"""simple docstring"""
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper

import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname

import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary

from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

lowercase__					       =	2

# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
lowercase__					       =	{
    # fairseq:
    'wmt19-ru-en': {'length_penalty': 1.1},
    'wmt19-en-ru': {'length_penalty': 1.15},
    'wmt19-en-de': {'length_penalty': 1.0},
    'wmt19-de-en': {'length_penalty': 1.1},
    # allenai:
    'wmt16-en-de-dist-12-1': {'length_penalty': 0.6},
    'wmt16-en-de-dist-6-1': {'length_penalty': 0.6},
    'wmt16-en-de-12-1': {'length_penalty': 0.8},
    'wmt19-de-en-6-6-base': {'length_penalty': 0.6},
    'wmt19-de-en-6-6-big': {'length_penalty': 0.6},
}

# this remaps the different models to their organization names
lowercase__					       =	{}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
 lowercase__					       =	'facebook'
for m in [
    "wmt16-en-de-dist-12-1",
    "wmt16-en-de-dist-6-1",
    "wmt16-en-de-12-1",
    "wmt19-de-en-6-6-base",
    "wmt19-de-en-6-6-big",
]:
 lowercase__					       =	'allenai'







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->str:
  # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
  # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
  a__:     Union[str, Any]										=	dict((re.sub(r'@@$'		,     ''		,     _SCREAMING_SNAKE_CASE   ), v) if k.endswith('@@'   ) else (re.sub(r'$'		,     '</w>'		,     _SCREAMING_SNAKE_CASE   ), v) for k, v in d.items()   )
  a__:     List[str]										=	'<s> <pad> </s> <unk>'.split()
  # restore the special tokens
  for k in keep_keys:
    del da[F'{k}</w>']
    a__:     Any										=	d[k]  # restore
  return da







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Optional[int]:
  # prep
  assert os.path.exists(_SCREAMING_SNAKE_CASE   )
  os.makedirs(_SCREAMING_SNAKE_CASE		,     exist_ok=_SCREAMING_SNAKE_CASE   )
  print(F'Writing results to {pytorch_dump_folder_path}'   )

  # handle various types of models

  a__:     Optional[int]										=	basename(_SCREAMING_SNAKE_CASE   )
  a__:     str										=	dirname(_SCREAMING_SNAKE_CASE   )

  a__:     Optional[Any]										=	fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
  a__:     List[Any]										=	cls.hub_models()
  a__:     str										=	{'bpe': 'fastbpe', 'tokenizer': 'moses'}
  a__:     int										=	'.'
  # note: since the model dump is old, fairseq has upgraded its model some
  # time later, and it does a whole lot of rewrites and splits on the saved
  # weights, therefore we can't use torch.load() directly on the model file.
  # see: upgrade_state_dict(state_dict) in fairseq_model.py
  print(F'using checkpoint {checkpoint_file}'   )
  a__:     List[str]										=	hub_utils.from_pretrained(
      _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     archive_map=_SCREAMING_SNAKE_CASE		,     **_SCREAMING_SNAKE_CASE   )

  a__:     int										=	vars(chkpt['args']['model']   )

  a__:     int										=	args['source_lang']
  a__:     Optional[Any]										=	args['target_lang']

  a__:     Optional[int]										=	dirname(_SCREAMING_SNAKE_CASE   )
  a__:     Optional[int]										=	basename(_SCREAMING_SNAKE_CASE   )

  # dicts
  a__:     Union[str, Any]										=	os.path.join(_SCREAMING_SNAKE_CASE		,     F'dict.{src_lang}.txt'   )
  a__:     Optional[int]										=	os.path.join(_SCREAMING_SNAKE_CASE		,     F'dict.{tgt_lang}.txt'   )

  a__:     int										=	Dictionary.load(_SCREAMING_SNAKE_CASE   )
  a__:     Tuple										=	rewrite_dict_keys(src_dict.indices   )
  a__:     Optional[int]										=	len(_SCREAMING_SNAKE_CASE   )
  a__:     Union[str, Any]										=	os.path.join(_SCREAMING_SNAKE_CASE		,     'vocab-src.json'   )
  print(F'Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records'   )
  with open(_SCREAMING_SNAKE_CASE		,     'w'		,     encoding='utf-8'   ) as f:
    f.write(json.dumps(_SCREAMING_SNAKE_CASE		,     ensure_ascii=_SCREAMING_SNAKE_CASE		,     indent=_SCREAMING_SNAKE_CASE   )   )

