



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_:Any             =						logging.get_logger(__name__)




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  Union[str, Any]:



    """simple docstring"""






    A :       List[str]          =   original_name.split("""."""		)[0]
    A :       Optional[Any]          =   key.split("""."""		)
    A :       str          =   int(key_list[key_list.index(_lowerCAmelCase		) - 2]		)
    A :       Optional[Any]          =   int(key_list[key_list.index(_lowerCAmelCase		) - 1]		)
    A :       Optional[int]          =   orig_block_num - offset

    A :       int          =   key.replace(f'''{orig_block_num}.{layer_num}.{original_name}'''      ,     f'''block.{new_block_num}.{layer_num}.{new_name}'''		)
    return key




def 	__UpperCamelCase	(  _lowerCAmelCase		)       ->  Dict:



    """simple docstring"""






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




def 	__UpperCamelCase	(  )       ->  Optional[Any]:



    """simple docstring"""






    A :       int          =   """http://images.cocodataset.org/val2017/000000039769.jpg"""
    A :       Tuple          =   Image.open(requests.get(_lowerCAmelCase      ,     stream=_lowerCAmelCase		).raw		)

    return image




@torch.no_grad()
def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  List[str]:



    """simple docstring"""






    A :       str          =   PoolFormerConfig()

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

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

    # load image processor
    A :       Dict          =   PoolFormerImageProcessor(crop_pct=_lowerCAmelCase		)

    # Prepare image
    A :       Tuple          =   prepare_img()
    A :       List[str]          =   image_processor(images=_lowerCAmelCase      ,     return_tensors="""pt"""		).pixel_values

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

    # load original state dict
    A :       Optional[Any]          =   torch.load(_lowerCAmelCase      ,     map_location=torch.device("""cpu"""		)		)

    # rename keys
    A :       List[str]          =   rename_keys(_lowerCAmelCase		)

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

    # Define image processor
    A :       Tuple          =   PoolFormerImageProcessor(crop_pct=_lowerCAmelCase		)
    A :       Tuple          =   image_processor(images=prepare_img()      ,     return_tensors="""pt"""		).pixel_values

    # forward pass
    A :       List[str]          =   model(_lowerCAmelCase		)
    A :       int          =   outputs.logits

    # define expected logit slices for different models
    if size == "s12":
        A :       Any          =   torch.tensor([-0.3_045, -0.6_758, -0.4_869]		)
    elif size == "s24":
        A :       List[Any]          =   torch.tensor([0.4_402, -0.1_374, -0.8_045]		)
    elif size == "s36":
        A :       Any          =   torch.tensor([-0.6_080, -0.5_133, -0.5_898]		)
    elif size == "m36":
        A :       Any          =   torch.tensor([0.3_952, 0.2_263, -1.2_668]		)
    elif size == "m48":
        A :       str          =   torch.tensor([0.1_167, -0.0_656, -0.3_423]		)
    else:
        raise ValueError(f'''Size {size} not supported'''		)

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

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


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







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE_:List[Any]             =						"""▁"""

SCREAMING_SNAKE_CASE_:int             =						{"""vocab_file""": """spiece.model"""}

SCREAMING_SNAKE_CASE_:Any             =						{
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""}
}

SCREAMING_SNAKE_CASE_:Optional[Any]             =						{
    """google/pegasus-xsum""": 512,
}


SCREAMING_SNAKE_CASE_:Tuple             =						logging.get_logger(__name__)






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Union[str, Any]					=      VOCAB_FILES_NAMES

    __lowerCamelCase			: List[Any]					=      VOCAB_FILES_NAMES
    __lowerCamelCase			: Optional[Any]					=      PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase			: str					=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase			: str					=      ["input_ids", "attention_mask"]






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__="<pad>",		lowerCamelCase__="</s>",		lowerCamelCase__="<unk>",		lowerCamelCase__="<mask_2>",		lowerCamelCase__="<mask_1>",		lowerCamelCase__=None,		lowerCamelCase__=103,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        A :       int          =   offset
        if additional_special_tokens is not None:
            if not isinstance(lowerCamelCase__,		lowerCamelCase__		):
                raise TypeError(
                    f'''additional_special_tokens should be of type {type(lowerCamelCase__		)}, but is'''
                    f''' {type(lowerCamelCase__		)}'''		)

            A :       Dict          =   (
                ([mask_token_sent] + additional_special_tokens)
                if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                else additional_special_tokens
            )
            # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
            additional_special_tokens_extended += [
                f'''<unk_{i}>''' for i in range(len(lowerCamelCase__		),		self.offset - 1		)
            ]

            if len(set(lowerCamelCase__		)		) != len(lowerCamelCase__		):
                raise ValueError(
                    """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
                    f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''		)
            A :       int          =   additional_special_tokens_extended
        else:
            A :       Optional[int]          =   [mask_token_sent] if mask_token_sent is not None else []
            additional_special_tokens += [f'''<unk_{i}>''' for i in range(2,		self.offset		)]

