



"""simple docstring"""


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,
				)


snake_case_								=			logging.get_logger(__name__)


snake_case_								=			Dict[str, Any]
snake_case_								=			List[Prediction]







@add_end_docstrings(SCREAMING_SNAKE_CASE_							)
class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""






						def __init__(   self					:List[Any]  ,    *lowercase_					:int  ,    **lowercase_					:Dict   )							->     int:
												super().__init__(*A_  ,    **A_   )

												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       UpperCAmelCase__	(   self					:List[Any]  ,    **lowercase_					:int   )							->     Union[str, Any]:
												UpperCAmelCase			 =					{}
												UpperCAmelCase			 =					{}
												if "subtask" in kwargs:
																		UpperCAmelCase			 =					kwargs["subtask"]
																		UpperCAmelCase			 =					kwargs["subtask"]
												if "threshold" in kwargs:
																		UpperCAmelCase			 =					kwargs["threshold"]
												if "mask_threshold" in kwargs:
																		UpperCAmelCase			 =					kwargs["mask_threshold"]
												if "overlap_mask_area_threshold" in kwargs:
																		UpperCAmelCase			 =					kwargs["overlap_mask_area_threshold"]

												return preprocess_kwargs, {}, postprocess_kwargs






						def __call__(   self					:Any  ,    lowercase_					:str  ,    **lowercase_					:Union[str, Any]   )							->     Union[str, Any]:
												return super().__call__(A_  ,    **A_   )






						def       UpperCAmelCase__	(   self					:int  ,    lowercase_					:Optional[Any]  ,    lowercase_					:Optional[Any]=None   )							->     Union[str, Any]:
												UpperCAmelCase			 =					load_image(A_   )
												UpperCAmelCase			 =					[(image.height, image.width)]
												if self.model.config.__class__.__name__ == "OneFormerConfig":
																		if subtask is None:
																								UpperCAmelCase			 =					{}
																		else:
																								UpperCAmelCase			 =					{"task_inputs": [subtask]}
																		UpperCAmelCase			 =					self.image_processor(images=[image]  ,    return_tensors='pt'  ,    **A_   )
																		UpperCAmelCase			 =					self.tokenizer(
																		    inputs['task_inputs']  ,    padding='max_length'  ,    max_length=self.model.config.task_seq_len  ,    return_tensors=self.framework  ,    )["input_ids"]
												else:
																		UpperCAmelCase			 =					self.image_processor(images=[image]  ,    return_tensors='pt'   )
												UpperCAmelCase			 =					target_size
												return inputs






						def       UpperCAmelCase__	(   self					:Optional[Any]  ,    lowercase_					:List[Any]   )							->     Tuple:
												UpperCAmelCase			 =					model_inputs.pop('target_size'   )
												UpperCAmelCase			 =					self.model(**A_   )
												UpperCAmelCase			 =					target_size
												return model_outputs






						def       UpperCAmelCase__	(   self					:Any  ,    lowercase_					:Union[str, Any]  ,    lowercase_					:Optional[Any]=None  ,    lowercase_					:Dict=0.9  ,    lowercase_					:List[str]=0.5  ,    lowercase_					:Optional[Any]=0.5   )							->     List[Any]:
												UpperCAmelCase			 =					None
												if subtask in {"panoptic", None} and hasattr(self.image_processor  ,    'post_process_panoptic_segmentation'   ):
																		UpperCAmelCase			 =					self.image_processor.post_process_panoptic_segmentation
												elif subtask in {"instance", None} and hasattr(self.image_processor  ,    'post_process_instance_segmentation'   ):
																		UpperCAmelCase			 =					self.image_processor.post_process_instance_segmentation

												if fn is not None:
																		UpperCAmelCase			 =					fn(
																		    A_  ,    threshold=A_  ,    mask_threshold=A_  ,    overlap_mask_area_threshold=A_  ,    target_sizes=model_outputs['target_size']  ,    )[0]

																		UpperCAmelCase			 =					[]
																		UpperCAmelCase			 =					outputs["segmentation"]

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

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

																		UpperCAmelCase			 =					[]
																		UpperCAmelCase			 =					outputs.numpy()
																		UpperCAmelCase			 =					np.unique(A_   )

																		for label in labels:
																								UpperCAmelCase			 =					(segmentation == label) * 2_55
																								UpperCAmelCase			 =					Image.fromarray(mask.astype(np.uinta   )  ,    mode='L'   )
																								UpperCAmelCase			 =					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 itertools
import operator
import sys
from collections import OrderedDict

from run_eval import datetime_now, run_generate

from utils import ROUGE_KEYS


# A table of supported tasks and the list of scores in the order of importance to be sorted by.
# To add a new task, simply list the score names that `run_eval.run_generate()` returns
__UpperCAmelCase     					=  {
    'translation': ['bleu'],
    'summarization': ROUGE_KEYS,
}







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Optional[Any]:

     """simple docstring"""







