





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_       (							UpperCamelCase_     )   ->						str:
       UpperCamelCase_      				=     "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        lowerCAmelCase_       (							)   ->						VivitConfig:
       UpperCamelCase_      				=     VivitConfig()

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

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




def        lowerCAmelCase_       (							)   ->						Optional[int]:
       UpperCamelCase_      				=     hf_hub_download(
           repo_id="hf-internal-testing/spaghetti-video"    ,						filename="eating_spaghetti_32_frames.npy"    ,						repo_type="dataset"     )
       UpperCamelCase_      				=     np.load(UpperCamelCase_     )
       return list(UpperCamelCase_     )




def        lowerCAmelCase_       (							UpperCamelCase_     )   ->						Union[str, Any]:
       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        lowerCAmelCase_       (							UpperCamelCase_     )   ->						Dict:
       return current.flatten()




def        lowerCAmelCase_       (							UpperCamelCase_     )   ->						Union[str, Any]:
       return np.reshape(UpperCamelCase_    ,						(current.shape[0], current.shape[1] * current.shape[2])     ).T




def        lowerCAmelCase_       (							UpperCamelCase_     )   ->						str:
       return np.reshape(UpperCamelCase_    ,						(current.shape[0] * current.shape[1], current.shape[2])     ).T




def        lowerCAmelCase_       (							UpperCamelCase_    ,						UpperCamelCase_     )   ->						str:
       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        lowerCAmelCase_       (							UpperCamelCase_     )   ->						Union[str, Any]:
       return sum([1 if "encoderblock_" in k else 0 for k in state_dict["optimizer"]["target"]["Transformer"].keys()]     )




def        lowerCAmelCase_       (							UpperCamelCase_    ,						UpperCamelCase_=False     )   ->						List[str]:
       UpperCamelCase_      				=     get_n_layers(UpperCamelCase_     )

       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(UpperCamelCase_     ):
              new_state.update(transform_state_encoder_block(UpperCamelCase_    ,						UpperCamelCase_     )     )

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




def        lowerCAmelCase_       (							)   ->						VivitImageProcessor:
       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        lowerCAmelCase_       (							UpperCamelCase_     )   ->						List[str]:
       UpperCamelCase_      				=     "checkpoint"

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

       UpperCamelCase_      				=     restore_checkpoint(UpperCamelCase_    ,						UpperCamelCase_     )
       UpperCamelCase_      				=     transform_state(UpperCamelCase_    ,						classification_head=UpperCamelCase_     )

       UpperCamelCase_      				=     get_vivit_config()

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

       UpperCamelCase_      				=     VivitForVideoClassification(UpperCamelCase_     )
       model.load_state_dict(UpperCamelCase_     )
       model.eval()

       UpperCamelCase_      				=     get_processor()

       UpperCamelCase_      				=     prepare_video()
       UpperCamelCase_      				=     extractor(UpperCamelCase_    ,						return_tensors="pt"     )

       UpperCamelCase_      				=     model(**UpperCamelCase_     )

       UpperCamelCase_      				=     torch.Size([1, 400]     )
       UpperCamelCase_      				=     torch.tensor([-1.05_43, 2.07_64, -0.21_04, 0.44_39, -0.96_58]     )

       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__":
     _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)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCAmelCase				       = {
    'configuration_mpnet': ['MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MPNetConfig'],
    'tokenization_mpnet': ['MPNetTokenizer'],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase				       = ['MPNetTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase				       = [
         'MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
         'MPNetForMaskedLM',
         'MPNetForMultipleChoice',
         'MPNetForQuestionAnswering',
         'MPNetForSequenceClassification',
         'MPNetForTokenClassification',
         'MPNetLayer',
         'MPNetModel',
         'MPNetPreTrainedModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase				       = [
         'TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFMPNetEmbeddings',
         'TFMPNetForMaskedLM',
         'TFMPNetForMultipleChoice',
         'TFMPNetForQuestionAnswering',
         'TFMPNetForSequenceClassification',
         'TFMPNetForTokenClassification',
         'TFMPNetMainLayer',
         'TFMPNetModel',
         'TFMPNetPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
     from .tokenization_mpnet import MPNetTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mpnet_fast import MPNetTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mpnet import (
              MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              MPNetForMaskedLM,
              MPNetForMultipleChoice,
              MPNetForQuestionAnswering,
              MPNetForSequenceClassification,
              MPNetForTokenClassification,
              MPNetLayer,
              MPNetModel,
              MPNetPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mpnet import (
              TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFMPNetEmbeddings,
              TFMPNetForMaskedLM,
              TFMPNetForMultipleChoice,
              TFMPNetForQuestionAnswering,
              TFMPNetForSequenceClassification,
              TFMPNetForTokenClassification,
              TFMPNetMainLayer,
              TFMPNetModel,
              TFMPNetPreTrainedModel,
          )

else:
     import sys

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

