




'''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()
lowercase      :   Any      =       logging.get_logger(__name__)



def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase				):
     snake_case			:      Dict        =			original_name.split("""."""				)[0]
     snake_case			:      int        =			key.split("""."""				)
     snake_case			:      Optional[int]        =			int(key_list[key_list.index(SCREAMING_SNAKE_CASE__				) - 2]				)
     snake_case			:      List[Any]        =			int(key_list[key_list.index(SCREAMING_SNAKE_CASE__				) - 1]				)
     snake_case			:      Optional[int]        =			orig_block_num - offset

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



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



def        lowerCamelCase__				(      ):
     snake_case			:      Any        =			"http://images.cocodataset.org/val2017/000000039769.jpg"
     snake_case			:      Union[str, Any]        =			Image.open(requests.get(SCREAMING_SNAKE_CASE__     ,   stream=SCREAMING_SNAKE_CASE__				).raw				)

     return image



@torch.no_grad()
def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase				):
     snake_case			:      Optional[int]        =			PoolFormerConfig()

     # set attributes based on model_name
     snake_case			:      Dict        =			"huggingface/label-files"
     snake_case			:      Tuple        =			model_name[-3:]
     snake_case			:      List[Any]        =			1_000
     snake_case			:      str        =			"imagenet-1k-id2label.json"
     snake_case			:      str        =			(1, 1_000)

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

     # load image processor
     snake_case			:      Union[str, Any]        =			PoolFormerImageProcessor(crop_pct=SCREAMING_SNAKE_CASE__				)

     # Prepare image
     snake_case			:      str        =			prepare_img()
     snake_case			:      Optional[int]        =			image_processor(images=SCREAMING_SNAKE_CASE__     ,   return_tensors="""pt"""				).pixel_values

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

     # load original state dict
     snake_case			:      List[Any]        =			torch.load(SCREAMING_SNAKE_CASE__     ,   map_location=torch.device("""cpu"""				)				)

     # rename keys
     snake_case			:      str        =			rename_keys(SCREAMING_SNAKE_CASE__				)

     # create HuggingFace model and load state dict
     snake_case			:      Tuple        =			PoolFormerForImageClassification(SCREAMING_SNAKE_CASE__				)
     model.load_state_dict(SCREAMING_SNAKE_CASE__				)
     model.eval()

     # Define image processor
     snake_case			:      int        =			PoolFormerImageProcessor(crop_pct=SCREAMING_SNAKE_CASE__				)
     snake_case			:      str        =			image_processor(images=prepare_img()     ,   return_tensors="""pt"""				).pixel_values

     # forward pass
     snake_case			:      List[str]        =			model(SCREAMING_SNAKE_CASE__				)
     snake_case			:      Any        =			outputs.logits

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








"""simple docstring"""




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation


_lowerCAmelCase							:   Any				=				logging.get_logger(__name__)

_lowerCAmelCase							:   Any				=				{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_lowerCAmelCase							:   List[str]				=				{
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
    },
}

_lowerCAmelCase							:   List[str]				=				{
    "gpt2": 10_24,
    "gpt2-medium": 10_24,
    "gpt2-large": 10_24,
    "gpt2-xl": 10_24,
    "distilgpt2": 10_24,
}






class       UpperCAmelCase_    (							_UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:  Optional[Any]						=					VOCAB_FILES_NAMES
      __SCREAMING_SNAKE_CASE		:  int						=					PRETRAINED_VOCAB_FILES_MAP
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					['input_ids', 'attention_mask']
      __SCREAMING_SNAKE_CASE		:  Dict						=					GPTaTokenizer



      def __init__(	self	:      Optional[Any]	,      A	:      Optional[int]=None	,      A	:      List[Any]=None	,      A	:      Optional[int]=None	,      A	:      Optional[Any]="<|endoftext|>"	,      A	:      Union[str, Any]="<|endoftext|>"	,      A	:      Optional[Any]="<|endoftext|>"	,      A	:      Optional[Any]=False	,      **A	:      str	,      ):
           super().__init__(
               A	,      A	,      tokenizer_file=A	,      unk_token=A	,      bos_token=A	,      eos_token=A	,      add_prefix_space=A	,      **A	,      )

           _UpperCAmelCase     :      List[Any]														=       kwargs.pop("add_bos_token"	,      A		)

           _UpperCAmelCase     :      List[str]														=       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
           if pre_tok_state.get("add_prefix_space"	,      A		) != add_prefix_space:
                _UpperCAmelCase     :      Dict														=       getattr(A	,      pre_tok_state.pop("type"		)		)
                _UpperCAmelCase     :      Optional[Any]														=       add_prefix_space
                _UpperCAmelCase     :      Optional[Any]														=       pre_tok_class(**A		)

           _UpperCAmelCase     :      List[Any]														=       add_prefix_space



      def 		snake_case_   (	self	:      List[Any]	,      *A	:      List[Any]	,      **A	:      List[str]		):
           _UpperCAmelCase     :      Union[str, Any]														=       kwargs.get("is_split_into_words"	,      A		)
           assert self.add_prefix_space or not is_split_into_words, (
               f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
               "to use it with pretokenized inputs."
           )

           return super()._batch_encode_plus(*A	,      **A		)



      def 		snake_case_   (	self	:      Optional[Any]	,      *A	:      List[Any]	,      **A	:      int		):
           _UpperCAmelCase     :      Union[str, Any]														=       kwargs.get("is_split_into_words"	,      A		)

           assert self.add_prefix_space or not is_split_into_words, (
               f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
               "to use it with pretokenized inputs."
           )

           return super()._encode_plus(*A	,      **A		)



      def 		snake_case_   (	self	:      Optional[int]	,      A	:      str	,      A	:      Optional[str] = None		):
           _UpperCAmelCase     :      List[Any]														=       self._tokenizer.model.save(A	,      name=A		)
           return tuple(A		)



      def 		snake_case_   (	self	:      int	,      A	:      "Conversation"		):
           _UpperCAmelCase     :      List[str]														=       []
           for is_user, text in conversation.iter_texts():
                input_ids.extend(self.encode(A	,      add_special_tokens=A		) + [self.eos_token_id]		)

           if len(A		) > self.model_max_length:
                _UpperCAmelCase     :      Optional[int]														=       input_ids[-self.model_max_length :]
           return input_ids
