"""simple docstring"""





import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


_UpperCamelCase     :		Dict											=					getLogger(__name__)


_UpperCamelCase     :		List[Any]											=					"cuda" if torch.cuda.is_available() else "cpu"





def        a_  (   _lowerCAmelCase						:				List[str]     ,				_lowerCAmelCase						:				str     ,				_lowerCAmelCase						:				str     ,				_lowerCAmelCase						:				int = 8     ,				_lowerCAmelCase						:				str = DEFAULT_DEVICE     ,				_lowerCAmelCase						:				Optional[Any]=False     ,				_lowerCAmelCase						:				Union[str, Any]="summarization"     ,				_lowerCAmelCase						:				Optional[Any]=None     ,				**_lowerCAmelCase						:				Optional[Any]     ,				):





       '''simple docstring'''

       lowercase__    :	List[str]     					=			Path(_lowerCAmelCase  ).open('w'     ,				encoding='utf-8'  )
       lowercase__    :	Any     					=			str(_lowerCAmelCase  )
       lowercase__    :	int     					=			AutoModelForSeqaSeqLM.from_pretrained(_lowerCAmelCase  ).to(_lowerCAmelCase  )
       if fpaa:
              lowercase__    :	Union[str, Any]     					=			model.half()

       lowercase__    :	Optional[int]     					=			AutoTokenizer.from_pretrained(_lowerCAmelCase  )
       logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""  )  # if this is wrong, check config.model_type.

       lowercase__    :	str     					=			time.time()
       # update config with task specific params
       use_task_specific_params(_lowerCAmelCase     ,				_lowerCAmelCase  )
       if prefix is None:
              lowercase__    :	Tuple     					=			prefix or getattr(model.config     ,				'prefix'     ,				''  ) or ''
       for examples_chunk in tqdm(list(chunks(_lowerCAmelCase     ,				_lowerCAmelCase  )  )  ):
              lowercase__    :	Tuple     					=			[prefix + text for text in examples_chunk]
              lowercase__    :	Optional[int]     					=			tokenizer(_lowerCAmelCase     ,				return_tensors='pt'     ,				truncation=_lowerCAmelCase     ,				padding='longest'  ).to(_lowerCAmelCase  )
              lowercase__    :	int     					=			model.generate(
                  input_ids=batch.input_ids     ,				attention_mask=batch.attention_mask     ,				**_lowerCAmelCase     ,				)
              lowercase__    :	str     					=			tokenizer.batch_decode(_lowerCAmelCase     ,				skip_special_tokens=_lowerCAmelCase     ,				clean_up_tokenization_spaces=_lowerCAmelCase  )
              for hypothesis in dec:
                     fout.write(hypothesis + '\n'  )
                     fout.flush()
       fout.close()
       lowercase__    :	str     					=			int(time.time() - start_time  )  # seconds
       lowercase__    :	Union[str, Any]     					=			len(_lowerCAmelCase  )
       return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs     ,				4  )}





def        a_  (   ):





       '''simple docstring'''

       return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'  )





def        a_  (   _lowerCAmelCase						:				Tuple=True  ):





       '''simple docstring'''

       lowercase__    :	List[Any]     					=			argparse.ArgumentParser()
       parser.add_argument('model_name'     ,				type=_lowerCAmelCase     ,				help='like facebook/bart-large-cnn,t5-base, etc.'  )
       parser.add_argument('input_path'     ,				type=_lowerCAmelCase     ,				help='like cnn_dm/test.source'  )
       parser.add_argument('save_path'     ,				type=_lowerCAmelCase     ,				help='where to save summaries'  )
       parser.add_argument('--reference_path'     ,				type=_lowerCAmelCase     ,				required=_lowerCAmelCase     ,				help='like cnn_dm/test.target'  )
       parser.add_argument('--score_path'     ,				type=_lowerCAmelCase     ,				required=_lowerCAmelCase     ,				default='metrics.json'     ,				help='where to save metrics'  )
       parser.add_argument('--device'     ,				type=_lowerCAmelCase     ,				required=_lowerCAmelCase     ,				default=_lowerCAmelCase     ,				help='cuda, cuda:1, cpu etc.'  )
       parser.add_argument(
           '--prefix'     ,				type=_lowerCAmelCase     ,				required=_lowerCAmelCase     ,				default=_lowerCAmelCase     ,				help='will be added to the begininng of src examples'  )
       parser.add_argument('--task'     ,				type=_lowerCAmelCase     ,				default='summarization'     ,				help='used for task_specific_params + metrics'  )
       parser.add_argument('--bs'     ,				type=_lowerCAmelCase     ,				default=8     ,				required=_lowerCAmelCase     ,				help='batch size'  )
       parser.add_argument(
           '--n_obs'     ,				type=_lowerCAmelCase     ,				default=-1     ,				required=_lowerCAmelCase     ,				help='How many observations. Defaults to all.'  )
       parser.add_argument('--fp16'     ,				action='store_true'  )
       parser.add_argument('--dump-args'     ,				action='store_true'     ,				help='print the custom hparams with the results'  )
       parser.add_argument(
           '--info'     ,				nargs='?'     ,				type=_lowerCAmelCase     ,				const=datetime_now()     ,				help=(
               'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
               ' lang=en-ru. If no value is passed, the current datetime string will be used.'
           )     ,				)
       # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
       lowercase__					,   lowercase__    :	Any     					=			parser.parse_known_args()
       lowercase__    :	List[str]     					=			parse_numeric_n_bool_cl_kwargs(_lowerCAmelCase  )
       if parsed_args and verbose:
              print(f"""parsed the following generate kwargs: {parsed_args}"""  )
       lowercase__    :	Union[str, Any]     					=			[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path  ).readlines()]
       if args.n_obs > 0:
              lowercase__    :	Tuple     					=			examples[: args.n_obs]
       Path(args.save_path  ).parent.mkdir(exist_ok=_lowerCAmelCase  )

