



'''simple docstring'''


import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

						if is_vision_available():
												from transformers import OneFormerImageProcessor
												from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
												from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
						from PIL import Image







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Optional[Any] ,  lowercase_    :      Dict="shi-labs/oneformer_demo"						):
						with open(hf_hub_download(lowercase_ ,  lowercase_ ,  repo_type="""dataset"""						) ,  """r"""						) as f:
												lowercase        =							json.load(lowercase_						)
						lowercase        =							{}
						lowercase        =							[]
						lowercase        =							[]
						for key, info in class_info.items():
												lowercase        =							info["""name"""]
												class_names.append(info["""name"""]						)
												if info["isthing"]:
																		thing_ids.append(int(lowercase_						)						)
						lowercase        =							thing_ids
						lowercase        =							class_names
						return metadata




class      __UpperCamelCase       (unittest.TestCase			):







	def __init__(    self ,				_lowerCAmelCase ,				_lowerCAmelCase=7 ,				_lowerCAmelCase=3 ,				_lowerCAmelCase=30 ,				_lowerCAmelCase=400 ,				_lowerCAmelCase=None ,				_lowerCAmelCase=True ,				_lowerCAmelCase=True ,				_lowerCAmelCase=[0.5, 0.5, 0.5] ,				_lowerCAmelCase=[0.5, 0.5, 0.5] ,				_lowerCAmelCase=10 ,				_lowerCAmelCase=False ,				_lowerCAmelCase=255 ,				_lowerCAmelCase="shi-labs/oneformer_demo" ,				_lowerCAmelCase="ade20k_panoptic.json" ,				_lowerCAmelCase=10 ,				)    ->    str:
							'''simple docstring'''






							lowercase        =							parent
							lowercase        =							batch_size
							lowercase        =							num_channels
							lowercase        =							min_resolution
							lowercase        =							max_resolution
							lowercase        =							do_resize
							lowercase        =							{"""shortest_edge""": 32, """longest_edge""": 1333} if size is None else size
							lowercase        =							do_normalize
							lowercase        =							image_mean
							lowercase        =							image_std
							lowercase        =							class_info_file
							lowercase        =							prepare_metadata(_lowerCAmelCase ,				_lowerCAmelCase						)
							lowercase        =							num_text
							lowercase        =							repo_path

							# for the post_process_functions
							lowercase        =							2
							lowercase        =							10
							lowercase        =							10
							lowercase        =							3
							lowercase        =							4
							lowercase        =							num_labels
							lowercase        =							do_reduce_labels
							lowercase        =							ignore_index







	def   _a   (    self						)    ->    str:
							'''simple docstring'''






							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "num_labels": self.num_labels,
							    "do_reduce_labels": self.do_reduce_labels,
							    "ignore_index": self.ignore_index,
							    "class_info_file": self.class_info_file,
							    "metadata": self.metadata,
							    "num_text": self.num_text,
							}







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase=False						)    ->    Tuple:
							'''simple docstring'''






							if not batched:
													lowercase        =							image_inputs[0]
													if isinstance(_lowerCAmelCase ,				Image.Image						):
																			lowercase     ,   lowercase        =							image.size
													else:
																			lowercase     ,   lowercase        =							image.shape[1], image.shape[2]
													if w < h:
																			lowercase        =							int(self.size["""shortest_edge"""] * h / w						)
																			lowercase        =							self.size["""shortest_edge"""]
													elif w > h:
																			lowercase        =							self.size["""shortest_edge"""]
																			lowercase        =							int(self.size["""shortest_edge"""] * w / h						)
													else:
																			lowercase        =							self.size["""shortest_edge"""]
																			lowercase        =							self.size["""shortest_edge"""]

							else:
													lowercase        =							[]
													for image in image_inputs:
																			lowercase     ,   lowercase        =							self.get_expected_values([image]						)
																			expected_values.append((expected_height, expected_width)						)
													lowercase        =							max(_lowerCAmelCase ,				key=lambda _lowerCAmelCase						: item[0]						)[0]
													lowercase        =							max(_lowerCAmelCase ,				key=lambda _lowerCAmelCase						: item[1]						)[1]

							return expected_height, expected_width







	def   _a   (    self						)    ->    List[str]:
							'''simple docstring'''






							return OneFormerForUniversalSegmentationOutput(
							    # +1 for null class
							    class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)						) ,				masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)						) ,				)




@require_torch
@require_vision
class      __UpperCamelCase       (_UpperCAmelCase			,   unittest.TestCase			):
	__A			  =					OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
	# only for test_image_processing_common.test_image_proc_to_json_string
	__A			  =					image_processing_class







	def   _a   (    self						)    ->    str:
							'''simple docstring'''






							lowercase        =							OneFormerImageProcessorTester(self						)







