


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						(				SCREAMING_SNAKE_CASE_   :		Dict							):






       """simple docstring"""





       UpperCamelCase     :						List[str] 						=		"""https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint"""

       with open(SCREAMING_SNAKE_CASE_				,      '''wb'''							) as f:
              with requests.get(SCREAMING_SNAKE_CASE_				,      stream=SCREAMING_SNAKE_CASE_							) as req:
                     for chunk in req.iter_content(chunk_size=2_0_4_8							):
                            f.write(SCREAMING_SNAKE_CASE_							)


def   a						(				):






       """simple docstring"""





       UpperCamelCase     :						Dict 						=		VivitConfig()

       UpperCamelCase     :						List[Any] 						=		4_0_0
       UpperCamelCase     :						Tuple 						=		"""huggingface/label-files"""
       UpperCamelCase     :						Any 						=		"""kinetics400-id2label.json"""

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


def   a						(				):






       """simple docstring"""





       UpperCamelCase     :						Any 						=		hf_hub_download(
           repo_id='''hf-internal-testing/spaghetti-video'''				,      filename='''eating_spaghetti_32_frames.npy'''				,      repo_type='''dataset'''							)
       UpperCamelCase     :						List[Any] 						=		np.load(SCREAMING_SNAKE_CASE_							)
       return list(SCREAMING_SNAKE_CASE_							)


def   a						(				SCREAMING_SNAKE_CASE_   :		Union[str, Any]							):






       """simple docstring"""





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

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

       else:
              raise Exception(F"""Invalid number of dimesions: {np.ndim(SCREAMING_SNAKE_CASE_							)}"""							)


def   a						(				SCREAMING_SNAKE_CASE_   :		str							):






       """simple docstring"""





       return current.flatten()


def   a						(				SCREAMING_SNAKE_CASE_   :		Optional[Any]							):






       """simple docstring"""





       return np.reshape(SCREAMING_SNAKE_CASE_				,      (current.shape[0], current.shape[1] * current.shape[2])							).T


def   a						(				SCREAMING_SNAKE_CASE_   :		Optional[Any]							):






       """simple docstring"""





       return np.reshape(SCREAMING_SNAKE_CASE_				,      (current.shape[0] * current.shape[1], current.shape[2])							).T


def   a						(				SCREAMING_SNAKE_CASE_   :		Union[str, Any]				,      SCREAMING_SNAKE_CASE_   :		List[Any]							):






       """simple docstring"""





       UpperCamelCase     :						Optional[int] 						=		state_dict["""optimizer"""]["""target"""]["""Transformer"""][F"""encoderblock_{i}"""]

       UpperCamelCase     :						str 						=		F"""encoder.layer.{i}."""
       UpperCamelCase     :						str 						=		{
           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						(				SCREAMING_SNAKE_CASE_   :		Dict							):






       """simple docstring"""





       return sum([1 if '''encoderblock_''' in k else 0 for k in state_dict['''optimizer''']['''target''']['''Transformer'''].keys()]							)


def   a						(				SCREAMING_SNAKE_CASE_   :		Tuple				,      SCREAMING_SNAKE_CASE_   :		List[str]=False							):






       """simple docstring"""





       UpperCamelCase     :						Union[str, Any] 						=		get_n_layers(SCREAMING_SNAKE_CASE_							)

       UpperCamelCase     :						Optional[Any] 						=		OrderedDict()

       UpperCamelCase     :						Any 						=		state_dict["""optimizer"""]["""target"""]["""Transformer"""]["""encoder_norm"""]["""bias"""]
       UpperCamelCase     :						Optional[int] 						=		state_dict["""optimizer"""]["""target"""]["""Transformer"""]["""encoder_norm"""]["""scale"""]

       UpperCamelCase     :						List[Any] 						=		np.transpose(
           state_dict['''optimizer''']['''target''']['''embedding''']['''kernel''']				,      (4, 3, 0, 1, 2)							)
       UpperCamelCase     :						Dict 						=		state_dict["""optimizer"""]["""target"""]["""embedding"""]["""bias"""]

