading."   ,      )

  UpperCamelCase_					:						str =					self.get_dummy_inputs(__a			)
  UpperCamelCase_					:						Tuple =					pipe_loaded(**__a			)[0]

  UpperCamelCase_					:						str =					np.abs(output - output_loaded			).max()
  self.assertLess(__a   ,      1e-4			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					'cpu'

  UpperCamelCase_					:						Union[str, Any] =					self.get_dummy_components()
  UpperCamelCase_					:						Optional[Any] =					self.pipeline_class(**__a			)
  pipe.to(__a			)
  pipe.set_progress_bar_config(disable=__a			)

  UpperCamelCase_					:						Any =					self.get_dummy_mask_inputs(__a			)
  UpperCamelCase_					:						Dict =					pipe.generate_mask(**__a			)
  UpperCamelCase_					:						Optional[int] =					mask[0, -3:, -3:]

  self.assertEqual(mask.shape   ,      (1, 1_6, 1_6)			)
  UpperCamelCase_					:						int =					np.array([0] * 9			)
  UpperCamelCase_					:						str =					np.abs(mask_slice.flatten() - expected_slice			).max()
  self.assertLessEqual(__a   ,      1e-3			)
  self.assertEqual(mask[0, -3, -4]   ,      0			)

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

  """simple docstring"""





  UpperCamelCase_					:						Any =					'cpu'

  UpperCamelCase_					:						List[Any] =					self.get_dummy_components()
  UpperCamelCase_					:						Optional[Any] =					self.pipeline_class(**__a			)
  pipe.to(__a			)
  pipe.set_progress_bar_config(disable=__a			)

  UpperCamelCase_					:						Dict =					self.get_dummy_inversion_inputs(__a			)
  UpperCamelCase_					:						Optional[int] =					pipe.invert(**__a			).images
  UpperCamelCase_					:						Optional[int] =					image[0, -1, -3:, -3:]

  self.assertEqual(image.shape   ,      (2, 3_2, 3_2, 3)			)
  UpperCamelCase_					:						Optional[Any] =					np.array(
      [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]   ,      )
  UpperCamelCase_					:						str =					np.abs(image_slice.flatten() - expected_slice			).max()
  self.assertLessEqual(__a   ,      1e-3			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						Tuple:

  """simple docstring"""





  super().test_inference_batch_single_identical(expected_max_diff=5e-3			)


 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						Any =					'cpu'

  UpperCamelCase_					:						str =					self.get_dummy_components()

  UpperCamelCase_					:						str =					{'beta_start': 0.00085, 'beta_end': 0.012, 'beta_schedule': 'scaled_linear'}
  UpperCamelCase_					:						Any =					DPMSolverMultistepScheduler(**__a			)
  UpperCamelCase_					:						Any =					DPMSolverMultistepInverseScheduler(**__a			)

  UpperCamelCase_					:						Tuple =					self.pipeline_class(**__a			)
  pipe.to(__a			)
  pipe.set_progress_bar_config(disable=__a			)

  UpperCamelCase_					:						str =					self.get_dummy_inversion_inputs(__a			)
  UpperCamelCase_					:						List[Any] =					pipe.invert(**__a			).images
  UpperCamelCase_					:						Any =					image[0, -1, -3:, -3:]

  self.assertEqual(image.shape   ,      (2, 3_2, 3_2, 3)			)
  UpperCamelCase_					:						int =					np.array(
      [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]   ,      )
  UpperCamelCase_					:						Union[str, Any] =					np.abs(image_slice.flatten() - expected_slice			).max()
  self.assertLessEqual(__a   ,      1e-3			)





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						Optional[int]:

  """simple docstring"""





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

 @classmethod
 def 						SCREAMING_SNAKE_CASE__   ( cls	:							Optional[int]			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					load_image(
      'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'			)

  UpperCamelCase_					:						str =					raw_image.convert('RGB'			).resize((7_6_8, 7_6_8)			)

  UpperCamelCase_					:						str =					raw_image

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

  """simple docstring"""





  UpperCamelCase_					:						Dict =					torch.manual_seed(0			)

  UpperCamelCase_					:						Any =					StableDiffusionDiffEditPipeline.from_pretrained(
      'stabilityai/stable-diffusion-2-1'   ,      safety_checker=__a   ,      torch_dtype=torch.floataa			)
  UpperCamelCase_					:						Tuple =					DDIMScheduler.from_config(pipe.scheduler.config			)
  UpperCamelCase_					:						List[Any] =					DDIMInverseScheduler.from_config(pipe.scheduler.config			)
  pipe.enable_model_cpu_offload()
  pipe.set_progress_bar_config(disable=__a			)

  UpperCamelCase_					:						int =					'a bowl of fruit'
  UpperCamelCase_					:						Dict =					'a bowl of pears'

  UpperCamelCase_					:						List[Any] =					pipe.generate_mask(
      image=self.raw_image   ,      source_prompt=__a   ,      target_prompt=__a   ,      generator=__a   ,      )

  UpperCamelCase_					:						str =					pipe.invert(
      prompt=__a   ,      image=self.raw_image   ,      inpaint_strength=0.7   ,      generator=__a			).latents

  UpperCamelCase_					:						Dict =					pipe(
      prompt=__a   ,      mask_image=__a   ,      image_latents=__a   ,      generator=__a   ,      negative_prompt=__a   ,      inpaint_strength=0.7   ,      output_type='numpy'   ,      ).images[0]

  UpperCamelCase_					:						str =					(
      np.array(
          load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/diffedit/pears.png'			).resize((7_6_8, 7_6_8)			)			)
      / 2_5_5
  )
  assert np.abs((expected_image - image).max()			) < 5e-1


