
"""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(A_	)
class       lowerCAmelCase__				(						A_	):







     def __init__(							self			:			List[str]    ,      **_lowerCamelCase			:			Union[str, Any]			):
       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			:			List[str]    ,      **_lowerCamelCase			:			Optional[Any]			):
       _snake_case					     =       {}
       _snake_case					     =       {}
       _snake_case					     =       {}
       # preprocess args
       if "points_per_batch" in kwargs:
         _snake_case					     =       kwargs['''points_per_batch''']
       if "points_per_crop" in kwargs:
         _snake_case					     =       kwargs['''points_per_crop''']
       if "crops_n_layers" in kwargs:
         _snake_case					     =       kwargs['''crops_n_layers''']
       if "crop_overlap_ratio" in kwargs:
         _snake_case					     =       kwargs['''crop_overlap_ratio''']
       if "crop_n_points_downscale_factor" in kwargs:
         _snake_case					     =       kwargs['''crop_n_points_downscale_factor''']
       # postprocess args
       if "pred_iou_thresh" in kwargs:
         _snake_case					     =       kwargs['''pred_iou_thresh''']
       if "stability_score_offset" in kwargs:
         _snake_case					     =       kwargs['''stability_score_offset''']
       if "mask_threshold" in kwargs:
         _snake_case					     =       kwargs['''mask_threshold''']
       if "stability_score_thresh" in kwargs:
         _snake_case					     =       kwargs['''stability_score_thresh''']
       if "crops_nms_thresh" in kwargs:
         _snake_case					     =       kwargs['''crops_nms_thresh''']
       if "output_rle_mask" in kwargs:
         _snake_case					     =       kwargs['''output_rle_mask''']
       if "output_bboxes_mask" in kwargs:
         _snake_case					     =       kwargs['''output_bboxes_mask''']
       return preprocess_kwargs, forward_params, postprocess_kwargs







     def __call__(							self			:			Tuple    ,      _lowerCamelCase			:			Union[str, Any]    ,      *_lowerCamelCase			:			List[Any]    ,      _lowerCamelCase			:			Optional[Any]=None    ,      _lowerCamelCase			:			Optional[int]=None    ,      **_lowerCamelCase			:			Optional[Any]			):
       return super().__call__(_lowerCamelCase    ,      *_lowerCamelCase    ,      num_workers=_lowerCamelCase    ,      batch_size=_lowerCamelCase    ,      **_lowerCamelCase			)







     def      lowercase	(							self			:			Any    ,      _lowerCamelCase			:			Optional[Any]    ,      _lowerCamelCase			:			Optional[int]=64    ,      _lowerCamelCase			:			int = 0    ,      _lowerCamelCase			:			float = 512 / 1500    ,      _lowerCamelCase			:			Optional[int] = 32    ,      _lowerCamelCase			:			Optional[int] = 1    ,      ):
       _snake_case					     =       load_image(_lowerCamelCase			)
       _snake_case					     =       self.image_processor.size['''longest_edge''']
       _snake_case     ,					_snake_case     ,					_snake_case     ,					_snake_case					     =       self.image_processor.generate_crop_boxes(
           _lowerCamelCase    ,      _lowerCamelCase    ,      _lowerCamelCase    ,      _lowerCamelCase    ,      _lowerCamelCase    ,      _lowerCamelCase			)
       _snake_case					     =       self.image_processor(images=_lowerCamelCase    ,      return_tensors='''pt'''			)

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

       _snake_case					     =       grid_points.shape[1]
       _snake_case					     =       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			):
         _snake_case					     =       grid_points[:, i : i + points_per_batch, :, :]
         _snake_case					     =       input_labels[:, i : i + points_per_batch]
         _snake_case					     =       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			:			str    ,      _lowerCamelCase			:			Tuple=0.8_8    ,      _lowerCamelCase			:			Dict=0.9_5    ,      _lowerCamelCase			:			Any=0    ,      _lowerCamelCase			:			Any=1    ,      ):
       _snake_case					     =       model_inputs.pop('''input_boxes'''			)
       _snake_case					     =       model_inputs.pop('''is_last'''			)
       _snake_case					     =       model_inputs.pop('''original_sizes'''			).tolist()
       _snake_case					     =       model_inputs.pop('''reshaped_input_sizes'''			).tolist()

