


"""simple docstring"""






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							:    str					   =	logging.get_logger(__name__)




@add_end_docstrings(__lowerCamelCase )
class 							A_      (       __lowerCamelCase ):


  def __init__(			self:						Any							,**__lowerCAmelCase:						str    ):







     '''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 							_lowercase					(			self:						Optional[int]							,**__lowerCAmelCase:						Dict    ):







     '''simple docstring'''







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


  def __call__(			self:						Optional[Any]							,__lowerCAmelCase:						Dict							,*__lowerCAmelCase:						Union[str, Any]							,__lowerCAmelCase:						Union[str, Any]=None							,__lowerCAmelCase:						Dict=None							,**__lowerCAmelCase:						List[str]    ):







     '''simple docstring'''







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


  def 							_lowercase					(			self:						Optional[Any]							,__lowerCAmelCase:						Optional[Any]							,__lowerCAmelCase:						Any=64							,__lowerCAmelCase:						int = 0							,__lowerCAmelCase:						float = 512 / 1_500							,__lowerCAmelCase:						Optional[int] = 32							,__lowerCAmelCase:						Optional[int] = 1							,):







     '''simple docstring'''







     _lowerCamelCase   :				Any							=     load_image(lowerCamelCase__    )
     _lowerCamelCase   :				int							=     self.image_processor.size["longest_edge"]
     _lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase   :				Any							=     self.image_processor.generate_crop_boxes(
         lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__    )
     _lowerCamelCase   :				List[str]							=     self.image_processor(images=lowerCamelCase__							,return_tensors="pt"    )

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

     _lowerCamelCase   :				Union[str, Any]							=     grid_points.shape[1]
     _lowerCamelCase   :				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__    ):
        _lowerCamelCase   :				List[Any]							=     grid_points[:, i : i + points_per_batch, :, :]
        _lowerCamelCase   :				str							=     input_labels[:, i : i + points_per_batch]
        _lowerCamelCase   :				Any							=     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 							_lowercase					(			self:						Tuple							,__lowerCAmelCase:						Union[str, Any]							,__lowerCAmelCase:						str=0.88							,__lowerCAmelCase:						Dict=0.95							,__lowerCAmelCase:						int=0							,__lowerCAmelCase:						int=1							,):







     '''simple docstring'''







     _lowerCamelCase   :				Dict							=     model_inputs.pop("input_boxes"    )
     _lowerCamelCase   :				List[str]							=     model_inputs.pop("is_last"    )
     _lowerCamelCase   :				str							=     model_inputs.pop("original_sizes"    ).tolist()
     _lowerCamelCase   :				List[str]							=     model_inputs.pop("reshaped_input_sizes"    ).tolist()

     _lowerCamelCase   :				int							=     self.model(**lowerCamelCase__    )

     # post processing happens here in order to avoid CPU GPU copies of ALL the masks
     _lowerCamelCase   :				int							=     model_outputs["pred_masks"]
     _lowerCamelCase   :				str							=     self.image_processor.post_process_masks(
         lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__							,binarize=lowerCamelCase__    )
     _lowerCamelCase   :				Optional[int]							=     model_outputs["iou_scores"]
     _lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase   :				List[Any]							=     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 							_lowercase					(			self:						Any							,__lowerCAmelCase:						List[Any]							,__lowerCAmelCase:						Dict=False							,__lowerCAmelCase:						str=False							,__lowerCAmelCase:						List[str]=0.7							,):







     '''simple docstring'''







     _lowerCamelCase   :				Any							=     []
     _lowerCamelCase   :				List[Any]							=     []
     _lowerCamelCase   :				Union[str, Any]							=     []
     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"    )    )

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

     _lowerCamelCase   :				str							=     defaultdict(lowerCamelCase__    )
     for output in model_outputs:
        for k, v in output.items():
           extra[k].append(lowerCamelCase__    )

     _lowerCamelCase   :				Dict							=     {}
     if output_rle_mask:
        _lowerCamelCase   :				Optional[Any]							=     rle_mask

     if output_bboxes_mask:
        _lowerCamelCase   :				int							=     bounding_boxes

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


import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          LiltForQuestionAnswering,
          LiltForSequenceClassification,
          LiltForTokenClassification,
          LiltModel,
      )
      from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST

class    _lowercase				:

   def __init__(				self	:    Tuple			,						lowerCamelCase__	:    Optional[int]			,						lowerCamelCase__	:    Dict=1_3			,						lowerCamelCase__	:    Any=7			,						lowerCamelCase__	:    Optional[Any]=True			,						lowerCamelCase__	:    Optional[Any]=True			,						lowerCamelCase__	:    List[Any]=True			,						lowerCamelCase__	:    Union[str, Any]=True			,						lowerCamelCase__	:    Optional[int]=9_9			,						lowerCamelCase__	:    Optional[int]=2_4			,						lowerCamelCase__	:    Optional[int]=2			,						lowerCamelCase__	:    Dict=6			,						lowerCamelCase__	:    List[str]=3_7			,						lowerCamelCase__	:    Optional[Any]="gelu"			,						lowerCamelCase__	:    Dict=0.1			,						lowerCamelCase__	:    Optional[int]=0.1			,						lowerCamelCase__	:    Any=5_1_2			,						lowerCamelCase__	:    int=1_6			,						lowerCamelCase__	:    Union[str, Any]=2			,						lowerCamelCase__	:    Optional[int]=0.02			,						lowerCamelCase__	:    Optional[Any]=3			,						lowerCamelCase__	:    Optional[int]=None			,						lowerCamelCase__	:    Any=1_0_0_0			,						) ->			Any:
         """simple docstring"""





         A_							       =		parent
         A_							       =		batch_size
         A_							       =		seq_length
         A_							       =		is_training
         A_							       =		use_input_mask
         A_							       =		use_token_type_ids
         A_							       =		use_labels
         A_							       =		vocab_size
         A_							       =		hidden_size
         A_							       =		num_hidden_layers
         A_							       =		num_attention_heads
         A_							       =		intermediate_size
         A_							       =		hidden_act
         A_							       =		hidden_dropout_prob
         A_							       =		attention_probs_dropout_prob
         A_							       =		max_position_embeddings
         A_							       =		type_vocab_size
         A_							       =		type_sequence_label_size
         A_							       =		initializer_range
         A_							       =		num_labels
         A_							       =		scope
         A_							       =		range_bbox

   def     UpperCamelCase					(				self	:    int       ) ->			int:
         """simple docstring"""





         A_							       =		ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size       )

         A_							       =		ids_tensor([self.batch_size, self.seq_length, 4]			,						self.range_bbox       )
         # Ensure that bbox is legal
         for i in range(bbox.shape[0]       ):
               for j in range(bbox.shape[1]       ):
                     if bbox[i, j, 3] < bbox[i, j, 1]:
                           A_							       =		bbox[i, j, 3]
                           A_							       =		bbox[i, j, 1]
                           A_							       =		t
                     if bbox[i, j, 2] < bbox[i, j, 0]:
                           A_							       =		bbox[i, j, 2]
                           A_							       =		bbox[i, j, 0]
                           A_							       =		t

         A_							       =		None
         if self.use_input_mask:
               A_							       =		ids_tensor([self.batch_size, self.seq_length]			,						vocab_size=2       )

         A_							       =		None
         if self.use_token_type_ids:
               A_							       =		ids_tensor([self.batch_size, self.seq_length]			,						self.type_vocab_size       )

         A_							       =		None
         A_							       =		None
         if self.use_labels:
               A_							       =		ids_tensor([self.batch_size]			,						self.type_sequence_label_size       )
               A_							       =		ids_tensor([self.batch_size, self.seq_length]			,						self.num_labels       )

         A_							       =		self.get_config()

         return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels

   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			List[str]:
         """simple docstring"""





         return LiltConfig(
             vocab_size=self.vocab_size			,						hidden_size=self.hidden_size			,						num_hidden_layers=self.num_hidden_layers			,						num_attention_heads=self.num_attention_heads			,						intermediate_size=self.intermediate_size			,						hidden_act=self.hidden_act			,						hidden_dropout_prob=self.hidden_dropout_prob			,						attention_probs_dropout_prob=self.attention_probs_dropout_prob			,						max_position_embeddings=self.max_position_embeddings			,						type_vocab_size=self.type_vocab_size			,						initializer_range=self.initializer_range			,						)

   def     UpperCamelCase					(				self	:    Dict			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    List[Any]			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    List[Any]			,						lowerCamelCase__	:    List[str]			,						lowerCamelCase__	:    Any			,						) ->			List[Any]:
         """simple docstring"""





         A_							       =		LiltModel(config=lowerCamelCase__       )
         model.to(lowerCamelCase__       )
         model.eval()
         A_							       =		model(lowerCamelCase__			,						bbox=lowerCamelCase__			,						attention_mask=lowerCamelCase__			,						token_type_ids=lowerCamelCase__       )
         A_							       =		model(lowerCamelCase__			,						bbox=lowerCamelCase__			,						token_type_ids=lowerCamelCase__       )
         A_							       =		model(lowerCamelCase__			,						bbox=lowerCamelCase__       )

         self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.seq_length, self.hidden_size)       )
         self.parent.assertEqual(result.pooler_output.shape			,						(self.batch_size, self.hidden_size)       )

   def     UpperCamelCase					(				self	:    Optional[int]			,						lowerCamelCase__	:    str			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    List[Any]			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    Optional[Any]			,						) ->			Union[str, Any]:
         """simple docstring"""





