


import json
import os
import tempfile
from unittest.mock import patch

import torch
from torch.utils.data import DataLoader, TensorDataset

from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, PartialState
from accelerate.test_utils import require_bnb, require_multi_gpu, slow
from accelerate.test_utils.testing import AccelerateTestCase, require_cuda
from accelerate.utils import patch_environment






def 							__lowercase       (			):
							a__					=					torch.nn.Linear(2 ,   4       )
							a__					=					torch.optim.AdamW(model.parameters() ,   lr=1.0       )
							a__					=					torch.optim.lr_scheduler.OneCycleLR(__lowerCAmelCase ,   max_lr=0.01 ,   steps_per_epoch=2 ,   epochs=1       )
							a__					=					DataLoader(TensorDataset(torch.tensor([1, 2, 3]       )       )       )
							a__					=					DataLoader(TensorDataset(torch.tensor([4, 5, 6]       )       )       )

							return model, optimizer, scheduler, train_dl, valid_dl






def 							__lowercase       (			__lowerCAmelCase							:    Optional[int]       ):
							return (model.weight.abs().sum() + model.bias.abs().sum()).item()






def 							__lowercase       (			__lowerCAmelCase							:    Dict       ):
							a__					=					torch.nn.Linear(*tuple(model.weight.T.shape       )       ).state_dict()
							model.load_state_dict(__lowerCAmelCase       )







class 	snake_case_       (lowerCamelCase_				):
					@require_cuda
					def 			lowerCamelCase__(				self       :Any				) ->   str:
												a__					=					Accelerator()
												assert PartialState._shared_state["_cpu"] is False
												assert PartialState._shared_state["device"].type == "cuda"
												with self.assertRaises(__snake_case				):
																			a__					=					Accelerator(cpu=__snake_case				)
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   Optional[int]:
												a__					=					Accelerator()
												a__					=					GradientState()
												assert state.num_steps == 1
												a__					=					4
												assert state.num_steps == 4

												assert state.sync_gradients is True
												a__					=					False
												assert state.sync_gradients is False
												GradientState._reset_state()
					def 			lowerCamelCase__(				self       :Dict				) ->   Union[str, Any]:
												a__					=					Accelerator()
												a__				, a__				, a__				, a__				, a__					=					create_components()

												(
												    (
												    a__
												)				, (
												    a__
												)				, (
												    a__
												)				, (
												    a__
												)				, (
												    a__
												)				, 
												)					=					accelerator.prepare(__snake_case      ,__snake_case      ,__snake_case      ,__snake_case      ,__snake_case				)

												self.assertTrue(prepared_model in accelerator._models				)
												self.assertTrue(prepared_optimizer in accelerator._optimizers				)
												self.assertTrue(prepared_scheduler in accelerator._schedulers				)
												self.assertTrue(prepared_train_dl in accelerator._dataloaders				)
												self.assertTrue(prepared_valid_dl in accelerator._dataloaders				)
					def 			lowerCamelCase__(				self       :Tuple				) ->   List[str]:
												a__					=					Accelerator()
												a__				, a__				, a__				, a__				, a__					=					create_components()
												accelerator.prepare(__snake_case      ,__snake_case      ,__snake_case      ,__snake_case      ,__snake_case				)
												accelerator.free_memory()

												self.assertTrue(len(accelerator._models				) == 0				)
												self.assertTrue(len(accelerator._optimizers				) == 0				)
												self.assertTrue(len(accelerator._schedulers				) == 0				)
												self.assertTrue(len(accelerator._dataloaders				) == 0				)
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   Optional[Any]:
												PartialState._reset_state()

												# Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
												def noop(*__snake_case       :Any      ,**__snake_case       :str				):
																			pass

												with patch('torch.cuda.set_device'      ,__snake_case				), patch_environment(ACCELERATE_TORCH_DEVICE='cuda:64'				):
																			a__					=					Accelerator()
																			self.assertEqual(str(accelerator.state.device				)      ,'cuda:64'				)
					def 			lowerCamelCase__(				self       :Dict				) ->   Dict:
												a__					=					Accelerator()
												a__				, a__				, a__				, a__				, a__					=					create_components()
												accelerator.prepare(__snake_case      ,__snake_case      ,__snake_case      ,__snake_case      ,__snake_case				)

												a__					=					get_signature(__snake_case				)

												with tempfile.TemporaryDirectory() as tmpdirname:
																			accelerator.save_state(__snake_case				)

