
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 		_lowerCamelCase       (   _a  ):
  """simple docstring"""




  _lowerCamelCase      =						'''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=2_0_4_8  ):
        f.write(_a  )


def 		_lowerCamelCase       (   ):
  """simple docstring"""




  _lowerCamelCase      =						VivitConfig()

  _lowerCamelCase      =						4_0_0
  _lowerCamelCase      =						'''huggingface/label-files'''
  _lowerCamelCase      =						'''kinetics400-id2label.json'''

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


def 		_lowerCamelCase       (   ):
  """simple docstring"""




  _lowerCamelCase      =						hf_hub_download(
      repo_id='''hf-internal-testing/spaghetti-video'''			,   filename='''eating_spaghetti_32_frames.npy'''			,   repo_type='''dataset'''  )
  _lowerCamelCase      =						np.load(_a  )
  return list(_a  )


def 		_lowerCamelCase       (   _a  ):
  """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 		_lowerCamelCase       (   _a  ):
  """simple docstring"""




  return current.flatten()


def 		_lowerCamelCase       (   _a  ):
  """simple docstring"""




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


def 		_lowerCamelCase       (   _a  ):
  """simple docstring"""




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


def 		_lowerCamelCase       (   _a			,   _a  ):
  """simple docstring"""




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

  _lowerCamelCase      =						F'''encoder.layer.{i}.'''
  _lowerCamelCase      =						{
      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 		_lowerCamelCase       (   _a  ):
  """simple docstring"""




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


def 		_lowerCamelCase       (   _a			,   _a=False  ):
  """simple docstring"""




  _lowerCamelCase      =						get_n_layers(_a  )

  _lowerCamelCase      =						OrderedDict()

  _lowerCamelCase      =						state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''bias''']
  _lowerCamelCase      =						state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''scale''']

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

  _lowerCamelCase      =						state_dict['''optimizer''']['''target''']['''cls''']
  _lowerCamelCase      =						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:
    _lowerCamelCase      =						{'''vivit.''' + k: v for k, v in new_state.items()}
    _lowerCamelCase      =						np.transpose(state_dict['''optimizer''']['''target''']['''output_projection''']['''kernel''']  )
    _lowerCamelCase      =						np.transpose(state_dict['''optimizer''']['''target''']['''output_projection''']['''bias''']  )

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


def 		_lowerCamelCase       (   ):
  """simple docstring"""




  _lowerCamelCase      =						VivitImageProcessor()

  assert extractor.do_resize is True
  assert extractor.size == {"shortest_edge": 2_5_6}
  assert extractor.do_center_crop is True
  assert extractor.crop_size == {"width": 2_2_4, "height": 2_2_4}
  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 / 2_5_5

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

  return extractor


def 		_lowerCamelCase       (   _a  ):
  """simple docstring"""




  _lowerCamelCase      =						'''checkpoint'''

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

  _lowerCamelCase      =						restore_checkpoint(_a			,   _a  )
  _lowerCamelCase      =						transform_state(_a			,   classification_head=_a  )

  _lowerCamelCase      =						get_vivit_config()

  assert config.image_size == 2_2_4
  assert config.num_frames == 3_2

  _lowerCamelCase      =						VivitForVideoClassification(_a  )
  model.load_state_dict(_a  )
  model.eval()

  _lowerCamelCase      =						get_processor()

  _lowerCamelCase      =						prepare_video()
  _lowerCamelCase      =						extractor(_a			,   return_tensors='''pt'''  )

  _lowerCamelCase      =						model(**_a  )

  _lowerCamelCase      =						torch.Size([1, 4_0_0]  )
  _lowerCamelCase      =						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__":
  _UpperCAmelCase    			=					argparse.ArgumentParser()

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

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






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class 		__magic_name__				( lowercase_					):


  """simple docstring"""





  _UpperCamelCase						 = ["image_processor", "tokenizer"]
  _UpperCamelCase						 = "AutoImageProcessor"
  _UpperCamelCase						 = "AutoTokenizer"


  def __init__(					self	,		a__=None	,		a__=None	,		**a__		):
    _lowerCamelCase      =						None
    if "feature_extractor" in kwargs:
      warnings.warn(
          '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
          ''' instead.'''	,		a__	,		)
      _lowerCamelCase      =						kwargs.pop('''feature_extractor'''		)

    _lowerCamelCase      =						image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
      raise ValueError('''You need to specify an `image_processor`.'''		)
    if tokenizer is None:
      raise ValueError('''You need to specify a `tokenizer`.'''		)

    super().__init__(a__	,		a__		)
    _lowerCamelCase      =						self.image_processor
    _lowerCamelCase      =						False


  def __call__(					self	,		*a__	,		**a__		):
    # For backward compatibility
    if self._in_target_context_manager:
      return self.current_processor(*a__	,		**a__		)

    _lowerCamelCase      =						kwargs.pop('''images'''	,		a__		)
    _lowerCamelCase      =						kwargs.pop('''text'''	,		a__		)
    if len(a__		) > 0:
      _lowerCamelCase      =						args[0]
      _lowerCamelCase      =						args[1:]

    if images is None and text is None:
      raise ValueError('''You need to specify either an `images` or `text` input to process.'''		)

    if images is not None:
      _lowerCamelCase      =						self.image_processor(a__	,		*a__	,		**a__		)
    if text is not None:
      _lowerCamelCase      =						self.tokenizer(a__	,		**a__		)

    if text is None:
      return inputs
    elif images is None:
      return encodings
    else:
      _lowerCamelCase      =						encodings['''input_ids''']
      return inputs


  def     _UpperCAmelCase  (					self	,		*a__	,		**a__		):
    return self.tokenizer.batch_decode(*a__	,		**a__		)


  def     _UpperCAmelCase  (					self	,		*a__	,		**a__		):
    return self.tokenizer.decode(*a__	,		**a__		)


  @contextmanager
  def     _UpperCAmelCase  (					self		):
    warnings.warn(
        '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
        '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
        '''your images inputs, or in a separate call.'''		)
    _lowerCamelCase      =						True
    _lowerCamelCase      =						self.tokenizer
    yield
    _lowerCamelCase      =						self.image_processor
    _lowerCamelCase      =						False


  @property
  def     _UpperCAmelCase  (					self		):
    warnings.warn(
        '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''	,		a__	,		)
    return self.image_processor_class


  @property
  def     _UpperCAmelCase  (					self		):
    warnings.warn(
        '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''	,		a__	,		)
    return self.image_processor




