


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

from tokenizers import processors

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


if is_sentencepiece_available():
 from .tokenization_mbart import MBartTokenizer
else:
 _SCREAMING_SNAKE_CASE      :			int      				=   None


_SCREAMING_SNAKE_CASE      :			Tuple      				=   logging.get_logger(__name__)


_SCREAMING_SNAKE_CASE      :			str      				=   {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

_SCREAMING_SNAKE_CASE      :			str      				=   {
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/mbart-large-en-ro": "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json",
        "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json",
    },
}

_SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   {
    "facebook/mbart-large-en-ro": 10_24,
    "facebook/mbart-large-cc25": 10_24,
}

# fmt: off
_SCREAMING_SNAKE_CASE      :			str      				=   ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		VOCAB_FILES_NAMES
    __magic_name__        =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __magic_name__        =		PRETRAINED_VOCAB_FILES_MAP
    __magic_name__        =		['input_ids', 'attention_mask']
    __magic_name__        =		MBartTokenizer

    __magic_name__        =		[]
    __magic_name__        =		[]
    def __init__(	self      , __snake_case=None      , __snake_case=None      , __snake_case="<s>"      , __snake_case="</s>"      , __snake_case="</s>"      , __snake_case="<s>"      , __snake_case="<unk>"      , __snake_case="<pad>"      , __snake_case="<mask>"      , __snake_case=None      , __snake_case=None      , __snake_case=None      , **__snake_case      , ):
         # Mask token behave like a normal word, i.e. include the space before it
         snake_case					=    AddedToken(__snake_case      , lstrip=__snake_case      , rstrip=__snake_case   ) if isinstance(__snake_case      , __snake_case   ) else mask_token

         super().__init__(
             vocab_file=__snake_case      , tokenizer_file=__snake_case      , bos_token=__snake_case      , eos_token=__snake_case      , sep_token=__snake_case      , cls_token=__snake_case      , unk_token=__snake_case      , pad_token=__snake_case      , mask_token=__snake_case      , src_lang=__snake_case      , tgt_lang=__snake_case      , additional_special_tokens=__snake_case      , **__snake_case      , )

         snake_case					=    vocab_file
         snake_case					=    False if not self.vocab_file else True

         snake_case					=    FAIRSEQ_LANGUAGE_CODES.copy()

         if additional_special_tokens is not None:
              # Only add those special tokens if they are not already there.
              _additional_special_tokens.extend(
                  [t for t in additional_special_tokens if t not in _additional_special_tokens]   )

         self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens}   )
         snake_case					=    {
             lang_code: self.convert_tokens_to_ids(__snake_case   ) for lang_code in FAIRSEQ_LANGUAGE_CODES
         }

         snake_case					=    src_lang if src_lang is not None else '''en_XX'''
         snake_case					=    self.convert_tokens_to_ids(self._src_lang   )
         snake_case					=    tgt_lang
         self.set_src_lang_special_tokens(self._src_lang   )
    @property
    def 		a_      (	self   ):
         return self._src_lang
    @src_lang.setter
    def 		a_      (	self      , __snake_case   ):
         snake_case					=    new_src_lang
         self.set_src_lang_special_tokens(self._src_lang   )
    def 		a_      (	self      , __snake_case      , __snake_case = None   ):
         if token_ids_a is None:
              return self.prefix_tokens + token_ids_a + self.suffix_tokens
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
    def 		a_      (	self      , __snake_case      , __snake_case = None   ):
         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 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , **__snake_case   ):
         if src_lang is None or tgt_lang is None:
              raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''   )
         snake_case					=    src_lang
         snake_case					=    self(__snake_case      , add_special_tokens=__snake_case      , return_tensors=__snake_case      , **__snake_case   )
         snake_case					=    self.convert_tokens_to_ids(__snake_case   )
         snake_case					=    tgt_lang_id
         return inputs
    def 		a_      (	self      , __snake_case      , __snake_case = "en_XX"      , __snake_case = None      , __snake_case = "ro_RO"      , **__snake_case      , ):
         snake_case					=    src_lang
         snake_case					=    tgt_lang
         return super().prepare_seqaseq_batch(__snake_case      , __snake_case      , **__snake_case   )
    def 		a_      (	self   ):
         return self.set_src_lang_special_tokens(self.src_lang   )
    def 		a_      (	self   ):
         return self.set_tgt_lang_special_tokens(self.tgt_lang   )
    def 		a_      (	self      , __snake_case   ):
         snake_case					=    self.convert_tokens_to_ids(__snake_case   )
         snake_case					=    []
         snake_case					=    [self.eos_token_id, self.cur_lang_code]

