

import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

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


if is_sentencepiece_available():
       from .tokenization_ta import TaTokenizer
else:
       _SCREAMING_SNAKE_CASE  :						Optional[int]         =    None


_SCREAMING_SNAKE_CASE  :						Union[str, Any]         =    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE  :						Optional[Any]         =    {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

_SCREAMING_SNAKE_CASE  :						int         =    {
    '''vocab_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/tokenizer.json''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/tokenizer.json''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/tokenizer.json''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/tokenizer.json''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/tokenizer.json''',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
_SCREAMING_SNAKE_CASE  :						str         =    {
    '''t5-small''': 512,
    '''t5-base''': 512,
    '''t5-large''': 512,
    '''t5-3b''': 512,
    '''t5-11b''': 512,
}






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  a     =      VOCAB_FILES_NAMES
  a     =      PRETRAINED_VOCAB_FILES_MAP
  a     =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a     =      ["input_ids", "attention_mask"]
  a     =      TaTokenizer

  a     =      []






  def __init__(							self		:	str			,     __lowerCamelCase		:	Optional[int]=None			,     __lowerCamelCase		:	Any=None			,     __lowerCamelCase		:	Dict="</s>"			,     __lowerCamelCase		:	List[str]="<unk>"			,     __lowerCamelCase		:	Any="<pad>"			,     __lowerCamelCase		:	Tuple=100			,     __lowerCamelCase		:	int=None			,     **__lowerCamelCase		:	List[str]			,     )	->							Any:
       # Add extra_ids to the special token list
       if extra_ids > 0 and additional_special_tokens is None:
            SCREAMING_SNAKE_CASE__					=					[f'''<extra_id_{i}>''' for i in range(__lowerCamelCase      )]
       elif extra_ids > 0 and additional_special_tokens is not None:
            # Check that we have the right number of extra special tokens
            SCREAMING_SNAKE_CASE__					=					len(set(filter(lambda __lowerCamelCase      : bool('''extra_id_''' in str(__lowerCamelCase      )      )			,     __lowerCamelCase      )      )      )
            if extra_tokens != extra_ids:
                 raise ValueError(
                     f'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
                     ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
                     ''' tokens'''      )

       super().__init__(
           __lowerCamelCase			,     tokenizer_file=__lowerCamelCase			,     eos_token=__lowerCamelCase			,     unk_token=__lowerCamelCase			,     pad_token=__lowerCamelCase			,     extra_ids=__lowerCamelCase			,     additional_special_tokens=__lowerCamelCase			,     **__lowerCamelCase			,     )

       SCREAMING_SNAKE_CASE__					=					vocab_file
       SCREAMING_SNAKE_CASE__					=					False if not self.vocab_file else True
       SCREAMING_SNAKE_CASE__					=					extra_ids






  @staticmethod
  def    lowercase_	(							__lowerCamelCase		:	Optional[Any]			,     __lowerCamelCase		:	Tuple			,     __lowerCamelCase		:	int      )	->							Tuple:
       if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
            SCREAMING_SNAKE_CASE__					=					TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
            if init_max_model_length is not None and init_max_model_length != max_model_length:
                 return init_max_model_length
            elif init_max_model_length is None:
                 warnings.warn(
                     '''This tokenizer was incorrectly instantiated with a model max length of'''
                     f''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
                     ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
                     ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
                     f''' {pretrained_model_name_or_path} automatically truncating your input to'''
                     f''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
                     f''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
                     ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
                     ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''			,     __lowerCamelCase			,     )

       return max_model_length






  def    lowercase_	(							self		:	Union[str, Any]			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	Optional[str] = None      )	->							Tuple[str]:
       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
       SCREAMING_SNAKE_CASE__					=					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      )
            logger.info(f'''Copy vocab file to {out_vocab_file}'''      )

       return (out_vocab_file,)






  def    lowercase_	(							self		:	str			,     __lowerCamelCase		:	List[int]			,     __lowerCamelCase		:	Optional[List[int]] = None      )	->							List[int]:
       SCREAMING_SNAKE_CASE__					=					token_ids_a + [self.eos_token_id]
       if token_ids_a is None:
            return self.prefix_tokens + token_ids_a
       else:
            SCREAMING_SNAKE_CASE__					=					token_ids_a + [self.eos_token_id]
            return self.prefix_tokens + token_ids_a + token_ids_a






  def    lowercase_	(							self		:	Dict			,     __lowerCamelCase		:	List[int]			,     __lowerCamelCase		:	Optional[List[int]] = None      )	->							List[int]:
       SCREAMING_SNAKE_CASE__					=					[self.eos_token_id]

       if token_ids_a is None:
            return len(token_ids_a + eos      ) * [0]
       return len(token_ids_a + eos + token_ids_a + eos      ) * [0]






  def    lowercase_	(							self		:	List[Any]      )	->							Optional[int]:
       return list(
           set(filter(lambda __lowerCamelCase      : bool(re.search(r'''<extra_id_\d+>'''			,     __lowerCamelCase      )      ) is not None			,     self.additional_special_tokens      )      )      )







  def    lowercase_	(							self		:	Any      )	->							Union[str, Any]:
       return [self.convert_tokens_to_ids(__lowerCamelCase      ) for token in self.get_sentinel_tokens()]





