



import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig, TimesformerForVideoClassification, VideoMAEImageProcessor


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Any       ):






 '''simple docstring'''

 a							=		TimesformerConfig()

 if "large" in model_name:
  a							=		96

 if "hr" in model_name:
  a							=		16
  a							=		4_48

 a							=		"huggingface/label-files"
 if "k400" in model_name:
  a							=		4_00
  a							=		"kinetics400-id2label.json"
 elif "k600" in model_name:
  a							=		6_00
  a							=		"kinetics600-id2label.json"
 elif "ssv2" in model_name:
  a							=		1_74
  a							=		"something-something-v2-id2label.json"
 else:
  raise ValueError("Model name should either contain 'k400', 'k600' or 'ssv2'."       )
 a							=		json.load(open(hf_hub_download(UpperCAmelCase__			,       UpperCAmelCase__			,       repo_type="dataset"       )			,       "r"       )       )
 a							=		{int(UpperCAmelCase__       ): v for k, v in idalabel.items()}
 a							=		idalabel
 a							=		{v: k for k, v in idalabel.items()}

 return config


def 						UpperCAmelCase__   (   UpperCAmelCase__			:int       ):






 '''simple docstring'''

 if "encoder." in name:
  a							=		name.replace("encoder."			,       ""       )
 if "cls_token" in name:
  a							=		name.replace("cls_token"			,       "timesformer.embeddings.cls_token"       )
 if "pos_embed" in name:
  a							=		name.replace("pos_embed"			,       "timesformer.embeddings.position_embeddings"       )
 if "time_embed" in name:
  a							=		name.replace("time_embed"			,       "timesformer.embeddings.time_embeddings"       )
 if "patch_embed.proj" in name:
  a							=		name.replace("patch_embed.proj"			,       "timesformer.embeddings.patch_embeddings.projection"       )
 if "patch_embed.norm" in name:
  a							=		name.replace("patch_embed.norm"			,       "timesformer.embeddings.norm"       )
 if "blocks" in name:
  a							=		name.replace("blocks"			,       "timesformer.encoder.layer"       )
 if "attn.proj" in name:
  a							=		name.replace("attn.proj"			,       "attention.output.dense"       )
 if "attn" in name and "bias" not in name and "temporal" not in name:
  a							=		name.replace("attn"			,       "attention.self"       )
 if "attn" in name and "temporal" not in name:
  a							=		name.replace("attn"			,       "attention.attention"       )
 if "temporal_norm1" in name:
  a							=		name.replace("temporal_norm1"			,       "temporal_layernorm"       )
 if "temporal_attn.proj" in name:
  a							=		name.replace("temporal_attn"			,       "temporal_attention.output.dense"       )
 if "temporal_fc" in name:
  a							=		name.replace("temporal_fc"			,       "temporal_dense"       )
 if "norm1" in name and "temporal" not in name:
  a							=		name.replace("norm1"			,       "layernorm_before"       )
 if "norm2" in name:
  a							=		name.replace("norm2"			,       "layernorm_after"       )
 if "mlp.fc1" in name:
  a							=		name.replace("mlp.fc1"			,       "intermediate.dense"       )
 if "mlp.fc2" in name:
  a							=		name.replace("mlp.fc2"			,       "output.dense"       )
 if "norm.weight" in name and "fc" not in name and "temporal" not in name:
  a							=		name.replace("norm.weight"			,       "timesformer.layernorm.weight"       )
 if "norm.bias" in name and "fc" not in name and "temporal" not in name:
  a							=		name.replace("norm.bias"			,       "timesformer.layernorm.bias"       )
 if "head" in name:
  a							=		name.replace("head"			,       "classifier"       )

 return name


def 						UpperCAmelCase__   (   UpperCAmelCase__			:List[str]			,       UpperCAmelCase__			:Optional[int]       ):






