'''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 			_UpperCamelCase			(  ):




    """simple docstring"""



    __UpperCamelCase						:						Optional[int]		     =	argparse.ArgumentParser(description='Run and time generations on a big model using Accelerate.'      )
    parser.add_argument('model_name'					,  type=_a					,  default=_a					,  help='The name of the model to try.'      )
    parser.add_argument(
        '--tokenizer_name'					,  type=_a					,  default=_a					,  help='The name of the tokenizer (if different from the model.'      )
    parser.add_argument('--is_causal'					,  type=_a					,  default=_a					,  help='Whether or not the model is causal.'      )
    parser.add_argument(
        '--model_revision'					,  type=_a					,  default=_a					,  help='The revision to use for the model checkpoint.'      )
    parser.add_argument('--torch_dtype'					,  type=_a					,  default=_a					,  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						:						Optional[int]		     =	DEFAULT_MODELS[args.model_name]
        __UpperCamelCase						:						Optional[Any]		     =	defaults['model']
        if args.tokenizer_name is None:
            __UpperCamelCase						:						Optional[int]		     =	defaults.get('tokenizer'					,  args.model_name      )
        if args.is_causal is None:
            __UpperCamelCase						:						Tuple		     =	defaults['is_causal']
        if args.model_revision is None:
            __UpperCamelCase						:						int		     =	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						:						str		     =	args.model_name
    if args.model_revision is None:
        __UpperCamelCase						:						Optional[int]		     =	'main'

    return args







def 			_UpperCamelCase			(  ):




    """simple docstring"""



    transformers.utils.logging.set_verbosity_error()
    __UpperCamelCase						:						Optional[int]		     =	parse_args()

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

    __UpperCamelCase						:						int		     =	start_measure()
    __UpperCamelCase						:						Optional[int]		     =	model_cls.from_pretrained(args.model_name					,  device_map='auto'					,  **_a      )
    __UpperCamelCase						:						List[str]		     =	end_measure(_a      )
    log_measures(_a					,  'Model loading'      )

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

        __UpperCamelCase						:						Optional[int]		     =	tokenizer.decode(_a					,  skip_special_tokens=_a      )
        texts_outs.append(_a      )
        generation_times.append(_a      )
        gen_tokens.append(_a      )
        print(f"""Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n"""      )

    __UpperCamelCase						:						int		     =	end_measure(_a      )
    log_measures(_a					,  'Model generation'      )

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


if __name__ == "__main__":
  main()




'''simple docstring'''
import numpy as np







def 			_UpperCamelCase			(  _a						:						Union[str, Any]					,  _a						:						Any					,  _a						:						Dict      ):




    """simple docstring"""



    __UpperCamelCase						:						Tuple		     =	n_draws * class_counts / class_counts.sum()
    # floored means we don't overshoot n_samples, but probably undershoot
    __UpperCamelCase						:						Optional[Any]		     =	np.floor(_a      )
    # we add samples according to how much "left over" probability
    # they had, until we arrive at n_samples
    __UpperCamelCase						:						Tuple		     =	int(n_draws - floored.sum()      )
    if need_to_add > 0:
        __UpperCamelCase						:						Any		     =	continuous - floored
        __UpperCamelCase						:						Any		     =	np.sort(np.unique(_a      )      )[::-1]
        # add according to remainder, but break ties
        # randomly to avoid biases
        for value in values:
            ((__UpperCamelCase)     ,       )						:						Any		     =	np.where(remainder == value      )
            # if we need_to_add less than what's in inds
            # we draw randomly from them.
            # if we need to add more, we add them all and
            # go to the next value
            __UpperCamelCase						:						List[Any]		     =	min(len(_a      )					,  _a      )
            __UpperCamelCase						:						Tuple		     =	rng.choice(_a					,  size=_a					,  replace=_a      )
            floored[inds] += 1
            need_to_add -= add_now
            if need_to_add == 0:
                break
    return floored.astype(np.intaa      )







def 			_UpperCamelCase			(  _a						:						Optional[int]					,  _a						:						Union[str, Any]					,  _a						:						int					,  _a						:						Union[str, Any]					,  _a						:						str=1_0      ):




    """simple docstring"""



    __UpperCamelCase     ,       __UpperCamelCase						:						int		     =	np.unique(_a					,  return_inverse=_a      )
    __UpperCamelCase						:						str		     =	classes.shape[0]
    __UpperCamelCase						:						str		     =	np.bincount(_a      )
    if np.min(_a      ) < 2:
        raise ValueError('Minimum class count error'      )
    if n_train < n_classes:
        raise ValueError(
            'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)      )
    if n_test < n_classes:
        raise ValueError(
            'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)      )
    __UpperCamelCase						:						Optional[int]		     =	np.split(np.argsort(_a					,  kind='mergesort'      )					,  np.cumsum(_a      )[:-1]      )
    for _ in range(_a      ):
        __UpperCamelCase						:						List[str]		     =	approximate_mode(_a					,  _a					,  _a      )
        __UpperCamelCase						:						Tuple		     =	class_counts - n_i
        __UpperCamelCase						:						int		     =	approximate_mode(_a					,  _a					,  _a      )

        __UpperCamelCase						:						List[Any]		     =	[]
        __UpperCamelCase						:						str		     =	[]

        for i in range(_a      ):
            __UpperCamelCase						:						Optional[Any]		     =	rng.permutation(class_counts[i]      )
            __UpperCamelCase						:						List[Any]		     =	class_indices[i].take(_a					,  mode='clip'      )
            train.extend(perm_indices_class_i[: n_i[i]]      )
            test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]      )
        __UpperCamelCase						:						int		     =	rng.permutation(_a      )
        __UpperCamelCase						:						int		     =	rng.permutation(_a      )

        yield train, test



