




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       __UpperCAmelCase      (				__UpperCamelCase					):
			__lowercase	:       Union[str, Any]				=   TimesformerConfig()

			if "large" in model_name:
						__lowercase	:       Optional[int]				=   96

			if "hr" in model_name:
						__lowercase	:       Optional[Any]				=   16
						__lowercase	:       Any				=   4_48

			__lowercase	:       Union[str, Any]				=   '''huggingface/label-files'''
			if "k400" in model_name:
						__lowercase	:       int				=   4_00
						__lowercase	:       List[Any]				=   '''kinetics400-id2label.json'''
			elif "k600" in model_name:
						__lowercase	:       List[Any]				=   6_00
						__lowercase	:       Optional[Any]				=   '''kinetics600-id2label.json'''
			elif "ssv2" in model_name:
						__lowercase	:       Tuple				=   1_74
						__lowercase	:       str				=   '''something-something-v2-id2label.json'''
			else:
						raise ValueError('''Model name should either contain \'k400\', \'k600\' or \'ssv2\'.'''					)
			__lowercase	:       Tuple				=   json.load(open(hf_hub_download(_lowerCAmelCase					,							_lowerCAmelCase					,							repo_type='''dataset'''					)					,							'''r'''					)					)
			__lowercase	:       List[str]				=   {int(_lowerCAmelCase					): v for k, v in idalabel.items()}
			__lowercase	:       Dict				=   idalabel
			__lowercase	:       List[str]				=   {v: k for k, v in idalabel.items()}

			return config







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

			return name







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase					):
			for key in orig_state_dict.copy().keys():
						__lowercase	:       Optional[Any]				=   orig_state_dict.pop(_lowerCAmelCase					)

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

						if "qkv" in key:
									__lowercase	:       Dict				=   key.split('''.'''					)
									__lowercase	:       Any				=   int(key_split[1]					)
									__lowercase	:       Dict				=   '''timesformer.encoder.layer.'''
									if "temporal" in key:
												__lowercase	:       int				=   '''.temporal_attention.attention.qkv.'''
									else:
												__lowercase	:       Optional[int]				=   '''.attention.attention.qkv.'''
									if "weight" in key:
												__lowercase	:       int				=   val
									else:
												__lowercase	:       Union[str, Any]				=   val
						else:
									__lowercase	:       Tuple				=   val

			return orig_state_dict







def       __UpperCAmelCase      (				):
			__lowercase	:       List[str]				=   hf_hub_download(
			    repo_id='''hf-internal-testing/spaghetti-video'''					,							filename='''eating_spaghetti.npy'''					,							repo_type='''dataset'''					)
			__lowercase	:       List[str]				=   np.load(_lowerCAmelCase					)
			return list(_lowerCAmelCase					)







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase					):
			__lowercase	:       Optional[Any]				=   get_timesformer_config(_lowerCAmelCase					)

			__lowercase	:       int				=   TimesformerForVideoClassification(_lowerCAmelCase					)

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

			model.load_state_dict(_lowerCAmelCase					)
			model.eval()

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

			__lowercase	:       Any				=   model(**_lowerCAmelCase					)
			__lowercase	:       Optional[int]				=   outputs.logits

			__lowercase	:       List[Any]				=   [
			    # 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	:       List[Any]				=   torch.Size([1, 4_00]					)
						__lowercase	:       Union[str, Any]				=   torch.tensor([-0.3_016, -0.7_713, -0.4_205]					)
			elif model_name == "timesformer-base-finetuned-k600":
						__lowercase	:       int				=   torch.Size([1, 6_00]					)
						__lowercase	:       int				=   torch.tensor([-0.7_267, -0.7_466, 3.2_404]					)
			elif model_name == "timesformer-base-finetuned-ssv2":
						__lowercase	:       Optional[int]				=   torch.Size([1, 1_74]					)
						__lowercase	:       List[str]				=   torch.tensor([-0.9_059, 0.6_433, -3.1_457]					)
			elif model_name == "timesformer-large-finetuned-k400":
						__lowercase	:       List[Any]				=   torch.Size([1, 4_00]					)
						__lowercase	:       List[Any]				=   torch.tensor([0, 0, 0]					)
			elif model_name == "timesformer-large-finetuned-k600":
						__lowercase	:       List[str]				=   torch.Size([1, 6_00]					)
						__lowercase	:       str				=   torch.tensor([0, 0, 0]					)
			elif model_name == "timesformer-large-finetuned-ssv2":
						__lowercase	:       Union[str, Any]				=   torch.Size([1, 1_74]					)
						__lowercase	:       List[Any]				=   torch.tensor([0, 0, 0]					)
			elif model_name == "timesformer-hr-finetuned-k400":
						__lowercase	:       Optional[int]				=   torch.Size([1, 4_00]					)
						__lowercase	:       Dict				=   torch.tensor([-0.9_617, -3.7_311, -3.7_708]					)
			elif model_name == "timesformer-hr-finetuned-k600":
						__lowercase	:       Any				=   torch.Size([1, 6_00]					)
						__lowercase	:       Any				=   torch.tensor([2.5_273, 0.7_127, 1.8_848]					)
			elif model_name == "timesformer-hr-finetuned-ssv2":
						__lowercase	:       Optional[Any]				=   torch.Size([1, 1_74]					)
						__lowercase	:       Dict				=   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]					,							_lowerCAmelCase					,							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(_lowerCAmelCase					)
						model.save_pretrained(_lowerCAmelCase					)

