


'''simple docstring'''







import argparse
import json
import os.path
from collections import OrderedDict

import numpy as np
import requests
import torch
from flax.training.checkpoints import restore_checkpoint
from huggingface_hub import hf_hub_download

from transformers import VivitConfig, VivitForVideoClassification, VivitImageProcessor
from transformers.image_utils import PILImageResampling







def _UpperCamelCase			(	__A       )				->						Any:


      '''simple docstring'''



      UpperCamelCase__      =							"https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint"

      with open(__A ,     "wb"       ) as f:
            with requests.get(__A ,     stream=__A       ) as req:
                  for chunk in req.iter_content(chunk_size=2048       ):
                        f.write(__A       )







def _UpperCamelCase			(	)				->						VivitConfig:


      '''simple docstring'''



      UpperCamelCase__      =							VivitConfig()

      UpperCamelCase__      =							400
      UpperCamelCase__      =							"huggingface/label-files"
      UpperCamelCase__      =							"kinetics400-id2label.json"

      UpperCamelCase__      =							json.load(open(hf_hub_download(__A ,     __A ,     repo_type="dataset"       ) ,     "r"       )       )
      UpperCamelCase__      =							{int(__A       ): v for k, v in idalabel.items()}
      UpperCamelCase__      =							idalabel
      UpperCamelCase__      =							{v: k for k, v in idalabel.items()}
      return config







def _UpperCamelCase			(	)				->						int:


      '''simple docstring'''



      UpperCamelCase__      =							hf_hub_download(
          repo_id="hf-internal-testing/spaghetti-video" ,     filename="eating_spaghetti_32_frames.npy" ,     repo_type="dataset"       )
      UpperCamelCase__      =							np.load(__A       )
      return list(__A       )







def _UpperCamelCase			(	__A       )				->						int:


      '''simple docstring'''



      if np.ndim(__A       ) == 2:
            return transform_attention_bias(__A       )

      elif np.ndim(__A       ) == 3:
            return transform_attention_kernel(__A       )

      else:
            raise Exception(F'''Invalid number of dimesions: {np.ndim(__A       )}'''       )







def _UpperCamelCase			(	__A       )				->						Dict:


      '''simple docstring'''



      return current.flatten()







def _UpperCamelCase			(	__A       )				->						List[Any]:


      '''simple docstring'''



      return np.reshape(__A ,     (current.shape[0], current.shape[1] * current.shape[2])       ).T







def _UpperCamelCase			(	__A       )				->						Tuple:


      '''simple docstring'''



      return np.reshape(__A ,     (current.shape[0] * current.shape[1], current.shape[2])       ).T







def _UpperCamelCase			(	__A ,     __A       )				->						Union[str, Any]:


      '''simple docstring'''



      UpperCamelCase__      =							state_dict["optimizer"]["target"]["Transformer"][F'''encoderblock_{i}''']

      UpperCamelCase__      =							F'''encoder.layer.{i}.'''
      UpperCamelCase__      =							{
          prefix + "intermediate.dense.bias": state["MlpBlock_0"]["Dense_0"]["bias"],
          prefix + "intermediate.dense.weight": np.transpose(state["MlpBlock_0"]["Dense_0"]["kernel"]       ),
          prefix + "output.dense.bias": state["MlpBlock_0"]["Dense_1"]["bias"],
          prefix + "output.dense.weight": np.transpose(state["MlpBlock_0"]["Dense_1"]["kernel"]       ),
          prefix + "layernorm_before.bias": state["LayerNorm_0"]["bias"],
          prefix + "layernorm_before.weight": state["LayerNorm_0"]["scale"],
          prefix + "layernorm_after.bias": state["LayerNorm_1"]["bias"],
          prefix + "layernorm_after.weight": state["LayerNorm_1"]["scale"],
          prefix
          + "attention.attention.query.bias": transform_attention(
              state["MultiHeadDotProductAttention_0"]["query"]["bias"]       ),
          prefix
          + "attention.attention.query.weight": transform_attention(
              state["MultiHeadDotProductAttention_0"]["query"]["kernel"]       ),
          prefix
          + "attention.attention.key.bias": transform_attention(state["MultiHeadDotProductAttention_0"]["key"]["bias"]       ),
          prefix
          + "attention.attention.key.weight": transform_attention(
              state["MultiHeadDotProductAttention_0"]["key"]["kernel"]       ),
          prefix
          + "attention.attention.value.bias": transform_attention(
              state["MultiHeadDotProductAttention_0"]["value"]["bias"]       ),
          prefix
          + "attention.attention.value.weight": transform_attention(
              state["MultiHeadDotProductAttention_0"]["value"]["kernel"]       ),
          prefix + "attention.output.dense.bias": state["MultiHeadDotProductAttention_0"]["out"]["bias"],
          prefix
          + "attention.output.dense.weight": transform_attention_output_weight(
              state["MultiHeadDotProductAttention_0"]["out"]["kernel"]       ),
      }

      return new_state







def _UpperCamelCase			(	__A       )				->						Any:


