
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


lowercase						:      Optional[Any]							=							getLogger(__name__)


lowercase						:      Any							=							"""cuda""" if torch.cuda.is_available() else """cpu"""







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__ = 8      ,			SCREAMING_SNAKE_CASE__ = DEFAULT_DEVICE      ,			SCREAMING_SNAKE_CASE__=False      ,			SCREAMING_SNAKE_CASE__="summarization"      ,			SCREAMING_SNAKE_CASE__=None      ,			**SCREAMING_SNAKE_CASE__      ,			)				->					Dict:
							lowercase							:  Optional[int] 			=   Path(SCREAMING_SNAKE_CASE__       ).open("""w"""      ,			encoding="""utf-8"""       )
							lowercase							:  Optional[int] 			=   str(SCREAMING_SNAKE_CASE__       )
							lowercase							:  List[str] 			=   AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE__       ).to(SCREAMING_SNAKE_CASE__       )
							if fpaa:
														lowercase							:  Optional[int] 			=   model.half()

							lowercase							:  int 			=   AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__       )
							logger.info(f"Inferred tokenizer type: {tokenizer.__class__}"       )  # if this is wrong, check config.model_type.

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







def 		_snake_case(						)				->					Any:
							return datetime.datetime.now().strftime("""%Y-%m-%d %H:%M:%S"""       )







def 		_snake_case(						SCREAMING_SNAKE_CASE__=True       )				->					int:
							lowercase							:  str 			=   argparse.ArgumentParser()
							parser.add_argument("""model_name"""      ,			type=SCREAMING_SNAKE_CASE__      ,			help="""like facebook/bart-large-cnn,t5-base, etc."""       )
							parser.add_argument("""input_path"""      ,			type=SCREAMING_SNAKE_CASE__      ,			help="""like cnn_dm/test.source"""       )
							parser.add_argument("""save_path"""      ,			type=SCREAMING_SNAKE_CASE__      ,			help="""where to save summaries"""       )
							parser.add_argument("""--reference_path"""      ,			type=SCREAMING_SNAKE_CASE__      ,			required=SCREAMING_SNAKE_CASE__      ,			help="""like cnn_dm/test.target"""       )
							parser.add_argument("""--score_path"""      ,			type=SCREAMING_SNAKE_CASE__      ,			required=SCREAMING_SNAKE_CASE__      ,			default="""metrics.json"""      ,			help="""where to save metrics"""       )
							parser.add_argument("""--device"""      ,			type=SCREAMING_SNAKE_CASE__      ,			required=SCREAMING_SNAKE_CASE__      ,			default=SCREAMING_SNAKE_CASE__      ,			help="""cuda, cuda:1, cpu etc."""       )
							parser.add_argument(
							    """--prefix"""      ,			type=SCREAMING_SNAKE_CASE__      ,			required=SCREAMING_SNAKE_CASE__      ,			default=SCREAMING_SNAKE_CASE__      ,			help="""will be added to the begininng of src examples"""       )
							parser.add_argument("""--task"""      ,			type=SCREAMING_SNAKE_CASE__      ,			default="""summarization"""      ,			help="""used for task_specific_params + metrics"""       )
							parser.add_argument("""--bs"""      ,			type=SCREAMING_SNAKE_CASE__      ,			default=8      ,			required=SCREAMING_SNAKE_CASE__      ,			help="""batch size"""       )
							parser.add_argument(
							    """--n_obs"""      ,			type=SCREAMING_SNAKE_CASE__      ,			default=-1      ,			required=SCREAMING_SNAKE_CASE__      ,			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=SCREAMING_SNAKE_CASE__      ,			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							:  str 			=   parser.parse_known_args()
							lowercase							:  Optional[int] 			=   parse_numeric_n_bool_cl_kwargs(SCREAMING_SNAKE_CASE__       )
							if parsed_args and verbose:
														print(f"parsed the following generate kwargs: {parsed_args}"       )
							with open(args.input_path       ) as f:
														lowercase							:  Optional[int] 			=   [""" """ + x.rstrip() if """t5""" in args.model_name else x.rstrip() for x in f.readlines()]
							if args.n_obs > 0:
														lowercase							:  List[str] 			=   examples[: args.n_obs]
							Path(args.save_path       ).parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE__       )
							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."       )
							lowercase							:  Optional[Any] 			=   generate_summaries_or_translations(
							    SCREAMING_SNAKE_CASE__      ,			args.save_path      ,			args.model_name      ,			batch_size=args.bs      ,			device=args.device      ,			fpaa=args.fpaa      ,			task=args.task      ,			prefix=args.prefix      ,			**SCREAMING_SNAKE_CASE__      ,			)

							if args.reference_path is None:
														return {}

							# Compute scores
							lowercase							:  Optional[Any] 			=   calculate_bleu if """translation""" in args.task else calculate_rouge
							lowercase							:  List[Any] 			=   [x.rstrip() for x in open(args.save_path       ).readlines()]
							lowercase							:  int 			=   [x.rstrip() for x in open(args.reference_path       ).readlines()][: len(SCREAMING_SNAKE_CASE__       )]
							lowercase							:  dict 			=   score_fn(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )
							scores.update(SCREAMING_SNAKE_CASE__       )

							if args.dump_args:
														scores.update(SCREAMING_SNAKE_CASE__       )
							if args.info:
														lowercase							:  List[Any] 			=   args.info

							if verbose:
														print(SCREAMING_SNAKE_CASE__       )

							if args.score_path is not None:
														json.dump(SCREAMING_SNAKE_CASE__      ,			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)


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_keras_nlp_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase						:      Any							=							{
    """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
    """tokenization_gpt2""": ["""GPT2Tokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase						:      List[Any]							=							["""GPT2TokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase						:      str							=							[
      """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """GPT2DoubleHeadsModel""",
      """GPT2ForQuestionAnswering""",
      """GPT2ForSequenceClassification""",
      """GPT2ForTokenClassification""",
      """GPT2LMHeadModel""",
      """GPT2Model""",
      """GPT2PreTrainedModel""",
      """load_tf_weights_in_gpt2""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase						:      Tuple							=							[
      """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFGPT2DoubleHeadsModel""",
      """TFGPT2ForSequenceClassification""",
      """TFGPT2LMHeadModel""",
      """TFGPT2MainLayer""",
      """TFGPT2Model""",
      """TFGPT2PreTrainedModel""",
  ]

try:
  if not is_keras_nlp_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase						:      List[str]							=							["""TFGPT2Tokenizer"""]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase						:      str							=							["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]

if TYPE_CHECKING:
  from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
  from .tokenization_gpta import GPTaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_gpta_fast import GPTaTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpta import (
        GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTaDoubleHeadsModel,
        GPTaForQuestionAnswering,
        GPTaForSequenceClassification,
        GPTaForTokenClassification,
        GPTaLMHeadModel,
        GPTaModel,
        GPTaPreTrainedModel,
        load_tf_weights_in_gpta,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_gpta import (
        TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFGPTaDoubleHeadsModel,
        TFGPTaForSequenceClassification,
        TFGPTaLMHeadModel,
        TFGPTaMainLayer,
        TFGPTaModel,
        TFGPTaPreTrainedModel,
    )

  try:
    if not is_keras_nlp_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_gpta_tf import TFGPTaTokenizer

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
  import sys

  lowercase						:      Union[str, Any]							=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
