



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

_UpperCAmelCase     =			logging.get_logger(__name__)
@add_end_docstrings(UpperCAmelCase__       )
class 					a	(  UpperCAmelCase__       ):



  def __init__(						self :					Dict       ,		**lowerCAmelCase :					int		)     ->					int:


     '''simple docstring'''




     super().__init__(**lowerCAmelCase		)
     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(lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					Any       ,		**lowerCAmelCase :					Optional[int]		)     ->					Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Union[str, Any]     			={}
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			={}
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			={}
     # preprocess args
     if "points_per_batch" in kwargs:
        SCREAMING_SNAKE_CASE_: Tuple     			=kwargs["""points_per_batch"""]
     if "points_per_crop" in kwargs:
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=kwargs["""points_per_crop"""]
     if "crops_n_layers" in kwargs:
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=kwargs["""crops_n_layers"""]
     if "crop_overlap_ratio" in kwargs:
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=kwargs["""crop_overlap_ratio"""]
     if "crop_n_points_downscale_factor" in kwargs:
        SCREAMING_SNAKE_CASE_: Any     			=kwargs["""crop_n_points_downscale_factor"""]
     # postprocess args
     if "pred_iou_thresh" in kwargs:
        SCREAMING_SNAKE_CASE_: List[Any]     			=kwargs["""pred_iou_thresh"""]
     if "stability_score_offset" in kwargs:
        SCREAMING_SNAKE_CASE_: Tuple     			=kwargs["""stability_score_offset"""]
     if "mask_threshold" in kwargs:
        SCREAMING_SNAKE_CASE_: List[str]     			=kwargs["""mask_threshold"""]
     if "stability_score_thresh" in kwargs:
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=kwargs["""stability_score_thresh"""]
     if "crops_nms_thresh" in kwargs:
        SCREAMING_SNAKE_CASE_: List[str]     			=kwargs["""crops_nms_thresh"""]
     if "output_rle_mask" in kwargs:
        SCREAMING_SNAKE_CASE_: Dict     			=kwargs["""output_rle_mask"""]
     if "output_bboxes_mask" in kwargs:
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=kwargs["""output_bboxes_mask"""]
     return preprocess_kwargs, forward_params, postprocess_kwargs



  def __call__(						self :					List[Any]       ,		lowerCAmelCase :					Union[str, Any]       ,		*lowerCAmelCase :					int       ,		lowerCAmelCase :					Any=None       ,		lowerCAmelCase :					List[str]=None       ,		**lowerCAmelCase :					Optional[int]		)     ->					str:


     '''simple docstring'''




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



  def 	lowerCamelCase__       (						self :					Optional[int]       ,		lowerCAmelCase :					List[Any]       ,		lowerCAmelCase :					List[Any]=64       ,		lowerCAmelCase :					int = 0       ,		lowerCAmelCase :					float = 512 / 1500       ,		lowerCAmelCase :					Optional[int] = 32       ,		lowerCAmelCase :					Optional[int] = 1       ,		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: str     			=load_image(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=self.image_processor.size["""longest_edge"""]
     SCREAMING_SNAKE_CASE_: int     			=self.image_processor.generate_crop_boxes(
         lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Tuple     			=self.image_processor(images=lowerCAmelCase       ,		return_tensors="""pt"""		)

     with self.device_placement():
        if self.framework == "pt":
           SCREAMING_SNAKE_CASE_: List[Any]     			=self.get_inference_context()
           with inference_context():
              SCREAMING_SNAKE_CASE_: Union[str, Any]     			=self._ensure_tensor_on_device(lowerCAmelCase       ,		device=self.device		)
              SCREAMING_SNAKE_CASE_: Union[str, Any]     			=self.model.get_image_embeddings(model_inputs.pop("""pixel_values"""		)		)
              SCREAMING_SNAKE_CASE_: List[Any]     			=image_embeddings

     SCREAMING_SNAKE_CASE_: Any     			=grid_points.shape[1]
     SCREAMING_SNAKE_CASE_: List[str]     			=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       ,		lowerCAmelCase       ,		lowerCAmelCase		):
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=grid_points[:, i : i + points_per_batch, :, :]
        SCREAMING_SNAKE_CASE_: Dict     			=input_labels[:, i : i + points_per_batch]
        SCREAMING_SNAKE_CASE_: Dict     			=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 :					int       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					List[str]=0.8_8       ,		lowerCAmelCase :					List[str]=0.9_5       ,		lowerCAmelCase :					List[Any]=0       ,		lowerCAmelCase :					Dict=1       ,		)     ->					Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[Any]     			=model_inputs.pop("""input_boxes"""		)
     SCREAMING_SNAKE_CASE_: List[str]     			=model_inputs.pop("""is_last"""		)
     SCREAMING_SNAKE_CASE_: Tuple     			=model_inputs.pop("""original_sizes"""		).tolist()
     SCREAMING_SNAKE_CASE_: Tuple     			=model_inputs.pop("""reshaped_input_sizes"""		).tolist()

     SCREAMING_SNAKE_CASE_: List[str]     			=self.model(**lowerCAmelCase		)

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





  def 	lowerCamelCase__       (						self :					Union[str, Any]       ,		lowerCAmelCase :					Optional[Any]       ,		lowerCAmelCase :					str=False       ,		lowerCAmelCase :					Optional[int]=False       ,		lowerCAmelCase :					str=0.7       ,		)     ->					Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: str     			=[]
     SCREAMING_SNAKE_CASE_: List[Any]     			=[]
     SCREAMING_SNAKE_CASE_: List[str]     			=[]
     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"""		)		)

