
'''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_       (		__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






    snake_case_	:      List[Any]            =  "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=2_0_4_8    ):
                f.write(_lowerCAmelCase    )

def 				UpperCamelCase_       (		):
    """simple docstring"""






    snake_case_	:      Any            =  VivitConfig()

    snake_case_	:      Union[str, Any]            =  4_0_0
    snake_case_	:      List[Any]            =  "huggingface/label-files"
    snake_case_	:      int            =  "kinetics400-id2label.json"

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

def 				UpperCamelCase_       (		):
    """simple docstring"""






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

def 				UpperCamelCase_       (		__SCREAMING_SNAKE_CASE    ):
    """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_       (		__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






    return current.flatten()

def 				UpperCamelCase_       (		__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






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

def 				UpperCamelCase_       (		__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






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

def 				UpperCamelCase_       (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






    snake_case_	:      str            =  state_dict["optimizer"]["target"]["Transformer"][f'encoderblock_{i}']

    snake_case_	:      Tuple            =  f'encoder.layer.{i}.'
    snake_case_	:      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_       (		__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






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

def 				UpperCamelCase_       (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE=False    ):
    """simple docstring"""






    snake_case_	:      List[str]            =  get_n_layers(_lowerCAmelCase    )

    snake_case_	:      List[str]            =  OrderedDict()

    snake_case_	:      List[Any]            =  state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["bias"]
    snake_case_	:      List[str]            =  state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["scale"]

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

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






    snake_case_	:      Optional[int]            =  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 				UpperCamelCase_       (		__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






    snake_case_	:      Union[str, Any]            =  "checkpoint"

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

    snake_case_	:      Dict            =  restore_checkpoint(_lowerCAmelCase,						_lowerCAmelCase    )
    snake_case_	:      List[str]            =  transform_state(_lowerCAmelCase,						classification_head=_lowerCAmelCase    )

    snake_case_	:      Optional[int]            =  get_vivit_config()

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

    snake_case_	:      Optional[int]            =  VivitForVideoClassification(_lowerCAmelCase    )
    model.load_state_dict(_lowerCAmelCase    )
    model.eval()

    snake_case_	:      str            =  get_processor()

    snake_case_	:      Union[str, Any]            =  prepare_video()
    snake_case_	:      List[Any]            =  extractor(_lowerCAmelCase,						return_tensors="pt"    )

    snake_case_	:      Any            =  model(**_lowerCAmelCase    )

    snake_case_	:      List[str]            =  torch.Size([1, 4_0_0]    )
    snake_case_	:      Any            =  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],						_lowerCAmelCase,						atol=1E-4    ), outputs.logits[0, :5]

    model.save_pretrained(_lowerCAmelCase    )
    extractor.save_pretrained(_lowerCAmelCase    )


if __name__ == "__main__":
  a_     =  argparse.ArgumentParser()

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

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






'''simple docstring'''
from __future__ import annotations

from typing import Any






class 				UpperCAmelCase_			:





 def __init__(      self	,    lowercase_	,    lowercase_):  # noqa: A002
     snake_case_	:      List[str]            =  item
     snake_case_	:      Dict            =  next






class 				UpperCAmelCase_			:





 def __init__(      self):
     snake_case_	:      Node | None            =  None
     snake_case_	:      List[str]            =  0





 def     snake_case__						(      self	,    lowercase_):
     snake_case_	:      List[Any]            =  Node(lowercase_	,    self.head)
     self.size += 1





 def     snake_case__						(      self):
     # Switched 'self.is_empty()' to 'self.head is None'
     # because mypy was considering the possibility that 'self.head'
     # can be None in below else part and giving error
     if self.head is None:
         return None
     else:
         snake_case_	:      List[Any]            =  self.head.item
         snake_case_	:      Any            =  self.head.next
         self.size -= 1
         return item





 def     snake_case__						(      self):
     return self.head is None





 def __str__(      self):
     if self.is_empty():
         return ""
     else:
         snake_case_	:      Dict            =  self.head
         snake_case_	:      Optional[Any]            =  ""
         snake_case_	:      list[str]            =  []
         while iterate:
             item_list.append(str(iterate.item))
             snake_case_	:      List[str]            =  iterate.next

         snake_case_	:      Optional[Any]            =  " --> ".join(lowercase_)

         return item_str





 def __len__(      self):
     return self.size




