






'''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()

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





def 		_UpperCAmelCase (				__A				: Union[str, Any]   ):
				# (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'''@@$'''    ,		''''''    ,		__A   ), v) if k.endswith('''@@'''   ) else (re.sub(R'''$'''    ,		'''</w>'''    ,		__A   ), v) for k, v in d.items()   )
				a_		:	str									=   '''<s> <pad> </s> <unk>'''.split()
				# restore the special tokens
				for k in keep_keys:
								del da[f'{k}</w>']
								a_		:	str									=   d[k]  # restore
				return da





def 		_UpperCAmelCase (				__A				: List[Any]    ,		__A				: List[str]   ):
				# 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_		:	Union[str, Any]									=   basename(__A   )
				a_		:	Optional[Any]									=   dirname(__A   )

				a_		:	List[Any]									=   fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
				a_		:	str									=   cls.hub_models()
				a_		:	List[str]									=   {'''bpe''': '''fastbpe''', '''tokenizer''': '''moses'''}
				a_		:	str									=   '''.'''
				# 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_		:	Any									=   hub_utils.from_pretrained(
				    __A    ,		__A    ,		__A    ,		archive_map=__A    ,		**__A   )

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

				a_		:	Any									=   args['''source_lang''']
				a_		:	List[Any]									=   args['''target_lang''']

				a_		:	Union[str, Any]									=   dirname(__A   )
				a_		:	int									=   basename(__A   )

				# dicts
				a_		:	Optional[Any]									=   os.path.join(__A    ,		f'dict.{src_lang}.txt'   )
				a_		:	int									=   os.path.join(__A    ,		f'dict.{tgt_lang}.txt'   )

				a_		:	Any									=   Dictionary.load(__A   )
				a_		:	Any									=   rewrite_dict_keys(src_dict.indices   )
				a_		:	List[Any]									=   len(__A   )
				a_		:	Optional[Any]									=   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_		:	Tuple									=   True
				for k in src_vocab.keys():
								if not k.islower():
												a_		:	Dict									=   False
												break

				a_		:	Any									=   Dictionary.load(__A   )
				a_		:	List[Any]									=   rewrite_dict_keys(tgt_dict.indices   )
				a_		:	int									=   len(__A   )
				a_		:	Any									=   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_		:	Optional[int]									=   os.path.join(__A    ,		VOCAB_FILES_NAMES['''merges_file''']   )
				for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
								a_		:	Optional[Any]									=   os.path.join(__A    ,		__A   )
								if os.path.exists(__A   ):
												break
				with open(__A    ,		encoding='''utf-8'''   ) as fin:
								a_		:	Dict									=   fin.read()
				a_		:	Any									=   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_		:	List[Any]									=   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_		:	int									=   {
				    '''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_		:	List[Any]									=   5
				a_		:	Tuple									=   False
				if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
								a_		:	Optional[int]									=   best_score_hparams[model_dir]['''length_penalty''']
				else:
								a_		:	Union[str, Any]									=   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_		:	Dict									=   os.path.join(__A    ,		__A   )

				a_		:	List[str]									=   {
				    '''langs''': [src_lang, tgt_lang],
				    '''model_max_length''': 10_24,
				    '''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_		:	Any									=   chkpt['''models'''][0]
				a_		:	Optional[int]									=   model.state_dict()

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

				# remove unneeded keys
				a_		:	Optional[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(__A    ,		__A   )

				a_		:	str									=   FSMTConfig.from_pretrained(__A   )
				a_		:	Optional[int]									=   FSMTForConditionalGeneration(__A   )

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

				# save
				a_		:	List[str]									=   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__":
		__lowerCAmelCase									=   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.'
		)
		__lowerCAmelCase									=   parser.parse_args()
		convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)












'''simple docstring'''







import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
		import torch


if is_vision_available():
		from PIL import Image

		from transformers import MgpstrProcessor, ViTImageProcessor





@require_torch
@require_vision
class 						SCREAMING_SNAKE_CASE       (       unittest.TestCase							):
			snake_case__	=					ViTImageProcessor if is_vision_available() else None