	@property
	def   _a   (    self						)    ->    Tuple:
							'''simple docstring'''






							return self.image_processing_tester.prepare_image_processor_dict()







	def   _a   (    self						)    ->    Union[str, Any]:
							'''simple docstring'''






							lowercase        =							self.image_processing_class(**self.image_processor_dict						)
							self.assertTrue(hasattr(_lowerCAmelCase ,				"""image_mean"""						)						)
							self.assertTrue(hasattr(_lowerCAmelCase ,				"""image_std"""						)						)
							self.assertTrue(hasattr(_lowerCAmelCase ,				"""do_normalize"""						)						)
							self.assertTrue(hasattr(_lowerCAmelCase ,				"""do_resize"""						)						)
							self.assertTrue(hasattr(_lowerCAmelCase ,				"""size"""						)						)
							self.assertTrue(hasattr(_lowerCAmelCase ,				"""ignore_index"""						)						)
							self.assertTrue(hasattr(_lowerCAmelCase ,				"""class_info_file"""						)						)
							self.assertTrue(hasattr(_lowerCAmelCase ,				"""num_text"""						)						)
							self.assertTrue(hasattr(_lowerCAmelCase ,				"""repo_path"""						)						)
							self.assertTrue(hasattr(_lowerCAmelCase ,				"""metadata"""						)						)
							self.assertTrue(hasattr(_lowerCAmelCase ,				"""do_reduce_labels"""						)						)







	def   _a   (    self						)    ->    str:
							'''simple docstring'''






							pass







	def   _a   (    self						)    ->    Tuple:
							'''simple docstring'''






							lowercase        =							self.image_processing_class(**self.image_processor_dict						)
							# create random PIL images
							lowercase        =							prepare_image_inputs(self.image_processing_tester ,				equal_resolution=_lowerCAmelCase						)
							for image in image_inputs:
													self.assertIsInstance(_lowerCAmelCase ,				Image.Image						)

							# Test not batched input
							lowercase        =							image_processor(image_inputs[0] ,				["""semantic"""] ,				return_tensors="""pt"""						).pixel_values

							lowercase     ,   lowercase        =							self.image_processing_tester.get_expected_values(_lowerCAmelCase						)

							self.assertEqual(
							    encoded_images.shape ,				(1, self.image_processing_tester.num_channels, expected_height, expected_width) ,				)

							# Test batched
							lowercase     ,   lowercase        =							self.image_processing_tester.get_expected_values(_lowerCAmelCase ,				batched=_lowerCAmelCase						)

							lowercase        =							image_processor(
							    _lowerCAmelCase ,				["""semantic"""] * len(_lowerCAmelCase						) ,				return_tensors="""pt"""						).pixel_values
							self.assertEqual(
							    encoded_images.shape ,				(
							        self.image_processing_tester.batch_size,
							        self.image_processing_tester.num_channels,
							        expected_height,
							        expected_width,
							    ) ,				)







	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






							lowercase        =							self.image_processing_class(**self.image_processor_dict						)
							# create random numpy tensors
							lowercase        =							prepare_image_inputs(self.image_processing_tester ,				equal_resolution=_lowerCAmelCase ,				numpify=_lowerCAmelCase						)
							for image in image_inputs:
													self.assertIsInstance(_lowerCAmelCase ,				np.ndarray						)

							# Test not batched input
							lowercase        =							image_processor(image_inputs[0] ,				["""semantic"""] ,				return_tensors="""pt"""						).pixel_values

							lowercase     ,   lowercase        =							self.image_processing_tester.get_expected_values(_lowerCAmelCase						)

							self.assertEqual(
							    encoded_images.shape ,				(1, self.image_processing_tester.num_channels, expected_height, expected_width) ,				)

							# Test batched
							lowercase     ,   lowercase        =							self.image_processing_tester.get_expected_values(_lowerCAmelCase ,				batched=_lowerCAmelCase						)

							lowercase        =							image_processor(
							    _lowerCAmelCase ,				["""semantic"""] * len(_lowerCAmelCase						) ,				return_tensors="""pt"""						).pixel_values
							self.assertEqual(
							    encoded_images.shape ,				(
							        self.image_processing_tester.batch_size,
							        self.image_processing_tester.num_channels,
							        expected_height,
							        expected_width,
							    ) ,				)







	def   _a   (    self						)    ->    Any:
							'''simple docstring'''






							lowercase        =							self.image_processing_class(**self.image_processor_dict						)
							# create random PyTorch tensors
							lowercase        =							prepare_image_inputs(self.image_processing_tester ,				equal_resolution=_lowerCAmelCase ,				torchify=_lowerCAmelCase						)
							for image in image_inputs:
													self.assertIsInstance(_lowerCAmelCase ,				torch.Tensor						)

