






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_			(		lowercase_	)							->Optional[int]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						TimesformerConfig()

 if "large" in model_name:
  SCREAMING_SNAKE_CASE							  =						9_6

 if "hr" in model_name:
  SCREAMING_SNAKE_CASE							  =						1_6
  SCREAMING_SNAKE_CASE							  =						4_4_8

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

 return config





def 		A_			(		lowercase_	)							->Any:







 """simple docstring"""

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

 return name





def 		A_			(		lowercase_					,       lowercase_	)							->List[Any]:







 """simple docstring"""

 for key in orig_state_dict.copy().keys():
  SCREAMING_SNAKE_CASE							  =						orig_state_dict.pop(lowercase_	)

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

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

 return orig_state_dict





def 		A_			(		)							->Any:







 """simple docstring"""

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





def 		A_			(		lowercase_					,       lowercase_					,       lowercase_					,       lowercase_	)							->List[Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						get_timesformer_config(lowercase_	)

 SCREAMING_SNAKE_CASE							  =						TimesformerForVideoClassification(lowercase_	)

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

 model.load_state_dict(lowercase_	)
 model.eval()

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

 SCREAMING_SNAKE_CASE							  =						model(**lowercase_	)
 SCREAMING_SNAKE_CASE							  =						outputs.logits

 SCREAMING_SNAKE_CASE							  =						[
     # 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":
  SCREAMING_SNAKE_CASE							  =						torch.Size([1, 4_0_0]	)
  SCREAMING_SNAKE_CASE							  =						torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5]	)
 elif model_name == "timesformer-base-finetuned-k600":
  SCREAMING_SNAKE_CASE							  =						torch.Size([1, 6_0_0]	)
  SCREAMING_SNAKE_CASE							  =						torch.tensor([-0.7_2_6_7, -0.7_4_6_6, 3.2_4_0_4]	)
 elif model_name == "timesformer-base-finetuned-ssv2":
  SCREAMING_SNAKE_CASE							  =						torch.Size([1, 1_7_4]	)
  SCREAMING_SNAKE_CASE							  =						torch.tensor([-0.9_0_5_9, 0.6_4_3_3, -3.1_4_5_7]	)
 elif model_name == "timesformer-large-finetuned-k400":
  SCREAMING_SNAKE_CASE							  =						torch.Size([1, 4_0_0]	)
  SCREAMING_SNAKE_CASE							  =						torch.tensor([0, 0, 0]	)
 elif model_name == "timesformer-large-finetuned-k600":
  SCREAMING_SNAKE_CASE							  =						torch.Size([1, 6_0_0]	)
  SCREAMING_SNAKE_CASE							  =						torch.tensor([0, 0, 0]	)
 elif model_name == "timesformer-large-finetuned-ssv2":
  SCREAMING_SNAKE_CASE							  =						torch.Size([1, 1_7_4]	)
  SCREAMING_SNAKE_CASE							  =						torch.tensor([0, 0, 0]	)
 elif model_name == "timesformer-hr-finetuned-k400":
  SCREAMING_SNAKE_CASE							  =						torch.Size([1, 4_0_0]	)
  SCREAMING_SNAKE_CASE							  =						torch.tensor([-0.9_6_1_7, -3.7_3_1_1, -3.7_7_0_8]	)
 elif model_name == "timesformer-hr-finetuned-k600":
  SCREAMING_SNAKE_CASE							  =						torch.Size([1, 6_0_0]	)
  SCREAMING_SNAKE_CASE							  =						torch.tensor([2.5_2_7_3, 0.7_1_2_7, 1.8_8_4_8]	)
 elif model_name == "timesformer-hr-finetuned-ssv2":
  SCREAMING_SNAKE_CASE							  =						torch.Size([1, 1_7_4]	)
  SCREAMING_SNAKE_CASE							  =						torch.tensor([-3.6_7_5_6, -0.7_5_1_3, 0.7_1_8_0]	)
 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]					,       lowercase_					,       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(lowercase_	)
  model.save_pretrained(lowercase_	)

 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 math
import sys
class 					a_:



     """simple docstring"""



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

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						0
      SCREAMING_SNAKE_CASE							  =						[]
      SCREAMING_SNAKE_CASE							  =						{}  # To store the pos of node in array





