



"""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_mbartaa import MBartaaTokenizer
else:
							A     =      None


A     =      logging.get_logger(__name__)

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

A     =      {
    '''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     =      {
    '''facebook/mbart-large-50-one-to-many-mmt''': 1_0_2_4,
}

# 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''', '''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   UpperCAmelCase__		(   a__  ):
		lowerCAmelCase_							:						Optional[int]							=  VOCAB_FILES_NAMES
		lowerCAmelCase_							:						List[Any]							=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCAmelCase_							:						List[str]							=  PRETRAINED_VOCAB_FILES_MAP
		lowerCAmelCase_							:						Union[str, Any]							=  ['input_ids', 'attention_mask']
		lowerCAmelCase_							:						int							=  MBartaaTokenizer

		lowerCAmelCase_							:						List[int]							=  []
		lowerCAmelCase_							:						List[int]							=  []



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






									'''simple docstring'''

									return self._src_lang



		@src_lang.setter
		def        A_		(			self	:     List[Any]   ,				snake_case	:     Optional[int]					)      ->    None:






									'''simple docstring'''

									A    =       new_src_lang
									self.set_src_lang_special_tokens(self._src_lang					)



		def        A_		(			self	:     Optional[Any]   ,				snake_case	:     Any   ,				snake_case	:     Union[str, Any] = 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	:     str   ,				snake_case	:     str   ,				snake_case	:     int = "en_XX"   ,				snake_case	:     Optional[int] = None   ,				snake_case	:     Any = "ro_RO"   ,				**snake_case	:     Union[str, Any]   ,				)      ->    BatchEncoding:






									'''simple docstring'''

									A    =       src_lang
									A    =       tgt_lang
									return super().prepare_seqaseq_batch(_lowerCamelCase   ,				_lowerCamelCase   ,				**_lowerCamelCase					)



		def        A_		(			self	:     Optional[int]					)      ->    Union[str, Any]:






									'''simple docstring'''

									return self.set_src_lang_special_tokens(self.src_lang					)



		def        A_		(			self	:     Dict					)      ->    Union[str, Any]:






									'''simple docstring'''

									return self.set_tgt_lang_special_tokens(self.tgt_lang					)



		def        A_		(			self	:     List[Any]   ,				snake_case	:     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	:     Optional[Any]   ,				snake_case	:     Tuple					)      ->    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	:     str   ,				snake_case	:     Optional[Any]   ,				snake_case	:     Union[str, Any]   ,				snake_case	:     Tuple   ,				snake_case	:     Tuple   ,				**snake_case	:     Optional[int]					)      ->    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	:     Tuple   ,				snake_case	:     List[Any]   ,				snake_case	:     List[Any] = 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,)





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_(    a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Any				      = TimesformerConfig()

    if "large" in model_name:
        SCREAMING_SNAKE_CASE				:				Dict				      = 96

    if "hr" in model_name:
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = 16
        SCREAMING_SNAKE_CASE				:				str				      = 448

    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = '''huggingface/label-files'''
    if "k400" in model_name:
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = 400
        SCREAMING_SNAKE_CASE				:				Tuple				      = '''kinetics400-id2label.json'''
    elif "k600" in model_name:
        SCREAMING_SNAKE_CASE				:				Any				      = 600
        SCREAMING_SNAKE_CASE				:				int				      = '''kinetics600-id2label.json'''
    elif "ssv2" in model_name:
        SCREAMING_SNAKE_CASE				:				Tuple				      = 174
        SCREAMING_SNAKE_CASE				:				int				      = '''something-something-v2-id2label.json'''
    else:
        raise ValueError('''Model name should either contain \'k400\', \'k600\' or \'ssv2\'.'''   )
    SCREAMING_SNAKE_CASE				:				List[str]				      = json.load(open(hf_hub_download(a__ ,						a__ ,						repo_type='''dataset'''   ) ,						'''r'''   )   )
    SCREAMING_SNAKE_CASE				:				List[Any]				      = {int(a__   ): v for k, v in idalabel.items()}
    SCREAMING_SNAKE_CASE				:				str				      = idalabel
    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = {v: k for k, v in idalabel.items()}

    return config







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



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

    return name







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    for key in orig_state_dict.copy().keys():
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = orig_state_dict.pop(a__   )

        if key.startswith('''model.'''   ):
            SCREAMING_SNAKE_CASE				:				Dict				      = key.replace('''model.''' ,						''''''   )