																			# make sure random weights don't match
																			load_random_weights(__snake_case				)
																			self.assertTrue(abs(model_signature - get_signature(__snake_case				)				) > 1E-3				)

																			# make sure loaded weights match
																			accelerator.load_state(__snake_case				)
																			self.assertTrue(abs(model_signature - get_signature(__snake_case				)				) < 1E-3				)
					def 			lowerCamelCase__(				self       :Dict				) ->   Union[str, Any]:
												a__					=					Accelerator()
												a__				, a__				, a__				, a__				, a__					=					create_components()
												accelerator.prepare(__snake_case      ,__snake_case      ,__snake_case      ,__snake_case      ,__snake_case				)

												a__					=					get_signature(__snake_case				)

												# saving hook
												def save_config(__snake_case       :Optional[int]      ,__snake_case       :Any      ,__snake_case       :List[Any]				):
																			a__					=					{'class_name': models[0].__class__.__name__}

																			with open(os.path.join(__snake_case      ,'data.json'				)      ,'w'				) as f:
																										json.dump(__snake_case      ,__snake_case				)

        # loading hook
												def load_config(__snake_case       :Any      ,__snake_case       :List[str]				):
																			with open(os.path.join(__snake_case      ,'data.json'				)      ,'r'				) as f:
																										a__					=					json.load(__snake_case				)

																			a__					=					config['class_name']

												a__					=					accelerator.register_save_state_pre_hook(__snake_case				)
												a__					=					accelerator.register_load_state_pre_hook(__snake_case				)

												with tempfile.TemporaryDirectory() as tmpdirname:
																			accelerator.save_state(__snake_case				)

																			# make sure random weights don't match with hooks
																			load_random_weights(__snake_case				)
																			self.assertTrue(abs(model_signature - get_signature(__snake_case				)				) > 1E-3				)

																			# random class name to verify correct one is loaded
																			a__					=					'random'

																			# make sure loaded weights match with hooks
																			accelerator.load_state(__snake_case				)
																			self.assertTrue(abs(model_signature - get_signature(__snake_case				)				) < 1E-3				)

																			# mode.class_name is loaded from config
																			self.assertTrue(model.class_name == model.__class__.__name__				)

												# remove hooks
												save_hook.remove()
												load_hook.remove()

												with tempfile.TemporaryDirectory() as tmpdirname:
																			accelerator.save_state(__snake_case				)

																			# make sure random weights don't match with hooks removed
																			load_random_weights(__snake_case				)
																			self.assertTrue(abs(model_signature - get_signature(__snake_case				)				) > 1E-3				)

																			# random class name to verify correct one is loaded
																			a__					=					'random'

																			# make sure loaded weights match with hooks removed
																			accelerator.load_state(__snake_case				)
																			self.assertTrue(abs(model_signature - get_signature(__snake_case				)				) < 1E-3				)

																			# mode.class_name is NOT loaded from config
																			self.assertTrue(model.class_name != model.__class__.__name__				)
					def 			lowerCamelCase__(				self       :int				) ->   Dict:
												a__					=					Accelerator()
												a__				, a__				, a__				, a__				, a__					=					create_components()
												a__					=					None

												# This should work
												a__				, a__				, a__				, a__				, a__				, a__					=					accelerator.prepare(
												    __snake_case      ,__snake_case      ,__snake_case      ,__snake_case      ,__snake_case      ,__snake_case				)
												self.assertTrue(dummy_obj is None				)
					def 			lowerCamelCase__(				self       :List[str]				) ->   Dict:
												a__					=					Accelerator()
												a__				, a__				, a__				, a__				, a__					=					create_components()
												a__					=					[1, 2, 3]

