


import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class     __UpperCAmelCase							(       a__ ,					unittest.TestCase							):


     """simple docstring"""
     _lowerCamelCase   =      RobertaTokenizer
     _lowerCamelCase   =      RobertaTokenizerFast
     _lowerCamelCase   =      True
     _lowerCamelCase   =      {"""cls_token""": """<s>"""}


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

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        __a					=  [
            """l""",
            """o""",
            """w""",
            """e""",
            """r""",
            """s""",
            """t""",
            """i""",
            """d""",
            """n""",
            """\u0120""",
            """\u0120l""",
            """\u0120n""",
            """\u0120lo""",
            """\u0120low""",
            """er""",
            """\u0120lowest""",
            """\u0120newer""",
            """\u0120wider""",
            """<unk>""",
        ]
        __a					=  dict(zip(__a  ,       range(len(__a    )    )    )    )
        __a					=  ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
        __a					=  {"""unk_token""": """<unk>"""}

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


     def 	snake_case_  (	self  ,       **__A    ):
        kwargs.update(self.special_tokens_map    )
        return self.tokenizer_class.from_pretrained(self.tmpdirname  ,       **__a    )


     def 	snake_case_  (	self  ,       **__A    ):
        kwargs.update(self.special_tokens_map    )
        return RobertaTokenizerFast.from_pretrained(self.tmpdirname  ,       **__a    )


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  """lower newer"""
        __a					=  """lower newer"""
        return input_text, output_text


     def 	snake_case_  (	self    ):
        __a					=  self.tokenizer_class(self.vocab_file  ,       self.merges_file  ,       **self.special_tokens_map    )
        __a					=  """lower newer"""
        __a					=  ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
        __a					=  tokenizer.tokenize(__a    )  # , add_prefix_space=True)
        self.assertListEqual(__a  ,       __a    )

        __a					=  tokens + [tokenizer.unk_token]
        __a					=  [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__a    )  ,       __a    )


     def 	snake_case_  (	self    ):
        __a					=  self.get_tokenizer()

        self.assertListEqual(tokenizer.encode("""Hello world!"""  ,       add_special_tokens=__a    )  ,       [0, 31414, 232, 328, 2]    )
        self.assertListEqual(
            tokenizer.encode("""Hello world! cécé herlolip 418"""  ,       add_special_tokens=__a    )  ,       [0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]  ,       )


     @slow
     def 	snake_case_  (	self    ):
        __a					=  self.tokenizer_class.from_pretrained("""roberta-base"""    )

        __a					=  tokenizer.encode("""sequence builders"""  ,       add_special_tokens=__a    )
        __a					=  tokenizer.encode("""multi-sequence build"""  ,       add_special_tokens=__a    )

        __a					=  tokenizer.encode(
            """sequence builders"""  ,       add_special_tokens=__a  ,       add_prefix_space=__a    )
        __a					=  tokenizer.encode(
            """sequence builders"""  ,       """multi-sequence build"""  ,       add_special_tokens=__a  ,       add_prefix_space=__a    )

        __a					=  tokenizer.build_inputs_with_special_tokens(__a    )
        __a					=  tokenizer.build_inputs_with_special_tokens(__a  ,       __a    )

        assert encoded_sentence == encoded_text_from_decode
        assert encoded_pair == encoded_pair_from_decode


     def 	snake_case_  (	self    ):
        __a					=  self.get_tokenizer()

        __a					=  """Encode this sequence."""
        __a					=  tokenizer.byte_encoder[""" """.encode("""utf-8"""    )[0]]

        # Testing encoder arguments
        __a					=  tokenizer.encode(__a  ,       add_special_tokens=__a  ,       add_prefix_space=__a    )
        __a					=  tokenizer.convert_ids_to_tokens(encoded[0]    )[0]
        self.assertNotEqual(__a  ,       __a    )

