

'''simple docstring'''


import json
import os
import unittest

from transformers.models.roc_bert.tokenization_roc_bert import (
    VOCAB_FILES_NAMES,
    RoCBertBasicTokenizer,
    RoCBertTokenizer,
    RoCBertWordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english



@require_tokenizers
class 					lowercase						(  a_,					unittest.TestCase ):
							_lowerCamelCase		:      int=   RoCBertTokenizer
							_lowerCamelCase		:      Optional[Any]=   None
							_lowerCamelCase		:      Optional[Any]=   False
							_lowerCamelCase		:      List[str]=   True
							_lowerCamelCase		:      Tuple=   filter_non_english


							def 						_snake_case							(			self)							->				int:
								super().setUp()

								UpperCAmelCase_		:      str       	=					['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', '你', '好', '是', '谁', 'a', 'b', 'c', 'd']
								UpperCAmelCase_		:      Optional[int]       	=					{}
								UpperCAmelCase_		:      Dict       	=					{}
								for i, value in enumerate(_snake_case):
									UpperCAmelCase_		:      Dict       	=					i
									UpperCAmelCase_		:      Any       	=					i
								UpperCAmelCase_		:      List[str]       	=					os.path.join(self.tmpdirname    ,			VOCAB_FILES_NAMES['vocab_file'])
								UpperCAmelCase_		:      List[Any]       	=					os.path.join(self.tmpdirname    ,			VOCAB_FILES_NAMES['word_shape_file'])
								UpperCAmelCase_		:      Optional[Any]       	=					os.path.join(self.tmpdirname    ,			VOCAB_FILES_NAMES['word_pronunciation_file'])
								with open(self.vocab_file    ,			'w'    ,			encoding='utf-8') as vocab_writer:
									vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]))
								with open(self.word_shape_file    ,			'w'    ,			encoding='utf-8') as word_shape_writer:
									json.dump(_snake_case    ,			_snake_case    ,			ensure_ascii=_snake_case)
								with open(self.word_pronunciation_file    ,			'w'    ,			encoding='utf-8') as word_pronunciation_writer:
									json.dump(_snake_case    ,			_snake_case    ,			ensure_ascii=_snake_case)


							def 						_snake_case							(			self)							->				Union[str, Any]:
								UpperCAmelCase_		:      Any       	=					self.tokenizer_class(self.vocab_file    ,			self.word_shape_file    ,			self.word_pronunciation_file)

								UpperCAmelCase_		:      Optional[Any]       	=					tokenizer.tokenize('你好[SEP]你是谁')
								self.assertListEqual(_snake_case    ,			['你', '好', '[SEP]', '你', '是', '谁'])
								self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case)    ,			[5, 6, 2, 5, 7, 8])
								self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(_snake_case)    ,			[5, 6, 2, 5, 7, 8])
								self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(_snake_case)    ,			[5, 6, 2, 5, 7, 8])


							def 						_snake_case							(			self)							->				str:
								UpperCAmelCase_		:      str       	=					RoCBertBasicTokenizer()

								self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz')    ,			['ah', '\u535A', '\u63A8', 'zz'])


							def 						_snake_case							(			self)							->				Union[str, Any]:
								UpperCAmelCase_		:      Tuple       	=					RoCBertBasicTokenizer(do_lower_case=_snake_case)

								self.assertListEqual(
								    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  ')    ,			['hello', '!', 'how', 'are', 'you', '?'])
								self.assertListEqual(tokenizer.tokenize('H\u00E9llo')    ,			['hello'])


							def 						_snake_case							(			self)							->				str:
								UpperCAmelCase_		:      Union[str, Any]       	=					RoCBertBasicTokenizer(do_lower_case=_snake_case    ,			strip_accents=_snake_case)

								self.assertListEqual(
								    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  ')    ,			['hällo', '!', 'how', 'are', 'you', '?'])
								self.assertListEqual(tokenizer.tokenize('H\u00E9llo')    ,			['h\u00E9llo'])


							def 						_snake_case							(			self)							->				Dict:
								UpperCAmelCase_		:      int       	=					RoCBertBasicTokenizer(do_lower_case=_snake_case    ,			strip_accents=_snake_case)

								self.assertListEqual(
								    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  ')    ,			['hallo', '!', 'how', 'are', 'you', '?'])
								self.assertListEqual(tokenizer.tokenize('H\u00E9llo')    ,			['hello'])


							def 						_snake_case							(			self)							->				str:
								UpperCAmelCase_		:      List[str]       	=					RoCBertBasicTokenizer(do_lower_case=_snake_case)

								self.assertListEqual(
								    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  ')    ,			['hallo', '!', 'how', 'are', 'you', '?'])
								self.assertListEqual(tokenizer.tokenize('H\u00E9llo')    ,			['hello'])


							def 						_snake_case							(			self)							->				List[Any]:
								UpperCAmelCase_		:      int       	=					RoCBertBasicTokenizer(do_lower_case=_snake_case)

								self.assertListEqual(
								    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  ')    ,			['HeLLo', '!', 'how', 'Are', 'yoU', '?'])


