




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_     (					lowerCamelCase:    Any  )   ->     str:


      '''simple docstring'''




      lowerCamelCase__ 					=			TimesformerConfig()

      if "large" in model_name:
            lowerCamelCase__ 					=			96

      if "hr" in model_name:
            lowerCamelCase__ 					=			16
            lowerCamelCase__ 					=			4_48

      lowerCamelCase__ 					=			'huggingface/label-files'
      if "k400" in model_name:
            lowerCamelCase__ 					=			4_00
            lowerCamelCase__ 					=			'kinetics400-id2label.json'
      elif "k600" in model_name:
            lowerCamelCase__ 					=			6_00
            lowerCamelCase__ 					=			'kinetics600-id2label.json'
      elif "ssv2" in model_name:
            lowerCamelCase__ 					=			1_74
            lowerCamelCase__ 					=			'something-something-v2-id2label.json'
      else:
            raise ValueError('Model name should either contain \'k400\', \'k600\' or \'ssv2\'.'  )
      lowerCamelCase__ 					=			json.load(open(hf_hub_download(lowerCamelCase       ,			lowerCamelCase       ,			repo_type='dataset'  )       ,			'r'  )  )
      lowerCamelCase__ 					=			{int(lowerCamelCase  ): v for k, v in idalabel.items()}
      lowerCamelCase__ 					=			idalabel
      lowerCamelCase__ 					=			{v: k for k, v in idalabel.items()}

      return config

def      lowerCAmelCase_     (					lowerCamelCase:    Dict  )   ->     Optional[int]:


      '''simple docstring'''




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

      return name

def      lowerCAmelCase_     (					lowerCamelCase:    int       ,			lowerCamelCase:    Optional[Any]  )   ->     List[Any]:


      '''simple docstring'''




      for key in orig_state_dict.copy().keys():
            lowerCamelCase__ 					=			orig_state_dict.pop(lowerCamelCase  )

            if key.startswith('model.'  ):
                  lowerCamelCase__ 					=			key.replace('model.'       ,			''  )

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

      return orig_state_dict

def      lowerCAmelCase_     (					)   ->     Optional[int]:


      '''simple docstring'''




      lowerCamelCase__ 					=			hf_hub_download(
          repo_id='hf-internal-testing/spaghetti-video'       ,			filename='eating_spaghetti.npy'       ,			repo_type='dataset'  )
      lowerCamelCase__ 					=			np.load(lowerCamelCase  )
      return list(lowerCamelCase  )

def      lowerCAmelCase_     (					lowerCamelCase:    Dict       ,			lowerCamelCase:    List[Any]       ,			lowerCamelCase:    Optional[Any]       ,			lowerCamelCase:    Optional[int]  )   ->     Union[str, Any]:


      '''simple docstring'''




      lowerCamelCase__ 					=			get_timesformer_config(lowerCamelCase  )

      lowerCamelCase__ 					=			TimesformerForVideoClassification(lowerCamelCase  )

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

      model.load_state_dict(lowerCamelCase  )
      model.eval()

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

      lowerCamelCase__ 					=			model(**lowerCamelCase  )
      lowerCamelCase__ 					=			outputs.logits

      lowerCamelCase__ 					=			[
          # 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__ 					=			torch.Size([1, 4_00]  )
            lowerCamelCase__ 					=			torch.tensor([-0.3016, -0.7713, -0.4205]  )
      elif model_name == "timesformer-base-finetuned-k600":
            lowerCamelCase__ 					=			torch.Size([1, 6_00]  )
            lowerCamelCase__ 					=			torch.tensor([-0.7267, -0.7466, 3.2404]  )
      elif model_name == "timesformer-base-finetuned-ssv2":
            lowerCamelCase__ 					=			torch.Size([1, 1_74]  )
            lowerCamelCase__ 					=			torch.tensor([-0.9059, 0.6433, -3.1457]  )
      elif model_name == "timesformer-large-finetuned-k400":
            lowerCamelCase__ 					=			torch.Size([1, 4_00]  )
            lowerCamelCase__ 					=			torch.tensor([0, 0, 0]  )
      elif model_name == "timesformer-large-finetuned-k600":
            lowerCamelCase__ 					=			torch.Size([1, 6_00]  )
            lowerCamelCase__ 					=			torch.tensor([0, 0, 0]  )
      elif model_name == "timesformer-large-finetuned-ssv2":
            lowerCamelCase__ 					=			torch.Size([1, 1_74]  )
            lowerCamelCase__ 					=			torch.tensor([0, 0, 0]  )
      elif model_name == "timesformer-hr-finetuned-k400":
            lowerCamelCase__ 					=			torch.Size([1, 4_00]  )
            lowerCamelCase__ 					=			torch.tensor([-0.9617, -3.7311, -3.7708]  )
      elif model_name == "timesformer-hr-finetuned-k600":
            lowerCamelCase__ 					=			torch.Size([1, 6_00]  )
            lowerCamelCase__ 					=			torch.tensor([2.5273, 0.7127, 1.8848]  )
      elif model_name == "timesformer-hr-finetuned-ssv2":
            lowerCamelCase__ 					=			torch.Size([1, 1_74]  )
            lowerCamelCase__ 					=			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]       ,			lowerCamelCase       ,			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(lowerCamelCase  )
            model.save_pretrained(lowerCamelCase  )

