



"""simple docstring"""


import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


__A      :							Optional[Any]   =      logging.get_logger(__name__)







def        lowercase		(					_SCREAMING_SNAKE_CASE      :  Optional[Any]=None    ,   _SCREAMING_SNAKE_CASE      :  List[str]=None ):






				'''simple docstring'''



				return field(default_factory=lambda: default    ,   metadata=_UpperCAmelCase )
@dataclass
class 	_a       :
			"""simple docstring"""
			UpperCamelCase__						=		list_field(
			    default=[]	,							metadata={
			        """help""": (
			            """Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"""
			            """ of all available models"""
			        )
			    }	,							)

			UpperCamelCase__						=		list_field(
			    default=[8]	,							metadata={"""help""": """List of batch sizes for which memory and time performance will be evaluated"""})

			UpperCamelCase__						=		list_field(
			    default=[8, 32, 128, 512]	,							metadata={"""help""": """List of sequence lengths for which memory and time performance will be evaluated"""}	,							)

			UpperCamelCase__						=		field(
			    default=__UpperCAmelCase	,							metadata={"""help""": """Whether to benchmark inference of model. Inference can be disabled via --no-inference."""}	,							)
			UpperCamelCase__						=		field(
			    default=__UpperCAmelCase	,							metadata={"""help""": """Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."""}	,							)
			UpperCamelCase__						=		field(
			    default=__UpperCAmelCase	,							metadata={"""help""": """Whether to run on available tpu devices. TPU can be disabled via --no-tpu."""})
			UpperCamelCase__						=		field(default=__UpperCAmelCase	,							metadata={"""help""": """Use FP16 to accelerate inference."""})
			UpperCamelCase__						=		field(default=__UpperCAmelCase	,							metadata={"""help""": """Benchmark training of model"""})
			UpperCamelCase__						=		field(default=__UpperCAmelCase	,							metadata={"""help""": """Verbose memory tracing"""})
			UpperCamelCase__						=		field(
			    default=__UpperCAmelCase	,							metadata={"""help""": """Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."""}	,							)
			UpperCamelCase__						=		field(
			    default=__UpperCAmelCase	,							metadata={
			        """help""": """Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"""
			    }	,							)
			UpperCamelCase__						=		field(default=__UpperCAmelCase	,							metadata={"""help""": """Trace memory line by line"""})
			UpperCamelCase__						=		field(default=__UpperCAmelCase	,							metadata={"""help""": """Save result to a CSV file"""})
			UpperCamelCase__						=		field(default=__UpperCAmelCase	,							metadata={"""help""": """Save all print statements in a log file"""})
			UpperCamelCase__						=		field(default=__UpperCAmelCase	,							metadata={"""help""": """Whether to print environment information"""})
			UpperCamelCase__						=		field(
			    default=__UpperCAmelCase	,							metadata={
			        """help""": (
			            """Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"""
			            """ multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"""
			            """ for debugging / testing and on TPU."""
			        )
			    }	,							)
			UpperCamelCase__						=		field(
			    default=F"inference_time_{round(time())}.csv"	,							metadata={"""help""": """CSV filename used if saving time results to csv."""}	,							)
			UpperCamelCase__						=		field(
			    default=F"inference_memory_{round(time())}.csv"	,							metadata={"""help""": """CSV filename used if saving memory results to csv."""}	,							)
			UpperCamelCase__						=		field(
			    default=F"train_time_{round(time())}.csv"	,							metadata={"""help""": """CSV filename used if saving time results to csv for training."""}	,							)
			UpperCamelCase__						=		field(
			    default=F"train_memory_{round(time())}.csv"	,							metadata={"""help""": """CSV filename used if saving memory results to csv for training."""}	,							)
			UpperCamelCase__						=		field(
			    default=F"env_info_{round(time())}.csv"	,							metadata={"""help""": """CSV filename used if saving environment information."""}	,							)
			UpperCamelCase__						=		field(
			    default=F"log_{round(time())}.csv"	,							metadata={"""help""": """Log filename used if print statements are saved in log."""}	,							)
			UpperCamelCase__						=		field(default=3	,							metadata={"""help""": """Times an experiment will be run."""})
			UpperCamelCase__						=		field(
			    default=__UpperCAmelCase	,							metadata={
			        """help""": (
			            """Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"""
			            """ model weights."""
			        )
			    }	,							)