      '''simple docstring'''



      return sum([1 if "encoderblock_" in k else 0 for k in state_dict["optimizer"]["target"]["Transformer"].keys()]       )







def _UpperCamelCase			(	__A ,     __A=False       )				->						Any:


      '''simple docstring'''



      UpperCamelCase__      =							get_n_layers(__A       )

      UpperCamelCase__      =							OrderedDict()

      UpperCamelCase__      =							state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["bias"]
      UpperCamelCase__      =							state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["scale"]

      UpperCamelCase__      =							np.transpose(
          state_dict["optimizer"]["target"]["embedding"]["kernel"] ,     (4, 3, 0, 1, 2)       )
      UpperCamelCase__      =							state_dict["optimizer"]["target"]["embedding"]["bias"]

      UpperCamelCase__      =							state_dict["optimizer"]["target"]["cls"]
      UpperCamelCase__      =							state_dict["optimizer"]["target"]["Transformer"]["posembed_input"][
          "pos_embedding"
      ]

      for i in range(__A       ):
            new_state.update(transform_state_encoder_block(__A ,     __A       )       )

      if classification_head:
            UpperCamelCase__      =							{"vivit." + k: v for k, v in new_state.items()}
            UpperCamelCase__      =							np.transpose(state_dict["optimizer"]["target"]["output_projection"]["kernel"]       )
            UpperCamelCase__      =							np.transpose(state_dict["optimizer"]["target"]["output_projection"]["bias"]       )

      return {k: torch.tensor(__A       ) for k, v in new_state.items()}







def _UpperCamelCase			(	)				->						VivitImageProcessor:


      '''simple docstring'''



      UpperCamelCase__      =							VivitImageProcessor()

      assert extractor.do_resize is True
      assert extractor.size == {"shortest_edge": 256}
      assert extractor.do_center_crop is True
      assert extractor.crop_size == {"width": 224, "height": 224}
      assert extractor.resample == PILImageResampling.BILINEAR

      # here: https://github.com/deepmind/dmvr/blob/master/dmvr/modalities.py
      # one can seen that add_image has default values for normalization_mean and normalization_std set to 0 and 1
      # which effectively means no normalization (and ViViT does not overwrite those when calling this func)
      assert extractor.do_normalize is False
      assert extractor.do_rescale is True
      assert extractor.rescale_factor == 1 / 255

      # zero-centering = True in original implementation
      assert extractor.do_zero_centering is True

      return extractor







def _UpperCamelCase			(	__A       )				->						Dict:


      '''simple docstring'''



      UpperCamelCase__      =							"checkpoint"

      if not os.path.exists(__A       ):
            download_checkpoint(__A       )

      UpperCamelCase__      =							restore_checkpoint(__A ,     __A       )
      UpperCamelCase__      =							transform_state(__A ,     classification_head=__A       )

      UpperCamelCase__      =							get_vivit_config()

      assert config.image_size == 224
      assert config.num_frames == 32

      UpperCamelCase__      =							VivitForVideoClassification(__A       )
      model.load_state_dict(__A       )
      model.eval()

      UpperCamelCase__      =							get_processor()

      UpperCamelCase__      =							prepare_video()
      UpperCamelCase__      =							extractor(__A ,     return_tensors="pt"       )

      UpperCamelCase__      =							model(**__A       )

      UpperCamelCase__      =							torch.Size([1, 400]       )
      UpperCamelCase__      =							torch.tensor([-1.0543, 2.0764, -0.2104, 0.4439, -0.9658]       )

      assert outputs.logits.shape == expected_shape
      assert torch.allclose(outputs.logits[0, :5] ,     __A ,     atol=1E-4       ), outputs.logits[0, :5]

      model.save_pretrained(__A       )
      extractor.save_pretrained(__A       )


if __name__ == "__main__":
  a__    :   Dict								=			argparse.ArgumentParser()

  parser.add_argument('--output_model_name', '-o', type=str, help='Output path for the converted HuggingFace model')

  a__    :   Tuple								=			parser.parse_args()
  convert(args.output_model_name)









'''simple docstring'''







import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						str:


      '''simple docstring'''



      UpperCamelCase__      =							RoFormerConfig.from_json_file(__A       )
      print(F'''Building PyTorch model from configuration: {config}'''       )
      UpperCamelCase__      =							RoFormerForMaskedLM(__A       )

      # Load weights from tf checkpoint
      load_tf_weights_in_roformer(__A ,     __A ,     __A       )

      # Save pytorch-model
      print(F'''Save PyTorch model to {pytorch_dump_path}'''       )
      torch.save(model.state_dict() ,     __A ,     _use_new_zipfile_serialization=__A       )


if __name__ == "__main__":
  a__    :   str								=			argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
  )
  parser.add_argument(
      '--bert_config_file',
      default=None,
      type=str,
      required=True,
      help=(
          'The config json file corresponding to the pre-trained BERT model. \n'
          'This specifies the model architecture.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  a__    :   Dict								=			parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