       if args.reference_path is None and Path(args.score_path  ).exists():
              warnings.warn(f"""score_path {args.score_path} will be overwritten unless you type ctrl-c."""  )

       if args.device == "cpu" and args.fpaa:
              # this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
              raise ValueError('Can\'t mix --fp16 and --device cpu'  )

       lowercase__    :	List[Any]     					=			generate_summaries_or_translations(
           _lowerCAmelCase     ,				args.save_path     ,				args.model_name     ,				batch_size=args.bs     ,				device=args.device     ,				fpaa=args.fpaa     ,				task=args.task     ,				prefix=args.prefix     ,				**_lowerCAmelCase     ,				)

       if args.reference_path is None:
              return {}

       # Compute scores
       lowercase__    :	Any     					=			calculate_bleu if 'translation' in args.task else calculate_rouge
       lowercase__    :	Any     					=			[x.rstrip() for x in open(args.save_path  ).readlines()]
       lowercase__    :	str     					=			[x.rstrip() for x in open(args.reference_path  ).readlines()][: len(_lowerCAmelCase  )]
       lowercase__    :	dict     					=			score_fn(_lowerCAmelCase     ,				_lowerCAmelCase  )
       scores.update(_lowerCAmelCase  )

       if args.dump_args:
              scores.update(_lowerCAmelCase  )
       if args.info:
              lowercase__    :	List[str]     					=			args.info

       if verbose:
              print(_lowerCAmelCase  )

       if args.score_path is not None:
              json.dump(_lowerCAmelCase     ,				open(args.score_path     ,				'w'  )  )

       return scores


if __name__ == "__main__":
       # Usage for MT:
       # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
       run_generate(verbose=True)


"""simple docstring"""





import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
       import tensorflow as tf

       from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments


@require_tf
class   UpperCAmelCase_			(					unittest.TestCase):


      def 	_UpperCAmelCase (     self	,    a							) ->      str:
             for model_result in results.values():
                    for batch_size, sequence_length in zip(model_result['bs']	,    model_result['ss']							):
                           lowercase__    :	str     					=			model_result['result'][batch_size][sequence_length]
                           self.assertIsNotNone(a							)


      def 	_UpperCAmelCase (     self							) ->      int:
             lowercase__    :	Dict     					=			'sshleifer/tiny-gpt2'
             lowercase__    :	int     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    eager_mode=a	,    multi_process=a	,    )
             lowercase__    :	str     					=			TensorFlowBenchmark(a							)
             lowercase__    :	Optional[int]     					=			benchmark.run()
             self.check_results_dict_not_empty(results.time_inference_result							)
             self.check_results_dict_not_empty(results.memory_inference_result							)


      def 	_UpperCAmelCase (     self							) ->      int:
             lowercase__    :	List[str]     					=			'sgugger/tiny-distilbert-classification'
             lowercase__    :	int     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=a	,    only_pretrain_model=a	,    )
             lowercase__    :	Optional[Any]     					=			TensorFlowBenchmark(a							)
             lowercase__    :	Optional[int]     					=			benchmark.run()
             self.check_results_dict_not_empty(results.time_inference_result							)
             self.check_results_dict_not_empty(results.memory_inference_result							)


      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             lowercase__    :	Optional[int]     					=			'sshleifer/tiny-gpt2'
             lowercase__    :	Union[str, Any]     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=a	,    )
             lowercase__    :	Optional[Any]     					=			TensorFlowBenchmark(a							)
             lowercase__    :	List[str]     					=			benchmark.run()
             self.check_results_dict_not_empty(results.time_inference_result							)
             self.check_results_dict_not_empty(results.memory_inference_result							)


