



# 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()

UpperCAmelCase__       =	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
UpperCAmelCase__       =	{
    # 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
UpperCAmelCase__       =	{}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
							UpperCAmelCase__       =	"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",
]:
							UpperCAmelCase__       =	"allenai"





def 							A     (					_UpperCAmelCase    :       Union[str, Any]   )    ->			Any:







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





def 							A     (					_UpperCAmelCase    :       str							,       _UpperCAmelCase    :       Union[str, Any]   )    ->			Dict:







			'''simple docstring'''
			assert os.path.exists(_UpperCAmelCase   )
			os.makedirs(_UpperCAmelCase							,       exist_ok=_UpperCAmelCase   )
			print(F"Writing results to {pytorch_dump_folder_path}"   )

			# handle various types of models

			_UpperCAmelCase			=       basename(_UpperCAmelCase   )
			_UpperCAmelCase			=       dirname(_UpperCAmelCase   )

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

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

			_UpperCAmelCase			=       args['source_lang']
			_UpperCAmelCase			=       args['target_lang']

			_UpperCAmelCase			=       dirname(_UpperCAmelCase   )
			_UpperCAmelCase			=       basename(_UpperCAmelCase   )

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

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

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

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

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

			# model config
			_UpperCAmelCase			=       os.path.join(_UpperCAmelCase							,       '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']}"

			_UpperCAmelCase			=       {
			    '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
			_UpperCAmelCase			=       5
			_UpperCAmelCase			=       False
			if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
						_UpperCAmelCase			=       best_score_hparams[model_dir]['length_penalty']
			else:
						_UpperCAmelCase			=       1.0

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

			# tokenizer config
			_UpperCAmelCase			=       os.path.join(_UpperCAmelCase							,       _UpperCAmelCase   )

			_UpperCAmelCase			=       {
			    'langs': [src_lang, tgt_lang],
			    'model_max_length': 1_024,
			    'do_lower_case': do_lower_case,
			}

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

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

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

			# remove unneeded keys
			_UpperCAmelCase			=       [
			    '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(_UpperCAmelCase							,       _UpperCAmelCase   )

			_UpperCAmelCase			=       FSMTConfig.from_pretrained(_UpperCAmelCase   )
			_UpperCAmelCase			=       FSMTForConditionalGeneration(_UpperCAmelCase   )

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

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

			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__":
							UpperCAmelCase__       =	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."
							)
							UpperCAmelCase__       =	parser.parse_args()
							convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)





import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class       __lowerCAmelCase   (      A    ,						unittest.TestCase						):
			UpperCamelCase        =					CLIPTokenizer
			UpperCamelCase        =					CLIPTokenizerFast
			UpperCamelCase        =					True
			UpperCamelCase        =					{}
			UpperCamelCase        =					False



			def    _lowerCamelCase	(						self       :  List[str])       ->   List[str]:



						"""simple docstring"""






						super().setUp()

						# fmt: off
						_UpperCAmelCase			=       ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
						# fmt: on
						_UpperCAmelCase			=       dict(zip(A			,		range(len(A))))
						_UpperCAmelCase			=       ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>']
						_UpperCAmelCase			=       {'unk_token': '<unk>'}

						_UpperCAmelCase			=       os.path.join(self.tmpdirname			,		VOCAB_FILES_NAMES['vocab_file'])
						_UpperCAmelCase			=       os.path.join(self.tmpdirname			,		VOCAB_FILES_NAMES['merges_file'])
						with open(self.vocab_file			,		'w'			,		encoding='utf-8') as fp:
									fp.write(json.dumps(A) + '\n')
						with open(self.merges_file			,		'w'			,		encoding='utf-8') as fp:
									fp.write('\n'.join(A))



			def    _lowerCamelCase	(						self       :  Optional[Any]			,		**A       :  str)       ->   Optional[int]:



						"""simple docstring"""






						kwargs.update(self.special_tokens_map)
						return CLIPTokenizer.from_pretrained(self.tmpdirname			,		**A)



			def    _lowerCamelCase	(						self       :  Any			,		**A       :  Dict)       ->   Union[str, Any]:



						"""simple docstring"""






						kwargs.update(self.special_tokens_map)
						return CLIPTokenizerFast.from_pretrained(self.tmpdirname			,		**A)



			def    _lowerCamelCase	(						self       :  Optional[int]			,		A       :  Union[str, Any])       ->   int:



						"""simple docstring"""






						_UpperCAmelCase			=       'lower newer'
						_UpperCAmelCase			=       'lower newer'
						return input_text, output_text



			def    _lowerCamelCase	(						self       :  Dict)       ->   Any:



						"""simple docstring"""