			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
			)









"""simple docstring"""




from __future__ import annotations

import random

from matplotlib import animation
from matplotlib import pyplot as plt

# Frame rate of the animation
a_            =   2_0

# Time between time steps in seconds
a_            =   INTERVAL / 1_0_0_0

class        UpperCAmelCase_  :
		def __init__(		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_ = 1.0							,		UpperCamelCase_ = 1.0							,		UpperCamelCase_ = "blue"							,		)		->   None:
					__lowercase	:       Optional[int]				=   position_x
					__lowercase	:       List[Any]				=   position_y
					__lowercase	:       Optional[int]				=   velocity_x
					__lowercase	:       List[str]				=   velocity_y
					__lowercase	:       List[Any]				=   mass
					__lowercase	:       Tuple				=   size
					__lowercase	:       Tuple				=   color
		@property
		def 					_lowerCamelCase    (		self   )		->   tuple[float, float]:
					return self.position_x, self.position_y
		@property
		def 					_lowerCamelCase    (		self   )		->   tuple[float, float]:
					return self.velocity_x, self.velocity_y
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )		->   None:
					self.velocity_x += force_x * delta_time
					self.velocity_y += force_y * delta_time
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_   )		->   None:
					self.position_x += self.velocity_x * delta_time
					self.position_y += self.velocity_y * delta_time


class        UpperCAmelCase_  :
		def __init__(		self							,		UpperCamelCase_							,		UpperCamelCase_ = 1.0							,		UpperCamelCase_ = 1.0							,		UpperCamelCase_ = 0.0							,		)		->   None:
					__lowercase	:       int				=   bodies
					__lowercase	:       Dict				=   gravitation_constant
					__lowercase	:       List[Any]				=   time_factor
					__lowercase	:       List[str]				=   softening_factor
		def __len__(		self   )		->   int:
					return len(self.bodies   )
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_   )		->   None:
					for bodya in self.bodies:
								__lowercase	:       Optional[Any]				=   0.0
								__lowercase	:       Any				=   0.0
								for bodya in self.bodies:
											if bodya != bodya:
														__lowercase	:       Union[str, Any]				=   bodya.position_x - bodya.position_x
														__lowercase	:       str				=   bodya.position_y - bodya.position_y

														# Calculation of the distance using Pythagoras's theorem
														# Extra factor due to the softening technique
														__lowercase	:       str				=   (dif_x**2 + dif_y**2 + self.softening_factor) ** (
														    1 / 2
														)

														# Newton's law of universal gravitation.
														force_x += (
														    self.gravitation_constant * bodya.mass * dif_x / distance**3
														)
														force_y += (
														    self.gravitation_constant * bodya.mass * dif_y / distance**3
														)

            # Update the body's velocity once all the force components have been added
								bodya.update_velocity(UpperCamelCase_							,		UpperCamelCase_							,		delta_time * self.time_factor   )

					# Update the positions only after all the velocities have been updated
					for body in self.bodies:
								body.update_position(delta_time * self.time_factor   )







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase					):
			body_system.update_system(__UpperCamelCase					)

			# Update the positions of the patches
			for patch, body in zip(__UpperCamelCase					,							body_system.bodies					):
						__lowercase	:       List[str]				=   (body.position_x, body.position_y)