     _lowerCAmelCase :       Dict	   =     search.split()
     _lowerCAmelCase :       int	   =     dict((g.split("="					) for g in groups)					)
     _lowerCAmelCase :       Dict	   =     list(entries.keys()					)
     _lowerCAmelCase :       Tuple	   =     [[f'''--{k} {v}''' for v in vs.split(":"					)] for k, vs in entries.items()]
     _lowerCAmelCase :       Dict	   =     [list(SCREAMING_SNAKE_CASE					) for x in itertools.product(*SCREAMING_SNAKE_CASE					)]
     return matrix, entry_names







def 						_snake_case		(     )							->   Tuple:

     """simple docstring"""







     _lowerCAmelCase :       List[str]	   =     sys.argv[0]

     _lowerCAmelCase :       Any	   =     argparse.ArgumentParser(
         usage=(
             "\n\nImportant: this script accepts all arguments `run_eval.py` accepts and then a few extra, therefore"
             " refer to `run_eval.py -h` for the complete list."
         )					)
     parser.add_argument(
         "--search"		,				type=SCREAMING_SNAKE_CASE		,				required=SCREAMING_SNAKE_CASE		,				help="param space to search, e.g. \"num_beams=5:10 length_penalty=0.8:1.0:1.2\""		,				)
     parser.add_argument(
         "--bs"		,				type=SCREAMING_SNAKE_CASE		,				default=8		,				required=SCREAMING_SNAKE_CASE		,				help="initial batch size (may get reduced if it's too big)"					)
     parser.add_argument("--task"		,				type=SCREAMING_SNAKE_CASE		,				help="used for task_specific_params + metrics"					)
     parser.add_argument(
         "--info"		,				nargs="?"		,				type=SCREAMING_SNAKE_CASE		,				const=datetime_now()		,				help=(
             "add custom notes to be printed before the results table. If no value is passed, the current datetime"
             " string will be used."
         )		,				)
     _lowerCAmelCase   ,      _lowerCAmelCase :       List[str]	   =     parser.parse_known_args()
     # we share some of the args
     args_main.extend(["--task", args.task]					)
     _lowerCAmelCase :       str	   =     [prog] + args_main

     # to support variations like translation_en_to_de"
     _lowerCAmelCase :       Any	   =     "translation" if "translation" in args.task else "summarization"

     _lowerCAmelCase   ,      _lowerCAmelCase :       int	   =     parse_search_arg(args.search					)
     _lowerCAmelCase :       Optional[Any]	   =     task_score_names[task]  # score cols first
     _lowerCAmelCase :       int	   =     {col: len(str(SCREAMING_SNAKE_CASE					)					) for col in col_names}
     _lowerCAmelCase :       Optional[int]	   =     []
     for r in matrix:
          _lowerCAmelCase :       List[str]	   =     dict((x.replace("--"		,				""					).split() for x in r)					)
          _lowerCAmelCase :       Any	   =     " ".join(SCREAMING_SNAKE_CASE					).split()
          args_exp.extend(["--bs", str(args.bs					)]					)  # in case we need to reduce its size due to CUDA OOM
          _lowerCAmelCase :       List[str]	   =     args_normal + args_exp

          # XXX: need to trap CUDA OOM and lower args.bs if that happens and retry

          _lowerCAmelCase :       Any	   =     run_generate(verbose=SCREAMING_SNAKE_CASE					)
          # make sure scores are first in the table
          _lowerCAmelCase :       Any	   =     OrderedDict()
          for score in task_score_names[task]:
               _lowerCAmelCase :       Dict	   =     scores[score]
          result.update(SCREAMING_SNAKE_CASE					)
          results.append(SCREAMING_SNAKE_CASE					)

          # find widest entries
          for k, v in result.items():
               _lowerCAmelCase :       Any	   =     len(str(SCREAMING_SNAKE_CASE					)					)
               if l > col_widths[k]:
                    _lowerCAmelCase :       str	   =     l

     _lowerCAmelCase :       Optional[Any]	   =     sorted(SCREAMING_SNAKE_CASE		,				key=operator.itemgetter(*task_score_names[task]					)		,				reverse=SCREAMING_SNAKE_CASE					)
     print(" | ".join([f'''{col:{col_widths[col]}}''' for col in col_names]					)					)
     print(" | ".join([f'''{'-'*col_widths[col]}''' for col in col_names]					)					)
     for row in results_sorted:
          print(" | ".join([f'''{row[col]:{col_widths[col]}}''' for col in col_names]					)					)

     _lowerCAmelCase :       Optional[int]	   =     results_sorted[0]
     for score in task_score_names[task]:
          del best[score]
     _lowerCAmelCase :       Tuple	   =     [f'''--{k} {v}''' for k, v in best.items()]
     _lowerCAmelCase :       Union[str, Any]	   =     ["--bs", str(args.bs					)]
     if args.info:
          print(f'''\nInfo: {args.info}'''					)
     print("\nBest score args:"					)
     print(" ".join(args_main + best_args + dyn_args					)					)

     return results_sorted


if __name__ == "__main__":
  # Usage:
  # [normal-run_eval_search.py cmd plus] \
  # --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
  #
  # Example:
  # PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py $MODEL_NAME \
  # $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target \
  # --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation \
  # --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
  run_search()