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


if __name__ == "__main__":
      __SCREAMING_SNAKE_CASE      :				List[str]				      =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."""
      )

      __SCREAMING_SNAKE_CASE      :				Tuple				      =parser.parse_args()
      convert_timesformer_checkpoint(
          args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
      )





from typing import Dict, Tuple, overload

import torch
import torch.types
from torch import nn

from . import residue_constants as rc
from .rigid_utils import Rigid, Rotation
from .tensor_utils import batched_gather

@overload
def      lowerCAmelCase_     (					lowerCamelCase:    torch.Tensor       ,			lowerCamelCase:    torch.Tensor       ,			lowerCamelCase:    None  )   ->     torch.Tensor:


      '''simple docstring'''




      ...

@overload
def      lowerCAmelCase_     (					lowerCamelCase:    torch.Tensor       ,			lowerCamelCase:    torch.Tensor       ,			lowerCamelCase:    torch.Tensor  )   ->     Tuple[torch.Tensor, torch.Tensor]:


      '''simple docstring'''




      ...

def      lowerCAmelCase_     (					lowerCamelCase:    Any       ,			lowerCamelCase:    Union[str, Any]       ,			lowerCamelCase:    Tuple  )   ->     int:


      '''simple docstring'''




      lowerCamelCase__ 					=			aatype == rc.restype_order['G']
      lowerCamelCase__ 					=			rc.atom_order['CA']
      lowerCamelCase__ 					=			rc.atom_order['CB']
      lowerCamelCase__ 					=			torch.where(
          is_gly[..., None].expand(*((-1,) * len(is_gly.shape  ))       ,			3  )       ,			all_atom_positions[..., ca_idx, :]       ,			all_atom_positions[..., cb_idx, :]       ,			)

      if all_atom_masks is not None:
            lowerCamelCase__ 					=			torch.where(
                lowerCamelCase       ,			all_atom_masks[..., ca_idx]       ,			all_atom_masks[..., cb_idx]       ,			)
            return pseudo_beta, pseudo_beta_mask
      else:
            return pseudo_beta

def      lowerCAmelCase_     (					lowerCamelCase:    torch.Tensor       ,			lowerCamelCase:    Dict[str, torch.Tensor]  )   ->     torch.Tensor:


      '''simple docstring'''




      lowerCamelCase__ 					=			batched_gather(
          lowerCamelCase       ,			batch['residx_atom37_to_atom14']       ,			dim=-2       ,			no_batch_dims=len(atomaa.shape[:-2]  )       ,			)

      lowerCamelCase__ 					=			atomaa_data * batch['atom37_atom_exists'][..., None]

      return atomaa_data

def      lowerCAmelCase_     (					lowerCamelCase:    Dict[str, torch.Tensor]  )   ->     torch.Tensor:


