



'''simple docstring'''
import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase :		int			    =       XCLIPTextConfig()

						# derive patch size from model name
						_UpperCamelCase :		Dict			    =       model_name.find('patch'							)
						_UpperCamelCase :		Tuple			    =       int(model_name[start_idx + len('patch'							) : start_idx + len('patch'							) + 2]							)
						_UpperCamelCase :		Optional[Any]			    =       XCLIPVisionConfig(patch_size=UpperCAmelCase_  ,  num_frames=UpperCAmelCase_							)

						if "large" in model_name:
												_UpperCamelCase :		Union[str, Any]			    =       7_6_8
												_UpperCamelCase :		int			    =       3_0_7_2
												_UpperCamelCase :		Dict			    =       1_2

												_UpperCamelCase :		Union[str, Any]			    =       1_0_2_4
												_UpperCamelCase :		List[str]			    =       4_0_9_6
												_UpperCamelCase :		Optional[Any]			    =       1_6
												_UpperCamelCase :		Tuple			    =       2_4
												_UpperCamelCase :		str			    =       7_6_8
												_UpperCamelCase :		str			    =       3_0_7_2

						if model_name == "xclip-large-patch14-16-frames":
												_UpperCamelCase :		List[str]			    =       3_3_6

						_UpperCamelCase :		List[Any]			    =       XCLIPConfig.from_text_vision_configs(UpperCAmelCase_  ,  UpperCAmelCase_							)

						if "large" in model_name:
												_UpperCamelCase :		Optional[Any]			    =       7_6_8

						return config






def A__   (					UpperCAmelCase_							):
						# text encoder
						if name == "token_embedding.weight":
												_UpperCamelCase :		Dict			    =       name.replace('token_embedding.weight'  ,  'text_model.embeddings.token_embedding.weight'							)
						if name == "positional_embedding":
												_UpperCamelCase :		int			    =       name.replace('positional_embedding'  ,  'text_model.embeddings.position_embedding.weight'							)
						if "ln_1" in name:
												_UpperCamelCase :		str			    =       name.replace('ln_1'  ,  'layer_norm1'							)
						if "ln_2" in name:
												_UpperCamelCase :		str			    =       name.replace('ln_2'  ,  'layer_norm2'							)
						if "c_fc" in name:
												_UpperCamelCase :		Tuple			    =       name.replace('c_fc'  ,  'fc1'							)
						if "c_proj" in name:
												_UpperCamelCase :		Optional[int]			    =       name.replace('c_proj'  ,  'fc2'							)
						if name.startswith('transformer.resblocks'							):
												_UpperCamelCase :		Optional[int]			    =       name.replace('transformer.resblocks'  ,  'text_model.encoder.layers'							)
						if "attn.out_proj" in name and "message" not in name:
												_UpperCamelCase :		List[Any]			    =       name.replace('attn.out_proj'  ,  'self_attn.out_proj'							)
						if "ln_final" in name:
												_UpperCamelCase :		List[Any]			    =       name.replace('ln_final'  ,  'text_model.final_layer_norm'							)
						# visual encoder
						if name == "visual.class_embedding":
												_UpperCamelCase :		str			    =       name.replace('visual.class_embedding'  ,  'vision_model.embeddings.class_embedding'							)
						if name == "visual.positional_embedding":
												_UpperCamelCase :		List[str]			    =       name.replace('visual.positional_embedding'  ,  'vision_model.embeddings.position_embedding.weight'							)
						if name.startswith('visual.transformer.resblocks'							):
												_UpperCamelCase :		int			    =       name.replace('visual.transformer.resblocks'  ,  'vision_model.encoder.layers'							)
						if "visual.conv1" in name:
												_UpperCamelCase :		Dict			    =       name.replace('visual.conv1'  ,  'vision_model.embeddings.patch_embedding'							)
						if "visual.ln_pre" in name:
												_UpperCamelCase :		int			    =       name.replace('visual.ln_pre'  ,  'vision_model.pre_layernorm'							)
						if "visual.ln_post" in name:
												_UpperCamelCase :		Tuple			    =       name.replace('visual.ln_post'  ,  'vision_model.post_layernorm'							)
						if "visual.proj" in name:
												_UpperCamelCase :		Optional[Any]			    =       name.replace('visual.proj'  ,  'visual_projection.weight'							)
						if "text_projection" in name:
												_UpperCamelCase :		str			    =       name.replace('text_projection'  ,  'text_projection.weight'							)
						# things on top
						if "prompts_visual_proj" in name:
												_UpperCamelCase :		Optional[Any]			    =       name.replace('prompts_visual_proj'  ,  'prompts_visual_projection'							)
						if "prompts_visual_ln" in name:
												_UpperCamelCase :		Optional[Any]			    =       name.replace('prompts_visual_ln'  ,  'prompts_visual_layernorm'							)
						# mit
						if name == "mit.positional_embedding":
												_UpperCamelCase :		Dict			    =       name.replace('positional'  ,  'position'							)
						if name.startswith('mit.resblocks'							):
												_UpperCamelCase :		Optional[Any]			    =       name.replace('mit.resblocks'  ,  'mit.encoder.layers'							)
						# prompts generator
						if name.startswith('prompts_generator.norm'							):
												_UpperCamelCase :		int			    =       name.replace('prompts_generator.norm'  ,  'prompts_generator.layernorm'							)

