


"""simple docstring"""




import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class    lowerCAmelCase      (   __a      ,       unittest.TestCase       ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_		:	Union[str, Any]														=	LxmertTokenizer
		SCREAMING_SNAKE_CASE_		:	List[str]														=	LxmertTokenizerFast
		SCREAMING_SNAKE_CASE_		:	str														=	True
		SCREAMING_SNAKE_CASE_		:	Any														=	True







		def 				__A		(      self							)	->				Optional[Any]:
								super().setUp()

								SCREAMING_SNAKE_CASE						=   [
								    '[UNK]',
								    '[CLS]',
								    '[SEP]',
								    'want',
								    '##want',
								    '##ed',
								    'wa',
								    'un',
								    'runn',
								    '##ing',
								    ',',
								    'low',
								    'lowest',
								]
								SCREAMING_SNAKE_CASE						=   os.path.join(self.tmpdirname		,			VOCAB_FILES_NAMES['vocab_file']							)
								with open(self.vocab_file		,			'w'		,			encoding='utf-8'							) as vocab_writer:
														vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]							)							)







		def 				__A		(      self		,			lowerCAmelCase__							)	->				int:
								SCREAMING_SNAKE_CASE						=   'UNwant\u00E9d,running'
								SCREAMING_SNAKE_CASE						=   'unwanted, running'
								return input_text, output_text







		def 				__A		(      self							)	->				Tuple:
								SCREAMING_SNAKE_CASE						=   self.tokenizer_class(self.vocab_file							)

								SCREAMING_SNAKE_CASE						=   tokenizer.tokenize('UNwant\u00E9d,running'							)
								self.assertListEqual(_UpperCamelCase		,			['un', '##want', '##ed', ',', 'runn', '##ing']							)
								self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase							)		,			[7, 4, 5, 10, 8, 9]							)







		def 				__A		(      self							)	->				Tuple:
								if not self.test_rust_tokenizer:
														return

								SCREAMING_SNAKE_CASE						=   self.get_tokenizer()
								SCREAMING_SNAKE_CASE						=   self.get_rust_tokenizer()

								SCREAMING_SNAKE_CASE						=   'I was born in 92000, and this is falsé.'

								SCREAMING_SNAKE_CASE						=   tokenizer.tokenize(_UpperCamelCase							)
								SCREAMING_SNAKE_CASE						=   rust_tokenizer.tokenize(_UpperCamelCase							)
								self.assertListEqual(_UpperCamelCase		,			_UpperCamelCase							)

								SCREAMING_SNAKE_CASE						=   tokenizer.encode(_UpperCamelCase		,			add_special_tokens=_UpperCamelCase							)
								SCREAMING_SNAKE_CASE						=   rust_tokenizer.encode(_UpperCamelCase		,			add_special_tokens=_UpperCamelCase							)
								self.assertListEqual(_UpperCamelCase		,			_UpperCamelCase							)

								SCREAMING_SNAKE_CASE						=   self.get_rust_tokenizer()
								SCREAMING_SNAKE_CASE						=   tokenizer.encode(_UpperCamelCase							)
								SCREAMING_SNAKE_CASE						=   rust_tokenizer.encode(_UpperCamelCase							)
								self.assertListEqual(_UpperCamelCase		,			_UpperCamelCase							)




import argparse
from pathlib import Path

import torch

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


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






def   lowerCamelCase__							(							__lowerCAmelCase    :    Any							):




			"""simple docstring"""


			lowerCAmelCase_         =      torch.load(__lowerCAmelCase       ,				map_location="cpu"							)
			if "model" in sd.keys():
						lowerCAmelCase_         =      torch.load(__lowerCAmelCase       ,				map_location="cpu"							)["model"]

			# pop unnecessary weights
			lowerCAmelCase_         =      [
			    "decoder.version",
			    "decoder.output_projection.weight",
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(__lowerCAmelCase							)

			lowerCAmelCase_         =      {
			    "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:
									lowerCAmelCase_         =      sd.pop(__lowerCAmelCase							)

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

									lowerCAmelCase_         =      key.replace(".qkv_proj."       ,				".q_proj."							)
									lowerCAmelCase_         =      key.replace(".qkv_proj."       ,				".k_proj."							)
									lowerCAmelCase_         =      key.replace(".qkv_proj."       ,				".v_proj."							)

									lowerCAmelCase_         =      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
									lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_         =      torch.split(__lowerCAmelCase       ,				depth // 3       ,				dim=0							)

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

			return sd






@torch.no_grad()
def   lowerCamelCase__							(							__lowerCAmelCase    :    Optional[int]       ,				__lowerCAmelCase    :    List[str]       ,				__lowerCAmelCase    :    Optional[Any]=None							):




			"""simple docstring"""


			lowerCAmelCase_         =      load_checkpoint(__lowerCAmelCase							)

			if config is not None:
						lowerCAmelCase_         =      OPTConfig.from_pretrained(__lowerCAmelCase							)
			else:
						lowerCAmelCase_         =      OPTConfig()

			lowerCAmelCase_         =      OPTModel(__lowerCAmelCase							).half().eval()
			model.load_state_dict(__lowerCAmelCase							)

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


if __name__ == "__main__":
			_A  =	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.")
			_A  =	parser.parse_args()
			convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)



