


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 			__lowercase	(						_UpperCamelCase     )    ->str:







      """simple docstring"""

      lowercase			:   Optional[int]					   =		'''https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint'''

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




def 			__lowercase	(						)    ->VivitConfig:







      """simple docstring"""

      lowercase			:   Tuple					   =		VivitConfig()

      lowercase			:   Optional[int]					   =		400
      lowercase			:   Union[str, Any]					   =		'''huggingface/label-files'''
      lowercase			:   int					   =		'''kinetics400-id2label.json'''

      lowercase			:   str					   =		json.load(open(hf_hub_download(_UpperCamelCase,    _UpperCamelCase,    repo_type='''dataset'''     ),    '''r'''     )     )
      lowercase			:   Optional[Any]					   =		{int(_UpperCamelCase     ): v for k, v in idalabel.items()}
      lowercase			:   str					   =		idalabel
      lowercase			:   int					   =		{v: k for k, v in idalabel.items()}
      return config




def 			__lowercase	(						)    ->Optional[int]:







      """simple docstring"""

      lowercase			:   Optional[int]					   =		hf_hub_download(
          repo_id='''hf-internal-testing/spaghetti-video''',    filename='''eating_spaghetti_32_frames.npy''',    repo_type='''dataset'''     )
      lowercase			:   Dict					   =		np.load(_UpperCamelCase     )
      return list(_UpperCamelCase     )




def 			__lowercase	(						_UpperCamelCase     )    ->List[str]:







      """simple docstring"""

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

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

      else:
            raise Exception(f"""Invalid number of dimesions: {np.ndim(_UpperCamelCase     )}"""     )




def 			__lowercase	(						_UpperCamelCase     )    ->str:







      """simple docstring"""

      return current.flatten()




def 			__lowercase	(						_UpperCamelCase     )    ->Optional[int]:







      """simple docstring"""

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




def 			__lowercase	(						_UpperCamelCase     )    ->str:







      """simple docstring"""

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




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->Tuple:







      """simple docstring"""

      lowercase			:   int					   =		state_dict['''optimizer''']['''target''']['''Transformer'''][f"""encoderblock_{i}"""]

      lowercase			:   Any					   =		f"""encoder.layer.{i}."""
      lowercase			:   Dict					   =		{
          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 			__lowercase	(						_UpperCamelCase     )    ->str:







      """simple docstring"""

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




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase=False     )    ->Dict:







      """simple docstring"""

      lowercase			:   Union[str, Any]					   =		get_n_layers(_UpperCamelCase     )

      lowercase			:   str					   =		OrderedDict()

      lowercase			:   Tuple					   =		state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''bias''']
      lowercase			:   Union[str, Any]					   =		state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''scale''']

      lowercase			:   List[str]					   =		np.transpose(
          state_dict['''optimizer''']['''target''']['''embedding''']['''kernel'''],    (4, 3, 0, 1, 2)     )
      lowercase			:   Optional[Any]					   =		state_dict['''optimizer''']['''target''']['''embedding''']['''bias''']

      lowercase			:   List[Any]					   =		state_dict['''optimizer''']['''target''']['''cls''']
      lowercase			:   Optional[Any]					   =		state_dict['''optimizer''']['''target''']['''Transformer''']['''posembed_input'''][
          '''pos_embedding'''
      ]

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

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

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




def 			__lowercase	(						)    ->VivitImageProcessor:







      """simple docstring"""

      lowercase			:   Optional[int]					   =		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 			__lowercase	(						_UpperCamelCase     )    ->Tuple:







      """simple docstring"""

      lowercase			:   str					   =		'''checkpoint'''

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

      lowercase			:   Optional[Any]					   =		restore_checkpoint(_UpperCamelCase,    _UpperCamelCase     )
      lowercase			:   Union[str, Any]					   =		transform_state(_UpperCamelCase,    classification_head=_UpperCamelCase     )

      lowercase			:   Tuple					   =		get_vivit_config()

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

      lowercase			:   Dict					   =		VivitForVideoClassification(_UpperCamelCase     )
      model.load_state_dict(_UpperCamelCase     )
      model.eval()

      lowercase			:   List[str]					   =		get_processor()

      lowercase			:   Optional[Any]					   =		prepare_video()
      lowercase			:   Tuple					   =		extractor(_UpperCamelCase,    return_tensors='''pt'''     )

      lowercase			:   Dict					   =		model(**_UpperCamelCase     )

      lowercase			:   Tuple					   =		torch.Size([1, 400]     )
      lowercase			:   Dict					   =		torch.tensor([-1.0_5_4_3, 2.0_7_6_4, -0.2_1_0_4, 0.4_4_3_9, -0.9_6_5_8]     )

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

      model.save_pretrained(_UpperCamelCase     )
      extractor.save_pretrained(_UpperCamelCase     )


if __name__ == "__main__":
  __a   				=      argparse.ArgumentParser()

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

  __a   				=      parser.parse_args()
  convert(args.output_model_name)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__a   				=      {
    '''configuration_decision_transformer''': [
        '''DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DecisionTransformerConfig''',
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   				=      [
      '''DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''DecisionTransformerGPT2Model''',
      '''DecisionTransformerGPT2PreTrainedModel''',
      '''DecisionTransformerModel''',
      '''DecisionTransformerPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_decision_transformer import (
      DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      DecisionTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_decision_transformer import (
        DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DecisionTransformerGPTaModel,
        DecisionTransformerGPTaPreTrainedModel,
        DecisionTransformerModel,
        DecisionTransformerPreTrainedModel,
    )


else:
  import sys

  __a   				=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