  # detect whether this is a do_lower_case situation, which can be derived by checking whether we
  # have at least one uppercase letter in the source vocab
  a__:     str										=	True
  for k in src_vocab.keys():
    if not k.islower():
      a__:     int										=	False
      break

  a__:     List[str]										=	Dictionary.load(_SCREAMING_SNAKE_CASE   )
  a__:     int										=	rewrite_dict_keys(tgt_dict.indices   )
  a__:     int										=	len(_SCREAMING_SNAKE_CASE   )
  a__:     Tuple										=	os.path.join(_SCREAMING_SNAKE_CASE		,     'vocab-tgt.json'   )
  print(F'Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records'   )
  with open(_SCREAMING_SNAKE_CASE		,     'w'		,     encoding='utf-8'   ) as f:
    f.write(json.dumps(_SCREAMING_SNAKE_CASE		,     ensure_ascii=_SCREAMING_SNAKE_CASE		,     indent=_SCREAMING_SNAKE_CASE   )   )

  # merges_file (bpecodes)
  a__:     Dict										=	os.path.join(_SCREAMING_SNAKE_CASE		,     VOCAB_FILES_NAMES['merges_file']   )
  for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
    a__:     Union[str, Any]										=	os.path.join(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
    if os.path.exists(_SCREAMING_SNAKE_CASE   ):
      break
  with open(_SCREAMING_SNAKE_CASE		,     encoding='utf-8'   ) as fin:
    a__:     Dict										=	fin.read()
  a__:     List[Any]										=	re.sub(r' \d+$'		,     ''		,     _SCREAMING_SNAKE_CASE		,     0		,     re.M   )  # remove frequency number
  print(F'Generating {merges_file}'   )
  with open(_SCREAMING_SNAKE_CASE		,     'w'		,     encoding='utf-8'   ) as fout:
    fout.write(_SCREAMING_SNAKE_CASE   )

  # model config
  a__:     List[Any]										=	os.path.join(_SCREAMING_SNAKE_CASE		,     'config.json'   )

  # validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
  # may have to modify the tokenizer if a different type is used by a future model
  assert args["bpe"] == "fastbpe", F'need to extend tokenizer to support bpe={args["bpe"]}'
  assert args["tokenizer"] == "moses", F'need to extend tokenizer to support bpe={args["tokenizer"]}'

  a__:     Any										=	{
      'architectures': ['FSMTForConditionalGeneration'],
      'model_type': 'fsmt',
      'activation_dropout': args['activation_dropout'],
      'activation_function': 'relu',
      'attention_dropout': args['attention_dropout'],
      'd_model': args['decoder_embed_dim'],
      'dropout': args['dropout'],
      'init_std': 0.02,
      'max_position_embeddings': args['max_source_positions'],
      'num_hidden_layers': args['encoder_layers'],
      'src_vocab_size': src_vocab_size,
      'tgt_vocab_size': tgt_vocab_size,
      'langs': [src_lang, tgt_lang],
      'encoder_attention_heads': args['encoder_attention_heads'],
      'encoder_ffn_dim': args['encoder_ffn_embed_dim'],
      'encoder_layerdrop': args['encoder_layerdrop'],
      'encoder_layers': args['encoder_layers'],
      'decoder_attention_heads': args['decoder_attention_heads'],
      'decoder_ffn_dim': args['decoder_ffn_embed_dim'],
      'decoder_layerdrop': args['decoder_layerdrop'],
      'decoder_layers': args['decoder_layers'],
      'bos_token_id': 0,
      'pad_token_id': 1,
      'eos_token_id': 2,
      'is_encoder_decoder': True,
      'scale_embedding': not args['no_scale_embedding'],
      'tie_word_embeddings': args['share_all_embeddings'],
  }

