




from typing import Any, Dict, List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_torch_available():
     from ..models.auto.modeling_auto import (
         MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
         MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
         MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
         MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
     )


_UpperCAmelCase						:    Tuple      =  logging.get_logger(__name__)


_UpperCAmelCase						:    List[str]      =  Dict[str, Any]
_UpperCAmelCase						:    Union[str, Any]      =  List[Prediction]




@add_end_docstrings(lowerCAmelCase)
class  __lowerCAmelCase							(				lowerCAmelCase):






       def __init__( self:       int							,       *_lowerCAmelCase:       Dict							,       **_lowerCAmelCase:       Optional[Any]					):
         super().__init__(*_lowerCAmelCase							,       **_lowerCAmelCase					)

         if self.framework == "tf":
           raise ValueError(F"The {self.__class__} is only available in PyTorch."					)

         requires_backends(self							,       "vision"					)
         self.check_model_type(
             dict(
                 MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
                 + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
                 + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
                 + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()					)					)






       def        SCREAMING_SNAKE_CASE     ( self:       int							,       **_lowerCAmelCase:       Union[str, Any]					):
         lowercase     :int   						=    {}
         lowercase     :Optional[int]   						=    {}
         if "subtask" in kwargs:
           lowercase     :List[str]   						=    kwargs["subtask"]
           lowercase     :Optional[Any]   						=    kwargs["subtask"]
         if "threshold" in kwargs:
           lowercase     :int   						=    kwargs["threshold"]
         if "mask_threshold" in kwargs:
           lowercase     :Optional[Any]   						=    kwargs["mask_threshold"]
         if "overlap_mask_area_threshold" in kwargs:
           lowercase     :Any   						=    kwargs["overlap_mask_area_threshold"]

         return preprocess_kwargs, {}, postprocess_kwargs






       def __call__( self:       Optional[Any]							,       _lowerCAmelCase:       str							,       **_lowerCAmelCase:       List[str]					):
         return super().__call__(_lowerCAmelCase							,       **_lowerCAmelCase					)






       def        SCREAMING_SNAKE_CASE     ( self:       List[Any]							,       _lowerCAmelCase:       Optional[Any]							,       _lowerCAmelCase:       Union[str, Any]=None					):
         lowercase     :Any   						=    load_image(_lowerCAmelCase					)
         lowercase     :Tuple   						=    [(image.height, image.width)]
         if self.model.config.__class__.__name__ == "OneFormerConfig":
           if subtask is None:
             lowercase     :int   						=    {}
           else:
             lowercase     :Optional[int]   						=    {"task_inputs": [subtask]}
           lowercase     :Optional[int]   						=    self.image_processor(images=[image]							,       return_tensors="pt"							,       **_lowerCAmelCase					)
           lowercase     :Tuple   						=    self.tokenizer(
               inputs["task_inputs"]							,       padding="max_length"							,       max_length=self.model.config.task_seq_len							,       return_tensors=self.framework							,       )["input_ids"]
         else:
           lowercase     :Dict   						=    self.image_processor(images=[image]							,       return_tensors="pt"					)
         lowercase     :Dict   						=    target_size
         return inputs






       def        SCREAMING_SNAKE_CASE     ( self:       int							,       _lowerCAmelCase:       int					):
         lowercase     :Tuple   						=    model_inputs.pop("target_size"					)
         lowercase     :Tuple   						=    self.model(**_lowerCAmelCase					)
         lowercase     :List[Any]   						=    target_size
         return model_outputs






       def        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]							,       _lowerCAmelCase:       Any							,       _lowerCAmelCase:       Optional[int]=None							,       _lowerCAmelCase:       Optional[Any]=0.9							,       _lowerCAmelCase:       int=0.5							,       _lowerCAmelCase:       Tuple=0.5					):
         lowercase     :Tuple   						=    None
         if subtask in {"panoptic", None} and hasattr(self.image_processor							,       "post_process_panoptic_segmentation"					):
           lowercase     :Dict   						=    self.image_processor.post_process_panoptic_segmentation
         elif subtask in {"instance", None} and hasattr(self.image_processor							,       "post_process_instance_segmentation"					):
           lowercase     :List[str]   						=    self.image_processor.post_process_instance_segmentation