							# Test not batched input
							lowercase        =							image_processor(image_inputs[0] ,				["""semantic"""] ,				return_tensors="""pt"""						).pixel_values

							lowercase     ,   lowercase        =							self.image_processing_tester.get_expected_values(_lowerCAmelCase						)

							self.assertEqual(
							    encoded_images.shape ,				(1, self.image_processing_tester.num_channels, expected_height, expected_width) ,				)

							# Test batched
							lowercase     ,   lowercase        =							self.image_processing_tester.get_expected_values(_lowerCAmelCase ,				batched=_lowerCAmelCase						)

							lowercase        =							image_processor(
							    _lowerCAmelCase ,				["""semantic"""] * len(_lowerCAmelCase						) ,				return_tensors="""pt"""						).pixel_values
							self.assertEqual(
							    encoded_images.shape ,				(
							        self.image_processing_tester.batch_size,
							        self.image_processing_tester.num_channels,
							        expected_height,
							        expected_width,
							    ) ,				)







	def   _a   (    self ,				_lowerCAmelCase=False ,				_lowerCAmelCase=False ,				_lowerCAmelCase="np"						)    ->    int:
							'''simple docstring'''






							lowercase        =							self.image_processing_class(**self.image_processor_dict						)
							# prepare image and target
							lowercase        =							self.image_processing_tester.num_labels
							lowercase        =							None
							lowercase        =							None
							lowercase        =							prepare_image_inputs(self.image_processing_tester ,				equal_resolution=_lowerCAmelCase						)
							if with_segmentation_maps:
													lowercase        =							num_labels
													if is_instance_map:
																			lowercase        =							list(range(_lowerCAmelCase						)						) * 2
																			lowercase        =							dict(enumerate(_lowerCAmelCase						)						)
													lowercase        =							[
													    np.random.randint(0 ,				high * 2 ,				(img.size[1], img.size[0])						).astype(np.uinta						) for img in image_inputs
													]
													if segmentation_type == "pil":
																			lowercase        =							[Image.fromarray(_lowerCAmelCase						) for annotation in annotations]

							lowercase        =							image_processor(
							    _lowerCAmelCase ,				["""semantic"""] * len(_lowerCAmelCase						) ,				_lowerCAmelCase ,				return_tensors="""pt""" ,				instance_id_to_semantic_id=_lowerCAmelCase ,				pad_and_return_pixel_mask=_lowerCAmelCase ,				)

							return inputs







	def   _a   (    self						)    ->    List[str]:
							'''simple docstring'''






							pass







	def   _a   (    self						)    ->    Tuple:
							'''simple docstring'''






							def common(_lowerCAmelCase=False ,				_lowerCAmelCase=None						):
													lowercase        =							self.comm_get_image_processor_inputs(
													    with_segmentation_maps=_lowerCAmelCase ,				is_instance_map=_lowerCAmelCase ,				segmentation_type=_lowerCAmelCase						)

													lowercase        =							inputs["""mask_labels"""]
													lowercase        =							inputs["""class_labels"""]
													lowercase        =							inputs["""pixel_values"""]
													lowercase        =							inputs["""text_inputs"""]

													# check the batch_size
													for mask_label, class_label, text_input in zip(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						):
																			self.assertEqual(mask_label.shape[0] ,				class_label.shape[0]						)
																			# this ensure padding has happened
																			self.assertEqual(mask_label.shape[1:] ,				pixel_values.shape[2:]						)
																			self.assertEqual(len(_lowerCAmelCase						) ,				self.image_processing_tester.num_text						)

							common()
							common(is_instance_map=_lowerCAmelCase						)
							common(is_instance_map=_lowerCAmelCase ,				segmentation_type="""pil"""						)
							common(is_instance_map=_lowerCAmelCase ,				segmentation_type="""pil"""						)







	def   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






							lowercase        =							np.zeros((20, 50)						)
							lowercase        =							1
							lowercase        =							1
							lowercase        =							1

							lowercase        =							binary_mask_to_rle(_lowerCAmelCase						)
							self.assertEqual(len(_lowerCAmelCase						) ,				4						)
							self.assertEqual(rle[0] ,				21						)
							self.assertEqual(rle[1] ,				45						)







	def   _a   (    self						)    ->    Optional[int]:
							'''simple docstring'''






							lowercase        =							self.image_processing_class(
							    num_labels=self.image_processing_tester.num_classes ,				max_seq_length=77 ,				task_seq_length=77 ,				class_info_file="""ade20k_panoptic.json""" ,				num_text=self.image_processing_tester.num_text ,				repo_path="""shi-labs/oneformer_demo""" ,				)
							lowercase        =							self.image_processing_tester.get_fake_oneformer_outputs()

							lowercase        =							fature_extractor.post_process_semantic_segmentation(_lowerCAmelCase						)

