
'''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


_lowercase          =						16
_lowercase          =						32


def A (__lowerCamelCase :Accelerator					,      __lowerCamelCase :int = 16					,      __lowerCamelCase :str = "bert-base-cased"						):
						_lowerCAmelCase    		=					AutoTokenizer.from_pretrained(__lowerCamelCase						)
						_lowerCAmelCase    		=					load_dataset("""glue"""					,      """mrpc"""						)

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

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

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

						def collate_fn(__lowerCamelCase :List[Any]						):
												# 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(__lowerCamelCase					,      padding="""max_length"""					,      max_length=128					,      return_tensors="""pt"""						)
												return tokenizer.pad(__lowerCamelCase					,      padding="""longest"""					,      return_tensors="""pt"""						)

						# Instantiate dataloaders.
						_lowerCAmelCase    		=					DataLoader(
						    tokenized_datasets["""train"""]					,      shuffle=__lowerCamelCase					,      collate_fn=__lowerCamelCase					,      batch_size=__lowerCamelCase						)
						_lowerCAmelCase    		=					DataLoader(
						    tokenized_datasets["""validation"""]					,      shuffle=__lowerCamelCase					,      collate_fn=__lowerCamelCase					,      batch_size=__lowerCamelCase						)

						return train_dataloader, eval_dataloader


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

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


def A (__lowerCamelCase :Tuple					,      __lowerCamelCase :Optional[Any]						):
						# Initialize accelerator
						_lowerCAmelCase    		=					Accelerator()

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

						set_seed(__lowerCamelCase						)
						_lowerCAmelCase		,       _lowerCAmelCase    		=					get_dataloaders(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase						)

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

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

						if accelerator.state.deepspeed_plugin is not None:
												_lowerCAmelCase    		=					accelerator.state.deepspeed_plugin.deepspeed_config[
												    """gradient_accumulation_steps"""
												]
						else:
												_lowerCAmelCase    		=					1
						_lowerCAmelCase    		=					(len(__lowerCamelCase						) * num_epochs) // gradient_accumulation_steps

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

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

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

						if args.resume_from_checkpoint:
												accelerator.load_state(args.resume_from_checkpoint						)
												_lowerCAmelCase    		=					args.resume_from_checkpoint.split("""epoch_"""						)[1]
												_lowerCAmelCase    		=					""""""
												for char in epoch_string:
																		if char.isdigit():
																								state_epoch_num += char
																		else:
																								break
												_lowerCAmelCase    		=					int(__lowerCamelCase						) + 1
												_lowerCAmelCase    		=					evaluation_loop(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase						)
												accelerator.print("""resumed checkpoint performance:"""					,      __lowerCamelCase						)
												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:
																		_lowerCAmelCase    		=					json.load(__lowerCamelCase						)
																		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
						_lowerCAmelCase    		=					{}
						for epoch in range(__lowerCamelCase					,      __lowerCamelCase						):
												model.train()
												for step, batch in enumerate(__lowerCamelCase						):
																		_lowerCAmelCase    		=					model(**__lowerCamelCase						)
																		_lowerCAmelCase    		=					outputs.loss
																		_lowerCAmelCase    		=					loss / gradient_accumulation_steps
																		accelerator.backward(__lowerCamelCase						)
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

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

												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(__lowerCamelCase					,      __lowerCamelCase						)


def A ():
						_lowerCAmelCase    		=					argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""						)
						parser.add_argument(
						    """--model_name_or_path"""					,      type=__lowerCamelCase					,      default="""bert-base-cased"""					,      help="""Path to pretrained model or model identifier from huggingface.co/models."""					,      required=__lowerCamelCase					,      )
						parser.add_argument(
						    """--output_dir"""					,      type=__lowerCamelCase					,      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=__lowerCamelCase					,      default=__lowerCamelCase					,      help="""If the training should continue from a checkpoint folder."""					,      )
						parser.add_argument(
						    """--partial_train_epoch"""					,      type=__lowerCamelCase					,      default=__lowerCamelCase					,      help="""If passed, the training will stop after this number of epochs."""					,      )
						parser.add_argument(
						    """--num_epochs"""					,      type=__lowerCamelCase					,      default=2					,      help="""Number of train epochs."""					,      )
						_lowerCAmelCase    		=					parser.parse_args()
						_lowerCAmelCase    		=					{"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

						training_function(__lowerCamelCase					,      __lowerCamelCase						)


if __name__ == "__main__":
						main()




"""simple docstring"""

from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


UpperCAmelCase__					=logging.get_logger(__name__)

