




"""simple docstring"""

import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig, TimesformerForVideoClassification, VideoMAEImageProcessor



def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	int:
 lowerCAmelCase__      :							Union[str, Any]    =							TimesformerConfig()

 if "large" in model_name:
  lowerCAmelCase__      :							Tuple    =							9_6

 if "hr" in model_name:
  lowerCAmelCase__      :							Tuple    =							1_6
  lowerCAmelCase__      :							Any    =							4_4_8

 lowerCAmelCase__      :							int    =							'''huggingface/label-files'''
 if "k400" in model_name:
  lowerCAmelCase__      :							Optional[Any]    =							4_0_0
  lowerCAmelCase__      :							Optional[Any]    =							'''kinetics400-id2label.json'''
 elif "k600" in model_name:
  lowerCAmelCase__      :							Union[str, Any]    =							6_0_0
  lowerCAmelCase__      :							Any    =							'''kinetics600-id2label.json'''
 elif "ssv2" in model_name:
  lowerCAmelCase__      :							Any    =							1_7_4
  lowerCAmelCase__      :							str    =							'''something-something-v2-id2label.json'''
 else:
  raise ValueError("Model name should either contain \'k400\', \'k600\' or \'ssv2\'."      )
 lowerCAmelCase__      :							Any    =							json.load(open(hf_hub_download(UpperCamelCase__				,				UpperCamelCase__				,				repo_type="dataset"      )				,				"r"      )      )
 lowerCAmelCase__      :							List[Any]    =							{int(UpperCamelCase__      ): v for k, v in idalabel.items()}
 lowerCAmelCase__      :							Tuple    =							idalabel
 lowerCAmelCase__      :							List[str]    =							{v: k for k, v in idalabel.items()}

 return config



def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	Optional[int]:
 if "encoder." in name:
  lowerCAmelCase__      :							Tuple    =							name.replace("encoder."				,				""      )
 if "cls_token" in name:
  lowerCAmelCase__      :							List[str]    =							name.replace("cls_token"				,				"timesformer.embeddings.cls_token"      )
 if "pos_embed" in name:
  lowerCAmelCase__      :							Tuple    =							name.replace("pos_embed"				,				"timesformer.embeddings.position_embeddings"      )
 if "time_embed" in name:
  lowerCAmelCase__      :							List[str]    =							name.replace("time_embed"				,				"timesformer.embeddings.time_embeddings"      )
 if "patch_embed.proj" in name:
  lowerCAmelCase__      :							Tuple    =							name.replace("patch_embed.proj"				,				"timesformer.embeddings.patch_embeddings.projection"      )
 if "patch_embed.norm" in name:
  lowerCAmelCase__      :							str    =							name.replace("patch_embed.norm"				,				"timesformer.embeddings.norm"      )
 if "blocks" in name:
  lowerCAmelCase__      :							Tuple    =							name.replace("blocks"				,				"timesformer.encoder.layer"      )
 if "attn.proj" in name:
  lowerCAmelCase__      :							Optional[Any]    =							name.replace("attn.proj"				,				"attention.output.dense"      )
 if "attn" in name and "bias" not in name and "temporal" not in name:
  lowerCAmelCase__      :							Dict    =							name.replace("attn"				,				"attention.self"      )
 if "attn" in name and "temporal" not in name:
  lowerCAmelCase__      :							Tuple    =							name.replace("attn"				,				"attention.attention"      )
 if "temporal_norm1" in name:
  lowerCAmelCase__      :							Any    =							name.replace("temporal_norm1"				,				"temporal_layernorm"      )
 if "temporal_attn.proj" in name:
  lowerCAmelCase__      :							List[str]    =							name.replace("temporal_attn"				,				"temporal_attention.output.dense"      )
 if "temporal_fc" in name:
  lowerCAmelCase__      :							List[str]    =							name.replace("temporal_fc"				,				"temporal_dense"      )
 if "norm1" in name and "temporal" not in name:
  lowerCAmelCase__      :							Tuple    =							name.replace("norm1"				,				"layernorm_before"      )
 if "norm2" in name:
  lowerCAmelCase__      :							List[Any]    =							name.replace("norm2"				,				"layernorm_after"      )
 if "mlp.fc1" in name:
  lowerCAmelCase__      :							List[str]    =							name.replace("mlp.fc1"				,				"intermediate.dense"      )
 if "mlp.fc2" in name:
  lowerCAmelCase__      :							Optional[int]    =							name.replace("mlp.fc2"				,				"output.dense"      )
 if "norm.weight" in name and "fc" not in name and "temporal" not in name:
  lowerCAmelCase__      :							List[str]    =							name.replace("norm.weight"				,				"timesformer.layernorm.weight"      )
 if "norm.bias" in name and "fc" not in name and "temporal" not in name:
  lowerCAmelCase__      :							str    =							name.replace("norm.bias"				,				"timesformer.layernorm.bias"      )
 if "head" in name:
  lowerCAmelCase__      :							List[str]    =							name.replace("head"				,				"classifier"      )

