


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
	from ...onnx.config import PatchingSpec
	from ...tokenization_utils_base import PreTrainedTokenizerBase


A       :		Union[str, Any]    =   logging.get_logger(__name__)

A       :		Tuple    =   {
    'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json',
    'allenai/longformer-large-4096': 'https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json',
    'allenai/longformer-large-4096-finetuned-triviaqa': (
        'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'
    ),
    'allenai/longformer-base-4096-extra.pos.embd.only': (
        'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'
    ),
    'allenai/longformer-large-4096-extra.pos.embd.only': (
        'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'
    ),
}



class 							A    (       lowerCamelCase__							):
					'''simple docstring'''






					A__			= 'longformer'

					def __init__(self    :						Optional[int]      ,					_UpperCAmelCase    :						Any = 512      ,					_UpperCAmelCase    :						Optional[Any] = 2      ,					_UpperCAmelCase    :						List[Any] = 1      ,					_UpperCAmelCase    :						Dict = 0      ,					_UpperCAmelCase    :						str = 2      ,					_UpperCAmelCase    :						Optional[int] = 3_0522      ,					_UpperCAmelCase    :						int = 768      ,					_UpperCAmelCase    :						Dict = 12      ,					_UpperCAmelCase    :						Optional[Any] = 12      ,					_UpperCAmelCase    :						Any = 3072      ,					_UpperCAmelCase    :						List[Any] = "gelu"      ,					_UpperCAmelCase    :						Union[str, Any] = 0.1      ,					_UpperCAmelCase    :						List[str] = 0.1      ,					_UpperCAmelCase    :						Optional[int] = 512      ,					_UpperCAmelCase    :						Dict = 2      ,					_UpperCAmelCase    :						Optional[int] = 0.02      ,					_UpperCAmelCase    :						Dict = 1E-1_2      ,					_UpperCAmelCase    :						str = False      ,					**_UpperCAmelCase    :						Union[str, Any]      ,					)						->   Dict:




							"""simple docstring"""


							super().__init__(pad_token_id=lowercase_      ,					**lowercase_		)

							lowercase__     			=		attention_window
							lowercase__     			=		sep_token_id
							lowercase__     			=		bos_token_id
							lowercase__     			=		eos_token_id
							lowercase__     			=		vocab_size
							lowercase__     			=		hidden_size
							lowercase__     			=		num_hidden_layers
							lowercase__     			=		num_attention_heads
							lowercase__     			=		hidden_act
							lowercase__     			=		intermediate_size
							lowercase__     			=		hidden_dropout_prob
							lowercase__     			=		attention_probs_dropout_prob
							lowercase__     			=		max_position_embeddings
							lowercase__     			=		type_vocab_size
							lowercase__     			=		initializer_range
							lowercase__     			=		layer_norm_eps
							lowercase__     			=		onnx_export






class 							A    (       lowerCamelCase__							):
					'''simple docstring'''






					def __init__(self    :						int      ,					_UpperCAmelCase    :						Dict      ,					_UpperCAmelCase    :						Tuple = "default"      ,					_UpperCAmelCase    :						str = None		)						->   Optional[int]:




							"""simple docstring"""


							super().__init__(lowercase_      ,					lowercase_      ,					lowercase_		)
							lowercase__     			=		True

					@property
					def   lowerCamelCase__     (self    :						int		)						->   Mapping[str, Mapping[int, str]]:




							"""simple docstring"""


							if self.task == "multiple-choice":
									lowercase__     			=		{0: """batch""", 1: """choice""", 2: """sequence"""}
							else:
									lowercase__     			=		{0: """batch""", 1: """sequence"""}
							return OrderedDict(
							    [
							        ("""input_ids""", dynamic_axis),
							        ("""attention_mask""", dynamic_axis),
							        ("""global_attention_mask""", dynamic_axis),
							    ]		)

					@property
					def   lowerCamelCase__     (self    :						Optional[int]		)						->   Mapping[str, Mapping[int, str]]:




							"""simple docstring"""


							lowercase__     			=		super().outputs
							if self.task == "default":
									lowercase__     			=		{0: """batch"""}
							return outputs

					@property
					def   lowerCamelCase__     (self    :						Any		)						->   float:




							"""simple docstring"""


							return 1E-4

					@property
					def   lowerCamelCase__     (self    :						str		)						->   int:




							"""simple docstring"""


							return max(super().default_onnx_opset      ,					14		)


					def   lowerCamelCase__     (self    :						str      ,					_UpperCAmelCase    :						int      ,					_UpperCAmelCase    :						Optional[int] = -1      ,					_UpperCAmelCase    :						Dict = -1      ,					_UpperCAmelCase    :						List[Any] = False      ,					_UpperCAmelCase    :						Tuple = None      ,					)						->   Mapping[str, Any]:




							"""simple docstring"""


							lowercase__     			=		super().generate_dummy_inputs(
							    preprocessor=lowercase_      ,					batch_size=lowercase_      ,					seq_length=lowercase_      ,					is_pair=lowercase_      ,					framework=lowercase_		)
							import torch

							# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
							# makes the export fail randomly
							lowercase__     			=		torch.zeros_like(inputs["""input_ids"""]		)
							# make every second token global
							lowercase__     			=		1

							return inputs




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

lowerCamelCase           =					logging.get_logger(__name__)







@add_end_docstrings(lowerCamelCase__							)
class 						__magic_name__			(							lowerCamelCase__							):

   '''simple docstring'''




   def __init__(				self,    **lowercase_       )							->    Optional[int]:






     """simple docstring"""






     super().__init__(**lowercase_       )
     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(lowercase_       )



   def  _UpperCAmelCase (				self,    **lowercase_       )							->    Optional[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,    lowercase_,    *lowercase_,    lowercase_=None,    lowercase_=None,    **lowercase_       )							->    List[Any]:






     """simple docstring"""






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



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_=64,    lowercase_ = 0,    lowercase_ = 512 / 1500,    lowercase_ = 32,    lowercase_ = 1,    )							->    Any:






     """simple docstring"""






     a__  =load_image(lowercase_       )
     a__  =self.image_processor.size['''longest_edge''']
     a__,      a__,      a__,      a__  =self.image_processor.generate_crop_boxes(
         lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_       )
     a__  =self.image_processor(images=lowercase_,    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(lowercase_,    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,    lowercase_,    lowercase_       ):
       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  _UpperCAmelCase (				self,    lowercase_,    lowercase_=0.88,    lowercase_=0.95,    lowercase_=0,    lowercase_=1,    )							->    int:






     """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(**lowercase_       )

     # 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(
         lowercase_,    lowercase_,    lowercase_,    lowercase_,    binarize=lowercase_       )
     a__  =model_outputs['''iou_scores''']
     a__,      a__,      a__  =self.image_processor.filter_masks(
         masks[0],    iou_scores[0],    original_sizes[0],    input_boxes[0],    lowercase_,    lowercase_,    lowercase_,    lowercase_,    )
     return {
         "masks": masks,
         "is_last": is_last,
         "boxes": boxes,
         "iou_scores": iou_scores,
     }



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_=False,    lowercase_=False,    lowercase_=0.7,    )							->    Any:






     """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(lowercase_       )
     a__  =torch.cat(lowercase_       )
     a__,      a__,      a__,      a__  =self.image_processor.post_process_for_mask_generation(
         lowercase_,    lowercase_,    lowercase_,    lowercase_       )

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

     a__  ={}
     if output_rle_mask:
       a__  =rle_mask

     if output_bboxes_mask:
       a__  =bounding_boxes

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