






import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import ViTMSNForImageClassification, ViTMSNModel
  from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import ViTImageProcessor
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  def __init__(      self		:			Union[str, Any]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			Tuple=13       ,				lowerCamelCase		:			str=30       ,				lowerCamelCase		:			Tuple=2       ,				lowerCamelCase		:			Optional[Any]=3       ,				lowerCamelCase		:			Optional[int]=True       ,				lowerCamelCase		:			str=True       ,				lowerCamelCase		:			Any=32       ,				lowerCamelCase		:			Optional[int]=5       ,				lowerCamelCase		:			List[Any]=4       ,				lowerCamelCase		:			int=37       ,				lowerCamelCase		:			Any="gelu"       ,				lowerCamelCase		:			int=0.1       ,				lowerCamelCase		:			Tuple=0.1       ,				lowerCamelCase		:			str=10       ,				lowerCamelCase		:			Union[str, Any]=0.02       ,				lowerCamelCase		:			Dict=None       ,				)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    parent
         _UpperCAmelCase							=    batch_size
         _UpperCAmelCase							=    image_size
         _UpperCAmelCase							=    patch_size
         _UpperCAmelCase							=    num_channels
         _UpperCAmelCase							=    is_training
         _UpperCAmelCase							=    use_labels
         _UpperCAmelCase							=    hidden_size
         _UpperCAmelCase							=    num_hidden_layers
         _UpperCAmelCase							=    num_attention_heads
         _UpperCAmelCase							=    intermediate_size
         _UpperCAmelCase							=    hidden_act
         _UpperCAmelCase							=    hidden_dropout_prob
         _UpperCAmelCase							=    attention_probs_dropout_prob
         _UpperCAmelCase							=    type_sequence_label_size
         _UpperCAmelCase							=    initializer_range
         _UpperCAmelCase							=    scope

         # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         _UpperCAmelCase							=    (image_size // patch_size) ** 2
         _UpperCAmelCase							=    num_patches + 1


  def  lowerCamelCase       (      self		:			str							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

         _UpperCAmelCase							=    None
         if self.use_labels:
                _UpperCAmelCase							=    ids_tensor([self.batch_size]       ,				self.type_sequence_label_size							)

         _UpperCAmelCase							=    self.get_config()

         return config, pixel_values, labels


  def  lowerCamelCase       (      self		:			Dict							)						->	Optional[Any]:


         """simple docstring"""
         return ViTMSNConfig(
             image_size=self.image_size       ,				patch_size=self.patch_size       ,				num_channels=self.num_channels       ,				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       ,				initializer_range=self.initializer_range       ,				)


  def  lowerCamelCase       (      self		:			Dict       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			List[Any]							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    ViTMSNModel(config=__lowerCAmelCase							)
         model.to(__lowerCAmelCase							)
         model.eval()
         _UpperCAmelCase							=    model(__lowerCAmelCase							)
         self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.seq_length, self.hidden_size)							)


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			Tuple							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.type_sequence_label_size
         _UpperCAmelCase							=    ViTMSNForImageClassification(__lowerCAmelCase							)
         model.to(__lowerCAmelCase							)
         model.eval()
         _UpperCAmelCase							=    model(__lowerCAmelCase       ,				labels=__lowerCAmelCase							)
         print("""Pixel and labels shape: {pixel_values.shape}, {labels.shape}"""							)
         print("""Labels: {labels}"""							)
         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.type_sequence_label_size)							)

         # test greyscale images
         _UpperCAmelCase							=    1
         _UpperCAmelCase							=    ViTMSNForImageClassification(__lowerCAmelCase							)
         model.to(__lowerCAmelCase							)
         model.eval()

         _UpperCAmelCase							=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
         _UpperCAmelCase							=    model(__lowerCAmelCase							)
         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.type_sequence_label_size)							)





  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    self.prepare_config_and_inputs()
         _UpperCAmelCase							=    config_and_inputs
         _UpperCAmelCase							=    {"""pixel_values""": pixel_values}
         return config, inputs_dict



@require_torch
class 		SCREAMING_SNAKE_CASE__	(  SCREAMING_SNAKE_CASE__					,      SCREAMING_SNAKE_CASE__					,      unittest.TestCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
  _lowerCamelCase    =  (
      {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
      if is_torch_available()
      else {}
  )

