


import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Tuple=2     ,	SCREAMING_SNAKE_CASE__     :   str=True     ,	SCREAMING_SNAKE_CASE__     :   Any=False							):
     __UpperCamelCase       				=Accelerator(step_scheduler_with_optimizer=SCREAMING_SNAKE_CASE__     ,	split_batches=SCREAMING_SNAKE_CASE__							)
     __UpperCamelCase       				=torch.nn.Linear(2     ,	4							)
     __UpperCamelCase       				=torch.optim.AdamW(model.parameters()     ,	lr=1.0							)
     __UpperCamelCase       				=torch.optim.lr_scheduler.OneCycleLR(SCREAMING_SNAKE_CASE__     ,	max_lr=0.01     ,	steps_per_epoch=2     ,	epochs=1							)
     __UpperCamelCase				, __UpperCamelCase				, __UpperCamelCase       				=accelerator.prepare(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

     # Optimizer has stepped
     scheduler.step()
     if step_scheduler_with_optimizer or (num_processes == 1):
          assert (
              scheduler.scheduler.last_epoch == num_processes
          ), F'Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})'
     else:
          assert (
              scheduler.scheduler.last_epoch != num_processes
          ), F'Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})'

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Dict=2     ,	SCREAMING_SNAKE_CASE__     :   Union[str, Any]=True     ,	SCREAMING_SNAKE_CASE__     :   Union[str, Any]=False							):
     __UpperCamelCase       				=Accelerator(step_scheduler_with_optimizer=SCREAMING_SNAKE_CASE__     ,	split_batches=SCREAMING_SNAKE_CASE__							)
     __UpperCamelCase       				=torch.nn.Linear(2     ,	4							)
     __UpperCamelCase       				=torch.optim.AdamW(model.parameters()     ,	lr=1.0							)
     __UpperCamelCase       				=torch.optim.lr_scheduler.LambdaLR(SCREAMING_SNAKE_CASE__     ,	lr_lambda=lambda SCREAMING_SNAKE_CASE__							: 1 - n / 10							)
     __UpperCamelCase				, __UpperCamelCase				, __UpperCamelCase       				=accelerator.prepare(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

     # Optimizer has stepped
     __UpperCamelCase       				=False
     scheduler.step()
     __UpperCamelCase       				=1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
     assert (
         scheduler.get_last_lr()[0] == expected_lr
     ), F'Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'

     # Optimizer has not stepped
     __UpperCamelCase       				=True
     scheduler.step()
     if not step_scheduler_with_optimizer:
          __UpperCamelCase       				=1 - 2 / 10
     assert (
         scheduler.get_last_lr()[0] == expected_lr
     ), F'Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   int = 2							):
     from transformers import get_linear_schedule_with_warmup

     __UpperCamelCase       				=[1, 2, 4]
     for num_steps in steps:
          __UpperCamelCase       				=GradientAccumulationPlugin(num_steps=SCREAMING_SNAKE_CASE__     ,	adjust_scheduler=num_steps > 1							)
          __UpperCamelCase       				=Accelerator(gradient_accumulation_plugin=SCREAMING_SNAKE_CASE__							)
          __UpperCamelCase       				=torch.nn.Linear(2     ,	4							)
          __UpperCamelCase       				=torch.optim.AdamW(model.parameters()     ,	lr=10.0							)
          __UpperCamelCase       				=get_linear_schedule_with_warmup(optimizer=SCREAMING_SNAKE_CASE__     ,	num_warmup_steps=0     ,	num_training_steps=20							)

          __UpperCamelCase				, __UpperCamelCase				, __UpperCamelCase       				=accelerator.prepare(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

          for i in range(10 * num_steps							):
               with accelerator.accumulate(SCREAMING_SNAKE_CASE__							):
                    optimizer.step()
                    scheduler.step()

               if i == (10 * num_steps - 2):
                    assert (
                        scheduler.get_last_lr()[0] != 0
                    ), F'Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}'
          assert (
              scheduler.get_last_lr()[0] == 0
          ), F'Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}'
          GradientState._reset_state()






@require_cpu
class        UpperCAmelCase__			(   unittest.TestCase						):



    """simple docstring"""






    def _a   (     self       )    ->			List[Any]:
         debug_launcher(partial(A_   ,  num_processes=1       )   ,  num_processes=1       )
         debug_launcher(partial(A_   ,  num_processes=1   ,  split_batches=A_       )   ,  num_processes=1       )






    def _a   (     self       )    ->			List[str]:
         debug_launcher(partial(A_   ,  num_processes=1       )   ,  num_processes=1       )
         debug_launcher(partial(A_   ,  num_processes=1   ,  split_batches=A_       )   ,  num_processes=1       )






    def _a   (     self       )    ->			List[Any]:
         debug_launcher(partial(A_   ,  num_processes=1   ,  step_scheduler_with_optimizer=A_       )   ,  num_processes=1       )






    def _a   (     self       )    ->			Tuple:
         debug_launcher(partial(A_   ,  num_processes=1   ,  step_scheduler_with_optimizer=A_       )   ,  num_processes=1       )






    def _a   (     self       )    ->			Dict:
         AcceleratorState._reset_state(A_       )
         debug_launcher(A_       )
         debug_launcher(partial(A_   ,  num_processes=1   ,  split_batches=A_       )   ,  num_processes=1       )






    def _a   (     self       )    ->			Union[str, Any]:
         AcceleratorState._reset_state(A_       )
         debug_launcher(A_       )
         debug_launcher(partial(A_   ,  num_processes=1   ,  split_batches=A_       )   ,  num_processes=1       )






    def _a   (     self       )    ->			Optional[Any]:
         AcceleratorState._reset_state(A_       )
         debug_launcher(partial(A_   ,  step_scheduler_with_optimizer=A_       )       )






    def _a   (     self       )    ->			int:
         AcceleratorState._reset_state(A_       )
         debug_launcher(partial(A_   ,  step_scheduler_with_optimizer=A_       )       )






