
'''simple docstring'''



import argparse
import time

import torch
import transformers
from measures_util import end_measure, log_measures, start_measure
from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForSeqaSeqLM, AutoTokenizer

from accelerate.utils import compute_module_sizes


UpperCamelCase__ :						Tuple    							=					{
    "gpt-j-6b": {"is_causal": True, "model": "sgugger/sharded-gpt-j-6B", "tokenizer": "EleutherAI/gpt-j-6B"},
    "gpt-neox": {"is_causal": True, "model": "EleutherAI/gpt-neox-20b"},
    "opt": {"is_causal": True, "model": "facebook/opt-30b"},
    "T0pp": {"is_causal": False, "model": "bigscience/T0pp", "model_revision": "sharded"},
}

UpperCamelCase__ :						int    							=					[
    "Hello, my name is",
    "Are unicorns real? Unicorns are",
    "For the first time in several years,",
    "My name is Julien and I am",
    "The goal of life is",
    "Whenever I'm sad, I like to",
]
def 				lowerCAmelCase_		(			)       ->       str:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			argparse.ArgumentParser(description="""Run and time generations on a big model using Accelerate."""  )
   parser.add_argument("""model_name"""      , type=SCREAMING_SNAKE_CASE_      , default=SCREAMING_SNAKE_CASE_      , help="""The name of the model to try."""  )
   parser.add_argument(
       """--tokenizer_name"""      , type=SCREAMING_SNAKE_CASE_      , default=SCREAMING_SNAKE_CASE_      , help="""The name of the tokenizer (if different from the model."""  )
   parser.add_argument("""--is_causal"""      , type=SCREAMING_SNAKE_CASE_      , default=SCREAMING_SNAKE_CASE_      , help="""Whether or not the model is causal."""  )
   parser.add_argument(
       """--model_revision"""      , type=SCREAMING_SNAKE_CASE_      , default=SCREAMING_SNAKE_CASE_      , help="""The revision to use for the model checkpoint."""  )
   parser.add_argument("""--torch_dtype"""      , type=SCREAMING_SNAKE_CASE_      , default=SCREAMING_SNAKE_CASE_      , help="""The dtype for the model."""  )
   parser.add_argument("""--disk_offload"""      , action="""store_true"""  )

   _SCREAMING_SNAKE_CASE       	=			parser.parse_args()

   # Sanitize args
   if args.model_name in DEFAULT_MODELS:
      _SCREAMING_SNAKE_CASE       	=			DEFAULT_MODELS[args.model_name]
      _SCREAMING_SNAKE_CASE       	=			defaults["""model"""]
      if args.tokenizer_name is None:
         _SCREAMING_SNAKE_CASE       	=			defaults.get("""tokenizer"""      , args.model_name  )
      if args.is_causal is None:
         _SCREAMING_SNAKE_CASE       	=			defaults["""is_causal"""]
      if args.model_revision is None:
         _SCREAMING_SNAKE_CASE       	=			defaults.get("""model_revision"""      , """main"""  )

   if args.is_causal is None:
      raise ValueError("""Could not infer the default for `--is_causal`, pass either True or False for it."""  )
   if args.tokenizer_name is None:
      _SCREAMING_SNAKE_CASE       	=			args.model_name
   if args.model_revision is None:
      _SCREAMING_SNAKE_CASE       	=			"""main"""

   return args
def 				lowerCAmelCase_		(			)       ->       Optional[int]:




   """simple docstring"""
   transformers.utils.logging.set_verbosity_error()
   _SCREAMING_SNAKE_CASE       	=			parse_args()

   if args.torch_dtype is None:
      _SCREAMING_SNAKE_CASE       	=			AutoConfig.from_pretrained(args.model_name  )
      _SCREAMING_SNAKE_CASE       	=			getattr(SCREAMING_SNAKE_CASE_      , """torch_dtype"""      , torch.floataa  )
   else:
      _SCREAMING_SNAKE_CASE       	=			getattr(SCREAMING_SNAKE_CASE_      , args.torch_dtype  )
   _SCREAMING_SNAKE_CASE       	=			AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
   _SCREAMING_SNAKE_CASE       	=			{
       """torch_dtype""": torch_dtype,
       """revision""": args.model_revision,
   }
   if args.disk_offload:
      _SCREAMING_SNAKE_CASE       	=			"""tmp_offload"""
      _SCREAMING_SNAKE_CASE       	=			True

   _SCREAMING_SNAKE_CASE       	=			start_measure()
   _SCREAMING_SNAKE_CASE       	=			model_cls.from_pretrained(args.model_name      , device_map="""auto"""      , **SCREAMING_SNAKE_CASE_  )
   _SCREAMING_SNAKE_CASE       	=			end_measure(SCREAMING_SNAKE_CASE_  )
   log_measures(SCREAMING_SNAKE_CASE_      , """Model loading"""  )

