
"""simple docstring"""

from __future__ import annotations

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

class __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ):
     lowercase		  =		1
     lowercase		  =		2
     lowercase		  =		3





     def __repr__(      self ):

      '''simple docstring'''




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






def 				A		(							snake_case :tuple[int, int]							,   snake_case :int							,   snake_case :int						) ->      float:
 __UpperCamelCase    ,      __UpperCamelCase							    =			point
 return degrees(atana(y - miny							,   x - minx						)						)






def 				A		(							snake_case :tuple[int, int]							,   snake_case :tuple[int, int]							,   snake_case :tuple[int, int]						) ->      Direction:
 __UpperCamelCase    ,      __UpperCamelCase							    =			starting
 __UpperCamelCase    ,      __UpperCamelCase							    =			via
 __UpperCamelCase    ,      __UpperCamelCase							    =			target
 __UpperCamelCase							    =			degrees(atana(ya - ya							,   xa - xa						)						)
 via_angle %= 3_6_0
 __UpperCamelCase							    =			degrees(atana(ya - ya							,   xa - xa						)						)
 target_angle %= 3_6_0
 # 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 				A		(							snake_case :list[tuple[int, int]]						) ->      list[tuple[int, int]]:

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

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

 __UpperCamelCase							    =			sorted(snake_case							,   key=lambda snake_case						: angle_comparer(snake_case							,   snake_case							,   snake_case						)						)
 # 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)						)

 __UpperCamelCase							    =			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.
 __UpperCamelCase							    =			Direction.left

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

   if next_direction == Direction.left:
    __UpperCamelCase							    =			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(snake_case						)





"""simple docstring"""

import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()






def 				A		(							snake_case :Dict							,   snake_case :int							,   snake_case :List[str]							,   snake_case :int							,   snake_case :List[str]						) ->      List[str]:
 # Initialise PyTorch model
 __UpperCamelCase							    =			BertConfig.from_pretrained(
     'bert-large-cased'							,   vocab_size=snake_case							,   max_position_embeddings=5_1_2							,   is_decoder=snake_case							,   add_cross_attention=snake_case							,   )
 __UpperCamelCase							    =			bert_config.to_dict()
 del bert_config_dict["type_vocab_size"]
 __UpperCamelCase							    =			BertGenerationConfig(**snake_case						)
 if is_encoder:
  __UpperCamelCase							    =			BertGenerationEncoder(snake_case						)
 else:
  __UpperCamelCase							    =			BertGenerationDecoder(snake_case						)
 print(f'Building PyTorch model from configuration: {config}'						)

 # Load weights from tf checkpoint
 load_tf_weights_in_bert_generation(
     snake_case							,   snake_case							,   model_class='bert'							,   is_encoder_named_decoder=snake_case							,   is_encoder=snake_case							,   )

 # Save pytorch-model
 print(f'Save PyTorch model and config to {pytorch_dump_path}'						)
 model.save_pretrained(snake_case						)


if __name__ == "__main__":
      UpperCamelCase      :							List[str]          =					argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--tf_hub_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
      )
      parser.add_argument(
          "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      parser.add_argument(
          "--is_encoder_named_decoder",
          action="store_true",
          help="If decoder has to be renamed to encoder in PyTorch model.",
      )
      parser.add_argument("--is_encoder", action="store_true", help="If model is an encoder.")
      parser.add_argument("--vocab_size", default=5_0_3_5_8, type=int, help="Vocab size of model")
      UpperCamelCase      :							Dict          =					parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_hub_path,
          args.pytorch_dump_path,
          args.is_encoder_named_decoder,
          args.vocab_size,
          is_encoder=args.is_encoder,
      )



