




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    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		Any:

    """simple docstring"""


    __UpperCamelCase											=  '''https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint'''

    with open(lowercase_   ,       '''wb''' ) as f:
        with requests.get(lowercase_   ,       stream=lowercase_ ) as req:
            for chunk in req.iter_content(chunk_size=20_48 ):
                f.write(lowercase_ )




def    __SCREAMING_SNAKE_CASE				(					)    ->		VivitConfig:

    """simple docstring"""


    __UpperCamelCase											=  VivitConfig()

    __UpperCamelCase											=  4_00
    __UpperCamelCase											=  '''huggingface/label-files'''
    __UpperCamelCase											=  '''kinetics400-id2label.json'''

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




def    __SCREAMING_SNAKE_CASE				(					)    ->		str:

    """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(lowercase_ )
    return list(lowercase_ )




def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		Dict:

    """simple docstring"""


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

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

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




def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		str:

    """simple docstring"""


    return current.flatten()




def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		int:

    """simple docstring"""


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




def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		List[str]:

    """simple docstring"""


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




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ )    ->		List[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    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		Optional[int]:

    """simple docstring"""


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




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_=False )    ->		Tuple:

    """simple docstring"""


    __UpperCamelCase											=  get_n_layers(lowercase_ )

    __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(lowercase_ ):
        new_state.update(transform_state_encoder_block(lowercase_   ,       lowercase_ ) )

    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(lowercase_ ) for k, v in new_state.items()}




def    __SCREAMING_SNAKE_CASE				(					)    ->		VivitImageProcessor:

    """simple docstring"""


    __UpperCamelCase											=  VivitImageProcessor()

    assert extractor.do_resize is True
    assert extractor.size == {"shortest_edge": 2_56}
    assert extractor.do_center_crop is True
    assert extractor.crop_size == {"width": 2_24, "height": 2_24}
    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_55

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

    return extractor




def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		Tuple:

    """simple docstring"""


    __UpperCamelCase											=  '''checkpoint'''

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

    __UpperCamelCase											=  restore_checkpoint(lowercase_   ,       lowercase_ )
    __UpperCamelCase											=  transform_state(lowercase_   ,       classification_head=lowercase_ )

    __UpperCamelCase											=  get_vivit_config()

    assert config.image_size == 2_24
    assert config.num_frames == 32

    __UpperCamelCase											=  VivitForVideoClassification(lowercase_ )
    model.load_state_dict(lowercase_ )
    model.eval()

    __UpperCamelCase											=  get_processor()

    __UpperCamelCase											=  prepare_video()
    __UpperCamelCase											=  extractor(lowercase_   ,       return_tensors='''pt''' )

    __UpperCamelCase											=  model(**lowercase_ )

    __UpperCamelCase											=  torch.Size([1, 4_00] )
    __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]   ,       lowercase_   ,       atol=1E-4 ), outputs.logits[0, :5]

    model.save_pretrained(lowercase_ )
    extractor.save_pretrained(lowercase_ )


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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_										=       {
    "configuration_marian": ["MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarianConfig", "MarianOnnxConfig"],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       ["MarianTokenizer"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       [
     "MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST",
     "MarianForCausalLM",
     "MarianModel",
     "MarianMTModel",
     "MarianPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       ["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       ["FlaxMarianModel", "FlaxMarianMTModel", "FlaxMarianPreTrainedModel"]

if TYPE_CHECKING:
 from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_marian import MarianTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_marian import (
      MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
      MarianForCausalLM,
      MarianModel,
      MarianMTModel,
      MarianPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
 import sys

 a_										=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






