


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 	A (		A__			):
     UpperCAmelCase    :					str			=      TimesformerConfig()

     if "large" in model_name:
          UpperCAmelCase    :					List[str]			=      9_6

     if "hr" in model_name:
          UpperCAmelCase    :					Optional[Any]			=      1_6
          UpperCAmelCase    :					str			=      4_4_8

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

     return config







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

     return name







def 	A (		A__		,		A__			):
     for key in orig_state_dict.copy().keys():
          UpperCAmelCase    :					Dict			=      orig_state_dict.pop(A__			)

          if key.startswith("model."			):
               UpperCAmelCase    :					Optional[int]			=      key.replace("model."		,		""			)

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

     return orig_state_dict







def 	A (		):
     UpperCAmelCase    :					Tuple			=      hf_hub_download(
         repo_id="hf-internal-testing/spaghetti-video"		,		filename="eating_spaghetti.npy"		,		repo_type="dataset"			)
     UpperCAmelCase    :					Optional[Any]			=      np.load(A__			)
     return list(A__			)







def 	A (		A__		,		A__		,		A__		,		A__			):
     UpperCAmelCase    :					int			=      get_timesformer_config(A__			)

     UpperCAmelCase    :					Any			=      TimesformerForVideoClassification(A__			)

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

     model.load_state_dict(A__			)
     model.eval()

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

     UpperCAmelCase    :					Dict			=      model(**A__			)
     UpperCAmelCase    :					Union[str, Any]			=      outputs.logits

     UpperCAmelCase    :					Optional[int]			=      [
         # 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    :					List[Any]			=      torch.Size([1, 4_0_0]			)
          UpperCAmelCase    :					List[Any]			=      torch.tensor([-0.3016, -0.7713, -0.4205]			)
     elif model_name == "timesformer-base-finetuned-k600":
          UpperCAmelCase    :					Optional[Any]			=      torch.Size([1, 6_0_0]			)
          UpperCAmelCase    :					Dict			=      torch.tensor([-0.7267, -0.7466, 3.2404]			)
     elif model_name == "timesformer-base-finetuned-ssv2":
          UpperCAmelCase    :					str			=      torch.Size([1, 1_7_4]			)
          UpperCAmelCase    :					Any			=      torch.tensor([-0.9059, 0.6433, -3.1457]			)
     elif model_name == "timesformer-large-finetuned-k400":
          UpperCAmelCase    :					Tuple			=      torch.Size([1, 4_0_0]			)
          UpperCAmelCase    :					Tuple			=      torch.tensor([0, 0, 0]			)
     elif model_name == "timesformer-large-finetuned-k600":
          UpperCAmelCase    :					Dict			=      torch.Size([1, 6_0_0]			)
          UpperCAmelCase    :					Union[str, Any]			=      torch.tensor([0, 0, 0]			)
     elif model_name == "timesformer-large-finetuned-ssv2":
          UpperCAmelCase    :					Optional[int]			=      torch.Size([1, 1_7_4]			)
          UpperCAmelCase    :					Any			=      torch.tensor([0, 0, 0]			)
     elif model_name == "timesformer-hr-finetuned-k400":
          UpperCAmelCase    :					int			=      torch.Size([1, 4_0_0]			)
          UpperCAmelCase    :					Union[str, Any]			=      torch.tensor([-0.9617, -3.7311, -3.7708]			)
     elif model_name == "timesformer-hr-finetuned-k600":
          UpperCAmelCase    :					Optional[Any]			=      torch.Size([1, 6_0_0]			)
          UpperCAmelCase    :					Union[str, Any]			=      torch.tensor([2.5273, 0.7127, 1.8848]			)
     elif model_name == "timesformer-hr-finetuned-ssv2":
          UpperCAmelCase    :					str			=      torch.Size([1, 1_7_4]			)
          UpperCAmelCase    :					int			=      torch.tensor([-3.6756, -0.7513, 0.7180]			)
     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]		,		A__		,		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(A__			)
          model.save_pretrained(A__			)

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


if __name__ == "__main__":
      _snake_case				=			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."
      )

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







from typing import TYPE_CHECKING

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


_snake_case				=			{
    "configuration_oneformer": ["ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "OneFormerConfig"],
    "processing_oneformer": ["OneFormerProcessor"],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case				=			["OneFormerImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case				=			[
          "ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "OneFormerForUniversalSegmentation",
          "OneFormerModel",
          "OneFormerPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_oneformer import ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, OneFormerConfig
      from .processing_oneformer import OneFormerProcessor

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_oneformer import OneFormerImageProcessor
      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_oneformer import (
                ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                OneFormerForUniversalSegmentation,
                OneFormerModel,
                OneFormerPreTrainedModel,
            )


else:
      import sys

      _snake_case				=			_LazyModule(__name__, globals()["__file__"], _import_structure)



