


"""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


SCREAMING_SNAKE_CASE__				:  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"},
}

SCREAMING_SNAKE_CASE__				:  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_					(					)     ->     Optional[int]:
   a   :	Tuple						=    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'				)

   a   :	Any						=    parser.parse_args()

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

   return args




def        A_					(					)     ->     List[str]:
   transformers.utils.logging.set_verbosity_error()
   a   :	Optional[Any]						=    parse_args()

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

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

   a   :	int						=    compute_module_sizes(UpperCAmelCase__				)
   a   :	List[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]
   a   :	Optional[Any]						=    '\n'.join([F'- {device}: {size // 2**20}MiB' for device, size in device_size.items()]				)
   print(F'\nTheoretical use:\n{message}'				)

   a   :	List[Any]						=    AutoTokenizer.from_pretrained(args.tokenizer_name				)

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

      a   :	int						=    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'				)

   a   :	List[str]						=    end_measure(UpperCAmelCase__				)
   log_measures(UpperCAmelCase__       ,   'Model generation'				)

   a   :	int						=    [gen / tok for gen, tok in zip(UpperCAmelCase__       ,   UpperCAmelCase__				)]
   a   :	Tuple						=    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'				)
   a   :	Optional[int]						=    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 importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


SCREAMING_SNAKE_CASE__				:  Optional[Any]						= "\\n@inproceedings{ramshaw-marcus-1995-text,\n    title = \"Text Chunking using Transformation-Based Learning\",\n    author = \"Ramshaw, Lance  and\n      Marcus, Mitch\",\n    booktitle = \"Third Workshop on Very Large Corpora\",\n    year = \"1995\",\n    url = \"https://www.aclweb.org/anthology/W95-0107\",\n}\n@misc{seqeval,\n  title={{seqeval}: A Python framework for sequence labeling evaluation},\n  url={https://github.com/chakki-works/seqeval},\n  note={Software available from https://github.com/chakki-works/seqeval},\n  author={Hiroki Nakayama},\n  year={2018},\n}\n"

SCREAMING_SNAKE_CASE__				:  str						= "\\nseqeval is a Python framework for sequence labeling evaluation.\nseqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.\n\nThis is well-tested by using the Perl script conlleval, which can be used for\nmeasuring the performance of a system that has processed the CoNLL-2000 shared task data.\n\nseqeval supports following formats:\nIOB1\nIOB2\nIOE1\nIOE2\nIOBES\n\nSee the [README.md] file at https://github.com/chakki-works/seqeval for more information.\n"

SCREAMING_SNAKE_CASE__				:  Any						= "\nProduces labelling scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)\n    references: List of List of reference labels (Ground truth (correct) target values)\n    suffix: True if the IOB prefix is after type, False otherwise. default: False\n    scheme: Specify target tagging scheme. Should be one of [\"IOB1\", \"IOB2\", \"IOE1\", \"IOE2\", \"IOBES\", \"BILOU\"].\n        default: None\n    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.\n        If you want to only count exact matches, pass mode=\"strict\". default: None.\n    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None\n    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,\n        \"warn\". \"warn\" acts as 0, but the warning is raised.\n\nReturns:\n    'scores': dict. Summary of the scores for overall and per type\n        Overall:\n            'accuracy': accuracy,\n            'precision': precision,\n            'recall': recall,\n            'f1': F1 score, also known as balanced F-score or F-measure,\n        Per type:\n            'precision': precision,\n            'recall': recall,\n            'f1': F1 score, also known as balanced F-score or F-measure\nExamples:\n\n    >>> predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]\n    >>> references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]\n    >>> seqeval = datasets.load_metric(\"seqeval\")\n    >>> results = seqeval.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    ['MISC', 'PER', 'overall_precision', 'overall_recall', 'overall_f1', 'overall_accuracy']\n    >>> print(results[\"overall_f1\"])\n    0.5\n    >>> print(results[\"PER\"][\"f1\"])\n    1.0\n"


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,  _KWARGS_DESCRIPTION   )
class 	A_       (     datasets.Metric   ):





  """simple docstring"""

  def 							lowercase_     (						self						)				->					Tuple:
     return datasets.MetricInfo(
         description=_DESCRIPTION  ,					citation=_CITATION  ,					homepage='https://github.com/chakki-works/seqeval'  ,					inputs_description=_KWARGS_DESCRIPTION  ,					features=datasets.Features(
             {
                 'predictions': datasets.Sequence(datasets.Value('string'  ,					id='label'						)  ,					id='sequence'						),
                 'references': datasets.Sequence(datasets.Value('string'  ,					id='label'						)  ,					id='sequence'						),
             }						)  ,					codebase_urls=['https://github.com/chakki-works/seqeval']  ,					reference_urls=['https://github.com/chakki-works/seqeval']  ,					)






  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = False  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = "warn"  ,					)				->					List[Any]:
     if scheme is not None:
        try:
           a   :	str						=    importlib.import_module('seqeval.scheme'						)
           a   :	List[str]						=    getattr(__UpperCAmelCase  ,					__UpperCAmelCase						)
        except AttributeError:
           raise ValueError(f'Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}'						)
     a   :	str						=    classification_report(
         y_true=__UpperCAmelCase  ,					y_pred=__UpperCAmelCase  ,					suffix=__UpperCAmelCase  ,					output_dict=__UpperCAmelCase  ,					scheme=__UpperCAmelCase  ,					mode=__UpperCAmelCase  ,					sample_weight=__UpperCAmelCase  ,					zero_division=__UpperCAmelCase  ,					)
     report.pop('macro avg'						)
     report.pop('weighted avg'						)
     a   :	Optional[Any]						=    report.pop('micro avg'						)

     a   :	Optional[int]						=    {
         type_name: {
             'precision': score['precision'],
             'recall': score['recall'],
             'f1': score['f1-score'],
             'number': score['support'],
         }
         for type_name, score in report.items()
     }
     a   :	int						=    overall_score['precision']
     a   :	int						=    overall_score['recall']
     a   :	int						=    overall_score['f1-score']
     a   :	List[str]						=    accuracy_score(y_true=__UpperCAmelCase  ,					y_pred=__UpperCAmelCase						)

     return scores





