


'''simple docstring'''
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_ :     Optional[Any]								=      logging.get_logger(__name__)


def 			_lowercase       (				UpperCamelCase__       :					List[str],				UpperCamelCase__       :					Any,				UpperCamelCase__       :					Tuple,				UpperCamelCase__       :					str			):
 __A  :		Any       =			original_name.split('.'			)[0]
 __A  :		Optional[int]       =			key.split('.'			)
 __A  :		Optional[Any]       =			int(key_list[key_list.index(_UpperCamelCase			) - 2]			)
 __A  :		Optional[Any]       =			int(key_list[key_list.index(_UpperCamelCase			) - 1]			)
 __A  :		Union[str, Any]       =			orig_block_num - offset

 __A  :		Optional[Any]       =			key.replace(f"""{orig_block_num}.{layer_num}.{original_name}""",				f"""block.{new_block_num}.{layer_num}.{new_name}"""			)
 return key


def 			_lowercase       (				UpperCamelCase__       :					int			):
 __A  :		Dict       =			OrderedDict()
 __A     ,__A  :		str       =			0, 0
 for key, value in state_dict.items():
  if key.startswith('network'			):
   __A  :		Dict       =			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  :		Optional[int]       =			key[: key.find('proj'			)]
   __A  :		List[str]       =			key.replace(_UpperCamelCase,				f"""patch_embeddings.{total_embed_found}."""			)
   __A  :		int       =			key.replace('proj',				'projection'			)
   if key.endswith('bias'			):
    total_embed_found += 1
  if "patch_embeddings" in key:
   __A  :		Union[str, Any]       =			'poolformer.encoder.' + key
  if "mlp.fc1" in key:
   __A  :		Tuple       =			replace_key_with_offset(_UpperCamelCase,				_UpperCamelCase,				'mlp.fc1',				'output.conv1'			)
  if "mlp.fc2" in key:
   __A  :		Any       =			replace_key_with_offset(_UpperCamelCase,				_UpperCamelCase,				'mlp.fc2',				'output.conv2'			)
  if "norm1" in key:
   __A  :		Any       =			replace_key_with_offset(_UpperCamelCase,				_UpperCamelCase,				'norm1',				'before_norm'			)
  if "norm2" in key:
   __A  :		List[str]       =			replace_key_with_offset(_UpperCamelCase,				_UpperCamelCase,				'norm2',				'after_norm'			)
  if "layer_scale_1" in key:
   __A  :		Tuple       =			replace_key_with_offset(_UpperCamelCase,				_UpperCamelCase,				'layer_scale_1',				'layer_scale_1'			)
  if "layer_scale_2" in key:
   __A  :		Any       =			replace_key_with_offset(_UpperCamelCase,				_UpperCamelCase,				'layer_scale_2',				'layer_scale_2'			)
  if "head" in key:
   __A  :		Dict       =			key.replace('head',				'classifier'			)
  __A  :		List[str]       =			value
 return new_state_dict


def 			_lowercase       (				):
 __A  :		Optional[Any]       =			'http://images.cocodataset.org/val2017/000000039769.jpg'
 __A  :		Optional[Any]       =			Image.open(requests.get(_UpperCamelCase,				stream=_UpperCamelCase			).raw			)

 return image


@torch.no_grad()
def 			_lowercase       (				UpperCamelCase__       :					Optional[Any],				UpperCamelCase__       :					Optional[int],				UpperCamelCase__       :					int			):
 __A  :		Any       =			PoolFormerConfig()

 # set attributes based on model_name
 __A  :		List[str]       =			'huggingface/label-files'
 __A  :		Optional[int]       =			model_name[-3:]
 __A  :		Any       =			1000
 __A  :		Dict       =			'imagenet-1k-id2label.json'
 __A  :		Dict       =			(1, 1000)

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

 # load image processor
 __A  :		str       =			PoolFormerImageProcessor(crop_pct=_UpperCamelCase			)

 # Prepare image
 __A  :		Dict       =			prepare_img()
 __A  :		Dict       =			image_processor(images=_UpperCamelCase,				return_tensors='pt'			).pixel_values

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

 # load original state dict
 __A  :		Optional[Any]       =			torch.load(_UpperCamelCase,				map_location=torch.device('cpu'			)			)

