


"""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()
__snake_case				=				logging.get_logger(__name__)
def 					A_      (      _lowerCAmelCase   :    Union[str, Any],   _lowerCAmelCase   :    Optional[Any],   _lowerCAmelCase   :    Union[str, Any],   _lowerCAmelCase   :    Any				):






     """simple docstring"""

     _a					= original_name.split('''.'''				)[0]
     _a					= key.split('''.'''				)
     _a					= int(key_list[key_list.index(_SCREAMING_SNAKE_CASE				) - 2]				)
     _a					= int(key_list[key_list.index(_SCREAMING_SNAKE_CASE				) - 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_      (      _lowerCAmelCase   :    str				):






     """simple docstring"""

     _a					= OrderedDict()
     _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(_SCREAMING_SNAKE_CASE,   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(_SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE,   '''mlp.fc1''',   '''output.conv1'''				)
          if "mlp.fc2" in key:
               _a					= replace_key_with_offset(_SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE,   '''mlp.fc2''',   '''output.conv2'''				)
          if "norm1" in key:
               _a					= replace_key_with_offset(_SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE,   '''norm1''',   '''before_norm'''				)
          if "norm2" in key:
               _a					= replace_key_with_offset(_SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE,   '''norm2''',   '''after_norm'''				)
          if "layer_scale_1" in key:
               _a					= replace_key_with_offset(_SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE,   '''layer_scale_1''',   '''layer_scale_1'''				)
          if "layer_scale_2" in key:
               _a					= replace_key_with_offset(_SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE,   '''layer_scale_2''',   '''layer_scale_2'''				)
          if "head" in key:
               _a					= key.replace('''head''',   '''classifier'''				)
          _a					= value
     return new_state_dict
def 					A_      (      ):






     """simple docstring"""

     _a					= 'http://images.cocodataset.org/val2017/000000039769.jpg'
     _a					= Image.open(requests.get(_SCREAMING_SNAKE_CASE,   stream=_SCREAMING_SNAKE_CASE				).raw				)

     return image
@torch.no_grad()
def 					A_      (      _lowerCAmelCase   :    Union[str, Any],   _lowerCAmelCase   :    Dict,   _lowerCAmelCase   :    Optional[Any]				):






     """simple docstring"""

     _a					= PoolFormerConfig()

     # set attributes based on model_name
     _a					= 'huggingface/label-files'
     _a					= model_name[-3:]
     _a					= 10_00
     _a					= 'imagenet-1k-id2label.json'
     _a					= (1, 10_00)

     # set config attributes
     _a					= json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE,   repo_type='''dataset'''				),   '''r'''				)				)
     _a					= {int(_SCREAMING_SNAKE_CASE				): 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, 1_28, 3_20, 5_12]
          _a					= 4.0
          _a					= 0.9
     elif size == "s24":
          _a					= [4, 4, 12, 4]
          _a					= [64, 1_28, 3_20, 5_12]
          _a					= 4.0
          _a					= 0.9
     elif size == "s36":
          _a					= [6, 6, 18, 6]
          _a					= [64, 1_28, 3_20, 5_12]
          _a					= 4.0
          _a					= 1e-6
          _a					= 0.9
     elif size == "m36":
          _a					= [6, 6, 18, 6]
          _a					= [96, 1_92, 3_84, 7_68]
          _a					= 4.0
          _a					= 1e-6
          _a					= 0.9_5
     elif size == "m48":
          _a					= [8, 8, 24, 8]
          _a					= [96, 1_92, 3_84, 7_68]
          _a					= 4.0
          _a					= 1e-6
          _a					= 0.9_5
     else:
          raise ValueError(f'Size {size} not supported'				)

