





"""simple docstring"""
import math
import sys
class lowerCamelCase__  :



   def __init__(       self	) ->    List[Any]:







      """simple docstring"""
      lowercase    :			Union[str, Any]  =       0
      lowercase    :			Tuple  =       []
      lowercase    :			Optional[Any]  =       {}  # To store the pos of node in array



   def    _UpperCAmelCase	(       self	) ->    Optional[Any]:







      """simple docstring"""
      return self.cur_size == 0



   def    _UpperCAmelCase	(       self      ,   snake_case	) ->    str:







      """simple docstring"""
      lowercase    :			str  =       self.left(_lowerCAmelCase	)
      lowercase    :			int  =       self.right(_lowerCAmelCase	)
      if lc < self.cur_size and self.array(_lowerCAmelCase	)[0] < self.array(_lowerCAmelCase	)[0]:
         lowercase    :			List[str]  =       lc
      else:
         lowercase    :			Optional[int]  =       idx
      if rc < self.cur_size and self.array(_lowerCAmelCase	)[0] < self.array(_lowerCAmelCase	)[0]:
         lowercase    :			Tuple  =       rc
      if smallest != idx:
         self.swap(_lowerCAmelCase      ,   _lowerCAmelCase	)
         self.min_heapify(_lowerCAmelCase	)



   def    _UpperCAmelCase	(       self      ,   snake_case	) ->    List[str]:







      """simple docstring"""
      # Inserts a node into the Priority Queue
      lowercase    :			List[Any]  =       self.cur_size
      self.cur_size += 1
      self.array.append((sys.maxsize, tup[1])	)
      self.decrease_key((sys.maxsize, tup[1])      ,   tup[0]	)



   def    _UpperCAmelCase	(       self	) ->    Union[str, Any]:







      """simple docstring"""
      # Removes and returns the min element at top of priority queue
      lowercase    :			Tuple  =       self.array[0][1]
      lowercase    :			Dict  =       self.array[self.cur_size - 1]
      self.cur_size -= 1
      self.min_heapify(1	)
      del self.pos[min_node]
      return min_node



   def    _UpperCAmelCase	(       self      ,   snake_case	) ->    Any:







      """simple docstring"""
      # returns the index of left child
      return 2 * i + 1



   def    _UpperCAmelCase	(       self      ,   snake_case	) ->    Dict:







      """simple docstring"""
      # returns the index of right child
      return 2 * i + 2



   def    _UpperCAmelCase	(       self      ,   snake_case	) ->    Any:







      """simple docstring"""
      # returns the index of parent
      return math.floor(i / 2	)



   def    _UpperCAmelCase	(       self      ,   snake_case      ,   snake_case	) ->    Dict:







      """simple docstring"""
      # swaps array elements at indices i and j
      # update the pos{}
      lowercase    :			Any  =       j
      lowercase    :			Any  =       i
      lowercase    :			Dict  =       self.array[i]
      lowercase    :			Union[str, Any]  =       self.array[j]
      lowercase    :			List[str]  =       temp





   def    _UpperCAmelCase	(       self      ,   snake_case      ,   snake_case	) ->    int:







      """simple docstring"""
      lowercase    :			Optional[Any]  =       self.pos[tup[1]]
      # assuming the new_d is atmost old_d
      lowercase    :			str  =       (new_d, tup[1])
      while idx > 0 and self.array[self.par(_lowerCAmelCase	)][0] > self.array[idx][0]:
         self.swap(_lowerCAmelCase      ,   self.par(_lowerCAmelCase	)	)
         lowercase    :			List[Any]  =       self.par(_lowerCAmelCase	)




class lowerCamelCase__  :



   def __init__(       self      ,   snake_case	) ->    Optional[Any]:







      """simple docstring"""
      lowercase    :			Optional[Any]  =       {}  # To store graph: u -> (v,w)
      lowercase    :			Union[str, Any]  =       num  # Number of nodes in graph
      # To store the distance from source vertex
      lowercase    :			List[str]  =       [0] * self.num_nodes
      lowercase    :			Dict  =       [-1] * self.num_nodes  # To store the path



   def    _UpperCAmelCase	(       self      ,   snake_case      ,   snake_case      ,   snake_case	) ->    Optional[int]:







      """simple docstring"""
      #  Edge going from node u to v and v to u with weight w
      # u (w)-> v, v (w) -> u
      # Check if u already in graph
      if u in self.adjList:
         self.adjList[u].append((v, w)	)
      else:
         lowercase    :			Optional[int]  =       [(v, w)]