						return name






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_							):
						for key in orig_state_dict.copy().keys():
												_UpperCamelCase :		List[Any]			    =       orig_state_dict.pop(UpperCAmelCase_							)

												if "attn.in_proj" in key:
																		_UpperCamelCase :		Optional[int]			    =       key.split('.'							)
																		if key.startswith('visual'							):
																								_UpperCamelCase :		List[str]			    =       key_split[3]
																								_UpperCamelCase :		List[str]			    =       config.vision_config.hidden_size
																								if "message_attn" in key:
																														if "weight" in key:
																																				_UpperCamelCase :		Optional[Any]			    =       val[
																																				    :dim, :
																																				]
																																				_UpperCamelCase :		Optional[Any]			    =       val[
																																				    dim : dim * 2, :
																																				]
																																				_UpperCamelCase :		Dict			    =       val[
																																				    -dim:, :
																																				]
																														else:
																																				_UpperCamelCase :		Optional[int]			    =       val[
																																				    :dim
																																				]
																																				_UpperCamelCase :		Union[str, Any]			    =       val[
																																				    dim : dim * 2
																																				]
																																				_UpperCamelCase :		Any			    =       val[
																																				    -dim:
																																				]
																								else:
																														if "weight" in key:
																																				_UpperCamelCase :		int			    =       val[
																																				    :dim, :
																																				]
																																				_UpperCamelCase :		Union[str, Any]			    =       val[
																																				    dim : dim * 2, :
																																				]
																																				_UpperCamelCase :		Any			    =       val[
																																				    -dim:, :
																																				]
																														else:
																																				_UpperCamelCase :		Optional[int]			    =       val[:dim]
																																				_UpperCamelCase :		Optional[int]			    =       val[
																																				    dim : dim * 2
																																				]
																																				_UpperCamelCase :		Union[str, Any]			    =       val[-dim:]
																		elif key.startswith('mit'							):
																								_UpperCamelCase :		Optional[Any]			    =       key_split[2]
																								_UpperCamelCase :		str			    =       config.vision_config.mit_hidden_size
																								if "weight" in key:
																														_UpperCamelCase :		List[str]			    =       val[:dim, :]
																														_UpperCamelCase :		Dict			    =       val[dim : dim * 2, :]
																														_UpperCamelCase :		str			    =       val[-dim:, :]
																								else:
																														_UpperCamelCase :		Tuple			    =       val[:dim]
																														_UpperCamelCase :		List[str]			    =       val[dim : dim * 2]
																														_UpperCamelCase :		Any			    =       val[-dim:]
																		else:
																								_UpperCamelCase :		Dict			    =       key_split[2]
																								_UpperCamelCase :		str			    =       config.text_config.hidden_size
																								if "weight" in key:
																														_UpperCamelCase :		Dict			    =       val[:dim, :]
																														_UpperCamelCase :		str			    =       val[
																														    dim : dim * 2, :
																														]
																														_UpperCamelCase :		str			    =       val[-dim:, :]
																								else:
																														_UpperCamelCase :		Optional[int]			    =       val[:dim]
																														_UpperCamelCase :		Union[str, Any]			    =       val[
																														    dim : dim * 2
																														]
																														_UpperCamelCase :		Union[str, Any]			    =       val[-dim:]
												else:
																		_UpperCamelCase :		int			    =       rename_key(UpperCAmelCase_							)
																		if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
																								_UpperCamelCase :		List[Any]			    =       val.T
																		_UpperCamelCase :		Optional[int]			    =       val