        __a					=  tokenizer.encode(__a  ,       add_special_tokens=__a  ,       add_prefix_space=__a    )
        __a					=  tokenizer.convert_ids_to_tokens(encoded[0]    )[0]
        self.assertEqual(__a  ,       __a    )

        tokenizer.add_special_tokens({"""bos_token""": """<s>"""}    )
        __a					=  tokenizer.encode(__a  ,       add_special_tokens=__a    )
        __a					=  tokenizer.convert_ids_to_tokens(encoded[1]    )[0]
        self.assertNotEqual(__a  ,       __a    )

        # Testing spaces after special tokens
        __a					=  """<mask>"""
        tokenizer.add_special_tokens(
            {"""mask_token""": AddedToken(__a  ,       lstrip=__a  ,       rstrip=__a    )}    )  # mask token has a left space
        __a					=  tokenizer.convert_tokens_to_ids(__a    )

        __a					=  """Encode <mask> sequence"""
        __a					=  """Encode <mask>sequence"""

        __a					=  tokenizer.encode(__a    )
        __a					=  encoded.index(__a    )
        __a					=  tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]    )[0]
        self.assertEqual(__a  ,       __a    )

        __a					=  tokenizer.encode(__a    )
        __a					=  encoded.index(__a    )
        __a					=  tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]    )[0]
        self.assertNotEqual(__a  ,       __a    )


     def 	snake_case_  (	self    ):
        pass


     def 	snake_case_  (	self    ):
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
              __a					=  self.rust_tokenizer_class.from_pretrained(__a  ,       **__a    )
              __a					=  self.tokenizer_class.from_pretrained(__a  ,       **__a    )
              __a					=  """A, <mask> AllenNLP sentence."""
              __a					=  tokenizer_r.encode_plus(__a  ,       add_special_tokens=__a  ,       return_token_type_ids=__a    )
              __a					=  tokenizer_p.encode_plus(__a  ,       add_special_tokens=__a  ,       return_token_type_ids=__a    )

              # token_type_ids should put 0 everywhere
              self.assertEqual(sum(tokens_r["""token_type_ids"""]    )  ,       sum(tokens_p["""token_type_ids"""]    )    )

              # attention_mask should put 1 everywhere, so sum over length should be 1
              self.assertEqual(
                  sum(tokens_r["""attention_mask"""]    ) / len(tokens_r["""attention_mask"""]    )  ,       sum(tokens_p["""attention_mask"""]    ) / len(tokens_p["""attention_mask"""]    )  ,       )

              __a					=  tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]    )
              __a					=  tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]    )

              # Rust correctly handles the space before the mask while python doesnt
              self.assertSequenceEqual(tokens_p["""input_ids"""]  ,       [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]    )
              self.assertSequenceEqual(tokens_r["""input_ids"""]  ,       [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]    )

              self.assertSequenceEqual(
                  __a  ,       ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]    )
              self.assertSequenceEqual(
                  __a  ,       ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]    )


     def 	snake_case_  (	self    ):
        for trim_offsets, add_prefix_space in itertools.product([True, False]  ,       repeat=2    ):
           __a					=  self.rust_tokenizer_class.from_pretrained(
               self.tmpdirname  ,       use_fast=__a  ,       add_prefix_space=__a  ,       trim_offsets=__a    )

           __a					=  json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()    )
           __a					=  json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()    )

           self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]  ,       __a    )

           self.assertEqual(post_processor_state["""add_prefix_space"""]  ,       __a    )
           self.assertEqual(post_processor_state["""trim_offsets"""]  ,       __a    )


     def 	snake_case_  (	self    ):
        # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
        # `trim_offsets`
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
              __a					=  """hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
              __a					=  f'''{text_of_1_token} {text_of_1_token}'''

              __a					=  self.rust_tokenizer_class.from_pretrained(
                  __a  ,       use_fast=__a  ,       add_prefix_space=__a  ,       trim_offsets=__a    )
              __a					=  tokenizer_r(__a  ,       return_offsets_mapping=__a  ,       add_special_tokens=__a    )
              self.assertEqual(encoding.offset_mapping[0]  ,       (0, len(__a    ))    )
              self.assertEqual(
                  encoding.offset_mapping[1]  ,       (len(__a    ) + 1, len(__a    ) + 1 + len(__a    ))  ,       )