 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						Any =					torch.manual_seed(0			)

  UpperCamelCase_					:						List[str] =					StableDiffusionDiffEditPipeline.from_pretrained(
      'stabilityai/stable-diffusion-2-1'   ,      safety_checker=__a   ,      torch_dtype=torch.floataa			)
  UpperCamelCase_					:						Union[str, Any] =					DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
  UpperCamelCase_					:						List[str] =					DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config			)
  pipe.enable_model_cpu_offload()
  pipe.set_progress_bar_config(disable=__a			)

  UpperCamelCase_					:						Dict =					'a bowl of fruit'
  UpperCamelCase_					:						Optional[Any] =					'a bowl of pears'

  UpperCamelCase_					:						Optional[int] =					pipe.generate_mask(
      image=self.raw_image   ,      source_prompt=__a   ,      target_prompt=__a   ,      generator=__a   ,      )

  UpperCamelCase_					:						Any =					pipe.invert(
      prompt=__a   ,      image=self.raw_image   ,      inpaint_strength=0.7   ,      generator=__a   ,      num_inference_steps=2_5   ,      ).latents

  UpperCamelCase_					:						List[str] =					pipe(
      prompt=__a   ,      mask_image=__a   ,      image_latents=__a   ,      generator=__a   ,      negative_prompt=__a   ,      inpaint_strength=0.7   ,      num_inference_steps=2_5   ,      output_type='numpy'   ,      ).images[0]

  UpperCamelCase_					:						int =					(
      np.array(
          load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/diffedit/pears.png'			).resize((7_6_8, 7_6_8)			)			)
      / 2_5_5
  )
  assert np.abs((expected_image - image).max()			) < 5e-1







'''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	:						Tuple       =	logging.get_logger(__name__)

__lowercase	:						List[Any]       =	torch.device('cpu')






def   lowerCamelCase							():
					__a			:       int					    =  'http://images.cocodataset.org/val2017/000000039769.jpg'
					__a			:       Tuple					    =  Image.open(requests.get(_SCREAMING_SNAKE_CASE						,      stream=_SCREAMING_SNAKE_CASE   ).raw   )
					return im






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					if swiftformer_name == "swiftformer_xs":
										return torch.tensor([-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0, 8.8_6_8_5e-0_1, 2.4_3_6_0e-0_1]   )

					elif swiftformer_name == "swiftformer_s":
										return torch.tensor([3.9_6_3_6e-0_1, 2.3_4_7_8e-0_1, -1.6_9_6_3e0_0, -1.7_3_8_1e0_0, -8.6_3_3_7e-0_1]   )

					elif swiftformer_name == "swiftformer_l1":
										return torch.tensor([-4.2_7_6_8e-0_1, -4.7_4_2_9e-0_1, -1.0_8_9_7e0_0, -1.0_2_4_8e0_0, 3.5_5_2_3e-0_2]   )

					elif swiftformer_name == "swiftformer_l3":
										return torch.tensor([-2.5_3_3_0e-0_1, 2.4_2_1_1e-0_1, -6.0_1_8_5e-0_1, -8.2_7_8_9e-0_1, -6.0_4_4_6e-0_2]   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					__a			:       int					    =  dct.pop(_SCREAMING_SNAKE_CASE   )
					__a			:       Tuple					    =  val






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






@torch.no_grad()
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str						,      _SCREAMING_SNAKE_CASE      :   Union[str, Any]						,      _SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					__a			:       Union[str, Any]					    =  SwiftFormerConfig()

					# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
					__a			:       List[str]					    =  1_000
					__a			:       Tuple					    =  'huggingface/label-files'
					__a			:       str					    =  'imagenet-1k-id2label.json'
					__a			:       Dict					    =  json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      repo_type='dataset'   )						,      'r'   )   )
					__a			:       Optional[int]					    =  {int(_SCREAMING_SNAKE_CASE   ): v for k, v in idalabel.items()}
					__a			:       Any					    =  idalabel
					__a			:       str					    =  {v: k for k, v in idalabel.items()}

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

					elif swiftformer_name == "swiftformer_s":
										__a			:       Dict					    =  [3, 3, 9, 6]
										__a			:       List[str]					    =  [48, 64, 168, 224]

					elif swiftformer_name == "swiftformer_l1":
										__a			:       Dict					    =  [4, 3, 10, 5]
										__a			:       Optional[int]					    =  [48, 96, 192, 384]

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

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

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

					# load HuggingFace model
					__a			:       Tuple					    =  SwiftFormerForImageClassification(_SCREAMING_SNAKE_CASE   ).eval()
					hf_model.load_state_dict(_SCREAMING_SNAKE_CASE   )

					# prepare test inputs
					__a			:       Tuple					    =  prepare_img()
					__a			:       str					    =  ViTImageProcessor.from_pretrained('preprocessor_config'   )
					__a			:       Tuple					    =  processor(images=_SCREAMING_SNAKE_CASE						,      return_tensors='pt'   )

					# compare outputs from both models
					__a			:       List[Any]					    =  get_expected_output(_SCREAMING_SNAKE_CASE   )
					__a			:       Dict					    =  hf_model(inputs['pixel_values']   ).logits

					assert hf_logits.shape == torch.Size([1, 1_000]   )
					assert torch.allclose(hf_logits[0, 0:5]						,      _SCREAMING_SNAKE_CASE						,      atol=1e-3   )

					Path(_SCREAMING_SNAKE_CASE   ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE   )
					print(F"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}"""   )
					hf_model.save_pretrained(_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
	__lowercase	:						Optional[int]       =	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	:						Tuple       =	parser.parse_args()
	convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)