						return orig_state_dict






def A__   (					UpperCAmelCase_							):
						if num_frames == 8:
												_UpperCamelCase :		List[str]			    =       'eating_spaghetti_8_frames.npy'
						elif num_frames == 1_6:
												_UpperCamelCase :		List[str]			    =       'eating_spaghetti.npy'
						elif num_frames == 3_2:
												_UpperCamelCase :		List[Any]			    =       'eating_spaghetti_32_frames.npy'
						_UpperCamelCase :		List[Any]			    =       hf_hub_download(
						    repo_id='hf-internal-testing/spaghetti-video'  ,  filename=UpperCAmelCase_  ,  repo_type='dataset'  ,  )
						_UpperCamelCase :		Dict			    =       np.load(UpperCAmelCase_							)
						return list(UpperCAmelCase_							)






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_=None  ,  UpperCAmelCase_=False							):
						_UpperCamelCase :		Optional[int]			    =       {
						    # fully supervised kinetics-400 checkpoints
						    'xclip-base-patch32': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth',
						    'xclip-base-patch32-16-frames': (
						        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth'
						    ),
						    'xclip-base-patch16': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth',
						    'xclip-base-patch16-16-frames': (
						        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth'
						    ),
						    'xclip-large-patch14': 'https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb',
						    'xclip-large-patch14-16-frames': 'https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f',
						    # fully supervised kinetics-600 checkpoints
						    'xclip-base-patch16-kinetics-600': (
						        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth'
						    ),
						    'xclip-base-patch16-kinetics-600-16-frames': (
						        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth'
						    ),
						    'xclip-large-patch14-kinetics-600': 'https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be',
						    # few shot
						    'xclip-base-patch16-hmdb-2-shot': (
						        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth'
						    ),
						    'xclip-base-patch16-hmdb-4-shot': (
						        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth'
						    ),
						    'xclip-base-patch16-hmdb-8-shot': (
						        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth'
						    ),
						    'xclip-base-patch16-hmdb-16-shot': (
						        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth'
						    ),
						    'xclip-base-patch16-ucf-2-shot': (
						        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth'
						    ),
						    'xclip-base-patch16-ucf-4-shot': (
						        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth'
						    ),
						    'xclip-base-patch16-ucf-8-shot': (
						        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth'
						    ),
						    'xclip-base-patch16-ucf-16-shot': (
						        'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth'
						    ),
						    # zero shot
						    'xclip-base-patch16-zero-shot': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth',
						}

						_UpperCamelCase :		List[str]			    =       model_to_url[model_name]
						_UpperCamelCase :		Union[str, Any]			    =       8
						if "16-frames" in model_name:
												_UpperCamelCase :		Union[str, Any]			    =       1_6
						elif "shot" in model_name:
												_UpperCamelCase :		List[str]			    =       3_2

						_UpperCamelCase :		Any			    =       get_xclip_config(UpperCAmelCase_  ,  UpperCAmelCase_							)
						_UpperCamelCase :		Optional[Any]			    =       XCLIPModel(UpperCAmelCase_							)
						model.eval()

						if "drive" in checkpoint_url:
												_UpperCamelCase :		List[str]			    =       'pytorch_model.bin'
												gdown.cached_download(UpperCAmelCase_  ,  UpperCAmelCase_  ,  quiet=UpperCAmelCase_							)
												_UpperCamelCase :		List[str]			    =       torch.load(UpperCAmelCase_  ,  map_location='cpu'							)['model']
						else:
												_UpperCamelCase :		List[str]			    =       torch.hub.load_state_dict_from_url(UpperCAmelCase_							)['model']

						_UpperCamelCase :		Tuple			    =       convert_state_dict(UpperCAmelCase_  ,  UpperCAmelCase_							)

						_UpperCamelCase :		Optional[int]			    =       XCLIPModel(UpperCAmelCase_							)
						_UpperCamelCase    ,      _UpperCamelCase :		Any			    =       model.load_state_dict(UpperCAmelCase_  ,  strict=UpperCAmelCase_							)
						assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
						model.eval()

						_UpperCamelCase :		List[str]			    =       3_3_6 if model_name == 'xclip-large-patch14-16-frames' else 2_2_4
						_UpperCamelCase :		Optional[int]			    =       VideoMAEImageProcessor(size=UpperCAmelCase_							)
						_UpperCamelCase :		Union[str, Any]			    =       CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'							)
						_UpperCamelCase :		List[str]			    =       CLIPTokenizerFast.from_pretrained('openai/clip-vit-base-patch32'							)
						_UpperCamelCase :		List[Any]			    =       XCLIPProcessor(image_processor=UpperCAmelCase_  ,  tokenizer=UpperCAmelCase_							)