  # good hparam defaults to start with
  a__:     Optional[Any]										=	5
  a__:     Union[str, Any]										=	False
  if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
    a__:     str										=	best_score_hparams[model_dir]['length_penalty']
  else:
    a__:     Tuple										=	1.0

  print(F'Generating {fsmt_model_config_file}'   )
  with open(_SCREAMING_SNAKE_CASE		,     'w'		,     encoding='utf-8'   ) as f:
    f.write(json.dumps(_SCREAMING_SNAKE_CASE		,     ensure_ascii=_SCREAMING_SNAKE_CASE		,     indent=_SCREAMING_SNAKE_CASE   )   )

  # tokenizer config
  a__:     List[Any]										=	os.path.join(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )

  a__:     List[Any]										=	{
      'langs': [src_lang, tgt_lang],
      'model_max_length': 1024,
      'do_lower_case': do_lower_case,
  }

  print(F'Generating {fsmt_tokenizer_config_file}'   )
  with open(_SCREAMING_SNAKE_CASE		,     'w'		,     encoding='utf-8'   ) as f:
    f.write(json.dumps(_SCREAMING_SNAKE_CASE		,     ensure_ascii=_SCREAMING_SNAKE_CASE		,     indent=_SCREAMING_SNAKE_CASE   )   )

  # model
  a__:     Union[str, Any]										=	chkpt['models'][0]
  a__:     str										=	model.state_dict()

  # rename keys to start with 'model.'
  a__:     Optional[int]										=	OrderedDict(('model.' + k, v) for k, v in model_state_dict.items()   )

  # remove unneeded keys
  a__:     Union[str, Any]										=	[
      'model.model',
      'model.encoder.version',
      'model.decoder.version',
      'model.encoder_embed_tokens.weight',
      'model.decoder_embed_tokens.weight',
      'model.encoder.embed_positions._float_tensor',
      'model.decoder.embed_positions._float_tensor',
  ]
  for k in ignore_keys:
    model_state_dict.pop(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )

  a__:     Dict										=	FSMTConfig.from_pretrained(_SCREAMING_SNAKE_CASE   )
  a__:     int										=	FSMTForConditionalGeneration(_SCREAMING_SNAKE_CASE   )

  # check that it loads ok
  model_new.load_state_dict(_SCREAMING_SNAKE_CASE		,     strict=_SCREAMING_SNAKE_CASE   )

  # save
  a__:     Union[str, Any]										=	os.path.join(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
  print(F'Generating {pytorch_weights_dump_path}'   )
  torch.save(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )

  print('Conversion is done!'   )
  print('\nLast step is to upload the files to s3'   )
  print(F'cd {data_root}'   )
  print(F'transformers-cli upload {model_dir}'   )


if __name__ == "__main__":
 lowercase__					       =	argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--fsmt_checkpoint_path',
     default=None,
     type=str,
     required=True,
     help=(
         'Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'
         ' bpecodes, etc.'
     ),
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
 )
 lowercase__					       =	parser.parse_args()
 convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)



"""simple docstring"""
from math import pi, sqrt, tan







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->float:
  if side_length < 0:
    raise ValueError('surface_area_cube() only accepts non-negative values'   )
  return 6 * side_length**2







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if length < 0 or breadth < 0 or height < 0:
    raise ValueError('surface_area_cuboid() only accepts non-negative values'   )
  return 2 * ((length * breadth) + (breadth * height) + (length * height))







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->float:
  if radius < 0:
    raise ValueError('surface_area_sphere() only accepts non-negative values'   )
  return 4 * pi * radius**2







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->float:
  if radius < 0:
    raise ValueError('surface_area_hemisphere() only accepts non-negative values'   )
  return 3 * pi * radius**2







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if radius < 0 or height < 0:
    raise ValueError('surface_area_cone() only accepts non-negative values'   )
  return pi * radius * (radius + (height**2 + radius**2) ** 0.5)







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if radius_a < 0 or radius_a < 0 or height < 0:
    raise ValueError(
        'surface_area_conical_frustum() only accepts non-negative values'   )
  a__:     List[Any]										=	(height**2 + (radius_a - radius_a) ** 2) ** 0.5
  return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if radius < 0 or height < 0:
    raise ValueError('surface_area_cylinder() only accepts non-negative values'   )
  return 2 * pi * radius * (height + radius)