			def        lowercase__  (						self   :			List[str]      )->Dict:
							warnings.warn(
							    F'The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'
							    ''' are deprecated in general and it is advised to use external Benchmarking libraries '''
							    ''' to benchmark Transformer models.''' ,      __SCREAMING_SNAKE_CASE ,      )



			def        lowercase__  (						self   :			Tuple      )->str:
							return json.dumps(dataclasses.asdict(self      ) ,      indent=2      )



			@property
			def        lowercase__  (						self   :			str      )->Any:

							if len(self.models      ) <= 0:
											raise ValueError(
											    '''Please make sure you provide at least one model name / model identifier, *e.g.* `--models'''
											    ''' bert-base-cased` or `args.models = [\'bert-base-cased\'].'''      )
							return self.models



			@property
			def        lowercase__  (						self   :			int      )->List[str]:

							if not self.multi_process:
											return False
							elif self.is_tpu:
											logger.info('''Multiprocessing is currently not possible on TPU.'''      )
											return False
							else:
											return True








from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
 import torch

 from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

__snake_case						:List[Any]      		=     logging.get_logger(__name__)

@add_end_docstrings(__UpperCAmelCase    )
class     _A       (	__UpperCAmelCase    ):



       def __init__(		self							:     Dict     ,		**__SCREAMING_SNAKE_CASE							:     Tuple):


           '''simple docstring'''

           super().__init__(**__SCREAMING_SNAKE_CASE)
           requires_backends(self     ,		'''vision''')
           requires_backends(self     ,		'''torch''')

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

           self.check_model_type(__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Any     ,		**__SCREAMING_SNAKE_CASE							:     Any):


           '''simple docstring'''

           __a						       =				{}
           __a						       =				{}
           __a						       =				{}
           # preprocess args
           if "points_per_batch" in kwargs:
               __a						       =				kwargs['''points_per_batch''']
           if "points_per_crop" in kwargs:
               __a						       =				kwargs['''points_per_crop''']
           if "crops_n_layers" in kwargs:
               __a						       =				kwargs['''crops_n_layers''']
           if "crop_overlap_ratio" in kwargs:
               __a						       =				kwargs['''crop_overlap_ratio''']
           if "crop_n_points_downscale_factor" in kwargs:
               __a						       =				kwargs['''crop_n_points_downscale_factor''']
           # postprocess args
           if "pred_iou_thresh" in kwargs:
               __a						       =				kwargs['''pred_iou_thresh''']
           if "stability_score_offset" in kwargs:
               __a						       =				kwargs['''stability_score_offset''']
           if "mask_threshold" in kwargs:
               __a						       =				kwargs['''mask_threshold''']
           if "stability_score_thresh" in kwargs:
               __a						       =				kwargs['''stability_score_thresh''']
           if "crops_nms_thresh" in kwargs:
               __a						       =				kwargs['''crops_nms_thresh''']
           if "output_rle_mask" in kwargs:
               __a						       =				kwargs['''output_rle_mask''']
           if "output_bboxes_mask" in kwargs:
               __a						       =				kwargs['''output_bboxes_mask''']
           return preprocess_kwargs, forward_params, postprocess_kwargs



       def __call__(		self							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     int     ,		*__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Tuple=None     ,		__SCREAMING_SNAKE_CASE							:     Any=None     ,		**__SCREAMING_SNAKE_CASE							:     str):


           '''simple docstring'''

           return super().__call__(__SCREAMING_SNAKE_CASE     ,		*__SCREAMING_SNAKE_CASE     ,		num_workers=__SCREAMING_SNAKE_CASE     ,		batch_size=__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     str     ,		__SCREAMING_SNAKE_CASE							:     Any=64     ,		__SCREAMING_SNAKE_CASE							:     int = 0     ,		__SCREAMING_SNAKE_CASE							:     float = 512 / 1_500     ,		__SCREAMING_SNAKE_CASE							:     Optional[int] = 32     ,		__SCREAMING_SNAKE_CASE							:     Optional[int] = 1     ,		):