 return name



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__      ) ->	Union[str, Any]:
 for key in orig_state_dict.copy().keys():
  lowerCAmelCase__      :							str    =							orig_state_dict.pop(UpperCamelCase__      )

  if key.startswith("model."      ):
   lowerCAmelCase__      :							Optional[Any]    =							key.replace("model."				,				""      )

  if "qkv" in key:
   lowerCAmelCase__      :							List[str]    =							key.split("."      )
   lowerCAmelCase__      :							int    =							int(key_split[1]      )
   lowerCAmelCase__      :							List[Any]    =							'''timesformer.encoder.layer.'''
   if "temporal" in key:
    lowerCAmelCase__      :							int    =							'''.temporal_attention.attention.qkv.'''
   else:
    lowerCAmelCase__      :							List[str]    =							'''.attention.attention.qkv.'''
   if "weight" in key:
    lowerCAmelCase__      :							List[Any]    =							val
   else:
    lowerCAmelCase__      :							Any    =							val
  else:
   lowerCAmelCase__      :							Union[str, Any]    =							val

 return orig_state_dict



def 			SCREAMING_SNAKE_CASE       (   ) ->	List[str]:
 lowerCAmelCase__      :							Dict    =							hf_hub_download(
     repo_id="hf-internal-testing/spaghetti-video"				,				filename="eating_spaghetti.npy"				,				repo_type="dataset"      )
 lowerCAmelCase__      :							Tuple    =							np.load(UpperCamelCase__      )
 return list(UpperCamelCase__      )



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__				,				lowercase__				,				lowercase__      ) ->	Optional[Any]:
 lowerCAmelCase__      :							Any    =							get_timesformer_config(UpperCamelCase__      )

 lowerCAmelCase__      :							int    =							TimesformerForVideoClassification(UpperCamelCase__      )

 # download original checkpoint, hosted on Google Drive
 lowerCAmelCase__      :							Optional[int]    =							'''pytorch_model.bin'''
 gdown.cached_download(UpperCamelCase__				,				UpperCamelCase__				,				quiet=UpperCamelCase__      )
 lowerCAmelCase__      :							Optional[Any]    =							torch.load(UpperCamelCase__				,				map_location="cpu"      )
 if "model" in files:
  lowerCAmelCase__      :							Tuple    =							files['''model''']
 elif "module" in files:
  lowerCAmelCase__      :							List[str]    =							files['''module''']
 else:
  lowerCAmelCase__      :							Union[str, Any]    =							files['''model_state''']
 lowerCAmelCase__      :							Dict    =							convert_state_dict(UpperCamelCase__				,				UpperCamelCase__      )

 model.load_state_dict(UpperCamelCase__      )
 model.eval()

 # verify model on basic input
 lowerCAmelCase__      :							List[str]    =							VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]				,				image_std=[0.5, 0.5, 0.5]      )
 lowerCAmelCase__      :							int    =							prepare_video()
 lowerCAmelCase__      :							List[str]    =							image_processor(video[:8]				,				return_tensors="pt"      )

 lowerCAmelCase__      :							int    =							model(**UpperCamelCase__      )
 lowerCAmelCase__      :							Optional[int]    =							outputs.logits

 lowerCAmelCase__      :							Union[str, Any]    =							[
     # Kinetics-400 checkpoints (hr = high resolution input of 448px instead of 224px)
     '''timesformer-base-finetuned-k400''',
     '''timesformer-large-finetuned-k400''',
     '''timesformer-hr-finetuned-k400''',
     # Kinetics-600 checkpoints (hr = high resolution input of 448px instead of 224px)
     '''timesformer-base-finetuned-k600''',
     '''timesformer-large-finetuned-k600''',
     '''timesformer-hr-finetuned-k600''',
     # Something-Something-v2 checkpoints (hr = high resolution input of 448px instead of 224px)
     '''timesformer-base-finetuned-ssv2''',
     '''timesformer-large-finetuned-ssv2''',
     '''timesformer-hr-finetuned-ssv2''',
 ]

 # NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
 if model_name == "timesformer-base-finetuned-k400":
  lowerCAmelCase__      :							List[Any]    =							torch.Size([1, 4_0_0]      )
  lowerCAmelCase__      :							Union[str, Any]    =							torch.tensor([-0.3_016, -0.7_713, -0.4_205]      )
 elif model_name == "timesformer-base-finetuned-k600":
  lowerCAmelCase__      :							Tuple    =							torch.Size([1, 6_0_0]      )
  lowerCAmelCase__      :							Optional[int]    =							torch.tensor([-0.7_267, -0.7_466, 3.2_404]      )
 elif model_name == "timesformer-base-finetuned-ssv2":
  lowerCAmelCase__      :							int    =							torch.Size([1, 1_7_4]      )
  lowerCAmelCase__      :							Optional[Any]    =							torch.tensor([-0.9_059, 0.6_433, -3.1_457]      )
 elif model_name == "timesformer-large-finetuned-k400":
  lowerCAmelCase__      :							Any    =							torch.Size([1, 4_0_0]      )
  lowerCAmelCase__      :							str    =							torch.tensor([0, 0, 0]      )
 elif model_name == "timesformer-large-finetuned-k600":
  lowerCAmelCase__      :							Tuple    =							torch.Size([1, 6_0_0]      )
  lowerCAmelCase__      :							Any    =							torch.tensor([0, 0, 0]      )
 elif model_name == "timesformer-large-finetuned-ssv2":
  lowerCAmelCase__      :							List[Any]    =							torch.Size([1, 1_7_4]      )
  lowerCAmelCase__      :							Optional[Any]    =							torch.tensor([0, 0, 0]      )
 elif model_name == "timesformer-hr-finetuned-k400":
  lowerCAmelCase__      :							List[Any]    =							torch.Size([1, 4_0_0]      )
  lowerCAmelCase__      :							Optional[int]    =							torch.tensor([-0.9_617, -3.7_311, -3.7_708]      )
 elif model_name == "timesformer-hr-finetuned-k600":
  lowerCAmelCase__      :							List[Any]    =							torch.Size([1, 6_0_0]      )
  lowerCAmelCase__      :							Tuple    =							torch.tensor([2.5_273, 0.7_127, 1.8_848]      )
 elif model_name == "timesformer-hr-finetuned-ssv2":
  lowerCAmelCase__      :							List[str]    =							torch.Size([1, 1_7_4]      )
  lowerCAmelCase__      :							Tuple    =							torch.tensor([-3.6_756, -0.7_513, 0.7_180]      )
 else:
  raise ValueError(F"""Model name not supported. Should be one of {model_names}"""      )

 # verify logits
 assert logits.shape == expected_shape
 assert torch.allclose(logits[0, :3]				,				UpperCamelCase__				,				atol=1E-4      )
 print("Logits ok!"      )

 if pytorch_dump_folder_path is not None:
  print(F"""Saving model and image processor to {pytorch_dump_folder_path}"""      )
  image_processor.save_pretrained(UpperCamelCase__      )
  model.save_pretrained(UpperCamelCase__      )

 if push_to_hub:
  print("Pushing to the hub..."      )
  model.push_to_hub(F"""fcakyon/{model_name}"""      )


if __name__ == "__main__":
    _UpperCamelCase		    =						argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--checkpoint_url""",
        default="""https://drive.google.com/u/1/uc?id=17yvuYp9L4mn-HpIcK5Zo6K3UoOy1kA5l&export=download""",
        type=str,
        help=(
            """URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct"""
            """ download link."""
        ),
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""",
        default="""""",
        type=str,
        help="""Path to the output PyTorch model directory.""",
    )
    parser.add_argument("""--model_name""", default="""timesformer-base-finetuned-k400""", type=str, help="""Name of the model.""")
    parser.add_argument(
        """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
    )

    _UpperCamelCase		    =						parser.parse_args()
    convert_timesformer_checkpoint(
        args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
    )









"""simple docstring"""

import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets



@contextmanager
def 			SCREAMING_SNAKE_CASE       (   ) ->	Any:
 def filter_log(lowercase__      ):
  return False if "This IS expected if you are initializing" in record.msg else True

 lowerCAmelCase__      :							Tuple    =							datasets.utils.logging.get_logger("transformers.modeling_utils"      )
 logger.addFilter(lowercase__      )
 try:
  yield
 finally:
  logger.removeFilter(lowercase__      )


_UpperCamelCase		    =						"""\
@inproceedings{bert-score,
  title={BERTScore: Evaluating Text Generation with BERT},
  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},
  booktitle={International Conference on Learning Representations},
  year={2020},
  url={https://openreview.net/forum?id=SkeHuCVFDr}
}
"""

_UpperCamelCase		    =						"""\
BERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference
sentences by cosine similarity.
It has been shown to correlate with human judgment on sentence-level and system-level evaluation.
Moreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language
generation tasks.

See the project's README at https://github.com/Tiiiger/bert_score#readme for more information.
"""

_UpperCamelCase		    =						"""
BERTScore Metrics with the hashcode from a source against one or more references.

