






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

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

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__							  =						logging.get_logger(__name__)
def 		_a       ( a					:int      ,				a					:Optional[Any]      ,				a					:int      ,				a					:Optional[Any]					)				->     Dict:
 a						    = original_name.split('''.'''					)[0]
 a						    = key.split('''.'''					)
 a						    = int(key_list[key_list.index(a					) - 2]					)
 a						    = int(key_list[key_list.index(a					) - 1]					)
 a						    = orig_block_num - offset

 a						    = key.replace(F"""{orig_block_num}.{layer_num}.{original_name}"""      ,				F"""block.{new_block_num}.{layer_num}.{new_name}"""					)
 return key
def 		_a       ( a					:Union[str, Any]					)				->     int:
 a						    = OrderedDict()
 a    ,	a						    = 0, 0
 for key, value in state_dict.items():
  if key.startswith('''network'''					):
   a						    = key.replace('''network'''      ,				'''poolformer.encoder'''					)
  if "proj" in key:
   # Works for the first embedding as well as the internal embedding layers
   if key.endswith('''bias'''					) and "patch_embed" not in key:
    patch_emb_offset += 1
   a						    = key[: key.find('''proj'''					)]
   a						    = key.replace(a      ,				F"""patch_embeddings.{total_embed_found}."""					)
   a						    = key.replace('''proj'''      ,				'''projection'''					)
   if key.endswith('''bias'''					):
    total_embed_found += 1
  if "patch_embeddings" in key:
   a						    = '''poolformer.encoder.''' + key
  if "mlp.fc1" in key:
   a						    = replace_key_with_offset(a      ,				a      ,				'''mlp.fc1'''      ,				'''output.conv1'''					)
  if "mlp.fc2" in key:
   a						    = replace_key_with_offset(a      ,				a      ,				'''mlp.fc2'''      ,				'''output.conv2'''					)
  if "norm1" in key:
   a						    = replace_key_with_offset(a      ,				a      ,				'''norm1'''      ,				'''before_norm'''					)
  if "norm2" in key:
   a						    = replace_key_with_offset(a      ,				a      ,				'''norm2'''      ,				'''after_norm'''					)
  if "layer_scale_1" in key:
   a						    = replace_key_with_offset(a      ,				a      ,				'''layer_scale_1'''      ,				'''layer_scale_1'''					)
  if "layer_scale_2" in key:
   a						    = replace_key_with_offset(a      ,				a      ,				'''layer_scale_2'''      ,				'''layer_scale_2'''					)
  if "head" in key:
   a						    = key.replace('''head'''      ,				'''classifier'''					)
  a						    = value
 return new_state_dict
def 		_a       ( )				->     Optional[int]:
 a						    = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 a						    = Image.open(requests.get(a      ,				stream=a					).raw					)

 return image
@torch.no_grad()
def 		_a       ( a					:Optional[Any]      ,				a					:Optional[int]      ,				a					:int					)				->     Dict:
 a						    = PoolFormerConfig()

 # set attributes based on model_name
 a						    = '''huggingface/label-files'''
 a						    = model_name[-3:]
 a						    = 1_000
 a						    = '''imagenet-1k-id2label.json'''
 a						    = (1, 1_000)

 # set config attributes
 a						    = json.load(open(hf_hub_download(a      ,				a      ,				repo_type='''dataset'''					)      ,				'''r'''					)					)
 a						    = {int(a					): v for k, v in idalabel.items()}
 a						    = idalabel
 a						    = {v: k for k, v in idalabel.items()}
 if size == "s12":
  a						    = [2, 2, 6, 2]
  a						    = [64, 128, 320, 512]
  a						    = 4.0
  a						    = 0.9
 elif size == "s24":
  a						    = [4, 4, 12, 4]
  a						    = [64, 128, 320, 512]
  a						    = 4.0
  a						    = 0.9
 elif size == "s36":
  a						    = [6, 6, 18, 6]
  a						    = [64, 128, 320, 512]
  a						    = 4.0
  a						    = 1e-6
  a						    = 0.9
 elif size == "m36":
  a						    = [6, 6, 18, 6]
  a						    = [96, 192, 384, 768]
  a						    = 4.0
  a						    = 1e-6
  a						    = 0.95
 elif size == "m48":
  a						    = [8, 8, 24, 8]
  a						    = [96, 192, 384, 768]
  a						    = 4.0
  a						    = 1e-6
  a						    = 0.95
 else:
  raise ValueError(F"""Size {size} not supported"""					)

 # load image processor
 a						    = PoolFormerImageProcessor(crop_pct=a					)

 # Prepare image
 a						    = prepare_img()
 a						    = image_processor(images=a      ,				return_tensors='''pt'''					).pixel_values

 logger.info(F"""Converting model {model_name}..."""					)

 # load original state dict
 a						    = torch.load(a      ,				map_location=torch.device('''cpu'''					)					)

 # rename keys
 a						    = rename_keys(a					)

 # create HuggingFace model and load state dict
 a						    = PoolFormerForImageClassification(a					)
 model.load_state_dict(a					)
 model.eval()