							def 						_snake_case							(			self)							->				Any:
								UpperCAmelCase_		:      int       	=					RoCBertBasicTokenizer(do_lower_case=_snake_case    ,			strip_accents=_snake_case)

								self.assertListEqual(
								    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  ')    ,			['HäLLo', '!', 'how', 'Are', 'yoU', '?'])


							def 						_snake_case							(			self)							->				int:
								UpperCAmelCase_		:      Optional[int]       	=					RoCBertBasicTokenizer(do_lower_case=_snake_case    ,			strip_accents=_snake_case)

								self.assertListEqual(
								    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  ')    ,			['HaLLo', '!', 'how', 'Are', 'yoU', '?'])


							def 						_snake_case							(			self)							->				List[Any]:
								UpperCAmelCase_		:      Optional[Any]       	=					RoCBertBasicTokenizer(do_lower_case=_snake_case    ,			never_split=['[UNK]'])

								self.assertListEqual(
								    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]')    ,			['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]'])


							def 						_snake_case							(			self)							->				str:
								UpperCAmelCase_		:      Tuple       	=					['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

								UpperCAmelCase_		:      str       	=					{}
								for i, token in enumerate(_snake_case):
									UpperCAmelCase_		:      Optional[int]       	=					i
								UpperCAmelCase_		:      Optional[int]       	=					RoCBertWordpieceTokenizer(vocab=_snake_case    ,			unk_token='[UNK]')

								self.assertListEqual(tokenizer.tokenize('')    ,			[])

								self.assertListEqual(tokenizer.tokenize('unwanted running')    ,			['un', '##want', '##ed', 'runn', '##ing'])

								self.assertListEqual(tokenizer.tokenize('unwantedX running')    ,			['[UNK]', 'runn', '##ing'])


							def 						_snake_case							(			self)							->				List[str]:
								self.assertTrue(_is_whitespace(' '))
								self.assertTrue(_is_whitespace('\t'))
								self.assertTrue(_is_whitespace('\r'))
								self.assertTrue(_is_whitespace('\n'))
								self.assertTrue(_is_whitespace('\u00A0'))

								self.assertFalse(_is_whitespace('A'))
								self.assertFalse(_is_whitespace('-'))


							def 						_snake_case							(			self)							->				Union[str, Any]:
								self.assertTrue(_is_control('\u0005'))

								self.assertFalse(_is_control('A'))
								self.assertFalse(_is_control(' '))
								self.assertFalse(_is_control('\t'))
								self.assertFalse(_is_control('\r'))


							def 						_snake_case							(			self)							->				Optional[Any]:
								self.assertTrue(_is_punctuation('-'))
								self.assertTrue(_is_punctuation('$'))
								self.assertTrue(_is_punctuation('`'))
								self.assertTrue(_is_punctuation('.'))

								self.assertFalse(_is_punctuation('A'))
								self.assertFalse(_is_punctuation(' '))


							def 						_snake_case							(			self)							->				Union[str, Any]:
								UpperCAmelCase_		:      Any       	=					self.get_tokenizer()

								# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
								self.assertListEqual([tokenizer.tokenize(_snake_case) for t in ['Test', '\xad', 'test']]    ,			[['[UNK]'], [], ['[UNK]']])

								if self.test_rust_tokenizer:
									UpperCAmelCase_		:      int       	=					self.get_rust_tokenizer()
									self.assertListEqual(
									    [rust_tokenizer.tokenize(_snake_case) for t in ['Test', '\xad', 'test']]    ,			[['[UNK]'], [], ['[UNK]']])


							def 						_snake_case							(			self)							->				Dict:
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
										UpperCAmelCase_		:      Optional[int]       	=					self.rust_tokenizer_class.from_pretrained(_snake_case    ,			**_snake_case)

										UpperCAmelCase_		:      Any       	=					F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
										UpperCAmelCase_		:      Union[str, Any]       	=					tokenizer_r.encode_plus(
										    _snake_case    ,			return_attention_mask=_snake_case    ,			return_token_type_ids=_snake_case    ,			return_offsets_mapping=_snake_case    ,			add_special_tokens=_snake_case    ,			)

