
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     lowerCamelCase__				(   a ) ->  Optional[Any]:
   _A:    List[str]						     =			TimesformerConfig()

   if "large" in model_name:
      _A:    Tuple						     =			96

   if "hr" in model_name:
      _A:    Any						     =			16
      _A:    Union[str, Any]						     =			4_48

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

   return config






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

   return name






def     lowerCamelCase__				(   a						,						a ) ->  Tuple:
   for key in orig_state_dict.copy().keys():
      _A:    List[Any]						     =			orig_state_dict.pop(a )

      if key.startswith('''model.''' ):
         _A:    List[str]						     =			key.replace('''model.'''						,						'''''' )

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

   return orig_state_dict






def     lowerCamelCase__				(   ) ->  Tuple:
   _A:    Union[str, Any]						     =			hf_hub_download(
       repo_id='''hf-internal-testing/spaghetti-video'''						,						filename='''eating_spaghetti.npy'''						,						repo_type='''dataset''' )
   _A:    Tuple						     =			np.load(a )
   return list(a )






def     lowerCamelCase__				(   a						,						a						,						a						,						a ) ->  Tuple:
   _A:    Optional[int]						     =			get_timesformer_config(a )

   _A:    List[str]						     =			TimesformerForVideoClassification(a )

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

   model.load_state_dict(a )
   model.eval()

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

   _A:    Tuple						     =			model(**a )
   _A:    Tuple						     =			outputs.logits

   _A:    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":
      _A:    List[str]						     =			torch.Size([1, 4_00] )
      _A:    int						     =			torch.tensor([-0.3016, -0.7713, -0.4205] )
   elif model_name == "timesformer-base-finetuned-k600":
      _A:    Dict						     =			torch.Size([1, 6_00] )
      _A:    Tuple						     =			torch.tensor([-0.7267, -0.7466, 3.2404] )
   elif model_name == "timesformer-base-finetuned-ssv2":
      _A:    Tuple						     =			torch.Size([1, 1_74] )
      _A:    Any						     =			torch.tensor([-0.9059, 0.6433, -3.1457] )
   elif model_name == "timesformer-large-finetuned-k400":
      _A:    Optional[int]						     =			torch.Size([1, 4_00] )
      _A:    List[str]						     =			torch.tensor([0, 0, 0] )
   elif model_name == "timesformer-large-finetuned-k600":
      _A:    int						     =			torch.Size([1, 6_00] )
      _A:    Tuple						     =			torch.tensor([0, 0, 0] )
   elif model_name == "timesformer-large-finetuned-ssv2":
      _A:    List[Any]						     =			torch.Size([1, 1_74] )
      _A:    str						     =			torch.tensor([0, 0, 0] )
   elif model_name == "timesformer-hr-finetuned-k400":
      _A:    Optional[int]						     =			torch.Size([1, 4_00] )
      _A:    Optional[Any]						     =			torch.tensor([-0.9617, -3.7311, -3.7708] )
   elif model_name == "timesformer-hr-finetuned-k600":
      _A:    int						     =			torch.Size([1, 6_00] )
      _A:    str						     =			torch.tensor([2.5273, 0.7127, 1.8848] )
   elif model_name == "timesformer-hr-finetuned-ssv2":
      _A:    List[str]						     =			torch.Size([1, 1_74] )
      _A:    List[str]						     =			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]						,						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__":
   UpperCAmelCase__						:	str	     = 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.'
   )

   UpperCAmelCase__						:	Dict	     = parser.parse_args()
   convert_timesformer_checkpoint(
       args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
   )





import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation


UpperCAmelCase__						:	str	     = logging.get_logger(__name__)

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

UpperCAmelCase__						:	int	     = {
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

UpperCAmelCase__						:	List[str]	     = {
    'microsoft/deberta-base': 512,
    'microsoft/deberta-large': 512,
    'microsoft/deberta-xlarge': 512,
    'microsoft/deberta-base-mnli': 512,
    'microsoft/deberta-large-mnli': 512,
    'microsoft/deberta-xlarge-mnli': 512,
}

UpperCAmelCase__						:	Optional[Any]	     = {
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''
   __UpperCamelCase						:       Union[str, Any]												=       VOCAB_FILES_NAMES
   __UpperCamelCase						:       Union[str, Any]												=       PRETRAINED_VOCAB_FILES_MAP
   __UpperCamelCase						:       Union[str, Any]												=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCamelCase						:       str												=       ['''input_ids''', '''attention_mask''', '''token_type_ids''']
   __UpperCamelCase						:       Any												=       DebertaTokenizer


   def __init__(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				Optional[Any]=None     ,     lowerCAmelCase_	:				int=None     ,     lowerCAmelCase_	:				Dict=None     ,     lowerCAmelCase_	:				int="replace"     ,     lowerCAmelCase_	:				List[str]="[CLS]"     ,     lowerCAmelCase_	:				str="[SEP]"     ,     lowerCAmelCase_	:				int="[SEP]"     ,     lowerCAmelCase_	:				Any="[CLS]"     ,     lowerCAmelCase_	:				Dict="[UNK]"     ,     lowerCAmelCase_	:				Optional[int]="[PAD]"     ,     lowerCAmelCase_	:				List[str]="[MASK]"     ,     lowerCAmelCase_	:				Optional[Any]=False     ,     **lowerCAmelCase_	:				List[Any]     ,     ):