 '''simple docstring'''

 for key in orig_state_dict.copy().keys():
  a							=		orig_state_dict.pop(UpperCAmelCase__       )

  if key.startswith("model."       ):
   a							=		key.replace("model."			,       ""       )

  if "qkv" in key:
   a							=		key.split("."       )
   a							=		int(key_split[1]       )
   a							=		"timesformer.encoder.layer."
   if "temporal" in key:
    a							=		".temporal_attention.attention.qkv."
   else:
    a							=		".attention.attention.qkv."
   if "weight" in key:
    a							=		val
   else:
    a							=		val
  else:
   a							=		val

 return orig_state_dict


def 						UpperCAmelCase__   (   ):






 '''simple docstring'''

 a							=		hf_hub_download(
     repo_id="hf-internal-testing/spaghetti-video"			,       filename="eating_spaghetti.npy"			,       repo_type="dataset"       )
 a							=		np.load(UpperCAmelCase__       )
 return list(UpperCAmelCase__       )


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Optional[Any]			,       UpperCAmelCase__			:Tuple			,       UpperCAmelCase__			:Union[str, Any]			,       UpperCAmelCase__			:Tuple       ):






 '''simple docstring'''

 a							=		get_timesformer_config(UpperCAmelCase__       )

 a							=		TimesformerForVideoClassification(UpperCAmelCase__       )

 # download original checkpoint, hosted on Google Drive
 a							=		"pytorch_model.bin"
 gdown.cached_download(UpperCAmelCase__			,       UpperCAmelCase__			,       quiet=UpperCAmelCase__       )
 a							=		torch.load(UpperCAmelCase__			,       map_location="cpu"       )
 if "model" in files:
  a							=		files["model"]
 elif "module" in files:
  a							=		files["module"]
 else:
  a							=		files["model_state"]
 a							=		convert_state_dict(UpperCAmelCase__			,       UpperCAmelCase__       )

 model.load_state_dict(UpperCAmelCase__       )
 model.eval()

 # verify model on basic input
 a							=		VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]			,       image_std=[0.5, 0.5, 0.5]       )
 a							=		prepare_video()
 a							=		image_processor(video[:8]			,       return_tensors="pt"       )

 a							=		model(**UpperCAmelCase__       )
 a							=		outputs.logits

 a							=		[
     # Kinetics-400 checkpoints (hr = high resolution input of 448px instead of 224px)
     "timesformer-base-finetuned-k400",
     "timesformer-large-finetuned-k400",
     "timesformer-hr-finetuned-k400",
     # Kinetics-600 checkpoints (hr = high resolution input of 448px instead of 224px)
     "timesformer-base-finetuned-k600",
     "timesformer-large-finetuned-k600",
     "timesformer-hr-finetuned-k600",
     # Something-Something-v2 checkpoints (hr = high resolution input of 448px instead of 224px)
     "timesformer-base-finetuned-ssv2",
     "timesformer-large-finetuned-ssv2",
     "timesformer-hr-finetuned-ssv2",
 ]

 # NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
 if model_name == "timesformer-base-finetuned-k400":
  a							=		torch.Size([1, 4_00]       )
  a							=		torch.tensor([-0.3016, -0.7713, -0.4205]       )
 elif model_name == "timesformer-base-finetuned-k600":
  a							=		torch.Size([1, 6_00]       )
  a							=		torch.tensor([-0.7267, -0.7466, 3.2404]       )
 elif model_name == "timesformer-base-finetuned-ssv2":
  a							=		torch.Size([1, 1_74]       )
  a							=		torch.tensor([-0.9059, 0.6433, -3.1457]       )
 elif model_name == "timesformer-large-finetuned-k400":
  a							=		torch.Size([1, 4_00]       )
  a							=		torch.tensor([0, 0, 0]       )
 elif model_name == "timesformer-large-finetuned-k600":
  a							=		torch.Size([1, 6_00]       )
  a							=		torch.tensor([0, 0, 0]       )
 elif model_name == "timesformer-large-finetuned-ssv2":
  a							=		torch.Size([1, 1_74]       )
  a							=		torch.tensor([0, 0, 0]       )
 elif model_name == "timesformer-hr-finetuned-k400":
  a							=		torch.Size([1, 4_00]       )
  a							=		torch.tensor([-0.9617, -3.7311, -3.7708]       )
 elif model_name == "timesformer-hr-finetuned-k600":
  a							=		torch.Size([1, 6_00]       )
  a							=		torch.tensor([2.5273, 0.7127, 1.8848]       )
 elif model_name == "timesformer-hr-finetuned-ssv2":
  a							=		torch.Size([1, 1_74]       )
  a							=		torch.tensor([-3.6756, -0.7513, 0.7180]       )
 else:
  raise ValueError(F"""Model name not supported. Should be one of {model_names}"""       )