  _lowerCamelCase    =  False
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    ViTMSNModelTester(self							)
         _UpperCAmelCase							=    ConfigTester(self       ,				config_class=__lowerCAmelCase       ,				has_text_modality=__lowerCAmelCase       ,				hidden_size=37							)


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


         """simple docstring"""
         self.config_tester.run_common_tests()


  @unittest.skip(reason="""ViTMSN does not use inputs_embeds"""							)
  def  lowerCamelCase       (      self		:			Tuple							)						->	List[str]:


         """simple docstring"""
         pass


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                _UpperCAmelCase							=    model_class(__lowerCAmelCase							)
                self.assertIsInstance(model.get_input_embeddings()       ,				(nn.Module)							)
                _UpperCAmelCase							=    model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(__lowerCAmelCase       ,				nn.Linear							)							)


  def  lowerCamelCase       (      self		:			List[str]							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                _UpperCAmelCase							=    model_class(__lowerCAmelCase							)
                _UpperCAmelCase							=    inspect.signature(model.forward							)
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                _UpperCAmelCase							=    [*signature.parameters.keys()]

                _UpperCAmelCase							=    ["""pixel_values"""]
                self.assertListEqual(arg_names[:1]       ,				__lowerCAmelCase							)


  def  lowerCamelCase       (      self		:			Dict							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__lowerCAmelCase							)


  def  lowerCamelCase       (      self		:			Any							)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase							)





  @slow
  def  lowerCamelCase       (      self		:			Any							)						->	str:


         """simple docstring"""
         for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _UpperCAmelCase							=    ViTMSNModel.from_pretrained(__lowerCAmelCase							)
                self.assertIsNotNone(__lowerCAmelCase							)




def       _SCREAMING_SNAKE_CASE (							)				->						Any:
       _UpperCAmelCase							=    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
       return image





@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  @cached_property
  def  lowerCamelCase       (      self		:			Tuple							)						->	Optional[int]:


         """simple docstring"""
         return ViTImageProcessor.from_pretrained("""facebook/vit-msn-small"""							) if is_vision_available() else None





  @slow
  def  lowerCamelCase       (      self		:			int							)						->	Any:


         """simple docstring"""
         torch.manual_seed(2							)
         _UpperCAmelCase							=    ViTMSNForImageClassification.from_pretrained("""facebook/vit-msn-small"""							).to(__lowerCAmelCase							)

         _UpperCAmelCase							=    self.default_image_processor
         _UpperCAmelCase							=    prepare_img()
         _UpperCAmelCase							=    image_processor(images=__lowerCAmelCase       ,				return_tensors="""pt"""							).to(__lowerCAmelCase							)

         # forward pass
         with torch.no_grad():
                _UpperCAmelCase							=    model(**__lowerCAmelCase							)

         # verify the logits
         _UpperCAmelCase							=    torch.Size((1, 1000)							)
         self.assertEqual(outputs.logits.shape       ,				__lowerCAmelCase							)

         _UpperCAmelCase							=    torch.tensor([-0.0803, -0.4454, -0.2375]							).to(__lowerCAmelCase							)

         self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,				__lowerCAmelCase       ,				atol=1E-4							)							)







'''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

UpperCamelCase      					=logging.get_logger(__name__)




