




"""simple docstring"""


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:
     A_      =    None


A_      =    logging.get_logger(__name__)


A_      =    {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

A_      =    {
    '''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''',
    },
}

A_      =    {
    '''facebook/mbart-large-en-ro''': 10_24,
    '''facebook/mbart-large-cc25''': 10_24,
}

# fmt: off
A_      =    ['''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  lowercase(					__a			):







 '''simple docstring'''





 lowercase__			 = VOCAB_FILES_NAMES
 lowercase__			 = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase__			 = PRETRAINED_VOCAB_FILES_MAP
 lowercase__			 = ["input_ids", "attention_mask"]
 lowercase__			 = MBartTokenizer

 lowercase__			 = []
 lowercase__			 = []


 def __init__(    self:		Dict,				a_:		str=None,				a_:		Tuple=None,				a_:		List[Any]="<s>",				a_:		int="</s>",				a_:		Tuple="</s>",				a_:		Union[str, Any]="<s>",				a_:		Any="<unk>",				a_:		List[Any]="<pad>",				a_:		List[Any]="<mask>",				a_:		Tuple=None,				a_:		Union[str, Any]=None,				a_:		int=None,				**a_:		Dict,				):




   '''simple docstring'''



   _snake_case							:		Dict								=  AddedToken(a_,				lstrip=a_,				rstrip=a_		) if isinstance(a_,				a_		) else mask_token

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

   _snake_case							:		str								=  vocab_file
   _snake_case							:		List[str]								=  False if not self.vocab_file else True

   _snake_case							:		Optional[Any]								=  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							:		Dict								=  {
       lang_code: self.convert_tokens_to_ids(a_		) for lang_code in FAIRSEQ_LANGUAGE_CODES
   }

   _snake_case							:		Optional[int]								=  src_lang if src_lang is not None else """en_XX"""
   _snake_case							:		int								=  self.convert_tokens_to_ids(self._src_lang		)
   _snake_case							:		List[Any]								=  tgt_lang
   self.set_src_lang_special_tokens(self._src_lang		)


 @property
 def 			UpperCamelCase_       (    self:		Tuple		):




   '''simple docstring'''



   return self._src_lang


 @src_lang.setter
 def 			UpperCamelCase_       (    self:		Union[str, Any],				a_:		str		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  new_src_lang
   self.set_src_lang_special_tokens(self._src_lang		)


 def 			UpperCamelCase_       (    self:		str,				a_:		List[int],				a_:		Optional[List[int]] = None		):




   '''simple docstring'''



   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 			UpperCamelCase_       (    self:		Dict,				a_:		List[int],				a_:		Optional[List[int]] = None		):




   '''simple docstring'''



   _snake_case							:		Any								=  [self.sep_token_id]
   _snake_case							:		List[str]								=  [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:		Union[str, Any],				a_:		Tuple,				a_:		str,				a_:		Optional[str],				a_:		Optional[str],				**a_:		str		):




   '''simple docstring'''



   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							:		Union[str, Any]								=  src_lang
   _snake_case							:		Union[str, Any]								=  self(a_,				add_special_tokens=a_,				return_tensors=a_,				**a_		)
   _snake_case							:		int								=  self.convert_tokens_to_ids(a_		)
   _snake_case							:		Tuple								=  tgt_lang_id
   return inputs


 def 			UpperCamelCase_       (    self:		Optional[int],				a_:		List[str],				a_:		str = "en_XX",				a_:		Optional[List[str]] = None,				a_:		str = "ro_RO",				**a_:		List[Any],				):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  src_lang
   _snake_case							:		str								=  tgt_lang
   return super().prepare_seqaseq_batch(a_,				a_,				**a_		)


 def 			UpperCamelCase_       (    self:		Optional[Any]		):




   '''simple docstring'''



   return self.set_src_lang_special_tokens(self.src_lang		)


 def 			UpperCamelCase_       (    self:		int		):




   '''simple docstring'''



   return self.set_tgt_lang_special_tokens(self.tgt_lang		)


 def 			UpperCamelCase_       (    self:		List[str],				a_:		str		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  self.convert_tokens_to_ids(a_		)
   _snake_case							:		str								=  []
   _snake_case							:		Tuple								=  [self.eos_token_id, self.cur_lang_code]

