"""simple docstring"""


import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__UpperCamelCase									=			16
__UpperCamelCase									=			32







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase = 16						,					UpperCAmelCase = "bert-base-cased"					)				->      Optional[Any]:
     snake_case_					       =					AutoTokenizer.from_pretrained(UpperCAmelCase					)
     snake_case_					       =					load_dataset('glue'						,					'mrpc'					)

     def tokenize_function(UpperCAmelCase					):
          # max_length=None => use the model max length (it's actually the default)
          snake_case_					       =					tokenizer(examples['sentence1']						,					examples['sentence2']						,					truncation=UpperCAmelCase						,					max_length=UpperCAmelCase					)
          return outputs

     # Apply the method we just defined to all the examples in all the splits of the dataset
     snake_case_					       =					datasets.map(
         UpperCAmelCase						,					batched=UpperCAmelCase						,					remove_columns=['idx', 'sentence1', 'sentence2']						,					load_from_cache_file=UpperCAmelCase					)

     # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
     # transformers library
     snake_case_					       =					tokenized_datasets.rename_column('label'						,					'labels'					)

     def collate_fn(UpperCAmelCase					):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          if accelerator.distributed_type == DistributedType.TPU:
               return tokenizer.pad(UpperCAmelCase						,					padding='max_length'						,					max_length=128						,					return_tensors='pt'					)
          return tokenizer.pad(UpperCAmelCase						,					padding='longest'						,					return_tensors='pt'					)

     # Instantiate dataloaders.
     snake_case_					       =					DataLoader(
         tokenized_datasets['train']						,					shuffle=UpperCAmelCase						,					collate_fn=UpperCAmelCase						,					batch_size=UpperCAmelCase					)
     snake_case_					       =					DataLoader(
         tokenized_datasets['validation']						,					shuffle=UpperCAmelCase						,					collate_fn=UpperCAmelCase						,					batch_size=UpperCAmelCase					)

     return train_dataloader, eval_dataloader







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					)				->      str:
     model.eval()
     snake_case_					       =					0
     for step, batch in enumerate(UpperCAmelCase					):
          # We could avoid this line since we set the accelerator with `device_placement=True`.
          batch.to(accelerator.device					)
          with torch.no_grad():
               snake_case_					       =					model(**UpperCAmelCase					)
          snake_case_					       =					outputs.logits.argmax(dim=-1					)
          # It is slightly faster to call this once, than multiple times
          snake_case_       ,	snake_case_					       =					accelerator.gather(
              (predictions, batch['labels'])					)  # If we are in a multiprocess environment, the last batch has duplicates
          if accelerator.use_distributed:
               if step == len(UpperCAmelCase					) - 1:
                    snake_case_					       =					predictions[: len(eval_dataloader.dataset					) - samples_seen]
                    snake_case_					       =					references[: len(eval_dataloader.dataset					) - samples_seen]
               else:
                    samples_seen += references.shape[0]
          metric.add_batch(
              predictions=UpperCAmelCase						,					references=UpperCAmelCase						,					)

     snake_case_					       =					metric.compute()
     return eval_metric["accuracy"]







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase					)				->      str:
     # Initialize accelerator
     snake_case_					       =					Accelerator()

     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     snake_case_					       =					config['lr']
     snake_case_					       =					int(config['num_epochs']					)
     snake_case_					       =					int(config['seed']					)
     snake_case_					       =					int(config['batch_size']					)
     snake_case_					       =					args.model_name_or_path

     set_seed(UpperCAmelCase					)
     snake_case_       ,	snake_case_					       =					get_dataloaders(UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					)

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     snake_case_					       =					AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase						,					return_dict=UpperCAmelCase					)

     # Instantiate optimizer
     snake_case_					       =					(
         AdamW
         if accelerator.state.deepspeed_plugin is None
         or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
         else DummyOptim
     )
     snake_case_					       =					optimizer_cls(params=model.parameters()						,					lr=UpperCAmelCase					)

     if accelerator.state.deepspeed_plugin is not None:
          snake_case_					       =					accelerator.state.deepspeed_plugin.deepspeed_config[
              'gradient_accumulation_steps'
          ]
     else:
          snake_case_					       =					1
     snake_case_					       =					(len(UpperCAmelCase					) * num_epochs) // gradient_accumulation_steps

     # Instantiate scheduler
     if (
         accelerator.state.deepspeed_plugin is None
         or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
     ):
          snake_case_					       =					get_linear_schedule_with_warmup(
              optimizer=UpperCAmelCase						,					num_warmup_steps=0						,					num_training_steps=UpperCAmelCase						,					)
     else:
          snake_case_					       =					DummyScheduler(UpperCAmelCase						,					total_num_steps=UpperCAmelCase						,					warmup_num_steps=0					)

     # Prepare everything
     # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
     # prepare method.
     snake_case_       ,	snake_case_       ,	snake_case_       ,	snake_case_       ,	snake_case_					       =					accelerator.prepare(
         UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					)