       _snake_case					     =       self.model(**_lowerCamelCase			)

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

       _snake_case					     =       torch.cat(_lowerCamelCase			)
       _snake_case					     =       torch.cat(_lowerCamelCase			)
       _snake_case     ,					_snake_case     ,					_snake_case     ,					_snake_case					     =       self.image_processor.post_process_for_mask_generation(
           _lowerCamelCase    ,      _lowerCamelCase    ,      _lowerCamelCase    ,      _lowerCamelCase			)

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

       _snake_case					     =       {}
       if output_rle_mask:
         _snake_case					     =       rle_mask

       if output_bboxes_mask:
         _snake_case					     =       bounding_boxes

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





"""simple docstring"""



from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TFResNetForImageClassification, TFResNetModel
 from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor

class       lowerCAmelCase__				:







     def __init__(							self			:			Dict    ,      _lowerCamelCase			:			int    ,      _lowerCamelCase			:			Optional[int]=3    ,      _lowerCamelCase			:			List[str]=32    ,      _lowerCamelCase			:			Optional[int]=3    ,      _lowerCamelCase			:			Dict=10    ,      _lowerCamelCase			:			Tuple=[10, 20, 30, 40]    ,      _lowerCamelCase			:			int=[1, 1, 2, 1]    ,      _lowerCamelCase			:			int=True    ,      _lowerCamelCase			:			Optional[int]=True    ,      _lowerCamelCase			:			Optional[int]="relu"    ,      _lowerCamelCase			:			List[Any]=3    ,      _lowerCamelCase			:			Dict=None    ,      ):
       _snake_case					     =       parent
       _snake_case					     =       batch_size
       _snake_case					     =       image_size
       _snake_case					     =       num_channels
       _snake_case					     =       embeddings_size
       _snake_case					     =       hidden_sizes
       _snake_case					     =       depths
       _snake_case					     =       is_training
       _snake_case					     =       use_labels
       _snake_case					     =       hidden_act
       _snake_case					     =       num_labels
       _snake_case					     =       scope
       _snake_case					     =       len(_lowerCamelCase			)







     def      lowercase	(							self			:			Optional[int]			):
       _snake_case					     =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

       _snake_case					     =       None
       if self.use_labels:
         _snake_case					     =       ids_tensor([self.batch_size]    ,      self.num_labels			)

       _snake_case					     =       self.get_config()

       return config, pixel_values, labels







     def      lowercase	(							self			:			Tuple			):
       return ResNetConfig(
           num_channels=self.num_channels    ,      embeddings_size=self.embeddings_size    ,      hidden_sizes=self.hidden_sizes    ,      depths=self.depths    ,      hidden_act=self.hidden_act    ,      num_labels=self.num_labels    ,      image_size=self.image_size    ,      )







     def      lowercase	(							self			:			List[Any]    ,      _lowerCamelCase			:			List[str]    ,      _lowerCamelCase			:			str    ,      _lowerCamelCase			:			List[Any]			):
       _snake_case					     =       TFResNetModel(config=_lowerCamelCase			)
       _snake_case					     =       model(_lowerCamelCase			)
       # expected last hidden states: B, C, H // 32, W // 32
       self.parent.assertEqual(
           result.last_hidden_state.shape    ,      (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)    ,      )







     def      lowercase	(							self			:			Dict    ,      _lowerCamelCase			:			str    ,      _lowerCamelCase			:			Optional[Any]    ,      _lowerCamelCase			:			Tuple			):
       _snake_case					     =       self.num_labels
       _snake_case					     =       TFResNetForImageClassification(_lowerCamelCase			)
       _snake_case					     =       model(_lowerCamelCase    ,      labels=_lowerCamelCase			)
       self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.num_labels)			)







     def      lowercase	(							self			:			Tuple			):
       _snake_case					     =       self.prepare_config_and_inputs()
       _snake_case     ,					_snake_case     ,					_snake_case					     =       config_and_inputs
       _snake_case					     =       {'''pixel_values''': pixel_values}
       return config, inputs_dict






