





import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
    datefmt='''%m/%d/%Y %H:%M:%S''',
    level=logging.INFO,
)
SCREAMING_SNAKE_CASE_      :			Dict		  =      logging.getLogger(__name__)



def    SCREAMING_SNAKE_CASE							(			snake_case       )		->						Any:
		np.random.seed(args.seed       )
		torch.manual_seed(args.seed       )
		if args.n_gpu > 0:
				torch.cuda.manual_seed_all(args.seed       )



def    SCREAMING_SNAKE_CASE							(			)		->						Union[str, Any]:
		__lowercase					=    argparse.ArgumentParser()
		parser.add_argument(
		    '--model_name_or_path'	,      default=snake_case	,      type=snake_case	,      required=snake_case	,      )
		parser.add_argument('--prompt'	,      type=snake_case	,      default=''       )
		parser.add_argument('--length'	,      type=snake_case	,      default=20       )
		parser.add_argument('--stop_token'	,      type=snake_case	,      default=snake_case	,      help='Token at which text generation is stopped'       )
		parser.add_argument(
		    '--temperature'	,      type=snake_case	,      default=1.0	,      help='temperature of 1.0 has no effect, lower tend toward greedy sampling'	,      )
		parser.add_argument(
		    '--repetition_penalty'	,      type=snake_case	,      default=1.0	,      help='primarily useful for CTRL model; in that case, use 1.2'       )
		parser.add_argument('--k'	,      type=snake_case	,      default=0       )
		parser.add_argument('--penalty_alpha'	,      type=snake_case	,      default=0.0       )
		parser.add_argument('--p'	,      type=snake_case	,      default=0.9       )

		parser.add_argument('--prefix'	,      type=snake_case	,      default=''	,      help='Text added prior to input.'       )
		parser.add_argument('--padding_text'	,      type=snake_case	,      default=''	,      help='Deprecated, the use of `--prefix` is preferred.'       )
		parser.add_argument('--xlm_language'	,      type=snake_case	,      default=''	,      help='Optional language when used with the XLM model.'       )

		parser.add_argument('--seed'	,      type=snake_case	,      default=42	,      help='random seed for initialization'       )
		parser.add_argument('--no_cuda'	,      action='store_true'	,      help='Avoid using CUDA when available'       )
		parser.add_argument(
		    '--fp16'	,      action='store_true'	,      help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'	,      )
		__lowercase					=    parser.parse_args()

		__lowercase					=    torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'       )
		__lowercase					=    0 if args.no_cuda else torch.cuda.device_count()

		logger.warning(F"device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}"       )

		set_seed(snake_case       )

		# Initialize the model and tokenizer
		__lowercase					=    AutoTokenizer.from_pretrained(args.model_name_or_path       )
		__lowercase					=    AutoModelForCausalLM.from_pretrained(args.model_name_or_path       )

		# tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
		# model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
		model.to(args.device       )

		if args.fpaa:
				model.half()

		logger.info(snake_case       )
		__lowercase					=    args.prompt if args.prompt else input('Model prompt >>> '       )

		__lowercase					=    tokenizer(snake_case	,      return_tensors='pt'	,      add_special_tokens=snake_case       )
		__lowercase					=    {key: value.to(args.device       ) for key, value in inputs.items()}

		__lowercase					=    model.generate(
		    **snake_case	,      max_length=args.length + len(inputs['input_ids'][0]       )	,      penalty_alpha=args.penalty_alpha	,      top_k=args.k	,      )

		__lowercase					=    []
		for generated_sequence_idx, generated_sequence in enumerate(snake_case       ):
				print(F"=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="       )
				__lowercase					=    generated_sequence.tolist()

				# Decode text
				__lowercase					=    tokenizer.decode(snake_case	,      clean_up_tokenization_spaces=snake_case	,      add_special_tokens=snake_case       )

				# Remove all text after the stop token
				__lowercase					=    text[: text.find(args.stop_token       ) if args.stop_token else None]

				# Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
				__lowercase					=    (
				    prompt_text + text[len(tokenizer.decode(inputs['input_ids'][0]	,      clean_up_tokenization_spaces=snake_case       )       ) :]
				)

				generated_sequences.append(snake_case       )
				print(snake_case       )

		return generated_sequences


if __name__ == "__main__":
		main()











import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


SCREAMING_SNAKE_CASE_      :			Any		  =      '''\
@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},
}
'''

SCREAMING_SNAKE_CASE_      :			Tuple		  =      '''\
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.
'''

SCREAMING_SNAKE_CASE_      :			Dict		  =      '''
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 		snake_case_      (       datasets.Metric	):







	'''simple docstring'''




	def       UpperCAmelCase (	self       :    Any      )					->   str:

			'''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       :    Optional[Any]				,			__lowerCamelCase       :    int				,			__lowerCamelCase       :    str				,			__lowerCamelCase       :    bool = False				,			__lowerCamelCase       :    Optional[str] = None				,			__lowerCamelCase       :    Optional[str] = None				,			__lowerCamelCase       :    Optional[List[int]] = None				,			__lowerCamelCase       :    Union[str, int] = "warn"				,			)					->   Tuple:

			'''simple docstring'''




			if scheme is not None:
					try:
							__lowercase					=    importlib.import_module('seqeval.scheme'      )
							__lowercase					=    getattr(__lowerCamelCase				,			__lowerCamelCase      )
					except AttributeError:
							raise ValueError(F"Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}"      )
			__lowercase					=    classification_report(
			    y_true=__lowerCamelCase				,			y_pred=__lowerCamelCase				,			suffix=__lowerCamelCase				,			output_dict=__lowerCamelCase				,			scheme=__lowerCamelCase				,			mode=__lowerCamelCase				,			sample_weight=__lowerCamelCase				,			zero_division=__lowerCamelCase				,			)
			report.pop('macro avg'      )
			report.pop('weighted avg'      )
			__lowercase					=    report.pop('micro avg'      )

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

			return scores




