



"""simple docstring"""

import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin







class    __lowercase     (					_UpperCAmelCase					,    unittest.TestCase):







						"""simple docstring"""







						_A		:							Optional[int]							       =		BlenderbotSmallTokenizer
						_A		:							str							       =		False







						def 						__UpperCamelCase							(self       ):
											super().setUp()

											snake_case_					:		Any 					=					['__start__', 'adapt', 'act', 'ap@@', 'te', '__end__', '__unk__']
											snake_case_					:		str 					=					dict(zip(lowercase__   ,		range(len(lowercase__       )       )       )       )

											snake_case_					:		Union[str, Any] 					=					['#version: 0.2', 'a p', 't e</w>', 'ap t</w>', 'a d', 'ad apt</w>', 'a c', 'ac t</w>', '']
											snake_case_					:		str 					=					{'unk_token': '__unk__', 'bos_token': '__start__', 'eos_token': '__end__'}

											snake_case_					:		Tuple 					=					os.path.join(self.tmpdirname   ,		VOCAB_FILES_NAMES["""vocab_file"""]       )
											snake_case_					:		int 					=					os.path.join(self.tmpdirname   ,		VOCAB_FILES_NAMES["""merges_file"""]       )
											with open(self.vocab_file   ,		"""w"""   ,		encoding="""utf-8"""       ) as fp:
																fp.write(json.dumps(lowercase__       ) + """\n"""       )
											with open(self.merges_file   ,		"""w"""   ,		encoding="""utf-8"""       ) as fp:
																fp.write("""\n""".join(lowercase__       )       )







						def 						__UpperCamelCase							(self   ,		**lowercase__       ):
											kwargs.update(self.special_tokens_map       )
											return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname   ,		**lowercase__       )







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											snake_case_					:		Union[str, Any] 					=					'adapt act apte'
											snake_case_					:		List[Any] 					=					'adapt act apte'
											return input_text, output_text







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Any 					=					BlenderbotSmallTokenizer(self.vocab_file   ,		self.merges_file   ,		**self.special_tokens_map       )
											snake_case_					:		Dict 					=					'adapt act apte'
											snake_case_					:		str 					=					['adapt', 'act', 'ap@@', 'te']
											snake_case_					:		Dict 					=					tokenizer.tokenize(lowercase__       )
											self.assertListEqual(lowercase__   ,		lowercase__       )

											snake_case_					:		int 					=					[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

											snake_case_					:		Optional[int] 					=					[0, 1, 2, 3, 4, 5]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase__       )   ,		lowercase__       )







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		List[Any] 					=					BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""       )
											assert tok("""sam"""       ).input_ids == [13_84]
											snake_case_					:		Any 					=					'I am a small frog.'
											snake_case_					:		List[str] 					=					tok([src_text]   ,		padding=lowercase__   ,		truncation=lowercase__       )['input_ids']
											snake_case_					:		Union[str, Any] 					=					tok.batch_decode(lowercase__   ,		skip_special_tokens=lowercase__   ,		clean_up_tokenization_spaces=lowercase__       )[0]
											assert src_text != decoded  # I wish it did!
											assert decoded == "i am a small frog ."







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		int 					=					BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""       )
											snake_case_					:		Optional[int] 					=					'I am a small frog .'
											snake_case_					:		Any 					=					'.'
											snake_case_					:		Optional[int] 					=					tok(lowercase__       )['input_ids']
											snake_case_					:		Any 					=					tok(lowercase__       )['input_ids']

											assert encoded[-1] == encoded_dot[0]









"""simple docstring"""

import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
a_			   =					logging.getLogger(__name__)

if __name__ == "__main__":
			a_			   =					argparse.ArgumentParser(
			    description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
			)
			parser.add_argument(
			    '''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
			)
			parser.add_argument(
			    '''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
			)
			parser.add_argument('''--vocab_size''', default=30522, type=int)
			a_			   =					parser.parse_args()

			logger.info(F'''Loading data from {args.data_file}''')
			with open(args.data_file, '''rb''') as fp:
						a_			   =					pickle.load(fp)

			logger.info('''Counting occurrences for MLM.''')
			a_			   =					Counter()
			for tk_ids in data:
						counter.update(tk_ids)
			a_			   =					[0] * args.vocab_size
			for k, v in counter.items():
						a_			   =					v

			logger.info(F'''Dump to {args.token_counts_dump}''')
			with open(args.token_counts_dump, '''wb''') as handle:
						pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)