 # rename keys
 __A  :		int       =			rename_keys(_UpperCamelCase			)

 # create HuggingFace model and load state dict
 __A  :		Union[str, Any]       =			PoolFormerForImageClassification(_UpperCamelCase			)
 model.load_state_dict(_UpperCamelCase			)
 model.eval()

 # Define image processor
 __A  :		str       =			PoolFormerImageProcessor(crop_pct=_UpperCamelCase			)
 __A  :		Dict       =			image_processor(images=prepare_img(),				return_tensors='pt'			).pixel_values

 # forward pass
 __A  :		Optional[Any]       =			model(_UpperCamelCase			)
 __A  :		str       =			outputs.logits

 # define expected logit slices for different models
 if size == "s12":
  __A  :		int       =			torch.tensor([-0.3045, -0.6758, -0.4869]			)
 elif size == "s24":
  __A  :		Optional[Any]       =			torch.tensor([0.4402, -0.1374, -0.8045]			)
 elif size == "s36":
  __A  :		Optional[int]       =			torch.tensor([-0.6080, -0.5133, -0.5898]			)
 elif size == "m36":
  __A  :		Union[str, Any]       =			torch.tensor([0.3952, 0.2263, -1.2668]			)
 elif size == "m48":
  __A  :		List[Any]       =			torch.tensor([0.1167, -0.0656, -0.3423]			)
 else:
  raise ValueError(f"""Size {size} not supported"""			)

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

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


if __name__ == "__main__":
  UpperCAmelCase_ :     Any								=      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_ :     List[str]								=      parser.parse_args()
  convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)










'''simple docstring'''
import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

class 						_lowerCamelCase					(    unittest.TestCase				):




 '''simple docstring'''


 def  snake_case__   (      self						):

  """simple docstring"""







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


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  torch.manual_seed(0						)
  __A  :		Optional[int]       =			UNetaDModel(
      sample_size=(32, 64)      ,      in_channels=1      ,      out_channels=1      ,      layers_per_block=2      ,      block_out_channels=(128, 128)      ,      down_block_types=('AttnDownBlock2D', 'DownBlock2D')      ,      up_block_types=('UpBlock2D', 'AttnUpBlock2D')      ,      )
  return model


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  torch.manual_seed(0						)
  __A  :		Dict       =			UNetaDConditionModel(
      sample_size=(64, 32)      ,      in_channels=1      ,      out_channels=1      ,      layers_per_block=2      ,      block_out_channels=(128, 128)      ,      down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')      ,      up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')      ,      cross_attention_dim=10      ,      )
  return model


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  torch.manual_seed(0						)
  __A  :		Union[str, Any]       =			AutoencoderKL(
      sample_size=(128, 64)      ,      in_channels=1      ,      out_channels=1      ,      latent_channels=1      ,      layers_per_block=2      ,      block_out_channels=(128, 128)      ,      down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D')      ,      up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D')      ,      )
  __A  :		Union[str, Any]       =			UNetaDModel(
      sample_size=(64, 32)      ,      in_channels=1      ,      out_channels=1      ,      layers_per_block=2      ,      block_out_channels=(128, 128)      ,      down_block_types=('AttnDownBlock2D', 'DownBlock2D')      ,      up_block_types=('UpBlock2D', 'AttnUpBlock2D')      ,      )
  return vqvae, unet







 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		str       =			'cpu'  # ensure determinism for the device-dependent torch.Generator
  __A  :		int       =			Mel(
      x_res=self.dummy_unet.config.sample_size[1]      ,      y_res=self.dummy_unet.config.sample_size[0]      ,      )

  __A  :		Tuple       =			DDPMScheduler()
  __A  :		Optional[int]       =			AudioDiffusionPipeline(vqvae=__lowercase      ,      unet=self.dummy_unet      ,      mel=__lowercase      ,      scheduler=__lowercase						)
  __A  :		Dict       =			pipe.to(__lowercase						)
  pipe.set_progress_bar_config(disable=__lowercase						)

  __A  :		Tuple       =			torch.Generator(device=__lowercase						).manual_seed(42						)
  __A  :		Tuple       =			pipe(generator=__lowercase      ,      steps=4						)
  __A  :		Union[str, Any]       =			output.audios[0]
  __A  :		Dict       =			output.images[0]

