


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


snake_case			:      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'},
}

snake_case			:      str 							=   [
    '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 				SCREAMING_SNAKE_CASE (     )     ->   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=snake_case__   ,default=snake_case__   ,help='The name of the model to try.'							)
				parser.add_argument(
				    '--tokenizer_name'   ,type=snake_case__   ,default=snake_case__   ,help='The name of the tokenizer (if different from the model.'							)
				parser.add_argument('--is_causal'   ,type=snake_case__   ,default=snake_case__   ,help='Whether or not the model is causal.'							)
				parser.add_argument(
				    '--model_revision'   ,type=snake_case__   ,default=snake_case__   ,help='The revision to use for the model checkpoint.'							)
				parser.add_argument('--torch_dtype'   ,type=snake_case__   ,default=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 				SCREAMING_SNAKE_CASE (     )     ->   List[str]:

				"""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(snake_case__   ,'torch_dtype'   ,torch.floataa							)
				else:
								_SCREAMING_SNAKE_CASE      = getattr(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'   ,**snake_case__							)
				_SCREAMING_SNAKE_CASE      = end_measure(snake_case__							)
				log_measures(snake_case__   ,'Model loading'							)

				_SCREAMING_SNAKE_CASE      = compute_module_sizes(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(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(snake_case__							) if outputs[: len(snake_case__							)] != tokens else len(snake_case__							) - len(snake_case__							)
								_SCREAMING_SNAKE_CASE      = after_generate - before_generate

								_SCREAMING_SNAKE_CASE      = tokenizer.decode(snake_case__   ,skip_special_tokens=snake_case__							)
								texts_outs.append(snake_case__							)
								generation_times.append(snake_case__							)
								gen_tokens.append(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(snake_case__							)
				log_measures(snake_case__   ,'Model generation'							)

				_SCREAMING_SNAKE_CASE      = [gen / tok for gen, tok in zip(snake_case__   ,snake_case__							)]
				_SCREAMING_SNAKE_CASE      = sum(snake_case__							) / len(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(snake_case__							) - 1)
				print(f'''Average time of generation per token (excluding the first): {avg_gen:.2f}s'''							)


if __name__ == "__main__":
							main()




import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()



class    __lowercase  (       unittest.TestCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					str        =						"hf-internal-testing/tiny-stable-diffusion-pipe"
							SCREAMING_SNAKE_CASE   :					str        =						1
							SCREAMING_SNAKE_CASE   :					Optional[Any]        =						77
							SCREAMING_SNAKE_CASE   :					Dict        =						32
							SCREAMING_SNAKE_CASE   :					Optional[Any]        =						4
							SCREAMING_SNAKE_CASE   :					Optional[int]        =						64
							SCREAMING_SNAKE_CASE   :					Tuple        =						torch.manual_seed(0							)





							def 		__magic_name__			(    self				, A_=0.9999	)->	Optional[int]:
											_SCREAMING_SNAKE_CASE      = UNetaDConditionModel.from_pretrained(self.model_id				, subfolder='unet'	)
											_SCREAMING_SNAKE_CASE      = unet.to(A_	)
											_SCREAMING_SNAKE_CASE      = EMAModel(unet.parameters()				, decay=A_				, model_cls=A_				, model_config=unet.config	)
											return unet, ema_unet





							def 		__magic_name__			(    self	)->	Optional[int]:
											_SCREAMING_SNAKE_CASE      = torch.randn(
											    self.batch_size				, self.num_in_channels				, self.latent_height				, self.latent_width				, generator=self.generator	).to(A_	)
											_SCREAMING_SNAKE_CASE      = torch.randint(0				, 1000				, size=(self.batch_size,)				, generator=self.generator	).to(A_	)
											_SCREAMING_SNAKE_CASE      = torch.randn(
											    self.batch_size				, self.prompt_length				, self.text_encoder_hidden_dim				, generator=self.generator	).to(A_	)
											return noisy_latents, timesteps, encoder_hidden_states





							def 		__magic_name__			(    self				, A_	)->	str:
											_SCREAMING_SNAKE_CASE      = {}
											for k, param in unet.state_dict().items():
															_SCREAMING_SNAKE_CASE      = torch.randn_like(A_	) + (param * torch.randn_like(A_	))
															updated_state_dict.update({k: updated_param}	)
											unet.load_state_dict(A_	)
											return unet





							def 		__magic_name__			(    self	)->	Dict:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.get_models()
											# Take the first (hypothetical) EMA step.
											ema_unet.step(unet.parameters()	)
											assert ema_unet.optimization_step == 1

