

"""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  :      str							      =		logging.get_logger(__name__)

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_						,     A_ ):
	lowerCAmelCase__	:						Union[str, Any]  				=	original_name.split('''.''' )[0]
	lowerCAmelCase__	:						List[Any]  				=	key.split('''.''' )
	lowerCAmelCase__	:						Union[str, Any]  				=	int(key_list[key_list.index(A_ ) - 2] )
	lowerCAmelCase__	:						Any  				=	int(key_list[key_list.index(A_ ) - 1] )
	lowerCAmelCase__	:						Any  				=	orig_block_num - offset

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

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

def   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						List[Any]  				=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
	lowerCAmelCase__	:						int  				=	Image.open(requests.get(A_						,     stream=A_ ).raw )

	return image

@torch.no_grad()
def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_ ):
	lowerCAmelCase__	:						Tuple  				=	PoolFormerConfig()

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

	# set config attributes
	lowerCAmelCase__	:						List[Any]  				=	json.load(open(hf_hub_download(A_						,     A_						,     repo_type='''dataset''' )						,     '''r''' ) )
	lowerCAmelCase__	:						Optional[Any]  				=	{int(A_ ): v for k, v in idalabel.items()}
	lowerCAmelCase__	:						Union[str, Any]  				=	idalabel
	lowerCAmelCase__	:						int  				=	{v: k for k, v in idalabel.items()}
	if size == "s12":
		lowerCAmelCase__	:						Dict  				=	[2, 2, 6, 2]
		lowerCAmelCase__	:						str  				=	[64, 1_28, 3_20, 5_12]
		lowerCAmelCase__	:						Tuple  				=	4.0
		lowerCAmelCase__	:						Any  				=	0.9
	elif size == "s24":
		lowerCAmelCase__	:						Optional[Any]  				=	[4, 4, 12, 4]
		lowerCAmelCase__	:						List[Any]  				=	[64, 1_28, 3_20, 5_12]
		lowerCAmelCase__	:						Any  				=	4.0
		lowerCAmelCase__	:						Tuple  				=	0.9
	elif size == "s36":
		lowerCAmelCase__	:						Optional[int]  				=	[6, 6, 18, 6]
		lowerCAmelCase__	:						Optional[Any]  				=	[64, 1_28, 3_20, 5_12]
		lowerCAmelCase__	:						Any  				=	4.0
		lowerCAmelCase__	:						int  				=	1e-6
		lowerCAmelCase__	:						List[Any]  				=	0.9
	elif size == "m36":
		lowerCAmelCase__	:						str  				=	[6, 6, 18, 6]
		lowerCAmelCase__	:						int  				=	[96, 1_92, 3_84, 7_68]
		lowerCAmelCase__	:						List[str]  				=	4.0
		lowerCAmelCase__	:						Union[str, Any]  				=	1e-6
		lowerCAmelCase__	:						Optional[int]  				=	0.95
	elif size == "m48":
		lowerCAmelCase__	:						Union[str, Any]  				=	[8, 8, 24, 8]
		lowerCAmelCase__	:						str  				=	[96, 1_92, 3_84, 7_68]
		lowerCAmelCase__	:						List[str]  				=	4.0
		lowerCAmelCase__	:						Optional[Any]  				=	1e-6
		lowerCAmelCase__	:						List[str]  				=	0.95
	else:
		raise ValueError(f'Size {size} not supported' )

	# load image processor
	lowerCAmelCase__	:						Any  				=	PoolFormerImageProcessor(crop_pct=A_ )

	# Prepare image
	lowerCAmelCase__	:						Optional[Any]  				=	prepare_img()
	lowerCAmelCase__	:						List[str]  				=	image_processor(images=A_						,     return_tensors='''pt''' ).pixel_values

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

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

	# rename keys
	lowerCAmelCase__	:						Any  				=	rename_keys(A_ )

	# create HuggingFace model and load state dict
	lowerCAmelCase__	:						List[str]  				=	PoolFormerForImageClassification(A_ )
	model.load_state_dict(A_ )
	model.eval()

	# Define image processor
	lowerCAmelCase__	:						Union[str, Any]  				=	PoolFormerImageProcessor(crop_pct=A_ )
	lowerCAmelCase__	:						List[Any]  				=	image_processor(images=prepare_img()						,     return_tensors='''pt''' ).pixel_values

