

from __future__ import annotations





class      snake_case_       :







   '''simple docstring'''

   def __init__(					self			:    Optional[Any]							,       _UpperCamelCase			:    int							)				->Any:
         snake_case_       =     vertices
         snake_case_       =     [[0] * vertices for _ in range(__UpperCamelCase							)]







   def     snake_case__(					self			:    Optional[Any]							,       _UpperCamelCase			:    list[int]							)				->Any:
         print('''Vertex \t Distance from Source'''							)
         for vertex in range(self.vertices							):
               print(__UpperCamelCase							,       '''\t\t'''							,       distances_from_source[vertex]							)







   def     snake_case__(					self			:    Union[str, Any]							,       _UpperCamelCase			:    list[int]							,       _UpperCamelCase			:    list[bool]							)				->List[str]:
         snake_case_       =     1e7
         snake_case_       =     0

         # Search not nearest vertex not in the shortest path tree
         for vertex in range(self.vertices							):
               if distances_from_source[vertex] < minimum and visited[vertex] is False:
                     snake_case_       =     distances_from_source[vertex]
                     snake_case_       =     vertex
         return min_index







   def     snake_case__(					self			:    List[Any]							,       _UpperCamelCase			:    int							)				->Optional[Any]:
         snake_case_       =     [int(1e7							)] * self.vertices  # distances from the source
         snake_case_       =     0
         snake_case_       =     [False] * self.vertices

         for _ in range(self.vertices							):
               snake_case_       =     self.minimum_distance(__UpperCamelCase							,       __UpperCamelCase							)
               snake_case_       =     True

               # Update dist value of the adjacent vertices
               # of the picked vertex only if the current
               # distance is greater than new distance and
               # the vertex in not in the shortest path tree
               for v in range(self.vertices							):
                     if (
                         self.graph[u][v] > 0
                         and visited[v] is False
                         and distances[v] > distances[u] + self.graph[u][v]
                     ):
                           snake_case_       =     distances[u] + self.graph[u][v]

         self.print_solution(__UpperCamelCase							)


if __name__ == "__main__":
     lowerCAmelCase_	     =     Graph(9)
     lowerCAmelCase_	     =     [
         [0, 4, 0, 0, 0, 0, 0, 8, 0],
         [4, 0, 8, 0, 0, 0, 0, 11, 0],
         [0, 8, 0, 7, 0, 4, 0, 0, 2],
         [0, 0, 7, 0, 9, 14, 0, 0, 0],
         [0, 0, 0, 9, 0, 10, 0, 0, 0],
         [0, 0, 4, 14, 10, 0, 2, 0, 0],
         [0, 0, 0, 0, 0, 2, 0, 1, 6],
         [8, 11, 0, 0, 0, 0, 1, 0, 7],
         [0, 0, 2, 0, 0, 0, 6, 7, 0],
     ]
     graph.dijkstra(0)


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   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__	):
						snake_case_       =     TimesformerConfig()

						if "large" in model_name:
												snake_case_       =     96

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

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

						return config


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

						return name


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						for key in orig_state_dict.copy().keys():
												snake_case_       =     orig_state_dict.pop(SCREAMING_SNAKE_CASE__	)

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

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

						return orig_state_dict


def   __SCREAMING_SNAKE_CASE ():
						snake_case_       =     hf_hub_download(
						    repo_id='''hf-internal-testing/spaghetti-video'''  ,		filename='''eating_spaghetti.npy'''  ,		repo_type='''dataset'''	)
						snake_case_       =     np.load(SCREAMING_SNAKE_CASE__	)
						return list(SCREAMING_SNAKE_CASE__	)


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						snake_case_       =     get_timesformer_config(SCREAMING_SNAKE_CASE__	)

						snake_case_       =     TimesformerForVideoClassification(SCREAMING_SNAKE_CASE__	)

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

						model.load_state_dict(SCREAMING_SNAKE_CASE__	)
						model.eval()

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

						snake_case_       =     model(**SCREAMING_SNAKE_CASE__	)
						snake_case_       =     outputs.logits

						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":
												snake_case_       =     torch.Size([1, 400]	)
												snake_case_       =     torch.tensor([-0.3016, -0.7713, -0.4205]	)
						elif model_name == "timesformer-base-finetuned-k600":
												snake_case_       =     torch.Size([1, 600]	)
												snake_case_       =     torch.tensor([-0.7267, -0.7466, 3.2404]	)
						elif model_name == "timesformer-base-finetuned-ssv2":
												snake_case_       =     torch.Size([1, 174]	)
												snake_case_       =     torch.tensor([-0.9059, 0.6433, -3.1457]	)
						elif model_name == "timesformer-large-finetuned-k400":
												snake_case_       =     torch.Size([1, 400]	)
												snake_case_       =     torch.tensor([0, 0, 0]	)
						elif model_name == "timesformer-large-finetuned-k600":
												snake_case_       =     torch.Size([1, 600]	)
												snake_case_       =     torch.tensor([0, 0, 0]	)
						elif model_name == "timesformer-large-finetuned-ssv2":
												snake_case_       =     torch.Size([1, 174]	)
												snake_case_       =     torch.tensor([0, 0, 0]	)
						elif model_name == "timesformer-hr-finetuned-k400":
												snake_case_       =     torch.Size([1, 400]	)
												snake_case_       =     torch.tensor([-0.9617, -3.7311, -3.7708]	)
						elif model_name == "timesformer-hr-finetuned-k600":
												snake_case_       =     torch.Size([1, 600]	)
												snake_case_       =     torch.tensor([2.5273, 0.7127, 1.8848]	)
						elif model_name == "timesformer-hr-finetuned-ssv2":
												snake_case_       =     torch.Size([1, 174]	)
												snake_case_       =     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]  ,		SCREAMING_SNAKE_CASE__  ,		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(SCREAMING_SNAKE_CASE__	)
												model.save_pretrained(SCREAMING_SNAKE_CASE__	)

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


if __name__ == "__main__":
					lowerCAmelCase_	     =     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_	     =     parser.parse_args()
					convert_timesformer_checkpoint(
					    args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
					)