@require_tf
class       lowerCAmelCase__				(						A_ ,	A_ ,	unittest.TestCase	):
     __a						=					(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
     __a						=					(
         {"""feature-extraction""": TFResNetModel, """image-classification""": TFResNetForImageClassification}
         if is_tf_available()
         else {}
     )

     __a						=					False
     __a						=					False
     __a						=					False
     __a						=					False
     __a						=					False







     def      lowercase	(							self			:			List[Any]			):
       _snake_case					     =       TFResNetModelTester(self			)
       _snake_case					     =       ConfigTester(self    ,      config_class=_lowerCamelCase    ,      has_text_modality=_lowerCamelCase			)







     def      lowercase	(							self			:			Tuple			):
       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()







     def      lowercase	(							self			:			List[Any]			):
       return







     @unittest.skip(reason='''ResNet does not use inputs_embeds'''			)
     def      lowercase	(							self			:			Any			):
       pass







     @unittest.skip(reason='''ResNet does not support input and output embeddings'''			)
     def      lowercase	(							self			:			List[str]			):
       pass







     def      lowercase	(							self			:			int			):
       _snake_case     ,					_snake_case					     =       self.model_tester.prepare_config_and_inputs_for_common()

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

         _snake_case					     =       ['''pixel_values''']
         self.assertListEqual(arg_names[:1]    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			List[str]			):
       _snake_case					     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCamelCase			)







     def      lowercase	(							self			:			Union[str, Any]			):
       def check_hidden_states_output(_lowerCamelCase			:			int    ,      _lowerCamelCase			:			List[Any]    ,      _lowerCamelCase			:			str			):
         _snake_case					     =       model_class(_lowerCamelCase			)
         _snake_case					     =       model(**self._prepare_for_class(_lowerCamelCase    ,      _lowerCamelCase			)			)

         _snake_case					     =       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

         _snake_case					     =       self.model_tester.num_stages
         self.assertEqual(len(_lowerCamelCase			)    ,      expected_num_stages + 1			)

         # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
         self.assertListEqual(
             list(hidden_states[0].shape[-2:]			)    ,      [self.model_tester.image_size // 4, self.model_tester.image_size // 4]    ,      )

       _snake_case     ,					_snake_case					     =       self.model_tester.prepare_config_and_inputs_for_common()
       _snake_case					     =       ['''basic''', '''bottleneck''']
       for model_class in self.all_model_classes:
         for layer_type in layers_type:
           _snake_case					     =       layer_type
           _snake_case					     =       True
           check_hidden_states_output(_lowerCamelCase    ,      _lowerCamelCase    ,      _lowerCamelCase			)

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           _snake_case					     =       True

           check_hidden_states_output(_lowerCamelCase    ,      _lowerCamelCase    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			Union[str, Any]			):
       _snake_case					     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase			)







     @slow
     def      lowercase	(							self			:			List[str]			):
       for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _snake_case					     =       TFResNetModel.from_pretrained(_lowerCamelCase			)
         self.assertIsNotNone(_lowerCamelCase			)






def 					_UpperCAmelCase      ( )     ->					Union[str, Any]:
  _snake_case					     =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
  return image

@require_tf
@require_vision
class       lowerCAmelCase__				(						unittest.TestCase	):







     @cached_property
     def      lowercase	(							self			:			Dict			):
       return (
           AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)
           if is_vision_available()
           else None
       )







     @slow
     def      lowercase	(							self			:			List[Any]			):
       _snake_case					     =       TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)

       _snake_case					     =       self.default_image_processor
       _snake_case					     =       prepare_img()
       _snake_case					     =       image_processor(images=_lowerCamelCase    ,      return_tensors='''tf'''			)

       # forward pass
       _snake_case					     =       model(**_lowerCamelCase			)

       # verify the logits
       _snake_case					     =       tf.TensorShape((1, 1000)			)
       self.assertEqual(outputs.logits.shape    ,      _lowerCamelCase			)

       _snake_case					     =       tf.constant([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7]			)

       self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()    ,      _lowerCamelCase    ,      atol=1e-4			)			)