	# forward pass
	lowerCAmelCase__	:						Union[str, Any]  				=	model(A_ )
	lowerCAmelCase__	:						Dict  				=	outputs.logits

	# define expected logit slices for different models
	if size == "s12":
		lowerCAmelCase__	:						int  				=	torch.tensor([-0.3_045, -0.6_758, -0.4_869] )
	elif size == "s24":
		lowerCAmelCase__	:						Optional[int]  				=	torch.tensor([0.4_402, -0.1_374, -0.8_045] )
	elif size == "s36":
		lowerCAmelCase__	:						Tuple  				=	torch.tensor([-0.6_080, -0.5_133, -0.5_898] )
	elif size == "m36":
		lowerCAmelCase__	:						Optional[int]  				=	torch.tensor([0.3_952, 0.2_263, -1.2_668] )
	elif size == "m48":
		lowerCAmelCase__	:						int  				=	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]						,     A_						,     atol=1e-2 )

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


if __name__ == "__main__":
							__UpperCamelCase  :      List[str]							      =		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  :      Optional[Any]							      =		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 List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_xlnet import XLNetTokenizer
else:
			_a 						=      None


_a 						=      logging.get_logger(__name__)

_a 						=      {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_a 						=      {
    "vocab_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
    },
}

_a 						=      {
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

_a 						=      "▁"

# Segments (not really needed)
_a 						=      0
_a 						=      1
_a 						=      2
_a 						=      3
_a 						=      4






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						VOCAB_FILES_NAMES
			lowerCAmelCase_							     =						PRETRAINED_VOCAB_FILES_MAP
			lowerCAmelCase_							     =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCAmelCase_							     =						"""left"""
			lowerCAmelCase_							     =						XLNetTokenizer





			def __init__(    self       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase=False       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=False       ,  __lowerCAmelCase="<s>"       ,  __lowerCAmelCase="</s>"       ,  __lowerCAmelCase="<unk>"       ,  __lowerCAmelCase="<sep>"       ,  __lowerCAmelCase="<pad>"       ,  __lowerCAmelCase="<cls>"       ,  __lowerCAmelCase="<mask>"       ,  __lowerCAmelCase=["<eop>", "<eod>"]       ,  **__lowerCAmelCase       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		AddedToken(__lowerCAmelCase       ,  lstrip=__lowerCAmelCase       ,  rstrip=__lowerCAmelCase							) if isinstance(__lowerCAmelCase       ,  __lowerCAmelCase							) else mask_token

				super().__init__(
				    vocab_file=__lowerCAmelCase       ,  tokenizer_file=__lowerCAmelCase       ,  do_lower_case=__lowerCAmelCase       ,  remove_space=__lowerCAmelCase       ,  keep_accents=__lowerCAmelCase       ,  bos_token=__lowerCAmelCase       ,  eos_token=__lowerCAmelCase       ,  unk_token=__lowerCAmelCase       ,  sep_token=__lowerCAmelCase       ,  pad_token=__lowerCAmelCase       ,  cls_token=__lowerCAmelCase       ,  mask_token=__lowerCAmelCase       ,  additional_special_tokens=__lowerCAmelCase       ,  **__lowerCAmelCase       ,  )

				lowerCamelCase__								=		3
				lowerCamelCase__								=		do_lower_case
				lowerCamelCase__								=		remove_space
				lowerCamelCase__								=		keep_accents
				lowerCamelCase__								=		vocab_file
				lowerCamelCase__								=		False if not self.vocab_file else True





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase = None							):



				'''simple docstring'''







				lowerCamelCase__								=		[self.sep_token_id]
				lowerCamelCase__								=		[self.cls_token_id]
				if token_ids_a is None:
					return token_ids_a + sep + cls
				return token_ids_a + sep + token_ids_a + sep + cls





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase = None							):



				'''simple docstring'''







				lowerCamelCase__								=		[self.sep_token_id]
				lowerCamelCase__								=		[2]

				if token_ids_a is None:
					return len(token_ids_a + sep							) * [0] + cls_segment_id
				return len(token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1] + cls_segment_id





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase = None							):



				'''simple docstring'''







				if not self.can_save_slow_tokenizer:
					raise ValueError(
					    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
					    '''tokenizer.'''							)

				if not os.path.isdir(__lowerCAmelCase							):
					logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
					return
				lowerCamelCase__								=		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							):
					copyfile(self.vocab_file       ,  __lowerCAmelCase							)

				return (out_vocab_file,)



