
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


lowerCamelCase__    :		Dict          =   getLogger(__name__)


lowerCamelCase__    :		Tuple          =   """cuda""" if torch.cuda.is_available() else """cpu"""

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_ , lowercase_ = 8 , lowercase_ = DEFAULT_DEVICE , lowercase_=False , lowercase_="summarization" , lowercase_=None , **lowercase_ , )		->	Dict:


  '''simple docstring'''






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

  lowercase__  :   Any    		=      AutoTokenizer.from_pretrained(lowercase_   )
  logger.info(F'Inferred tokenizer type: {tokenizer.__class__}'   )  # if this is wrong, check config.model_type.

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

def 		UpperCamelCase	(      )		->	int:


  '''simple docstring'''






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

def 		UpperCamelCase	(      lowercase_=True   )		->	int:


  '''simple docstring'''






  lowercase__  :   Dict    		=      argparse.ArgumentParser()
  parser.add_argument("""model_name""" , type=lowercase_ , help="""like facebook/bart-large-cnn,t5-base, etc."""   )
  parser.add_argument("""input_path""" , type=lowercase_ , help="""like cnn_dm/test.source"""   )
  parser.add_argument("""save_path""" , type=lowercase_ , help="""where to save summaries"""   )
  parser.add_argument("""--reference_path""" , type=lowercase_ , required=lowercase_ , help="""like cnn_dm/test.target"""   )
  parser.add_argument("""--score_path""" , type=lowercase_ , required=lowercase_ , default="""metrics.json""" , help="""where to save metrics"""   )
  parser.add_argument("""--device""" , type=lowercase_ , required=lowercase_ , default=lowercase_ , help="""cuda, cuda:1, cpu etc."""   )
  parser.add_argument(
      """--prefix""" , type=lowercase_ , required=lowercase_ , default=lowercase_ , help="""will be added to the begininng of src examples"""   )
  parser.add_argument("""--task""" , type=lowercase_ , default="""summarization""" , help="""used for task_specific_params + metrics"""   )
  parser.add_argument("""--bs""" , type=lowercase_ , default=8 , required=lowercase_ , help="""batch size"""   )
  parser.add_argument(
      """--n_obs""" , type=lowercase_ , default=-1 , required=lowercase_ , 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=lowercase_ , 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__  :   Optional[Any]    		=      parser.parse_known_args()
  lowercase__  :   List[Any]    		=      parse_numeric_n_bool_cl_kwargs(lowercase_   )
  if parsed_args and verbose:
    print(F'parsed the following generate kwargs: {parsed_args}'   )
  lowercase__  :   int    		=      [""" """ + 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__  :   List[Any]    		=      examples[: args.n_obs]
  Path(args.save_path   ).parent.mkdir(exist_ok=lowercase_   )

  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__  :   Dict    		=      generate_summaries_or_translations(
      lowercase_ , args.save_path , args.model_name , batch_size=args.bs , device=args.device , fpaa=args.fpaa , task=args.task , prefix=args.prefix , **lowercase_ , )

  if args.reference_path is None:
    return {}

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

  if args.dump_args:
    scores.update(lowercase_   )
  if args.info:
    lowercase__  :   str    		=      args.info

  if verbose:
    print(lowercase_   )

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








lowerCamelCase__    :		Any          =   """0.18.2"""

from .configuration_utils import ConfigMixin
from .utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    logging,
)


try:
     if not is_onnx_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_onnx_objects import *  # noqa F403
else:
     from .pipelines import OnnxRuntimeModel

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_pt_objects import *  # noqa F403
else:
     from .models import (
         AutoencoderKL,
         ControlNetModel,
         ModelMixin,
         PriorTransformer,
         TaFilmDecoder,
         TransformeraDModel,
         UNetaDModel,
         UNetaDConditionModel,
         UNetaDModel,
         UNetaDConditionModel,
         VQModel,
     )
     from .optimization import (
         get_constant_schedule,
         get_constant_schedule_with_warmup,
         get_cosine_schedule_with_warmup,
         get_cosine_with_hard_restarts_schedule_with_warmup,
         get_linear_schedule_with_warmup,
         get_polynomial_decay_schedule_with_warmup,
         get_scheduler,
     )
     from .pipelines import (
         AudioPipelineOutput,
         ConsistencyModelPipeline,
         DanceDiffusionPipeline,
         DDIMPipeline,
         DDPMPipeline,
         DiffusionPipeline,
         DiTPipeline,
         ImagePipelineOutput,
         KarrasVePipeline,
         LDMPipeline,
         LDMSuperResolutionPipeline,
         PNDMPipeline,
         RePaintPipeline,
         ScoreSdeVePipeline,
     )
     from .schedulers import (
         CMStochasticIterativeScheduler,
         DDIMInverseScheduler,
         DDIMParallelScheduler,
         DDIMScheduler,
         DDPMParallelScheduler,
         DDPMScheduler,
         DEISMultistepScheduler,
         DPMSolverMultistepInverseScheduler,
         DPMSolverMultistepScheduler,
         DPMSolverSinglestepScheduler,
         EulerAncestralDiscreteScheduler,
         EulerDiscreteScheduler,
         HeunDiscreteScheduler,
         IPNDMScheduler,
         KarrasVeScheduler,
         KDPMaAncestralDiscreteScheduler,
         KDPMaDiscreteScheduler,
         PNDMScheduler,
         RePaintScheduler,
         SchedulerMixin,
         ScoreSdeVeScheduler,
         UnCLIPScheduler,
         UniPCMultistepScheduler,
         VQDiffusionScheduler,
     )
     from .training_utils import EMAModel