UpperCAmelCase__					={
    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}





class  lowerCamelCase__    (		_a     ):
      a			:  Tuple           =		"""gptj"""
      a			:  Union[str, Any]           =		{
          """max_position_embeddings""": """n_positions""",
          """hidden_size""": """n_embd""",
          """num_attention_heads""": """n_head""",
          """num_hidden_layers""": """n_layer""",
      }
      def __init__(		self						:						int   ,							A_						:						Optional[int]=5_0_4_0_0   ,							A_						:						Optional[Any]=2_0_4_8   ,							A_						:						Optional[Any]=4_0_9_6   ,							A_						:						Any=2_8   ,							A_						:						Union[str, Any]=1_6   ,							A_						:						int=6_4   ,							A_						:						int=None   ,							A_						:						str="gelu_new"   ,							A_						:						str=0.0   ,							A_						:						Optional[Any]=0.0   ,							A_						:						Dict=0.0   ,							A_						:						Dict=1e-5   ,							A_						:						Optional[int]=0.02   ,							A_						:						List[str]=True   ,							A_						:						List[Any]=5_0_2_5_6   ,							A_						:						Optional[int]=5_0_2_5_6   ,							A_						:						List[Any]=False   ,							**A_						:						int   ,							):






       '''simple docstring'''


       __lowercase						=						vocab_size
       __lowercase						=						n_positions
       __lowercase						=						n_embd
       __lowercase						=						n_layer
       __lowercase						=						n_head
       __lowercase						=						n_inner
       __lowercase						=						rotary_dim
       __lowercase						=						activation_function
       __lowercase						=						resid_pdrop
       __lowercase						=						embd_pdrop
       __lowercase						=						attn_pdrop
       __lowercase						=						layer_norm_epsilon
       __lowercase						=						initializer_range
       __lowercase						=						use_cache

       __lowercase						=						bos_token_id
       __lowercase						=						eos_token_id

       super().__init__(
           bos_token_id=A_   ,							eos_token_id=A_   ,							tie_word_embeddings=A_   ,							**A_				)






class  lowerCamelCase__    (		_a     ):
      def __init__(		self						:						int   ,							A_						:						PretrainedConfig   ,							A_						:						str = "default"   ,							A_						:						List[PatchingSpec] = None   ,							A_						:						bool = False   ,							):






       '''simple docstring'''


       super().__init__(A_   ,							task=A_   ,							patching_specs=A_   ,							use_past=A_				)
       if not getattr(self._config   ,							"""pad_token_id"""   ,							A_				):
        # TODO: how to do that better?
        __lowercase						=						0
      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]				):






       '''simple docstring'''


       __lowercase						=						OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}				)
       if self.use_past:
        self.fill_with_past_key_values_(A_   ,							direction="""inputs"""				)
        __lowercase						=						{0: """batch""", 1: """past_sequence + sequence"""}
       else:
        __lowercase						=						{0: """batch""", 1: """sequence"""}

       return common_inputs
      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       return self._config.n_layer
      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]				):






       '''simple docstring'''


       return self._config.n_head
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]   ,							A_						:						PreTrainedTokenizer   ,							A_						:						int = -1   ,							A_						:						int = -1   ,							A_						:						bool = False   ,							A_						:						Optional[TensorType] = None   ,							):






       '''simple docstring'''


       __lowercase						=						super(A_   ,							self				).generate_dummy_inputs(
           A_   ,							batch_size=A_   ,							seq_length=A_   ,							is_pair=A_   ,							framework=A_				)

       # We need to order the input in the way they appears in the forward()
       __lowercase						=						OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}				)

       # Need to add the past_keys
       if self.use_past:
        if not is_torch_available():
         raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""				)
        else:
         import torch

         __lowercase				,		__lowercase						=						common_inputs["""input_ids"""].shape
         # Not using the same length for past_key_values
         __lowercase						=						seqlen + 2
         __lowercase						=						(
             batch,
             self.num_attention_heads,
             past_key_values_length,
             self._config.hidden_size // self.num_attention_heads,
         )
         __lowercase						=						[
             (torch.zeros(A_				), torch.zeros(A_				)) for _ in range(self.num_layers				)
         ]

       __lowercase						=						common_inputs["""attention_mask"""]
       if self.use_past:
        __lowercase						=						ordered_inputs["""attention_mask"""].dtype
        __lowercase						=						torch.cat(
            [ordered_inputs["""attention_mask"""], torch.ones(A_   ,							A_   ,							dtype=A_				)]   ,							dim=1				)

       return ordered_inputs






      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       return 1_3