       UpperCamelCase     :						str 						=		state_dict["""optimizer"""]["""target"""]["""cls"""]
       UpperCamelCase     :						List[str] 						=		state_dict["""optimizer"""]["""target"""]["""Transformer"""]["""posembed_input"""][
           """pos_embedding"""
       ]

       for i in range(SCREAMING_SNAKE_CASE_							):
              new_state.update(transform_state_encoder_block(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							)							)

       if classification_head:
              UpperCamelCase     :						Tuple 						=		{"""vivit.""" + k: v for k, v in new_state.items()}
              UpperCamelCase     :						List[str] 						=		np.transpose(state_dict['''optimizer''']['''target''']['''output_projection''']['''kernel''']							)
              UpperCamelCase     :						Optional[Any] 						=		np.transpose(state_dict['''optimizer''']['''target''']['''output_projection''']['''bias''']							)

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


def   a						(				):






       """simple docstring"""





       UpperCamelCase     :						List[Any] 						=		VivitImageProcessor()

       assert extractor.do_resize is True
       assert extractor.size == {"shortest_edge": 2_5_6}
       assert extractor.do_center_crop is True
       assert extractor.crop_size == {"width": 2_2_4, "height": 2_2_4}
       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_5_5

       # zero-centering = True in original implementation
       assert extractor.do_zero_centering is True

       return extractor


def   a						(				SCREAMING_SNAKE_CASE_   :		str							):






       """simple docstring"""





       UpperCamelCase     :						Dict 						=		"""checkpoint"""

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

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

       UpperCamelCase     :						List[str] 						=		get_vivit_config()

       assert config.image_size == 2_2_4
       assert config.num_frames == 3_2

       UpperCamelCase     :						Optional[int] 						=		VivitForVideoClassification(SCREAMING_SNAKE_CASE_							)
       model.load_state_dict(SCREAMING_SNAKE_CASE_							)
       model.eval()

       UpperCamelCase     :						int 						=		get_processor()

       UpperCamelCase     :						List[str] 						=		prepare_video()
       UpperCamelCase     :						Optional[int] 						=		extractor(SCREAMING_SNAKE_CASE_				,      return_tensors='''pt'''							)

       UpperCamelCase     :						Optional[int] 						=		model(**SCREAMING_SNAKE_CASE_							)

       UpperCamelCase     :						Optional[int] 						=		torch.Size([1, 4_0_0]							)
       UpperCamelCase     :						Optional[int] 						=		torch.tensor([-1.0543, 2.0764, -0.2104, 0.4439, -0.9658]							)

       assert outputs.logits.shape == expected_shape
       assert torch.allclose(outputs.logits[0, :5]				,      SCREAMING_SNAKE_CASE_				,      atol=1E-4							), outputs.logits[0, :5]

       model.save_pretrained(SCREAMING_SNAKE_CASE_							)
       extractor.save_pretrained(SCREAMING_SNAKE_CASE_							)


if __name__ == "__main__":
  __UpperCAmelCase			:			List[str]			      =					argparse.ArgumentParser()

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

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









import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union
class 							__snake_case(       enum.Enum   ):
 lowercase			:							List[str]		   =		'''timm'''
 lowercase			:							Any		   =		'''transformers'''
def    a__    (	A		,					A		,					A       ):
   if stage_names is None:
      raise ValueError("""Stage_names must be set for transformers backbones"""       )

   if out_features is not None:
      if not isinstance(A		,					(list,)       ):
         raise ValueError(F'''out_features must be a list {type(A       )}'''       )
      if any(feat not in stage_names for feat in out_features       ):
         raise ValueError(F'''out_features must be a subset of stage_names: {stage_names} got {out_features}'''       )

   if out_indices is not None:
      if not isinstance(A		,					(list, tuple)       ):
         raise ValueError(F'''out_indices must be a list or tuple, got {type(A       )}'''       )
      if any(idx >= len(A       ) for idx in out_indices       ):
         raise ValueError("""out_indices must be valid indices for stage_names {stage_names}, got {out_indices}"""       )

