
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      :				Tuple          = logging.get_logger(__name__)


def    A (      lowercase					, lowercase					, lowercase					, lowercase				)     ->						List[str]:
		'''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    A (      lowercase				)     ->						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    A (      )     ->						Tuple:
		'''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    A (      lowercase					, lowercase					, lowercase				)     ->						List[Any]:
		'''simple docstring'''

		UpperCamelCase						=  PoolFormerConfig()

		# set attributes based on model_name
		UpperCamelCase						=  'huggingface/label-files'
		UpperCamelCase						=  model_name[-3:]
		UpperCamelCase						=  1_000
		UpperCamelCase						=  'imagenet-1k-id2label.json'
		UpperCamelCase						=  (1, 1_000)

		# 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						=  0.9_5
		elif size == "m48":
				UpperCamelCase						=  [8, 8, 24, 8]
				UpperCamelCase						=  [96, 192, 384, 768]
				UpperCamelCase						=  4.0
				UpperCamelCase						=  1e-6
				UpperCamelCase						=  0.9_5
		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([-0.3_0_4_5, -0.6_7_5_8, -0.4_8_6_9]				)
		elif size == "s24":
				UpperCamelCase						=  torch.tensor([0.4_4_0_2, -0.1_3_7_4, -0.8_0_4_5]				)
		elif size == "s36":
				UpperCamelCase						=  torch.tensor([-0.6_0_8_0, -0.5_1_3_3, -0.5_8_9_8]				)
		elif size == "m36":
				UpperCamelCase						=  torch.tensor([0.3_9_5_2, 0.2_2_6_3, -1.2_6_6_8]				)
		elif size == "m48":
				UpperCamelCase						=  torch.tensor([0.1_1_6_7, -0.0_6_5_6, -0.3_4_2_3]				)
		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      :				Union[str, 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."
					)
					_UpperCAmelCase      :				Union[str, Any]          = parser.parse_args()
					convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)


import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

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


_UpperCAmelCase      :				Dict          = logging.get_logger(__name__)


_UpperCAmelCase      :				Optional[Any]          = {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

_UpperCAmelCase      :				str          = {
    "vocab_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
    },
    "merges_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
    },
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": (
            "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
        )
    },
}

_UpperCAmelCase      :				List[str]          = {"facebook/blenderbot_small-90M": 512}


def    A (      lowercase				)     ->						Optional[Any]:
		'''simple docstring'''

		UpperCamelCase						=  set()
		UpperCamelCase						=  word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)				)
				UpperCamelCase						=  char

		UpperCamelCase						=  set(lowercase				)
		return pairs


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Optional[Any]			  =       VOCAB_FILES_NAMES
		__lowercase : Tuple			  =       PRETRAINED_VOCAB_FILES_MAP
		__lowercase : List[Any]			  =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowercase : Any			  =       ["input_ids", "attention_mask"]



		def __init__(				self       ,   A_       ,   A_       ,   A_="__start__"       ,   A_="__end__"       ,   A_="__unk__"       ,   A_="__null__"       ,   **A_       ,   )						->      List[Any]:
				"""simple docstring"""
				super().__init__(unk_token=A_       ,   bos_token=A_       ,   eos_token=A_       ,   pad_token=A_       ,   **A_ )

				with open(A_       ,   encoding='utf-8' ) as vocab_handle:
						UpperCamelCase						=  json.load(A_ )
				UpperCamelCase						=  {v: k for k, v in self.encoder.items()}
				with open(A_       ,   encoding='utf-8' ) as merges_handle:
						UpperCamelCase						=  merges_handle.read().split('\n' )[1:-1]
				UpperCamelCase						=  [tuple(merge.split() ) for merge in merges]
				UpperCamelCase						=  dict(zip(A_       ,   range(len(A_ ) ) ) )
				UpperCamelCase						=  {}



		@property
		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				return len(self.encoder )



