
# 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()

A     :	Dict    = 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
A     :	str    = {
    # 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
A     :	Union[str, Any]    = {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
					A     :	Any    = '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",
]:
					A     :	Dict    = 'allenai'


def     __lowerCAmelCase			(			a__  )       ->  int:
				# (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										=    dict((re.sub(R'''@@$'''						,    ''''''						,    a__  ), v) if k.endswith('''@@'''  ) else (re.sub(R'''$'''						,    '''</w>'''						,    a__  ), v) for k, v in d.items()  )
				__a										=    '''<s> <pad> </s> <unk>'''.split()
				# restore the special tokens
				for k in keep_keys:
								del da[F"""{k}</w>"""]
								__a										=    d[k]  # restore
				return da


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

				# handle various types of models

				__a										=    basename(a__  )
				__a										=    dirname(a__  )

				__a										=    fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
				__a										=    cls.hub_models()
				__a										=    {'''bpe''': '''fastbpe''', '''tokenizer''': '''moses'''}
				__a										=    '''.'''
				# 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										=    hub_utils.from_pretrained(
				    a__						,    a__						,    a__						,    archive_map=a__						,    **a__  )

				__a										=    vars(chkpt['''args''']['''model''']  )

				__a										=    args['''source_lang''']
				__a										=    args['''target_lang''']

				__a										=    dirname(a__  )
				__a										=    basename(a__  )

				# dicts
				__a										=    os.path.join(a__						,    F"""dict.{src_lang}.txt"""  )
				__a										=    os.path.join(a__						,    F"""dict.{tgt_lang}.txt"""  )

				__a										=    Dictionary.load(a__  )
				__a										=    rewrite_dict_keys(src_dict.indices  )
				__a										=    len(a__  )
				__a										=    os.path.join(a__						,    '''vocab-src.json'''  )
				print(F"""Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records"""  )
				with open(a__						,    '''w'''						,    encoding='''utf-8'''  ) as f:
								f.write(json.dumps(a__						,    ensure_ascii=a__						,    indent=a__  )  )

				# 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										=    True
				for k in src_vocab.keys():
								if not k.islower():
												__a										=    False
												break

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

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

				# model config
				__a										=    os.path.join(a__						,    '''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										=    {
				    '''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										=    5
				__a										=    False
				if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
								__a										=    best_score_hparams[model_dir]['''length_penalty''']
				else:
								__a										=    1.0

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

				# tokenizer config
				__a										=    os.path.join(a__						,    a__  )

				__a										=    {
				    '''langs''': [src_lang, tgt_lang],
				    '''model_max_length''': 1024,
				    '''do_lower_case''': do_lower_case,
				}

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

				# model
				__a										=    chkpt['''models'''][0]
				__a										=    model.state_dict()

				# rename keys to start with 'model.'
				__a										=    OrderedDict(('''model.''' + k, v) for k, v in model_state_dict.items()  )

				# remove unneeded keys
				__a										=    [
				    '''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(a__						,    a__  )

				__a										=    FSMTConfig.from_pretrained(a__  )
				__a										=    FSMTForConditionalGeneration(a__  )

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

				# save
				__a										=    os.path.join(a__						,    a__  )
				print(F"""Generating {pytorch_weights_dump_path}"""  )
				torch.save(a__						,    a__  )

				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__":
					A     :	Union[str, Any]    = 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.'
					)
					A     :	List[str]    = parser.parse_args()
					convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)

from __future__ import annotations

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

from matplotlib import pyplot as plt


def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  tuple[int | None, int | None, float]:
				if not arr:
								return None, None, 0
				if low == high:
								return low, high, arr[low]

				__a										=    (low + high) // 2
				__a							,     __a							,     __a										=    max_subarray(a__						,    a__						,    a__  )
				__a							,     __a							,     __a										=    max_subarray(a__						,    mid + 1						,    a__  )
				__a							,     __a							,     __a										=    max_cross_sum(a__						,    a__						,    a__						,    a__  )
				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     __lowerCAmelCase			(			a__						,    a__						,    a__						,    a__  )       ->  tuple[int, int, float]:
				__a							,     __a										=    float('''-inf'''  ), -1
				__a							,     __a										=    float('''-inf'''  ), -1

				__a										=    0
				for i in range(a__						,    low - 1						,    -1  ):
								summ += arr[i]
								if summ > left_sum:
												__a										=    summ
												__a										=    i

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

				return max_left, max_right, (left_sum + right_sum)


def     __lowerCAmelCase			(			a__  )       ->  float:
				__a										=    [randint(1						,    a__  ) for _ in range(a__  )]
				__a										=    time.time()
				max_subarray(a__						,    0						,    input_size - 1  )
				__a										=    time.time()
				return end - start


def     __lowerCAmelCase			(			)       ->  None:
				__a										=    [10, 100, 1000, 1_0000, 5_0000, 10_0000, 20_0000, 30_0000, 40_0000, 50_0000]
				__a										=    [time_max_subarray(a__  ) for input_size in input_sizes]
				print('''No of Inputs\t\tTime Taken'''  )
				for input_size, runtime in zip(a__						,    a__  ):
								print(a__						,    '''\t\t'''						,    a__  )
				plt.plot(a__						,    a__  )
				plt.xlabel('''Number of Inputs'''  )
				plt.ylabel('''Time taken in seconds'''  )
				plt.show()


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

					testmod()