      # Assuming undirected graph
      if v in self.adjList:
         self.adjList[v].append((u, w)	)
      else:
         lowercase    :			List[Any]  =       [(u, w)]



   def    _UpperCAmelCase	(       self	) ->    Optional[int]:







      """simple docstring"""
      # u -> v(w)
      for u in self.adjList:
         print(_lowerCAmelCase      ,   """->"""      ,   """ -> """.join(str(f'''{v}({w})'''	) for v, w in self.adjList[u]	)	)



   def    _UpperCAmelCase	(       self      ,   snake_case	) ->    str:







      """simple docstring"""
      # Flush old junk values in par[]
      lowercase    :			Dict  =       [-1] * self.num_nodes
      # src is the source node
      lowercase    :			Any  =       0
      lowercase    :			Optional[Any]  =       PriorityQueue()
      q.insert((0, src)	)  # (dist from src, node)
      for u in self.adjList:
         if u != src:
            lowercase    :			List[Any]  =       sys.maxsize  # Infinity
            lowercase    :			Tuple  =       -1

      while not q.is_empty():
         lowercase    :			List[Any]  =       q.extract_min()  # Returns node with the min dist from source
         # Update the distance of all the neighbours of u and
         # if their prev dist was INFINITY then push them in Q
         for v, w in self.adjList[u]:
            lowercase    :			Optional[Any]  =       self.dist[u] + w
            if self.dist[v] > new_dist:
               if self.dist[v] == sys.maxsize:
                  q.insert((new_dist, v)	)
               else:
                  q.decrease_key((self.dist[v], v)      ,   _lowerCAmelCase	)
               lowercase    :			Dict  =       new_dist
               lowercase    :			List[Any]  =       u

        # Show the shortest distances from src
      self.show_distances(_lowerCAmelCase	)



   def    _UpperCAmelCase	(       self      ,   snake_case	) ->    Any:







      """simple docstring"""
      print(f'''Distance from node: {src}'''	)
      for u in range(self.num_nodes	):
         print(f'''Node {u} has distance: {self.dist[u]}'''	)





   def    _UpperCAmelCase	(       self      ,   snake_case      ,   snake_case	) ->    int:







      """simple docstring"""
      # To show the shortest path from src to dest
      # WARNING: Use it *after* calling dijkstra
      lowercase    :			str  =       []
      lowercase    :			List[str]  =       0
      lowercase    :			int  =       dest
      # Backtracking from dest to src
      while self.par[temp] != -1:
         path.append(_lowerCAmelCase	)
         if temp != src:
            for v, w in self.adjList[temp]:
               if v == self.par[temp]:
                  cost += w
                  break
         lowercase    :			str  =       self.par[temp]
      path.append(_lowerCAmelCase	)
      path.reverse()

      print(f'''----Path to reach {dest} from {src}----'''	)
      for u in path:
         print(f'''{u}'''      ,   end=""" """	)
         if u != dest:
            print("""-> """      ,   end=""""""	)

      print("""\nTotal cost of path: """      ,   _lowerCAmelCase	)


if __name__ == "__main__":
   lowerCAmelCase:	Any							      =Graph(9)
   graph.add_edge(0, 1, 4)
   graph.add_edge(0, 7, 8)
   graph.add_edge(1, 2, 8)
   graph.add_edge(1, 7, 11)
   graph.add_edge(2, 3, 7)
   graph.add_edge(2, 8, 2)
   graph.add_edge(2, 5, 4)
   graph.add_edge(3, 4, 9)
   graph.add_edge(3, 5, 14)
   graph.add_edge(4, 5, 10)
   graph.add_edge(5, 6, 2)
   graph.add_edge(6, 7, 1)
   graph.add_edge(6, 8, 6)
   graph.add_edge(7, 8, 7)
   graph.show_graph()
   graph.dijkstra(0)
   graph.show_path(0, 4)

