'''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  _lowerCAmelCase				(				__a      )  ->      List[str]:
     '''simple docstring'''







     _UpperCamelCase  :Dict       ="""https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint"""

     with open(__a			,      """wb"""      ) as f:
          with requests.get(__a			,      stream=__a      ) as req:
               for chunk in req.iter_content(chunk_size=20_48      ):
                    f.write(__a      )



def  _lowerCAmelCase				(				)  ->      VivitConfig:
     '''simple docstring'''







     _UpperCamelCase  :List[Any]       =VivitConfig()

     _UpperCamelCase  :List[str]       =4_00
     _UpperCamelCase  :Any       ="""huggingface/label-files"""
     _UpperCamelCase  :str       ="""kinetics400-id2label.json"""

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



def  _lowerCAmelCase				(				)  ->      int:
     '''simple docstring'''







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



def  _lowerCAmelCase				(				__a      )  ->      Tuple:
     '''simple docstring'''







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

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

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



def  _lowerCAmelCase				(				__a      )  ->      Union[str, Any]:
     '''simple docstring'''







     return current.flatten()



def  _lowerCAmelCase				(				__a      )  ->      str:
     '''simple docstring'''







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



def  _lowerCAmelCase				(				__a      )  ->      List[Any]:
     '''simple docstring'''







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



def  _lowerCAmelCase				(				__a			,      __a      )  ->      List[Any]:
     '''simple docstring'''







     _UpperCamelCase  :Union[str, Any]       =state_dict["""optimizer"""]["""target"""]["""Transformer"""][F'''encoderblock_{i}''']

     _UpperCamelCase  :int       =F'''encoder.layer.{i}.'''
     _UpperCamelCase  :List[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  _lowerCAmelCase				(				__a      )  ->      str:
     '''simple docstring'''







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



def  _lowerCAmelCase				(				__a			,      __a=False      )  ->      Any:
     '''simple docstring'''







     _UpperCamelCase  :Dict       =get_n_layers(__a      )

     _UpperCamelCase  :int       =OrderedDict()

     _UpperCamelCase  :List[Any]       =state_dict["""optimizer"""]["""target"""]["""Transformer"""]["""encoder_norm"""]["""bias"""]
     _UpperCamelCase  :int       =state_dict["""optimizer"""]["""target"""]["""Transformer"""]["""encoder_norm"""]["""scale"""]

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

     _UpperCamelCase  :List[Any]       =state_dict["""optimizer"""]["""target"""]["""cls"""]
     _UpperCamelCase  :Optional[int]       =state_dict["""optimizer"""]["""target"""]["""Transformer"""]["""posembed_input"""][
         """pos_embedding"""
     ]

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

     if classification_head:
          _UpperCamelCase  :List[str]       ={"""vivit.""" + k: v for k, v in new_state.items()}
          _UpperCamelCase  :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(__a      ) for k, v in new_state.items()}



def  _lowerCAmelCase				(				)  ->      VivitImageProcessor:
     '''simple docstring'''







     _UpperCamelCase  :List[str]       =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  _lowerCAmelCase				(				__a      )  ->      str:
     '''simple docstring'''







     _UpperCamelCase  :Optional[int]       ="""checkpoint"""

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

     _UpperCamelCase  :str       =restore_checkpoint(__a			,      __a      )
     _UpperCamelCase  :List[Any]       =transform_state(__a			,      classification_head=__a      )

     _UpperCamelCase  :str       =get_vivit_config()

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

     _UpperCamelCase  :int       =VivitForVideoClassification(__a      )
     model.load_state_dict(__a      )
     model.eval()

     _UpperCamelCase  :List[str]       =get_processor()

     _UpperCamelCase  :Any       =prepare_video()
     _UpperCamelCase  :Union[str, Any]       =extractor(__a			,      return_tensors="""pt"""      )

     _UpperCamelCase  :str       =model(**__a      )

     _UpperCamelCase  :str       =torch.Size([1, 4_00]      )
     _UpperCamelCase  :Optional[int]       =torch.tensor([-1.05_43, 2.07_64, -0.21_04, 0.44_39, -0.96_58]      )

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

     model.save_pretrained(__a      )
     extractor.save_pretrained(__a      )


if __name__ == "__main__":
      _lowerCamelCase :				Optional[int]														= argparse.ArgumentParser()

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

      _lowerCamelCase :				int														= parser.parse_args()
      convert(args.output_model_name)
'''simple docstring'''

from sklearn.metrics import mean_absolute_error

import datasets


_lowerCamelCase :				Union[str, Any]														= """\
@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}
"""

_lowerCamelCase :				List[Any]														= """\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
"""


_lowerCamelCase :				Union[str, Any]														= """
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

                 \"raw_values\" : Returns a full set of errors in case of multioutput input.

                 \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

Returns:
    mae : mean absolute error.
        If multioutput is \"raw_values\", then mean absolute error is returned for each output separately. If multioutput is \"uniform_average\" or an ndarray of weights, then the weighted average of all output errors is returned.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric(\"mae\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.5}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results)
    {'mae': array([0.5, 1. ])}
"""




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,				_KWARGS_DESCRIPTION     )
class    lowerCamelCase__  (   datasets.Metric     ):



   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        return datasets.MetricInfo(
            description=_DESCRIPTION					,      citation=_CITATION					,      inputs_description=_KWARGS_DESCRIPTION					,      features=datasets.Features(self._get_feature_types()     )					,      reference_urls=[
                """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html"""
            ]					,      )



   def  _UpperCamelCase		(    self     )				->     Tuple:
        """simple docstring"""





        if self.config_name == "multilist":
             return {
                 "predictions": datasets.Sequence(datasets.Value("""float"""     )     ),
                 "references": datasets.Sequence(datasets.Value("""float"""     )     ),
             }
        else:
             return {
                 "predictions": datasets.Value("""float"""     ),
                 "references": datasets.Value("""float"""     ),
             }




   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__=None					,      lowerCAmelCase__="uniform_average"     )				->     List[str]:
        """simple docstring"""





        _UpperCamelCase  :Tuple       =mean_absolute_error(lowerCAmelCase__					,      lowerCAmelCase__					,      sample_weight=lowerCAmelCase__					,      multioutput=lowerCAmelCase__     )

        return {"mae": mae_score}