





'''simple docstring'''



import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
	from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


_SCREAMING_SNAKE_CASE					=    get_tests_dir('''fixtures/test_sentencepiece.model''')

_SCREAMING_SNAKE_CASE					=    {'''target_lang''': '''fi''', '''source_lang''': '''en'''}
_SCREAMING_SNAKE_CASE					=    '''>>zh<<'''
_SCREAMING_SNAKE_CASE					=    '''Helsinki-NLP/'''

if is_torch_available():
	_SCREAMING_SNAKE_CASE					=    '''pt'''
elif is_tf_available():
	_SCREAMING_SNAKE_CASE					=    '''tf'''
else:
	_SCREAMING_SNAKE_CASE					=    '''jax'''






@require_sentencepiece
class 							__lowercase       (      lowerCAmelCase__ ,      unittest.TestCase						):







	'''simple docstring'''

	a    :				int											=       MarianTokenizer
	a    :				Dict											=       False
	a    :				int											=       True


	def 						_UpperCAmelCase     (self )							->							int:







				'''simple docstring'''





				super().setUp()
				__lowercase										=							['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
				__lowercase										=							dict(zip(_lowerCamelCase     ,range(len(_lowerCamelCase ) ) ) )
				__lowercase										=							Path(self.tmpdirname )
				save_json(_lowerCamelCase     ,save_dir / VOCAB_FILES_NAMES['''vocab'''] )
				save_json(_lowerCamelCase     ,save_dir / VOCAB_FILES_NAMES['''tokenizer_config_file'''] )
				if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
							copyfile(_lowerCamelCase     ,save_dir / VOCAB_FILES_NAMES['''source_spm'''] )
							copyfile(_lowerCamelCase     ,save_dir / VOCAB_FILES_NAMES['''target_spm'''] )

				__lowercase										=							MarianTokenizer.from_pretrained(self.tmpdirname )
				tokenizer.save_pretrained(self.tmpdirname )


	def 						_UpperCAmelCase     (self     ,**_lowerCamelCase )							->							MarianTokenizer:







				'''simple docstring'''





				return MarianTokenizer.from_pretrained(self.tmpdirname     ,**_lowerCamelCase )


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







				'''simple docstring'''





				return (
				    "This is a test",
				    "This is a test",
				)


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







				'''simple docstring'''





				__lowercase										=							'''</s>'''
				__lowercase										=							0

				self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCamelCase )     ,_lowerCamelCase )
				self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCamelCase )     ,_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							Optional[Any]:







				'''simple docstring'''





				__lowercase										=							list(self.get_tokenizer().get_vocab().keys() )

				self.assertEqual(vocab_keys[0]     ,'''</s>''' )
				self.assertEqual(vocab_keys[1]     ,'''<unk>''' )
				self.assertEqual(vocab_keys[-1]     ,'''<pad>''' )
				self.assertEqual(len(_lowerCamelCase )     ,9 )


	def 						_UpperCAmelCase     (self )							->							Tuple:







				'''simple docstring'''





				self.assertEqual(self.get_tokenizer().vocab_size     ,9 )


	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





				__lowercase										=							MarianTokenizer.from_pretrained(f"{ORG_NAME}opus-mt-en-de" )
				__lowercase										=							en_de_tokenizer(['''I am a small frog''']     ,return_tensors=_lowerCamelCase )
				self.assertIsInstance(_lowerCamelCase     ,_lowerCamelCase )
				__lowercase										=							[38, 121, 14, 697, 38848, 0]
				self.assertListEqual(_lowerCamelCase     ,batch.input_ids[0] )

				__lowercase										=							tempfile.mkdtemp()
				en_de_tokenizer.save_pretrained(_lowerCamelCase )
				__lowercase										=							[x.name for x in Path(_lowerCamelCase ).glob('''*''' )]
				self.assertIn('''source.spm'''     ,_lowerCamelCase )
				MarianTokenizer.from_pretrained(_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							int:







				'''simple docstring'''





				__lowercase										=							self.get_tokenizer()

				__lowercase										=							tok(
				    ['''I am a small frog''' * 1000, '''I am a small frog''']     ,padding=_lowerCamelCase     ,truncation=_lowerCamelCase     ,return_tensors=_lowerCamelCase )
				self.assertIsInstance(_lowerCamelCase     ,_lowerCamelCase )
				self.assertEqual(batch.input_ids.shape     ,(2, 512) )


	def 						_UpperCAmelCase     (self )							->							Any:







				'''simple docstring'''





				__lowercase										=							self.get_tokenizer()
				__lowercase										=							tok(['''I am a tiny frog''', '''I am a small frog''']     ,padding=_lowerCamelCase     ,return_tensors=_lowerCamelCase )
				self.assertIsInstance(_lowerCamelCase     ,_lowerCamelCase )
				self.assertEqual(batch_smaller.input_ids.shape     ,(2, 10) )


