





'''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()
__SCREAMING_SNAKE_CASE     :int       =				logging.get_logger(__name__)
def 	UpperCAmelCase_							(					__lowercase							:      Optional[Any]			,     __lowercase							:      str			,     __lowercase							:      Tuple			,     __lowercase							:      Tuple       )		->       List[Any]:


     '''simple docstring'''



     _UpperCAmelCase     							=   original_name.split("."       )[0]
     _UpperCAmelCase     							=   key.split("."       )
     _UpperCAmelCase     							=   int(key_list[key_list.index(__lowercase       ) - 2]       )
     _UpperCAmelCase     							=   int(key_list[key_list.index(__lowercase       ) - 1]       )
     _UpperCAmelCase     							=   orig_block_num - offset

     _UpperCAmelCase     							=   key.replace(f'{orig_block_num}.{layer_num}.{original_name}'			,     f'block.{new_block_num}.{layer_num}.{new_name}'       )
     return key
def 	UpperCAmelCase_							(					__lowercase							:      List[str]       )		->       Any:


     '''simple docstring'''



     _UpperCAmelCase     							=   OrderedDict()
     _UpperCAmelCase							,  _UpperCAmelCase     							=   0, 0
     for key, value in state_dict.items():
          if key.startswith("network"       ):
               _UpperCAmelCase     							=   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
               _UpperCAmelCase     							=   key[: key.find("proj"       )]
               _UpperCAmelCase     							=   key.replace(__lowercase			,     f'patch_embeddings.{total_embed_found}.'       )
               _UpperCAmelCase     							=   key.replace("proj"			,     "projection"       )
               if key.endswith("bias"       ):
                    total_embed_found += 1
          if "patch_embeddings" in key:
               _UpperCAmelCase     							=   "poolformer.encoder." + key
          if "mlp.fc1" in key:
               _UpperCAmelCase     							=   replace_key_with_offset(__lowercase			,     __lowercase			,     "mlp.fc1"			,     "output.conv1"       )
          if "mlp.fc2" in key:
               _UpperCAmelCase     							=   replace_key_with_offset(__lowercase			,     __lowercase			,     "mlp.fc2"			,     "output.conv2"       )
          if "norm1" in key:
               _UpperCAmelCase     							=   replace_key_with_offset(__lowercase			,     __lowercase			,     "norm1"			,     "before_norm"       )
          if "norm2" in key:
               _UpperCAmelCase     							=   replace_key_with_offset(__lowercase			,     __lowercase			,     "norm2"			,     "after_norm"       )
          if "layer_scale_1" in key:
               _UpperCAmelCase     							=   replace_key_with_offset(__lowercase			,     __lowercase			,     "layer_scale_1"			,     "layer_scale_1"       )
          if "layer_scale_2" in key:
               _UpperCAmelCase     							=   replace_key_with_offset(__lowercase			,     __lowercase			,     "layer_scale_2"			,     "layer_scale_2"       )
          if "head" in key:
               _UpperCAmelCase     							=   key.replace("head"			,     "classifier"       )
          _UpperCAmelCase     							=   value
     return new_state_dict
def 	UpperCAmelCase_							(					)		->       Any:


     '''simple docstring'''



     _UpperCAmelCase     							=   "http://images.cocodataset.org/val2017/000000039769.jpg"
     _UpperCAmelCase     							=   Image.open(requests.get(__lowercase			,     stream=__lowercase       ).raw       )

     return image
@torch.no_grad()
def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      Dict			,     __lowercase							:      Dict       )		->       Optional[int]:


     '''simple docstring'''



     _UpperCAmelCase     							=   PoolFormerConfig()

     # set attributes based on model_name
     _UpperCAmelCase     							=   "huggingface/label-files"
     _UpperCAmelCase     							=   model_name[-3:]
     _UpperCAmelCase     							=   1000
     _UpperCAmelCase     							=   "imagenet-1k-id2label.json"
     _UpperCAmelCase     							=   (1, 1000)

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

     # load image processor
     _UpperCAmelCase     							=   PoolFormerImageProcessor(crop_pct=__lowercase       )

     # Prepare image
     _UpperCAmelCase     							=   prepare_img()
     _UpperCAmelCase     							=   image_processor(images=__lowercase			,     return_tensors="pt"       ).pixel_values

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

     # load original state dict
     _UpperCAmelCase     							=   torch.load(__lowercase			,     map_location=torch.device("cpu"       )       )

     # rename keys
     _UpperCAmelCase     							=   rename_keys(__lowercase       )

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

     # Define image processor
     _UpperCAmelCase     							=   PoolFormerImageProcessor(crop_pct=__lowercase       )
     _UpperCAmelCase     							=   image_processor(images=prepare_img()			,     return_tensors="pt"       ).pixel_values

     # forward pass
     _UpperCAmelCase     							=   model(__lowercase       )
     _UpperCAmelCase     							=   outputs.logits

