
'''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__				:	Optional[int]					=						{
    "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__				:	Optional[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 					A       ( )   ->    List[str]:







			'''simple docstring'''



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







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

			lowerCAmelCase__									=							compute_module_sizes(UpperCamelCase_ )
			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(UpperCamelCase_  ,		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(UpperCamelCase_ ) if outputs[: len(UpperCamelCase_ )] != tokens else len(UpperCamelCase_ ) - len(UpperCamelCase_ )
						lowerCAmelCase__									=							after_generate - before_generate

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

			lowerCAmelCase__									=							end_measure(UpperCamelCase_ )
			log_measures(UpperCamelCase_  ,		"Model generation" )

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


if __name__ == "__main__":
				main()







'''simple docstring'''

import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize






class 				A       :





	def __init__(   self     :    Union[str, Any] ,							__magic_name__     :    Union[str, Any] ,							__magic_name__     :    Optional[Any]=sys.maxsize							):


				"""simple docstring"""

				lowerCAmelCase__									=							"bilinear"
				lowerCAmelCase__									=							max_size
				lowerCAmelCase__									=							short_edge_length







	def __call__(   self     :    List[str] ,							__magic_name__     :    int							):


				"""simple docstring"""

				lowerCAmelCase__									=							[]
				for img in imgs:
							lowerCAmelCase__		,lowerCAmelCase__									=							img.shape[:2]
							# later: provide list and randomly choose index for resize
							lowerCAmelCase__									=							np.random.randint(self.short_edge_length[0] ,							self.short_edge_length[1] + 1							)
							if size == 0:
										return img
							lowerCAmelCase__									=							size * 1.0 / min(__magic_name__ ,							__magic_name__							)
							if h < w:
										lowerCAmelCase__		,lowerCAmelCase__									=							size, scale * w
							else:
										lowerCAmelCase__		,lowerCAmelCase__									=							scale * h, size
							if max(__magic_name__ ,							__magic_name__							) > self.max_size:
										lowerCAmelCase__									=							self.max_size * 1.0 / max(__magic_name__ ,							__magic_name__							)
										lowerCAmelCase__									=							newh * scale
										lowerCAmelCase__									=							neww * scale
							lowerCAmelCase__									=							int(neww + 0.5							)
							lowerCAmelCase__									=							int(newh + 0.5							)

							if img.dtype == np.uinta:
										lowerCAmelCase__									=							Image.fromarray(__magic_name__							)
										lowerCAmelCase__									=							pil_image.resize((neww, newh) ,							PILImageResampling.BILINEAR							)
										lowerCAmelCase__									=							np.asarray(__magic_name__							)
							else:
										lowerCAmelCase__									=							img.permute(2 ,							0 ,							1							).unsqueeze(0							)  # 3, 0, 1)  # hw(c) -> nchw
										lowerCAmelCase__									=							nn.functional.interpolate(
										    __magic_name__ ,							(newh, neww) ,							mode=self.interp_method ,							align_corners=__magic_name__							).squeeze(0							)
							img_augs.append(__magic_name__							)

				return img_augs






class 				A       :





	def __init__(   self     :    str ,							__magic_name__     :    Union[str, Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST] ,							cfg.INPUT.MAX_SIZE_TEST							)
				lowerCAmelCase__									=							cfg.INPUT.FORMAT
				lowerCAmelCase__									=							cfg.SIZE_DIVISIBILITY
				lowerCAmelCase__									=							cfg.PAD_VALUE
				lowerCAmelCase__									=							cfg.INPUT.MAX_SIZE_TEST
				lowerCAmelCase__									=							cfg.MODEL.DEVICE
				lowerCAmelCase__									=							torch.tensor(cfg.MODEL.PIXEL_STD							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							) ,							1 ,							1							)
				lowerCAmelCase__									=							torch.tensor(cfg.MODEL.PIXEL_MEAN							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							) ,							1 ,							1							)
				lowerCAmelCase__									=							lambda __magic_name__							: (x - self.pixel_mean) / self.pixel_std





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any] ,							__magic_name__     :    Optional[int]							):


				"""simple docstring"""

				lowerCAmelCase__									=							tuple(max(__magic_name__							) for s in zip(*[img.shape for img in images]							)							)
				lowerCAmelCase__									=							[im.shape[-2:] for im in images]
				lowerCAmelCase__									=							[
				    nn.functional.pad(
				        __magic_name__ ,							[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]] ,							value=self.pad_value ,							)
				    for size, im in zip(__magic_name__ ,							__magic_name__							)
				]

				return torch.stack(__magic_name__							), torch.tensor(__magic_name__							)







	def __call__(   self     :    List[str] ,							__magic_name__     :    int ,							__magic_name__     :    Optional[int]=False							):


				"""simple docstring"""

				with torch.no_grad():
							if not isinstance(__magic_name__ ,							__magic_name__							):
										lowerCAmelCase__									=							[images]
							if single_image:
										assert len(__magic_name__							) == 1
							for i in range(len(__magic_name__							)							):
										if isinstance(images[i] ,							torch.Tensor							):
													images.insert(__magic_name__ ,							images.pop(__magic_name__							).to(self.device							).float()							)
										elif not isinstance(images[i] ,							torch.Tensor							):
													images.insert(
													    __magic_name__ ,							torch.as_tensor(img_tensorize(images.pop(__magic_name__							) ,							input_format=self.input_format							)							)
													    .to(self.device							)
													    .float() ,							)
            # resize smallest edge
							lowerCAmelCase__									=							torch.tensor([im.shape[:2] for im in images]							)
							lowerCAmelCase__									=							self.aug(__magic_name__							)
							# transpose images and convert to torch tensors
							# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
							# now normalize before pad to avoid useless arithmetic
							lowerCAmelCase__									=							[self.normalizer(__magic_name__							) for x in images]
							# now pad them to do the following operations
							lowerCAmelCase__		,lowerCAmelCase__									=							self.pad(__magic_name__							)
							# Normalize

							if self.size_divisibility > 0:
										raise NotImplementedError()
							# pad
							lowerCAmelCase__									=							torch.true_divide(__magic_name__ ,							__magic_name__							)
							if single_image:
										return images[0], sizes[0], scales_yx[0]
							else:
										return images, sizes, scales_yx







def 					A       ( UpperCamelCase_   :       int  ,		UpperCamelCase_   :       Optional[Any] )   ->    List[str]:







			'''simple docstring'''



			boxes[:, 0::2] *= scale_yx[:, 1]
			boxes[:, 1::2] *= scale_yx[:, 0]
			return boxes







def 					A       ( UpperCamelCase_   :       Optional[Any]  ,		UpperCamelCase_   :       Tuple[int, int] )   ->    Tuple:







			'''simple docstring'''



			assert torch.isfinite(UpperCamelCase_ ).all(), "Box tensor contains infinite or NaN!"
			lowerCAmelCase__		,lowerCAmelCase__									=							box_size
			tensor[:, 0].clamp_(min=0  ,		max=UpperCamelCase_ )
			tensor[:, 1].clamp_(min=0  ,		max=UpperCamelCase_ )
			tensor[:, 2].clamp_(min=0  ,		max=UpperCamelCase_ )
			tensor[:, 3].clamp_(min=0  ,		max=UpperCamelCase_ )