         if fn is not None:
           lowercase     :List[str]   						=    fn(
               _lowerCAmelCase							,       threshold=_lowerCAmelCase							,       mask_threshold=_lowerCAmelCase							,       overlap_mask_area_threshold=_lowerCAmelCase							,       target_sizes=model_outputs["target_size"]							,       )[0]

           lowercase     :Optional[int]   						=    []
           lowercase     :List[Any]   						=    outputs["segmentation"]

           for segment in outputs["segments_info"]:
             lowercase     :Union[str, Any]   						=    (segmentation == segment["id"]) * 2_55
             lowercase     :List[str]   						=    Image.fromarray(mask.numpy().astype(np.uinta					)							,       mode="L"					)
             lowercase     :str   						=    self.model.config.idalabel[segment["label_id"]]
             lowercase     :List[str]   						=    segment["score"]
             annotation.append({"score": score, "label": label, "mask": mask}					)

         elif subtask in {"semantic", None} and hasattr(self.image_processor							,       "post_process_semantic_segmentation"					):
           lowercase     :Tuple   						=    self.image_processor.post_process_semantic_segmentation(
               _lowerCAmelCase							,       target_sizes=model_outputs["target_size"]					)[0]

           lowercase     :str   						=    []
           lowercase     :str   						=    outputs.numpy()
           lowercase     :List[Any]   						=    np.unique(_lowerCAmelCase					)

           for label in labels:
             lowercase     :Optional[Any]   						=    (segmentation == label) * 2_55
             lowercase     :List[str]   						=    Image.fromarray(mask.astype(np.uinta					)							,       mode="L"					)
             lowercase     :Optional[Any]   						=    self.model.config.idalabel[label]
             annotation.append({"score": None, "label": label, "mask": mask}					)
         else:
           raise ValueError(F"Subtask {subtask} is not supported for model {type(self.model					)}"					)
         return annotation









import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


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



def    UpperCAmelCase__	(  ):
  lowercase     :int   						=    argparse.ArgumentParser(description="PyTorch Transformer Language Model" )
  parser.add_argument("--model_name",      type=lowerCamelCase,      default="transfo-xl-wt103",      help="pretrained model name" )
  parser.add_argument(
      "--split",      type=lowerCamelCase,      default="test",      choices=["all", "valid", "test"],      help="which split to evaluate" )
  parser.add_argument("--batch_size",      type=lowerCamelCase,      default=10,      help="batch size" )
  parser.add_argument("--tgt_len",      type=lowerCamelCase,      default=128,      help="number of tokens to predict" )
  parser.add_argument("--ext_len",      type=lowerCamelCase,      default=0,      help="length of the extended context" )
  parser.add_argument("--mem_len",      type=lowerCamelCase,      default=1600,      help="length of the retained previous heads" )
  parser.add_argument("--clamp_len",      type=lowerCamelCase,      default=1000,      help="max positional embedding index" )
  parser.add_argument("--no_cuda",      action="store_true",      help="Do not use CUDA even though CUA is available" )
  parser.add_argument("--work_dir",      type=lowerCamelCase,      required=lowerCamelCase,      help="path to the work_dir" )
  parser.add_argument("--no_log",      action="store_true",      help="do not log the eval result" )
  parser.add_argument("--same_length",      action="store_true",      help="set same length attention with masking" )
  parser.add_argument("--server_ip",      type=lowerCamelCase,      default="",      help="Can be used for distant debugging." )
  parser.add_argument("--server_port",      type=lowerCamelCase,      default="",      help="Can be used for distant debugging." )
  lowercase     :Any   						=    parser.parse_args()
  assert args.ext_len >= 0, "extended context length must be non-negative"

  if args.server_ip and args.server_port:
    # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
    import ptvsd

    print("Waiting for debugger attach" )
    ptvsd.enable_attach(address=(args.server_ip, args.server_port),      redirect_output=lowerCamelCase )
    ptvsd.wait_for_attach()

  lowercase     :Any   						=    torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" )
  logger.info("device: {}".format(lowerCamelCase ) )