						_UpperCAmelCase			=       CLIPTokenizer(self.vocab_file			,		self.merges_file			,		**self.special_tokens_map)
						_UpperCAmelCase			=       'lower newer'
						_UpperCAmelCase			=       ['lo', 'w', 'er</w>', 'n', 'e', 'w', 'er</w>']
						_UpperCAmelCase			=       tokenizer.tokenize(A)
						self.assertListEqual(A			,		A)

						_UpperCAmelCase			=       tokens + [tokenizer.unk_token]
						_UpperCAmelCase			=       [10, 2, 16, 9, 3, 2, 16, 20]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(A)			,		A)



			@require_ftfy
			def    _lowerCamelCase	(						self       :  List[Any])       ->   Dict:



						"""simple docstring"""






						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"):
												_UpperCAmelCase			=       self.tokenizer_class.from_pretrained(A			,		**A)
												_UpperCAmelCase			=       self.rust_tokenizer_class.from_pretrained(A			,		**A)

												_UpperCAmelCase			=       'A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'
												_UpperCAmelCase			=       tokenizer_s.tokenize(A)
												_UpperCAmelCase			=       tokenizer_r.tokenize(A)

												self.assertListEqual(A			,		A)

												# Test that the tokenization is identical on an example containing a character (Latin Small Letter A
												# with Tilde) encoded in 2 different ways
												_UpperCAmelCase			=       'xa\u0303y' + ' ' + 'x\xe3y'
												_UpperCAmelCase			=       tokenizer_s.tokenize(A)
												_UpperCAmelCase			=       tokenizer_r.tokenize(A)

												self.assertListEqual(A			,		A)

												# Test that the tokenization is identical on unicode of space type
												_UpperCAmelCase			=       [
												    '\u0009',  # (horizontal tab, '\t')
												    '\u000B',  # (vertical tab)
												    '\u000C',  # (form feed)
												    '\u0020',  # (space, ' ')
												    '\u200E',  # (left-to-right mark):w
												    '\u200F',  # (right-to-left mark)
												]
												for unicode_seq in spaces_unicodes:
															_UpperCAmelCase			=       tokenizer_s.tokenize(A)
															_UpperCAmelCase			=       tokenizer_r.tokenize(A)

															self.assertListEqual(A			,		A)

												# Test that the tokenization is identical on unicode of line break type
												_UpperCAmelCase			=       [
												    '\u000A',  # (line feed, '\n')
												    '\r\n',  # (carriage return and line feed, '\r\n')
												    '\u000D',  # (carriage return, '\r')
												    '\r',  # (carriage return, '\r')
												    '\u000D',  # (carriage return, '\r')
												    '\u2028',  # (line separator)
												    '\u2029',  # (paragraph separator)
												    # "\u0085", # (next line)
												]

												# The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
												# it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
												# space (and thus into an empty list).

												for unicode_seq in line_break_unicodes:
															_UpperCAmelCase			=       tokenizer_s.tokenize(A)
															_UpperCAmelCase			=       tokenizer_r.tokenize(A)

															self.assertListEqual(A			,		A)



			def    _lowerCamelCase	(						self       :  str)       ->   Optional[Any]:



						"""simple docstring"""






						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"):
												_UpperCAmelCase			=       'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
												_UpperCAmelCase			=       F"{text_of_1_token} {text_of_1_token}"

												_UpperCAmelCase			=       self.rust_tokenizer_class.from_pretrained(
												    A			,		use_fast=A			,		)
												_UpperCAmelCase			=       tokenizer_r(A			,		return_offsets_mapping=A			,		add_special_tokens=A)
												self.assertEqual(encoding.offset_mapping[0]			,		(0, len(A)))
												self.assertEqual(
												    encoding.offset_mapping[1]			,		(len(A) + 1, len(A) + 1 + len(A))			,		)

												_UpperCAmelCase			=       F" {text}"

												_UpperCAmelCase			=       self.rust_tokenizer_class.from_pretrained(
												    A			,		use_fast=A			,		)
												_UpperCAmelCase			=       tokenizer_r(A			,		return_offsets_mapping=A			,		add_special_tokens=A)
												self.assertEqual(encoding.offset_mapping[0]			,		(1, 1 + len(A)))
												self.assertEqual(
												    encoding.offset_mapping[1]			,		(1 + len(A) + 1, 1 + len(A) + 1 + len(A))			,		)



			def    _lowerCamelCase	(						self       :  Tuple)       ->   str:



						"""simple docstring"""






						with self.assertRaises(A) as context:
									self.rust_tokenizer_class.from_pretrained('robot-test/old-clip-tokenizer')

						self.assertTrue(
						    context.exception.args[0].startswith(
						        'The `backend_tokenizer` provided does not match the expected format.'))



			@require_ftfy
			def    _lowerCamelCase	(						self       :  int)       ->   int:



						"""simple docstring"""






						super().test_tokenization_python_rust_equals()



			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   Any:



						"""simple docstring"""






						pass