      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             lowercase__    :	Any     					=			'sshleifer/tiny-gpt2'
             lowercase__    :	List[Any]     					=			AutoConfig.from_pretrained(a							)
             lowercase__    :	Any     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    eager_mode=a	,    multi_process=a	,    )
             lowercase__    :	Tuple     					=			TensorFlowBenchmark(a	,    [config]							)
             lowercase__    :	Dict     					=			benchmark.run()
             self.check_results_dict_not_empty(results.time_inference_result							)
             self.check_results_dict_not_empty(results.memory_inference_result							)


      def 	_UpperCAmelCase (     self							) ->      int:
             lowercase__    :	Optional[Any]     					=			'sshleifer/tiny-gpt2'
             lowercase__    :	List[str]     					=			AutoConfig.from_pretrained(a							)
             lowercase__    :	Any     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=a	,    )
             lowercase__    :	List[str]     					=			TensorFlowBenchmark(a	,    [config]							)
             lowercase__    :	Any     					=			benchmark.run()
             self.check_results_dict_not_empty(results.time_inference_result							)
             self.check_results_dict_not_empty(results.memory_inference_result							)


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	Optional[Any]     					=			'sshleifer/tiny-gpt2'
             lowercase__    :	Any     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=a	,    )
             lowercase__    :	Optional[Any]     					=			TensorFlowBenchmark(a							)
             lowercase__    :	Tuple     					=			benchmark.run()
             self.check_results_dict_not_empty(results.time_train_result							)
             self.check_results_dict_not_empty(results.memory_train_result							)


      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__    :	Optional[Any]     					=			'sshleifer/tiny-gpt2'
             lowercase__    :	Optional[int]     					=			AutoConfig.from_pretrained(a							)
             lowercase__    :	str     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=a	,    )
             lowercase__    :	str     					=			TensorFlowBenchmark(a	,    [config]							)
             lowercase__    :	Optional[int]     					=			benchmark.run()
             self.check_results_dict_not_empty(results.time_train_result							)
             self.check_results_dict_not_empty(results.memory_train_result							)


      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             lowercase__    :	List[str]     					=			'patrickvonplaten/t5-tiny-random'
             lowercase__    :	Any     					=			AutoConfig.from_pretrained(a							)
             lowercase__    :	List[str]     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    multi_process=a	,    )
             lowercase__    :	int     					=			TensorFlowBenchmark(a	,    configs=[config]							)
             lowercase__    :	Union[str, Any]     					=			benchmark.run()
             self.check_results_dict_not_empty(results.time_inference_result							)
             self.check_results_dict_not_empty(results.memory_inference_result							)


      @unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('GPU'							)							) == 0	,    'Cannot do xla on CPU.'							)
      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__    :	Any     					=			'sshleifer/tiny-gpt2'
             lowercase__    :	Optional[Any]     					=			TensorFlowBenchmarkArguments(
                 models=[MODEL_ID]	,    training=a	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    use_xla=a	,    multi_process=a	,    )
             lowercase__    :	Any     					=			TensorFlowBenchmark(a							)
             lowercase__    :	Dict     					=			benchmark.run()
             self.check_results_dict_not_empty(results.time_inference_result							)
             self.check_results_dict_not_empty(results.memory_inference_result							)


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__    :	Any     					=			'sshleifer/tiny-gpt2'
             with tempfile.TemporaryDirectory() as tmp_dir:
                    lowercase__    :	List[Any]     					=			TensorFlowBenchmarkArguments(
                        models=[MODEL_ID]	,    inference=a	,    save_to_csv=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    inference_time_csv_file=os.path.join(a	,    'inf_time.csv'							)	,    inference_memory_csv_file=os.path.join(a	,    'inf_mem.csv'							)	,    env_info_csv_file=os.path.join(a	,    'env.csv'							)	,    multi_process=a	,    )
                    lowercase__    :	Union[str, Any]     					=			TensorFlowBenchmark(a							)
                    benchmark.run()
                    self.assertTrue(Path(os.path.join(a	,    'inf_time.csv'							)							).exists()							)
                    self.assertTrue(Path(os.path.join(a	,    'inf_mem.csv'							)							).exists()							)
                    self.assertTrue(Path(os.path.join(a	,    'env.csv'							)							).exists()							)


      def 	_UpperCAmelCase (     self							) ->      Dict:
             lowercase__    :	Tuple     					=			'sshleifer/tiny-gpt2'

             def _check_summary_is_not_empty(a							):
                    self.assertTrue(hasattr(a	,    'sequential'							)							)
                    self.assertTrue(hasattr(a	,    'cumulative'							)							)
                    self.assertTrue(hasattr(a	,    'current'							)							)
                    self.assertTrue(hasattr(a	,    'total'							)							)

             with tempfile.TemporaryDirectory() as tmp_dir:
                    lowercase__    :	Optional[Any]     					=			TensorFlowBenchmarkArguments(
                        models=[MODEL_ID]	,    inference=a	,    sequence_lengths=[8]	,    batch_sizes=[1]	,    log_filename=os.path.join(a	,    'log.txt'							)	,    log_print=a	,    trace_memory_line_by_line=a	,    eager_mode=a	,    multi_process=a	,    )
                    lowercase__    :	Optional[int]     					=			TensorFlowBenchmark(a							)
                    lowercase__    :	Optional[Any]     					=			benchmark.run()
                    _check_summary_is_not_empty(result.inference_summary							)
                    self.assertTrue(Path(os.path.join(a	,    'log.txt'							)							).exists()							)

