



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


a__   :					List[Any] 						=      "true"

def 	_lowerCAmelCase (    A__    ,	A__=82    ,	A__=16						):
      set_seed(42						)
      lowercase__  =	RegressionModel()
      lowercase__  =	deepcopy(A__						)
      lowercase__  =	RegressionDataset(length=A__						)
      lowercase__  =	DataLoader(A__    ,	batch_size=A__						)
      model.to(accelerator.device						)
      lowercase__,						lowercase__  =	accelerator.prepare(A__    ,	A__						)
      return model, ddp_model, dataloader

def 	_lowerCAmelCase (    A__    ,	A__=False						):
      lowercase__  =	AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'						)
      lowercase__  =	load_dataset('glue'    ,	'mrpc'    ,	split='validation'						)

      def tokenize_function(A__						):
            lowercase__  =	tokenizer(examples['sentence1']    ,	examples['sentence2']    ,	truncation=A__    ,	max_length=A__						)
            return outputs

      with accelerator.main_process_first():
            lowercase__  =	dataset.map(
                A__    ,	batched=A__    ,	remove_columns=['idx', 'sentence1', 'sentence2']    ,	)

      lowercase__  =	tokenized_datasets.rename_column('label'    ,	'labels'						)

      def collate_fn(A__						):
            if use_longest:
                  return tokenizer.pad(A__    ,	padding='longest'    ,	return_tensors='pt'						)
            return tokenizer.pad(A__    ,	padding='max_length'    ,	max_length=128    ,	return_tensors='pt'						)

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

def 	_lowerCAmelCase (    A__    ,	A__						):
      lowercase__  =	Accelerator(dispatch_batches=A__    ,	split_batches=A__						)
      lowercase__  =	get_dataloader(A__    ,	not dispatch_batches						)
      lowercase__  =	AutoModelForSequenceClassification.from_pretrained(
          'hf-internal-testing/mrpc-bert-base-cased'    ,	return_dict=A__						)
      lowercase__,						lowercase__  =	accelerator.prepare(A__    ,	A__						)
      return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator

def 	_lowerCAmelCase (    A__    ,	A__    ,	A__						):
      lowercase__  =	[]
      for batch in dataloader:
            lowercase__,						lowercase__  =	batch.values()
            with torch.no_grad():
                  lowercase__  =	model(A__						)
                  lowercase__,						lowercase__  =	accelerator.gather_for_metrics((logit, target)						)
                  logits_and_targets.append((logit, target)						)
      lowercase__,						lowercase__  =	[], []
      for logit, targ in logits_and_targets:
            logits.append(A__						)
            targs.append(A__						)
      lowercase__,						lowercase__  =	torch.cat(A__						), torch.cat(A__						)
      return logits, targs

def 	_lowerCAmelCase (    A__    ,	A__=82    ,	A__=False    ,	A__=False    ,	A__=16						):
      lowercase__,						lowercase__,						lowercase__  =	get_basic_setup(A__    ,	A__    ,	A__						)
      lowercase__,						lowercase__  =	generate_predictions(A__    ,	A__    ,	A__						)
      assert (
          len(A__						) == num_samples
      ), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(A__						)}'''

def 	_lowerCAmelCase (    A__ = False    ,	A__ = False						):
      lowercase__  =	evaluate.load('glue'    ,	'mrpc'						)
      lowercase__,						lowercase__  =	get_mrpc_setup(A__    ,	A__						)
      # First do baseline
      lowercase__,						lowercase__,						lowercase__  =	setup['no']
      model.to(A__						)
      model.eval()
      for batch in dataloader:
            batch.to(A__						)
            with torch.inference_mode():
                  lowercase__  =	model(**A__						)
            lowercase__  =	outputs.logits.argmax(dim=-1						)
            metric.add_batch(predictions=A__    ,	references=batch['labels']						)
      lowercase__  =	metric.compute()

      # Then do distributed
      lowercase__,						lowercase__,						lowercase__  =	setup['ddp']
      model.eval()
      for batch in dataloader:
            with torch.inference_mode():
                  lowercase__  =	model(**A__						)
            lowercase__  =	outputs.logits.argmax(dim=-1						)
            lowercase__  =	batch['labels']
            lowercase__,						lowercase__  =	accelerator.gather_for_metrics((preds, references)						)
            metric.add_batch(predictions=A__    ,	references=A__						)
      lowercase__  =	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 	_lowerCAmelCase (    ):
      lowercase__  =	Accelerator(split_batches=A__    ,	dispatch_batches=A__						)
      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(A__    ,	A__						)
                        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]:
                  lowercase__  =	Accelerator(split_batches=A__    ,	dispatch_batches=A__						)
                  if accelerator.is_local_main_process:
                        print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'''						)
                  test_torch_metrics(A__    ,	99						)
                  accelerator.state._reset_state()
      if accelerator.is_local_main_process:
            print('**Test last batch is not dropped when perfectly divisible**'						)
      lowercase__  =	Accelerator()
      test_torch_metrics(A__    ,	512						)
      accelerator.state._reset_state()

def 	_lowerCAmelCase (    A__						):
      # For xla_spawn (TPUs)
      main()


if __name__ == "__main__":
       main()





import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class    UpperCAmelCase__(				lowerCamelCase  ,				unittest.TestCase						):

   '''simple docstring'''




   A     :	int												=     CTRLTokenizer
   A     :	Tuple												=     False
   A     :	Any												=     False


   def       UpperCAmelCase		(    self	:      int)						->					Any:




         """simple docstring"""

         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         lowercase__  =	['adapt', 're@@', 'a@@', 'apt', 'c@@', 't', '<unk>']
         lowercase__  =	dict(zip(lowerCAmelCase      ,					range(len(lowerCAmelCase))))
         lowercase__  =	['#version: 0.2', 'a p', 'ap t</w>', 'r e', 'a d', 'ad apt</w>', '']
         lowercase__  =	{'unk_token': '<unk>'}

         lowercase__  =	os.path.join(self.tmpdirname      ,					VOCAB_FILES_NAMES['vocab_file'])
         lowercase__  =	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       UpperCAmelCase		(    self	:      int      ,					**lowerCAmelCase	:      str)						->					Dict:




         """simple docstring"""

         kwargs.update(self.special_tokens_map)
         return CTRLTokenizer.from_pretrained(self.tmpdirname      ,					**lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      List[str]      ,					lowerCAmelCase	:      Optional[int])						->					Optional[Any]:




         """simple docstring"""

         lowercase__  =	'adapt react readapt apt'
         lowercase__  =	'adapt react readapt apt'
         return input_text, output_text



   def       UpperCAmelCase		(    self	:      Optional[int])						->					str:




         """simple docstring"""

         lowercase__  =	CTRLTokenizer(self.vocab_file      ,					self.merges_file      ,					**self.special_tokens_map)
         lowercase__  =	'adapt react readapt apt'
         lowercase__  =	'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
         lowercase__  =	tokenizer.tokenize(lowerCAmelCase)
         self.assertListEqual(lowerCAmelCase      ,					lowerCAmelCase)

         lowercase__  =	tokens + [tokenizer.unk_token]

         lowercase__  =	[0, 1, 2, 4, 5, 1, 0, 3, 6]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase)      ,					lowerCAmelCase)