     # load image processor
     _a					= PoolFormerImageProcessor(crop_pct=_SCREAMING_SNAKE_CASE				)

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

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

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

     # rename keys
     _a					= rename_keys(_SCREAMING_SNAKE_CASE				)

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

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

     # forward pass
     _a					= model(_SCREAMING_SNAKE_CASE				)
     _a					= outputs.logits

     # define expected logit slices for different models
     if size == "s12":
          _a					= torch.tensor([-0.3_0_4_5, -0.6_7_5_8, -0.4_8_6_9]				)
     elif size == "s24":
          _a					= torch.tensor([0.4_4_0_2, -0.1_3_7_4, -0.8_0_4_5]				)
     elif size == "s36":
          _a					= torch.tensor([-0.6_0_8_0, -0.5_1_3_3, -0.5_8_9_8]				)
     elif size == "m36":
          _a					= torch.tensor([0.3_9_5_2, 0.2_2_6_3, -1.2_6_6_8]				)
     elif size == "m48":
          _a					= torch.tensor([0.1_1_6_7, -0.0_6_5_6, -0.3_4_2_3]				)
     else:
          raise ValueError(f'Size {size} not supported'				)

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

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


if __name__ == "__main__":
   __snake_case				=				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.'''
   )
   __snake_case				=				parser.parse_args()
   convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)



'''simple docstring'''



import gc
import random
import unittest

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

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

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


enable_full_determinism()





class 							__UpperCamelCase				(		lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_		,      unittest.TestCase					):
	A_				=    StableDiffusionInpaintPipeline
	A_				=    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
	A_				=    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	A_				=    frozenset(
	    []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	A_				=    frozenset([]					)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						torch.manual_seed(0							)
						__a			:       int					    =  UNetaDConditionModel(
						    block_out_channels=(32, 64)						,				layers_per_block=2						,				sample_size=32						,				in_channels=9						,				out_channels=4						,				down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')						,				up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')						,				cross_attention_dim=32						,				attention_head_dim=(2, 4)						,				use_linear_projection=__a						,				)
						__a			:       str					    =  PNDMScheduler(skip_prk_steps=__a							)
						torch.manual_seed(0							)
						__a			:       Union[str, Any]					    =  AutoencoderKL(
						    block_out_channels=[32, 64]						,				in_channels=3						,				out_channels=3						,				down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']						,				up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']						,				latent_channels=4						,				sample_size=128						,				)
						torch.manual_seed(0							)
						__a			:       List[str]					    =  CLIPTextConfig(
						    bos_token_id=0						,				eos_token_id=2						,				hidden_size=32						,				intermediate_size=37						,				layer_norm_eps=1E-0_5						,				num_attention_heads=4						,				num_hidden_layers=5						,				pad_token_id=1						,				vocab_size=1000						,				hidden_act='gelu'						,				projection_dim=512						,				)
						__a			:       Dict					    =  CLIPTextModel(__a							)
						__a			:       Union[str, Any]					    =  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)

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




	def       __UpperCAmelCase							(   self						,				__a						,				__a=0							):

						'''simple docstring'''





						__a			:       Union[str, Any]					    =  floats_tensor((1, 3, 32, 32)						,				rng=random.Random(__a							)							).to(__a							)
						__a			:       List[Any]					    =  image.cpu().permute(0						,				2						,				3						,				1							)[0]
						__a			:       Tuple					    =  Image.fromarray(np.uinta(__a							)							).convert('RGB'							).resize((64, 64)							)
						__a			:       Tuple					    =  Image.fromarray(np.uinta(image + 4							)							).convert('RGB'							).resize((64, 64)							)
						if str(__a							).startswith('mps'							):
											__a			:       Any					    =  torch.manual_seed(__a							)
						else:
											__a			:       str					    =  torch.Generator(device=__a							).manual_seed(__a							)
						__a			:       Dict					    =  {
						    'prompt': 'A painting of a squirrel eating a burger',
						    'image': init_image,
						    'mask_image': mask_image,
						    'generator': generator,
						    'num_inference_steps': 2,
						    'guidance_scale': 6.0,
						    'output_type': 'numpy',
						}
						return inputs