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if torus_radius < 0 or tube_radius < 0:
    raise ValueError('surface_area_torus() only accepts non-negative values'   )
  if torus_radius < tube_radius:
    raise ValueError(
        'surface_area_torus() does not support spindle or self intersecting tori'   )
  return 4 * pow(_SCREAMING_SNAKE_CASE		,     2   ) * torus_radius * tube_radius







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if length < 0 or width < 0:
    raise ValueError('area_rectangle() only accepts non-negative values'   )
  return length * width







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->float:
  if side_length < 0:
    raise ValueError('area_square() only accepts non-negative values'   )
  return side_length**2







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if base < 0 or height < 0:
    raise ValueError('area_triangle() only accepts non-negative values'   )
  return (base * height) / 2







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if sidea < 0 or sidea < 0 or sidea < 0:
    raise ValueError('area_triangle_three_sides() only accepts non-negative values'   )
  elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
    raise ValueError('Given three sides do not form a triangle'   )
  a__:     int										=	(sidea + sidea + sidea) / 2
  a__:     Tuple										=	sqrt(
      semi_perimeter
      * (semi_perimeter - sidea)
      * (semi_perimeter - sidea)
      * (semi_perimeter - sidea)   )
  return area







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if base < 0 or height < 0:
    raise ValueError('area_parallelogram() only accepts non-negative values'   )
  return base * height







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if basea < 0 or basea < 0 or height < 0:
    raise ValueError('area_trapezium() only accepts non-negative values'   )
  return 1 / 2 * (basea + basea) * height







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->float:
  if radius < 0:
    raise ValueError('area_circle() only accepts non-negative values'   )
  return pi * radius**2







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if radius_x < 0 or radius_y < 0:
    raise ValueError('area_ellipse() only accepts non-negative values'   )
  return pi * radius_x * radius_y







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if diagonal_a < 0 or diagonal_a < 0:
    raise ValueError('area_rhombus() only accepts non-negative values'   )
  return 1 / 2 * diagonal_a * diagonal_a







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float:
  if not isinstance(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ) or sides < 3:
    raise ValueError(
        'area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides'   )
  elif length < 0:
    raise ValueError(
        'area_reg_polygon() only accepts non-negative values as \
length of a side'   )
  return (sides * length**2) / (4 * tan(pi / sides   ))
  return (sides * length**2) / (4 * tan(pi / sides   ))


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)  # verbose so we can see methods missing tests

 print('[DEMO] Areas of various geometric shapes: \n')
 print(f"Rectangle: {area_rectangle(10, 20) = }")
 print(f"Square: {area_square(10) = }")
 print(f"Triangle: {area_triangle(10, 10) = }")
 print(f"Triangle: {area_triangle_three_sides(5, 12, 13) = }")
 print(f"Parallelogram: {area_parallelogram(10, 20) = }")
 print(f"Rhombus: {area_rhombus(10, 20) = }")
 print(f"Trapezium: {area_trapezium(10, 20, 30) = }")
 print(f"Circle: {area_circle(20) = }")
 print(f"Ellipse: {area_ellipse(10, 20) = }")
 print('\nSurface Areas of various geometric shapes: \n')
 print(f"Cube: {surface_area_cube(20) = }")
 print(f"Cuboid: {surface_area_cuboid(10, 20, 30) = }")
 print(f"Sphere: {surface_area_sphere(20) = }")
 print(f"Hemisphere: {surface_area_hemisphere(20) = }")
 print(f"Cone: {surface_area_cone(10, 20) = }")
 print(f"Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }")
 print(f"Cylinder: {surface_area_cylinder(10, 20) = }")
 print(f"Torus: {surface_area_torus(20, 10) = }")
 print(f"Equilateral Triangle: {area_reg_polygon(3, 10) = }")
 print(f"Square: {area_reg_polygon(4, 10) = }")
 print(f"Reqular Pentagon: {area_reg_polygon(5, 10) = }")