						_UpperCamelCase :		Tuple			    =       prepare_video(UpperCAmelCase_							)
						_UpperCamelCase :		Tuple			    =       processor(
						    text=['playing sports', 'eating spaghetti', 'go shopping']  ,  videos=UpperCAmelCase_  ,  return_tensors='pt'  ,  padding=UpperCAmelCase_							)

						print('Shape of pixel values:'  ,  inputs.pixel_values.shape							)

						with torch.no_grad():
												_UpperCamelCase :		str			    =       model(**UpperCAmelCase_							)

						# Verify outputs
						_UpperCamelCase :		Union[str, Any]			    =       outputs.logits_per_video
						_UpperCamelCase :		Dict			    =       logits_per_video.softmax(dim=1							)
						print('Probs:'  ,  UpperCAmelCase_							)
						# kinetics-400
						if model_name == "xclip-base-patch32":
												_UpperCamelCase :		Tuple			    =       torch.tensor([[0.0_019, 0.9_951, 0.0_030]]							)
						elif model_name == "xclip-base-patch32-16-frames":
												_UpperCamelCase :		Optional[Any]			    =       torch.tensor([[7.0999E-04, 9.9883E-01, 4.5580E-04]]							)
						elif model_name == "xclip-base-patch16":
												_UpperCamelCase :		Optional[Any]			    =       torch.tensor([[0.0_083, 0.9_681, 0.0_236]]							)
						elif model_name == "xclip-base-patch16-16-frames":
												_UpperCamelCase :		str			    =       torch.tensor([[7.6937E-04, 9.9728E-01, 1.9473E-03]]							)
						elif model_name == "xclip-large-patch14":
												_UpperCamelCase :		Optional[Any]			    =       torch.tensor([[0.0_062, 0.9_864, 0.0_075]]							)
						elif model_name == "xclip-large-patch14-16-frames":
												_UpperCamelCase :		List[str]			    =       torch.tensor([[3.3877E-04, 9.9937E-01, 2.8888E-04]]							)
						# kinetics-600
						elif model_name == "xclip-base-patch16-kinetics-600":
												_UpperCamelCase :		Any			    =       torch.tensor([[0.0_555, 0.8_914, 0.0_531]]							)
						elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
												_UpperCamelCase :		Union[str, Any]			    =       torch.tensor([[3.8554E-04, 9.9929E-01, 3.2754E-04]]							)
						elif model_name == "xclip-large-patch14-kinetics-600":
												_UpperCamelCase :		Dict			    =       torch.tensor([[0.0_036, 0.9_920, 0.0_045]]							)
						# few shot
						elif model_name == "xclip-base-patch16-hmdb-2-shot":
												_UpperCamelCase :		Optional[Any]			    =       torch.tensor([[7.1890E-06, 9.9994E-01, 5.6559E-05]]							)
						elif model_name == "xclip-base-patch16-hmdb-4-shot":
												_UpperCamelCase :		Optional[Any]			    =       torch.tensor([[1.0320E-05, 9.9993E-01, 6.2435E-05]]							)
						elif model_name == "xclip-base-patch16-hmdb-8-shot":
												_UpperCamelCase :		Optional[Any]			    =       torch.tensor([[4.1377E-06, 9.9990E-01, 9.8386E-05]]							)
						elif model_name == "xclip-base-patch16-hmdb-16-shot":
												_UpperCamelCase :		str			    =       torch.tensor([[4.1347E-05, 9.9962E-01, 3.3411E-04]]							)
						elif model_name == "xclip-base-patch16-ucf-2-shot":
												_UpperCamelCase :		List[str]			    =       torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-04]]							)
						elif model_name == "xclip-base-patch16-ucf-4-shot":
												_UpperCamelCase :		Dict			    =       torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-04]]							)
						elif model_name == "xclip-base-patch16-ucf-8-shot":
												_UpperCamelCase :		Tuple			    =       torch.tensor([[0.0_027, 0.9_904, 0.0_070]]							)
						elif model_name == "xclip-base-patch16-ucf-16-shot":
												_UpperCamelCase :		int			    =       torch.tensor([[9.8219E-04, 9.9593E-01, 3.0863E-03]]							)
						# zero shot
						elif model_name == "xclip-base-patch16-zero-shot":
												_UpperCamelCase :		Tuple			    =       torch.tensor([[3.5082E-04, 9.9785E-01, 1.7966E-03]]							)
						else:
												raise ValueError(f'Model name {model_name} not supported'							)
						assert torch.allclose(UpperCAmelCase_  ,  UpperCAmelCase_  ,  atol=1E-3							)
						print('Looks ok!'							)