 # Define image processor
 a						    = PoolFormerImageProcessor(crop_pct=a					)
 a						    = image_processor(images=prepare_img()      ,				return_tensors='''pt'''					).pixel_values

 # forward pass
 a						    = model(a					)
 a						    = outputs.logits

 # define expected logit slices for different models
 if size == "s12":
  a						    = torch.tensor([-0.3_045, -0.6_758, -0.4_869]					)
 elif size == "s24":
  a						    = torch.tensor([0.4_402, -0.1_374, -0.8_045]					)
 elif size == "s36":
  a						    = torch.tensor([-0.6_080, -0.5_133, -0.5_898]					)
 elif size == "m36":
  a						    = torch.tensor([0.3_952, 0.2_263, -1.2_668]					)
 elif size == "m48":
  a						    = torch.tensor([0.1_167, -0.0_656, -0.3_423]					)
 else:
  raise ValueError(F"""Size {size} not supported"""					)

 # verify logits
 assert logits.shape == expected_shape
 assert torch.allclose(logits[0, :3]      ,				a      ,				atol=1e-2					)

 # finally, save model and image processor
 logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""					)
 Path(a					).mkdir(exist_ok=a					)
 model.save_pretrained(a					)
 print(F"""Saving image processor to {pytorch_dump_folder_path}"""					)
 image_processor.save_pretrained(a					)


if __name__ == "__main__":
     UpperCAmelCase__							  =						argparse.ArgumentParser()

     parser.add_argument(
         "--model_name",
         default="poolformer_s12",
         type=str,
         help="Name of the model you'd like to convert.",
     )
     parser.add_argument(
         "--checkpoint_path", default=None, type=str, help="Path to the original PyTorch checkpoint (.pth file)."
     )
     parser.add_argument(
         "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
     )
     UpperCAmelCase__							  =						parser.parse_args()
     convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)










import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 						lowercase_   (      lowercase					,    unittest.TestCase  ):






      '''simple docstring'''




      __snake_case	    =    KandinskyVaaInpaintPipeline
      __snake_case	    =    ['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
      __snake_case	    =    [
          '''image_embeds''',
          '''negative_image_embeds''',
          '''image''',
          '''mask_image''',
      ]
      __snake_case	    =    [
          '''generator''',
          '''height''',
          '''width''',
          '''latents''',
          '''guidance_scale''',
          '''num_inference_steps''',
          '''return_dict''',
          '''guidance_scale''',
          '''num_images_per_prompt''',
          '''output_type''',
          '''return_dict''',
      ]
      __snake_case	    =    False







      @property
      def  __lowerCAmelCase   ( self :		Optional[int]    )       ->Tuple:




       """simple docstring"""


       return 32







      @property
      def  __lowerCAmelCase   ( self :		Optional[Any]    )       ->Dict:




       """simple docstring"""


       return 32







      @property
      def  __lowerCAmelCase   ( self :		List[Any]    )       ->Union[str, Any]:




       """simple docstring"""


       return self.time_input_dim







      @property
      def  __lowerCAmelCase   ( self :		Tuple    )       ->Optional[Any]:




       """simple docstring"""


       return self.time_input_dim * 4







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




       """simple docstring"""


       return 100







      @property
      def  __lowerCAmelCase   ( self :		Tuple    )       ->Optional[int]:




       """simple docstring"""


       torch.manual_seed(0    )

       a						    = {
           '''in_channels''': 9,
           # Out channels is double in channels because predicts mean and variance
           '''out_channels''': 8,
           '''addition_embed_type''': '''image''',
           '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
           '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
           '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
           '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
           '''layers_per_block''': 1,
           '''encoder_hid_dim''': self.text_embedder_hidden_size,
           '''encoder_hid_dim_type''': '''image_proj''',
           '''cross_attention_dim''': self.cross_attention_dim,
           '''attention_head_dim''': 4,
           '''resnet_time_scale_shift''': '''scale_shift''',
           '''class_embed_type''': None,
       }

       a						    = UNetaDConditionModel(**__UpperCAmelCase    )
       return model







      @property
      def  __lowerCAmelCase   ( self :		List[Any]    )       ->List[Any]:




       """simple docstring"""


       return {
           "block_out_channels": [32, 64],
           "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 12,
           "out_channels": 3,
           "up_block_types": [
               "AttnUpDecoderBlock2D",
               "UpDecoderBlock2D",
           ],
           "vq_embed_dim": 4,
       }







      @property
      def  __lowerCAmelCase   ( self :		str    )       ->List[Any]:




       """simple docstring"""


       torch.manual_seed(0    )
       a						    = VQModel(**self.dummy_movq_kwargs    )
       return model







      def  __lowerCAmelCase   ( self :		Optional[Any]    )       ->Any:




