


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_       ):



     """simple docstring"""






     snake_case					=       TimesformerConfig()

     if "large" in model_name:
          snake_case					=       96

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

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

     return config
def     UpperCAmelCase__		(UpperCamelCase_       ):



     """simple docstring"""






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

     return name
def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_       ):



     """simple docstring"""






     for key in orig_state_dict.copy().keys():
          snake_case					=       orig_state_dict.pop(UpperCamelCase_       )

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

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

     return orig_state_dict
def     UpperCAmelCase__		():



     """simple docstring"""






     snake_case					=       hf_hub_download(
         repo_id='''hf-internal-testing/spaghetti-video'''							,filename='''eating_spaghetti.npy'''							,repo_type='''dataset'''       )
     snake_case					=       np.load(UpperCamelCase_       )
     return list(UpperCamelCase_       )
def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):



     """simple docstring"""






     snake_case					=       get_timesformer_config(UpperCamelCase_       )

     snake_case					=       TimesformerForVideoClassification(UpperCamelCase_       )

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

     model.load_state_dict(UpperCamelCase_       )
     model.eval()

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

     snake_case					=       model(**UpperCamelCase_       )
     snake_case					=       outputs.logits

     snake_case					=       [
         # 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":
          snake_case					=       torch.Size([1, 4_00]       )
          snake_case					=       torch.tensor([-0.3016, -0.7713, -0.4205]       )
     elif model_name == "timesformer-base-finetuned-k600":
          snake_case					=       torch.Size([1, 6_00]       )
          snake_case					=       torch.tensor([-0.7267, -0.7466, 3.2404]       )
     elif model_name == "timesformer-base-finetuned-ssv2":
          snake_case					=       torch.Size([1, 1_74]       )
          snake_case					=       torch.tensor([-0.9059, 0.6433, -3.1457]       )
     elif model_name == "timesformer-large-finetuned-k400":
          snake_case					=       torch.Size([1, 4_00]       )
          snake_case					=       torch.tensor([0, 0, 0]       )
     elif model_name == "timesformer-large-finetuned-k600":
          snake_case					=       torch.Size([1, 6_00]       )
          snake_case					=       torch.tensor([0, 0, 0]       )
     elif model_name == "timesformer-large-finetuned-ssv2":
          snake_case					=       torch.Size([1, 1_74]       )
          snake_case					=       torch.tensor([0, 0, 0]       )
     elif model_name == "timesformer-hr-finetuned-k400":
          snake_case					=       torch.Size([1, 4_00]       )
          snake_case					=       torch.tensor([-0.9617, -3.7311, -3.7708]       )
     elif model_name == "timesformer-hr-finetuned-k600":
          snake_case					=       torch.Size([1, 6_00]       )
          snake_case					=       torch.tensor([2.5273, 0.7127, 1.8848]       )
     elif model_name == "timesformer-hr-finetuned-ssv2":
          snake_case					=       torch.Size([1, 1_74]       )
          snake_case					=       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__":
 _SCREAMING_SNAKE_CASE    :				Optional[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."
 )

 _SCREAMING_SNAKE_CASE    :				Optional[int]       =					parser.parse_args()
 convert_timesformer_checkpoint(
     args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
 )








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE    :				Optional[int]       =					{
    "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
    "tokenization_layoutlm": ["LayoutLMTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE    :				str       =					["LayoutLMTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE    :				List[str]       =					[
     "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
     "LayoutLMForMaskedLM",
     "LayoutLMForSequenceClassification",
     "LayoutLMForTokenClassification",
     "LayoutLMForQuestionAnswering",
     "LayoutLMModel",
     "LayoutLMPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE    :				Optional[int]       =					[
     "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFLayoutLMForMaskedLM",
     "TFLayoutLMForSequenceClassification",
     "TFLayoutLMForTokenClassification",
     "TFLayoutLMForQuestionAnswering",
     "TFLayoutLMMainLayer",
     "TFLayoutLMModel",
     "TFLayoutLMPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
 from .tokenization_layoutlm import LayoutLMTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_layoutlm import (
      LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      LayoutLMForMaskedLM,
      LayoutLMForQuestionAnswering,
      LayoutLMForSequenceClassification,
      LayoutLMForTokenClassification,
      LayoutLMModel,
      LayoutLMPreTrainedModel,
  )
 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_layoutlm import (
      TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFLayoutLMForMaskedLM,
      TFLayoutLMForQuestionAnswering,
      TFLayoutLMForSequenceClassification,
      TFLayoutLMForTokenClassification,
      TFLayoutLMMainLayer,
      TFLayoutLMModel,
      TFLayoutLMPreTrainedModel,
  )

else:
 import sys

 _SCREAMING_SNAKE_CASE    :				Optional[int]       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