   if out_features is not None and out_indices is not None:
      if len(A       ) != len(A       ):
         raise ValueError("""out_features and out_indices should have the same length if both are set"""       )
      if out_features != [stage_names[idx] for idx in out_indices]:
         raise ValueError("""out_features and out_indices should correspond to the same stages if both are set"""       )
def    a__    (	A		,					A		,					A		,					):
   if out_indices is None and out_features is None:
      lowercase__    :						int       =			[len(A       ) - 1]
      lowercase__    :						Dict       =			[stage_names[-1]]
   elif out_indices is None and out_features is not None:
      lowercase__    :						Any       =			[stage_names.index(A       ) for layer in out_features]
   elif out_features is None and out_indices is not None:
      lowercase__    :						List[str]       =			[stage_names[idx] for idx in out_indices]
   return out_features, out_indices
def    a__    (	A		,					A		,					A		,					):
   verify_out_features_out_indices(out_features=A		,					out_indices=A		,					stage_names=A       )
   lowercase__  ,       lowercase__    :						Dict       =			_align_output_features_output_indices(
       out_features=A		,					out_indices=A		,					stage_names=A       )
   # Verify that the aligned out_features and out_indices are valid
   verify_out_features_out_indices(out_features=A		,					out_indices=A		,					stage_names=A       )
   return output_features, output_indices
class 							__snake_case:
 lowercase			:							Optional[BackboneType]		   =		None



 def     UpperCAmelCase     (						self : Dict			,		UpperCAmelCase__ : int						):

    """simple docstring"""






    if getattr(self			,		"""_backbone"""			,		UpperCAmelCase__						) is None:
       raise ValueError("""self._backbone must be set before calling _init_timm_backbone"""						)

    # These will diagree with the defaults for the transformers models e.g. for resnet50
    # the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
    # the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
    lowercase__    :						List[str]       =			[stage["""module"""] for stage in self._backbone.feature_info.info]
    lowercase__    :						int       =			[stage["""num_chs"""] for stage in self._backbone.feature_info.info]
    lowercase__    :						Optional[int]       =			self._backbone.feature_info.out_indices
    lowercase__    :						Optional[Any]       =			self._backbone.feature_info.module_name()

    # We verify the out indices and out features are valid
    verify_out_features_out_indices(
        out_features=UpperCAmelCase__			,		out_indices=UpperCAmelCase__			,		stage_names=self.stage_names						)
    lowercase__  ,       lowercase__    :						int       =			out_features, out_indices



 def     UpperCAmelCase     (						self : Optional[Any]			,		UpperCAmelCase__ : str						):

    """simple docstring"""






    lowercase__    :						List[str]       =			getattr(UpperCAmelCase__			,		"""stage_names"""						)
    lowercase__    :						Union[str, Any]       =			getattr(UpperCAmelCase__			,		"""out_features"""			,		UpperCAmelCase__						)
    lowercase__    :						List[str]       =			getattr(UpperCAmelCase__			,		"""out_indices"""			,		UpperCAmelCase__						)

    lowercase__    :						Tuple       =			stage_names
    lowercase__  ,       lowercase__    :						List[str]       =			get_aligned_output_features_output_indices(
        out_features=UpperCAmelCase__			,		out_indices=UpperCAmelCase__			,		stage_names=UpperCAmelCase__						)
    # Number of channels for each stage. This is set in the transformer backbone model init
    lowercase__    :						Optional[int]       =			None



 def     UpperCAmelCase     (						self : Tuple			,		UpperCAmelCase__ : Optional[Any]						):

    """simple docstring"""






    lowercase__    :						List[Any]       =			config

    lowercase__    :						Optional[Any]       =			getattr(UpperCAmelCase__			,		"""use_timm_backbone"""			,		UpperCAmelCase__						)
    lowercase__    :						Optional[int]       =			BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

    if self.backbone_type == BackboneType.TIMM:
       self._init_timm_backbone(UpperCAmelCase__						)
    elif self.backbone_type == BackboneType.TRANSFORMERS:
       self._init_transformers_backbone(UpperCAmelCase__						)
    else:
       raise ValueError(f'''backbone_type {self.backbone_type} not supported.'''						)



