




"""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__						=			logging.get_logger(__name__)





def 							UpperCAmelCase		(		lowercase_			:					Optional[int]      ,       lowercase_			:					Optional[int]      ,       lowercase_			:					Optional[int]      ,       lowercase_			:					Union[str, Any]    ):
					_lowerCAmelCase:Any									=  original_name.split('''.'''    )[0]
					_lowerCAmelCase:Union[str, Any]									=  key.split('''.'''    )
					_lowerCAmelCase:Any									=  int(key_list[key_list.index(lowercase_    ) - 2]    )
					_lowerCAmelCase:Any									=  int(key_list[key_list.index(lowercase_    ) - 1]    )
					_lowerCAmelCase:str									=  orig_block_num - offset

					_lowerCAmelCase:Dict									=  key.replace(F'{orig_block_num}.{layer_num}.{original_name}'      ,       F'block.{new_block_num}.{layer_num}.{new_name}'    )
					return key





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





def 							UpperCAmelCase		(		):
					_lowerCAmelCase:Tuple									=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					_lowerCAmelCase:List[Any]									=  Image.open(requests.get(lowercase_      ,       stream=lowercase_    ).raw    )

					return image





@torch.no_grad()
def 							UpperCAmelCase		(		lowercase_			:					Dict      ,       lowercase_			:					str      ,       lowercase_			:					int    ):
					_lowerCAmelCase:Optional[Any]									=  PoolFormerConfig()

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

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

					# load image processor
					_lowerCAmelCase:Optional[Any]									=  PoolFormerImageProcessor(crop_pct=lowercase_    )

					# Prepare image
					_lowerCAmelCase:int									=  prepare_img()
					_lowerCAmelCase:int									=  image_processor(images=lowercase_      ,       return_tensors='''pt'''    ).pixel_values

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

					# load original state dict
					_lowerCAmelCase:int									=  torch.load(lowercase_      ,       map_location=torch.device('''cpu'''    )    )

					# rename keys
					_lowerCAmelCase:int									=  rename_keys(lowercase_    )

					# create HuggingFace model and load state dict
					_lowerCAmelCase:Dict									=  PoolFormerForImageClassification(lowercase_    )
					model.load_state_dict(lowercase_    )
					model.eval()

					# Define image processor
					_lowerCAmelCase:Dict									=  PoolFormerImageProcessor(crop_pct=lowercase_    )
					_lowerCAmelCase:Optional[Any]									=  image_processor(images=prepare_img()      ,       return_tensors='''pt'''    ).pixel_values

					# forward pass
					_lowerCAmelCase:Tuple									=  model(lowercase_    )
					_lowerCAmelCase:str									=  outputs.logits

					# define expected logit slices for different models
					if size == "s12":
										_lowerCAmelCase:List[str]									=  torch.tensor([-0.30_45, -0.67_58, -0.48_69]    )
					elif size == "s24":
										_lowerCAmelCase:Any									=  torch.tensor([0.44_02, -0.13_74, -0.80_45]    )
					elif size == "s36":
										_lowerCAmelCase:Tuple									=  torch.tensor([-0.60_80, -0.51_33, -0.58_98]    )
					elif size == "m36":
										_lowerCAmelCase:List[str]									=  torch.tensor([0.39_52, 0.22_63, -1.26_68]    )
					elif size == "m48":
										_lowerCAmelCase:Optional[int]									=  torch.tensor([0.11_67, -0.06_56, -0.34_23]    )
					else:
										raise ValueError(F'Size {size} not supported'    )

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

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


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)












"""simple docstring"""






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import torch

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available




@dataclass
class   a__			(       UpperCamelCase_  ):
						snake_case__             = 42


try:
				if not (is_transformers_available() and is_torch_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
				from .pipeline_text_to_video_synth import TextToVideoSDPipeline
				from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline  # noqa: F401
				from .pipeline_text_to_video_zero import TextToVideoZeroPipeline






