



"""simple docstring"""






import shutil
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_tf_cross_test,
    require_tf,
    require_torch,
    require_torchvision,
    require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import AutoProcessor, SamImageProcessor, SamProcessor

if is_torch_available():
		import torch

if is_tf_available():
		import tensorflow as tf

@require_vision
@require_torchvision
class 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					def 		snake_case	(	self							:  Optional[Any]					)-> Optional[Any]:
								lowerCamelCase__		:		int		     =tempfile.mkdtemp()
								lowerCamelCase__		:		Any		     =SamImageProcessor()
								lowerCamelCase__		:		Tuple		     =SamProcessor(lowerCamelCase					)
								processor.save_pretrained(self.tmpdirname					)



					def 		snake_case	(	self							:  List[Any],  **lowerCamelCase							:  List[str]					)-> List[str]:
								return AutoProcessor.from_pretrained(self.tmpdirname,  **lowerCamelCase					).image_processor



					def 		snake_case	(	self							:  str					)-> Tuple:
								shutil.rmtree(self.tmpdirname					)



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

								lowerCamelCase__		:		str		     =[Image.fromarray(np.moveaxis(lowerCamelCase,  0,  -1					)					) for x in image_inputs]

								return image_inputs



					def 		snake_case	(	self							:  int					)-> List[Any]:
								lowerCamelCase__		:		int		     =SamProcessor(image_processor=self.get_image_processor()					)
								processor.save_pretrained(self.tmpdirname					)

								lowerCamelCase__		:		List[str]		     =self.get_image_processor(do_normalize=lowerCamelCase,  padding_value=1.0					)

								lowerCamelCase__		:		Any		     =SamProcessor.from_pretrained(self.tmpdirname,  do_normalize=lowerCamelCase,  padding_value=1.0					)

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



					def 		snake_case	(	self							:  str					)-> Optional[int]:
								lowerCamelCase__		:		Optional[Any]		     =self.get_image_processor()

								lowerCamelCase__		:		int		     =SamProcessor(image_processor=lowerCamelCase					)

								lowerCamelCase__		:		str		     =self.prepare_image_inputs()

								lowerCamelCase__		:		str		     =image_processor(lowerCamelCase,  return_tensors='''np'''					)
								lowerCamelCase__		:		List[str]		     =processor(images=lowerCamelCase,  return_tensors='''np'''					)

								input_feat_extract.pop('''original_sizes'''					)  # pop original_sizes as it is popped in the processor
								input_feat_extract.pop('''reshaped_input_sizes'''					)  # pop original_sizes as it is popped in the processor

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




					@require_torch
					def 		snake_case	(	self							:  int					)-> List[Any]:
								lowerCamelCase__		:		List[Any]		     =self.get_image_processor()

								lowerCamelCase__		:		Tuple		     =SamProcessor(image_processor=lowerCamelCase					)
								lowerCamelCase__		:		List[str]		     =[torch.ones((1, 3, 5, 5)					)]

								lowerCamelCase__		:		List[str]		     =[[1764, 2646]]

								lowerCamelCase__		:		str		     =[[683, 1024]]
								lowerCamelCase__		:		Any		     =processor.post_process_masks(lowerCamelCase,  lowerCamelCase,  lowerCamelCase					)
								self.assertEqual(masks[0].shape,  (1, 3, 1764, 2646)					)

								lowerCamelCase__		:		Union[str, Any]		     =processor.post_process_masks(
								    lowerCamelCase,  torch.tensor(lowerCamelCase					),  torch.tensor(lowerCamelCase					)					)
								self.assertEqual(masks[0].shape,  (1, 3, 1764, 2646)					)

								# should also work with np
								lowerCamelCase__		:		Union[str, Any]		     =[np.ones((1, 3, 5, 5)					)]
								lowerCamelCase__		:		Tuple		     =processor.post_process_masks(lowerCamelCase,  np.array(lowerCamelCase					),  np.array(lowerCamelCase					)					)

								self.assertEqual(masks[0].shape,  (1, 3, 1764, 2646)					)

								lowerCamelCase__		:		Optional[int]		     =[[1, 0], [0, 1]]
								with self.assertRaises(lowerCamelCase					):
											lowerCamelCase__		:		Tuple		     =processor.post_process_masks(lowerCamelCase,  np.array(lowerCamelCase					),  np.array(lowerCamelCase					)					)





