'''simple docstring'''

from typing import TYPE_CHECKING

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


A_        =   {
    "configuration_splinter": ["SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SplinterConfig"],
    "tokenization_splinter": ["SplinterTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_        =   ["SplinterTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_        =   [
						    "SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "SplinterForQuestionAnswering",
						    "SplinterForPreTraining",
						    "SplinterLayer",
						    "SplinterModel",
						    "SplinterPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
						from .tokenization_splinter import SplinterTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_splinter_fast import SplinterTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_splinter import (
												    SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SplinterForPreTraining,
												    SplinterForQuestionAnswering,
												    SplinterLayer,
												    SplinterModel,
												    SplinterPreTrainedModel,
												)


else:
						import sys

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






'''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						: Optional[Any])						->		List[Any]:



						"""simple docstring"""

						snake_case   :					List[str]							=						"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 					UpperCAmelCase__      (							)						->		VivitConfig:



						"""simple docstring"""

						snake_case   :					Any							=						VivitConfig()

						snake_case   :					Optional[int]							=						400
						snake_case   :					Tuple							=						"huggingface/label-files"
						snake_case   :					Tuple							=						"kinetics400-id2label.json"

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


def 					UpperCAmelCase__      (							)						->		int:



						"""simple docstring"""

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


def 					UpperCAmelCase__      (							_lowerCAmelCase						: np.ndarray)						->		str:



						"""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						: np.ndarray)						->		Optional[Any]:



						"""simple docstring"""

						return current.flatten()


def 					UpperCAmelCase__      (							_lowerCAmelCase						: np.ndarray)						->		List[Any]:



						"""simple docstring"""

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


def 					UpperCAmelCase__      (							_lowerCAmelCase						: np.ndarray)						->		Optional[Any]:



						"""simple docstring"""

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


def 					UpperCAmelCase__      (							_lowerCAmelCase						: Union[str, Any],			_lowerCAmelCase						: List[Any])						->		Union[str, Any]:



						"""simple docstring"""

						snake_case   :					Union[str, Any]							=						state_dict["optimizer"]["target"]["Transformer"][f"""encoderblock_{i}"""]

						snake_case   :					Any							=						f"""encoder.layer.{i}."""
						snake_case   :					Optional[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__      (							_lowerCAmelCase						: List[Any])						->		List[Any]:



						"""simple docstring"""

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


def 					UpperCAmelCase__      (							_lowerCAmelCase						: List[Any],			_lowerCAmelCase						: List[Any]=False)						->		List[Any]:



						"""simple docstring"""

						snake_case   :					Dict							=						get_n_layers(_lowerCAmelCase)

						snake_case   :					Tuple							=						OrderedDict()

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

						snake_case   :					Tuple							=						np.transpose(
						    state_dict["optimizer"]["target"]["embedding"]["kernel"],			(4, 3, 0, 1, 2))
						snake_case   :					Optional[Any]							=						state_dict["optimizer"]["target"]["embedding"]["bias"]

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

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


def 					UpperCAmelCase__      (							)						->		VivitImageProcessor:



						"""simple docstring"""

						snake_case   :					int							=						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 					UpperCAmelCase__      (							_lowerCAmelCase						: str)						->		Union[str, Any]:



						"""simple docstring"""

						snake_case   :					str							=						"checkpoint"

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

						snake_case   :					Union[str, Any]							=						restore_checkpoint(_lowerCAmelCase,			_lowerCAmelCase)
						snake_case   :					Optional[Any]							=						transform_state(_lowerCAmelCase,			classification_head=_lowerCAmelCase)

						snake_case   :					List[Any]							=						get_vivit_config()

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

						snake_case   :					Union[str, Any]							=						VivitForVideoClassification(_lowerCAmelCase)
						model.load_state_dict(_lowerCAmelCase)
						model.eval()

						snake_case   :					Any							=						get_processor()

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

						snake_case   :					Tuple							=						model(**_lowerCAmelCase)

						snake_case   :					Any							=						torch.Size([1, 400])
						snake_case   :					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],			_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)