        if "qkv" in key:
            SCREAMING_SNAKE_CASE				:				int				      = key.split('''.'''   )
            SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = int(key_split[1]   )
            SCREAMING_SNAKE_CASE				:				int				      = '''timesformer.encoder.layer.'''
            if "temporal" in key:
                SCREAMING_SNAKE_CASE				:				Tuple				      = '''.temporal_attention.attention.qkv.'''
            else:
                SCREAMING_SNAKE_CASE				:				List[Any]				      = '''.attention.attention.qkv.'''
            if "weight" in key:
                SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = val
            else:
                SCREAMING_SNAKE_CASE				:				Optional[int]				      = val
        else:
            SCREAMING_SNAKE_CASE				:				Tuple				      = val

    return orig_state_dict







def 				UpperCAmelCase_(    ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Optional[int]				      = hf_hub_download(
        repo_id='''hf-internal-testing/spaghetti-video''' ,						filename='''eating_spaghetti.npy''' ,						repo_type='''dataset'''   )
    SCREAMING_SNAKE_CASE				:				int				      = np.load(a__   )
    return list(a__   )







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Dict				      = get_timesformer_config(a__   )

    SCREAMING_SNAKE_CASE				:				Tuple				      = TimesformerForVideoClassification(a__   )

    # download original checkpoint, hosted on Google Drive
    SCREAMING_SNAKE_CASE				:				Tuple				      = '''pytorch_model.bin'''
    gdown.cached_download(a__ ,						a__ ,						quiet=a__   )
    SCREAMING_SNAKE_CASE				:				int				      = torch.load(a__ ,						map_location='''cpu'''   )
    if "model" in files:
        SCREAMING_SNAKE_CASE				:				Tuple				      = files['''model''']
    elif "module" in files:
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = files['''module''']
    else:
        SCREAMING_SNAKE_CASE				:				Any				      = files['''model_state''']
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = convert_state_dict(a__ ,						a__   )

    model.load_state_dict(a__   )
    model.eval()

    # verify model on basic input
    SCREAMING_SNAKE_CASE				:				str				      = VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] ,						image_std=[0.5, 0.5, 0.5]   )
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = prepare_video()
    SCREAMING_SNAKE_CASE				:				str				      = image_processor(video[:8] ,						return_tensors='''pt'''   )

    SCREAMING_SNAKE_CASE				:				Any				      = model(**a__   )
    SCREAMING_SNAKE_CASE				:				List[str]				      = outputs.logits

    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = [
        # 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":
        SCREAMING_SNAKE_CASE				:				Tuple				      = torch.Size([1, 400]   )
        SCREAMING_SNAKE_CASE				:				int				      = torch.tensor([-0.3_016, -0.7_713, -0.4_205]   )
    elif model_name == "timesformer-base-finetuned-k600":
        SCREAMING_SNAKE_CASE				:				Tuple				      = torch.Size([1, 600]   )
        SCREAMING_SNAKE_CASE				:				str				      = torch.tensor([-0.7_267, -0.7_466, 3.2_404]   )
    elif model_name == "timesformer-base-finetuned-ssv2":
        SCREAMING_SNAKE_CASE				:				Dict				      = torch.Size([1, 174]   )
        SCREAMING_SNAKE_CASE				:				int				      = torch.tensor([-0.9_059, 0.6_433, -3.1_457]   )
    elif model_name == "timesformer-large-finetuned-k400":
        SCREAMING_SNAKE_CASE				:				Any				      = torch.Size([1, 400]   )
        SCREAMING_SNAKE_CASE				:				int				      = torch.tensor([0, 0, 0]   )
    elif model_name == "timesformer-large-finetuned-k600":
        SCREAMING_SNAKE_CASE				:				str				      = torch.Size([1, 600]   )
        SCREAMING_SNAKE_CASE				:				str				      = torch.tensor([0, 0, 0]   )
    elif model_name == "timesformer-large-finetuned-ssv2":
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = torch.Size([1, 174]   )
        SCREAMING_SNAKE_CASE				:				str				      = torch.tensor([0, 0, 0]   )
    elif model_name == "timesformer-hr-finetuned-k400":
        SCREAMING_SNAKE_CASE				:				List[str]				      = torch.Size([1, 400]   )
        SCREAMING_SNAKE_CASE				:				int				      = torch.tensor([-0.9_617, -3.7_311, -3.7_708]   )
    elif model_name == "timesformer-hr-finetuned-k600":
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = torch.Size([1, 600]   )
        SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = torch.tensor([2.5_273, 0.7_127, 1.8_848]   )
    elif model_name == "timesformer-hr-finetuned-ssv2":
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = torch.Size([1, 174]   )
        SCREAMING_SNAKE_CASE				:				str				      = torch.tensor([-3.6_756, -0.7_513, 0.7_180]   )
    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] ,						a__ ,						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(a__   )
        model.save_pretrained(a__   )

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


if __name__ == "__main__":
      a__				:      Optional[int]    		=     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__				:      str    		=     parser.parse_args()
      convert_timesformer_checkpoint(
          args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
      )