@require_vision
@require_tf
class 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					def 		snake_case	(	self							:  Union[str, Any]					)-> Optional[int]:
								lowerCamelCase__		:		Optional[Any]		     =tempfile.mkdtemp()
								lowerCamelCase__		:		Tuple		     =SamImageProcessor()
								lowerCamelCase__		:		List[str]		     =SamProcessor(lowerCamelCase					)
								processor.save_pretrained(self.tmpdirname					)



					def 		snake_case	(	self							:  Optional[Any],  **lowerCamelCase							:  Tuple					)-> str:
								return AutoProcessor.from_pretrained(self.tmpdirname,  **lowerCamelCase					).image_processor



					def 		snake_case	(	self							:  List[Any]					)-> Tuple:
								shutil.rmtree(self.tmpdirname					)



					def 		snake_case	(	self							:  Union[str, Any]					)-> List[Any]:
								lowerCamelCase__		:		List[Any]		     =[np.random.randint(255,  size=(3, 30, 400),  dtype=np.uinta					)]

								lowerCamelCase__		:		Union[str, Any]		     =[Image.fromarray(np.moveaxis(lowerCamelCase,  0,  -1					)					) for x in image_inputs]

								return image_inputs



					def 		snake_case	(	self							:  str					)-> Dict:
								lowerCamelCase__		:		Tuple		     =SamProcessor(image_processor=self.get_image_processor()					)
								processor.save_pretrained(self.tmpdirname					)

								lowerCamelCase__		:		Union[str, Any]		     =self.get_image_processor(do_normalize=lowerCamelCase,  padding_value=1.0					)

								lowerCamelCase__		:		List[str]		     =SamProcessor.from_pretrained(self.tmpdirname,  do_normalize=lowerCamelCase,  padding_value=1.0					)

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



					def 		snake_case	(	self							:  Optional[int]					)-> Any:
								lowerCamelCase__		:		Optional[Any]		     =self.get_image_processor()

								lowerCamelCase__		:		Optional[Any]		     =SamProcessor(image_processor=lowerCamelCase					)

								lowerCamelCase__		:		List[str]		     =self.prepare_image_inputs()

								lowerCamelCase__		:		List[Any]		     =image_processor(lowerCamelCase,  return_tensors='''np'''					)
								lowerCamelCase__		:		Union[str, Any]		     =processor(images=lowerCamelCase,  return_tensors='''np'''					)

								input_feat_extract.pop('''original_sizes'''					)  # pop original_sizes as it is popped in the processor
								input_feat_extract.pop('''reshaped_input_sizes'''					)  # pop reshaped_input_sizes as it is popped in the processor

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




					@require_tf
					def 		snake_case	(	self							:  List[Any]					)-> Optional[int]:
								lowerCamelCase__		:		Dict		     =self.get_image_processor()

								lowerCamelCase__		:		Dict		     =SamProcessor(image_processor=lowerCamelCase					)
								lowerCamelCase__		:		Union[str, Any]		     =[tf.ones((1, 3, 5, 5)					)]

								lowerCamelCase__		:		Any		     =[[1764, 2646]]

								lowerCamelCase__		:		List[str]		     =[[683, 1024]]
								lowerCamelCase__		:		Any		     =processor.post_process_masks(lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  return_tensors='''tf'''					)
								self.assertEqual(masks[0].shape,  (1, 3, 1764, 2646)					)

								lowerCamelCase__		:		List[str]		     =processor.post_process_masks(
								    lowerCamelCase,  tf.convert_to_tensor(lowerCamelCase					),  tf.convert_to_tensor(lowerCamelCase					),  return_tensors='''tf''',  )
								self.assertEqual(masks[0].shape,  (1, 3, 1764, 2646)					)