 # verify logits
 assert logits.shape == expected_shape
 assert torch.allclose(logits[0, :3]			,       UpperCAmelCase__			,       atol=1E-4       )
 print("Logits ok!"       )

 if pytorch_dump_folder_path is not None:
  print(F"""Saving model and image processor to {pytorch_dump_folder_path}"""       )
  image_processor.save_pretrained(UpperCAmelCase__       )
  model.save_pretrained(UpperCAmelCase__       )

 if push_to_hub:
  print("Pushing to the hub..."       )
  model.push_to_hub(F"""fcakyon/{model_name}"""       )


if __name__ == "__main__":
     A_		:  List[Any]										=  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--checkpoint_url''',
         default='''https://drive.google.com/u/1/uc?id=17yvuYp9L4mn-HpIcK5Zo6K3UoOy1kA5l&export=download''',
         type=str,
         help=(
             '''URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct'''
             ''' download link.'''
         ),
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''',
         default='''''',
         type=str,
         help='''Path to the output PyTorch model directory.''',
     )
     parser.add_argument('''--model_name''', default='''timesformer-base-finetuned-k400''', type=str, help='''Name of the model.''')
     parser.add_argument(
         '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
     )

     A_		:  Any										=  parser.parse_args()
     convert_timesformer_checkpoint(
         args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
     )






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_mbartaa import MBartaaTokenizer
else:
     A_		:  Any										=  None


A_		:  Tuple										=  logging.get_logger(__name__)

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

A_		:  List[Any]										=  {
    '''vocab_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'''
        ),
    },
}

A_		:  Optional[Any]										=  {
    '''facebook/mbart-large-50-one-to-many-mmt''': 10_24,
}

# fmt: off
A_		:  List[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''', '''af_ZA''', '''az_AZ''', '''bn_IN''', '''fa_IR''', '''he_IL''', '''hr_HR''', '''id_ID''', '''ka_GE''', '''km_KH''', '''mk_MK''', '''ml_IN''', '''mn_MN''', '''mr_IN''', '''pl_PL''', '''ps_AF''', '''pt_XX''', '''sv_SE''', '''sw_KE''', '''ta_IN''', '''te_IN''', '''th_TH''', '''tl_XX''', '''uk_UA''', '''ur_PK''', '''xh_ZA''', '''gl_ES''', '''sl_SI''']



class 		_lowercase		(							UpperCAmelCase__						):
   _UpperCAmelCase  						=      VOCAB_FILES_NAMES
   _UpperCAmelCase  						=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCAmelCase  						=      PRETRAINED_VOCAB_FILES_MAP
   _UpperCAmelCase  						=      ['''input_ids''', '''attention_mask''']
   _UpperCAmelCase  						=      MBartaaTokenizer

   _UpperCAmelCase  						=      []
   _UpperCAmelCase  						=      []