										UpperCAmelCase_		:      List[Any]       	=					tokenizer_r.do_lower_case if hasattr(_snake_case    ,			'do_lower_case') else False
										UpperCAmelCase_		:      List[str]       	=					(
										    [
										        ((0, 0), tokenizer_r.cls_token),
										        ((0, 1), 'A'),
										        ((1, 2), ','),
										        ((3, 5), 'na'),
										        ((5, 6), '##ï'),
										        ((6, 8), '##ve'),
										        ((9, 15), tokenizer_r.mask_token),
										        ((16, 21), 'Allen'),
										        ((21, 23), '##NL'),
										        ((23, 24), '##P'),
										        ((25, 33), 'sentence'),
										        ((33, 34), '.'),
										        ((0, 0), tokenizer_r.sep_token),
										    ]
										    if not do_lower_case
										    else [
										        ((0, 0), tokenizer_r.cls_token),
										        ((0, 1), 'a'),
										        ((1, 2), ','),
										        ((3, 8), 'naive'),
										        ((9, 15), tokenizer_r.mask_token),
										        ((16, 21), 'allen'),
										        ((21, 23), '##nl'),
										        ((23, 24), '##p'),
										        ((25, 33), 'sentence'),
										        ((33, 34), '.'),
										        ((0, 0), tokenizer_r.sep_token),
										    ]
										)

										self.assertEqual(
										    [e[1] for e in expected_results]    ,			tokenizer_r.convert_ids_to_tokens(tokens['input_ids']))
										self.assertEqual([e[0] for e in expected_results]    ,			tokens['offset_mapping'])


							def 						_snake_case							(			self)							->				List[str]:
								UpperCAmelCase_		:      List[Any]       	=					['的', '人', '有']
								UpperCAmelCase_		:      int       	=					''.join(_snake_case)
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
										UpperCAmelCase_		:      Any       	=					True
										UpperCAmelCase_		:      Optional[int]       	=					self.tokenizer_class.from_pretrained(_snake_case    ,			**_snake_case)
										UpperCAmelCase_		:      Tuple       	=					self.rust_tokenizer_class.from_pretrained(_snake_case    ,			**_snake_case)

										UpperCAmelCase_		:      Tuple       	=					tokenizer_p.encode(_snake_case    ,			add_special_tokens=_snake_case)
										UpperCAmelCase_		:      Optional[int]       	=					tokenizer_r.encode(_snake_case    ,			add_special_tokens=_snake_case)

										UpperCAmelCase_		:      Dict       	=					tokenizer_r.convert_ids_to_tokens(_snake_case)
										UpperCAmelCase_		:      str       	=					tokenizer_p.convert_ids_to_tokens(_snake_case)

										# it is expected that each Chinese character is not preceded by "##"
										self.assertListEqual(_snake_case    ,			_snake_case)
										self.assertListEqual(_snake_case    ,			_snake_case)

										UpperCAmelCase_		:      Optional[Any]       	=					False
										UpperCAmelCase_		:      Any       	=					self.rust_tokenizer_class.from_pretrained(_snake_case    ,			**_snake_case)
										UpperCAmelCase_		:      List[str]       	=					self.tokenizer_class.from_pretrained(_snake_case    ,			**_snake_case)

										UpperCAmelCase_		:      int       	=					tokenizer_r.encode(_snake_case    ,			add_special_tokens=_snake_case)
										UpperCAmelCase_		:      int       	=					tokenizer_p.encode(_snake_case    ,			add_special_tokens=_snake_case)

										UpperCAmelCase_		:      int       	=					tokenizer_r.convert_ids_to_tokens(_snake_case)
										UpperCAmelCase_		:      Tuple       	=					tokenizer_p.convert_ids_to_tokens(_snake_case)

										# it is expected that only the first Chinese character is not preceded by "##".
										UpperCAmelCase_		:      Dict       	=					[
										    F"""##{token}""" if idx != 0 else token for idx, token in enumerate(_snake_case)
										]
										self.assertListEqual(_snake_case    ,			_snake_case)
										self.assertListEqual(_snake_case    ,			_snake_case)


							@slow
							def 						_snake_case							(			self)							->				str:
								UpperCAmelCase_		:      List[Any]       	=					self.tokenizer_class(self.vocab_file    ,			self.word_shape_file    ,			self.word_pronunciation_file)

								UpperCAmelCase_		:      Dict       	=					tokenizer.encode('你好'    ,			add_special_tokens=_snake_case)
								UpperCAmelCase_		:      int       	=					tokenizer.encode('你是谁'    ,			add_special_tokens=_snake_case)