           '''simple docstring'''

           __a						       =				load_image(__SCREAMING_SNAKE_CASE)
           __a						       =				self.image_processor.size['''longest_edge''']
           __a       ,   __a       ,   __a       ,   __a						       =				self.image_processor.generate_crop_boxes(
               __SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				self.image_processor(images=__SCREAMING_SNAKE_CASE     ,		return_tensors='''pt''')

           with self.device_placement():
               if self.framework == "pt":
                   __a						       =				self.get_inference_context()
                   with inference_context():
                       __a						       =				self._ensure_tensor_on_device(__SCREAMING_SNAKE_CASE     ,		device=self.device)
                       __a						       =				self.model.get_image_embeddings(model_inputs.pop('''pixel_values'''))
                       __a						       =				image_embeddings

           __a						       =				grid_points.shape[1]
           __a						       =				points_per_batch if points_per_batch is not None else n_points

           if points_per_batch <= 0:
               raise ValueError(
                   '''Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. '''
                   '''To return all points at once, set points_per_batch to None''')

           for i in range(0     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE):
               __a						       =				grid_points[:, i : i + points_per_batch, :, :]
               __a						       =				input_labels[:, i : i + points_per_batch]
               __a						       =				i == n_points - points_per_batch
               yield {
                   "input_points": batched_points,
                   "input_labels": labels,
                   "input_boxes": crop_boxes,
                   "is_last": is_last,
                   **model_inputs,
               }



       def 		_lowerCamelCase			(		self							:     Any     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     int=0.88     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=0.95     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=0     ,		__SCREAMING_SNAKE_CASE							:     int=1     ,		):


           '''simple docstring'''

           __a						       =				model_inputs.pop('''input_boxes''')
           __a						       =				model_inputs.pop('''is_last''')
           __a						       =				model_inputs.pop('''original_sizes''').tolist()
           __a						       =				model_inputs.pop('''reshaped_input_sizes''').tolist()

           __a						       =				self.model(**__SCREAMING_SNAKE_CASE)

           # post processing happens here in order to avoid CPU GPU copies of ALL the masks
           __a						       =				model_outputs['''pred_masks''']
           __a						       =				self.image_processor.post_process_masks(
               __SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		binarize=__SCREAMING_SNAKE_CASE)
           __a						       =				model_outputs['''iou_scores''']
           __a       ,   __a       ,   __a						       =				self.image_processor.filter_masks(
               masks[0]     ,		iou_scores[0]     ,		original_sizes[0]     ,		input_boxes[0]     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		)
           return {
               "masks": masks,
               "is_last": is_last,
               "boxes": boxes,
               "iou_scores": iou_scores,
           }





       def 		_lowerCamelCase			(		self							:     int     ,		__SCREAMING_SNAKE_CASE							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     Dict=False     ,		__SCREAMING_SNAKE_CASE							:     Tuple=False     ,		__SCREAMING_SNAKE_CASE							:     int=0.7     ,		):


           '''simple docstring'''

           __a						       =				[]
           __a						       =				[]
           __a						       =				[]
           for model_output in model_outputs:
               all_scores.append(model_output.pop('''iou_scores'''))
               all_masks.extend(model_output.pop('''masks'''))
               all_boxes.append(model_output.pop('''boxes'''))

           __a						       =				torch.cat(__SCREAMING_SNAKE_CASE)
           __a						       =				torch.cat(__SCREAMING_SNAKE_CASE)
           __a       ,   __a       ,   __a       ,   __a						       =				self.image_processor.post_process_for_mask_generation(
               __SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

           __a						       =				defaultdict(__SCREAMING_SNAKE_CASE)
           for output in model_outputs:
               for k, v in output.items():
                   extra[k].append(__SCREAMING_SNAKE_CASE)

           __a						       =				{}
           if output_rle_mask:
               __a						       =				rle_mask

           if output_bboxes_mask:
               __a						       =				bounding_boxes

           return {"masks": output_masks, "scores": iou_scores, **optional, **extra}