         A_							       =		self.num_labels
         A_							       =		LiltForTokenClassification(config=lowerCamelCase__       )
         model.to(lowerCamelCase__       )
         model.eval()
         A_							       =		model(
             lowerCamelCase__			,						bbox=lowerCamelCase__			,						attention_mask=lowerCamelCase__			,						token_type_ids=lowerCamelCase__			,						labels=lowerCamelCase__       )
         self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.seq_length, self.num_labels)       )

   def     UpperCamelCase					(				self	:    Dict			,						lowerCamelCase__	:    List[Any]			,						lowerCamelCase__	:    str			,						lowerCamelCase__	:    str			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    List[str]			,						) ->			Any:
         """simple docstring"""





         A_							       =		LiltForQuestionAnswering(config=lowerCamelCase__       )
         model.to(lowerCamelCase__       )
         model.eval()
         A_							       =		model(
             lowerCamelCase__			,						bbox=lowerCamelCase__			,						attention_mask=lowerCamelCase__			,						token_type_ids=lowerCamelCase__			,						start_positions=lowerCamelCase__			,						end_positions=lowerCamelCase__			,						)
         self.parent.assertEqual(result.start_logits.shape			,						(self.batch_size, self.seq_length)       )
         self.parent.assertEqual(result.end_logits.shape			,						(self.batch_size, self.seq_length)       )





   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			List[Any]:
         """simple docstring"""





         A_							       =		self.prepare_config_and_inputs()
         (
             (
             A_
         )       ,(
             A_
         )       ,(
             A_
         )       ,(
             A_
         )       ,(
             A_
         )       ,(
             A_
         )       ,(
             A_
         )       ,
         )							       =		config_and_inputs
         A_							       =		{
             '''input_ids''': input_ids,
             '''bbox''': bbox,
             '''token_type_ids''': token_type_ids,
             '''attention_mask''': input_mask,
         }
         return config, inputs_dict



@require_torch
class    _lowercase				(					__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,unittest.TestCase			):
   _lowercase     :       Dict  					=    (
       (
           LiltModel,
           LiltForSequenceClassification,
           LiltForTokenClassification,
           LiltForQuestionAnswering,
       )
       if is_torch_available()
       else ()
   )
   _lowercase     :       List[Any]  					=    (
       {
           'feature-extraction': LiltModel,
           'question-answering': LiltForQuestionAnswering,
           'text-classification': LiltForSequenceClassification,
           'token-classification': LiltForTokenClassification,
           'zero-shot': LiltForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   _lowercase     :       Dict  					=    False
   _lowercase     :       Optional[int]  					=    False

   def     UpperCamelCase					(				self	:    Optional[Any]			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    List[str]			,						lowerCamelCase__	:    List[str]			,						lowerCamelCase__	:    List[str]       ) ->			Any:
         """simple docstring"""





         return True

   def     UpperCamelCase					(				self	:    int       ) ->			Any:
         """simple docstring"""





         A_							       =		LiltModelTester(self       )
         A_							       =		ConfigTester(self			,						config_class=lowerCamelCase__			,						hidden_size=3_7       )

   def     UpperCamelCase					(				self	:    int       ) ->			Optional[Any]:
         """simple docstring"""





         self.config_tester.run_common_tests()

   def     UpperCamelCase					(				self	:    Union[str, Any]       ) ->			Dict:
         """simple docstring"""





         A_							       =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    Optional[Any]       ) ->			int:
         """simple docstring"""





         A_							       =		self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
               A_							       =		type
               self.model_tester.create_and_check_model(*lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    List[Any]       ) ->			int:
         """simple docstring"""





         A_							       =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    List[str]       ) ->			Any:
         """simple docstring"""





         A_							       =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__       )





   @slow
   def     UpperCamelCase					(				self	:    Any       ) ->			Dict:
         """simple docstring"""





         for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               A_							       =		LiltModel.from_pretrained(lowerCamelCase__       )
               self.assertIsNotNone(lowerCamelCase__       )



@require_torch
@slow
class    _lowercase				(					unittest.TestCase			):

   def     UpperCamelCase					(				self	:    List[str]       ) ->			Optional[Any]:
         """simple docstring"""





         A_							       =		LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base'''       ).to(lowerCamelCase__       )

         A_							       =		torch.tensor([[1, 2]]			,						device=lowerCamelCase__       )
         A_							       =		torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]			,						device=lowerCamelCase__       )

         # forward pass
         with torch.no_grad():
               A_							       =		model(input_ids=lowerCamelCase__			,						bbox=lowerCamelCase__       )

         A_							       =		torch.Size([1, 2, 7_6_8]       )
         A_							       =		torch.tensor(
             [[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]]			,						device=lowerCamelCase__			,						)

         self.assertTrue(outputs.last_hidden_state.shape			,						lowerCamelCase__       )
         self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]			,						lowerCamelCase__			,						atol=1e-3       )       )