								UpperCAmelCase_		:      int       	=					tokenizer.build_inputs_with_special_tokens(_snake_case)
								UpperCAmelCase_		:      Optional[int]       	=					tokenizer.build_inputs_with_special_tokens(_snake_case    ,			_snake_case)

								assert encoded_sentence == [1] + text + [2]
								assert encoded_pair == [1] + text + [2] + text_a + [2]





							def 						_snake_case							(			self)							->				Tuple:
								UpperCAmelCase_		:      Optional[int]       	=					self.get_tokenizers(do_lower_case=_snake_case)
								for tokenizer in tokenizers:
									with self.subTest(F"""{tokenizer.__class__.__name__}"""):
										UpperCAmelCase_		:      Dict       	=					'你好，你是谁'
										UpperCAmelCase_		:      List[str]       	=					tokenizer.tokenize(_snake_case)
										UpperCAmelCase_		:      Tuple       	=					tokenizer.convert_tokens_to_ids(_snake_case)
										UpperCAmelCase_		:      Tuple       	=					tokenizer.convert_tokens_to_shape_ids(_snake_case)
										UpperCAmelCase_		:      Union[str, Any]       	=					tokenizer.convert_tokens_to_pronunciation_ids(_snake_case)
										UpperCAmelCase_		:      Tuple       	=					tokenizer.prepare_for_model(
										    _snake_case    ,			_snake_case    ,			_snake_case    ,			add_special_tokens=_snake_case)

										UpperCAmelCase_		:      Dict       	=					tokenizer.encode_plus(_snake_case    ,			add_special_tokens=_snake_case)

										self.assertEqual(_snake_case    ,			_snake_case)









'''simple docstring'''


import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


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

lowerCAmelCase__										= [
    ["attention", "attn"],
    ["encoder_attention", "encoder_attn"],
    ["q_lin", "q_proj"],
    ["k_lin", "k_proj"],
    ["v_lin", "v_proj"],
    ["out_lin", "out_proj"],
    ["norm_embeddings", "layernorm_embedding"],
    ["position_embeddings", "embed_positions"],
    ["embeddings", "embed_tokens"],
    ["ffn.lin", "fc"],
]







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    List[Any]:
	if k == "embeddings.weight":
		return "shared.weight"

	for parlai_name, hf_name in PATTERNS:
		UpperCAmelCase_		:      Optional[int]       	=					k.replace(UpperCamelCase   ,UpperCamelCase  )

	if k.startswith('encoder'  ):
		UpperCAmelCase_		:      Dict       	=					k.replace('.attn'   ,'.self_attn'  )
		UpperCAmelCase_		:      int       	=					k.replace('norm1'   ,'self_attn_layer_norm'  )
		UpperCAmelCase_		:      int       	=					k.replace('norm2'   ,'final_layer_norm'  )
	elif k.startswith('decoder'  ):
		UpperCAmelCase_		:      Union[str, Any]       	=					k.replace('norm1'   ,'self_attn_layer_norm'  )
		UpperCAmelCase_		:      int       	=					k.replace('norm2'   ,'encoder_attn_layer_norm'  )
		UpperCAmelCase_		:      List[str]       	=					k.replace('norm3'   ,'final_layer_norm'  )
	return k







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    Optional[Any]:
	UpperCAmelCase_		:      Union[str, Any]       	=					[
	    'model.encoder.layernorm_embedding.weight',
	    'model.encoder.layernorm_embedding.bias',
	    'model.decoder.layernorm_embedding.weight',
	    'model.decoder.layernorm_embedding.bias',
	]
	for k in keys:
		UpperCAmelCase_		:      Optional[int]       	=					sd.pop(UpperCamelCase  )
		UpperCAmelCase_		:      List[Any]       	=					k.replace('layernorm_embedding'   ,'layer_norm'  )
		assert new_k not in sd
		UpperCAmelCase_		:      Any       	=					v


lowerCAmelCase__										= ["START"]







@torch.no_grad()
def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )    ->    Dict:
	UpperCAmelCase_		:      str       	=					torch.load(UpperCamelCase   ,map_location='cpu'  )
	UpperCAmelCase_		:      Tuple       	=					model['model']
	UpperCAmelCase_		:      Union[str, Any]       	=					BlenderbotConfig.from_json_file(UpperCamelCase  )
	UpperCAmelCase_		:      Union[str, Any]       	=					BlenderbotForConditionalGeneration(UpperCamelCase  )
	UpperCAmelCase_		:      Union[str, Any]       	=					m.model.state_dict().keys()
	UpperCAmelCase_		:      Optional[Any]       	=					[]
	UpperCAmelCase_		:      Dict       	=					{}
	for k, v in sd.items():
		if k in IGNORE_KEYS:
			continue

		UpperCAmelCase_		:      Union[str, Any]       	=					rename_state_dict_key(UpperCamelCase  )
		if new_k not in valid_keys:
			failures.append([k, new_k]  )
		else:
			UpperCAmelCase_		:      List[str]       	=					v
	if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
		rename_layernorm_keys(UpperCamelCase  )
	m.model.load_state_dict(UpperCamelCase   ,strict=UpperCamelCase  )
	m.half()
	m.save_pretrained(UpperCamelCase  )


if __name__ == "__main__":
						lowerCAmelCase__										= argparse.ArgumentParser()
						# Required parameters
						parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
						parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
						parser.add_argument(
						    "--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
						)
						lowerCAmelCase__										= parser.parse_args()
						convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)






