



"""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     A__     (						__SCREAMING_SNAKE_CASE						,	unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			MvpTokenizer
	SCREAMING_SNAKE_CASE					   =			MvpTokenizerFast
	SCREAMING_SNAKE_CASE					   =			True
	SCREAMING_SNAKE_CASE					   =			filter_roberta_detectors

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str])       -> List[Any]:

				"""simple docstring"""

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

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int] ,    **_SCREAMING_SNAKE_CASE:	List[Any])       -> List[Any]:

				"""simple docstring"""

				kwargs.update(self.special_tokens_map)
				return self.tokenizer_class.from_pretrained(self.tmpdirname ,    **__lowerCAmelCase)

	def 							_SCREAMING_SNAKE_CASE     (  self:	str ,    **_SCREAMING_SNAKE_CASE:	Dict)       -> Any:

				"""simple docstring"""

				kwargs.update(self.special_tokens_map)
				return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,    **__lowerCAmelCase)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	Tuple)       -> str:

				"""simple docstring"""

				return "lower newer", "lower newer"

	@cached_property
	def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any])       -> int:

				"""simple docstring"""

				return MvpTokenizer.from_pretrained("RUCAIBox/mvp")

	@cached_property
	def 							_SCREAMING_SNAKE_CASE     (  self:	Any)       -> Any:

				"""simple docstring"""

				return MvpTokenizerFast.from_pretrained("RUCAIBox/mvp")

	@require_torch
	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> List[str]:

				"""simple docstring"""

				__lowerCAmelCase   :						List[str]				= ["A long paragraph for summarization.", "Another paragraph for summarization."]
				__lowerCAmelCase   :						Optional[Any]				= [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

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

							self.assertEqual((2, 9) ,    batch.input_ids.shape)
							self.assertEqual((2, 9) ,    batch.attention_mask.shape)
							__lowerCAmelCase   :						Optional[Any]				= batch.input_ids.tolist()[0]
							self.assertListEqual(__lowerCAmelCase ,    __lowerCAmelCase)
							# Test that special tokens are reset

	@require_torch
	def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> Tuple:

				"""simple docstring"""

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

	@require_torch
	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> Union[str, Any]:

				"""simple docstring"""

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

	@require_torch
	def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any])       -> Any:

				"""simple docstring"""

				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							__lowerCAmelCase   :						List[str]				= tokenizer(
							    ["I am a small frog" * 1024, "I am a small frog"] ,    padding=__lowerCAmelCase ,    truncation=__lowerCAmelCase ,    return_tensors="pt")
							self.assertIsInstance(__lowerCAmelCase ,    __lowerCAmelCase)
							self.assertEqual(batch.input_ids.shape ,    (2, 1024))

	@require_torch
	def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> str:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[int]				= ["A long paragraph for summarization."]
				__lowerCAmelCase   :						int				= [
				    "Summary of the text.",
				]
				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							__lowerCAmelCase   :						Optional[int]				= tokenizer(__lowerCAmelCase ,    text_target=__lowerCAmelCase ,    return_tensors="pt")
							__lowerCAmelCase   :						Any				= inputs["input_ids"]
							__lowerCAmelCase   :						Tuple				= 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 							_SCREAMING_SNAKE_CASE     (  self:	Dict)       -> str:

				"""simple docstring"""

				pass







	def 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> List[Any]:

				"""simple docstring"""

				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
										__lowerCAmelCase   :						List[Any]				= self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase ,    **__lowerCAmelCase)
										__lowerCAmelCase   :						str				= self.tokenizer_class.from_pretrained(__lowerCAmelCase ,    **__lowerCAmelCase)
										__lowerCAmelCase   :						int				= "A, <mask> AllenNLP sentence."
										__lowerCAmelCase   :						Tuple				= tokenizer_r.encode_plus(__lowerCAmelCase ,    add_special_tokens=__lowerCAmelCase ,    return_token_type_ids=__lowerCAmelCase)
										__lowerCAmelCase   :						Optional[int]				= tokenizer_p.encode_plus(__lowerCAmelCase ,    add_special_tokens=__lowerCAmelCase ,    return_token_type_ids=__lowerCAmelCase)

										# 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   :						str				= tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
										__lowerCAmelCase   :						str				= 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, 5_0264, 3823, 487, 2_1992, 3645, 4, 2])
										self.assertSequenceEqual(tokens_r["input_ids"] ,    [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2])

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




"""simple docstring"""






from abc import ABC, abstractmethod
from argparse import ArgumentParser



class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	@staticmethod
	@abstractmethod
	def 							_SCREAMING_SNAKE_CASE     (  _SCREAMING_SNAKE_CASE:	ArgumentParser)       -> str:

				"""simple docstring"""

				raise NotImplementedError()







	@abstractmethod
	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str])       -> str:

				"""simple docstring"""

				raise NotImplementedError()