

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


lowerCAmelCase__             =		{
    """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"""},
}

lowerCAmelCase__             =		[
    """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_  (				)    ->					List[Any]:




	'''simple docstring'''




	_UpperCamelCase :		str								=    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'						)

	_UpperCamelCase :		Union[str, Any]								=    parser.parse_args()

	# Sanitize args
	if args.model_name in DEFAULT_MODELS:
		_UpperCamelCase :		Any								=    DEFAULT_MODELS[args.model_name]
		_UpperCamelCase :		Optional[int]								=    defaults['''model''']
		if args.tokenizer_name is None:
			_UpperCamelCase :		List[Any]								=    defaults.get('tokenizer'				,							args.model_name						)
		if args.is_causal is None:
			_UpperCamelCase :		int								=    defaults['''is_causal''']
		if args.model_revision is None:
			_UpperCamelCase :		Union[str, Any]								=    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:
		_UpperCamelCase :		Tuple								=    args.model_name
	if args.model_revision is None:
		_UpperCamelCase :		Optional[int]								=    '''main'''

	return args






def        lowerCamelCase_  (				)    ->					Dict:




	'''simple docstring'''




	transformers.utils.logging.set_verbosity_error()
	_UpperCamelCase :		List[Any]								=    parse_args()

	if args.torch_dtype is None:
		_UpperCamelCase :		Any								=    AutoConfig.from_pretrained(args.model_name						)
		_UpperCamelCase :		Optional[int]								=    getattr(snake_case_				,							'torch_dtype'				,							torch.floataa						)
	else:
		_UpperCamelCase :		Dict								=    getattr(snake_case_				,							args.torch_dtype						)
	_UpperCamelCase :		Tuple								=    AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
	_UpperCamelCase :		List[str]								=    {
	    '''torch_dtype''': torch_dtype,
	    '''revision''': args.model_revision,
	}
	if args.disk_offload:
		_UpperCamelCase :		Dict								=    '''tmp_offload'''
		_UpperCamelCase :		Any								=    True

	_UpperCamelCase :		Union[str, Any]								=    start_measure()
	_UpperCamelCase :		Any								=    model_cls.from_pretrained(args.model_name				,							device_map='auto'				,							**snake_case_						)
	_UpperCamelCase :		Optional[Any]								=    end_measure(snake_case_						)
	log_measures(snake_case_				,							'Model loading'						)

	_UpperCamelCase :		List[str]								=    compute_module_sizes(snake_case_						)
	_UpperCamelCase :		str								=    {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]
	_UpperCamelCase :		Union[str, Any]								=    '''\n'''.join([F'''- {device}: {size // 2**2_0}MiB''' for device, size in device_size.items()]						)
	print(F'''\nTheoretical use:\n{message}'''						)

	_UpperCamelCase :		Tuple								=    AutoTokenizer.from_pretrained(args.tokenizer_name						)

	_UpperCamelCase :		Union[str, Any]								=    start_measure()
	_UpperCamelCase :		int								=    []
	_UpperCamelCase :		str								=    []
	_UpperCamelCase :		Union[str, Any]								=    []
	for prompt in PROMPTS:
		_UpperCamelCase :		Union[str, Any]								=    tokenizer(snake_case_				,							return_tensors='pt'						).to(0						)
		_UpperCamelCase :		int								=    inputs['''input_ids'''][0].tolist()
		_UpperCamelCase :		int								=    time.time()
		_UpperCamelCase :		List[str]								=    model.generate(inputs['input_ids']						)
		_UpperCamelCase :		Tuple								=    time.time()
		_UpperCamelCase :		str								=    outputs[0].tolist()
		_UpperCamelCase :		str								=    len(snake_case_						) if outputs[: len(snake_case_						)] != tokens else len(snake_case_						) - len(snake_case_						)
		_UpperCamelCase :		str								=    after_generate - before_generate

		_UpperCamelCase :		str								=    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'''						)

	_UpperCamelCase :		Union[str, Any]								=    end_measure(snake_case_						)
	log_measures(snake_case_				,							'Model generation'						)

	_UpperCamelCase :		Any								=    [gen / tok for gen, tok in zip(snake_case_				,							snake_case_						)]
	_UpperCamelCase :		Optional[int]								=    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'''						)
	_UpperCamelCase :		List[str]								=    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()









'''simple docstring'''






import warnings

from .generation import GenerationMixin

class 	_lowerCAmelCase     (    __A       ):






		'''simple docstring'''
		# warning at import time
		warnings.warn(
		    'Importing `GenerationMixin` from `src/transformers/generation_utils.py` is deprecated and will '
		    'be removed in Transformers v5. Import as `from transformers import GenerationMixin` instead.'     ,		__A     ,		)