						if pytorch_dump_folder_path is not None:
												print(f'Saving model {model_name} to {pytorch_dump_folder_path}'							)
												model.save_pretrained(UpperCAmelCase_							)

						if push_to_hub:
												print('Pushing model, processor and slow tokenizer files to the hub...'							)
												model.push_to_hub(UpperCAmelCase_  ,  organization='nielsr'							)
												processor.push_to_hub(UpperCAmelCase_  ,  organization='nielsr'							)
												slow_tokenizer.push_to_hub(UpperCAmelCase_  ,  organization='nielsr'							)


if __name__ == "__main__":
					snake_case_			:     List[str]  	=  argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--model_name',
					    default='xclip-base-patch32',
					    type=str,
					    help='Name of the model.',
					)
					parser.add_argument(
					    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
					)
					parser.add_argument(
					    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
					)

					snake_case_			:     List[str]  	=  parser.parse_args()
					convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''
from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						if not arr:
												return None, None, 0
						if low == high:
												return low, high, arr[low]

						_UpperCamelCase :		str			    =       (low + high) // 2
						_UpperCamelCase    ,      _UpperCamelCase    ,      _UpperCamelCase :		Dict			    =       max_subarray(UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							)
						_UpperCamelCase    ,      _UpperCamelCase    ,      _UpperCamelCase :		List[Any]			    =       max_subarray(UpperCAmelCase_  ,  mid + 1  ,  UpperCAmelCase_							)
						_UpperCamelCase    ,      _UpperCamelCase    ,      _UpperCamelCase :		Tuple			    =       max_cross_sum(UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							)
						if left_sum >= right_sum and left_sum >= cross_sum:
												return left_low, left_high, left_sum
						elif right_sum >= left_sum and right_sum >= cross_sum:
												return right_low, right_high, right_sum
						return cross_left, cross_right, cross_sum






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase    ,      _UpperCamelCase :		Optional[Any]			    =       float('-inf'							), -1
						_UpperCamelCase    ,      _UpperCamelCase :		int			    =       float('-inf'							), -1

						_UpperCamelCase :		int | float			    =       0
						for i in range(UpperCAmelCase_  ,  low - 1  ,  -1							):
												summ += arr[i]
												if summ > left_sum:
																		_UpperCamelCase :		Optional[int]			    =       summ
																		_UpperCamelCase :		Union[str, Any]			    =       i

						_UpperCamelCase :		List[Any]			    =       0
						for i in range(mid + 1  ,  high + 1							):
												summ += arr[i]
												if summ > right_sum:
																		_UpperCamelCase :		List[Any]			    =       summ
																		_UpperCamelCase :		List[str]			    =       i

						return max_left, max_right, (left_sum + right_sum)






def A__   (					UpperCAmelCase_							):
						_UpperCamelCase :		List[str]			    =       [randint(1  ,  UpperCAmelCase_							) for _ in range(UpperCAmelCase_							)]
						_UpperCamelCase :		Optional[Any]			    =       time.time()
						max_subarray(UpperCAmelCase_  ,  0  ,  input_size - 1							)
						_UpperCamelCase :		str			    =       time.time()
						return end - start






def A__   (					):
						_UpperCamelCase :		Any			    =       [1_0, 1_0_0, 1_0_0_0, 1_0_0_0_0, 5_0_0_0_0, 1_0_0_0_0_0, 2_0_0_0_0_0, 3_0_0_0_0_0, 4_0_0_0_0_0, 5_0_0_0_0_0]
						_UpperCamelCase :		Dict			    =       [time_max_subarray(UpperCAmelCase_							) for input_size in input_sizes]
						print('No of Inputs\t\tTime Taken'							)
						for input_size, runtime in zip(UpperCAmelCase_  ,  UpperCAmelCase_							):
												print(UpperCAmelCase_  ,  '\t\t'  ,  UpperCAmelCase_							)
						plt.plot(UpperCAmelCase_  ,  UpperCAmelCase_							)
						plt.xlabel('Number of Inputs'							)
						plt.ylabel('Time taken in seconds'							)
						plt.show()


if __name__ == "__main__":
					from doctest import testmod

					testmod()


