
"""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

_lowercase							   =				logging.get_logger(__name__)







@add_end_docstrings(_lowercase    )
class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						def __init__( self     :      Dict  ,**A_     :      Tuple      )     ->   int:
							super().__init__(**A_      )
							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(A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,**A_     :      List[Any]      )     ->   Union[str, Any]:
							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     :      Dict  ,A_     :      Tuple  ,*A_     :      Tuple  ,A_     :      Tuple=None  ,A_     :      Any=None  ,**A_     :      Union[str, Any]      )     ->   Any:
							return super().__call__(A_  ,*A_  ,num_workers=A_  ,batch_size=A_  ,**A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      Optional[int]  ,A_     :      Optional[int]=64  ,A_     :      int = 0  ,A_     :      float = 512 / 1500  ,A_     :      Optional[int] = 32  ,A_     :      Optional[int] = 1  ,)     ->   Dict:
							A						   =   load_image(A_      )
							A						   =   self.image_processor.size['longest_edge']
							A   ,      A   ,      A   ,      A						   =   self.image_processor.generate_crop_boxes(
							    A_  ,A_  ,A_  ,A_  ,A_  ,A_      )
							A						   =   self.image_processor(images=A_  ,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(A_  ,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  ,A_  ,A_      ):
								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 						_SCREAMING_SNAKE_CASE      ( self     :      Dict  ,A_     :      Optional[Any]  ,A_     :      Union[str, Any]=0.88  ,A_     :      List[Any]=0.95  ,A_     :      Union[str, Any]=0  ,A_     :      str=1  ,)     ->   int:
							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(**A_      )

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




						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      Union[str, Any]  ,A_     :      List[Any]=False  ,A_     :      Any=False  ,A_     :      str=0.7  ,)     ->   List[str]:
							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(A_      )
							A						   =   torch.cat(A_      )
							A   ,      A   ,      A   ,      A						   =   self.image_processor.post_process_for_mask_generation(
							    A_  ,A_  ,A_  ,A_      )

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

							A						   =   {}
							if output_rle_mask:
								A						   =   rle_mask

							if output_bboxes_mask:
								A						   =   bounding_boxes

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

"""simple docstring"""


import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import ImageGPTImageProcessor







class 				lowerCAmelCase_				(      unittest.TestCase    ):



						'''simple docstring'''


						def __init__( self     :      int  ,A_     :      Tuple  ,A_     :      str=7  ,A_     :      Tuple=3  ,A_     :      List[Any]=18  ,A_     :      List[str]=30  ,A_     :      Optional[Any]=400  ,A_     :      Any=True  ,A_     :      Optional[Any]=None  ,A_     :      List[str]=True  ,)     ->   str:
							A						   =   size if size is not None else {'height': 18, 'width': 18}
							A						   =   parent
							A						   =   batch_size
							A						   =   num_channels
							A						   =   image_size
							A						   =   min_resolution
							A						   =   max_resolution
							A						   =   do_resize
							A						   =   size
							A						   =   do_normalize




						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   List[Any]:
							return {
							    # here we create 2 clusters for the sake of simplicity
							    "clusters": np.asarray(
							        [
							            [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
							            [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
							        ]      ),
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							}







@require_torch
@require_vision
class 				lowerCAmelCase_				(      _lowercase		,					unittest.TestCase    ):



						'''simple docstring'''


						_lowerCamelCase:    List[Any]      =    ImageGPTImageProcessor if is_vision_available() else None


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Any:
							A						   =   ImageGPTImageProcessingTester(self      )


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Union[str, Any]:
							return self.image_processor_tester.prepare_image_processor_dict()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   List[Any]:
							A						   =   self.image_processing_class(**self.image_processor_dict      )
							self.assertTrue(hasattr(A_  ,'clusters'      )      )
							self.assertTrue(hasattr(A_  ,'do_resize'      )      )
							self.assertTrue(hasattr(A_  ,'size'      )      )
							self.assertTrue(hasattr(A_  ,'do_normalize'      )      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Tuple:
							A						   =   self.image_processing_class.from_dict(self.image_processor_dict      )
							self.assertEqual(image_processor.size  ,{'height': 18, 'width': 18}      )

							A						   =   self.image_processing_class.from_dict(self.image_processor_dict  ,size=42      )
							self.assertEqual(image_processor.size  ,{'height': 42, 'width': 42}      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   str:
							A						   =   self.image_processing_class(**self.image_processor_dict      )
							A						   =   json.loads(image_processor.to_json_string()      )
							for key, value in self.image_processor_dict.items():
								if key == "clusters":
									self.assertTrue(np.array_equal(A_  ,obj[key]      )      )
								else:
									self.assertEqual(obj[key]  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Union[str, Any]:
							A						   =   self.image_processing_class(**self.image_processor_dict      )

							with tempfile.TemporaryDirectory() as tmpdirname:
								A						   =   os.path.join(A_  ,'image_processor.json'      )
								image_processor_first.to_json_file(A_      )
								A						   =   self.image_processing_class.from_json_file(A_      ).to_dict()

							A						   =   image_processor_first.to_dict()
							for key, value in image_processor_first.items():
								if key == "clusters":
									self.assertTrue(np.array_equal(A_  ,image_processor_second[key]      )      )
								else:
									self.assertEqual(image_processor_first[key]  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   int:
							A						   =   self.image_processing_class(**self.image_processor_dict      )

							with tempfile.TemporaryDirectory() as tmpdirname:
								image_processor_first.save_pretrained(A_      )
								A						   =   self.image_processing_class.from_pretrained(A_      ).to_dict()

							A						   =   image_processor_first.to_dict()
							for key, value in image_processor_first.items():
								if key == "clusters":
									self.assertTrue(np.array_equal(A_  ,image_processor_second[key]      )      )
								else:
									self.assertEqual(image_processor_first[key]  ,A_      )




						@unittest.skip('ImageGPT requires clusters at initialization'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Union[str, Any]:
							pass






def       _snake_case				(		):
	A						   =   load_dataset('hf-internal-testing/fixtures_image_utils'	,      split='test'     )

	A						   =   Image.open(dataset[4]['file']     )
	A						   =   Image.open(dataset[5]['file']     )

	A						   =   [imagea, imagea]

	return images







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



						'''simple docstring'''


						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   int:
							A						   =   ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'      )

							A						   =   prepare_images()

							# test non-batched
							A						   =   image_processing(images[0]  ,return_tensors='pt'      )

							self.assertIsInstance(encoding.input_ids  ,torch.LongTensor      )
							self.assertEqual(encoding.input_ids.shape  ,(1, 1024)      )

							A						   =   [306, 191, 191]
							self.assertEqual(encoding.input_ids[0, :3].tolist()  ,A_      )

							# test batched
							A						   =   image_processing(A_  ,return_tensors='pt'      )

							self.assertIsInstance(encoding.input_ids  ,torch.LongTensor      )
							self.assertEqual(encoding.input_ids.shape  ,(2, 1024)      )

							A						   =   [303, 13, 13]
							self.assertEqual(encoding.input_ids[1, -3:].tolist()  ,A_      )