 @property
 def     UpperCAmelCase     (						self : int						):

    """simple docstring"""






    return self._out_features



 @out_features.setter
 def     UpperCAmelCase     (						self : Dict			,		UpperCAmelCase__ : List[str]						):

    """simple docstring"""






    lowercase__  ,       lowercase__    :						Optional[Any]       =			get_aligned_output_features_output_indices(
        out_features=UpperCAmelCase__			,		out_indices=UpperCAmelCase__			,		stage_names=self.stage_names						)



 @property
 def     UpperCAmelCase     (						self : List[str]						):

    """simple docstring"""






    return self._out_indices



 @out_indices.setter
 def     UpperCAmelCase     (						self : Optional[Any]			,		UpperCAmelCase__ : Union[Tuple[int], List[int]]						):

    """simple docstring"""






    lowercase__  ,       lowercase__    :						Any       =			get_aligned_output_features_output_indices(
        out_features=UpperCAmelCase__			,		out_indices=UpperCAmelCase__			,		stage_names=self.stage_names						)



 @property
 def     UpperCAmelCase     (						self : int						):

    """simple docstring"""






    return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names						)}



 @property
 def     UpperCAmelCase     (						self : Optional[Any]						):

    """simple docstring"""






    return [self.out_feature_channels[name] for name in self.out_features]



 def     UpperCAmelCase     (						self : Optional[Any]			,		*UpperCAmelCase__ : Tuple			,		**UpperCAmelCase__ : Dict						):

    """simple docstring"""






    lowercase__    :						Optional[Any]       =			dict(inspect.signature(self.forward						).parameters						)
    lowercase__    :						Tuple       =			{k: v for k, v in kwargs.items() if k in signature}
    return self(*UpperCAmelCase__			,		**UpperCAmelCase__						)



 def     UpperCAmelCase     (						self : int			,		UpperCAmelCase__ : List[str]			,		UpperCAmelCase__ : Optional[bool] = None			,		UpperCAmelCase__ : Optional[bool] = None			,		UpperCAmelCase__ : Optional[bool] = None			,		):

    """simple docstring"""






    raise NotImplementedError("""This method should be implemented by the derived class."""						)







 def     UpperCAmelCase     (						self : Any						):

    """simple docstring"""






    lowercase__    :						Union[str, Any]       =			super().to_dict()
    lowercase__    :						Tuple       =			output.pop("""_out_features"""						)
    lowercase__    :						Union[str, Any]       =			output.pop("""_out_indices"""						)
    return output
class 							__snake_case:



 @property
 def     UpperCAmelCase     (						self : str						):

    """simple docstring"""






    return self._out_features



 @out_features.setter
 def     UpperCAmelCase     (						self : Union[str, Any]			,		UpperCAmelCase__ : List[str]						):

    """simple docstring"""






    lowercase__  ,       lowercase__    :						Dict       =			get_aligned_output_features_output_indices(
        out_features=UpperCAmelCase__			,		out_indices=UpperCAmelCase__			,		stage_names=self.stage_names						)



 @property
 def     UpperCAmelCase     (						self : Tuple						):

    """simple docstring"""






    return self._out_indices



 @out_indices.setter
 def     UpperCAmelCase     (						self : int			,		UpperCAmelCase__ : Union[Tuple[int], List[int]]						):

    """simple docstring"""






    lowercase__  ,       lowercase__    :						Any       =			get_aligned_output_features_output_indices(
        out_features=UpperCAmelCase__			,		out_indices=UpperCAmelCase__			,		stage_names=self.stage_names						)







 def     UpperCAmelCase     (						self : List[Any]						):

    """simple docstring"""






    lowercase__    :						Dict       =			super().to_dict()
    lowercase__    :						Union[str, Any]       =			output.pop("""_out_features"""						)
    lowercase__    :						Dict       =			output.pop("""_out_indices"""						)
    return output