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


    """simple docstring"""






    a							=		AddedToken(__lowerCAmelCase				, lstrip=__lowerCAmelCase				, rstrip=__lowerCAmelCase				) if isinstance(__lowerCAmelCase				, __lowerCAmelCase				) else mask_token

    a							=		kwargs.get("additional_special_tokens"				, []				)
    kwargs["additional_special_tokens"] += [
        code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
    ]

    super().__init__(
        __lowerCAmelCase				, src_lang=__lowerCAmelCase				, tgt_lang=__lowerCAmelCase				, tokenizer_file=__lowerCAmelCase				, eos_token=__lowerCAmelCase				, sep_token=__lowerCAmelCase				, cls_token=__lowerCAmelCase				, unk_token=__lowerCAmelCase				, pad_token=__lowerCAmelCase				, mask_token=__lowerCAmelCase				, **__lowerCAmelCase				, )

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

    a							=		{
        lang_code: self.convert_tokens_to_ids(__lowerCAmelCase				) for lang_code in FAIRSEQ_LANGUAGE_CODES
    }

    a							=		src_lang if src_lang is not None else "en_XX"
    a							=		tgt_lang
    a							=		self.lang_code_to_id[self._src_lang]
    self.set_src_lang_special_tokens(self._src_lang				)

   @property
   def 		A				(   self      :							Dict				)							->					str:


    """simple docstring"""






    return self._src_lang

   @src_lang.setter
   def 		A				(   self      :							Dict				, __lowerCAmelCase      :							str				)							->					None:


    """simple docstring"""






    a							=		new_src_lang
    self.set_src_lang_special_tokens(self._src_lang				)

   def 		A				(   self      :							Dict				, __lowerCAmelCase      :							List[int]				, __lowerCAmelCase      :							Optional[List[int]] = None				)							->					List[int]:


    """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 		A				(   self      :							Union[str, Any]				, __lowerCAmelCase      :							List[str]				, __lowerCAmelCase      :							str = "en_XX"				, __lowerCAmelCase      :							Optional[List[str]] = None				, __lowerCAmelCase      :							str = "ro_RO"				, **__lowerCAmelCase      :							Dict				, )							->					BatchEncoding:


    """simple docstring"""






    a							=		src_lang
    a							=		tgt_lang
    return super().prepare_seqaseq_batch(__lowerCAmelCase				, __lowerCAmelCase				, **__lowerCAmelCase				)

   def 		A				(   self      :							List[str]				)							->					List[str]:


    """simple docstring"""






    return self.set_src_lang_special_tokens(self.src_lang				)

   def 		A				(   self      :							str				)							->					int:


    """simple docstring"""






    return self.set_tgt_lang_special_tokens(self.tgt_lang				)

   def 		A				(   self      :							Union[str, Any]				, __lowerCAmelCase      :							str				)							->					None:


    """simple docstring"""






    a							=		self.convert_tokens_to_ids(__lowerCAmelCase				)
    a							=		[self.cur_lang_code_id]
    a							=		[self.eos_token_id]

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

    a							=		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      :							List[str]				, __lowerCAmelCase      :							str				)							->					None:


    """simple docstring"""






    a							=		self.convert_tokens_to_ids(__lowerCAmelCase				)
    a							=		[self.cur_lang_code_id]
    a							=		[self.eos_token_id]

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

    a							=		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      :							Union[str, Any]				, __lowerCAmelCase      :							Dict				, __lowerCAmelCase      :							str				, __lowerCAmelCase      :							Optional[str]				, __lowerCAmelCase      :							Optional[str]				, **__lowerCAmelCase      :							Any				)							->					Union[str, Any]:


    """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"				)
    a							=		src_lang
    a							=		self(__lowerCAmelCase				, add_special_tokens=__lowerCAmelCase				, return_tensors=__lowerCAmelCase				, **__lowerCAmelCase				)
    a							=		self.convert_tokens_to_ids(__lowerCAmelCase				)
    a							=		tgt_lang_id
    return inputs




   def 		A				(   self      :							Union[str, Any]				, __lowerCAmelCase      :							str				, __lowerCAmelCase      :							Optional[str] = None				)							->					Tuple[str]:


    """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(__lowerCAmelCase				):
     logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
     return
    a							=		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				)

    return (out_vocab_file,)

