

"""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()

__UpperCamelCase    : str				 =						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    : List[Any]				 =						{
    # fairseq:
    '''wmt19-ru-en''': {'''length_penalty''': 1.1},
    '''wmt19-en-ru''': {'''length_penalty''': 1.1_5},
    '''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    : str				 =						{}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
							__UpperCamelCase    : int				 =						'''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    : Optional[int]				 =						'''allenai'''

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

def     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):
	# 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

	lowerCAmelCase__      :		int    							=      basename(A_					)
	lowerCAmelCase__      :		List[str]    							=      dirname(A_					)

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

	lowerCAmelCase__      :		Any    							=      vars(chkpt['''args''']['''model''']					)

	lowerCAmelCase__      :		List[str]    							=      args['''source_lang''']
	lowerCAmelCase__      :		Optional[int]    							=      args['''target_lang''']

	lowerCAmelCase__      :		Optional[int]    							=      dirname(A_					)
	lowerCAmelCase__      :		List[Any]    							=      basename(A_					)

	# dicts
	lowerCAmelCase__      :		Optional[Any]    							=      os.path.join(A_		,	f'dict.{src_lang}.txt'					)
	lowerCAmelCase__      :		Union[str, Any]    							=      os.path.join(A_		,	f'dict.{tgt_lang}.txt'					)

	lowerCAmelCase__      :		Optional[int]    							=      Dictionary.load(A_					)
	lowerCAmelCase__      :		str    							=      rewrite_dict_keys(src_dict.indices					)
	lowerCAmelCase__      :		Union[str, Any]    							=      len(A_					)
	lowerCAmelCase__      :		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
	lowerCAmelCase__      :		str    							=      True
	for k in src_vocab.keys():
		if not k.islower():
			lowerCAmelCase__      :		List[str]    							=      False
			break

	lowerCAmelCase__      :		List[Any]    							=      Dictionary.load(A_					)
	lowerCAmelCase__      :		List[str]    							=      rewrite_dict_keys(tgt_dict.indices					)
	lowerCAmelCase__      :		Tuple    							=      len(A_					)
	lowerCAmelCase__      :		Dict    							=      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)
	lowerCAmelCase__      :		int    							=      os.path.join(A_		,	VOCAB_FILES_NAMES['''merges_file''']					)
	for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
		lowerCAmelCase__      :		Optional[int]    							=      os.path.join(A_		,	A_					)
		if os.path.exists(A_					):
			break
	with open(A_		,	encoding='''utf-8'''					) as fin:
		lowerCAmelCase__      :		str    							=      fin.read()
	lowerCAmelCase__      :		int    							=      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
	lowerCAmelCase__      :		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"]}'

	lowerCAmelCase__      :		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
	lowerCAmelCase__      :		Optional[Any]    							=      5
	lowerCAmelCase__      :		Dict    							=      False
	if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
		lowerCAmelCase__      :		Any    							=      best_score_hparams[model_dir]['''length_penalty''']
	else:
		lowerCAmelCase__      :		int    							=      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
	lowerCAmelCase__      :		Union[str, Any]    							=      os.path.join(A_		,	A_					)

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

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

	# remove unneeded keys
	lowerCAmelCase__      :		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_					)

	lowerCAmelCase__      :		Optional[int]    							=      FSMTConfig.from_pretrained(A_					)
	lowerCAmelCase__      :		Optional[int]    							=      FSMTForConditionalGeneration(A_					)

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

	# save
	lowerCAmelCase__      :		Union[str, Any]    							=      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__":
							__UpperCamelCase    : Dict				 =						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    : List[str]				 =						parser.parse_args()
							convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)







"""simple docstring"""





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import LevitImageProcessor







class 							SCREAMING_SNAKE_CASE   (						unittest.TestCase			):






					"""simple docstring"""



					def __init__(			self     :							Any  ,lowercase_     :							Dict  ,lowercase_     :							List[str]=7  ,lowercase_     :							Tuple=3  ,lowercase_     :							List[str]=1_8  ,lowercase_     :							Optional[Any]=3_0  ,lowercase_     :							List[Any]=4_0_0  ,lowercase_     :							List[Any]=True  ,lowercase_     :							Any=None  ,lowercase_     :							Optional[Any]=True  ,lowercase_     :							str=None  ,lowercase_     :							List[Any]=True  ,lowercase_     :							Dict=[0.5, 0.5, 0.5]  ,lowercase_     :							Dict=[0.5, 0.5, 0.5]  ,):
						lowerCAmelCase__      :		Any    							=      size if size is not None else {'''shortest_edge''': 1_8}
						lowerCAmelCase__      :		Union[str, Any]    							=      crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
						lowerCAmelCase__      :		Dict    							=      parent
						lowerCAmelCase__      :		Dict    							=      batch_size
						lowerCAmelCase__      :		List[str]    							=      num_channels
						lowerCAmelCase__      :		Any    							=      image_size
						lowerCAmelCase__      :		Union[str, Any]    							=      min_resolution
						lowerCAmelCase__      :		Dict    							=      max_resolution
						lowerCAmelCase__      :		List[str]    							=      do_resize
						lowerCAmelCase__      :		Optional[Any]    							=      size
						lowerCAmelCase__      :		Tuple    							=      do_center_crop
						lowerCAmelCase__      :		Optional[int]    							=      crop_size
						lowerCAmelCase__      :		List[str]    							=      do_normalize
						lowerCAmelCase__      :		Tuple    							=      image_mean
						lowerCAmelCase__      :		int    							=      image_std