         snake_case					=    self.convert_ids_to_tokens(self.prefix_tokens   )
         snake_case					=    self.convert_ids_to_tokens(self.suffix_tokens   )

         snake_case					=    processors.TemplateProcessing(
             single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str      , pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str      , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str      , self.prefix_tokens + self.suffix_tokens   )   )      , )
    def 		a_      (	self      , __snake_case   ):
         snake_case					=    self.convert_tokens_to_ids(__snake_case   )
         snake_case					=    []
         snake_case					=    [self.eos_token_id, self.cur_lang_code]

         snake_case					=    self.convert_ids_to_tokens(self.prefix_tokens   )
         snake_case					=    self.convert_ids_to_tokens(self.suffix_tokens   )

         snake_case					=    processors.TemplateProcessing(
             single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str      , pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str      , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str      , self.prefix_tokens + self.suffix_tokens   )   )      , )




    def 		a_      (	self      , __snake_case      , __snake_case = None   ):
         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(__snake_case   ):
              logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''   )
              return
         snake_case					=    os.path.join(
             __snake_case      , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

         if os.path.abspath(self.vocab_file   ) != os.path.abspath(__snake_case   ):
              copyfile(self.vocab_file      , __snake_case   )

         return (out_vocab_file,)








import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_=False    ):



     """simple docstring"""






     snake_case					=    []
     for i in range(config.num_hidden_layers    ):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append((F'''module.blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''')    )
          rename_keys.append((F'''module.blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''')    )
          rename_keys.append(
              (F'''module.blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''')    )
          rename_keys.append((F'''module.blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''')    )
          rename_keys.append((F'''module.blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''')    )
          rename_keys.append((F'''module.blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''')    )
          rename_keys.append((F'''module.blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''')    )
          rename_keys.append((F'''module.blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''')    )
          rename_keys.append((F'''module.blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''')    )
          rename_keys.append((F'''module.blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''')    )

     # projection layer + position embeddings
     rename_keys.extend(
         [
             ('''module.cls_token''', '''vit.embeddings.cls_token'''),
             ('''module.patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
             ('''module.patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
             ('''module.pos_embed''', '''vit.embeddings.position_embeddings'''),
         ]    )

     if base_model:
          # layernorm + pooler
          rename_keys.extend(
              [
                  ('''module.norm.weight''', '''layernorm.weight'''),
                  ('''module.norm.bias''', '''layernorm.bias'''),
              ]    )

          # if just the base model, we should remove "vit" from all keys that start with "vit"
          snake_case					=    [(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''    ) else pair for pair in rename_keys]
     else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ('''norm.weight''', '''vit.layernorm.weight'''),
                  ('''norm.bias''', '''vit.layernorm.bias'''),
                  ('''head.weight''', '''classifier.weight'''),
                  ('''head.bias''', '''classifier.bias'''),
              ]    )

     return rename_keys
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_=False    ):



     """simple docstring"""






     for i in range(config.num_hidden_layers    ):
          if base_model:
               snake_case					=    ''''''
          else:
               snake_case					=    '''vit.'''
          # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
          snake_case					=    state_dict.pop(F'''module.blocks.{i}.attn.qkv.weight'''    )
          snake_case					=    state_dict.pop(F'''module.blocks.{i}.attn.qkv.bias'''    )
          # next, add query, keys and values (in that order) to the state dict
          snake_case					=    in_proj_weight[
              : config.hidden_size, :
          ]
          snake_case					=    in_proj_bias[: config.hidden_size]
          snake_case					=    in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          snake_case					=    in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          snake_case					=    in_proj_weight[
              -config.hidden_size :, :
          ]
          snake_case					=    in_proj_bias[-config.hidden_size :]
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    ['''head.weight''', '''head.bias''']
     for k in ignore_keys:
          state_dict.pop(UpperCamelCase_							,UpperCamelCase_    )
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    [
         '''module.fc.fc1.weight''',
         '''module.fc.fc1.bias''',
         '''module.fc.bn1.weight''',
         '''module.fc.bn1.bias''',
         '''module.fc.bn1.running_mean''',
         '''module.fc.bn1.running_var''',
         '''module.fc.bn1.num_batches_tracked''',
         '''module.fc.fc2.weight''',
         '''module.fc.fc2.bias''',
         '''module.fc.bn2.weight''',
         '''module.fc.bn2.bias''',
         '''module.fc.bn2.running_mean''',
         '''module.fc.bn2.running_var''',
         '''module.fc.bn2.num_batches_tracked''',
         '''module.fc.fc3.weight''',
         '''module.fc.fc3.bias''',
     ]
     for k in ignore_keys:
          state_dict.pop(UpperCamelCase_							,UpperCamelCase_    )
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    dct.pop(UpperCamelCase_    )
     snake_case					=    val
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    ViTMSNConfig()
     snake_case					=    10_00