# OUTPUT
# 0 -> 1(4) -> 7(8)
# 1 -> 0(4) -> 2(8) -> 7(11)
# 7 -> 0(8) -> 1(11) -> 6(1) -> 8(7)
# 2 -> 1(8) -> 3(7) -> 8(2) -> 5(4)
# 3 -> 2(7) -> 4(9) -> 5(14)
# 8 -> 2(2) -> 6(6) -> 7(7)
# 5 -> 2(4) -> 3(14) -> 4(10) -> 6(2)
# 4 -> 3(9) -> 5(10)
# 6 -> 5(2) -> 7(1) -> 8(6)
# Distance from node: 0
# Node 0 has distance: 0
# Node 1 has distance: 4
# Node 2 has distance: 12
# Node 3 has distance: 19
# Node 4 has distance: 21
# Node 5 has distance: 11
# Node 6 has distance: 9
# Node 7 has distance: 8
# Node 8 has distance: 14
# ----Path to reach 4 from 0----
# 0 -> 7 -> 6 -> 5 -> 4
# Total cost of path:  21









"""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 			__snake_case						(      __A    )		->				Optional[int]:
   lowercase    :			int  =       TimesformerConfig()

   if "large" in model_name:
      lowercase    :			str  =       96

   if "hr" in model_name:
      lowercase    :			Tuple  =       16
      lowercase    :			Any  =       448

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

   return config




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

   return name




def 			__snake_case						(      __A ,__A    )		->				List[str]:
   for key in orig_state_dict.copy().keys():
      lowercase    :			str  =       orig_state_dict.pop(__A    )

      if key.startswith("""model."""    ):
         lowercase    :			Union[str, Any]  =       key.replace("""model.""" ,""""""    )

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

   return orig_state_dict




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




def 			__snake_case						(      __A ,__A ,__A ,__A    )		->				Union[str, Any]:
   lowercase    :			Union[str, Any]  =       get_timesformer_config(__A    )

   lowercase    :			Any  =       TimesformerForVideoClassification(__A    )

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

   model.load_state_dict(__A    )
   model.eval()

   # verify model on basic input
   lowercase    :			Union[str, Any]  =       VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] ,image_std=[0.5, 0.5, 0.5]    )
   lowercase    :			Dict  =       prepare_video()
   lowercase    :			Optional[int]  =       image_processor(video[:8] ,return_tensors="""pt"""    )

   lowercase    :			Dict  =       model(**__A    )
   lowercase    :			int  =       outputs.logits

   lowercase    :			List[str]  =       [
       # 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":
      lowercase    :			Optional[int]  =       torch.Size([1, 400]    )
      lowercase    :			Dict  =       torch.tensor([-0.30_16, -0.77_13, -0.42_05]    )
   elif model_name == "timesformer-base-finetuned-k600":
      lowercase    :			str  =       torch.Size([1, 600]    )
      lowercase    :			List[Any]  =       torch.tensor([-0.72_67, -0.74_66, 3.24_04]    )
   elif model_name == "timesformer-base-finetuned-ssv2":
      lowercase    :			Tuple  =       torch.Size([1, 174]    )
      lowercase    :			Optional[Any]  =       torch.tensor([-0.90_59, 0.64_33, -3.14_57]    )
   elif model_name == "timesformer-large-finetuned-k400":
      lowercase    :			str  =       torch.Size([1, 400]    )
      lowercase    :			List[str]  =       torch.tensor([0, 0, 0]    )
   elif model_name == "timesformer-large-finetuned-k600":
      lowercase    :			List[Any]  =       torch.Size([1, 600]    )
      lowercase    :			str  =       torch.tensor([0, 0, 0]    )
   elif model_name == "timesformer-large-finetuned-ssv2":
      lowercase    :			Dict  =       torch.Size([1, 174]    )
      lowercase    :			List[Any]  =       torch.tensor([0, 0, 0]    )
   elif model_name == "timesformer-hr-finetuned-k400":
      lowercase    :			Tuple  =       torch.Size([1, 400]    )
      lowercase    :			Union[str, Any]  =       torch.tensor([-0.96_17, -3.73_11, -3.77_08]    )
   elif model_name == "timesformer-hr-finetuned-k600":
      lowercase    :			Union[str, Any]  =       torch.Size([1, 600]    )
      lowercase    :			Any  =       torch.tensor([2.52_73, 0.71_27, 1.88_48]    )
   elif model_name == "timesformer-hr-finetuned-ssv2":
      lowercase    :			int  =       torch.Size([1, 174]    )
      lowercase    :			Optional[Any]  =       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] ,__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__":
   lowerCAmelCase:	Any							      =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."
   )

   lowerCAmelCase:	int							      =parser.parse_args()
   convert_timesformer_checkpoint(
       args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
   )