  # Load a pre-processed dataset
  # You can also build the corpus yourself using TransfoXLCorpus methods
  # The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
  # and tokenizing the dataset
  # The pre-processed corpus is a convertion (using the conversion script )
  lowercase     :Dict   						=    TransfoXLCorpus.from_pretrained(args.model_name )

  lowercase     :Optional[int]   						=    corpus.get_iterator("valid",      args.batch_size,      args.tgt_len,      device=lowerCamelCase,      ext_len=args.ext_len )
  lowercase     :Union[str, Any]   						=    corpus.get_iterator("test",      args.batch_size,      args.tgt_len,      device=lowerCamelCase,      ext_len=args.ext_len )

  # Load a pre-trained model
  lowercase     :List[Any]   						=    TransfoXLLMHeadModel.from_pretrained(args.model_name )
  model.to(lowerCamelCase )

  logger.info(
      "Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}".format(
          args.batch_size,      args.tgt_len,      args.ext_len,      args.mem_len,      args.clamp_len ) )

  model.reset_memory_length(args.mem_len )
  if args.clamp_len > 0:
    lowercase     :Tuple   						=    args.clamp_len
  if args.same_length:
    lowercase     :Tuple   						=    True

  ###############################################################################
  # Evaluation code
  ###############################################################################
  def evaluate(lowerCamelCase ):
    # Turn on evaluation mode which disables dropout.
    model.eval()
    lowercase						,     lowercase     :Optional[Any]   						=    0, 0.0
    lowercase     :Optional[Any]   						=    time.time()
    with torch.no_grad():
      lowercase     :Dict   						=    None
      for idx, (data, target, seq_len) in enumerate(lowerCamelCase ):
        lowercase     :Optional[Any]   						=    model(lowerCamelCase,      lm_labels=lowerCamelCase,      mems=lowerCamelCase )
        lowercase						,     lowercase						,     lowercase     :Optional[int]   						=    ret
        lowercase     :Optional[Any]   						=    loss.mean()
        total_loss += seq_len * loss.item()
        total_len += seq_len
      lowercase     :List[Any]   						=    time.time() - start_time
    logger.info("Time : {:.2f}s, {:.2f}ms/segment".format(lowerCamelCase,      1000 * total_time / (idx + 1) ) )
    return total_loss / total_len

  # Run on test data.
  if args.split == "all":
    lowercase     :Union[str, Any]   						=    evaluate(lowerCamelCase )
    lowercase     :List[str]   						=    evaluate(lowerCamelCase )
  elif args.split == "valid":
    lowercase     :Optional[int]   						=    evaluate(lowerCamelCase )
    lowercase     :Dict   						=    None
  elif args.split == "test":
    lowercase     :int   						=    evaluate(lowerCamelCase )
    lowercase     :Optional[Any]   						=    None

  def format_log(lowerCamelCase,      lowerCamelCase ):
    lowercase     :List[str]   						=    "| {0} loss {1:5.2f} | {0} ppl {2:9.3f} ".format(lowerCamelCase,      lowerCamelCase,      math.exp(lowerCamelCase ) )
    return log_str

  lowercase     :Optional[int]   						=    ""
  if valid_loss is not None:
    log_str += format_log(lowerCamelCase,      "valid" )
  if test_loss is not None:
    log_str += format_log(lowerCamelCase,      "test" )

  logger.info("=" * 100 )
  logger.info(lowerCamelCase )
  logger.info("=" * 100 )


if __name__ == "__main__":
     main()



