





"""simple docstring"""
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 			__snake_case						(      __A    )		->				Optional[int]:
   lowercase    :			int  =       TimesformerConfig()

   if "large" in model_name:
      lowercase    :			str  =       96

   if "hr" in model_name:
      lowercase    :			Tuple  =       16
      lowercase    :			Any  =       448

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

   return config




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

   return name




def 			__snake_case						(      __A ,__A    )		->				List[str]:
   for key in orig_state_dict.copy().keys():
      lowercase    :			str  =       orig_state_dict.pop(__A    )

      if key.startswith("""model."""    ):
         lowercase    :			Union[str, Any]  =       key.replace("""model.""" ,""""""    )

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

   return orig_state_dict




def 			__snake_case						(      )		->				Dict:
   lowercase    :			str  =       hf_hub_download(
       repo_id="""hf-internal-testing/spaghetti-video""" ,filename="""eating_spaghetti.npy""" ,repo_type="""dataset"""    )
   lowercase    :			List[Any]  =       np.load(__A    )
   return list(__A    )




def 			__snake_case						(      __A ,__A ,__A ,__A    )		->				Union[str, Any]:
   lowercase    :			Union[str, Any]  =       get_timesformer_config(__A    )

   lowercase    :			Any  =       TimesformerForVideoClassification(__A    )

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

   model.load_state_dict(__A    )
   model.eval()

   # verify model on basic input
   lowercase    :			Union[str, Any]  =       VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] ,image_std=[0.5, 0.5, 0.5]    )
   lowercase    :			Dict  =       prepare_video()
   lowercase    :			Optional[int]  =       image_processor(video[:8] ,return_tensors="""pt"""    )

   lowercase    :			Dict  =       model(**__A    )
   lowercase    :			int  =       outputs.logits

   lowercase    :			List[str]  =       [
       # 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":
      lowercase    :			Optional[int]  =       torch.Size([1, 400]    )
      lowercase    :			Dict  =       torch.tensor([-0.30_16, -0.77_13, -0.42_05]    )
   elif model_name == "timesformer-base-finetuned-k600":
      lowercase    :			str  =       torch.Size([1, 600]    )
      lowercase    :			List[Any]  =       torch.tensor([-0.72_67, -0.74_66, 3.24_04]    )
   elif model_name == "timesformer-base-finetuned-ssv2":
      lowercase    :			Tuple  =       torch.Size([1, 174]    )
      lowercase    :			Optional[Any]  =       torch.tensor([-0.90_59, 0.64_33, -3.14_57]    )
   elif model_name == "timesformer-large-finetuned-k400":
      lowercase    :			str  =       torch.Size([1, 400]    )
      lowercase    :			List[str]  =       torch.tensor([0, 0, 0]    )
   elif model_name == "timesformer-large-finetuned-k600":
      lowercase    :			List[Any]  =       torch.Size([1, 600]    )
      lowercase    :			str  =       torch.tensor([0, 0, 0]    )
   elif model_name == "timesformer-large-finetuned-ssv2":
      lowercase    :			Dict  =       torch.Size([1, 174]    )
      lowercase    :			List[Any]  =       torch.tensor([0, 0, 0]    )
   elif model_name == "timesformer-hr-finetuned-k400":
      lowercase    :			Tuple  =       torch.Size([1, 400]    )
      lowercase    :			Union[str, Any]  =       torch.tensor([-0.96_17, -3.73_11, -3.77_08]    )
   elif model_name == "timesformer-hr-finetuned-k600":
      lowercase    :			Union[str, Any]  =       torch.Size([1, 600]    )
      lowercase    :			Any  =       torch.tensor([2.52_73, 0.71_27, 1.88_48]    )
   elif model_name == "timesformer-hr-finetuned-ssv2":
      lowercase    :			int  =       torch.Size([1, 174]    )
      lowercase    :			Optional[Any]  =       torch.tensor([-3.67_56, -0.75_13, 0.71_80]    )
   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__":
   lowerCAmelCase:	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."
   )

   lowerCAmelCase:	int							      =parser.parse_args()
   convert_timesformer_checkpoint(
       args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
   )







"""simple docstring"""
from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy
class lowerCamelCase__  (    __UpperCamelCase				):
   __UpperCAmelCase        =					"""MarkupLMFeatureExtractor"""
   __UpperCAmelCase        =					("""MarkupLMTokenizer""", """MarkupLMTokenizerFast""")
   __UpperCAmelCase        =					True



   def __call__(       self      ,   snake_case=None      ,   snake_case=None      ,   snake_case=None      ,   snake_case=None      ,   snake_case=None      ,   snake_case = True      ,   snake_case = False      ,   snake_case = None      ,   snake_case = None      ,   snake_case = 0      ,   snake_case = None      ,   snake_case = None      ,   snake_case = None      ,   snake_case = False      ,   snake_case = False      ,   snake_case = False      ,   snake_case = False      ,   snake_case = True      ,   snake_case = None      ,   **snake_case      ,   ) ->    BatchEncoding:







      """simple docstring"""
      # first, create nodes and xpaths
      if self.parse_html:
         if html_strings is None:
            raise ValueError("""Make sure to pass HTML strings in case `parse_html` is set to `True`"""	)

         if nodes is not None or xpaths is not None or node_labels is not None:
            raise ValueError(
                """Please don't pass nodes, xpaths nor node labels in case `parse_html` is set to `True`"""	)

         lowercase    :			List[Any]  =       self.feature_extractor(snake_case	)
         lowercase    :			Union[str, Any]  =       features["""nodes"""]
         lowercase    :			List[Any]  =       features["""xpaths"""]
      else:
         if html_strings is not None:
            raise ValueError("""You have passed HTML strings but `parse_html` is set to `False`."""	)
         if nodes is None or xpaths is None:
            raise ValueError("""Make sure to pass nodes and xpaths in case `parse_html` is set to `False`"""	)

        # # second, apply the tokenizer
      if questions is not None and self.parse_html:
         if isinstance(snake_case      ,   snake_case	):
            lowercase    :			Dict  =       [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

      lowercase    :			Dict  =       self.tokenizer(
          text=questions if questions is not None else nodes      ,   text_pair=nodes if questions is not None else None      ,   xpaths=snake_case      ,   node_labels=snake_case      ,   add_special_tokens=snake_case      ,   padding=snake_case      ,   truncation=snake_case      ,   max_length=snake_case      ,   stride=snake_case      ,   pad_to_multiple_of=snake_case      ,   return_token_type_ids=snake_case      ,   return_attention_mask=snake_case      ,   return_overflowing_tokens=snake_case      ,   return_special_tokens_mask=snake_case      ,   return_offsets_mapping=snake_case      ,   return_length=snake_case      ,   verbose=snake_case      ,   return_tensors=snake_case      ,   **snake_case      ,   )

      return encoded_inputs



   def    _UpperCAmelCase	(       self      ,   *snake_case      ,   **snake_case	) ->    Optional[Any]:







      """simple docstring"""
      return self.tokenizer.batch_decode(*snake_case      ,   **snake_case	)



   def    _UpperCAmelCase	(       self      ,   *snake_case      ,   **snake_case	) ->    Tuple:







      """simple docstring"""
      return self.tokenizer.decode(*snake_case      ,   **snake_case	)





   @property
   def    _UpperCAmelCase	(       self	) ->    Dict:







      """simple docstring"""
      lowercase    :			Any  =       self.tokenizer.model_input_names
      return tokenizer_input_names