												# This should work
												a__				, a__				, a__				, a__				, a__				, a__					=					accelerator.prepare(
												    __snake_case      ,__snake_case      ,__snake_case      ,__snake_case      ,__snake_case      ,__snake_case				)
												self.assertEqual(
												    getattr(__snake_case      ,'_is_accelerate_prepared'      ,__snake_case				)      ,__snake_case      ,'Dummy object should have `_is_accelerate_prepared` set to `True`'      ,)
												self.assertEqual(
												    getattr(__snake_case      ,'_is_accelerate_prepared'      ,__snake_case				)      ,__snake_case      ,'Model is missing `_is_accelerator_prepared` or is set to `False`'      ,)
												self.assertEqual(
												    getattr(__snake_case      ,'_is_accelerate_prepared'      ,__snake_case				)      ,__snake_case      ,'Optimizer is missing `_is_accelerator_prepared` or is set to `False`'      ,)
												self.assertEqual(
												    getattr(__snake_case      ,'_is_accelerate_prepared'      ,__snake_case				)      ,__snake_case      ,'Scheduler is missing `_is_accelerator_prepared` or is set to `False`'      ,)
												self.assertEqual(
												    getattr(__snake_case      ,'_is_accelerate_prepared'      ,__snake_case				)      ,__snake_case      ,'Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`'      ,)
												self.assertEqual(
												    getattr(__snake_case      ,'_is_accelerate_prepared'      ,__snake_case				)      ,__snake_case      ,'Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`'      ,)
					@slow
					@require_bnb
					def 			lowerCamelCase__(				self       :int				) ->   Tuple:
												from transformers import AutoModelForCausalLM

												a__					=					AutoModelForCausalLM.from_pretrained(
												    'EleutherAI/gpt-neo-125m'      ,load_in_abit=__snake_case      ,device_map={'': 0}      ,)
												a__					=					Accelerator()

												# This should work
												a__					=					accelerator.prepare(__snake_case				)
					@slow
					@require_bnb
					def 			lowerCamelCase__(				self       :Tuple				) ->   Any:
												from transformers import AutoModelForCausalLM

												a__					=					Accelerator()

												with init_empty_weights():
																			a__					=					AutoModelForCausalLM.from_pretrained(
																			    'EleutherAI/gpt-neo-125m'      ,)
																			model.tie_weights()
																			a__					=					infer_auto_device_map(__snake_case				)
																			a__					=					'cpu'

												a__					=					AutoModelForCausalLM.from_pretrained(
												    'EleutherAI/gpt-neo-125m'      ,device_map=__snake_case      ,load_in_abit=__snake_case      ,llm_inta_enable_fpaa_cpu_offload=__snake_case				)

												# This should not work and get value error
												with self.assertRaises(__snake_case				):
																			a__					=					accelerator.prepare(__snake_case				)
					@slow
					@require_bnb
					@require_multi_gpu
					def 			lowerCamelCase__(				self       :List[str]				) ->   Union[str, Any]:
												from transformers import AutoModelForCausalLM

												a__					=					{'distributed_type': DistributedType.MULTI_GPU}

												with init_empty_weights():
																			a__					=					AutoModelForCausalLM.from_pretrained(
																			    'EleutherAI/gpt-neo-125m'      ,)
																			model.tie_weights()
																			a__					=					infer_auto_device_map(__snake_case				)
																			a__					=					1

												a__					=					AutoModelForCausalLM.from_pretrained(
												    'EleutherAI/gpt-neo-125m'      ,load_in_abit=__snake_case      ,device_map=__snake_case      ,)
												a__					=					Accelerator()

												# This should not work and get value error
												with self.assertRaises(__snake_case				):
																			a__					=					accelerator.prepare(__snake_case				)

												PartialState._reset_state()
					@slow
					@require_bnb
					@require_multi_gpu
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   Dict:
												from transformers import AutoModelForCausalLM

												with init_empty_weights():
																			a__					=					AutoModelForCausalLM.from_pretrained(
																			    'EleutherAI/gpt-neo-125m'      ,)
																			a__					=					infer_auto_device_map(__snake_case				)
																			a__					=					1

												a__					=					AutoModelForCausalLM.from_pretrained(
												    'EleutherAI/gpt-neo-125m'      ,load_in_abit=__snake_case      ,device_map=__snake_case      ,)
												a__					=					Accelerator()

												# This should work
												a__					=					accelerator.prepare(__snake_case				)



					@require_cuda
					def 			lowerCamelCase__(				self       :str				) ->   int:
												a__					=					torch.nn.Linear(10      ,10				)
												a__					=					torch.optim.SGD(model.parameters()      ,lr=0.01				)
												a__					=					Accelerator(cpu=__snake_case				)
												a__					=					accelerator.prepare(__snake_case				)








