






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					(       UpperCamelCase_   :			Union[str, Any]				)	->				Optional[int]:
						"""simple docstring"""







						lowerCAmelCase__	 =  TimesformerConfig()

						if "large" in model_name:
												lowerCAmelCase__	 =  96

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

						lowerCAmelCase__	 =  "huggingface/label-files"
						if "k400" in model_name:
												lowerCAmelCase__	 =  400
												lowerCAmelCase__	 =  "kinetics400-id2label.json"
						elif "k600" in model_name:
												lowerCAmelCase__	 =  600
												lowerCAmelCase__	 =  "kinetics600-id2label.json"
						elif "ssv2" in model_name:
												lowerCAmelCase__	 =  174
												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(UpperCamelCase_      ,				UpperCamelCase_      ,				repo_type="dataset"				)      ,				"r"				)				)
						lowerCAmelCase__	 =  {int(UpperCamelCase_				): v for k, v in idalabel.items()}
						lowerCAmelCase__	 =  idalabel
						lowerCAmelCase__	 =  {v: k for k, v in idalabel.items()}

						return config


def     _a					(       UpperCamelCase_   :			Union[str, Any]				)	->				Dict:
						"""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     _a					(       UpperCamelCase_   :			Any      ,				UpperCamelCase_   :			List[str]				)	->				List[Any]:
						"""simple docstring"""







						for key in orig_state_dict.copy().keys():
												lowerCAmelCase__	 =  orig_state_dict.pop(UpperCamelCase_				)

												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     _a					(       )	->				List[Any]:
						"""simple docstring"""







						lowerCAmelCase__	 =  hf_hub_download(
						    repo_id="hf-internal-testing/spaghetti-video"      ,				filename="eating_spaghetti.npy"      ,				repo_type="dataset"				)
						lowerCAmelCase__	 =  np.load(UpperCamelCase_				)
						return list(UpperCamelCase_				)


def     _a					(       UpperCamelCase_   :			Union[str, Any]      ,				UpperCamelCase_   :			List[str]      ,				UpperCamelCase_   :			Union[str, Any]      ,				UpperCamelCase_   :			int				)	->				Optional[Any]:
						"""simple docstring"""







						lowerCAmelCase__	 =  get_timesformer_config(UpperCamelCase_				)

						lowerCAmelCase__	 =  TimesformerForVideoClassification(UpperCamelCase_				)

						# download original checkpoint, hosted on Google Drive
						lowerCAmelCase__	 =  "pytorch_model.bin"
						gdown.cached_download(UpperCamelCase_      ,				UpperCamelCase_      ,				quiet=UpperCamelCase_				)
						lowerCAmelCase__	 =  torch.load(UpperCamelCase_      ,				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(UpperCamelCase_      ,				UpperCamelCase_				)

						model.load_state_dict(UpperCamelCase_				)
						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(**UpperCamelCase_				)
						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, 400]				)
												lowerCAmelCase__	 =  torch.tensor([-0.3_016, -0.7_713, -0.4_205]				)
						elif model_name == "timesformer-base-finetuned-k600":
												lowerCAmelCase__	 =  torch.Size([1, 600]				)
												lowerCAmelCase__	 =  torch.tensor([-0.7_267, -0.7_466, 3.2_404]				)
						elif model_name == "timesformer-base-finetuned-ssv2":
												lowerCAmelCase__	 =  torch.Size([1, 174]				)
												lowerCAmelCase__	 =  torch.tensor([-0.9_059, 0.6_433, -3.1_457]				)
						elif model_name == "timesformer-large-finetuned-k400":
												lowerCAmelCase__	 =  torch.Size([1, 400]				)
												lowerCAmelCase__	 =  torch.tensor([0, 0, 0]				)
						elif model_name == "timesformer-large-finetuned-k600":
												lowerCAmelCase__	 =  torch.Size([1, 600]				)
												lowerCAmelCase__	 =  torch.tensor([0, 0, 0]				)
						elif model_name == "timesformer-large-finetuned-ssv2":
												lowerCAmelCase__	 =  torch.Size([1, 174]				)
												lowerCAmelCase__	 =  torch.tensor([0, 0, 0]				)
						elif model_name == "timesformer-hr-finetuned-k400":
												lowerCAmelCase__	 =  torch.Size([1, 400]				)
												lowerCAmelCase__	 =  torch.tensor([-0.9_617, -3.7_311, -3.7_708]				)
						elif model_name == "timesformer-hr-finetuned-k600":
												lowerCAmelCase__	 =  torch.Size([1, 600]				)
												lowerCAmelCase__	 =  torch.tensor([2.5_273, 0.7_127, 1.8_848]				)
						elif model_name == "timesformer-hr-finetuned-ssv2":
												lowerCAmelCase__	 =  torch.Size([1, 174]				)
												lowerCAmelCase__	 =  torch.tensor([-3.6_756, -0.7_513, 0.7_180]				)
						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]      ,				UpperCamelCase_      ,				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(UpperCamelCase_				)
												model.save_pretrained(UpperCamelCase_				)

