





"""simple docstring"""


import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


lowerCamelCase__						= """true"""




def        lowercase__     (       lowercase_		,lowercase_=82		,lowercase_=16      )       ->		List[Any]:




      """simple docstring"""

      set_seed(42      )
      _UpperCamelCase   :						Dict    =							RegressionModel()
      _UpperCamelCase   :						Dict    =							deepcopy(snake_case__      )
      _UpperCamelCase   :						List[Any]    =							RegressionDataset(length=snake_case__      )
      _UpperCamelCase   :						Optional[int]    =							DataLoader(snake_case__		,batch_size=snake_case__      )
      model.to(accelerator.device      )
      _UpperCamelCase   :						List[str]    =							accelerator.prepare(snake_case__		,snake_case__      )
      return model, ddp_model, dataloader




def        lowercase__     (       lowercase_		,lowercase_=False      )       ->		Optional[int]:




      """simple docstring"""

      _UpperCamelCase   :						Optional[Any]    =							AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased"      )
      _UpperCamelCase   :						int    =							load_dataset("glue"		,"mrpc"		,split="validation"      )

      def tokenize_function(lowercase_      ):
            _UpperCamelCase   :						Optional[int]    =							tokenizer(examples["sentence1"]		,examples["sentence2"]		,truncation=snake_case__		,max_length=snake_case__      )
            return outputs

      with accelerator.main_process_first():
            _UpperCamelCase   :						Optional[int]    =							dataset.map(
                snake_case__		,batched=snake_case__		,remove_columns=["idx", "sentence1", "sentence2"]		,)

      _UpperCamelCase   :						Tuple    =							tokenized_datasets.rename_column("label"		,"labels"      )

      def collate_fn(lowercase_      ):
            if use_longest:
                  return tokenizer.pad(snake_case__		,padding="longest"		,return_tensors="pt"      )
            return tokenizer.pad(snake_case__		,padding="max_length"		,max_length=128		,return_tensors="pt"      )

      return DataLoader(snake_case__		,shuffle=snake_case__		,collate_fn=snake_case__		,batch_size=16      )




def        lowercase__     (       lowercase_		,lowercase_      )       ->		Tuple:




      """simple docstring"""

      _UpperCamelCase   :						Union[str, Any]    =							Accelerator(dispatch_batches=snake_case__		,split_batches=snake_case__      )
      _UpperCamelCase   :						Any    =							get_dataloader(snake_case__		,not dispatch_batches      )
      _UpperCamelCase   :						Optional[int]    =							AutoModelForSequenceClassification.from_pretrained(
          "hf-internal-testing/mrpc-bert-base-cased"		,return_dict=snake_case__      )
      _UpperCamelCase   :						int    =							accelerator.prepare(snake_case__		,snake_case__      )
      return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_      )       ->		List[Any]:




      """simple docstring"""

      _UpperCamelCase   :						Any    =							[]
      for batch in dataloader:
            _UpperCamelCase   :						List[Any]    =							batch.values()
            with torch.no_grad():
                  _UpperCamelCase   :						List[Any]    =							model(snake_case__      )
                  _UpperCamelCase   :						Optional[int]    =							accelerator.gather_for_metrics((logit, target)      )
                  logits_and_targets.append((logit, target)      )
      _UpperCamelCase   :						Optional[Any]    =							[], []
      for logit, targ in logits_and_targets:
            logits.append(snake_case__      )
            targs.append(snake_case__      )
      _UpperCamelCase   :						List[str]    =							torch.cat(snake_case__      ), torch.cat(snake_case__      )
      return logits, targs




def        lowercase__     (       lowercase_		,lowercase_=82		,lowercase_=False		,lowercase_=False		,lowercase_=16      )       ->		Dict:




      """simple docstring"""

      _UpperCamelCase   :						List[str]    =							get_basic_setup(snake_case__		,snake_case__		,snake_case__      )
      _UpperCamelCase   :						Tuple    =							generate_predictions(snake_case__		,snake_case__		,snake_case__      )
      assert (
          len(snake_case__      ) == num_samples
      ), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(snake_case__      )}'''




def        lowercase__     (       lowercase_ = False		,lowercase_ = False      )       ->		Optional[Any]:




      """simple docstring"""

      _UpperCamelCase   :						Any    =							evaluate.load("glue"		,"mrpc"      )
      _UpperCamelCase   :						Optional[int]    =							get_mrpc_setup(snake_case__		,snake_case__      )
      # First do baseline
      _UpperCamelCase   :						Tuple    =							setup['''no''']
      model.to(snake_case__      )
      model.eval()
      for batch in dataloader:
            batch.to(snake_case__      )
            with torch.inference_mode():
                  _UpperCamelCase   :						str    =							model(**snake_case__      )
            _UpperCamelCase   :						Dict    =							outputs.logits.argmax(dim=-1      )
            metric.add_batch(predictions=snake_case__		,references=batch["labels"]      )
      _UpperCamelCase   :						int    =							metric.compute()

