






'''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    _snake_case   (				lowerCAmelCase__     ,					unittest.TestCase					):

					"""simple docstring"""





					_UpperCamelCase		 =      BartTokenizer
					_UpperCamelCase		 =      BartTokenizerFast
					_UpperCamelCase		 =      True
					_UpperCamelCase		 =      filter_roberta_detectors




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Dict:
										super().setUp()
										a_									=   [
										    'l',
										    'o',
										    'w',
										    'e',
										    'r',
										    's',
										    't',
										    'i',
										    'd',
										    'n',
										    '\u0120',
										    '\u0120l',
										    '\u0120n',
										    '\u0120lo',
										    '\u0120low',
										    'er',
										    '\u0120lowest',
										    '\u0120newer',
										    '\u0120wider',
										    '<unk>',
										]
										a_									=   dict(zip(lowerCAmelCase_   , range(len(lowerCAmelCase_     )     )     )     )
										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(lowerCAmelCase_     ) + '\n'     )
										with open(self.merges_file   , 'w'   , encoding='utf-8'     ) as fp:
															fp.write('\n'.join(lowerCAmelCase_     )     )




					def  __SCREAMING_SNAKE_CASE (     self   , **UpperCAmelCase__     )      ->	Union[str, Any]:
										kwargs.update(self.special_tokens_map     )
										return self.tokenizer_class.from_pretrained(self.tmpdirname   , **lowerCAmelCase_     )




					def  __SCREAMING_SNAKE_CASE (     self   , **UpperCAmelCase__     )      ->	int:
										kwargs.update(self.special_tokens_map     )
										return self.rust_tokenizer_class.from_pretrained(self.tmpdirname   , **lowerCAmelCase_     )




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	int:
										return "lower newer", "lower newer"




					@cached_property
					def  __SCREAMING_SNAKE_CASE (     self     )      ->	List[str]:
										return BartTokenizer.from_pretrained('facebook/bart-large'     )




					@cached_property
					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Dict:
										return BartTokenizerFast.from_pretrained('facebook/bart-large'     )




					@require_torch
					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Optional[int]:
										a_									=   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
										a_									=   [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															a_									=   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     )
															a_									=   batch.input_ids.tolist()[0]
															self.assertListEqual(lowerCAmelCase_   , lowerCAmelCase_     )
															# Test that special tokens are reset




					@require_torch
					def  __SCREAMING_SNAKE_CASE (     self     )      ->	List[Any]:
										a_									=   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															a_									=   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     )      ->	Dict:
										a_									=   [
										    'Summary of the text.',
										    'Another summary.',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															a_									=   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     )      ->	List[str]:






										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															a_									=   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     )      ->	Union[str, Any]:
										a_									=   ['A long paragraph for summarization.']
										a_									=   [
										    'Summary of the text.',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															a_									=   tokenizer(lowerCAmelCase_   , return_tensors='pt'     )
															a_									=   tokenizer(text_target=lowerCAmelCase_   , return_tensors='pt'     )
															a_									=   inputs['input_ids']
															a_									=   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     )      ->	Union[str, Any]:
										pass




					def  __SCREAMING_SNAKE_CASE (     self     )      ->	int:






										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(lowerCAmelCase_   , **lowerCAmelCase_     )
																				a_									=   self.tokenizer_class.from_pretrained(lowerCAmelCase_   , **lowerCAmelCase_     )
																				a_									=   'A, <mask> AllenNLP sentence.'
																				a_									=   tokenizer_r.encode_plus(lowerCAmelCase_   , add_special_tokens=lowerCAmelCase_   , return_token_type_ids=lowerCAmelCase_     )
																				a_									=   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']     )   , )

																				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, 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'''
import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
						import torch_xla.core.xla_model as xm

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase = False )				->				List[str]:






					"""simple docstring"""







					if device_specific:
										seed += AcceleratorState().process_index
					random.seed(_UpperCAmelCase )
					np.random.seed(_UpperCAmelCase )
					torch.manual_seed(_UpperCAmelCase )
					if is_xpu_available():
										torch.xpu.manual_seed_all(_UpperCAmelCase )
					elif is_npu_available():
										torch.npu.manual_seed_all(_UpperCAmelCase )
					else:
										torch.cuda.manual_seed_all(_UpperCAmelCase )
					# ^^ safe to call this function even if cuda is not available
					if is_tpu_available():
										xm.set_rng_state(_UpperCAmelCase )

def 							a	(   _UpperCAmelCase = None					,  _UpperCAmelCase = None )				->				Tuple:






					"""simple docstring"""







					if rng_type == RNGType.TORCH:
										a_									=   torch.get_rng_state()
					elif rng_type == RNGType.CUDA:
										a_									=   torch.cuda.get_rng_state()
					elif rng_type == RNGType.XLA:
										assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
										a_									=   torch.tensor(xm.get_rng_state() )
					elif rng_type == RNGType.NPU:
										assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
										a_									=   torch.npu.get_rng_state()
					elif rng_type == RNGType.XPU:
										assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
										a_									=   torch.xpu.get_rng_state()
					elif rng_type == RNGType.GENERATOR:
										assert generator is not None, "Need a generator to synchronize its seed."
										a_									=   generator.get_state()

					# Broadcast the rng state from device 0 to other devices
					a_									=   AcceleratorState()
					if state.distributed_type == DistributedType.TPU:
										a_									=   rng_state.to(xm.xla_device() )
										xm.collective_broadcast([rng_state] )
										xm.mark_step()
										a_									=   rng_state.cpu()
					elif (
					    state.distributed_type in CUDA_DISTRIBUTED_TYPES
					    or state.distributed_type == DistributedType.MULTI_NPU
					    or state.distributed_type == DistributedType.MULTI_XPU
					):
										a_									=   rng_state.to(state.device )
										torch.distributed.broadcast(_UpperCAmelCase					,  0 )
										a_									=   rng_state.cpu()
					elif state.distributed_type == DistributedType.MULTI_CPU:
										torch.distributed.broadcast(_UpperCAmelCase					,  0 )

					# Set the broadcast rng state
					if rng_type == RNGType.TORCH:
										torch.set_rng_state(_UpperCAmelCase )
					elif rng_type == RNGType.CUDA:
										torch.cuda.set_rng_state(_UpperCAmelCase )
					elif rng_type == RNGType.NPU:
										torch.npu.set_rng_state(_UpperCAmelCase )
					elif rng_type == RNGType.XPU:
										torch.xpu.set_rng_state(_UpperCAmelCase )
					elif rng_type == RNGType.XLA:
										xm.set_rng_state(rng_state.item() )
					elif rng_type == RNGType.GENERATOR:
										generator.set_state(_UpperCAmelCase )





def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase = None )				->				Any:






					"""simple docstring"""







					for rng_type in rng_types:
										synchronize_rng_state(RNGType(_UpperCAmelCase )					,  generator=_UpperCAmelCase )