							self.assertEqual(len(_lowerCAmelCase						) ,				self.image_processing_tester.batch_size						)
							self.assertEqual(
							    segmentation[0].shape ,				(
							        self.image_processing_tester.height,
							        self.image_processing_tester.width,
							    ) ,				)

							lowercase        =							[(1, 4) for i in range(self.image_processing_tester.batch_size						)]
							lowercase        =							fature_extractor.post_process_semantic_segmentation(_lowerCAmelCase ,				target_sizes=_lowerCAmelCase						)

							self.assertEqual(segmentation[0].shape ,				target_sizes[0]						)







	def   _a   (    self						)    ->    Union[str, Any]:
							'''simple docstring'''






							lowercase        =							self.image_processing_class(
							    num_labels=self.image_processing_tester.num_classes ,				max_seq_length=77 ,				task_seq_length=77 ,				class_info_file="""ade20k_panoptic.json""" ,				num_text=self.image_processing_tester.num_text ,				repo_path="""shi-labs/oneformer_demo""" ,				)
							lowercase        =							self.image_processing_tester.get_fake_oneformer_outputs()
							lowercase        =							image_processor.post_process_instance_segmentation(_lowerCAmelCase ,				threshold=0						)

							self.assertTrue(len(_lowerCAmelCase						) == self.image_processing_tester.batch_size						)
							for el in segmentation:
													self.assertTrue("""segmentation""" in el						)
													self.assertTrue("""segments_info""" in el						)
													self.assertEqual(type(el["""segments_info"""]						) ,				_lowerCAmelCase						)
													self.assertEqual(
													    el["""segmentation"""].shape ,				(self.image_processing_tester.height, self.image_processing_tester.width)						)







	def   _a   (    self						)    ->    Union[str, Any]:
							'''simple docstring'''






							lowercase        =							self.image_processing_class(
							    num_labels=self.image_processing_tester.num_classes ,				max_seq_length=77 ,				task_seq_length=77 ,				class_info_file="""ade20k_panoptic.json""" ,				num_text=self.image_processing_tester.num_text ,				repo_path="""shi-labs/oneformer_demo""" ,				)
							lowercase        =							self.image_processing_tester.get_fake_oneformer_outputs()
							lowercase        =							image_processor.post_process_panoptic_segmentation(_lowerCAmelCase ,				threshold=0						)

							self.assertTrue(len(_lowerCAmelCase						) == self.image_processing_tester.batch_size						)
							for el in segmentation:
													self.assertTrue("""segmentation""" in el						)
													self.assertTrue("""segments_info""" in el						)
													self.assertEqual(type(el["""segments_info"""]						) ,				_lowerCAmelCase						)
													self.assertEqual(
													    el["""segmentation"""].shape ,				(self.image_processing_tester.height, self.image_processing_tester.width)						)







'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_      :				Optional[Any]   					=    logging.get_logger(__name__)

lowercase_      :				int   					=    {
    '''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json''',
}




class      __UpperCamelCase       (_UpperCAmelCase			):
	__A			  =					'''gpt_bigcode'''
	__A			  =					['''past_key_values''']
	__A			  =					{
	    '''hidden_size''': '''n_embd''',
	    '''max_position_embeddings''': '''n_positions''',
	    '''num_attention_heads''': '''n_head''',
	    '''num_hidden_layers''': '''n_layer''',
	}







	def __init__(    self ,				_lowerCAmelCase=5_0257 ,				_lowerCAmelCase=1024 ,				_lowerCAmelCase=768 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=None ,				_lowerCAmelCase="gelu_pytorch_tanh" ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=1E-5 ,				_lowerCAmelCase=0.02 ,				_lowerCAmelCase=True ,				_lowerCAmelCase=True ,				_lowerCAmelCase=5_0256 ,				_lowerCAmelCase=5_0256 ,				_lowerCAmelCase=True ,				_lowerCAmelCase=True ,				_lowerCAmelCase=True ,				**_lowerCAmelCase ,				)    ->    Optional[int]:
							'''simple docstring'''






							lowercase        =							vocab_size
							lowercase        =							n_positions
							lowercase        =							n_embd
							lowercase        =							n_layer
							lowercase        =							n_head
							lowercase        =							n_inner
							lowercase        =							activation_function
							lowercase        =							resid_pdrop
							lowercase        =							embd_pdrop
							lowercase        =							attn_pdrop
							lowercase        =							layer_norm_epsilon
							lowercase        =							initializer_range
							lowercase        =							scale_attn_weights
							lowercase        =							use_cache
							lowercase        =							attention_softmax_in_fpaa
							lowercase        =							scale_attention_softmax_in_fpaa
							lowercase        =							multi_query

							lowercase        =							bos_token_id
							lowercase        =							eos_token_id

							super().__init__(bos_token_id=_lowerCAmelCase ,				eos_token_id=_lowerCAmelCase ,				**_lowerCAmelCase						)


