
'''simple docstring'''

import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
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    ):
	snake_case__      :Optional[int]        =     BartTokenizer
	snake_case__      :List[Any]        =     BartTokenizerFast
	snake_case__      :List[Any]        =     True
	snake_case__      :Union[str, Any]        =     filter_roberta_detectors





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Any							):


				"""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(__magic_name__      ,							range(len(__magic_name__							)							)							)							)
				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(__magic_name__							) + "\n"							)
				with open(self.merges_file      ,							"w"      ,							encoding="utf-8"							) as fp:
							fp.write("\n".join(__magic_name__							)							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[Any]      ,							**__magic_name__			:       int							):


				"""simple docstring"""

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





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Dict      ,							**__magic_name__			:       Tuple							):


				"""simple docstring"""

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





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Tuple      ,							__magic_name__			:       Optional[Any]							):


				"""simple docstring"""

				return "lower newer", "lower newer"





	@cached_property
	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[Any]							):


				"""simple docstring"""

				return BartTokenizer.from_pretrained("facebook/bart-large"							)





	@cached_property
	def 							__SCREAMING_SNAKE_CASE  (					self			:       int							):


				"""simple docstring"""

				return BartTokenizerFast.from_pretrained("facebook/bart-large"							)





	@require_torch
	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[Any]							):


				"""simple docstring"""

				lowerCAmelCase__								=							["A long paragraph for summarization.", "Another paragraph for summarization."]
				lowerCAmelCase__								=							[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

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

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





	@require_torch
	def 							__SCREAMING_SNAKE_CASE  (					self			:       Any							):


				"""simple docstring"""

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





	@require_torch
	def 							__SCREAMING_SNAKE_CASE  (					self			:       List[str]							):


				"""simple docstring"""

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





	@require_torch
	def 							__SCREAMING_SNAKE_CASE  (					self			:       Dict							):


				"""simple docstring"""

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





	@require_torch
	def 							__SCREAMING_SNAKE_CASE  (					self			:       List[Any]							):


				"""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(__magic_name__      ,							return_tensors="pt"							)
							lowerCAmelCase__								=							tokenizer(text_target=__magic_name__      ,							return_tensors="pt"							)
							lowerCAmelCase__								=							inputs["input_ids"]
							lowerCAmelCase__								=							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()							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       List[str]							):


				"""simple docstring"""

				pass







	def 							__SCREAMING_SNAKE_CASE  (					self			:       Any							):


				"""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(__magic_name__      ,							**__magic_name__							)
										lowerCAmelCase__								=							self.tokenizer_class.from_pretrained(__magic_name__      ,							**__magic_name__							)
										lowerCAmelCase__								=							"A, <mask> AllenNLP sentence."
										lowerCAmelCase__								=							tokenizer_r.encode_plus(__magic_name__      ,							add_special_tokens=__magic_name__      ,							return_token_type_ids=__magic_name__							)
										lowerCAmelCase__								=							tokenizer_p.encode_plus(__magic_name__      ,							add_special_tokens=__magic_name__      ,							return_token_type_ids=__magic_name__							)

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







'''simple docstring'''

from __future__ import annotations







def   A     (      UpperCamelCase_					:     int      )					->       list[int]:







			'''simple docstring'''



			assert (
			    isinstance(UpperCamelCase_						,	UpperCamelCase_      ) and mask > 0
			), F"""mask needs to be positive integer, your input {mask}"""
			lowerCAmelCase__								=							[]
			lowerCAmelCase__								=							mask

			while submask:
						all_submasks.append(UpperCamelCase_      )
						lowerCAmelCase__								=							(submask - 1) & mask

			return all_submasks


if __name__ == "__main__":
		import doctest

		doctest.testmod()





