






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


__snake_case     :     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"""},
}

__snake_case     :     List[Any]       =  [
    """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 (     )	-> 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 						_UpperCamelCase (     )	-> Any:

						"""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()













import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


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

__snake_case     :     Any       =  """\
seqeval is a Python framework for sequence labeling evaluation.
seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.

This is well-tested by using the Perl script conlleval, which can be used for
measuring the performance of a system that has processed the CoNLL-2000 shared task data.

seqeval supports following formats:
IOB1
IOB2
IOE1
IOE2
IOBES

See the [README.md] file at https://github.com/chakki-works/seqeval for more information.
"""

__snake_case     :     Optional[int]       =  """
Produces labelling scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)
    references: List of List of reference labels (Ground truth (correct) target values)
    suffix: True if the IOB prefix is after type, False otherwise. default: False
    scheme: Specify target tagging scheme. Should be one of [\"IOB1\", \"IOB2\", \"IOE1\", \"IOE2\", \"IOBES\", \"BILOU\"].
        default: None
    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.
        If you want to only count exact matches, pass mode=\"strict\". default: None.
    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None
    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,
        \"warn\". \"warn\" acts as 0, but the warning is raised.

Returns:
    'scores': dict. Summary of the scores for overall and per type
        Overall:
            'accuracy': accuracy,
            'precision': precision,
            'recall': recall,
            'f1': F1 score, also known as balanced F-score or F-measure,
        Per type:
            'precision': precision,
            'recall': recall,
            'f1': F1 score, also known as balanced F-score or F-measure
Examples:

    >>> predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
    >>> references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
    >>> seqeval = datasets.load_metric(\"seqeval\")
    >>> results = seqeval.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['MISC', 'PER', 'overall_precision', 'overall_recall', 'overall_f1', 'overall_accuracy']
    >>> print(results[\"overall_f1\"])
    0.5
    >>> print(results[\"PER\"][\"f1\"])
    1.0
"""


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






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												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    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase = False  ,    _UpperCamelCase = None  ,    _UpperCamelCase = None  ,    _UpperCamelCase = None  ,    _UpperCamelCase = "warn"  ,    ):






												"""simple docstring"""
												if scheme is not None:
																		try:
																								lowerCAmelCase__         =    importlib.import_module('seqeval.scheme' )
																								lowerCAmelCase__         =    getattr(_UpperCamelCase  ,    _UpperCamelCase )
																		except AttributeError:
																								raise ValueError(F"Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}" )
												lowerCAmelCase__         =    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' )
												lowerCAmelCase__         =    report.pop('micro avg' )

												lowerCAmelCase__         =    {
												    type_name: {
												        'precision': score['precision'],
												        'recall': score['recall'],
												        'f1': score['f1-score'],
												        'number': score['support'],
												    }
												    for type_name, score in report.items()
												}
												lowerCAmelCase__         =    overall_score['precision']
												lowerCAmelCase__         =    overall_score['recall']
												lowerCAmelCase__         =    overall_score['f1-score']
												lowerCAmelCase__         =    accuracy_score(y_true=_UpperCamelCase  ,    y_pred=_UpperCamelCase )

												return scores