			@property
			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      List[str]:
							return self.image_processor_tester.prepare_image_processor_dict()






			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple       )      ->      Optional[int]:
							a_		:	Optional[Any]									=   (3, 32, 128)
							a_		:	Optional[Any]									=   tempfile.mkdtemp()

							# fmt: off
							a_		:	Union[str, Any]									=   ['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
							# fmt: on
							a_		:	List[Any]									=   dict(zip(__SCREAMING_SNAKE_CASE	,    range(len(__SCREAMING_SNAKE_CASE       )       )       )       )

							a_		:	Union[str, Any]									=   os.path.join(self.tmpdirname	,    VOCAB_FILES_NAMES['''vocab_file''']       )
							with open(self.vocab_file	,    '''w'''	,    encoding='''utf-8'''       ) as fp:
											fp.write(json.dumps(__SCREAMING_SNAKE_CASE       ) + '''\n'''       )

							a_		:	Union[str, Any]									=   {
							    '''do_normalize''': False,
							    '''do_resize''': True,
							    '''image_processor_type''': '''ViTImageProcessor''',
							    '''resample''': 3,
							    '''size''': {'''height''': 32, '''width''': 128},
							}
							a_		:	Any									=   os.path.join(self.tmpdirname	,    __SCREAMING_SNAKE_CASE       )
							with open(self.image_processor_file	,    '''w'''	,    encoding='''utf-8'''       ) as fp:
											json.dump(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]	,    **__SCREAMING_SNAKE_CASE   :      Union[str, Any]       )      ->      Dict:
							return MgpstrTokenizer.from_pretrained(self.tmpdirname	,    **__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]	,    **__SCREAMING_SNAKE_CASE   :      Union[str, Any]       )      ->      List[Any]:
							return ViTImageProcessor.from_pretrained(self.tmpdirname	,    **__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]       )      ->      Union[str, Any]:
							shutil.rmtree(self.tmpdirname       )






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      str:
							a_		:	Optional[Any]									=   np.random.randint(255	,    size=(3, 30, 400)	,    dtype=np.uinta       )

							a_		:	Optional[int]									=   Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE	,    0	,    -1       )       )

							return image_input






			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      int:
							a_		:	str									=   self.get_tokenizer()
							a_		:	List[Any]									=   self.get_image_processor()

							a_		:	Union[str, Any]									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )
							processor.save_pretrained(self.tmpdirname       )
							a_		:	Tuple									=   MgpstrProcessor.from_pretrained(self.tmpdirname	,    use_fast=__SCREAMING_SNAKE_CASE       )

							self.assertEqual(processor.char_tokenizer.get_vocab()	,    tokenizer.get_vocab()       )
							self.assertIsInstance(processor.char_tokenizer	,    __SCREAMING_SNAKE_CASE       )

							self.assertEqual(processor.image_processor.to_json_string()	,    image_processor.to_json_string()       )
							self.assertIsInstance(processor.image_processor	,    __SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]       )      ->      int:
							a_		:	Any									=   self.get_tokenizer()
							a_		:	Dict									=   self.get_image_processor()

							a_		:	Optional[Any]									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )
							processor.save_pretrained(self.tmpdirname       )

							a_		:	str									=   self.get_tokenizer(bos_token='''(BOS)'''	,    eos_token='''(EOS)'''       )
							a_		:	str									=   self.get_image_processor(do_normalize=__SCREAMING_SNAKE_CASE	,    padding_value=1.0       )

							a_		:	Optional[int]									=   MgpstrProcessor.from_pretrained(
							    self.tmpdirname	,    bos_token='''(BOS)'''	,    eos_token='''(EOS)'''	,    do_normalize=__SCREAMING_SNAKE_CASE	,    padding_value=1.0       )