      '''simple docstring'''




      lowerCamelCase__ 					=			template_feats['template_aatype']
      lowerCamelCase__ 					=			template_feats['template_torsion_angles_sin_cos']
      lowerCamelCase__ 					=			template_feats['template_alt_torsion_angles_sin_cos']
      lowerCamelCase__ 					=			template_feats['template_torsion_angles_mask']
      lowerCamelCase__ 					=			torch.cat(
          [
              nn.functional.one_hot(lowerCamelCase       ,			22  ),
              torsion_angles_sin_cos.reshape(*torsion_angles_sin_cos.shape[:-2]       ,			14  ),
              alt_torsion_angles_sin_cos.reshape(*alt_torsion_angles_sin_cos.shape[:-2]       ,			14  ),
              torsion_angles_mask,
          ]       ,			dim=-1       ,			)

      return template_angle_feat

def      lowerCAmelCase_     (					lowerCamelCase:    Dict[str, torch.Tensor]       ,			lowerCamelCase:    torch.types.Number       ,			lowerCamelCase:    torch.types.Number       ,			lowerCamelCase:    int       ,			lowerCamelCase:    bool = False       ,			lowerCamelCase:    float = 1e-20       ,			lowerCamelCase:    float = 1e8       ,			)   ->     torch.Tensor:


      '''simple docstring'''




      lowerCamelCase__ 					=			batch['template_pseudo_beta_mask']
      lowerCamelCase__ 					=			template_mask[..., None] * template_mask[..., None, :]

      # Compute distogram (this seems to differ slightly from Alg. 5)
      lowerCamelCase__ 					=			batch['template_pseudo_beta']
      lowerCamelCase__ 					=			torch.sum((tpb[..., None, :] - tpb[..., None, :, :]) ** 2       ,			dim=-1       ,			keepdim=lowerCamelCase  )
      lowerCamelCase__ 					=			torch.linspace(lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase       ,			device=tpb.device  ) ** 2
      lowerCamelCase__ 					=			torch.cat([lower[1:], lower.new_tensor([inf]  )]       ,			dim=-1  )
      lowerCamelCase__ 					=			((dgram > lower) * (dgram < upper)).type(dgram.dtype  )

      lowerCamelCase__ 					=			[dgram, template_mask_ad[..., None]]

      lowerCamelCase__ 					=			nn.functional.one_hot(
          batch['template_aatype']       ,			rc.restype_num + 2       ,			)

      lowerCamelCase__ 					=			batch['template_aatype'].shape[-1]
      to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2]       ,			lowerCamelCase       ,			-1       ,			-1  )  )
      to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2]       ,			-1       ,			lowerCamelCase       ,			-1  )  )

      lowerCamelCase__					,						lowerCamelCase__					,						lowerCamelCase__ 					=			[rc.atom_order[a] for a in ['N', 'CA', 'C']]
      lowerCamelCase__ 					=			Rigid.make_transform_from_reference(
          n_xyz=batch['template_all_atom_positions'][..., n, :]       ,			ca_xyz=batch['template_all_atom_positions'][..., ca, :]       ,			c_xyz=batch['template_all_atom_positions'][..., c, :]       ,			eps=lowerCamelCase       ,			)
      lowerCamelCase__ 					=			rigids.get_trans()[..., None, :, :]
      lowerCamelCase__ 					=			rigids[..., None].invert_apply(lowerCamelCase  )

      lowerCamelCase__ 					=			torch.rsqrt(eps + torch.sum(rigid_vec**2       ,			dim=-1  )  )

      lowerCamelCase__ 					=			batch['template_all_atom_mask']
      lowerCamelCase__ 					=			t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
      lowerCamelCase__ 					=			template_mask[..., None] * template_mask[..., None, :]

      lowerCamelCase__ 					=			inv_distance_scalar * template_mask_ad
      lowerCamelCase__ 					=			rigid_vec * inv_distance_scalar[..., None]

      if not use_unit_vector:
            lowerCamelCase__ 					=			unit_vector * 0.0

      to_concat.extend(torch.unbind(unit_vector[..., None, :]       ,			dim=-1  )  )
      to_concat.append(template_mask_ad[..., None]  )

      lowerCamelCase__ 					=			torch.cat(lowerCamelCase       ,			dim=-1  )
      lowerCamelCase__ 					=			act * template_mask_ad[..., None]

      return act

def      lowerCAmelCase_     (					lowerCamelCase:    Dict[str, torch.Tensor]  )   ->     torch.Tensor:


      '''simple docstring'''




      lowerCamelCase__ 					=			nn.functional.one_hot(batch['extra_msa']       ,			23  )
      lowerCamelCase__ 					=			[
          msa_ahot,
          batch['extra_has_deletion'].unsqueeze(-1  ),
          batch['extra_deletion_value'].unsqueeze(-1  ),
      ]
      return torch.cat(lowerCamelCase       ,			dim=-1  )

def      lowerCAmelCase_     (					lowerCamelCase:    Rigid       ,			lowerCamelCase:    torch.Tensor       ,			lowerCamelCase:    torch.Tensor       ,			lowerCamelCase:    torch.Tensor       ,			)   ->     Rigid:


      '''simple docstring'''




      lowerCamelCase__ 					=			rrgdf[aatype, ...]

