
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    _a (						lowerCamelCase							):
 lowerCamelCase		:     Dict          =				"""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=2048							):
    f.write(lowerCamelCase							)





def    _a (						):
 lowerCamelCase		:     Tuple          =				VivitConfig()

 lowerCamelCase		:     List[Any]          =				400
 lowerCamelCase		:     List[Any]          =				"""huggingface/label-files"""
 lowerCamelCase		:     Union[str, Any]          =				"""kinetics400-id2label.json"""

 lowerCamelCase		:     List[str]          =				json.load(open(hf_hub_download(lowerCamelCase,     lowerCamelCase,     repo_type="""dataset"""							),     """r"""							)							)
 lowerCamelCase		:     Tuple          =				{int(lowerCamelCase							): v for k, v in idalabel.items()}
 lowerCamelCase		:     List[Any]          =				idalabel
 lowerCamelCase		:     Union[str, Any]          =				{v: k for k, v in idalabel.items()}
 return config





def    _a (						):
 lowerCamelCase		:     Dict          =				hf_hub_download(
     repo_id="""hf-internal-testing/spaghetti-video""",     filename="""eating_spaghetti_32_frames.npy""",     repo_type="""dataset"""							)
 lowerCamelCase		:     Dict          =				np.load(lowerCamelCase							)
 return list(lowerCamelCase							)





def    _a (						lowerCamelCase							):
 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    _a (						lowerCamelCase							):
 return current.flatten()





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





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





def    _a (						lowerCamelCase,     lowerCamelCase							):
 lowerCamelCase		:     Optional[Any]          =				state_dict["""optimizer"""]["""target"""]["""Transformer"""][F'''encoderblock_{i}''']

 lowerCamelCase		:     Optional[int]          =				F'''encoder.layer.{i}.'''
 lowerCamelCase		:     Dict          =				{
     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    _a (						lowerCamelCase							):
 return sum([1 if """encoderblock_""" in k else 0 for k in state_dict["""optimizer"""]["""target"""]["""Transformer"""].keys()]							)





def    _a (						lowerCamelCase,     lowerCamelCase=False							):
 lowerCamelCase		:     List[str]          =				get_n_layers(lowerCamelCase							)

 lowerCamelCase		:     List[Any]          =				OrderedDict()

 lowerCamelCase		:     int          =				state_dict["""optimizer"""]["""target"""]["""Transformer"""]["""encoder_norm"""]["""bias"""]
 lowerCamelCase		:     str          =				state_dict["""optimizer"""]["""target"""]["""Transformer"""]["""encoder_norm"""]["""scale"""]

 lowerCamelCase		:     Union[str, Any]          =				np.transpose(
     state_dict["""optimizer"""]["""target"""]["""embedding"""]["""kernel"""],     (4, 3, 0, 1, 2)							)
 lowerCamelCase		:     Any          =				state_dict["""optimizer"""]["""target"""]["""embedding"""]["""bias"""]

 lowerCamelCase		:     Dict          =				state_dict["""optimizer"""]["""target"""]["""cls"""]
 lowerCamelCase		:     Any          =				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		:     List[str]          =				{"""vivit.""" + k: v for k, v in new_state.items()}
  lowerCamelCase		:     List[str]          =				np.transpose(state_dict["""optimizer"""]["""target"""]["""output_projection"""]["""kernel"""]							)
  lowerCamelCase		:     List[str]          =				np.transpose(state_dict["""optimizer"""]["""target"""]["""output_projection"""]["""bias"""]							)

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





def    _a (						):
 lowerCamelCase		:     Dict          =				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    _a (						lowerCamelCase							):
 lowerCamelCase		:     List[Any]          =				"""checkpoint"""

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

 lowerCamelCase		:     Any          =				restore_checkpoint(lowerCamelCase,     lowerCamelCase							)
 lowerCamelCase		:     Optional[int]          =				transform_state(lowerCamelCase,     classification_head=lowerCamelCase							)

 lowerCamelCase		:     Tuple          =				get_vivit_config()

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

 lowerCamelCase		:     int          =				VivitForVideoClassification(lowerCamelCase							)
 model.load_state_dict(lowerCamelCase							)
 model.eval()

 lowerCamelCase		:     Optional[Any]          =				get_processor()

 lowerCamelCase		:     Optional[Any]          =				prepare_video()
 lowerCamelCase		:     Union[str, Any]          =				extractor(lowerCamelCase,     return_tensors="""pt"""							)

 lowerCamelCase		:     List[Any]          =				model(**lowerCamelCase							)

 lowerCamelCase		:     Union[str, Any]          =				torch.Size([1, 400]							)
 lowerCamelCase		:     Any          =				torch.tensor([-1.0_5_4_3, 2.0_7_6_4, -0.2_1_0_4, 0.4_4_3_9, -0.9_6_5_8]							)

 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__":
      _lowerCamelCase        =argparse.ArgumentParser()

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

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






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

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


_lowerCamelCase        ={
    """configuration_tvlt""": ["""TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TvltConfig"""],
    """feature_extraction_tvlt""": ["""TvltFeatureExtractor"""],
    """processing_tvlt""": ["""TvltProcessor"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase        =[
          """TVLT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TvltModel""",
          """TvltForPreTraining""",
          """TvltForAudioVisualClassification""",
          """TvltPreTrainedModel""",
      ]

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase        =["""TvltImageProcessor"""]


if TYPE_CHECKING:
      from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
      from .processing_tvlt import TvltProcessor
      from .feature_extraction_tvlt import TvltFeatureExtractor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tvlt import (
                TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TvltForAudioVisualClassification,
                TvltForPreTraining,
                TvltModel,
                TvltPreTrainedModel,
            )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_tvlt import TvltImageProcessor


else:
      import sys

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