       """simple docstring"""


       a						    = self.dummy_unet
       a						    = self.dummy_movq

       a						    = DDIMScheduler(
           num_train_timesteps=1_000					,   beta_schedule='''linear'''					,   beta_start=0.00085					,   beta_end=0.012					,   clip_sample=__UpperCAmelCase					,   set_alpha_to_one=__UpperCAmelCase					,   steps_offset=1					,   prediction_type='''epsilon'''					,   thresholding=__UpperCAmelCase					,   )

       a						    = {
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''movq''': movq,
       }

       return components







      def  __lowerCAmelCase   ( self :		Dict					,   __UpperCAmelCase :		List[str]					,   __UpperCAmelCase :		int=0    )       ->Optional[int]:




       """simple docstring"""


       a						    = floats_tensor((1, self.text_embedder_hidden_size)					,   rng=random.Random(__UpperCAmelCase    )    ).to(__UpperCAmelCase    )
       a						    = floats_tensor((1, self.text_embedder_hidden_size)					,   rng=random.Random(seed + 1    )    ).to(
           __UpperCAmelCase    )
       # create init_image
       a						    = floats_tensor((1, 3, 64, 64)					,   rng=random.Random(__UpperCAmelCase    )    ).to(__UpperCAmelCase    )
       a						    = image.cpu().permute(0					,   2					,   3					,   1    )[0]
       a						    = Image.fromarray(np.uinta(__UpperCAmelCase    )    ).convert('''RGB'''    ).resize((256, 256)    )
       # create mask
       a						    = np.ones((64, 64)					,   dtype=np.floataa    )
       a						    = 0

       if str(__UpperCAmelCase    ).startswith('''mps'''    ):
        a						    = torch.manual_seed(__UpperCAmelCase    )
       else:
        a						    = torch.Generator(device=__UpperCAmelCase    ).manual_seed(__UpperCAmelCase    )
       a						    = {
           '''image''': init_image,
           '''mask_image''': mask,
           '''image_embeds''': image_embeds,
           '''negative_image_embeds''': negative_image_embeds,
           '''generator''': generator,
           '''height''': 64,
           '''width''': 64,
           '''num_inference_steps''': 2,
           '''guidance_scale''': 4.0,
           '''output_type''': '''np''',
       }
       return inputs







      def  __lowerCAmelCase   ( self :		Dict    )       ->int:




       """simple docstring"""


       a						    = '''cpu'''

       a						    = self.get_dummy_components()

       a						    = self.pipeline_class(**__UpperCAmelCase    )
       a						    = pipe.to(__UpperCAmelCase    )

       pipe.set_progress_bar_config(disable=__UpperCAmelCase    )

       a						    = pipe(**self.get_dummy_inputs(__UpperCAmelCase    )    )
       a						    = output.images

       a						    = pipe(
           **self.get_dummy_inputs(__UpperCAmelCase    )					,   return_dict=__UpperCAmelCase					,   )[0]

       a						    = image[0, -3:, -3:, -1]
       a						    = image_from_tuple[0, -3:, -3:, -1]

       print(F"""image.shape {image.shape}"""    )

       assert image.shape == (1, 64, 64, 3)

       a						    = np.array(
           [0.50775903, 0.49527195, 0.48824543, 0.50192237, 0.48644906, 0.49373814, 0.4780598, 0.47234827, 0.48327848]    )

       assert (
           np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
       ), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
       assert (
           np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1e-2
       ), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""







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




       """simple docstring"""


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






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






      '''simple docstring'''




      def  __lowerCAmelCase   ( self :		Any    )       ->List[Any]:




       """simple docstring"""


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







      def  __lowerCAmelCase   ( self :		List[str]    )       ->str:




       """simple docstring"""


       a						    = load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'''    )

       a						    = load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''    )
       a						    = np.ones((768, 768)					,   dtype=np.floataa    )
       a						    = 0

       a						    = '''a hat'''

       a						    = KandinskyVaaPriorPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-prior'''					,   torch_dtype=torch.floataa    )
       pipe_prior.to(__UpperCAmelCase    )

       a						    = KandinskyVaaInpaintPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''					,   torch_dtype=torch.floataa    )
       a						    = pipeline.to(__UpperCAmelCase    )
       pipeline.set_progress_bar_config(disable=__UpperCAmelCase    )

       a						    = torch.Generator(device='''cpu'''    ).manual_seed(0    )
       a    ,	a						    = pipe_prior(
           __UpperCAmelCase					,   generator=__UpperCAmelCase					,   num_inference_steps=5					,   negative_prompt=''''''					,   ).to_tuple()

       a						    = pipeline(
           image=__UpperCAmelCase					,   mask_image=__UpperCAmelCase					,   image_embeds=__UpperCAmelCase					,   negative_image_embeds=__UpperCAmelCase					,   generator=__UpperCAmelCase					,   num_inference_steps=100					,   height=768					,   width=768					,   output_type='''np'''					,   )

       a						    = output.images[0]

       assert image.shape == (768, 768, 3)

       assert_mean_pixel_difference(__UpperCAmelCase					,   __UpperCAmelCase    )