    @require_huggingface_suite
    def _a   (     self       )    ->			Tuple:
         AcceleratorState._reset_state(A_       )
         debug_launcher(partial(A_   ,  num_processes=1       )       )
         debug_launcher(A_       )









import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)
_A									=		logging.getLogger(__name__)

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   str							):
     np.random.seed(args.seed							)
     torch.manual_seed(args.seed							)
     if args.n_gpu > 0:
          torch.cuda.manual_seed_all(args.seed							)

def 		_UpperCAmelCase							(     ):
     __UpperCamelCase       				=argparse.ArgumentParser()
     parser.add_argument(
         '--model_name_or_path'     ,	default=SCREAMING_SNAKE_CASE__     ,	type=SCREAMING_SNAKE_CASE__     ,	required=SCREAMING_SNAKE_CASE__     ,	)
     parser.add_argument('--prompt'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=''							)
     parser.add_argument('--length'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=20							)
     parser.add_argument('--stop_token'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=SCREAMING_SNAKE_CASE__     ,	help='Token at which text generation is stopped'							)
     parser.add_argument(
         '--temperature'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=1.0     ,	help='temperature of 1.0 has no effect, lower tend toward greedy sampling'     ,	)
     parser.add_argument(
         '--repetition_penalty'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=1.0     ,	help='primarily useful for CTRL model; in that case, use 1.2'							)
     parser.add_argument('--k'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=0							)
     parser.add_argument('--penalty_alpha'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=0.0							)
     parser.add_argument('--p'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=0.9							)

     parser.add_argument('--prefix'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=''     ,	help='Text added prior to input.'							)
     parser.add_argument('--padding_text'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=''     ,	help='Deprecated, the use of `--prefix` is preferred.'							)
     parser.add_argument('--xlm_language'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=''     ,	help='Optional language when used with the XLM model.'							)

     parser.add_argument('--seed'     ,	type=SCREAMING_SNAKE_CASE__     ,	default=42     ,	help='random seed for initialization'							)
     parser.add_argument('--no_cuda'     ,	action='store_true'     ,	help='Avoid using CUDA when available'							)
     parser.add_argument(
         '--fp16'     ,	action='store_true'     ,	help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'     ,	)
     __UpperCamelCase       				=parser.parse_args()

     __UpperCamelCase       				=torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'							)
     __UpperCamelCase       				=0 if args.no_cuda else torch.cuda.device_count()

     logger.warning(F'device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}'							)

     set_seed(SCREAMING_SNAKE_CASE__							)

     # Initialize the model and tokenizer
     __UpperCamelCase       				=AutoTokenizer.from_pretrained(args.model_name_or_path							)
     __UpperCamelCase       				=AutoModelForCausalLM.from_pretrained(args.model_name_or_path							)

     # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
     # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
     model.to(args.device							)

     if args.fpaa:
          model.half()

     logger.info(SCREAMING_SNAKE_CASE__							)
     __UpperCamelCase       				=args.prompt if args.prompt else input('Model prompt >>> '							)

     __UpperCamelCase       				=tokenizer(SCREAMING_SNAKE_CASE__     ,	return_tensors='pt'     ,	add_special_tokens=SCREAMING_SNAKE_CASE__							)
     __UpperCamelCase       				={key: value.to(args.device							) for key, value in inputs.items()}

     __UpperCamelCase       				=model.generate(
         **SCREAMING_SNAKE_CASE__     ,	max_length=args.length + len(inputs['input_ids'][0]							)     ,	penalty_alpha=args.penalty_alpha     ,	top_k=args.k     ,	)

     __UpperCamelCase       				=[]
     for generated_sequence_idx, generated_sequence in enumerate(SCREAMING_SNAKE_CASE__							):
          print(F'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='							)
          __UpperCamelCase       				=generated_sequence.tolist()

          # Decode text
          __UpperCamelCase       				=tokenizer.decode(SCREAMING_SNAKE_CASE__     ,	clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__     ,	add_special_tokens=SCREAMING_SNAKE_CASE__							)

          # Remove all text after the stop token
          __UpperCamelCase       				=text[: text.find(args.stop_token							) if args.stop_token else None]

          # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
          __UpperCamelCase       				=(
              prompt_text + text[len(tokenizer.decode(inputs['input_ids'][0]     ,	clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__							)							) :]
          )

          generated_sequences.append(SCREAMING_SNAKE_CASE__							)
          print(SCREAMING_SNAKE_CASE__							)

     return generated_sequences


if __name__ == "__main__":
   main()





