





'''simple docstring'''







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

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


_lowercase       						=			logging.get_logger(__name__)


_lowercase       						=			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

_lowercase       						=			{
    'vocab_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
    },
    'merges_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
    },
}

_lowercase       						=			{
    'allenai/led-base-16384': 16_384,
}
class 			_lowercase						(       __a					):
     _UpperCAmelCase										=					VOCAB_FILES_NAMES
     _UpperCAmelCase										=					PRETRAINED_VOCAB_FILES_MAP
     _UpperCAmelCase										=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _UpperCAmelCase										=					LEDTokenizer
     _UpperCAmelCase										=					['''input_ids''', '''attention_mask''']







     def __init__( self    ,   A__=None    ,   A__=None    ,   A__=None    ,   A__="replace"    ,   A__="<s>"    ,   A__="</s>"    ,   A__="</s>"    ,   A__="<s>"    ,   A__="<unk>"    ,   A__="<pad>"    ,   A__="<mask>"    ,   A__=False    ,   A__=True    ,   **A__    ,   ) ->	List[Any]:
      super().__init__(
          A__    ,   A__    ,   tokenizer_file=A__    ,   errors=A__    ,   bos_token=A__    ,   eos_token=A__    ,   sep_token=A__    ,   cls_token=A__    ,   unk_token=A__    ,   pad_token=A__    ,   mask_token=A__    ,   add_prefix_space=A__    ,   trim_offsets=A__    ,   **A__    ,   )

      snake_case				=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
      if pre_tok_state.get('''add_prefix_space'''    ,   A__    ) != add_prefix_space:
       snake_case				=			getattr(A__    ,   pre_tok_state.pop('''type'''    )    )
       snake_case				=			add_prefix_space
       snake_case				=			pre_tok_class(**A__    )

      snake_case				=			add_prefix_space

      # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
      snake_case				=			'''post_processor'''
      snake_case				=			getattr(self.backend_tokenizer    ,   A__    ,   A__    )
      if tokenizer_component_instance:
       snake_case				=			json.loads(tokenizer_component_instance.__getstate__()    )

       # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
       if "sep" in state:
        snake_case				=			tuple(state['''sep''']    )
       if "cls" in state:
        snake_case				=			tuple(state['''cls''']    )

       snake_case				=			False

       if state.get('''add_prefix_space'''    ,   A__    ) != add_prefix_space:
        snake_case				=			add_prefix_space
        snake_case				=			True

       if state.get('''trim_offsets'''    ,   A__    ) != trim_offsets:
        snake_case				=			trim_offsets
        snake_case				=			True

       if changes_to_apply:
        snake_case				=			getattr(A__    ,   state.pop('''type'''    )    )
        snake_case				=			component_class(**A__    )
        setattr(self.backend_tokenizer    ,   A__    ,   A__    )







     @property
     # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
     def     UpperCamelCase			( self    ) ->	str:
      if self._mask_token is None:
       if self.verbose:
        logger.error('''Using mask_token, but it is not set yet.'''    )
       return None
      return str(self._mask_token    )







     @mask_token.setter
     def     UpperCamelCase			( self    ,   A__    ) ->	List[Any]:
      snake_case				=			AddedToken(A__    ,   lstrip=A__    ,   rstrip=A__    ) if isinstance(A__    ,   A__    ) else value
      snake_case				=			value







     def     UpperCamelCase			( self    ,   *A__    ,   **A__    ) ->	BatchEncoding:
      snake_case				=			kwargs.get('''is_split_into_words'''    ,   A__    )

      if is_split_into_words and not self.add_prefix_space:
       raise ValueError(
           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     UpperCamelCase			( self    ,   *A__    ,   **A__    ) ->	BatchEncoding:
      snake_case				=			kwargs.get('''is_split_into_words'''    ,   A__    )

      if is_split_into_words and not self.add_prefix_space:
       raise ValueError(
           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     UpperCamelCase			( self    ,   A__    ,   A__ = None    ) ->	Tuple[str]:
      snake_case				=			self._tokenizer.model.save(A__    ,   name=A__    )
      return tuple(A__    )







     def     UpperCamelCase			( self    ,   A__    ,   A__=None    ) ->	Any:
      snake_case				=			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
      if token_ids_a is None:
       return output

      return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







     def     UpperCamelCase			( self    ,   A__    ,   A__ = None    ) ->	List[int]:
      snake_case				=			[self.sep_token_id]
      snake_case				=			[self.cls_token_id]

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







     def     UpperCamelCase			( self    ,   A__    ,   A__ = None    ,   A__ = PaddingStrategy.DO_NOT_PAD    ,   A__ = None    ,   A__ = None    ,   ) ->	dict:
      snake_case				=			super()._pad(
          encoded_inputs=A__    ,   max_length=A__    ,   padding_strategy=A__    ,   pad_to_multiple_of=A__    ,   return_attention_mask=A__    ,   )

      # Load from model defaults
      if return_attention_mask is None:
       snake_case				=			'''attention_mask''' in self.model_input_names

      if return_attention_mask and "global_attention_mask" in encoded_inputs:
       snake_case				=			encoded_inputs[self.model_input_names[0]]
       # `global_attention_mask` need to have the same length as other (sequential) inputs.
       snake_case				=			len(encoded_inputs['''global_attention_mask''']    ) != len(A__    )

       if needs_to_be_padded:
        snake_case				=			len(A__    ) - len(encoded_inputs['''global_attention_mask''']    )

        if self.padding_side == "right":
         # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
         snake_case				=			(
             encoded_inputs['''global_attention_mask'''] + [-1] * difference
         )
        elif self.padding_side == "left":
         snake_case				=			[-1] * difference + encoded_inputs[
             '''global_attention_mask'''
         ]
        else:
         raise ValueError('''Invalid padding strategy:''' + str(self.padding_side    )    )

      return encoded_inputs








'''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       						=			logging.get_logger(__name__)

def    __UpperCamelCase	(	a    : Dict					,   a    : Optional[int]					,   a    : Dict					,   a    : Dict     ) ->Union[str, Any]:
 snake_case				=			original_name.split('''.'''     )[0]
 snake_case				=			key.split('''.'''     )
 snake_case				=			int(key_list[key_list.index(a     ) - 2]     )
 snake_case				=			int(key_list[key_list.index(a     ) - 1]     )
 snake_case				=			orig_block_num - offset

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

def    __UpperCamelCase	(	) ->Optional[int]:
 snake_case				=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
 snake_case				=			Image.open(requests.get(a					,   stream=a     ).raw     )

 return image

@torch.no_grad()
def    __UpperCamelCase	(	a    : Dict					,   a    : Optional[Any]					,   a    : Tuple     ) ->List[str]:
 snake_case				=			PoolFormerConfig()

 # set attributes based on model_name
 snake_case				=			'''huggingface/label-files'''
 snake_case				=			model_name[-3:]
 snake_case				=			1000
 snake_case				=			'''imagenet-1k-id2label.json'''
 snake_case				=			(1, 1000)

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

 # load image processor
 snake_case				=			PoolFormerImageProcessor(crop_pct=a     )

 # Prepare image
 snake_case				=			prepare_img()
 snake_case				=			image_processor(images=a					,   return_tensors='''pt'''     ).pixel_values

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

 # load original state dict
 snake_case				=			torch.load(a					,   map_location=torch.device('''cpu'''     )     )

 # rename keys
 snake_case				=			rename_keys(a     )

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

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

 # forward pass
 snake_case				=			model(a     )
 snake_case				=			outputs.logits

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

