





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        lowerCAmelCase_       (							UpperCamelCase_     )   ->						Tuple:
       UpperCamelCase_      				=     TimesformerConfig()

       if "large" in model_name:
              UpperCamelCase_      				=     96

       if "hr" in model_name:
              UpperCamelCase_      				=     16
              UpperCamelCase_      				=     448

       UpperCamelCase_      				=     "huggingface/label-files"
       if "k400" in model_name:
              UpperCamelCase_      				=     400
              UpperCamelCase_      				=     "kinetics400-id2label.json"
       elif "k600" in model_name:
              UpperCamelCase_      				=     600
              UpperCamelCase_      				=     "kinetics600-id2label.json"
       elif "ssv2" in model_name:
              UpperCamelCase_      				=     174
              UpperCamelCase_      				=     "something-something-v2-id2label.json"
       else:
              raise ValueError("Model name should either contain 'k400', 'k600' or 'ssv2'."     )
       UpperCamelCase_      				=     json.load(open(hf_hub_download(UpperCamelCase_    ,						UpperCamelCase_    ,						repo_type="dataset"     )    ,						"r"     )     )
       UpperCamelCase_      				=     {int(UpperCamelCase_     ): v for k, v in idalabel.items()}
       UpperCamelCase_      				=     idalabel
       UpperCamelCase_      				=     {v: k for k, v in idalabel.items()}

       return config




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

       return name




def        lowerCAmelCase_       (							UpperCamelCase_    ,						UpperCamelCase_     )   ->						str:
       for key in orig_state_dict.copy().keys():
              UpperCamelCase_      				=     orig_state_dict.pop(UpperCamelCase_     )

              if key.startswith("model."     ):
                     UpperCamelCase_      				=     key.replace("model."    ,						""     )

              if "qkv" in key:
                     UpperCamelCase_      				=     key.split("."     )
                     UpperCamelCase_      				=     int(key_split[1]     )
                     UpperCamelCase_      				=     "timesformer.encoder.layer."
                     if "temporal" in key:
                            UpperCamelCase_      				=     ".temporal_attention.attention.qkv."
                     else:
                            UpperCamelCase_      				=     ".attention.attention.qkv."
                     if "weight" in key:
                            UpperCamelCase_      				=     val
                     else:
                            UpperCamelCase_      				=     val
              else:
                     UpperCamelCase_      				=     val

       return orig_state_dict




def        lowerCAmelCase_       (							)   ->						Tuple:
       UpperCamelCase_      				=     hf_hub_download(
           repo_id="hf-internal-testing/spaghetti-video"    ,						filename="eating_spaghetti.npy"    ,						repo_type="dataset"     )
       UpperCamelCase_      				=     np.load(UpperCamelCase_     )
       return list(UpperCamelCase_     )




def        lowerCAmelCase_       (							UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_     )   ->						List[Any]:
       UpperCamelCase_      				=     get_timesformer_config(UpperCamelCase_     )

       UpperCamelCase_      				=     TimesformerForVideoClassification(UpperCamelCase_     )

       # download original checkpoint, hosted on Google Drive
       UpperCamelCase_      				=     "pytorch_model.bin"
       gdown.cached_download(UpperCamelCase_    ,						UpperCamelCase_    ,						quiet=UpperCamelCase_     )
       UpperCamelCase_      				=     torch.load(UpperCamelCase_    ,						map_location="cpu"     )
       if "model" in files:
              UpperCamelCase_      				=     files["model"]
       elif "module" in files:
              UpperCamelCase_      				=     files["module"]
       else:
              UpperCamelCase_      				=     files["model_state"]
       UpperCamelCase_      				=     convert_state_dict(UpperCamelCase_    ,						UpperCamelCase_     )

       model.load_state_dict(UpperCamelCase_     )
       model.eval()

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

       UpperCamelCase_      				=     model(**UpperCamelCase_     )
       UpperCamelCase_      				=     outputs.logits

       UpperCamelCase_      				=     [
           # 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":
              UpperCamelCase_      				=     torch.Size([1, 400]     )
              UpperCamelCase_      				=     torch.tensor([-0.30_16, -0.77_13, -0.42_05]     )
       elif model_name == "timesformer-base-finetuned-k600":
              UpperCamelCase_      				=     torch.Size([1, 600]     )
              UpperCamelCase_      				=     torch.tensor([-0.72_67, -0.74_66, 3.24_04]     )
       elif model_name == "timesformer-base-finetuned-ssv2":
              UpperCamelCase_      				=     torch.Size([1, 174]     )
              UpperCamelCase_      				=     torch.tensor([-0.90_59, 0.64_33, -3.14_57]     )
       elif model_name == "timesformer-large-finetuned-k400":
              UpperCamelCase_      				=     torch.Size([1, 400]     )
              UpperCamelCase_      				=     torch.tensor([0, 0, 0]     )
       elif model_name == "timesformer-large-finetuned-k600":
              UpperCamelCase_      				=     torch.Size([1, 600]     )
              UpperCamelCase_      				=     torch.tensor([0, 0, 0]     )
       elif model_name == "timesformer-large-finetuned-ssv2":
              UpperCamelCase_      				=     torch.Size([1, 174]     )
              UpperCamelCase_      				=     torch.tensor([0, 0, 0]     )
       elif model_name == "timesformer-hr-finetuned-k400":
              UpperCamelCase_      				=     torch.Size([1, 400]     )
              UpperCamelCase_      				=     torch.tensor([-0.96_17, -3.73_11, -3.77_08]     )
       elif model_name == "timesformer-hr-finetuned-k600":
              UpperCamelCase_      				=     torch.Size([1, 600]     )
              UpperCamelCase_      				=     torch.tensor([2.52_73, 0.71_27, 1.88_48]     )
       elif model_name == "timesformer-hr-finetuned-ssv2":
              UpperCamelCase_      				=     torch.Size([1, 174]     )
              UpperCamelCase_      				=     torch.tensor([-3.67_56, -0.75_13, 0.71_80]     )
       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
     )








import io
import json
import subprocess


_UpperCAmelCase				       = [
    ['en', 'ru'],
    ['ru', 'en'],
    ['en', 'de'],
    ['de', 'en'],
]

_UpperCAmelCase				       = 8




def        lowerCAmelCase_       (							UpperCamelCase_    ,						UpperCamelCase_     )   ->						Union[str, Any]:
       UpperCamelCase_      				=     {}
       for src, tgt in pairs:
              UpperCamelCase_      				=     F'''{src}-{tgt}'''
              UpperCamelCase_      				=     F'''sacrebleu -t wmt19 -l {pair} --echo src'''.split()
              UpperCamelCase_      				=     subprocess.run(UpperCamelCase_    ,						stdout=subprocess.PIPE     ).stdout.decode("utf-8"     ).splitlines()
              UpperCamelCase_      				=     F'''sacrebleu -t wmt19 -l {pair} --echo ref'''.split()
              UpperCamelCase_      				=     subprocess.run(UpperCamelCase_    ,						stdout=subprocess.PIPE     ).stdout.decode("utf-8"     ).splitlines()
              UpperCamelCase_      				=     {"src": src_lines[:n_objs], "tgt": tgt_lines[:n_objs]}
       return text


_UpperCAmelCase				       = get_all_data(pairs, n_objs)
_UpperCAmelCase				       = './fsmt_val_data.json'
with io.open(filename, 'w', encoding='utf-8') as f:
     _UpperCAmelCase				       = json.dump(text, f, indent=2, ensure_ascii=False)

