





'''simple docstring'''







import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta 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, filter_roberta_detectors





@require_tokenizers
class        _UpperCAmelCase				(		lowerCAmelCase_      ,   unittest.TestCase  ):
		a  :  Optional[Any]      =MvpTokenizer
		a  :  Tuple      =MvpTokenizerFast
		a  :  Dict      =True
		a  :  Dict      =filter_roberta_detectors
		def     lowerCamelCase__     (    self     ):



					'''simple docstring'''

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

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



					'''simple docstring'''

					kwargs.update(self.special_tokens_map     )
					return self.tokenizer_class.from_pretrained(self.tmpdirname,**__SCREAMING_SNAKE_CASE     )
		def     lowerCamelCase__     (    self,**__SCREAMING_SNAKE_CASE     ):



					'''simple docstring'''

					kwargs.update(self.special_tokens_map     )
					return self.rust_tokenizer_class.from_pretrained(self.tmpdirname,**__SCREAMING_SNAKE_CASE     )
		def     lowerCamelCase__     (    self,__SCREAMING_SNAKE_CASE     ):



					'''simple docstring'''

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



					'''simple docstring'''

					return MvpTokenizer.from_pretrained("""RUCAIBox/mvp"""     )
		@cached_property
		def     lowerCamelCase__     (    self     ):



					'''simple docstring'''

					return MvpTokenizerFast.from_pretrained("""RUCAIBox/mvp"""     )
		@require_torch
		def     lowerCamelCase__     (    self     ):



					'''simple docstring'''

					__lowerCAmelCase   		=							["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
					__lowerCAmelCase   		=							[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

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

								self.assertEqual((2, 9),batch.input_ids.shape     )
								self.assertEqual((2, 9),batch.attention_mask.shape     )
								__lowerCAmelCase   		=							batch.input_ids.tolist()[0]
								self.assertListEqual(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE     )
								# Test that special tokens are reset
		@require_torch
		def     lowerCamelCase__     (    self     ):



					'''simple docstring'''

					__lowerCAmelCase   		=							["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								__lowerCAmelCase   		=							tokenizer(__SCREAMING_SNAKE_CASE,padding=__SCREAMING_SNAKE_CASE,return_tensors="""pt"""     )
								# check if input_ids are returned and no labels
								self.assertIn("""input_ids""",__SCREAMING_SNAKE_CASE     )
								self.assertIn("""attention_mask""",__SCREAMING_SNAKE_CASE     )
								self.assertNotIn("""labels""",__SCREAMING_SNAKE_CASE     )
								self.assertNotIn("""decoder_attention_mask""",__SCREAMING_SNAKE_CASE     )
		@require_torch
		def     lowerCamelCase__     (    self     ):



					'''simple docstring'''

					__lowerCAmelCase   		=							[
					    """Summary of the text.""",
					    """Another summary.""",
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								__lowerCAmelCase   		=							tokenizer(text_target=__SCREAMING_SNAKE_CASE,max_length=32,padding="""max_length""",return_tensors="""pt"""     )
								self.assertEqual(32,targets["""input_ids"""].shape[1]     )
		@require_torch
		def     lowerCamelCase__     (    self     ):



					'''simple docstring'''

					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								__lowerCAmelCase   		=							tokenizer(
								    ["""I am a small frog""" * 10_24, """I am a small frog"""],padding=__SCREAMING_SNAKE_CASE,truncation=__SCREAMING_SNAKE_CASE,return_tensors="""pt"""     )
								self.assertIsInstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE     )
								self.assertEqual(batch.input_ids.shape,(2, 10_24)     )
		@require_torch
		def     lowerCamelCase__     (    self     ):



					'''simple docstring'''

					__lowerCAmelCase   		=							["""A long paragraph for summarization."""]
					__lowerCAmelCase   		=							[
					    """Summary of the text.""",
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								__lowerCAmelCase   		=							tokenizer(__SCREAMING_SNAKE_CASE,text_target=__SCREAMING_SNAKE_CASE,return_tensors="""pt"""     )
								__lowerCAmelCase   		=							inputs["""input_ids"""]
								__lowerCAmelCase   		=							inputs["""labels"""]
								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()     )
		def     lowerCamelCase__     (    self     ):



					'''simple docstring'''

					pass







		def     lowerCamelCase__     (    self     ):



					'''simple docstring'''

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

											# 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"""]     ),)

											__lowerCAmelCase   		=							tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]     )
											__lowerCAmelCase   		=							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, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]     )
											self.assertSequenceEqual(tokens_r["""input_ids"""],[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]     )

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





'''simple docstring'''
import string
from math import logaa







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		document.translate(
							    str.maketrans(""						,  ""						,  string.punctuation ) ).replace("\n"						,  "" )
							A_			    =		document_without_punctuation.split(" " )  # word tokenization
							return len([word for word in tokenize_document if word.lower() == term.lower()] )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		corpus.lower().translate(
							    str.maketrans(""						,  ""						,  string.punctuation ) )  # strip all punctuation and replace it with ''
							A_			    =		corpus_without_punctuation.split("\n" )
							A_			    =		term.lower()
							return (len([doc for doc in docs if term in doc] ), len(UpperCamelCase_ ))







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_=False ):
							if smoothing:
														if n == 0:
																					raise ValueError("log10(0) is undefined." )
														return round(1 + logaa(n / (1 + df) )						,  3 )

							if df == 0:
														raise ZeroDivisionError("df must be > 0" )
							elif n == 0:
														raise ValueError("log10(0) is undefined." )
							return round(logaa(n / df )						,  3 )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							return round(tf * idf						,  3 )