   _snake_case							:		str								=  self.convert_ids_to_tokens(self.prefix_tokens		)
   _snake_case							:		Optional[Any]								=  self.convert_ids_to_tokens(self.suffix_tokens		)

   _snake_case							:		Dict								=  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 			UpperCamelCase_       (    self:		Dict,				a_:		str		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  self.convert_tokens_to_ids(a_		)
   _snake_case							:		Optional[Any]								=  []
   _snake_case							:		Union[str, Any]								=  [self.eos_token_id, self.cur_lang_code]

   _snake_case							:		Tuple								=  self.convert_ids_to_tokens(self.prefix_tokens		)
   _snake_case							:		Optional[Any]								=  self.convert_ids_to_tokens(self.suffix_tokens		)

   _snake_case							:		Dict								=  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 			UpperCamelCase_       (    self:		Optional[Any],				a_:		str,				a_:		Optional[str] = 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(a_		):
     logger.error(f"Vocabulary path ({save_directory}) should be a directory."		)
     return
   _snake_case							:		Any								=  os.path.join(
       a_,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)

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

   return (out_vocab_file,)








"""simple docstring"""


import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
A_      =    logging.get_logger()






@dataclass
class  lowercase:







 '''simple docstring'''





 lowercase__			 = 42
 lowercase__			 = field(default_factory=__a			)
 lowercase__			 = field(default_factory=__a			)


 def 			UpperCamelCase_       (    self:		Optional[Any],				a_:		Union[str, Any],				a_:		Tensor,				a_:		Tensor		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  len(list(m.modules()		)		) == 1 or isinstance(a_,				nn.Convad		) or isinstance(a_,				nn.BatchNormad		)
   if has_not_submodules:
     self.traced.append(a_		)


 def __call__(    self:		List[Any],				a_:		Tensor		):




   '''simple docstring'''



   for m in self.module.modules():
     self.handles.append(m.register_forward_hook(self._forward_hook		)		)
   self.module(a_		)
   [x.remove() for x in self.handles]
   return self


 @property
 def 			UpperCamelCase_       (    self:		Union[str, Any]		):




   '''simple docstring'''



   return list(filter(lambda a_		: len(list(x.state_dict().keys()		)		) > 0,				self.traced		)		)







@dataclass
class  lowercase:







 '''simple docstring'''





 lowercase__			 = 42
 lowercase__			 = 42
 lowercase__			 = 0
 lowercase__			 = field(default_factory=__a			)
 lowercase__			 = field(default_factory=__a			)


 def __call__(    self:		Dict,				a_:		Tensor		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  Tracker(self.dest		)(a_		).parametrized
   _snake_case							:		int								=  Tracker(self.src		)(a_		).parametrized

   _snake_case							:		Tuple								=  list(filter(lambda a_		: type(a_		) not in self.src_skip,				a_		)		)
   _snake_case							:		Union[str, Any]								=  list(filter(lambda a_		: type(a_		) not in self.dest_skip,				a_		)		)

   if len(a_		) != len(a_		):
     raise Exception(
         f"Numbers of operations are different. Source module has {len(a_		)} operations while"
         f" destination module has {len(a_		)}."		)

   for dest_m, src_m in zip(a_,				a_		):
     dest_m.load_state_dict(src_m.state_dict()		)
     if self.verbose == 1:
       print(f"Transfered from={src_m} to={dest_m}"		)







def  UpperCAmelCase__	(snake_case__							: str		,       snake_case__							: ResNetConfig		,       snake_case__							: Path		,       snake_case__							: bool = True   ):
  """simple docstring"""

  print(F"Converting {name}..."   )
  with torch.no_grad():
    _snake_case							:		Dict								=  timm.create_model(snake_case__		,       pretrained=snake_case__   ).eval()
    _snake_case							:		List[Any]								=  ResNetForImageClassification(snake_case__   ).eval()
    _snake_case							:		List[str]								=  ModuleTransfer(src=snake_case__		,       dest=snake_case__   )
    _snake_case							:		Optional[Any]								=  torch.randn((1, 3, 2_24, 2_24)   )
    module_transfer(snake_case__   )

  assert torch.allclose(from_model(snake_case__   )		,       our_model(snake_case__   ).logits   ), "The model logits don't match the original one."

