





'''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__   (	lowerCAmelCase						):





      """simple docstring"""


      _lowerCAmelCase							  =							"""https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint"""

      with open(lowerCAmelCase							,			"""wb"""						) as f:
            with requests.get(lowerCAmelCase							,			stream=lowerCAmelCase						) as req:
                  for chunk in req.iter_content(chunk_size=20_48						):
                        f.write(lowerCAmelCase						)
def      UpperCamelCase__   (	):





      """simple docstring"""


      _lowerCAmelCase							  =							VivitConfig()

      _lowerCAmelCase							  =							4_00
      _lowerCAmelCase							  =							"""huggingface/label-files"""
      _lowerCAmelCase							  =							"""kinetics400-id2label.json"""

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





      """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(lowerCAmelCase						)
      return list(lowerCAmelCase						)
def      UpperCamelCase__   (	lowerCAmelCase						):





      """simple docstring"""


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

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

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





      """simple docstring"""


      return current.flatten()
def      UpperCamelCase__   (	lowerCAmelCase						):





      """simple docstring"""


      return np.reshape(lowerCAmelCase							,			(current.shape[0], current.shape[1] * current.shape[2])						).T
def      UpperCamelCase__   (	lowerCAmelCase						):





      """simple docstring"""


      return np.reshape(lowerCAmelCase							,			(current.shape[0] * current.shape[1], current.shape[2])						).T
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase						):





      """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      UpperCamelCase__   (	lowerCAmelCase						):





      """simple docstring"""


      return sum([1 if """encoderblock_""" in k else 0 for k in state_dict["""optimizer"""]["""target"""]["""Transformer"""].keys()]						)
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase=False						):





      """simple docstring"""


      _lowerCAmelCase							  =							get_n_layers(lowerCAmelCase						)

      _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(lowerCAmelCase						):
            new_state.update(transform_state_encoder_block(lowerCAmelCase							,			lowerCAmelCase						)						)

      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(lowerCAmelCase						) for k, v in new_state.items()}
def      UpperCamelCase__   (	):





      """simple docstring"""


      _lowerCAmelCase							  =							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__   (	lowerCAmelCase						):





      """simple docstring"""


      _lowerCAmelCase							  =							"""checkpoint"""

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

      _lowerCAmelCase							  =							restore_checkpoint(lowerCAmelCase							,			lowerCAmelCase						)
      _lowerCAmelCase							  =							transform_state(lowerCAmelCase							,			classification_head=lowerCAmelCase						)

      _lowerCAmelCase							  =							get_vivit_config()

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

      _lowerCAmelCase							  =							VivitForVideoClassification(lowerCAmelCase						)
      model.load_state_dict(lowerCAmelCase						)
      model.eval()

      _lowerCAmelCase							  =							get_processor()

      _lowerCAmelCase							  =							prepare_video()
      _lowerCAmelCase							  =							extractor(lowerCAmelCase							,			return_tensors="""pt"""						)

      _lowerCAmelCase							  =							model(**lowerCAmelCase						)

      _lowerCAmelCase							  =							torch.Size([1, 4_00]						)
      _lowerCAmelCase							  =							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]							,			lowerCAmelCase							,			atol=1e-4						), outputs.logits[0, :5]

      model.save_pretrained(lowerCAmelCase						)
      extractor.save_pretrained(lowerCAmelCase						)


if __name__ == "__main__":
      A__       :     Tuple          =argparse.ArgumentParser()

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

      A__       :     Union[str, Any]          =parser.parse_args()
      convert(args.output_model_name)













'''simple docstring'''






from typing import TYPE_CHECKING

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


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

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     List[Any]          =[
          '''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:
      A__       :     str          =[
          '''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

      A__       :     List[Any]          =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






