





from __future__ import annotations

from collections import deque
from enum import Enum
from math import atana, degrees
from sys import maxsize




class 					UpperCamelCase_    (       UpperCamelCase__      ):
							lowerCamelCase_          =   1
							lowerCamelCase_          =   2
							lowerCamelCase_          =   3


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



									"""simple docstring"""



									return f'''{self.__class__.__name__}.{self.name}'''






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							tuple[int, int]   ,				UpperCamelCase__:							int   ,				UpperCamelCase__:							int      ):
		SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= point
		return degrees(atana(y - miny   ,				x - minx      )      )






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							tuple[int, int]   ,				UpperCamelCase__:							tuple[int, int]   ,				UpperCamelCase__:							tuple[int, int]      ):
		SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= starting
		SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= via
		SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= target
		SCREAMING_SNAKE_CASE__								= degrees(atana(ya - ya   ,				xa - xa      )      )
		via_angle %= 360
		SCREAMING_SNAKE_CASE__								= degrees(atana(ya - ya   ,				xa - xa      )      )
		target_angle %= 360
		# t-
		#  \ \
		#   \ v
		#    \|
		#     s
		# via_angle is always lower than target_angle, if direction is left.
		# If they are same, it means they are on a same line of convex hull.
		if target_angle > via_angle:
				return Direction.left
		elif target_angle == via_angle:
				return Direction.straight
		else:
				return Direction.right






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							list[tuple[int, int]]      ):

		if len(UpperCamelCase__      ) <= 2:
				# There is no convex hull
				raise ValueError("""graham_scan: argument must contain more than 3 points."""      )
		if len(UpperCamelCase__      ) == 3:
				return points
		# find the lowest and the most left point
		SCREAMING_SNAKE_CASE__								= 0
		SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= maxsize, maxsize
		for i, point in enumerate(UpperCamelCase__      ):
				SCREAMING_SNAKE_CASE__								= point[0]
				SCREAMING_SNAKE_CASE__								= point[1]
				if y < miny:
						SCREAMING_SNAKE_CASE__								= y
						SCREAMING_SNAKE_CASE__								= x
						SCREAMING_SNAKE_CASE__								= i
				if y == miny and x < minx:
						SCREAMING_SNAKE_CASE__								= x
						SCREAMING_SNAKE_CASE__								= i

    # remove the lowest and the most left point from points for preparing for sort
		points.pop(UpperCamelCase__      )

		SCREAMING_SNAKE_CASE__								= sorted(UpperCamelCase__   ,				key=lambda UpperCamelCase__      : angle_comparer(UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__      )      )
		# This insert actually costs complexity,
		# and you should instead add (minx, miny) into stack later.
		# I'm using insert just for easy understanding.
		sorted_points.insert(0   ,				(minx, miny)      )

		SCREAMING_SNAKE_CASE__								= deque()
		stack.append(sorted_points[0]      )
		stack.append(sorted_points[1]      )
		stack.append(sorted_points[2]      )
		# In any ways, the first 3 points line are towards left.
		# Because we sort them the angle from minx, miny.
		SCREAMING_SNAKE_CASE__								= Direction.left

		for i in range(3   ,				len(UpperCamelCase__      )      ):
				while True:
						SCREAMING_SNAKE_CASE__								= stack[-2]
						SCREAMING_SNAKE_CASE__								= stack[-1]
						SCREAMING_SNAKE_CASE__								= sorted_points[i]
						SCREAMING_SNAKE_CASE__								= check_direction(UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__      )

						if next_direction == Direction.left:
								SCREAMING_SNAKE_CASE__								= Direction.left
								break
						if next_direction == Direction.straight:
								if current_direction == Direction.left:
										# We keep current_direction as left.
										# Because if the straight line keeps as straight,
										# we want to know if this straight line is towards left.
										break
								elif current_direction == Direction.right:
										# If the straight line is towards right,
										# every previous points on those straigh line is not convex hull.
										stack.pop()
						if next_direction == Direction.right:
								stack.pop()
				stack.append(sorted_points[i]      )
		return list(UpperCamelCase__      )






import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Any   ,				UpperCamelCase__:							List[Any]   ,				UpperCamelCase__:							Union[str, Any]      ):
		# Construct configuration depending on size
		SCREAMING_SNAKE_CASE__								= {"""small""": (512, 8, 24), """medium""": (1_024, 8, 36), """large""": (1_536, 16, 48)}
		SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= MODELS[model_size]  # set model hyperparameters
		SCREAMING_SNAKE_CASE__								= ImageGPTConfig(n_embd=UpperCamelCase__   ,				n_layer=UpperCamelCase__   ,				n_head=UpperCamelCase__      )
		SCREAMING_SNAKE_CASE__								= ImageGPTForCausalLM(UpperCamelCase__      )

		# Load weights from numpy
		load_tf_weights_in_imagegpt(UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__      )

		# Save pytorch-model
		SCREAMING_SNAKE_CASE__								= pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
		SCREAMING_SNAKE_CASE__								= pytorch_dump_folder_path + """/""" + CONFIG_NAME
		print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''      )
		torch.save(model.state_dict()   ,				UpperCamelCase__      )
		print(f'''Save configuration file to {pytorch_config_dump_path}'''      )
		with open(UpperCamelCase__   ,				"""w"""   ,				encoding="""utf-8"""      ) as f:
				f.write(config.to_json_string()      )


if __name__ == "__main__":
	_lowerCamelCase							=     argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--imagegpt_checkpoint_path',
	    default=None,
	    type=str,
	    required=True,
	    help='Path to the TensorFlow checkpoint path.',
	)
	parser.add_argument(
	    '--model_size',
	    default=None,
	    type=str,
	    required=True,
	    help='Size of the model (can be either \'small\', \'medium\' or \'large\').',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	_lowerCamelCase							=     parser.parse_args()
	convert_imagegpt_checkpoint_to_pytorch(
	    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
	)