  _snake_case							:		Optional[int]								=  F"resnet{'-'.join(name.split('resnet'   )   )}"
  print(snake_case__   )

  if push_to_hub:
    our_model.push_to_hub(
        repo_path_or_name=save_directory / checkpoint_name		,       commit_message="""Add model"""		,       use_temp_dir=snake_case__		,       )

    # we can use the convnext one
    _snake_case							:		Union[str, Any]								=  AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""   )
    image_processor.push_to_hub(
        repo_path_or_name=save_directory / checkpoint_name		,       commit_message="""Add image processor"""		,       use_temp_dir=snake_case__		,       )

    print(F"Pushed {checkpoint_name}"   )


def  UpperCAmelCase__	(snake_case__							: Path		,       snake_case__							: str = None		,       snake_case__							: bool = True   ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  """imagenet-1k-id2label.json"""
  _snake_case							:		Optional[Any]								=  10_00
  _snake_case							:		str								=  (1, num_labels)

  _snake_case							:		List[Any]								=  """huggingface/label-files"""
  _snake_case							:		Union[str, Any]								=  num_labels
  _snake_case							:		Optional[int]								=  json.load(open(hf_hub_download(snake_case__		,       snake_case__		,       repo_type="""dataset"""   )		,       """r"""   )   )
  _snake_case							:		Union[str, Any]								=  {int(snake_case__   ): v for k, v in idalabel.items()}

  _snake_case							:		str								=  idalabel
  _snake_case							:		Union[str, Any]								=  {v: k for k, v in idalabel.items()}

  _snake_case							:		Tuple								=  partial(snake_case__		,       num_labels=snake_case__		,       idalabel=snake_case__		,       labelaid=snake_case__   )

  _snake_case							:		Optional[int]								=  {
      """resnet18""": ImageNetPreTrainedConfig(
          depths=[2, 2, 2, 2]		,       hidden_sizes=[64, 1_28, 2_56, 5_12]		,       layer_type="""basic"""   ),
      """resnet26""": ImageNetPreTrainedConfig(
          depths=[2, 2, 2, 2]		,       hidden_sizes=[2_56, 5_12, 10_24, 20_48]		,       layer_type="""bottleneck"""   ),
      """resnet34""": ImageNetPreTrainedConfig(
          depths=[3, 4, 6, 3]		,       hidden_sizes=[64, 1_28, 2_56, 5_12]		,       layer_type="""basic"""   ),
      """resnet50""": ImageNetPreTrainedConfig(
          depths=[3, 4, 6, 3]		,       hidden_sizes=[2_56, 5_12, 10_24, 20_48]		,       layer_type="""bottleneck"""   ),
      """resnet101""": ImageNetPreTrainedConfig(
          depths=[3, 4, 23, 3]		,       hidden_sizes=[2_56, 5_12, 10_24, 20_48]		,       layer_type="""bottleneck"""   ),
      """resnet152""": ImageNetPreTrainedConfig(
          depths=[3, 8, 36, 3]		,       hidden_sizes=[2_56, 5_12, 10_24, 20_48]		,       layer_type="""bottleneck"""   ),
  }

  if model_name:
    convert_weight_and_push(snake_case__		,       names_to_config[model_name]		,       snake_case__		,       snake_case__   )
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(snake_case__		,       snake_case__		,       snake_case__		,       snake_case__   )
  return config, expected_shape


if __name__ == "__main__":
     A_      =    argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--model_name''',
         default=None,
         type=str,
         help=(
             '''The name of the model you wish to convert, it must be one of the supported resnet* architecture,'''
             ''' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'''
         ),
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''',
         default=None,
         type=Path,
         required=True,
         help='''Path to the output PyTorch model directory.''',
     )
     parser.add_argument(
         '''--push_to_hub''',
         default=True,
         type=bool,
         required=False,
         help='''If True, push model and image processor to the hub.''',
     )

     A_      =    parser.parse_args()
     A_      =    args.pytorch_dump_folder_path
     pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
     convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)