@add_end_docstrings(SCREAMING_SNAKE_CASE__    )
class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 def __init__(				self      ,  **__lowerCAmelCase			):
       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 	_UpperCAmelCase							(				self      ,  **__lowerCAmelCase			):
       UpperCamelCase_					: int            =				{}
       UpperCamelCase_					: int            =				{}
       UpperCamelCase_					: Any            =				{}
       # preprocess args
       if "points_per_batch" in kwargs:
             UpperCamelCase_					: Dict            =				kwargs["""points_per_batch"""]
       if "points_per_crop" in kwargs:
             UpperCamelCase_					: str            =				kwargs["""points_per_crop"""]
       if "crops_n_layers" in kwargs:
             UpperCamelCase_					: Any            =				kwargs["""crops_n_layers"""]
       if "crop_overlap_ratio" in kwargs:
             UpperCamelCase_					: str            =				kwargs["""crop_overlap_ratio"""]
       if "crop_n_points_downscale_factor" in kwargs:
             UpperCamelCase_					: Tuple            =				kwargs["""crop_n_points_downscale_factor"""]
       # postprocess args
       if "pred_iou_thresh" in kwargs:
             UpperCamelCase_					: Any            =				kwargs["""pred_iou_thresh"""]
       if "stability_score_offset" in kwargs:
             UpperCamelCase_					: List[str]            =				kwargs["""stability_score_offset"""]
       if "mask_threshold" in kwargs:
             UpperCamelCase_					: Tuple            =				kwargs["""mask_threshold"""]
       if "stability_score_thresh" in kwargs:
             UpperCamelCase_					: Optional[Any]            =				kwargs["""stability_score_thresh"""]
       if "crops_nms_thresh" in kwargs:
             UpperCamelCase_					: Dict            =				kwargs["""crops_nms_thresh"""]
       if "output_rle_mask" in kwargs:
             UpperCamelCase_					: Optional[int]            =				kwargs["""output_rle_mask"""]
       if "output_bboxes_mask" in kwargs:
             UpperCamelCase_					: List[Any]            =				kwargs["""output_bboxes_mask"""]
       return preprocess_kwargs, forward_params, postprocess_kwargs
 def __call__(				self      ,  __lowerCAmelCase      ,  *__lowerCAmelCase      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=None      ,  **__lowerCAmelCase			):
       return super().__call__(__lowerCAmelCase      ,  *__lowerCAmelCase      ,  num_workers=__lowerCAmelCase      ,  batch_size=__lowerCAmelCase      ,  **__lowerCAmelCase			)
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase=64      ,  __lowerCAmelCase = 0      ,  __lowerCAmelCase = 5_12 / 15_00      ,  __lowerCAmelCase = 32      ,  __lowerCAmelCase = 1      ,  ):
       UpperCamelCase_					: Tuple            =				load_image(__lowerCAmelCase			)
       UpperCamelCase_					: Tuple            =				self.image_processor.size["""longest_edge"""]
       UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_					: Dict            =				self.image_processor.generate_crop_boxes(
           __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase			)
       UpperCamelCase_					: Any            =				self.image_processor(images=__lowerCAmelCase      ,  return_tensors="""pt"""			)

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

       UpperCamelCase_					: Any            =				grid_points.shape[1]
       UpperCamelCase_					: 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			):
             UpperCamelCase_					: Dict            =				grid_points[:, i : i + points_per_batch, :, :]
             UpperCamelCase_					: List[Any]            =				input_labels[:, i : i + points_per_batch]
             UpperCamelCase_					: 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 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase=0.88      ,  __lowerCAmelCase=0.95      ,  __lowerCAmelCase=0      ,  __lowerCAmelCase=1      ,  ):
       UpperCamelCase_					: Union[str, Any]            =				model_inputs.pop("""input_boxes"""			)
       UpperCamelCase_					: Tuple            =				model_inputs.pop("""is_last"""			)
       UpperCamelCase_					: Dict            =				model_inputs.pop("""original_sizes"""			).tolist()
       UpperCamelCase_					: int            =				model_inputs.pop("""reshaped_input_sizes"""			).tolist()

       UpperCamelCase_					: Optional[Any]            =				self.model(**__lowerCAmelCase			)

       # post processing happens here in order to avoid CPU GPU copies of ALL the masks
       UpperCamelCase_					: List[str]            =				model_outputs["""pred_masks"""]
       UpperCamelCase_					: Optional[int]            =				self.image_processor.post_process_masks(
           __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  binarize=__lowerCAmelCase			)
       UpperCamelCase_					: List[str]            =				model_outputs["""iou_scores"""]
       UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_					: Optional[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 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase=False      ,  __lowerCAmelCase=False      ,  __lowerCAmelCase=0.7      ,  ):
       UpperCamelCase_					: Tuple            =				[]
       UpperCamelCase_					: Optional[Any]            =				[]
       UpperCamelCase_					: Tuple            =				[]
       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_					: Any            =				torch.cat(__lowerCAmelCase			)
       UpperCamelCase_					: Union[str, Any]            =				torch.cat(__lowerCAmelCase			)
       UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_					: Any            =				self.image_processor.post_process_for_mask_generation(
           __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase			)

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

       UpperCamelCase_					: Optional[Any]            =				{}
       if output_rle_mask:
             UpperCamelCase_					: int            =				rle_mask

       if output_bboxes_mask:
             UpperCamelCase_					: int            =				bounding_boxes

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






