


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__      (			UpperCAmelCase_					:       Dict							)  ->	int:
     __lowerCamelCase     :			Any              =	'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=20_48							):
                    f.write(UpperCAmelCase_							)



def     UpperCAmelCase__      (			)  ->	VivitConfig:
     __lowerCamelCase     :			Union[str, Any]              =	VivitConfig()

     __lowerCamelCase     :			Optional[int]              =	4_00
     __lowerCamelCase     :			str              =	'huggingface/label-files'
     __lowerCamelCase     :			Optional[Any]              =	'kinetics400-id2label.json'

     __lowerCamelCase     :			Tuple              =	json.load(open(hf_hub_download(UpperCAmelCase_   ,					UpperCAmelCase_   ,					repo_type='dataset'							)   ,					'r'							)							)
     __lowerCamelCase     :			Optional[Any]              =	{int(UpperCAmelCase_							): v for k, v in idalabel.items()}
     __lowerCamelCase     :			int              =	idalabel
     __lowerCamelCase     :			Any              =	{v: k for k, v in idalabel.items()}
     return config



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



def     UpperCAmelCase__      (			UpperCAmelCase_					:       np.ndarray							)  ->	Dict:
     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     UpperCAmelCase__      (			UpperCAmelCase_					:       np.ndarray							)  ->	List[Any]:
     return current.flatten()



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



def     UpperCAmelCase__      (			UpperCAmelCase_					:       np.ndarray							)  ->	List[Any]:
     return np.reshape(UpperCAmelCase_   ,					(current.shape[0] * current.shape[1], current.shape[2])							).T



def     UpperCAmelCase__      (			UpperCAmelCase_					:       int   ,					UpperCAmelCase_					:       Tuple							)  ->	Any:
     __lowerCamelCase     :			Union[str, Any]              =	state_dict['optimizer']['target']['Transformer'][F'encoderblock_{i}']

     __lowerCamelCase     :			Dict              =	F'encoder.layer.{i}.'
     __lowerCamelCase     :			Any              =	{
         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__      (			UpperCAmelCase_					:       int							)  ->	Optional[int]:
     return sum([1 if 'encoderblock_' in k else 0 for k in state_dict['optimizer']['target']['Transformer'].keys()]							)



def     UpperCAmelCase__      (			UpperCAmelCase_					:       List[str]   ,					UpperCAmelCase_					:       int=False							)  ->	Optional[int]:
     __lowerCamelCase     :			Tuple              =	get_n_layers(UpperCAmelCase_							)

     __lowerCamelCase     :			int              =	OrderedDict()

     __lowerCamelCase     :			Union[str, Any]              =	state_dict['optimizer']['target']['Transformer']['encoder_norm']['bias']
     __lowerCamelCase     :			Optional[Any]              =	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     :			str              =	state_dict['optimizer']['target']['embedding']['bias']

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

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



def     UpperCAmelCase__      (			)  ->	VivitImageProcessor:
     __lowerCamelCase     :			List[Any]              =	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__      (			UpperCAmelCase_					:       str							)  ->	Tuple:
     __lowerCamelCase     :			int              =	'checkpoint'

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

     __lowerCamelCase     :			int              =	restore_checkpoint(UpperCAmelCase_   ,					UpperCAmelCase_							)
     __lowerCamelCase     :			Union[str, Any]              =	transform_state(UpperCAmelCase_   ,					classification_head=UpperCAmelCase_							)

     __lowerCamelCase     :			Union[str, Any]              =	get_vivit_config()

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

     __lowerCamelCase     :			int              =	VivitForVideoClassification(UpperCAmelCase_							)
     model.load_state_dict(UpperCAmelCase_							)
     model.eval()

     __lowerCamelCase     :			Optional[Any]              =	get_processor()

     __lowerCamelCase     :			Tuple              =	prepare_video()
     __lowerCamelCase     :			Any              =	extractor(UpperCAmelCase_   ,					return_tensors='pt'							)

     __lowerCamelCase     :			Tuple              =	model(**UpperCAmelCase_							)

     __lowerCamelCase     :			Union[str, Any]              =	torch.Size([1, 4_00]							)
     __lowerCamelCase     :			int              =	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]   ,					UpperCAmelCase_   ,					atol=1e-4							), outputs.logits[0, :5]

     model.save_pretrained(UpperCAmelCase_							)
     extractor.save_pretrained(UpperCAmelCase_							)


if __name__ == "__main__":
      A__	:      Union[str, Any]				=						argparse.ArgumentParser()

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

      A__	:      List[str]				=						parser.parse_args()
      convert(args.output_model_name)










'''simple docstring'''



from typing import TYPE_CHECKING

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


A__	:      Optional[Any]				=						{
    """configuration_xlm_prophetnet""": ["""XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMProphetNetConfig"""],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__	:      List[Any]				=						["""XLMProphetNetTokenizer"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__	:      Optional[Any]				=						[
          """XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """XLMProphetNetDecoder""",
          """XLMProphetNetEncoder""",
          """XLMProphetNetForCausalLM""",
          """XLMProphetNetForConditionalGeneration""",
          """XLMProphetNetModel""",
          """XLMProphetNetPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_xlm_prophetnet import (
                XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                XLMProphetNetDecoder,
                XLMProphetNetEncoder,
                XLMProphetNetForCausalLM,
                XLMProphetNetForConditionalGeneration,
                XLMProphetNetModel,
                XLMProphetNetPreTrainedModel,
            )

else:
      import sys

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