     # We need to keep track of how many total steps we have iterated over
     snake_case_					       =					0
     # We also need to keep track of the stating epoch so files are named properly
     snake_case_					       =					0
     snake_case_					       =					evaluate.load('glue'						,					'mrpc'					)
     snake_case_					       =					num_epochs

     if args.partial_train_epoch is not None:
          snake_case_					       =					args.partial_train_epoch

     if args.resume_from_checkpoint:
          accelerator.load_state(args.resume_from_checkpoint					)
          snake_case_					       =					args.resume_from_checkpoint.split('epoch_'					)[1]
          snake_case_					       =					''
          for char in epoch_string:
               if char.isdigit():
                    state_epoch_num += char
               else:
                    break
          snake_case_					       =					int(UpperCAmelCase					) + 1
          snake_case_					       =					evaluation_loop(UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					)
          accelerator.print('resumed checkpoint performance:'						,					UpperCAmelCase					)
          accelerator.print('resumed checkpoint\'s scheduler\'s lr:'						,					lr_scheduler.get_lr()[0]					)
          accelerator.print('resumed optimizers\'s lr:'						,					optimizer.param_groups[0]['lr']					)
          with open(os.path.join(args.output_dir						,					f'state_{starting_epoch-1}.json'					)						,					'r'					) as f:
               snake_case_					       =					json.load(UpperCAmelCase					)
               assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
               assert (
                   resumed_state["lr"] == lr_scheduler.get_lr()[0]
               ), "Scheduler learning rate mismatch, loading from checkpoint failed"
               assert (
                   resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
               ), "Optimizer learning rate mismatch, loading from checkpoint failed"
               assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
               return

    # Now we train the model
     snake_case_					       =					{}
     for epoch in range(UpperCAmelCase						,					UpperCAmelCase					):
          model.train()
          for step, batch in enumerate(UpperCAmelCase					):
               snake_case_					       =					model(**UpperCAmelCase					)
               snake_case_					       =					outputs.loss
               snake_case_					       =					loss / gradient_accumulation_steps
               accelerator.backward(UpperCAmelCase					)
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               overall_step += 1
          snake_case_					       =					f'epoch_{epoch}'
          snake_case_					       =					os.path.join(args.output_dir						,					UpperCAmelCase					)
          accelerator.save_state(UpperCAmelCase					)
          snake_case_					       =					evaluation_loop(UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					)
          snake_case_					       =					accuracy
          snake_case_					       =					lr_scheduler.get_lr()[0]
          snake_case_					       =					optimizer.param_groups[0]['lr']
          snake_case_					       =					epoch
          snake_case_					       =					overall_step
          accelerator.print(f'epoch {epoch}:'						,					UpperCAmelCase					)

          accelerator.wait_for_everyone()
          if accelerator.is_main_process:
               with open(os.path.join(args.output_dir						,					f'state_{epoch}.json'					)						,					'w'					) as f:
                    json.dump(UpperCAmelCase						,					UpperCAmelCase					)







def 					UpperCAmelCase    (		)				->      int:
     snake_case_					       =					argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'					)
     parser.add_argument(
         '--model_name_or_path'						,					type=UpperCAmelCase						,					default='bert-base-cased'						,					help='Path to pretrained model or model identifier from huggingface.co/models.'						,					required=UpperCAmelCase						,					)
     parser.add_argument(
         '--output_dir'						,					type=UpperCAmelCase						,					default='.'						,					help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'						,					)
     parser.add_argument(
         '--resume_from_checkpoint'						,					type=UpperCAmelCase						,					default=UpperCAmelCase						,					help='If the training should continue from a checkpoint folder.'						,					)
     parser.add_argument(
         '--partial_train_epoch'						,					type=UpperCAmelCase						,					default=UpperCAmelCase						,					help='If passed, the training will stop after this number of epochs.'						,					)
     parser.add_argument(
         '--num_epochs'						,					type=UpperCAmelCase						,					default=2						,					help='Number of train epochs.'						,					)
     snake_case_					       =					parser.parse_args()
     snake_case_					       =					{'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}

     training_function(UpperCAmelCase						,					UpperCAmelCase					)


if __name__ == "__main__":
    main()




"""simple docstring"""


import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class     snake_case	(				__snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_   :      List[str]         =  None
				SCREAMING_SNAKE_CASE_   :      Tuple         =  BloomTokenizerFast
				SCREAMING_SNAKE_CASE_   :      str         =  BloomTokenizerFast
				SCREAMING_SNAKE_CASE_   :      int         =  True
				SCREAMING_SNAKE_CASE_   :      Tuple         =  False
				SCREAMING_SNAKE_CASE_   :      int         =  """tokenizer_file"""
				SCREAMING_SNAKE_CASE_   :      List[str]         =  {"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}

				def 							lowercase_     (	self							:				List[Any])->   Dict:



						'''simple docstring'''


						super().setUp()
						__lowerCAmelCase:       Optional[Any]          =			BloomTokenizerFast.from_pretrained("bigscience/tokenizer")
						tokenizer.save_pretrained(self.tmpdirname)

				def 							lowercase_     (	self							:				List[Any]     ,  **UpperCamelCase__							:				Union[str, Any])->   Optional[Any]:



						'''simple docstring'''


						kwargs.update(self.special_tokens_map)
						return BloomTokenizerFast.from_pretrained(self.tmpdirname     ,  **UpperCamelCase__)

				def 							lowercase_     (	self							:				Union[str, Any])->   Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:       str          =			self.get_rust_tokenizer()

						__lowerCAmelCase:       int          =			["The quick brown fox</s>", "jumps over the lazy dog</s>"]
						__lowerCAmelCase:       List[str]          =			[[2_1_7_5, 2_3_7_1_4, 7_3_1_7_3, 1_4_4_2_5_2, 2], [7_7, 1_3_2_6_1_9, 3_4_7_8, 3_6_8, 1_0_9_5_8_6, 3_5_4_3_3, 2]]

						__lowerCAmelCase:       List[str]          =			tokenizer.batch_encode_plus(UpperCamelCase__)["input_ids"]
						self.assertListEqual(UpperCamelCase__     ,  UpperCamelCase__)

						__lowerCAmelCase:       List[Any]          =			tokenizer.batch_decode(UpperCamelCase__)
						self.assertListEqual(UpperCamelCase__     ,  UpperCamelCase__)

				def 							lowercase_     (	self							:				Optional[Any]     ,  UpperCamelCase__							:				Tuple=6)->   Tuple:



						'''simple docstring'''


						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
								with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
										__lowerCAmelCase:       Optional[Any]          =			self.rust_tokenizer_class.from_pretrained(UpperCamelCase__     ,  **UpperCamelCase__)
										# tokenizer_r.pad_token = None # Hotfixing padding = None
										# Simple input
										__lowerCAmelCase:       Dict          =			"This is a simple input"
										__lowerCAmelCase:       str          =			["This is a simple input 1", "This is a simple input 2"]
										__lowerCAmelCase:       int          =			("This is a simple input", "This is a pair")
										__lowerCAmelCase:       Union[str, Any]          =			[
										    ("This is a simple input 1", "This is a simple input 2"),
										    ("This is a simple pair 1", "This is a simple pair 2"),
										]

										# Simple input tests
										try:
												tokenizer_r.encode(UpperCamelCase__     ,  max_length=UpperCamelCase__)
												tokenizer_r.encode_plus(UpperCamelCase__     ,  max_length=UpperCamelCase__)

												tokenizer_r.batch_encode_plus(UpperCamelCase__     ,  max_length=UpperCamelCase__)
												tokenizer_r.encode(UpperCamelCase__     ,  max_length=UpperCamelCase__)
												tokenizer_r.batch_encode_plus(UpperCamelCase__     ,  max_length=UpperCamelCase__)
										except ValueError:
												self.fail("Bloom Tokenizer should be able to deal with padding")

										__lowerCAmelCase:       Tuple          =			None  # Hotfixing padding = None
										self.assertRaises(UpperCamelCase__     ,  tokenizer_r.encode     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length")

										# Simple input
										self.assertRaises(UpperCamelCase__     ,  tokenizer_r.encode_plus     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length")

										# Simple input
										self.assertRaises(
										    UpperCamelCase__     ,  tokenizer_r.batch_encode_plus     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length"     ,  )

										# Pair input
										self.assertRaises(UpperCamelCase__     ,  tokenizer_r.encode     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length")

										# Pair input
										self.assertRaises(UpperCamelCase__     ,  tokenizer_r.encode_plus     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length")

										# Pair input
										self.assertRaises(
										    UpperCamelCase__     ,  tokenizer_r.batch_encode_plus     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length"     ,  )

				def 							lowercase_     (	self							:				Optional[Any])->   List[str]:



						'''simple docstring'''


						__lowerCAmelCase:       Dict          =			self.get_rust_tokenizer()
						__lowerCAmelCase:       List[str]          =			load_dataset("xnli"     ,  "all_languages"     ,  split="test"     ,  streaming=UpperCamelCase__)

						__lowerCAmelCase:       Union[str, Any]          =			next(iter(UpperCamelCase__))["premise"]  # pick up one data
						__lowerCAmelCase:       Any          =			list(sample_data.values())

						__lowerCAmelCase:       int          =			list(map(tokenizer.encode     ,  UpperCamelCase__))
						__lowerCAmelCase:       str          =			[tokenizer.decode(UpperCamelCase__     ,  clean_up_tokenization_spaces=UpperCamelCase__) for x in output_tokens]
						self.assertListEqual(UpperCamelCase__     ,  UpperCamelCase__)




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



						'''simple docstring'''


						self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map)     ,  1)
						self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values())[0])     ,  1)