     SCREAMING_SNAKE_CASE_: List[str]     			=torch.cat(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Any     			=torch.cat(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=self.image_processor.post_process_for_mask_generation(
         lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: Tuple     			=defaultdict(lowerCAmelCase		)
     for output in model_outputs:
        for k, v in output.items():
           extra[k].append(lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: List[str]     			={}
     if output_rle_mask:
        SCREAMING_SNAKE_CASE_: Dict     			=rle_mask

     if output_bboxes_mask:
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=bounding_boxes

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








"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType
class 					a	:



  def __init__(						self :					Union[str, Any]       ,		lowerCAmelCase :					List[str]=2       ,		lowerCAmelCase :					int=3       ,		lowerCAmelCase :					Optional[Any]=64       ,		lowerCAmelCase :					Union[str, Any]=None		)     ->					Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=np.random.default_rng(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=length
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=rng.normal(size=(length,)		).astype(np.floataa		)
     SCREAMING_SNAKE_CASE_: Tuple     			=a * self.x + b + rng.normal(scale=0.1       ,		size=(length,)		).astype(np.floataa		)



  def __len__(						self :					List[Any]		)     ->					str:


     '''simple docstring'''




     return self.length





  def __getitem__(						self :					Union[str, Any]       ,		lowerCAmelCase :					Any		)     ->					List[str]:


     '''simple docstring'''




     return {"x": self.x[i], "y": self.y[i]}







class 					a	(  torch.nn.Module       ):



  def __init__(						self :					Optional[int]       ,		lowerCAmelCase :					str=0       ,		lowerCAmelCase :					List[Any]=0       ,		lowerCAmelCase :					Optional[int]=False		)     ->					Tuple:


     '''simple docstring'''




     super().__init__()
     SCREAMING_SNAKE_CASE_: int     			=torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
     SCREAMING_SNAKE_CASE_: int     			=torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
     SCREAMING_SNAKE_CASE_: Dict     			=True





  def 	lowerCamelCase__       (						self :					str       ,		lowerCAmelCase :					Tuple=None		)     ->					int:


     '''simple docstring'''




     if self.first_batch:
        print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''		)
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=False
     return x * self.a[0] + self.b[0]







class 					a	(  torch.nn.Module       ):



  def __init__(						self :					Union[str, Any]       ,		lowerCAmelCase :					Any=0       ,		lowerCAmelCase :					List[Any]=0       ,		lowerCAmelCase :					List[Any]=False		)     ->					str:


     '''simple docstring'''




     super().__init__()
     SCREAMING_SNAKE_CASE_: List[str]     			=torch.nn.Parameter(torch.tensor(lowerCAmelCase		).float()		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=torch.nn.Parameter(torch.tensor(lowerCAmelCase		).float()		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=True





  def 	lowerCamelCase__       (						self :					List[str]       ,		lowerCAmelCase :					int=None		)     ->					Any:


     '''simple docstring'''




     if self.first_batch:
        print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''		)
        SCREAMING_SNAKE_CASE_: Optional[int]     			=False
     return x * self.a + self.b







def      __magic_name__				(							lowercase							,			lowercase = 16 ):
   from datasets import load_dataset
   from transformers import AutoTokenizer

   SCREAMING_SNAKE_CASE_: Optional[Any]     			=AutoTokenizer.from_pretrained("""bert-base-cased""" )
   SCREAMING_SNAKE_CASE_: Optional[int]     			={"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
   SCREAMING_SNAKE_CASE_: Any     			=load_dataset("""csv"""							,			data_files=lowercase )
   SCREAMING_SNAKE_CASE_: Any     			=datasets["""train"""].unique("""label""" )

   SCREAMING_SNAKE_CASE_: List[Any]     			={v: i for i, v in enumerate(lowercase )}

   def tokenize_function(lowercase ):
      # max_length=None => use the model max length (it's actually the default)
      SCREAMING_SNAKE_CASE_: Dict     			=tokenizer(
          examples["""sentence1"""]							,			examples["""sentence2"""]							,			truncation=lowercase							,			max_length=lowercase							,			padding="""max_length""" )
      if "label" in examples:
         SCREAMING_SNAKE_CASE_: Optional[int]     			=[label_to_id[l] for l in examples["""label"""]]
      return outputs

   # Apply the method we just defined to all the examples in all the splits of the dataset
   SCREAMING_SNAKE_CASE_: List[Any]     			=datasets.map(
       lowercase							,			batched=lowercase							,			remove_columns=["""sentence1""", """sentence2""", """label"""]							,			)

   def collate_fn(lowercase ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      if accelerator.distributed_type == DistributedType.TPU:
         return tokenizer.pad(lowercase							,			padding="""max_length"""							,			max_length=128							,			return_tensors="""pt""" )
      return tokenizer.pad(lowercase							,			padding="""longest"""							,			return_tensors="""pt""" )

   # Instantiate dataloaders.
   SCREAMING_SNAKE_CASE_: Optional[int]     			=DataLoader(tokenized_datasets["""train"""]							,			shuffle=lowercase							,			collate_fn=lowercase							,			batch_size=2 )
   SCREAMING_SNAKE_CASE_: Dict     			=DataLoader(tokenized_datasets["""validation"""]							,			shuffle=lowercase							,			collate_fn=lowercase							,			batch_size=1 )

   return train_dataloader, eval_dataloader