							self.assertEqual(processor.char_tokenizer.get_vocab()	,    tokenizer_add_kwargs.get_vocab()       )
							self.assertIsInstance(processor.char_tokenizer	,    __SCREAMING_SNAKE_CASE       )

							self.assertEqual(processor.image_processor.to_json_string()	,    image_processor_add_kwargs.to_json_string()       )
							self.assertIsInstance(processor.image_processor	,    __SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      Optional[Any]:
							a_		:	Dict									=   self.get_image_processor()
							a_		:	Tuple									=   self.get_tokenizer()

							a_		:	Any									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	Optional[Any]									=   self.prepare_image_inputs()

							a_		:	List[Any]									=   image_processor(__SCREAMING_SNAKE_CASE	,    return_tensors='''np'''       )
							a_		:	int									=   processor(images=__SCREAMING_SNAKE_CASE	,    return_tensors='''np'''       )

							for key in input_image_proc.keys():
											self.assertAlmostEqual(input_image_proc[key].sum()	,    input_processor[key].sum()	,    delta=1e-2       )






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      Optional[Any]:
							a_		:	Dict									=   self.get_image_processor()
							a_		:	List[Any]									=   self.get_tokenizer()

							a_		:	int									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	Tuple									=   '''test'''

							a_		:	Any									=   processor(text=__SCREAMING_SNAKE_CASE       )

							a_		:	Optional[int]									=   tokenizer(__SCREAMING_SNAKE_CASE       )
							for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]	,    encoded_processor[key]       )






			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      int:
							a_		:	Union[str, Any]									=   self.get_image_processor()
							a_		:	Tuple									=   self.get_tokenizer()

							a_		:	Optional[Any]									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	Union[str, Any]									=   '''test'''
							a_		:	str									=   self.prepare_image_inputs()

							a_		:	Tuple									=   processor(text=__SCREAMING_SNAKE_CASE	,    images=__SCREAMING_SNAKE_CASE       )

							self.assertListEqual(list(inputs.keys()       )	,    ['''pixel_values''', '''labels''']       )

							# test if it raises when no input is passed
							with pytest.raises(__SCREAMING_SNAKE_CASE       ):
											processor()






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      int:
							a_		:	int									=   self.get_image_processor()
							a_		:	List[Any]									=   self.get_tokenizer()

							a_		:	Dict									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	Tuple									=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

							a_		:	List[Any]									=   processor.char_decode(__SCREAMING_SNAKE_CASE       )
							a_		:	Optional[int]									=   tokenizer.batch_decode(__SCREAMING_SNAKE_CASE       )
							a_		:	Union[str, Any]									=   [seq.replace(''' '''	,    ''''''       ) for seq in decoded_tok]

							self.assertListEqual(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Any       )      ->      List[Any]:
							a_		:	int									=   self.get_image_processor()
							a_		:	Tuple									=   self.get_tokenizer()

							a_		:	Any									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	Dict									=   None
							a_		:	Tuple									=   self.prepare_image_inputs()

							a_		:	Optional[int]									=   processor(text=__SCREAMING_SNAKE_CASE	,    images=__SCREAMING_SNAKE_CASE       )

							self.assertListEqual(list(inputs.keys()       )	,    processor.model_input_names       )







			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple       )      ->      Union[str, Any]:
							a_		:	List[str]									=   self.get_image_processor()
							a_		:	str									=   self.get_tokenizer()

							a_		:	Any									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	str									=   torch.randn(1	,    27	,    38       )
							a_		:	int									=   torch.randn(1	,    27	,    5_0257       )
							a_		:	Union[str, Any]									=   torch.randn(1	,    27	,    3_0522       )

							a_		:	Tuple									=   processor.batch_decode([char_input, bpe_input, wp_input]       )

							self.assertListEqual(list(results.keys()       )	,    ['''generated_text''', '''scores''', '''char_preds''', '''bpe_preds''', '''wp_preds''']       )