								# should also work with np
								lowerCamelCase__		:		int		     =[np.ones((1, 3, 5, 5)					)]
								lowerCamelCase__		:		Optional[Any]		     =processor.post_process_masks(
								    lowerCamelCase,  np.array(lowerCamelCase					),  np.array(lowerCamelCase					),  return_tensors='''tf'''					)

								self.assertEqual(masks[0].shape,  (1, 3, 1764, 2646)					)

								lowerCamelCase__		:		Union[str, Any]		     =[[1, 0], [0, 1]]
								with self.assertRaises(tf.errors.InvalidArgumentError					):
											lowerCamelCase__		:		Dict		     =processor.post_process_masks(
											    lowerCamelCase,  np.array(lowerCamelCase					),  np.array(lowerCamelCase					),  return_tensors='''tf'''					)





@require_vision
@require_torchvision
class 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					def 		snake_case	(	self							:  Optional[Any]					)-> Any:
								lowerCamelCase__		:		Tuple		     =tempfile.mkdtemp()
								lowerCamelCase__		:		List[str]		     =SamImageProcessor()
								lowerCamelCase__		:		Any		     =SamProcessor(lowerCamelCase					)
								processor.save_pretrained(self.tmpdirname					)



					def 		snake_case	(	self							:  Tuple,  **lowerCamelCase							:  Optional[Any]					)-> Optional[int]:
								return AutoProcessor.from_pretrained(self.tmpdirname,  **lowerCamelCase					).image_processor



					def 		snake_case	(	self							:  List[Any]					)-> str:
								shutil.rmtree(self.tmpdirname					)



					def 		snake_case	(	self							:  Optional[Any]					)-> Tuple:
								lowerCamelCase__		:		int		     =[np.random.randint(255,  size=(3, 30, 400),  dtype=np.uinta					)]

								lowerCamelCase__		:		Optional[int]		     =[Image.fromarray(np.moveaxis(lowerCamelCase,  0,  -1					)					) for x in image_inputs]

								return image_inputs



					@is_pt_tf_cross_test
					def 		snake_case	(	self							:  str					)-> Optional[int]:
								lowerCamelCase__		:		str		     =self.get_image_processor()

								lowerCamelCase__		:		List[Any]		     =SamProcessor(image_processor=lowerCamelCase					)
								lowerCamelCase__		:		Dict		     =np.random.randint(0,  2,  size=(1, 3, 5, 5)					).astype(np.floataa					)
								lowerCamelCase__		:		List[Any]		     =[tf.convert_to_tensor(lowerCamelCase					)]
								lowerCamelCase__		:		Optional[int]		     =[torch.tensor(lowerCamelCase					)]

								lowerCamelCase__		:		str		     =[[1764, 2646]]

								lowerCamelCase__		:		str		     =[[683, 1024]]
								lowerCamelCase__		:		List[str]		     =processor.post_process_masks(
								    lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  return_tensors='''tf'''					)
								lowerCamelCase__		:		List[str]		     =processor.post_process_masks(
								    lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  return_tensors='''pt'''					)

								self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy()					)					)




					@is_pt_tf_cross_test
					def 		snake_case	(	self							:  Tuple					)-> Tuple:
								lowerCamelCase__		:		Dict		     =self.get_image_processor()

								lowerCamelCase__		:		Dict		     =SamProcessor(image_processor=lowerCamelCase					)

								lowerCamelCase__		:		str		     =self.prepare_image_inputs()

								lowerCamelCase__		:		str		     =image_processor(lowerCamelCase,  return_tensors='''pt'''					)['''pixel_values'''].numpy()
								lowerCamelCase__		:		Tuple		     =processor(images=lowerCamelCase,  return_tensors='''pt'''					)['''pixel_values'''].numpy()

								lowerCamelCase__		:		int		     =image_processor(lowerCamelCase,  return_tensors='''tf'''					)['''pixel_values'''].numpy()
								lowerCamelCase__		:		List[Any]		     =processor(images=lowerCamelCase,  return_tensors='''tf'''					)['''pixel_values'''].numpy()

								self.assertTrue(np.allclose(lowerCamelCase,  lowerCamelCase					)					)
								self.assertTrue(np.allclose(lowerCamelCase,  lowerCamelCase					)					)
								self.assertTrue(np.allclose(lowerCamelCase,  lowerCamelCase					)					)