     # define expected logit slices for different models
     if size == "s12":
          _UpperCAmelCase     							=   torch.tensor([-0.3045, -0.6758, -0.4869]       )
     elif size == "s24":
          _UpperCAmelCase     							=   torch.tensor([0.4402, -0.1374, -0.8045]       )
     elif size == "s36":
          _UpperCAmelCase     							=   torch.tensor([-0.6080, -0.5133, -0.5898]       )
     elif size == "m36":
          _UpperCAmelCase     							=   torch.tensor([0.3952, 0.2263, -1.2668]       )
     elif size == "m48":
          _UpperCAmelCase     							=   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]			,     __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__":
  __SCREAMING_SNAKE_CASE     :int       =				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.'''
  )
  __SCREAMING_SNAKE_CASE     :Optional[Any]       =				parser.parse_args()
  convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)












"""simple docstring"""
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowerCamelCase_					=      logging.get_logger(__name__)

lowerCamelCase_					=      {
    '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/config.json''',
    '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/config.json''',
    '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/config.json''',
    '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/config.json''',
    '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/config.json''',
}







class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				'''t5'''
	__magic_name__	      =				['''past_key_values''']
	__magic_name__	      =				{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}





	def __init__(      self					: str	,	lowerCAmelCase_					: List[Any]=32_128	,	lowerCAmelCase_					: Tuple=512	,	lowerCAmelCase_					: Optional[int]=64	,	lowerCAmelCase_					: List[str]=2_048	,	lowerCAmelCase_					: Tuple=6	,	lowerCAmelCase_					: Dict=None	,	lowerCAmelCase_					: str=8	,	lowerCAmelCase_					: Optional[int]=32	,	lowerCAmelCase_					: Dict=128	,	lowerCAmelCase_					: Optional[Any]=0.1	,	lowerCAmelCase_					: str=1e-6	,	lowerCAmelCase_					: Dict=1.0	,	lowerCAmelCase_					: str="relu"	,	lowerCAmelCase_					: List[Any]=True	,	lowerCAmelCase_					: List[str]=True	,	lowerCAmelCase_					: Any=0	,	lowerCAmelCase_					: Tuple=1	,	**lowerCAmelCase_					: Optional[int]	,	)						->			int:
				UpperCAmelCase_     : int			     =					vocab_size
				UpperCAmelCase_     : Optional[Any]			     =					d_model
				UpperCAmelCase_     : str			     =					d_kv
				UpperCAmelCase_     : Any			     =					d_ff
				UpperCAmelCase_     : int			     =					num_layers
				UpperCAmelCase_     : Union[str, Any]			     =					(
				    num_decoder_layers if num_decoder_layers is not None else self.num_layers
				)  # default = symmetry
				UpperCAmelCase_     : Optional[Any]			     =					num_heads
				UpperCAmelCase_     : Any			     =					relative_attention_num_buckets
				UpperCAmelCase_     : Optional[Any]			     =					relative_attention_max_distance
				UpperCAmelCase_     : Optional[Any]			     =					dropout_rate
				UpperCAmelCase_     : Tuple			     =					layer_norm_epsilon
				UpperCAmelCase_     : int			     =					initializer_factor
				UpperCAmelCase_     : int			     =					feed_forward_proj
				UpperCAmelCase_     : str			     =					use_cache

				UpperCAmelCase_     : Tuple			     =					self.feed_forward_proj.split("-"						)
				UpperCAmelCase_     : List[Any]			     =					act_info[-1]
				UpperCAmelCase_     : Optional[int]			     =					act_info[0] == "gated"

				if len(lowerCAmelCase_						) > 1 and act_info[0] != "gated" or len(lowerCAmelCase_						) > 2:
							raise ValueError(
							    f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
							    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
							    "'gated-gelu' or 'relu'"						)

				# for backwards compatibility
				if feed_forward_proj == "gated-gelu":
							UpperCAmelCase_     : int			     =					"gelu_new"

				super().__init__(
				    pad_token_id=lowerCAmelCase_	,	eos_token_id=lowerCAmelCase_	,	is_encoder_decoder=lowerCAmelCase_	,	**lowerCAmelCase_	,	)







class 			UpperCamelCase_       (__A     ):





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Dict						)						->			Mapping[str, Mapping[int, str]]:
				UpperCAmelCase_     : Any			     =					{
				    "input_ids": {0: "batch", 1: "encoder_sequence"},
				    "attention_mask": {0: "batch", 1: "encoder_sequence"},
				}
				if self.use_past:
							UpperCAmelCase_     : List[Any]			     =					"past_encoder_sequence + sequence"
							UpperCAmelCase_     : Union[str, Any]			     =					{0: "batch"}
							UpperCAmelCase_     : Optional[Any]			     =					{0: "batch", 1: "past_decoder_sequence + sequence"}
				else:
							UpperCAmelCase_     : List[Any]			     =					{0: "batch", 1: "decoder_sequence"}
							UpperCAmelCase_     : Tuple			     =					{0: "batch", 1: "decoder_sequence"}

				if self.use_past:
							self.fill_with_past_key_values_(lowerCAmelCase_	,	direction="inputs"						)

				return common_inputs





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Any						)						->			int:
				return 13