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase = -1					,							__UpperCamelCase = 1					,							__UpperCamelCase = -1					,							__UpperCamelCase = 1					,							):
			__lowercase	:       List[str]				=   plt.figure()
			fig.canvas.set_window_title(__UpperCamelCase					)
			__lowercase	:       List[Any]				=   plt.axes(
			    xlim=(x_start, x_end)					,							ylim=(y_start, y_end)					)  # Set section to be plotted
			plt.gca().set_aspect('''equal'''					)  # Fix aspect ratio

			# Each body is drawn as a patch by the plt-function
			__lowercase	:       Any				=   [
			    plt.Circle((body.position_x, body.position_y)					,							body.size					,							fc=body.color					)
			    for body in body_system.bodies
			]

			for patch in patches:
						ax.add_patch(__UpperCamelCase					)

			# Function called at each step of the animation
			def update(__UpperCamelCase					) -> list[plt.Circle]:
						update_step(__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase					)
						return patches

			__lowercase	:       Tuple				=   animation.FuncAnimation(  # noqa: F841
			    __UpperCamelCase					,							__UpperCamelCase					,							interval=__UpperCamelCase					,							blit=__UpperCamelCase					)

			plt.show()







def       __UpperCAmelCase      (				):
			__lowercase	:       Dict				=   0.9_700_436
			__lowercase	:       Tuple				=   -0.24_308_753
			__lowercase	:       Any				=   0.466_203_685
			__lowercase	:       str				=   0.43_236_573

			__lowercase	:       int				=   [
			    Body(__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase					,							size=0.2					,							color='''red'''					),
			    Body(-position_x					,							-position_y					,							__UpperCamelCase					,							__UpperCamelCase					,							size=0.2					,							color='''green'''					),
			    Body(0					,							0					,							-2 * velocity_x					,							-2 * velocity_y					,							size=0.2					,							color='''blue'''					),
			]
			return BodySystem(__UpperCamelCase					,							time_factor=3					)







def       __UpperCAmelCase      (				):
			__lowercase	:       Tuple				=   7.3_476e22
			__lowercase	:       Optional[int]				=   5.972e24
			__lowercase	:       str				=   10_22
			__lowercase	:       Any				=   3_84_39_90_00
			__lowercase	:       Optional[int]				=   6.674e-11

			# Calculation of the respective velocities so that total impulse is zero,
			# i.e. the two bodies together don't move
			__lowercase	:       Optional[Any]				=   earth_mass * velocity_dif / (earth_mass + moon_mass)
			__lowercase	:       Tuple				=   moon_velocity - velocity_dif

			__lowercase	:       Tuple				=   Body(-earth_moon_distance					,							0					,							0					,							__UpperCamelCase					,							__UpperCamelCase					,							10_00_00_00					,							'''grey'''					)
			__lowercase	:       Dict				=   Body(0					,							0					,							0					,							__UpperCamelCase					,							__UpperCamelCase					,							50_00_00_00					,							'''blue'''					)
			return BodySystem([earth, moon]					,							__UpperCamelCase					,							time_factor=1_00_00_00					)







def       __UpperCAmelCase      (				):
			__lowercase	:       Optional[Any]				=   []
			for _ in range(10					):
						__lowercase	:       str				=   random.uniform(-0.5					,							0.5					)
						__lowercase	:       int				=   random.uniform(-0.5					,							0.5					)

						# Bodies are created pairwise with opposite velocities so that the
						# total impulse remains zero
						bodies.append(
						    Body(
						        random.uniform(-0.5					,							0.5					)					,							random.uniform(-0.5					,							0.5					)					,							__UpperCamelCase					,							__UpperCamelCase					,							size=0.05					,							)					)
						bodies.append(
						    Body(
						        random.uniform(-0.5					,							0.5					)					,							random.uniform(-0.5					,							0.5					)					,							-velocity_x					,							-velocity_y					,							size=0.05					,							)					)
			return BodySystem(__UpperCamelCase					,							0.01					,							10					,							0.1					)


if __name__ == "__main__":
			plot('Figure-8 solution to the 3-body-problem', example_a(), -2, 2, -2, 2)
			plot(
			    'Moon\'s orbit around the earth',
			    example_a(),
			    -4_3_0_0_0_0_0_0_0,
			    4_3_0_0_0_0_0_0_0,
			    -4_3_0_0_0_0_0_0_0,
			    4_3_0_0_0_0_0_0_0,
			)
			plot('Random system with many bodies', example_a(), -1.5, 1.5, -1.5, 1.5)