     snake_case					=    '''datasets/huggingface/label-files'''
     snake_case					=    '''imagenet-1k-id2label.json'''
     snake_case					=    json.load(open(hf_hub_download(UpperCamelCase_							,UpperCamelCase_    )							,'''r'''    )    )
     snake_case					=    {int(UpperCamelCase_    ): v for k, v in idalabel.items()}
     snake_case					=    idalabel
     snake_case					=    {v: k for k, v in idalabel.items()}

     if "s16" in checkpoint_url:
          snake_case					=    3_84
          snake_case					=    15_36
          snake_case					=    6
     elif "l16" in checkpoint_url:
          snake_case					=    10_24
          snake_case					=    40_96
          snake_case					=    24
          snake_case					=    16
          snake_case					=    0.1
     elif "b4" in checkpoint_url:
          snake_case					=    4
     elif "l7" in checkpoint_url:
          snake_case					=    7
          snake_case					=    10_24
          snake_case					=    40_96
          snake_case					=    24
          snake_case					=    16
          snake_case					=    0.1

     snake_case					=    ViTMSNModel(UpperCamelCase_    )

     snake_case					=    torch.hub.load_state_dict_from_url(UpperCamelCase_							,map_location='''cpu'''    )['''target_encoder''']

     snake_case					=    ViTImageProcessor(size=config.image_size    )

     remove_projection_head(UpperCamelCase_    )
     snake_case					=    create_rename_keys(UpperCamelCase_							,base_model=UpperCamelCase_    )

     for src, dest in rename_keys:
          rename_key(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    )
     read_in_q_k_v(UpperCamelCase_							,UpperCamelCase_							,base_model=UpperCamelCase_    )

     model.load_state_dict(UpperCamelCase_    )
     model.eval()

     snake_case					=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''

     snake_case					=    Image.open(requests.get(UpperCamelCase_							,stream=UpperCamelCase_    ).raw    )
     snake_case					=    ViTImageProcessor(
         size=config.image_size							,image_mean=UpperCamelCase_							,image_std=UpperCamelCase_    )
     snake_case					=    image_processor(images=UpperCamelCase_							,return_tensors='''pt'''    )

     # forward pass
     torch.manual_seed(2    )
     snake_case					=    model(**UpperCamelCase_    )
     snake_case					=    outputs.last_hidden_state

     # The following Colab Notebook was used to generate these outputs:
     # https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
     if "s16" in checkpoint_url:
          snake_case					=    torch.tensor([[-1.0915, -1.4876, -1.1809]]    )
     elif "b16" in checkpoint_url:
          snake_case					=    torch.tensor([[14.2889, -18.9045, 11.7281]]    )
     elif "l16" in checkpoint_url:
          snake_case					=    torch.tensor([[41.5028, -22.8681, 45.6475]]    )
     elif "b4" in checkpoint_url:
          snake_case					=    torch.tensor([[-4.3868, 5.2932, -0.4137]]    )
     else:
          snake_case					=    torch.tensor([[-0.1792, -0.6465, 2.4263]]    )

     # verify logits
     assert torch.allclose(last_hidden_state[:, 0, :3]							,UpperCamelCase_							,atol=1e-4    )

     print(F'''Saving model to {pytorch_dump_folder_path}'''    )
     model.save_pretrained(UpperCamelCase_    )

     print(F'''Saving image processor to {pytorch_dump_folder_path}'''    )
     image_processor.save_pretrained(UpperCamelCase_    )


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE      :			List[Any]      				=   argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--checkpoint_url",
     default="https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar",
     type=str,
     help="URL of the checkpoint you'd like to convert.",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
 )

 _SCREAMING_SNAKE_CASE      :			Any      				=   parser.parse_args()
 convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