try:
     if not (is_torch_available() and is_scipy_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
     from .schedulers import LMSDiscreteScheduler

try:
     if not (is_torch_available() and is_torchsde_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
     from .schedulers import DPMSolverSDEScheduler

try:
     if not (is_torch_available() and is_transformers_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
     from .pipelines import (
         AltDiffusionImgaImgPipeline,
         AltDiffusionPipeline,
         AudioLDMPipeline,
         CycleDiffusionPipeline,
         IFImgaImgPipeline,
         IFImgaImgSuperResolutionPipeline,
         IFInpaintingPipeline,
         IFInpaintingSuperResolutionPipeline,
         IFPipeline,
         IFSuperResolutionPipeline,
         ImageTextPipelineOutput,
         KandinskyImgaImgPipeline,
         KandinskyInpaintPipeline,
         KandinskyPipeline,
         KandinskyPriorPipeline,
         KandinskyVaaControlnetImgaImgPipeline,
         KandinskyVaaControlnetPipeline,
         KandinskyVaaImgaImgPipeline,
         KandinskyVaaInpaintPipeline,
         KandinskyVaaPipeline,
         KandinskyVaaPriorEmbaEmbPipeline,
         KandinskyVaaPriorPipeline,
         LDMTextToImagePipeline,
         PaintByExamplePipeline,
         SemanticStableDiffusionPipeline,
         ShapEImgaImgPipeline,
         ShapEPipeline,
         StableDiffusionAttendAndExcitePipeline,
         StableDiffusionControlNetImgaImgPipeline,
         StableDiffusionControlNetInpaintPipeline,
         StableDiffusionControlNetPipeline,
         StableDiffusionDepthaImgPipeline,
         StableDiffusionDiffEditPipeline,
         StableDiffusionImageVariationPipeline,
         StableDiffusionImgaImgPipeline,
         StableDiffusionInpaintPipeline,
         StableDiffusionInpaintPipelineLegacy,
         StableDiffusionInstructPixaPixPipeline,
         StableDiffusionLatentUpscalePipeline,
         StableDiffusionLDMaDPipeline,
         StableDiffusionModelEditingPipeline,
         StableDiffusionPanoramaPipeline,
         StableDiffusionParadigmsPipeline,
         StableDiffusionPipeline,
         StableDiffusionPipelineSafe,
         StableDiffusionPixaPixZeroPipeline,
         StableDiffusionSAGPipeline,
         StableDiffusionUpscalePipeline,
         StableUnCLIPImgaImgPipeline,
         StableUnCLIPPipeline,
         TextToVideoSDPipeline,
         TextToVideoZeroPipeline,
         UnCLIPImageVariationPipeline,
         UnCLIPPipeline,
         UniDiffuserModel,
         UniDiffuserPipeline,
         UniDiffuserTextDecoder,
         VersatileDiffusionDualGuidedPipeline,
         VersatileDiffusionImageVariationPipeline,
         VersatileDiffusionPipeline,
         VersatileDiffusionTextToImagePipeline,
         VideoToVideoSDPipeline,
         VQDiffusionPipeline,
     )

try:
     if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
     from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
     if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
     from .pipelines import StableDiffusionKDiffusionPipeline

try:
     if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
     from .pipelines import (
         OnnxStableDiffusionImgaImgPipeline,
         OnnxStableDiffusionInpaintPipeline,
         OnnxStableDiffusionInpaintPipelineLegacy,
         OnnxStableDiffusionPipeline,
         OnnxStableDiffusionUpscalePipeline,
         StableDiffusionOnnxPipeline,
     )

try:
     if not (is_torch_available() and is_librosa_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
     from .pipelines import AudioDiffusionPipeline, Mel

try:
     if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
     from .pipelines import SpectrogramDiffusionPipeline

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_flax_objects import *  # noqa F403
else:
     from .models.controlnet_flax import FlaxControlNetModel
     from .models.modeling_flax_utils import FlaxModelMixin
     from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
     from .models.vae_flax import FlaxAutoencoderKL
     from .pipelines import FlaxDiffusionPipeline
     from .schedulers import (
         FlaxDDIMScheduler,
         FlaxDDPMScheduler,
         FlaxDPMSolverMultistepScheduler,
         FlaxKarrasVeScheduler,
         FlaxLMSDiscreteScheduler,
         FlaxPNDMScheduler,
         FlaxSchedulerMixin,
         FlaxScoreSdeVeScheduler,
     )


try:
     if not (is_flax_available() and is_transformers_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
     from .pipelines import (
         FlaxStableDiffusionControlNetPipeline,
         FlaxStableDiffusionImgaImgPipeline,
         FlaxStableDiffusionInpaintPipeline,
         FlaxStableDiffusionPipeline,
     )

try:
     if not (is_note_seq_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_note_seq_objects import *  # noqa F403
else:
     from .pipelines import MidiProcessor