Args:
    predictions (list of str): Prediction/candidate sentences.
    references (list of str or list of list of str): Reference sentences.
    lang (str): Language of the sentences; required (e.g. 'en').
    model_type (str): Bert specification, default using the suggested
        model for the target language; has to specify at least one of
        `model_type` or `lang`.
    num_layers (int): The layer of representation to use,
        default using the number of layers tuned on WMT16 correlation data.
    verbose (bool): Turn on intermediate status update.
    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.
    device (str): On which the contextual embedding model will be allocated on.
        If this argument is None, the model lives on cuda:0 if cuda is available.
    nthreads (int): Number of threads.
    batch_size (int): Bert score processing batch size,
        at least one of `model_type` or `lang`. `lang` needs to be
        specified when `rescale_with_baseline` is True.
    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.
    baseline_path (str): Customized baseline file.
    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.

Returns:
    precision: Precision.
    recall: Recall.
    f1: F1 score.
    hashcode: Hashcode of the library.

Examples:

    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> bertscore = datasets.load_metric(\"bertscore\")
    >>> results = bertscore.compute(predictions=predictions, references=references, lang=\"en\")
    >>> print([round(v, 2) for v in results[\"f1\"]])
    [1.0, 1.0]
"""






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





       """simple docstring"""


       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        return datasets.MetricInfo(
            description=_DESCRIPTION ,				citation=_CITATION ,				homepage="https://github.com/Tiiiger/bert_score" ,				inputs_description=_KWARGS_DESCRIPTION ,				features=datasets.Features(
                {
                    "predictions": datasets.Value("string" ,				id="sequence"      ),
                    "references": datasets.Sequence(datasets.Value("string" ,				id="sequence"      ) ,				id="references"      ),
                }      ) ,				codebase_urls=["https://github.com/Tiiiger/bert_score"] ,				reference_urls=[
                "https://github.com/Tiiiger/bert_score",
                "https://arxiv.org/abs/1904.09675",
            ] ,				)






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case=None ,				snake_case=None ,				snake_case=None ,				snake_case=False ,				snake_case=False ,				snake_case=None ,				snake_case=64 ,				snake_case=4 ,				snake_case=False ,				snake_case=False ,				snake_case=None ,				snake_case=False ,				):





        """simple docstring"""

        lowerCAmelCase__      :							Any    =							bert_score.utils.get_hash
        lowerCAmelCase__      :							List[Any]    =							bert_score.BERTScorer

        if version.parse(bert_score.__version__      ) >= version.parse("0.3.10"      ):
         lowerCAmelCase__      :							str    =							functools.partial(snake_case ,				use_fast_tokenizer=snake_case      )
         lowerCAmelCase__      :							Tuple    =							functools.partial(snake_case ,				use_fast_tokenizer=snake_case      )
        elif use_fast_tokenizer:
         raise ImportWarning(
             "To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn't match this condition.\n"
             "You can install it with `pip install \"bert-score>=0.3.10\"`."      )

        if model_type is None:
         assert lang is not None, "either lang or model_type should be specified"
         lowerCAmelCase__      :							Dict    =							bert_score.utils.langamodel[lang.lower()]

        if num_layers is None:
         lowerCAmelCase__      :							int    =							bert_score.utils.modelalayers[model_type]

        lowerCAmelCase__      :							Optional[int]    =							get_hash(
            model=snake_case ,				num_layers=snake_case ,				idf=snake_case ,				rescale_with_baseline=snake_case ,				use_custom_baseline=baseline_path is not None ,				)

        with filter_logging_context():
         if not hasattr(self ,				"cached_bertscorer"      ) or self.cached_bertscorer.hash != hashcode:
          lowerCAmelCase__      :							Dict    =							scorer(
              model_type=snake_case ,				num_layers=snake_case ,				batch_size=snake_case ,				nthreads=snake_case ,				all_layers=snake_case ,				idf=snake_case ,				device=snake_case ,				lang=snake_case ,				rescale_with_baseline=snake_case ,				baseline_path=snake_case ,				)

        ((lowerCAmelCase__) ,	(lowerCAmelCase__) ,	(lowerCAmelCase__))      :							int    =							self.cached_bertscorer.score(
            cands=snake_case ,				refs=snake_case ,				verbose=snake_case ,				batch_size=snake_case ,				)
        lowerCAmelCase__      :							List[str]    =							{
            "precision": P.tolist(),
            "recall": R.tolist(),
            "f1": F.tolist(),
            "hashcode": hashcode,
        }
        return output_dict






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case=None ,				snake_case=None ,				**snake_case      ):





        """simple docstring"""

        if references is not None:
         lowerCAmelCase__      :							Union[str, Any]    =							[[ref] if isinstance(snake_case ,				snake_case      ) else ref for ref in references]
        super().add_batch(predictions=snake_case ,				references=snake_case ,				**snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case=None ,				snake_case=None ,				**snake_case      ):





        """simple docstring"""

        if isinstance(snake_case ,				snake_case      ):
         lowerCAmelCase__      :							str    =							[reference]
        super().add(prediction=snake_case ,				reference=snake_case ,				**snake_case      )