   _SCREAMING_SNAKE_CASE       	=			compute_module_sizes(SCREAMING_SNAKE_CASE_  )
   _SCREAMING_SNAKE_CASE       	=			{v: 0 for v in model.hf_device_map.values()}
   for module, device in model.hf_device_map.items():
      device_size[device] += module_sizes[module]
   _SCREAMING_SNAKE_CASE       	=			"""\n""".join([F"- {device}: {size // 2**20}MiB" for device, size in device_size.items()]  )
   print(F"\nTheoretical use:\n{message}"  )

   _SCREAMING_SNAKE_CASE       	=			AutoTokenizer.from_pretrained(args.tokenizer_name  )

   _SCREAMING_SNAKE_CASE       	=			start_measure()
   _SCREAMING_SNAKE_CASE       	=			[]
   _SCREAMING_SNAKE_CASE       	=			[]
   _SCREAMING_SNAKE_CASE       	=			[]
   for prompt in PROMPTS:
      _SCREAMING_SNAKE_CASE       	=			tokenizer(SCREAMING_SNAKE_CASE_      , return_tensors="""pt"""  ).to(0  )
      _SCREAMING_SNAKE_CASE       	=			inputs["""input_ids"""][0].tolist()
      _SCREAMING_SNAKE_CASE       	=			time.time()
      _SCREAMING_SNAKE_CASE       	=			model.generate(inputs["""input_ids"""]  )
      _SCREAMING_SNAKE_CASE       	=			time.time()
      _SCREAMING_SNAKE_CASE       	=			outputs[0].tolist()
      _SCREAMING_SNAKE_CASE       	=			len(SCREAMING_SNAKE_CASE_  ) if outputs[: len(SCREAMING_SNAKE_CASE_  )] != tokens else len(SCREAMING_SNAKE_CASE_  ) - len(SCREAMING_SNAKE_CASE_  )
      _SCREAMING_SNAKE_CASE       	=			after_generate - before_generate

      _SCREAMING_SNAKE_CASE       	=			tokenizer.decode(SCREAMING_SNAKE_CASE_      , skip_special_tokens=SCREAMING_SNAKE_CASE_  )
      texts_outs.append(SCREAMING_SNAKE_CASE_  )
      generation_times.append(SCREAMING_SNAKE_CASE_  )
      gen_tokens.append(SCREAMING_SNAKE_CASE_  )
      print(F"Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n"  )

   _SCREAMING_SNAKE_CASE       	=			end_measure(SCREAMING_SNAKE_CASE_  )
   log_measures(SCREAMING_SNAKE_CASE_      , """Model generation"""  )

   _SCREAMING_SNAKE_CASE       	=			[gen / tok for gen, tok in zip(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )]
   _SCREAMING_SNAKE_CASE       	=			sum(SCREAMING_SNAKE_CASE_  ) / len(SCREAMING_SNAKE_CASE_  )
   print(F"Average time of generation per token: {avg_gen:.2f}s"  )
   print(F"First generation (avg time per token): {generation_times_per_token[0]:.2f}s"  )
   _SCREAMING_SNAKE_CASE       	=			sum(generation_times_per_token[1:]  ) / (len(SCREAMING_SNAKE_CASE_  ) - 1)
   print(F"Average time of generation per token (excluding the first): {avg_gen:.2f}s"  )


if __name__ == "__main__":
     main()








'''simple docstring'''



import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest

class 				_a			(_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE =					(DDIMScheduler,)
       SCREAMING_SNAKE_CASE =					(('eta', 0.0), ('num_inference_steps', 50))






       def     UpperCamelCase			(							self    ,							**A__ )      ->     Optional[int]:
          _SCREAMING_SNAKE_CASE       	=			{
              """num_train_timesteps""": 10_00,
              """beta_start""": 0.0001,
              """beta_end""": 0.02,
              """beta_schedule""": """linear""",
              """clip_sample""": True,
          }

          config.update(**A__ )
          return config






       def     UpperCamelCase			(							self    ,							**A__ )      ->     List[str]:
          _SCREAMING_SNAKE_CASE       	=			self.scheduler_classes[0]
          _SCREAMING_SNAKE_CASE       	=			self.get_scheduler_config(**A__ )
          _SCREAMING_SNAKE_CASE       	=			scheduler_class(**A__ )

          _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			10, 0.0

          _SCREAMING_SNAKE_CASE       	=			self.dummy_model()
          _SCREAMING_SNAKE_CASE       	=			self.dummy_sample_deter

          scheduler.set_timesteps(A__ )

          for t in scheduler.timesteps:
             _SCREAMING_SNAKE_CASE       	=			model(A__    ,							A__ )
             _SCREAMING_SNAKE_CASE       	=			scheduler.step(A__    ,							A__    ,							A__    ,							A__ ).prev_sample

          return sample






       def     UpperCamelCase			(							self )      ->     str:
          for timesteps in [1_00, 5_00, 10_00]:
             self.check_over_configs(num_train_timesteps=A__ )






       def     UpperCamelCase			(							self )      ->     int:
          for steps_offset in [0, 1]:
             self.check_over_configs(steps_offset=A__ )