"""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			:			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
_lowercase			:			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
_lowercase			:			str										=						{}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
		_lowercase			:			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",
]:
		_lowercase			:			List[Any]										=						"allenai"






def 					snake_case__       (		__lowerCamelCase    :   Optional[Any]     ):
			"""simple docstring"""
			# (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__		:		Optional[Any]		     =dict((re.sub(R'''@@$'''							,		''''''							,		__lowerCamelCase     ), v) if k.endswith('''@@'''     ) else (re.sub(R'''$'''							,		'''</w>'''							,		__lowerCamelCase     ), v) for k, v in d.items()     )
			lowerCamelCase__		:		Tuple		     ='''<s> <pad> </s> <unk>'''.split()
			# restore the special tokens
			for k in keep_keys:
						del da[f'''{k}</w>''']
						lowerCamelCase__		:		str		     =d[k]  # restore
			return da






def 					snake_case__       (		__lowerCamelCase    :   Union[str, Any]							,		__lowerCamelCase    :   Dict     ):
			"""simple docstring"""
			# prep
			assert os.path.exists(__lowerCamelCase     )
			os.makedirs(__lowerCamelCase							,		exist_ok=__lowerCamelCase     )
			print(f'''Writing results to {pytorch_dump_folder_path}'''     )

			# handle various types of models

			lowerCamelCase__		:		Union[str, Any]		     =basename(__lowerCamelCase     )
			lowerCamelCase__		:		str		     =dirname(__lowerCamelCase     )

			lowerCamelCase__		:		Dict		     =fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
			lowerCamelCase__		:		Union[str, Any]		     =cls.hub_models()
			lowerCamelCase__		:		Optional[Any]		     ={'''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__		:		Optional[int]		     =hub_utils.from_pretrained(
			    __lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		archive_map=__lowerCamelCase							,		**__lowerCamelCase     )

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

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

			lowerCamelCase__		:		Dict		     =dirname(__lowerCamelCase     )
			lowerCamelCase__		:		str		     =basename(__lowerCamelCase     )

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

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

			# 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__		:		Optional[int]		     =True
			for k in src_vocab.keys():
						if not k.islower():
									lowerCamelCase__		:		int		     =False
									break

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

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

			# model config
			lowerCamelCase__		:		List[Any]		     =os.path.join(__lowerCamelCase							,		'''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__		:		str		     ={
			    '''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[int]		     =5
			lowerCamelCase__		:		List[str]		     =False
			if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
						lowerCamelCase__		:		Optional[int]		     =best_score_hparams[model_dir]['''length_penalty''']
			else:
						lowerCamelCase__		:		Union[str, Any]		     =1.0

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

			# tokenizer config
			lowerCamelCase__		:		Any		     =os.path.join(__lowerCamelCase							,		__lowerCamelCase     )

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

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

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

			# rename keys to start with 'model.'
			lowerCamelCase__		:		Union[str, Any]		     =OrderedDict(('''model.''' + k, v) for k, v in model_state_dict.items()     )

			# remove unneeded keys
			lowerCamelCase__		:		str		     =[
			    '''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(__lowerCamelCase							,		__lowerCamelCase     )

			lowerCamelCase__		:		Dict		     =FSMTConfig.from_pretrained(__lowerCamelCase     )
			lowerCamelCase__		:		int		     =FSMTForConditionalGeneration(__lowerCamelCase     )

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

			# save
			lowerCamelCase__		:		Optional[int]		     =os.path.join(__lowerCamelCase							,		__lowerCamelCase     )
			print(f'''Generating {pytorch_weights_dump_path}'''     )
			torch.save(__lowerCamelCase							,		__lowerCamelCase     )

			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			:			List[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."
		)
		_lowercase			:			str										=						parser.parse_args()
		convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)