              __a					=  self.rust_tokenizer_class.from_pretrained(
                  __a  ,       use_fast=__a  ,       add_prefix_space=__a  ,       trim_offsets=__a    )
              __a					=  tokenizer_r(__a  ,       return_offsets_mapping=__a  ,       add_special_tokens=__a    )
              self.assertEqual(encoding.offset_mapping[0]  ,       (0, len(__a    ))    )
              self.assertEqual(
                  encoding.offset_mapping[1]  ,       (len(__a    ) + 1, len(__a    ) + 1 + len(__a    ))  ,       )

              __a					=  self.rust_tokenizer_class.from_pretrained(
                  __a  ,       use_fast=__a  ,       add_prefix_space=__a  ,       trim_offsets=__a    )
              __a					=  tokenizer_r(__a  ,       return_offsets_mapping=__a  ,       add_special_tokens=__a    )
              self.assertEqual(encoding.offset_mapping[0]  ,       (0, len(__a    ))    )
              self.assertEqual(
                  encoding.offset_mapping[1]  ,       (len(__a    ), len(__a    ) + 1 + len(__a    ))  ,       )

              __a					=  self.rust_tokenizer_class.from_pretrained(
                  __a  ,       use_fast=__a  ,       add_prefix_space=__a  ,       trim_offsets=__a    )
              __a					=  tokenizer_r(__a  ,       return_offsets_mapping=__a  ,       add_special_tokens=__a    )
              self.assertEqual(encoding.offset_mapping[0]  ,       (0, len(__a    ))    )
              self.assertEqual(
                  encoding.offset_mapping[1]  ,       (len(__a    ), len(__a    ) + 1 + len(__a    ))  ,       )

              __a					=  f''' {text}'''

              # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
              #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
              # )
              # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
              # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
              # self.assertEqual(
              #     encoding.offset_mapping[1],
              #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
              # )

              __a					=  self.rust_tokenizer_class.from_pretrained(
                  __a  ,       use_fast=__a  ,       add_prefix_space=__a  ,       trim_offsets=__a    )
              __a					=  tokenizer_r(__a  ,       return_offsets_mapping=__a  ,       add_special_tokens=__a    )
              self.assertEqual(encoding.offset_mapping[0]  ,       (1, 1 + len(__a    ))    )
              self.assertEqual(
                  encoding.offset_mapping[1]  ,       (1 + len(__a    ) + 1, 1 + len(__a    ) + 1 + len(__a    ))  ,       )

              __a					=  self.rust_tokenizer_class.from_pretrained(
                  __a  ,       use_fast=__a  ,       add_prefix_space=__a  ,       trim_offsets=__a    )
              __a					=  tokenizer_r(__a  ,       return_offsets_mapping=__a  ,       add_special_tokens=__a    )
              self.assertEqual(encoding.offset_mapping[0]  ,       (0, 1 + len(__a    ))    )
              self.assertEqual(
                  encoding.offset_mapping[1]  ,       (1 + len(__a    ), 1 + len(__a    ) + 1 + len(__a    ))  ,       )

              __a					=  self.rust_tokenizer_class.from_pretrained(
                  __a  ,       use_fast=__a  ,       add_prefix_space=__a  ,       trim_offsets=__a    )
              __a					=  tokenizer_r(__a  ,       return_offsets_mapping=__a  ,       add_special_tokens=__a    )
              self.assertEqual(encoding.offset_mapping[0]  ,       (0, 1 + len(__a    ))    )
              self.assertEqual(
                  encoding.offset_mapping[1]  ,       (1 + len(__a    ), 1 + len(__a    ) + 1 + len(__a    ))  ,       )









'''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
)
lowercase      :   Any      =       logging.getLogger(__name__)

if __name__ == "__main__":
      lowercase      :   List[Any]      =       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=3_0522, type=int)
      lowercase      :   str      =       parser.parse_args()

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

      logger.info("""Counting occurrences for MLM.""")
      lowercase      :   List[Any]      =       Counter()
      for tk_ids in data:
            counter.update(tk_ids)
      lowercase      :   int      =       [0] * args.vocab_size
      for k, v in counter.items():
            lowercase      :   List[Any]      =       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)