      # [*, N, 8] transformations, i.e.
      #   One [*, N, 8, 3, 3] rotation matrix and
      #   One [*, N, 8, 3]    translation matrix
      lowerCamelCase__ 					=			r.from_tensor_axa(lowerCamelCase  )

      lowerCamelCase__ 					=			alpha.new_zeros((*((1,) * len(alpha.shape[:-1]  )), 2)  )
      lowerCamelCase__ 					=			1

      # [*, N, 8, 2]
      lowerCamelCase__ 					=			torch.cat([bb_rot.expand(*alpha.shape[:-2]       ,			-1       ,			-1  ), alpha]       ,			dim=-2  )

      # [*, N, 8, 3, 3]
      # Produces rotation matrices of the form:
      # [
      #   [1, 0  , 0  ],
      #   [0, a_2,-a_1],
      #   [0, a_1, a_2]
      # ]
      # This follows the original code rather than the supplement, which uses
      # different indices.

      lowerCamelCase__ 					=			alpha.new_zeros(default_r.get_rots().get_rot_mats().shape  )
      lowerCamelCase__ 					=			1
      lowerCamelCase__ 					=			alpha[..., 1]
      lowerCamelCase__ 					=			-alpha[..., 0]
      lowerCamelCase__ 					=			alpha

      lowerCamelCase__ 					=			default_r.compose(Rigid(Rotation(rot_mats=lowerCamelCase  )       ,			lowerCamelCase  )  )

      lowerCamelCase__ 					=			all_frames[..., 5]
      lowerCamelCase__ 					=			all_frames[..., 6]
      lowerCamelCase__ 					=			all_frames[..., 7]

      lowerCamelCase__ 					=			all_frames[..., 4]
      lowerCamelCase__ 					=			chia_frame_to_bb.compose(lowerCamelCase  )
      lowerCamelCase__ 					=			chia_frame_to_bb.compose(lowerCamelCase  )
      lowerCamelCase__ 					=			chia_frame_to_bb.compose(lowerCamelCase  )

      lowerCamelCase__ 					=			Rigid.cat(
          [
              all_frames[..., :5],
              chia_frame_to_bb.unsqueeze(-1  ),
              chia_frame_to_bb.unsqueeze(-1  ),
              chia_frame_to_bb.unsqueeze(-1  ),
          ]       ,			dim=-1       ,			)

      lowerCamelCase__ 					=			r[..., None].compose(lowerCamelCase  )

      return all_frames_to_global


def      lowerCAmelCase_     (					lowerCamelCase:    Rigid       ,			lowerCamelCase:    torch.Tensor       ,			lowerCamelCase:    torch.Tensor       ,			lowerCamelCase:    torch.Tensor       ,			lowerCamelCase:    torch.Tensor       ,			lowerCamelCase:    torch.Tensor       ,			)   ->     torch.Tensor:


      '''simple docstring'''




      lowerCamelCase__ 					=			group_idx[aatype, ...]

      # [*, N, 14, 8]
      lowerCamelCase__ 					=			nn.functional.one_hot(
          lowerCamelCase       ,			num_classes=default_frames.shape[-3]       ,			)

      # [*, N, 14, 8]
      lowerCamelCase__ 					=			r[..., None, :] * group_mask_one_hot

      # [*, N, 14]
      lowerCamelCase__ 					=			t_atoms_to_global.map_tensor_fn(lambda lowerCamelCase  : torch.sum(lowerCamelCase       ,			dim=-1  )  )

      # [*, N, 14, 1]
      lowerCamelCase__ 					=			atom_mask[aatype, ...].unsqueeze(-1  )

      # [*, N, 14, 3]
      lowerCamelCase__ 					=			lit_positions[aatype, ...]
      lowerCamelCase__ 					=			t_atoms_to_global.apply(lowerCamelCase  )
      lowerCamelCase__ 					=			pred_positions * atom_mask

      return pred_positions