	@slow
	def 						_UpperCAmelCase     (self )							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase										=							{'''input_ids''': [[43495, 462, 20, 42164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 38999, 6, 8, 464, 132, 1703, 492, 13, 4669, 37867, 13, 7525, 27, 1593, 988, 13, 33972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 12338, 2, 13958, 387, 2, 3629, 6953, 188, 2900, 2, 13958, 8011, 11501, 23, 8460, 4073, 34009, 20, 435, 11439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 37867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 26453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10767, 6, 316, 304, 4239, 3, 0], [148, 15722, 19, 1839, 12, 1350, 13, 22327, 5082, 5418, 47567, 35938, 59, 318, 19552, 108, 2183, 54, 14976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 19088, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100], [36, 6395, 12570, 39147, 11597, 6, 266, 4, 45405, 7296, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
				# fmt: on

				self.tokenizer_integration_test_util(
				    expected_encoding=_lowerCamelCase     ,model_name='''Helsinki-NLP/opus-mt-en-de'''     ,revision='''1a8c2263da11e68e50938f97e10cd57820bd504c'''     ,decode_kwargs={'''use_source_tokenizer''': True}     ,)


	def 						_UpperCAmelCase     (self )							->							Dict:







				'''simple docstring'''





				__lowercase										=							MarianTokenizer.from_pretrained('''hf-internal-testing/test-marian-two-vocabs''' )

				__lowercase										=							'''Tämä on testi'''
				__lowercase										=							'''This is a test'''

				__lowercase										=							[76, 7, 2047, 2]
				__lowercase										=							[69, 12, 11, 940, 2]

				__lowercase										=							tokenizer(_lowerCamelCase ).input_ids
				self.assertListEqual(_lowerCamelCase     ,_lowerCamelCase )

				__lowercase										=							tokenizer(text_target=_lowerCamelCase ).input_ids
				self.assertListEqual(_lowerCamelCase     ,_lowerCamelCase )

				__lowercase										=							tokenizer.decode(_lowerCamelCase     ,skip_special_tokens=_lowerCamelCase )
				self.assertEqual(_lowerCamelCase     ,_lowerCamelCase )












'''simple docstring'''



import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE					=    logging.get_logger(__name__)




def    _lowerCAmelCase						(				lowerCamelCase_			:  int				):
			__lowercase										=							torch.load(lowerCamelCase_		,       map_location='''cpu'''				)
			if "model" in sd.keys():
						__lowercase										=							torch.load(lowerCamelCase_		,       map_location='''cpu'''				)['''model''']

			# pop unnecessary weights
			__lowercase										=							[
			    '''decoder.version''',
			    '''decoder.output_projection.weight''',
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(lowerCamelCase_				)

			__lowercase										=							{
			    '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
			    '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
			    '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
			    '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
			}
			for old_key, new_key in keys_to_rename.items():
						if old_key in sd:
									__lowercase										=							sd.pop(lowerCamelCase_				)

			__lowercase										=							list(sd.keys()				)
			for key in keys:
						if ".qkv_proj." in key:
									__lowercase										=							sd[key]
									# We split QKV in separate Q,K,V

									__lowercase										=							key.replace('''.qkv_proj.'''		,       '''.q_proj.'''				)
									__lowercase										=							key.replace('''.qkv_proj.'''		,       '''.k_proj.'''				)
									__lowercase										=							key.replace('''.qkv_proj.'''		,       '''.v_proj.'''				)

									__lowercase										=							value.shape[0]
									assert depth % 3 == 0
									# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
									# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
									__lowercase						,		__lowercase						,		__lowercase										=							torch.split(lowerCamelCase_		,       depth // 3		,       dim=0				)

									__lowercase										=							q
									__lowercase										=							k
									__lowercase										=							v
									del sd[key]

			return sd




@torch.no_grad()
def    _lowerCAmelCase						(				lowerCamelCase_			:  Union[str, Any]		,       lowerCamelCase_			:  Tuple		,       lowerCamelCase_			:  Union[str, Any]=None				):
			__lowercase										=							load_checkpoint(lowerCamelCase_				)

			if config is not None:
						__lowercase										=							OPTConfig.from_pretrained(lowerCamelCase_				)
			else:
						__lowercase										=							OPTConfig()

			__lowercase										=							OPTModel(lowerCamelCase_				).half().eval()
			model.load_state_dict(lowerCamelCase_				)

			# Check results
			Path(lowerCamelCase_				).mkdir(exist_ok=lowerCamelCase_				)
			model.save_pretrained(lowerCamelCase_				)


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE					=    argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--fairseq_path''',
	    type=str,
	    help=(
	        '''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
	        ''' https://huggingface.co/models?other=opt_metasq'''
	    ),
	)
	parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
	parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''')
	_SCREAMING_SNAKE_CASE					=    parser.parse_args()
	convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)