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Dict					    =  'cpu'  # ensure determinism for the device-dependent torch.Generator
						__a			:       str					    =  self.get_dummy_components()
						__a			:       Union[str, Any]					    =  StableDiffusionInpaintPipeline(**__a							)
						__a			:       List[Any]					    =  sd_pipe.to(__a							)
						sd_pipe.set_progress_bar_config(disable=__a							)

						__a			:       List[Any]					    =  self.get_dummy_inputs(__a							)
						__a			:       Dict					    =  sd_pipe(**__a							).images
						__a			:       Optional[Any]					    =  image[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						__a			:       List[Any]					    =  np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]							)

						assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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





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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[str]					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/sd2-inpaint/init_image.png'							)
						__a			:       List[str]					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'							)
						__a			:       str					    =  load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
						    '/yellow_cat_sitting_on_a_park_bench.npy'							)

						__a			:       Optional[int]					    =  'stabilityai/stable-diffusion-2-inpainting'
						__a			:       Optional[int]					    =  StableDiffusionInpaintPipeline.from_pretrained(__a						,				safety_checker=__a							)
						pipe.to(__a							)
						pipe.set_progress_bar_config(disable=__a							)
						pipe.enable_attention_slicing()

						__a			:       Dict					    =  'Face of a yellow cat, high resolution, sitting on a park bench'

						__a			:       Tuple					    =  torch.manual_seed(0							)
						__a			:       int					    =  pipe(
						    prompt=__a						,				image=__a						,				mask_image=__a						,				generator=__a						,				output_type='np'						,				)
						__a			:       Dict					    =  output.images[0]

						assert image.shape == (512, 512, 3)
						assert np.abs(expected_image - image							).max() < 9E-3




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Tuple					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/sd2-inpaint/init_image.png'							)
						__a			:       int					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'							)
						__a			:       Any					    =  load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
						    '/yellow_cat_sitting_on_a_park_bench_fp16.npy'							)

						__a			:       str					    =  'stabilityai/stable-diffusion-2-inpainting'
						__a			:       List[str]					    =  StableDiffusionInpaintPipeline.from_pretrained(
						    __a						,				torch_dtype=torch.floataa						,				safety_checker=__a						,				)
						pipe.to(__a							)
						pipe.set_progress_bar_config(disable=__a							)
						pipe.enable_attention_slicing()

						__a			:       Union[str, Any]					    =  'Face of a yellow cat, high resolution, sitting on a park bench'

						__a			:       int					    =  torch.manual_seed(0							)
						__a			:       Optional[Any]					    =  pipe(
						    prompt=__a						,				image=__a						,				mask_image=__a						,				generator=__a						,				output_type='np'						,				)
						__a			:       int					    =  output.images[0]

						assert image.shape == (512, 512, 3)
						assert np.abs(expected_image - image							).max() < 5E-1





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()

						__a			:       str					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/sd2-inpaint/init_image.png'							)
						__a			:       List[Any]					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'							)

						__a			:       str					    =  'stabilityai/stable-diffusion-2-inpainting'
						__a			:       Any					    =  PNDMScheduler.from_pretrained(__a						,				subfolder='scheduler'							)
						__a			:       str					    =  StableDiffusionInpaintPipeline.from_pretrained(
						    __a						,				safety_checker=__a						,				scheduler=__a						,				torch_dtype=torch.floataa						,				)
						pipe.to(__a							)
						pipe.set_progress_bar_config(disable=__a							)
						pipe.enable_attention_slicing(1							)
						pipe.enable_sequential_cpu_offload()

						__a			:       str					    =  'Face of a yellow cat, high resolution, sitting on a park bench'

						__a			:       Tuple					    =  torch.manual_seed(0							)
						__a			:       str					    =  pipe(
						    prompt=__a						,				image=__a						,				mask_image=__a						,				generator=__a						,				num_inference_steps=2						,				output_type='np'						,				)

						__a			:       List[str]					    =  torch.cuda.max_memory_allocated()
						# make sure that less than 2.65 GB is allocated
						assert mem_bytes < 2.65 * 10**9



