
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_  :	str 				=					"""true"""







def     A__   (  lowerCamelCase       ,      lowerCamelCase=82       ,      lowerCamelCase=16  )					->		List[str]:
				set_seed(42  )
				UpperCamelCase_:							str		=  RegressionModel()
				UpperCamelCase_:							str		=  deepcopy(lowerCamelCase  )
				UpperCamelCase_:							Dict		=  RegressionDataset(length=lowerCamelCase  )
				UpperCamelCase_:							Union[str, Any]		=  DataLoader(lowerCamelCase       ,      batch_size=lowerCamelCase  )
				model.to(accelerator.device  )
				UpperCamelCase_,				UpperCamelCase_:							Optional[int]		=  accelerator.prepare(lowerCamelCase       ,      lowerCamelCase  )
				return model, ddp_model, dataloader







def     A__   (  lowerCamelCase       ,      lowerCamelCase=False  )					->		Any:
				UpperCamelCase_:							Tuple		=  AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased"""  )
				UpperCamelCase_:							str		=  load_dataset("""glue"""       ,      """mrpc"""       ,      split="""validation"""  )

				def tokenize_function(lowerCamelCase  ):
								UpperCamelCase_:							str		=  tokenizer(examples["""sentence1"""]       ,      examples["""sentence2"""]       ,      truncation=lowerCamelCase       ,      max_length=lowerCamelCase  )
								return outputs

				with accelerator.main_process_first():
								UpperCamelCase_:							Any		=  dataset.map(
								    lowerCamelCase       ,      batched=lowerCamelCase       ,      remove_columns=["""idx""", """sentence1""", """sentence2"""]       ,      )

				UpperCamelCase_:							List[Any]		=  tokenized_datasets.rename_column("""label"""       ,      """labels"""  )

				def collate_fn(lowerCamelCase  ):
								if use_longest:
												return tokenizer.pad(lowerCamelCase       ,      padding="""longest"""       ,      return_tensors="""pt"""  )
								return tokenizer.pad(lowerCamelCase       ,      padding="""max_length"""       ,      max_length=1_28       ,      return_tensors="""pt"""  )

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







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







def     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		str:
				UpperCamelCase_:							Union[str, Any]		=  []
				for batch in dataloader:
								UpperCamelCase_,				UpperCamelCase_:							Union[str, Any]		=  batch.values()
								with torch.no_grad():
												UpperCamelCase_:							List[Any]		=  model(lowerCamelCase  )
												UpperCamelCase_,				UpperCamelCase_:							Dict		=  accelerator.gather_for_metrics((logit, target)  )
												logits_and_targets.append((logit, target)  )
				UpperCamelCase_,				UpperCamelCase_:							str		=  [], []
				for logit, targ in logits_and_targets:
								logits.append(lowerCamelCase  )
								targs.append(lowerCamelCase  )
				UpperCamelCase_,				UpperCamelCase_:							Optional[Any]		=  torch.cat(lowerCamelCase  ), torch.cat(lowerCamelCase  )
				return logits, targs







def     A__   (  lowerCamelCase       ,      lowerCamelCase=82       ,      lowerCamelCase=False       ,      lowerCamelCase=False       ,      lowerCamelCase=16  )					->		Any:
				UpperCamelCase_,				UpperCamelCase_,				UpperCamelCase_:							int		=  get_basic_setup(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )
				UpperCamelCase_,				UpperCamelCase_:							Any		=  generate_predictions(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )
				assert (
				    len(lowerCamelCase  ) == num_samples
				), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowerCamelCase  )}'''







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

				# Then do distributed
				UpperCamelCase_,				UpperCamelCase_,				UpperCamelCase_:							Dict		=  setup["""ddp"""]
				model.eval()
				for batch in dataloader:
								with torch.inference_mode():
												UpperCamelCase_:							Optional[Any]		=  model(**lowerCamelCase  )
								UpperCamelCase_:							str		=  outputs.logits.argmax(dim=-1  )
								UpperCamelCase_:							str		=  batch["""labels"""]
								UpperCamelCase_,				UpperCamelCase_:							Optional[Any]		=  accelerator.gather_for_metrics((preds, references)  )
								metric.add_batch(predictions=lowerCamelCase       ,      references=lowerCamelCase  )
				UpperCamelCase_:							Tuple		=  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     A__   (  )					->		Union[str, Any]:
				UpperCamelCase_:							int		=  Accelerator(split_batches=lowerCamelCase       ,      dispatch_batches=lowerCamelCase  )
				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(lowerCamelCase       ,      lowerCamelCase  )
																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_:							Dict		=  Accelerator(split_batches=lowerCamelCase       ,      dispatch_batches=lowerCamelCase  )
												if accelerator.is_local_main_process:
																print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'''  )
												test_torch_metrics(lowerCamelCase       ,      99  )
												accelerator.state._reset_state()
				if accelerator.is_local_main_process:
								print("""**Test last batch is not dropped when perfectly divisible**"""  )
				UpperCamelCase_:							str		=  Accelerator()
				test_torch_metrics(lowerCamelCase       ,      5_12  )
				accelerator.state._reset_state()







def     A__   (  lowerCamelCase  )					->		List[Any]:
				# 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			(   _A       ,    unittest.TestCase					):






			'''simple docstring'''

			__UpperCamelCase :  Tuple    =	CTRLTokenizer
			__UpperCamelCase :  int    =	False
			__UpperCamelCase :  List[str]    =	False

			def  lowerCAmelCase__		(    self	:    List[str] ):
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							UpperCamelCase_:							int		=  ["""adapt""", """re@@""", """a@@""", """apt""", """c@@""", """t""", """<unk>"""]
							UpperCamelCase_:							int		=  dict(zip(snake_case_  ,		range(len(snake_case_ ) ) ) )
							UpperCamelCase_:							Union[str, Any]		=  ["""#version: 0.2""", """a p""", """ap t</w>""", """r e""", """a d""", """ad apt</w>""", """"""]
							UpperCamelCase_:							Tuple		=  {"""unk_token""": """<unk>"""}

							UpperCamelCase_:							Optional[Any]		=  os.path.join(self.tmpdirname  ,		VOCAB_FILES_NAMES["""vocab_file"""] )
							UpperCamelCase_:							Dict		=  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[int]  ,		**snake_case_	:    int ):
							kwargs.update(self.special_tokens_map )
							return CTRLTokenizer.from_pretrained(self.tmpdirname  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    Optional[int]  ,		snake_case_	:    List[str] ):
							UpperCamelCase_:							Dict		=  """adapt react readapt apt"""
							UpperCamelCase_:							List[str]		=  """adapt react readapt apt"""
							return input_text, output_text




			def  lowerCAmelCase__		(    self	:    List[str] ):
							UpperCamelCase_:							str		=  CTRLTokenizer(self.vocab_file  ,		self.merges_file  ,		**self.special_tokens_map )
							UpperCamelCase_:							List[Any]		=  """adapt react readapt apt"""
							UpperCamelCase_:							Optional[int]		=  """adapt re@@ a@@ c@@ t re@@ adapt apt""".split()
							UpperCamelCase_:							int		=  tokenizer.tokenize(snake_case_ )
							self.assertListEqual(snake_case_  ,		snake_case_ )

							UpperCamelCase_:							List[Any]		=  tokens + [tokenizer.unk_token]

							UpperCamelCase_:							Union[str, Any]		=  [0, 1, 2, 4, 5, 1, 0, 3, 6]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_ )  ,		snake_case_ )