      """simple docstring"""
      super().__init__(
          lowerCAmelCase_     ,     lowerCAmelCase_     ,     tokenizer_file=lowerCAmelCase_     ,     errors=lowerCAmelCase_     ,     bos_token=lowerCAmelCase_     ,     eos_token=lowerCAmelCase_     ,     unk_token=lowerCAmelCase_     ,     sep_token=lowerCAmelCase_     ,     cls_token=lowerCAmelCase_     ,     pad_token=lowerCAmelCase_     ,     mask_token=lowerCAmelCase_     ,     add_prefix_space=lowerCAmelCase_     ,     **lowerCAmelCase_     ,     )
      _A:    List[str]						     =			kwargs.pop('''add_bos_token'''     ,     lowerCAmelCase_	)

      _A:    Optional[int]						     =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
      if pre_tok_state.get('''add_prefix_space'''     ,     lowerCAmelCase_	) != add_prefix_space:
         _A:    Dict						     =			getattr(lowerCAmelCase_     ,     pre_tok_state.pop('''type'''	)	)
         _A:    List[Any]						     =			add_prefix_space
         _A:    Optional[int]						     =			pre_tok_class(**lowerCAmelCase_	)

      _A:    Optional[Any]						     =			add_prefix_space


   @property
   def 					__magic_name__							(   self	:				Optional[Any]	):

      """simple docstring"""
      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 					__magic_name__							(   self	:				str     ,     lowerCAmelCase_	:				str	):

      """simple docstring"""
      _A:    Dict						     =			AddedToken(lowerCAmelCase_     ,     lstrip=lowerCAmelCase_     ,     rstrip=lowerCAmelCase_	) if isinstance(lowerCAmelCase_     ,     lowerCAmelCase_	) else value
      _A:    str						     =			value


   def 					__magic_name__							(   self	:				Optional[Any]     ,     lowerCAmelCase_	:				List[int]     ,     lowerCAmelCase_	:				Optional[List[int]] = None	):

      """simple docstring"""
      if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      _A:    str						     =			[self.cls_token_id]
      _A:    Any						     =			[self.sep_token_id]
      return cls + token_ids_a + sep + token_ids_a + sep


   def 					__magic_name__							(   self	:				Optional[int]     ,     lowerCAmelCase_	:				List[int]     ,     lowerCAmelCase_	:				Optional[List[int]] = None	):

      """simple docstring"""
      _A:    Dict						     =			[self.sep_token_id]
      _A:    Any						     =			[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	) * [0] + len(token_ids_a + sep	) * [1]


   def 					__magic_name__							(   self	:				Tuple     ,     *lowerCAmelCase_	:				Dict     ,     **lowerCAmelCase_	:				Tuple	):

      """simple docstring"""
      _A:    Any						     =			kwargs.get('''is_split_into_words'''     ,     lowerCAmelCase_	)
      assert self.add_prefix_space or not is_split_into_words, (
          F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
          "to use it with pretokenized inputs."
      )

      return super()._batch_encode_plus(*lowerCAmelCase_     ,     **lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				List[Any]     ,     *lowerCAmelCase_	:				Optional[Any]     ,     **lowerCAmelCase_	:				Any	):

      """simple docstring"""
      _A:    Optional[Any]						     =			kwargs.get('''is_split_into_words'''     ,     lowerCAmelCase_	)

      assert self.add_prefix_space or not is_split_into_words, (
          F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
          "to use it with pretokenized inputs."
      )

      return super()._encode_plus(*lowerCAmelCase_     ,     **lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				Optional[int]     ,     lowerCAmelCase_	:				str     ,     lowerCAmelCase_	:				Optional[str] = None	):

      """simple docstring"""
      _A:    Union[str, Any]						     =			self._tokenizer.model.save(lowerCAmelCase_     ,     name=lowerCAmelCase_	)
      return tuple(lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				"Conversation"	):

      """simple docstring"""
      _A:    List[Any]						     =			[]
      for is_user, text in conversation.iter_texts():
         input_ids.extend(self.encode(lowerCAmelCase_     ,     add_special_tokens=lowerCAmelCase_	) + [self.eos_token_id]	)

      if len(lowerCAmelCase_	) > self.model_max_length:
         _A:    Union[str, Any]						     =			input_ids[-self.model_max_length :]
      return input_ids