          _SCREAMING_SNAKE_CASE       	=			self.scheduler_classes[0]
          _SCREAMING_SNAKE_CASE       	=			self.get_scheduler_config(steps_offset=1 )
          _SCREAMING_SNAKE_CASE       	=			scheduler_class(**A__ )
          scheduler.set_timesteps(5 )
          assert torch.equal(scheduler.timesteps    ,							torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1] ) )






       def     UpperCamelCase			(							self )      ->     Tuple:
          for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]    ,							[0.002, 0.02, 0.2, 2] ):
             self.check_over_configs(beta_start=A__    ,							beta_end=A__ )






       def     UpperCamelCase			(							self )      ->     Optional[Any]:
          for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=A__ )






       def     UpperCamelCase			(							self )      ->     List[str]:
          for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=A__ )






       def     UpperCamelCase			(							self )      ->     Tuple:
          for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=A__ )






       def     UpperCamelCase			(							self )      ->     Any:
          for timestep_spacing in ["trailing", "leading"]:
             self.check_over_configs(timestep_spacing=A__ )






       def     UpperCamelCase			(							self )      ->     int:
          for rescale_betas_zero_snr in [True, False]:
             self.check_over_configs(rescale_betas_zero_snr=A__ )






       def     UpperCamelCase			(							self )      ->     List[Any]:
          self.check_over_configs(thresholding=A__ )
          for threshold in [0.5, 1.0, 2.0]:
             for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(
                    thresholding=A__    ,							prediction_type=A__    ,							sample_max_value=A__    ,							)






       def     UpperCamelCase			(							self )      ->     Optional[int]:
          for t in [1, 10, 49]:
             self.check_over_forward(time_step=A__ )






       def     UpperCamelCase			(							self )      ->     Optional[Any]:
          for t, num_inference_steps in zip([1, 10, 50]    ,							[10, 50, 5_00] ):
             self.check_over_forward(time_step=A__    ,							num_inference_steps=A__ )






       def     UpperCamelCase			(							self )      ->     str:
          for t, eta in zip([1, 10, 49]    ,							[0.0, 0.5, 1.0] ):
             self.check_over_forward(time_step=A__    ,							eta=A__ )






       def     UpperCamelCase			(							self )      ->     Optional[int]:
          _SCREAMING_SNAKE_CASE       	=			self.scheduler_classes[0]
          _SCREAMING_SNAKE_CASE       	=			self.get_scheduler_config()
          _SCREAMING_SNAKE_CASE       	=			scheduler_class(**A__ )

          assert torch.sum(torch.abs(scheduler._get_variance(0    ,							0 ) - 0.0 ) ) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(4_20    ,							4_00 ) - 0.1_4771 ) ) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_80    ,							9_60 ) - 0.3_2460 ) ) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(0    ,							0 ) - 0.0 ) ) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(4_87    ,							4_86 ) - 0.0_0979 ) ) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_99    ,							9_98 ) - 0.02 ) ) < 1E-5






       def     UpperCamelCase			(							self )      ->     List[Any]:
          _SCREAMING_SNAKE_CASE       	=			self.full_loop()

          _SCREAMING_SNAKE_CASE       	=			torch.sum(torch.abs(A__ ) )
          _SCREAMING_SNAKE_CASE       	=			torch.mean(torch.abs(A__ ) )

          assert abs(result_sum.item() - 172.0067 ) < 1E-2
          assert abs(result_mean.item() - 0.22_3967 ) < 1E-3






       def     UpperCamelCase			(							self )      ->     str:
          _SCREAMING_SNAKE_CASE       	=			self.full_loop(prediction_type="""v_prediction""" )

          _SCREAMING_SNAKE_CASE       	=			torch.sum(torch.abs(A__ ) )
          _SCREAMING_SNAKE_CASE       	=			torch.mean(torch.abs(A__ ) )

          assert abs(result_sum.item() - 52.5302 ) < 1E-2
          assert abs(result_mean.item() - 0.0684 ) < 1E-3






       def     UpperCamelCase			(							self )      ->     List[Any]:
          # We specify different beta, so that the first alpha is 0.99
          _SCREAMING_SNAKE_CASE       	=			self.full_loop(set_alpha_to_one=A__    ,							beta_start=0.01 )
          _SCREAMING_SNAKE_CASE       	=			torch.sum(torch.abs(A__ ) )
          _SCREAMING_SNAKE_CASE       	=			torch.mean(torch.abs(A__ ) )

          assert abs(result_sum.item() - 149.8295 ) < 1E-2
          assert abs(result_mean.item() - 0.1951 ) < 1E-3






       def     UpperCamelCase			(							self )      ->     Any:
          # We specify different beta, so that the first alpha is 0.99
          _SCREAMING_SNAKE_CASE       	=			self.full_loop(set_alpha_to_one=A__    ,							beta_start=0.01 )
          _SCREAMING_SNAKE_CASE       	=			torch.sum(torch.abs(A__ ) )
          _SCREAMING_SNAKE_CASE       	=			torch.mean(torch.abs(A__ ) )

          assert abs(result_sum.item() - 149.0784 ) < 1E-2
          assert abs(result_mean.item() - 0.1941 ) < 1E-3