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


if __name__ == "__main__":
	a_							=      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.'''
	)

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














from __future__ import annotations

import random

# Adjacency list representation of this graph:
# https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
a_							=      {
    '''1''': ['''2''', '''3''', '''4''', '''5'''],
    '''2''': ['''1''', '''3''', '''4''', '''5'''],
    '''3''': ['''1''', '''2''', '''4''', '''5''', '''10'''],
    '''4''': ['''1''', '''2''', '''3''', '''5''', '''6'''],
    '''5''': ['''1''', '''2''', '''3''', '''4''', '''7'''],
    '''6''': ['''7''', '''8''', '''9''', '''10''', '''4'''],
    '''7''': ['''6''', '''8''', '''9''', '''10''', '''5'''],
    '''8''': ['''6''', '''7''', '''9''', '''10'''],
    '''9''': ['''6''', '''7''', '''8''', '''10'''],
    '''10''': ['''6''', '''7''', '''8''', '''9''', '''3'''],
}


def     _a					(       UpperCamelCase_   :			dict[str, list[str]]				)	->				set[tuple[str, str]]:
						"""simple docstring"""







						lowerCAmelCase__	 =  {node: {node} for node in graph}

						lowerCAmelCase__	 =  {node: graph[node][:] for node in graph}

						while len(UpperCamelCase_				) > 2:
												# Choose a random edge.
												lowerCAmelCase__	 =  random.choice(list(graph_copy.keys()				)				)
												lowerCAmelCase__	 =  random.choice(graph_copy[u]				)

												# Contract edge (u, v) to new node uv
												lowerCAmelCase__	 =  u + v
												lowerCAmelCase__	 =  list(set(graph_copy[u] + graph_copy[v]				)				)
												uv_neighbors.remove(UpperCamelCase_				)
												uv_neighbors.remove(UpperCamelCase_				)
												lowerCAmelCase__	 =  uv_neighbors
												for neighbor in uv_neighbors:
																		graph_copy[neighbor].append(UpperCamelCase_				)

												lowerCAmelCase__	 =  set(contracted_nodes[u].union(contracted_nodes[v]				)				)

												# Remove nodes u and v.
												del graph_copy[u]
												del graph_copy[v]
												for neighbor in uv_neighbors:
																		if u in graph_copy[neighbor]:
																								graph_copy[neighbor].remove(UpperCamelCase_				)
																		if v in graph_copy[neighbor]:
																								graph_copy[neighbor].remove(UpperCamelCase_				)

    # Find cutset.
						lowerCAmelCase__	 =  [contracted_nodes[node] for node in graph_copy]
						return {
						    (node, neighbor)
						    for node in groups[0]
						    for neighbor in graph[node]
						    if neighbor in groups[1]
						}


if __name__ == "__main__":
	print(partition_graph(TEST_GRAPH))