        A :       Any          =   {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            eos_token=lowerCamelCase__,		unk_token=lowerCamelCase__,		mask_token=lowerCamelCase__,		pad_token=lowerCamelCase__,		mask_token_sent=lowerCamelCase__,		offset=lowerCamelCase__,		additional_special_tokens=lowerCamelCase__,		sp_model_kwargs=self.sp_model_kwargs,		**lowerCamelCase__,		)
        A :       Union[str, Any]          =   mask_token_sent
        A :       Optional[Any]          =   vocab_file
        A :       Optional[int]          =   spm.SentencePieceProcessor(**self.sp_model_kwargs		)
        self.sp_model.Load(lowerCamelCase__		)

        # add special tokens to encoder dict
        A :       Dict[int, str]          =   {
            0: self.pad_token,
            1: self.eos_token,
        }

        if self.mask_token_sent is not None:
            self.encoder.update(
                {
                    2: self.mask_token_sent,
                    3: self.mask_token,
                }		)

        if self.offset > 0:
            # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
            # mask_token_sent is already added to list -> so start at 1
            self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1,		self.offset - 1		)}		)

        A :       Dict[str, int]          =   {v: k for k, v in self.encoder.items()}






    @property
    def 			_lowerCAmelCase					(       self		):
        return len(self.sp_model		) + self.offset






    def 			_lowerCAmelCase					(       self		):
        A :       Optional[int]          =   {self.convert_ids_to_tokens(lowerCamelCase__		): i for i in range(self.vocab_size		)}
        vocab.update(self.added_tokens_encoder		)
        return vocab






    def __getstate__(       self		):
        A :       List[Any]          =   self.__dict__.copy()
        A :       Union[str, Any]          =   None
        return state






    def __setstate__(       self,		lowerCamelCase__		):
        A :       List[Any]          =   d

        # for backward compatibility
        if not hasattr(self,		"""sp_model_kwargs"""		):
            A :       int          =   {}

        A :       Tuple          =   spm.SentencePieceProcessor(**self.sp_model_kwargs		)
        self.sp_model.Load(self.vocab_file		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        return self.sp_model.encode(lowerCamelCase__,		out_type=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        if token in self.decoder:
            return self.decoder[token]
        elif token in self.added_tokens_decoder:
            return self.added_tokens_decoder[token]
        A :       List[str]          =   self.sp_model.piece_to_id(lowerCamelCase__		)
        return sp_id + self.offset






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        if index in self.encoder:
            return self.encoder[index]
        elif index in self.added_tokens_encoder:
            return self.added_tokens_encoder[index]
        else:
            A :       Dict          =   self.sp_model.IdToPiece(index - self.offset		)
        return token






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A :       Optional[int]          =   []
        A :       Optional[Any]          =   """"""
        for token in tokens:
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
                out_string += self.sp_model.decode(lowerCamelCase__		) + token
                A :       int          =   []
            else:
                current_sub_tokens.append(lowerCamelCase__		)
        out_string += self.sp_model.decode(lowerCamelCase__		)
        return out_string.strip()






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__=False		):
        return 1






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A :       Optional[int]          =   set(self.all_special_ids		)  # call it once instead of inside list comp
        all_special_ids.remove(self.unk_token_id		)  # <unk> is only sometimes special

        return [1 if x in all_special_ids else 0 for x in seq]






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__ = None,		lowerCamelCase__ = False		):
        if already_has_special_tokens:
            return self._special_token_mask(lowerCamelCase__		)
        elif token_ids_a is None:
            return self._special_token_mask(lowerCamelCase__		) + [1]
        else:
            return self._special_token_mask(token_ids_a + token_ids_a		) + [1]






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__=None		):
        if token_ids_a is None:
            return token_ids_a + [self.eos_token_id]
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return token_ids_a + token_ids_a + [self.eos_token_id]






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__ = None		):
        if not os.path.isdir(lowerCamelCase__		):
            logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
            return
        A :       Any          =   os.path.join(
            lowerCamelCase__,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)

        if os.path.abspath(self.vocab_file		) != os.path.abspath(lowerCamelCase__		) and os.path.isfile(self.vocab_file		):
            copyfile(self.vocab_file,		lowerCamelCase__		)
        elif not os.path.isfile(self.vocab_file		):
            with open(lowerCamelCase__,		"""wb"""		) as fi:
                A :       Dict          =   self.sp_model.serialized_model_proto()
                fi.write(lowerCamelCase__		)

        return (out_vocab_file,)