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						return {
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_normalize": self.do_normalize,
						    "do_resize": self.do_resize,
						    "do_center_crop": self.do_center_crop,
						    "size": self.size,
						    "crop_size": self.crop_size,
						}







@require_torch
@require_vision
class 							SCREAMING_SNAKE_CASE   (						a_       ,				unittest.TestCase			):






					"""simple docstring"""



					lowercase__												=			LevitImageProcessor if is_vision_available() else None







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						lowerCAmelCase__      :		Optional[Any]    							=      LevitImageProcessingTester(self						)







					@property
					def 			__lowerCAmelCase					(			self     :							Any						):
						return self.image_processor_tester.prepare_image_processor_dict()







					def 			__lowerCAmelCase					(			self     :							str						):
						lowerCAmelCase__      :		Optional[int]    							=      self.image_processing_class(**self.image_processor_dict						)
						self.assertTrue(hasattr(lowercase_  ,'''image_mean'''						)						)
						self.assertTrue(hasattr(lowercase_  ,'''image_std'''						)						)
						self.assertTrue(hasattr(lowercase_  ,'''do_normalize'''						)						)
						self.assertTrue(hasattr(lowercase_  ,'''do_resize'''						)						)
						self.assertTrue(hasattr(lowercase_  ,'''do_center_crop'''						)						)
						self.assertTrue(hasattr(lowercase_  ,'''size'''						)						)







					def 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		int    							=      self.image_processing_class.from_dict(self.image_processor_dict						)
						self.assertEqual(image_processor.size  ,{'''shortest_edge''': 1_8}						)
						self.assertEqual(image_processor.crop_size  ,{'''height''': 1_8, '''width''': 1_8}						)

						lowerCAmelCase__      :		List[str]    							=      self.image_processing_class.from_dict(self.image_processor_dict  ,size=4_2  ,crop_size=8_4						)
						self.assertEqual(image_processor.size  ,{'''shortest_edge''': 4_2}						)
						self.assertEqual(image_processor.crop_size  ,{'''height''': 8_4, '''width''': 8_4}						)







					def 			__lowerCAmelCase					(			self     :							Union[str, Any]						):
						pass







					def 			__lowerCAmelCase					(			self     :							Optional[int]						):
						# Initialize image_processing
						lowerCAmelCase__      :		List[str]    							=      self.image_processing_class(**self.image_processor_dict						)
						# create random PIL images
						lowerCAmelCase__      :		Union[str, Any]    							=      prepare_image_inputs(self.image_processor_tester  ,equal_resolution=lowercase_						)
						for image in image_inputs:
							self.assertIsInstance(lowercase_  ,Image.Image						)

						# Test not batched input
						lowerCAmelCase__      :		Any    							=      image_processing(image_inputs[0]  ,return_tensors='''pt'''						).pixel_values
						self.assertEqual(
						    encoded_images.shape  ,(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )  ,)

						# Test batched
						lowerCAmelCase__      :		Optional[int]    							=      image_processing(lowercase_  ,return_tensors='''pt'''						).pixel_values
						self.assertEqual(
						    encoded_images.shape  ,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )  ,)







					def 			__lowerCAmelCase					(			self     :							Union[str, Any]						):
						# Initialize image_processing
						lowerCAmelCase__      :		str    							=      self.image_processing_class(**self.image_processor_dict						)
						# create random numpy tensors
						lowerCAmelCase__      :		Optional[Any]    							=      prepare_image_inputs(self.image_processor_tester  ,equal_resolution=lowercase_  ,numpify=lowercase_						)
						for image in image_inputs:
							self.assertIsInstance(lowercase_  ,np.ndarray						)

						# Test not batched input
						lowerCAmelCase__      :		Tuple    							=      image_processing(image_inputs[0]  ,return_tensors='''pt'''						).pixel_values
						self.assertEqual(
						    encoded_images.shape  ,(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )  ,)

						# Test batched
						lowerCAmelCase__      :		Union[str, Any]    							=      image_processing(lowercase_  ,return_tensors='''pt'''						).pixel_values
						self.assertEqual(
						    encoded_images.shape  ,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )  ,)







					def 			__lowerCAmelCase					(			self     :							int						):
						# Initialize image_processing
						lowerCAmelCase__      :		Tuple    							=      self.image_processing_class(**self.image_processor_dict						)
						# create random PyTorch tensors
						lowerCAmelCase__      :		Dict    							=      prepare_image_inputs(self.image_processor_tester  ,equal_resolution=lowercase_  ,torchify=lowercase_						)
						for image in image_inputs:
							self.assertIsInstance(lowercase_  ,torch.Tensor						)

						# Test not batched input
						lowerCAmelCase__      :		Dict    							=      image_processing(image_inputs[0]  ,return_tensors='''pt'''						).pixel_values
						self.assertEqual(
						    encoded_images.shape  ,(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )  ,)

						# Test batched
						lowerCAmelCase__      :		Optional[int]    							=      image_processing(lowercase_  ,return_tensors='''pt'''						).pixel_values
						self.assertEqual(
						    encoded_images.shape  ,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )  ,)