      # Then do distributed
      _UpperCamelCase   :						Optional[int]    =							setup['''ddp''']
      model.eval()
      for batch in dataloader:
            with torch.inference_mode():
                  _UpperCamelCase   :						Dict    =							model(**snake_case__      )
            _UpperCamelCase   :						str    =							outputs.logits.argmax(dim=-1      )
            _UpperCamelCase   :						Union[str, Any]    =							batch['''labels''']
            _UpperCamelCase   :						Tuple    =							accelerator.gather_for_metrics((preds, references)      )
            metric.add_batch(predictions=snake_case__		,references=snake_case__      )
      _UpperCamelCase   :						Optional[Any]    =							metric.compute()

      for key in "accuracy f1".split():
            assert math.isclose(
                baseline[key]		,distributed[key]      ), F'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''




def        lowercase__     (       )       ->		Optional[Any]:




      """simple docstring"""

      _UpperCamelCase   :						int    =							Accelerator(split_batches=snake_case__		,dispatch_batches=snake_case__      )
      if accelerator.is_local_main_process:
            datasets.utils.logging.set_verbosity_warning()
            transformers.utils.logging.set_verbosity_warning()
      else:
            datasets.utils.logging.set_verbosity_error()
            transformers.utils.logging.set_verbosity_error()
      # These are a bit slower so they should only be ran on the GPU or TPU
      if torch.cuda.is_available() or is_tpu_available():
            if accelerator.is_local_main_process:
                  print("**Testing gather_for_metrics**"      )
            for split_batches in [True, False]:
                  for dispatch_batches in [True, False]:
                        if accelerator.is_local_main_process:
                              print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`'''      )
                        test_mrpc(snake_case__		,snake_case__      )
                        accelerator.state._reset_state()
      if accelerator.is_local_main_process:
            print("**Test torch metrics**"      )
      for split_batches in [True, False]:
            for dispatch_batches in [True, False]:
                  _UpperCamelCase   :						Tuple    =							Accelerator(split_batches=snake_case__		,dispatch_batches=snake_case__      )
                  if accelerator.is_local_main_process:
                        print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'''      )
                  test_torch_metrics(snake_case__		,99      )
                  accelerator.state._reset_state()
      if accelerator.is_local_main_process:
            print("**Test last batch is not dropped when perfectly divisible**"      )
      _UpperCamelCase   :						Any    =							Accelerator()
      test_torch_metrics(snake_case__		,512      )
      accelerator.state._reset_state()




def        lowercase__     (       lowercase_      )       ->		Dict:




      """simple docstring"""

      main()


if __name__ == "__main__":
  main()




"""simple docstring"""






import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin






@require_jieba
class      A(  lowerCamelCase__				,							unittest.TestCase		):

		"""simple docstring"""





		A    =							CpmAntTokenizer
		A    =							False
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					super().setUp()

					_UpperCamelCase					:str					     =	[
					    '''<d>''',
					    '''</d>''',
					    '''<s>''',
					    '''</s>''',
					    '''</_>''',
					    '''<unk>''',
					    '''<pad>''',
					    '''</n>''',
					    '''我''',
					    '''是''',
					    '''C''',
					    '''P''',
					    '''M''',
					    '''A''',
					    '''n''',
					    '''t''',
					]
					_UpperCamelCase					:List[str]					     =	os.path.join(self.tmpdirname					,				VOCAB_FILES_NAMES['''vocab_file''']   )
					with open(self.vocab_file					,				'''w'''					,				encoding='''utf-8'''   ) as vocab_writer:
								vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]   )   )





		@tooslow
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:Any					     =	CpmAntTokenizer.from_pretrained('''openbmb/cpm-ant-10b'''   )
					_UpperCamelCase					:List[str]					     =	'''今天天气真好！'''
					_UpperCamelCase					:Tuple					     =	['''今天''', '''天气''', '''真''', '''好''', '''！''']
					_UpperCamelCase					:Tuple					     =	tokenizer.tokenize(SCREAMING_SNAKE_CASE__   )
					self.assertListEqual(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Dict					     =	'''今天天气真好！'''
					_UpperCamelCase					:Optional[int]					     =	[tokenizer.bos_token] + tokens

					_UpperCamelCase					:List[str]					     =	[6, 98_02, 1_49_62, 20_82, 8_31, 2_44]
					self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__   )					,				SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:str					     =	tokenizer.decode(SCREAMING_SNAKE_CASE__   )
					self.assertEqual(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )