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

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





     def   __UpperCamelCase		(						self			:					List[str]       ,       lowerCAmelCase__			:					str)    ->					Dict:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						self.left(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						self.right(lowerCAmelCase__)
      if lc < self.cur_size and self.array(lowerCAmelCase__)[0] < self.array(lowerCAmelCase__)[0]:
       SCREAMING_SNAKE_CASE							  =						lc
      else:
       SCREAMING_SNAKE_CASE							  =						idx
      if rc < self.cur_size and self.array(lowerCAmelCase__)[0] < self.array(lowerCAmelCase__)[0]:
       SCREAMING_SNAKE_CASE							  =						rc
      if smallest != idx:
       self.swap(lowerCAmelCase__       ,       lowerCAmelCase__)
       self.min_heapify(lowerCAmelCase__)





     def   __UpperCamelCase		(						self			:					Any       ,       lowerCAmelCase__			:					Tuple)    ->					Optional[Any]:

      """simple docstring"""
      # Inserts a node into the Priority Queue
      SCREAMING_SNAKE_CASE							  =						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			:					Dict)    ->					Optional[Any]:

      """simple docstring"""
      # Removes and returns the min element at top of priority queue
      SCREAMING_SNAKE_CASE							  =						self.array[0][1]
      SCREAMING_SNAKE_CASE							  =						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			:					List[Any]       ,       lowerCAmelCase__			:					Dict)    ->					int:

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





     def   __UpperCamelCase		(						self			:					Any       ,       lowerCAmelCase__			:					List[str])    ->					Optional[Any]:

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





     def   __UpperCamelCase		(						self			:					Tuple       ,       lowerCAmelCase__			:					List[Any])    ->					Dict:

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





     def   __UpperCamelCase		(						self			:					List[str]       ,       lowerCAmelCase__			:					List[str]       ,       lowerCAmelCase__			:					Any)    ->					List[str]:

      """simple docstring"""
      # swaps array elements at indices i and j
      # update the pos{}
      SCREAMING_SNAKE_CASE							  =						j
      SCREAMING_SNAKE_CASE							  =						i
      SCREAMING_SNAKE_CASE							  =						self.array[i]
      SCREAMING_SNAKE_CASE							  =						self.array[j]
      SCREAMING_SNAKE_CASE							  =						temp





     def   __UpperCamelCase		(						self			:					Union[str, Any]       ,       lowerCAmelCase__			:					Dict       ,       lowerCAmelCase__			:					Optional[Any])    ->					Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						self.pos[tup[1]]
      # assuming the new_d is atmost old_d
      SCREAMING_SNAKE_CASE							  =						(new_d, tup[1])
      while idx > 0 and self.array[self.par(lowerCAmelCase__)][0] > self.array[idx][0]:
       self.swap(lowerCAmelCase__       ,       self.par(lowerCAmelCase__))
       SCREAMING_SNAKE_CASE							  =						self.par(lowerCAmelCase__)

class 					a_:



     """simple docstring"""



     def __init__(						self			:					Any       ,       lowerCAmelCase__			:					str)    ->					str:

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





     def   __UpperCamelCase		(						self			:					List[str]       ,       lowerCAmelCase__			:					Optional[Any]       ,       lowerCAmelCase__			:					Union[str, Any]       ,       lowerCAmelCase__			:					List[Any])    ->					Tuple:

      """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:
       SCREAMING_SNAKE_CASE							  =						[(v, w)]

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





     def   __UpperCamelCase		(						self			:					Optional[int])    ->					Tuple:

      """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			:					int       ,       lowerCAmelCase__			:					List[str])    ->					List[Any]:

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

      while not q.is_empty():
       SCREAMING_SNAKE_CASE							  =						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]:
        SCREAMING_SNAKE_CASE							  =						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__)
         SCREAMING_SNAKE_CASE							  =						new_dist
         SCREAMING_SNAKE_CASE							  =						u

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





     def   __UpperCamelCase		(						self			:					Dict       ,       lowerCAmelCase__			:					List[str])    ->					Tuple:

      """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			:					List[Any]       ,       lowerCAmelCase__			:					Optional[Any]       ,       lowerCAmelCase__			:					Any)    ->					Optional[int]:

      """simple docstring"""
      # To show the shortest path from src to dest
      # WARNING: Use it *after* calling dijkstra
      SCREAMING_SNAKE_CASE							  =						[]
      SCREAMING_SNAKE_CASE							  =						0
      SCREAMING_SNAKE_CASE							  =						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
       SCREAMING_SNAKE_CASE							  =						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__":
      __UpperCAmelCase      				=							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, 1_1)
      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, 1_4)
      graph.add_edge(4, 5, 1_0)
      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