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  :						Dict         =    logging.get_logger(__name__)
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					original_name.split('''.'''       )[0]
     SCREAMING_SNAKE_CASE__					=					key.split('''.'''       )
     SCREAMING_SNAKE_CASE__					=					int(key_list[key_list.index(_A       ) - 2]       )
     SCREAMING_SNAKE_CASE__					=					int(key_list[key_list.index(_A       ) - 1]       )
     SCREAMING_SNAKE_CASE__					=					orig_block_num - offset

     SCREAMING_SNAKE_CASE__					=					key.replace(F'''{orig_block_num}.{layer_num}.{original_name}'''   ,	F'''block.{new_block_num}.{layer_num}.{new_name}'''       )
     return key
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




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





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     SCREAMING_SNAKE_CASE__					=					Image.open(requests.get(_A   ,	stream=_A       ).raw       )

     return image
@torch.no_grad()
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					PoolFormerConfig()

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

     # set config attributes
     SCREAMING_SNAKE_CASE__					=					json.load(open(hf_hub_download(_A   ,	_A   ,	repo_type='''dataset'''       )   ,	'''r'''       )       )
     SCREAMING_SNAKE_CASE__					=					{int(_A       ): v for k, v in idalabel.items()}
     SCREAMING_SNAKE_CASE__					=					idalabel
     SCREAMING_SNAKE_CASE__					=					{v: k for k, v in idalabel.items()}
     if size == "s12":
          SCREAMING_SNAKE_CASE__					=					[2, 2, 6, 2]
          SCREAMING_SNAKE_CASE__					=					[64, 1_28, 3_20, 5_12]
          SCREAMING_SNAKE_CASE__					=					4.0
          SCREAMING_SNAKE_CASE__					=					0.9
     elif size == "s24":
          SCREAMING_SNAKE_CASE__					=					[4, 4, 12, 4]
          SCREAMING_SNAKE_CASE__					=					[64, 1_28, 3_20, 5_12]
          SCREAMING_SNAKE_CASE__					=					4.0
          SCREAMING_SNAKE_CASE__					=					0.9
     elif size == "s36":
          SCREAMING_SNAKE_CASE__					=					[6, 6, 18, 6]
          SCREAMING_SNAKE_CASE__					=					[64, 1_28, 3_20, 5_12]
          SCREAMING_SNAKE_CASE__					=					4.0
          SCREAMING_SNAKE_CASE__					=					1e-6
          SCREAMING_SNAKE_CASE__					=					0.9
     elif size == "m36":
          SCREAMING_SNAKE_CASE__					=					[6, 6, 18, 6]
          SCREAMING_SNAKE_CASE__					=					[96, 1_92, 3_84, 7_68]
          SCREAMING_SNAKE_CASE__					=					4.0
          SCREAMING_SNAKE_CASE__					=					1e-6
          SCREAMING_SNAKE_CASE__					=					0.9_5
     elif size == "m48":
          SCREAMING_SNAKE_CASE__					=					[8, 8, 24, 8]
          SCREAMING_SNAKE_CASE__					=					[96, 1_92, 3_84, 7_68]
          SCREAMING_SNAKE_CASE__					=					4.0
          SCREAMING_SNAKE_CASE__					=					1e-6
          SCREAMING_SNAKE_CASE__					=					0.9_5
     else:
          raise ValueError(F'''Size {size} not supported'''       )

     # load image processor
     SCREAMING_SNAKE_CASE__					=					PoolFormerImageProcessor(crop_pct=_A       )

     # Prepare image
     SCREAMING_SNAKE_CASE__					=					prepare_img()
     SCREAMING_SNAKE_CASE__					=					image_processor(images=_A   ,	return_tensors='''pt'''       ).pixel_values

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

     # load original state dict
     SCREAMING_SNAKE_CASE__					=					torch.load(_A   ,	map_location=torch.device('''cpu'''       )       )

     # rename keys
     SCREAMING_SNAKE_CASE__					=					rename_keys(_A       )

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

     # Define image processor
     SCREAMING_SNAKE_CASE__					=					PoolFormerImageProcessor(crop_pct=_A       )
     SCREAMING_SNAKE_CASE__					=					image_processor(images=prepare_img()   ,	return_tensors='''pt'''       ).pixel_values

     # forward pass
     SCREAMING_SNAKE_CASE__					=					model(_A       )
     SCREAMING_SNAKE_CASE__					=					outputs.logits

     # define expected logit slices for different models
     if size == "s12":
          SCREAMING_SNAKE_CASE__					=					torch.tensor([-0.3_0_4_5, -0.6_7_5_8, -0.4_8_6_9]       )
     elif size == "s24":
          SCREAMING_SNAKE_CASE__					=					torch.tensor([0.4_4_0_2, -0.1_3_7_4, -0.8_0_4_5]       )
     elif size == "s36":
          SCREAMING_SNAKE_CASE__					=					torch.tensor([-0.6_0_8_0, -0.5_1_3_3, -0.5_8_9_8]       )
     elif size == "m36":
          SCREAMING_SNAKE_CASE__					=					torch.tensor([0.3_9_5_2, 0.2_2_6_3, -1.2_6_6_8]       )
     elif size == "m48":
          SCREAMING_SNAKE_CASE__					=					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]   ,	_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__":
       _SCREAMING_SNAKE_CASE  :						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.'''
       )
       _SCREAMING_SNAKE_CASE  :						Tuple         =    parser.parse_args()
       convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)