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 	snake_case_       (lowerCamelCase_     ,						unittest.TestCase				):
					UpperCAmelCase__ :   int   				=      MvpTokenizer
					UpperCAmelCase__ :   List[Any]   				=      MvpTokenizerFast
					UpperCAmelCase__ :   Optional[int]   				=      True
					UpperCAmelCase__ :   Tuple   				=      filter_roberta_detectors
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   Union[str, Any]:
												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(__snake_case      ,range(len(__snake_case				)				)				)				)
												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(__snake_case				) + '\n'				)
												with open(self.merges_file      ,'w'      ,encoding='utf-8'				) as fp:
																			fp.write('\n'.join(__snake_case				)				)
					def 			lowerCamelCase__(				self       :Optional[Any]      ,**__snake_case       :Tuple				) ->   int:
												kwargs.update(self.special_tokens_map				)
												return self.tokenizer_class.from_pretrained(self.tmpdirname      ,**__snake_case				)
					def 			lowerCamelCase__(				self       :Optional[Any]      ,**__snake_case       :Dict				) ->   Dict:
												kwargs.update(self.special_tokens_map				)
												return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,**__snake_case				)
					def 			lowerCamelCase__(				self       :Any      ,__snake_case       :Union[str, Any]				) ->   List[Any]:
												return "lower newer", "lower newer"
					@cached_property
					def 			lowerCamelCase__(				self       :Tuple				) ->   Optional[int]:
												return MvpTokenizer.from_pretrained('RUCAIBox/mvp'				)
					@cached_property
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   str:
												return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'				)
					@require_torch
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   str:
												a__					=					['A long paragraph for summarization.', 'Another paragraph for summarization.']
												a__					=					[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]:
																			a__					=					tokenizer(__snake_case      ,max_length=len(__snake_case				)      ,padding=__snake_case      ,return_tensors='pt'				)
																			self.assertIsInstance(__snake_case      ,__snake_case				)

																			self.assertEqual((2, 9)      ,batch.input_ids.shape				)
																			self.assertEqual((2, 9)      ,batch.attention_mask.shape				)
																			a__					=					batch.input_ids.tolist()[0]
																			self.assertListEqual(__snake_case      ,__snake_case				)
																			# Test that special tokens are reset
					@require_torch
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   List[Any]:
												a__					=					['A long paragraph for summarization.', 'Another paragraph for summarization.']
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a__					=					tokenizer(__snake_case      ,padding=__snake_case      ,return_tensors='pt'				)
																			# check if input_ids are returned and no labels
																			self.assertIn('input_ids'      ,__snake_case				)
																			self.assertIn('attention_mask'      ,__snake_case				)
																			self.assertNotIn('labels'      ,__snake_case				)
																			self.assertNotIn('decoder_attention_mask'      ,__snake_case				)
					@require_torch
					def 			lowerCamelCase__(				self       :Any				) ->   int:
												a__					=					[
												    'Summary of the text.',
												    'Another summary.',
												]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a__					=					tokenizer(text_target=__snake_case      ,max_length=32      ,padding='max_length'      ,return_tensors='pt'				)
																			self.assertEqual(32      ,targets['input_ids'].shape[1]				)
					@require_torch
					def 			lowerCamelCase__(				self       :int				) ->   Union[str, Any]:
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a__					=					tokenizer(
																			    ['I am a small frog' * 10_24, 'I am a small frog']      ,padding=__snake_case      ,truncation=__snake_case      ,return_tensors='pt'				)
																			self.assertIsInstance(__snake_case      ,__snake_case				)
																			self.assertEqual(batch.input_ids.shape      ,(2, 10_24)				)
					@require_torch
					def 			lowerCamelCase__(				self       :List[Any]				) ->   Any:
												a__					=					['A long paragraph for summarization.']
												a__					=					[
												    'Summary of the text.',
												]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a__					=					tokenizer(__snake_case      ,text_target=__snake_case      ,return_tensors='pt'				)
																			a__					=					inputs['input_ids']
																			a__					=					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       :Union[str, Any]				) ->   Tuple:
												pass



					def 			lowerCamelCase__(				self       :Tuple				) ->   Optional[Any]:
												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(__snake_case      ,**__snake_case				)
																										a__					=					self.tokenizer_class.from_pretrained(__snake_case      ,**__snake_case				)
																										a__					=					'A, <mask> AllenNLP sentence.'
																										a__					=					tokenizer_r.encode_plus(__snake_case      ,add_special_tokens=__snake_case      ,return_token_type_ids=__snake_case				)
																										a__					=					tokenizer_p.encode_plus(__snake_case      ,add_special_tokens=__snake_case      ,return_token_type_ids=__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']				)      ,)

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