  __A  :		Optional[Any]       =			torch.Generator(device=__lowercase						).manual_seed(42						)
  __A  :		Optional[int]       =			pipe(generator=__lowercase      ,      steps=4      ,      return_dict=__lowercase						)
  __A  :		int       =			output[0][0]

  assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
  assert (
      image.height == self.dummy_unet.config.sample_size[0]
      and image.width == self.dummy_unet.config.sample_size[1]
  )
  __A  :		Optional[int]       =			np.frombuffer(image.tobytes()      ,      dtype='uint8'						)[:10]
  __A  :		List[Any]       =			np.frombuffer(image_from_tuple.tobytes()      ,      dtype='uint8'						)[:10]
  __A  :		Optional[Any]       =			np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127]						)

  assert np.abs(image_slice.flatten() - expected_slice						).max() == 0
  assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() == 0

  __A  :		Optional[int]       =			Mel(
      x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1]      ,      y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0]      ,      )

  __A  :		Optional[Any]       =			DDIMScheduler()
  __A  :		List[Any]       =			self.dummy_vqvae_and_unet
  __A  :		int       =			AudioDiffusionPipeline(
      vqvae=self.dummy_vqvae_and_unet[0]      ,      unet=dummy_vqvae_and_unet[1]      ,      mel=__lowercase      ,      scheduler=__lowercase						)
  __A  :		Dict       =			pipe.to(__lowercase						)
  pipe.set_progress_bar_config(disable=__lowercase						)

  np.random.seed(0						)
  __A  :		Dict       =			np.random.uniform(-1      ,      1      ,      ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)						)
  __A  :		Dict       =			torch.Generator(device=__lowercase						).manual_seed(42						)
  __A  :		List[Any]       =			pipe(raw_audio=__lowercase      ,      generator=__lowercase      ,      start_step=5      ,      steps=10						)
  __A  :		Tuple       =			output.images[0]

  assert (
      image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
      and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
  )
  __A  :		int       =			np.frombuffer(image.tobytes()      ,      dtype='uint8'						)[:10]
  __A  :		List[str]       =			np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121]						)

  assert np.abs(image_slice.flatten() - expected_slice						).max() == 0

  __A  :		Any       =			self.dummy_unet_condition
  __A  :		Optional[int]       =			AudioDiffusionPipeline(
      vqvae=self.dummy_vqvae_and_unet[0]      ,      unet=__lowercase      ,      mel=__lowercase      ,      scheduler=__lowercase						)
  __A  :		Union[str, Any]       =			pipe.to(__lowercase						)
  pipe.set_progress_bar_config(disable=__lowercase						)

  np.random.seed(0						)
  __A  :		Dict       =			torch.rand((1, 1, 10)						)
  __A  :		Any       =			pipe(generator=__lowercase      ,      encoding=__lowercase						)
  __A  :		Union[str, Any]       =			output.images[0]
  __A  :		List[Any]       =			np.frombuffer(image.tobytes()      ,      dtype='uint8'						)[:10]
  __A  :		int       =			np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111]						)

  assert np.abs(image_slice.flatten() - expected_slice						).max() == 0


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




 '''simple docstring'''


 def  snake_case__   (      self						):

  """simple docstring"""







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







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			torch_device

  __A  :		Union[str, Any]       =			DiffusionPipeline.from_pretrained('teticio/audio-diffusion-ddim-256'						)
  __A  :		Dict       =			pipe.to(__lowercase						)
  pipe.set_progress_bar_config(disable=__lowercase						)

  __A  :		Any       =			torch.Generator(device=__lowercase						).manual_seed(42						)
  __A  :		Optional[Any]       =			pipe(generator=__lowercase						)
  __A  :		Union[str, Any]       =			output.audios[0]
  __A  :		List[Any]       =			output.images[0]

  assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
  assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
  __A  :		Union[str, Any]       =			np.frombuffer(image.tobytes()      ,      dtype='uint8'						)[:10]
  __A  :		Optional[Any]       =			np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26]						)

  assert np.abs(image_slice.flatten() - expected_slice						).max() == 0