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				return dict(self.encoder       ,   **self.added_tokens_encoder )



		def __UpperCamelCase				(				self       ,   A_ )						->      str:
				"""simple docstring"""
				if token in self.cache:
						return self.cache[token]
				UpperCamelCase						=  re.sub('([.,!?()])'       ,   r' \1'       ,   A_ )
				UpperCamelCase						=  re.sub('(\')'       ,   r' \1 '       ,   A_ )
				UpperCamelCase						=  re.sub(r'\s{2,}'       ,   ' '       ,   A_ )
				if "\n" in token:
						UpperCamelCase						=  token.replace('\n'       ,   ' __newln__' )

				UpperCamelCase						=  token.split(' ' )
				UpperCamelCase						=  []
				for token in tokens:
						if not len(A_ ):
								continue

						UpperCamelCase						=  token.lower()
						UpperCamelCase						=  tuple(A_ )
						UpperCamelCase						=  tuple(list(word[:-1] ) + [word[-1] + '</w>'] )
						UpperCamelCase						=  get_pairs(A_ )

						if not pairs:
								words.append(A_ )
								continue

						while True:
								UpperCamelCase						=  min(A_       ,   key=lambda A_ : self.bpe_ranks.get(A_       ,   float('inf' ) ) )
								if bigram not in self.bpe_ranks:
										break
								UpperCamelCase			,						UpperCamelCase						=  bigram
								UpperCamelCase						=  []
								UpperCamelCase						=  0

								while i < len(A_ ):
										try:
												UpperCamelCase						=  word.index(A_       ,   A_ )
												new_word.extend(word[i:j] )
												UpperCamelCase						=  j
										except ValueError:
												new_word.extend(word[i:] )
												break

										if word[i] == first and i < len(A_ ) - 1 and word[i + 1] == second:
												new_word.append(first + second )
												i += 2
										else:
												new_word.append(word[i] )
												i += 1
								UpperCamelCase						=  tuple(A_ )
								UpperCamelCase						=  new_word
								if len(A_ ) == 1:
										break
								else:
										UpperCamelCase						=  get_pairs(A_ )
						UpperCamelCase						=  '@@ '.join(A_ )
						UpperCamelCase						=  word[:-4]

						UpperCamelCase						=  word
						words.append(A_ )
				return " ".join(A_ )



		def __UpperCamelCase				(				self       ,   A_ )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  []

				UpperCamelCase						=  re.findall(r'\S+\n?'       ,   A_ )

				for token in words:
						split_tokens.extend(list(self.bpe(A_ ).split(' ' ) ) )
				return split_tokens



		def __UpperCamelCase				(				self       ,   A_ )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  token.lower()
				return self.encoder.get(A_       ,   self.encoder.get(self.unk_token ) )



		def __UpperCamelCase				(				self       ,   A_ )						->      str:
				"""simple docstring"""
				return self.decoder.get(A_       ,   self.unk_token )



		def __UpperCamelCase				(				self       ,   A_ )						->      str:
				"""simple docstring"""
				UpperCamelCase						=  ' '.join(A_ ).replace('@@ '       ,   '' ).strip()
				return out_string



		def __UpperCamelCase				(				self       ,   A_       ,   A_ = None )						->      Tuple[str]:
				"""simple docstring"""
				if not os.path.isdir(A_ ):
						logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
						return
				UpperCamelCase						=  os.path.join(
				    A_       ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
				UpperCamelCase						=  os.path.join(
				    A_       ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )

				with open(A_       ,   'w'       ,   encoding='utf-8' ) as f:
						f.write(json.dumps(self.encoder       ,   indent=2       ,   sort_keys=A_       ,   ensure_ascii=A_ ) + '\n' )

				UpperCamelCase						=  0
				with open(A_       ,   'w'       ,   encoding='utf-8' ) as writer:
						writer.write('#version: 0.2\n' )
						for bpe_tokens, token_index in sorted(self.bpe_ranks.items()       ,   key=lambda A_ : kv[1] ):
								if index != token_index:
										logger.warning(
										    F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
										    ' Please check that the tokenizer is not corrupted!' )
										UpperCamelCase						=  token_index
								writer.write(' '.join(A_ ) + '\n' )
								index += 1

				return vocab_file, merge_file
