
"""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_      :		int					)  ->							List[Any]:
   _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=20_48					):
            f.write(a_					)
def       __UpperCAmelCase		(		)  ->							VivitConfig:
   _UpperCAmelCase 						=       VivitConfig()

   _UpperCAmelCase 						=       4_00
   _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		(		)  ->							List[Any]:
   _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_      :		np.ndarray					)  ->							Dict:
   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_      :		np.ndarray					)  ->							Tuple:
   return current.flatten()
def       __UpperCAmelCase		(		a_      :		np.ndarray					)  ->							Dict:
   return np.reshape(a_     ,	(current.shape[0], current.shape[1] * current.shape[2])					).T
def       __UpperCAmelCase		(		a_      :		np.ndarray					)  ->							Union[str, Any]:
   return np.reshape(a_     ,	(current.shape[0] * current.shape[1], current.shape[2])					).T
def       __UpperCAmelCase		(		a_      :		Dict     ,	a_      :		Optional[int]					)  ->							Any:
   _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_      :		int					)  ->							Dict:
   return sum([1 if "encoderblock_" in k else 0 for k in state_dict["optimizer"]["target"]["Transformer"].keys()]					)
def       __UpperCAmelCase		(		a_      :		Dict     ,	a_      :		Optional[int]=False					)  ->							Union[str, Any]:
   _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:
   _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       __UpperCAmelCase		(		a_      :		str					)  ->							List[str]:
   _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 == 2_24
   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, 4_00]					)
   _UpperCAmelCase 						=       torch.tensor([-1.0_543, 2.0_764, -0.2_104, 0.4_439, -0.9_658]					)

   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__":
  SCREAMING_SNAKE_CASE_  					= argparse.ArgumentParser()

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

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



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE_  					= {
    'configuration_deformable_detr': ['DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeformableDetrConfig'],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE_  					= ['DeformableDetrFeatureExtractor']
  SCREAMING_SNAKE_CASE_  					= ['DeformableDetrImageProcessor']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE_  					= [
      'DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
      'DeformableDetrForObjectDetection',
      'DeformableDetrModel',
      'DeformableDetrPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
    from .image_processing_deformable_detr import DeformableDetrImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_deformable_detr import (
        DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
        DeformableDetrForObjectDetection,
        DeformableDetrModel,
        DeformableDetrPreTrainedModel,
    )

else:
  import sys

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