											# Take two more.
											for _ in range(2	):
															ema_unet.step(unet.parameters()	)
											assert ema_unet.optimization_step == 3





							def 		__magic_name__			(    self	)->	Optional[int]:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.get_models()
											# Since the `unet` is not being updated (i.e., backprop'd)
											# there won't be any difference between the `params` of `unet`
											# and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
											ema_unet.step(unet.parameters()	)
											_SCREAMING_SNAKE_CASE      = list(unet.parameters()	)
											for s_param, param in zip(ema_unet.shadow_params				, A_	):
															assert torch.allclose(A_				, A_	)

											# The above holds true even if we call `ema.step()` multiple times since
											# `unet` params are still not being updated.
											for _ in range(4	):
															ema_unet.step(unet.parameters()	)
											for s_param, param in zip(ema_unet.shadow_params				, A_	):
															assert torch.allclose(A_				, A_	)





							def 		__magic_name__			(    self	)->	Union[str, Any]:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.get_models()
											# Here we simulate the parameter updates for `unet`. Since there might
											# be some parameters which are initialized to zero we take extra care to
											# initialize their values to something non-zero before the multiplication.
											_SCREAMING_SNAKE_CASE      = self.simulate_backprop(A_	)

											# Take the EMA step.
											ema_unet.step(unet_pseudo_updated_step_one.parameters()	)

											# Now the EMA'd parameters won't be equal to the original model parameters.
											_SCREAMING_SNAKE_CASE      = list(unet_pseudo_updated_step_one.parameters()	)
											for s_param, param in zip(ema_unet.shadow_params				, A_	):
															assert ~torch.allclose(A_				, A_	)

											# Ensure this is the case when we take multiple EMA steps.
											for _ in range(4	):
															ema_unet.step(unet.parameters()	)
											for s_param, param in zip(ema_unet.shadow_params				, A_	):
															assert ~torch.allclose(A_				, A_	)





							def 		__magic_name__			(    self	)->	Optional[int]:
											# If we call EMA step after a backpropagation consecutively for two times,
											# the shadow params from those two steps should be different.
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.get_models()

											# First backprop + EMA
											_SCREAMING_SNAKE_CASE      = self.simulate_backprop(A_	)
											ema_unet.step(unet_step_one.parameters()	)
											_SCREAMING_SNAKE_CASE      = ema_unet.shadow_params

											# Second backprop + EMA
											_SCREAMING_SNAKE_CASE      = self.simulate_backprop(A_	)
											ema_unet.step(unet_step_two.parameters()	)
											_SCREAMING_SNAKE_CASE      = ema_unet.shadow_params

											for step_one, step_two in zip(A_				, A_	):
															assert ~torch.allclose(A_				, A_	)





							def 		__magic_name__			(    self	)->	Dict:
											# If there's no decay even if there are backprops, EMA steps
											# won't take any effect i.e., the shadow params would remain the
											# same.
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.get_models(decay=0.0	)
											_SCREAMING_SNAKE_CASE      = self.simulate_backprop(A_	)
											ema_unet.step(unet_step_one.parameters()	)
											_SCREAMING_SNAKE_CASE      = ema_unet.shadow_params

											_SCREAMING_SNAKE_CASE      = self.simulate_backprop(A_	)
											ema_unet.step(unet_step_two.parameters()	)
											_SCREAMING_SNAKE_CASE      = ema_unet.shadow_params

											for step_one, step_two in zip(A_				, A_	):
															assert torch.allclose(A_				, A_	)





							@skip_mps
							def 		__magic_name__			(    self	)->	Optional[Any]:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.get_models()
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.get_dummy_inputs()

											with tempfile.TemporaryDirectory() as tmpdir:
															ema_unet.save_pretrained(A_	)
															_SCREAMING_SNAKE_CASE      = UNetaDConditionModel.from_pretrained(A_				, model_cls=A_	)
															_SCREAMING_SNAKE_CASE      = loaded_unet.to(unet.device	)

											# Since no EMA step has been performed the outputs should match.
											_SCREAMING_SNAKE_CASE      = unet(A_				, A_				, A_	).sample
											_SCREAMING_SNAKE_CASE      = loaded_unet(A_				, A_				, A_	).sample

											assert torch.allclose(A_				, A_				, atol=1e-4	)
