"""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


a							={
    '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'},
}

a							=[
    '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_			( )							-> Any:




						'''simple docstring'''



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

						lowerCamelCase__ =parser.parse_args()

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

						return args





def       lowerCamelCase_			( )							-> List[str]:




						'''simple docstring'''



						transformers.utils.logging.set_verbosity_error()
						lowerCamelCase__ =parse_args()

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

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

						lowerCamelCase__ =compute_module_sizes(__lowerCAmelCase    )
						lowerCamelCase__ ={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]
						lowerCamelCase__ ="\n".join([F'''- {device}: {size // 2**20}MiB''' for device, size in device_size.items()]    )
						print(F'''\nTheoretical use:\n{message}'''    )

						lowerCamelCase__ =AutoTokenizer.from_pretrained(args.tokenizer_name    )

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

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

						lowerCamelCase__ =end_measure(__lowerCAmelCase    )
						log_measures(__lowerCAmelCase       ,   "Model generation"    )

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


if __name__ == "__main__":
	main()



"""simple docstring"""


import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class        __UpperCAmelCase						(     __lowerCAmelCase				,				__lowerCAmelCase				,				unittest.TestCase     ):
				A__				:       Any  							=	IFSuperResolutionPipeline
				A__				:       Tuple  							=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
				A__				:       Optional[int]  							=	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				A__				:       List[Any]  							=	PipelineTesterMixin.required_optional_params - {'''latents'''}

				def 		_a     (				self				):
										return self._get_superresolution_dummy_components()

				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase=0				):
										if str(_lowerCamelCase				).startswith("mps"				):
																lowerCamelCase__ =torch.manual_seed(_lowerCamelCase				)
										else:
																lowerCamelCase__ =torch.Generator(device=_lowerCamelCase				).manual_seed(_lowerCamelCase				)

										lowerCamelCase__ =floats_tensor((1, 3, 32, 32)     ,			rng=random.Random(_lowerCamelCase				)				).to(_lowerCamelCase				)

										lowerCamelCase__ ={
										    "prompt": "A painting of a squirrel eating a burger",
										    "image": image,
										    "generator": generator,
										    "num_inference_steps": 2,
										    "output_type": "numpy",
										}

										return inputs

				@unittest.skipIf(
				    torch_device != "cuda" or not is_xformers_available()     ,			reason="XFormers attention is only available with CUDA and `xformers` installed"     ,			)
				def 		_a     (				self				):
										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3				)

				def 		_a     (				self				):
										self._test_save_load_optional_components()

				@unittest.skipIf(torch_device != "cuda"     ,			reason="float16 requires CUDA"				)
				def 		_a     (				self				):
										# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
										super().test_save_load_floataa(expected_max_diff=1E-1				)

				def 		_a     (				self				):
										self._test_attention_slicing_forward_pass(expected_max_diff=1E-2				)

				def 		_a     (				self				):
										self._test_save_load_local()






				def 		_a     (				self				):
										self._test_inference_batch_single_identical(
										    expected_max_diff=1E-2     ,			)


