

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

					A     =   parser.parse_args()

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

					return args
def   __SCREAMING_SNAKE_CASE		(		):
					"""simple docstring"""
					transformers.utils.logging.set_verbosity_error()
					A     =   parse_args()

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

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

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

					A     =   AutoTokenizer.from_pretrained(args.tokenizer_name )

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

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

					A     =   end_measure(lowercase__ )
					log_measures(lowercase__     ,				"Model generation" )

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


if __name__ == "__main__":
	main()







'''simple docstring'''





import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


UpperCAmelCase            = logging.get_logger(__name__)

UpperCAmelCase            = {
    "google/vit-hybrid-base-bit-384": "https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}

class     a (					__magic_name__     ):
				_snake_case		     =			'''vit-hybrid'''




				def __init__(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=None,     SCREAMING_SNAKE_CASE_						:					Tuple=7_68,     SCREAMING_SNAKE_CASE_						:					List[str]=12,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=12,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=30_72,     SCREAMING_SNAKE_CASE_						:					List[Any]="gelu",     SCREAMING_SNAKE_CASE_						:					Any=0.0,     SCREAMING_SNAKE_CASE_						:					Dict=0.0,     SCREAMING_SNAKE_CASE_						:					List[Any]=0.02,     SCREAMING_SNAKE_CASE_						:					List[Any]=1e-12,     SCREAMING_SNAKE_CASE_						:					int=2_24,     SCREAMING_SNAKE_CASE_						:					int=1,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=3,     SCREAMING_SNAKE_CASE_						:					Any=[1, 10_24, 24, 24],     SCREAMING_SNAKE_CASE_						:					List[str]=True,     **SCREAMING_SNAKE_CASE_						:					List[str],     ):
										super().__init__(**SCREAMING_SNAKE_CASE_   )

										if backbone_config is None:
																logger.info('''`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'''   )
																snake_case							:  int			=     {
																    '''global_padding''': '''same''',
																    '''layer_type''': '''bottleneck''',
																    '''depths''': [3, 4, 9],
																    '''out_features''': ['''stage3'''],
																    '''embedding_dynamic_padding''': True,
																}

										if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ):
																if "model_type" in backbone_config:
																						snake_case							:  Any			=     CONFIG_MAPPING[backbone_config['''model_type''']]
																else:
																						logger.info(
																						    '''`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'''   )
																						snake_case							:  Any			=     BitConfig
																snake_case							:  Optional[Any]			=     backbone_config_class(**SCREAMING_SNAKE_CASE_   )

										snake_case							:  str			=     backbone_featmap_shape
										snake_case							:  Union[str, Any]			=     backbone_config
										snake_case							:  List[str]			=     hidden_size
										snake_case							:  List[Any]			=     num_hidden_layers
										snake_case							:  Dict			=     num_attention_heads
										snake_case							:  List[str]			=     intermediate_size
										snake_case							:  Optional[int]			=     hidden_act
										snake_case							:  List[str]			=     hidden_dropout_prob
										snake_case							:  Tuple			=     attention_probs_dropout_prob
										snake_case							:  Optional[int]			=     initializer_range
										snake_case							:  Optional[int]			=     layer_norm_eps
										snake_case							:  List[Any]			=     image_size
										snake_case							:  Any			=     patch_size
										snake_case							:  str			=     num_channels
										snake_case							:  List[str]			=     qkv_bias




				def 						__snake_case				(				self						:					Any   ):
										snake_case							:  Tuple			=     copy.deepcopy(self.__dict__   )
										snake_case							:  List[Any]			=     self.backbone_config.to_dict()
										snake_case							:  Optional[Any]			=     self.__class__.model_type
										return output

