






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__   :						str													=   logging.get_logger(__name__)


@add_end_docstrings(UpperCAmelCase__     )
class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  def __init__(  self		,		**_UpperCAmelCase			)      ->			Optional[Any]:
     super().__init__(**_UpperCAmelCase			)
     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(_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		**_UpperCAmelCase			)      ->			Any:
     UpperCamelCase_						=  {}
     UpperCamelCase_						=  {}
     UpperCamelCase_						=  {}
     # preprocess args
     if "points_per_batch" in kwargs:
        UpperCamelCase_						=  kwargs['points_per_batch']
     if "points_per_crop" in kwargs:
        UpperCamelCase_						=  kwargs['points_per_crop']
     if "crops_n_layers" in kwargs:
        UpperCamelCase_						=  kwargs['crops_n_layers']
     if "crop_overlap_ratio" in kwargs:
        UpperCamelCase_						=  kwargs['crop_overlap_ratio']
     if "crop_n_points_downscale_factor" in kwargs:
        UpperCamelCase_						=  kwargs['crop_n_points_downscale_factor']
     # postprocess args
     if "pred_iou_thresh" in kwargs:
        UpperCamelCase_						=  kwargs['pred_iou_thresh']
     if "stability_score_offset" in kwargs:
        UpperCamelCase_						=  kwargs['stability_score_offset']
     if "mask_threshold" in kwargs:
        UpperCamelCase_						=  kwargs['mask_threshold']
     if "stability_score_thresh" in kwargs:
        UpperCamelCase_						=  kwargs['stability_score_thresh']
     if "crops_nms_thresh" in kwargs:
        UpperCamelCase_						=  kwargs['crops_nms_thresh']
     if "output_rle_mask" in kwargs:
        UpperCamelCase_						=  kwargs['output_rle_mask']
     if "output_bboxes_mask" in kwargs:
        UpperCamelCase_						=  kwargs['output_bboxes_mask']
     return preprocess_kwargs, forward_params, postprocess_kwargs




  def __call__(  self		,		_UpperCAmelCase		,		*_UpperCAmelCase		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None		,		**_UpperCAmelCase			)      ->			List[str]:
     return super().__call__(_UpperCAmelCase		,		*_UpperCAmelCase		,		num_workers=_UpperCAmelCase		,		batch_size=_UpperCAmelCase		,		**_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase=64		,		_UpperCAmelCase = 0		,		_UpperCAmelCase = 512 / 1500		,		_UpperCAmelCase = 32		,		_UpperCAmelCase = 1		,		)      ->			Optional[Any]:
     UpperCamelCase_						=  load_image(_UpperCAmelCase			)
     UpperCamelCase_						=  self.image_processor.size['longest_edge']
     UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_						=  self.image_processor.generate_crop_boxes(
         _UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)
     UpperCamelCase_						=  self.image_processor(images=_UpperCAmelCase		,		return_tensors='pt'			)

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

     UpperCamelCase_						=  grid_points.shape[1]
     UpperCamelCase_						=  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		,		_UpperCAmelCase		,		_UpperCAmelCase			):
        UpperCamelCase_						=  grid_points[:, i : i + points_per_batch, :, :]
        UpperCamelCase_						=  input_labels[:, i : i + points_per_batch]
        UpperCamelCase_						=  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  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase=0.8_8		,		_UpperCAmelCase=0.9_5		,		_UpperCAmelCase=0		,		_UpperCAmelCase=1		,		)      ->			List[Any]:
     UpperCamelCase_						=  model_inputs.pop('input_boxes'			)
     UpperCamelCase_						=  model_inputs.pop('is_last'			)
     UpperCamelCase_						=  model_inputs.pop('original_sizes'			).tolist()
     UpperCamelCase_						=  model_inputs.pop('reshaped_input_sizes'			).tolist()

     UpperCamelCase_						=  self.model(**_UpperCAmelCase			)

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




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase=False		,		_UpperCAmelCase=False		,		_UpperCAmelCase=0.7		,		)      ->			Any:
     UpperCamelCase_						=  []
     UpperCamelCase_						=  []
     UpperCamelCase_						=  []
     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'			)			)

     UpperCamelCase_						=  torch.cat(_UpperCAmelCase			)
     UpperCamelCase_						=  torch.cat(_UpperCAmelCase			)
     UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_						=  self.image_processor.post_process_for_mask_generation(
         _UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)

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

     UpperCamelCase_						=  {}
     if output_rle_mask:
        UpperCamelCase_						=  rle_mask

     if output_bboxes_mask:
        UpperCamelCase_						=  bounding_boxes

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











"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a							      =     logging.get_logger(__name__)

a							      =     {
    '''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json''',
}





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	int     =      '''gpt_bigcode'''
 UpperCAmelCase       :	str     =      ['''past_key_values''']
 UpperCAmelCase       :	Dict     =      {
     '''hidden_size''': '''n_embd''',
     '''max_position_embeddings''': '''n_positions''',
     '''num_attention_heads''': '''n_head''',
     '''num_hidden_layers''': '''n_layer''',
 }

 def __init__(					self			:			Tuple       ,				_UpperCAmelCase			:			Dict=50_257       ,				_UpperCAmelCase			:			List[Any]=1_024       ,				_UpperCAmelCase			:			Any=768       ,				_UpperCAmelCase			:			int=12       ,				_UpperCAmelCase			:			Any=12       ,				_UpperCAmelCase			:			Tuple=None       ,				_UpperCAmelCase			:			str="gelu_pytorch_tanh"       ,				_UpperCAmelCase			:			str=0.1       ,				_UpperCAmelCase			:			Optional[Any]=0.1       ,				_UpperCAmelCase			:			Tuple=0.1       ,				_UpperCAmelCase			:			List[Any]=1E-5       ,				_UpperCAmelCase			:			List[Any]=0.02       ,				_UpperCAmelCase			:			Optional[Any]=True       ,				_UpperCAmelCase			:			Dict=True       ,				_UpperCAmelCase			:			List[Any]=50_256       ,				_UpperCAmelCase			:			Dict=50_256       ,				_UpperCAmelCase			:			int=True       ,				_UpperCAmelCase			:			Union[str, Any]=True       ,				_UpperCAmelCase			:			Any=True       ,				**_UpperCAmelCase			:			Any       ,				):
       _A     =  vocab_size
       _A     =  n_positions
       _A     =  n_embd
       _A     =  n_layer
       _A     =  n_head
       _A     =  n_inner
       _A     =  activation_function
       _A     =  resid_pdrop
       _A     =  embd_pdrop
       _A     =  attn_pdrop
       _A     =  layer_norm_epsilon
       _A     =  initializer_range
       _A     =  scale_attn_weights
       _A     =  use_cache
       _A     =  attention_softmax_in_fpaa
       _A     =  scale_attention_softmax_in_fpaa
       _A     =  multi_query

       _A     =  bos_token_id
       _A     =  eos_token_id

       super().__init__(bos_token_id=_UpperCAmelCase       ,				eos_token_id=_UpperCAmelCase       ,				**_UpperCAmelCase						)

