


"""simple docstring"""

import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class    lowerCamelCase__						(		_lowercase			,   unittest.TestCase    ):
      a :     List[Any]											=		LEDTokenizer
      a :     Optional[int]											=		LEDTokenizerFast
      a :     Union[str, Any]											=		True
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       super().setUp()
       __lowercase									=	[
           """l""",
           """o""",
           """w""",
           """e""",
           """r""",
           """s""",
           """t""",
           """i""",
           """d""",
           """n""",
           """\u0120""",
           """\u0120l""",
           """\u0120n""",
           """\u0120lo""",
           """\u0120low""",
           """er""",
           """\u0120lowest""",
           """\u0120newer""",
           """\u0120wider""",
           """<unk>""",
       ]
       __lowercase									=	dict(zip(A_      ,       range(len(A_							)							)							)							)
       __lowercase									=	["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
       __lowercase									=	{"""unk_token""": """<unk>"""}

       __lowercase									=	os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES["""vocab_file"""]							)
       __lowercase									=	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 SCREAMING_SNAKE_CASE_	(		self	:	Dict      ,       **A_	:	int							):






       '''simple docstring'''


       kwargs.update(self.special_tokens_map							)
       return self.tokenizer_class.from_pretrained(self.tmpdirname      ,       **A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]      ,       **A_	:	Any							):






       '''simple docstring'''


       kwargs.update(self.special_tokens_map							)
       return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,       **A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]      ,       A_	:	Optional[Any]							):






       '''simple docstring'''


       return "lower newer", "lower newer"
      @cached_property
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


       return LEDTokenizer.from_pretrained("""allenai/led-base-16384"""							)
      @cached_property
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       return LEDTokenizerFast.from_pretrained("""allenai/led-base-16384"""							)
      @require_torch
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


       __lowercase									=	["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
       __lowercase									=	[0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        __lowercase									=	tokenizer(A_      ,       max_length=len(A_							)      ,       padding=A_      ,       return_tensors="""pt"""							)
        self.assertIsInstance(A_      ,       A_							)

        self.assertEqual((2, 9)      ,       batch.input_ids.shape							)
        self.assertEqual((2, 9)      ,       batch.attention_mask.shape							)
        __lowercase									=	batch.input_ids.tolist()[0]
        self.assertListEqual(A_      ,       A_							)
      @require_torch
      def SCREAMING_SNAKE_CASE_	(		self	:	List[str]							):






       '''simple docstring'''


       __lowercase									=	["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        __lowercase									=	tokenizer(A_      ,       padding=A_      ,       return_tensors="""pt"""							)
        self.assertIn("""input_ids"""      ,       A_							)
        self.assertIn("""attention_mask"""      ,       A_							)
        self.assertNotIn("""labels"""      ,       A_							)
        self.assertNotIn("""decoder_attention_mask"""      ,       A_							)
      @require_torch
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       __lowercase									=	[
           """Summary of the text.""",
           """Another summary.""",
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        __lowercase									=	tokenizer(text_target=A_      ,       max_length=3_2      ,       padding="""max_length"""      ,       return_tensors="""pt"""							)
        self.assertEqual(3_2      ,       targets["""input_ids"""].shape[1]							)
      @require_torch
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        __lowercase									=	tokenizer(
            ["""I am a small frog""" * 1_0_2_4, """I am a small frog"""]      ,       padding=A_      ,       truncation=A_      ,       return_tensors="""pt"""							)
        self.assertIsInstance(A_      ,       A_							)
        self.assertEqual(batch.input_ids.shape      ,       (2, 5_1_2_2)							)
      @require_torch
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	["""A long paragraph for summarization."""]
       __lowercase									=	[
           """Summary of the text.""",
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        __lowercase									=	tokenizer(A_      ,       return_tensors="""pt"""							)
        __lowercase									=	tokenizer(text_target=A_      ,       return_tensors="""pt"""							)
        __lowercase									=	inputs["""input_ids"""]
        __lowercase									=	targets["""input_ids"""]
        self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()							)
        self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()							)
        self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()							)
        self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()							)
      @require_torch
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        __lowercase									=	["""Summary of the text.""", """Another summary."""]
        __lowercase									=	[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

        __lowercase									=	tokenizer(A_      ,       padding=A_							)
        __lowercase									=	[[0] * len(A_							) for x in encoded_output["""input_ids"""]]
        __lowercase									=	tokenizer.pad(A_							)
        self.assertSequenceEqual(outputs["""global_attention_mask"""]      ,       A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       pass






      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple							):






       '''simple docstring'''


       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
         __lowercase									=	self.rust_tokenizer_class.from_pretrained(A_      ,       **A_							)
         __lowercase									=	self.tokenizer_class.from_pretrained(A_      ,       **A_							)
         __lowercase									=	"""A, <mask> AllenNLP sentence."""
         __lowercase									=	tokenizer_r.encode_plus(A_      ,       add_special_tokens=A_      ,       return_token_type_ids=A_							)
         __lowercase									=	tokenizer_p.encode_plus(A_      ,       add_special_tokens=A_      ,       return_token_type_ids=A_							)
         self.assertEqual(sum(tokens_r["""token_type_ids"""]							)      ,       sum(tokens_p["""token_type_ids"""]							)							)
         self.assertEqual(
             sum(tokens_r["""attention_mask"""]							) / len(tokens_r["""attention_mask"""]							)      ,       sum(tokens_p["""attention_mask"""]							) / len(tokens_p["""attention_mask"""]							)      ,       )

         __lowercase									=	tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]							)
         __lowercase									=	tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]							)
         self.assertSequenceEqual(tokens_p["""input_ids"""]      ,       [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]							)
         self.assertSequenceEqual(tokens_r["""input_ids"""]      ,       [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]							)

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




"""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
)
UpperCAmelCase__  							=logging.getLogger(__name__)

if __name__ == "__main__":
       UpperCAmelCase__  							=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)
       UpperCAmelCase__  							=parser.parse_args()

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

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