

'''simple docstring'''



import json
import pathlib
import unittest

import numpy as np

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, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import ConditionalDetrImageProcessor



class lowerCamelCase			(  unittest.TestCase						):



			'''simple docstring'''






			def __init__(				self	:						int		,      lowerCAmelCase_	:						Tuple		,      lowerCAmelCase_	:						Dict=7		,      lowerCAmelCase_	:						List[Any]=3		,      lowerCAmelCase_	:						Dict=30		,      lowerCAmelCase_	:						List[str]=4_00		,      lowerCAmelCase_	:						Optional[int]=True		,      lowerCAmelCase_	:						str=None		,      lowerCAmelCase_	:						Optional[Any]=True		,      lowerCAmelCase_	:						str=[0.5, 0.5, 0.5]		,      lowerCAmelCase_	:						int=[0.5, 0.5, 0.5]		,      lowerCAmelCase_	:						str=True		,      lowerCAmelCase_	:						Optional[Any]=1 / 2_55		,      lowerCAmelCase_	:						int=True		,      )      ->		Union[str, Any]:





									'''simple docstring'''
									# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
									A__					:       Optional[Any]     =size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
									A__					:       Union[str, Any]     =parent
									A__					:       str     =batch_size
									A__					:       int     =num_channels
									A__					:       Tuple     =min_resolution
									A__					:       str     =max_resolution
									A__					:       List[str]     =do_resize
									A__					:       Optional[Any]     =size
									A__					:       Dict     =do_normalize
									A__					:       List[str]     =image_mean
									A__					:       Union[str, Any]     =image_std
									A__					:       List[Any]     =do_rescale
									A__					:       List[Any]     =rescale_factor
									A__					:       Any     =do_pad


			def lowercase__						(				self	:						Union[str, Any]			)      ->		Optional[Any]:





									'''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,
									    "do_rescale": self.do_rescale,
									    "rescale_factor": self.rescale_factor,
									    "do_pad": self.do_pad,
									}



			def lowercase__						(				self	:						Optional[int]		,      lowerCAmelCase_	:						Optional[Any]		,      lowerCAmelCase_	:						Any=False			)      ->		List[str]:





									'''simple docstring'''
									if not batched:
															A__					:       Union[str, Any]     =image_inputs[0]
															if isinstance(lowerCAmelCase_		,      Image.Image			):
																					A__   ,						A__					:       Any     =image.size
															else:
																					A__   ,						A__					:       Dict     =image.shape[1], image.shape[2]
															if w < h:
																					A__					:       Tuple     =int(self.size["""shortest_edge"""] * h / w			)
																					A__					:       Dict     =self.size["""shortest_edge"""]
															elif w > h:
																					A__					:       Any     =self.size["""shortest_edge"""]
																					A__					:       int     =int(self.size["""shortest_edge"""] * w / h			)
															else:
																					A__					:       Optional[int]     =self.size["""shortest_edge"""]
																					A__					:       List[str]     =self.size["""shortest_edge"""]

									else:
															A__					:       List[Any]     =[]
															for image in image_inputs:
																					A__   ,						A__					:       Optional[int]     =self.get_expected_values([image]			)
																					expected_values.append((expected_height, expected_width)			)
															A__					:       Any     =max(lowerCAmelCase_		,      key=lambda lowerCAmelCase_			: item[0]			)[0]
															A__					:       Union[str, Any]     =max(lowerCAmelCase_		,      key=lambda lowerCAmelCase_			: item[1]			)[1]

									return expected_height, expected_width



@require_torch
@require_vision
class lowerCamelCase			(  __snake_case	,						unittest.TestCase						):



			'''simple docstring'''






			__snake_case        =	ConditionalDetrImageProcessor if is_vision_available() else None


			def lowercase__						(				self	:						Tuple			)      ->		List[str]:





									'''simple docstring'''
									A__					:       Any     =ConditionalDetrImageProcessingTester(self			)


			@property
			def lowercase__						(				self	:						str			)      ->		int:





									'''simple docstring'''
									return self.image_processor_tester.prepare_image_processor_dict()


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





									'''simple docstring'''
									A__					:       int     =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"""			)			)


			def lowercase__						(				self	:						Any			)      ->		int:





									'''simple docstring'''
									A__					:       List[str]     =self.image_processing_class.from_dict(self.image_processor_dict			)
									self.assertEqual(image_processor.size		,      {"""shortest_edge""": 18, """longest_edge""": 13_33}			)
									self.assertEqual(image_processor.do_pad		,      lowerCAmelCase_			)

									A__					:       Any     =self.image_processing_class.from_dict(
									    self.image_processor_dict		,      size=42		,      max_size=84		,      pad_and_return_pixel_mask=lowerCAmelCase_			)
									self.assertEqual(image_processor.size		,      {"""shortest_edge""": 42, """longest_edge""": 84}			)
									self.assertEqual(image_processor.do_pad		,      lowerCAmelCase_			)


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





									'''simple docstring'''
									pass


			def lowercase__						(				self	:						Any			)      ->		Any:





									'''simple docstring'''
									# Initialize image_processing
									A__					:       Union[str, Any]     =self.image_processing_class(**self.image_processor_dict			)
									# create random PIL images
									A__					:       List[Any]     =prepare_image_inputs(self.image_processor_tester		,      equal_resolution=lowerCAmelCase_			)
									for image in image_inputs:
															self.assertIsInstance(lowerCAmelCase_		,      Image.Image			)

									# Test not batched input
									A__					:       Optional[Any]     =image_processing(image_inputs[0]		,      return_tensors="""pt"""			).pixel_values

									A__   ,						A__					:       Dict     =self.image_processor_tester.get_expected_values(lowerCAmelCase_			)

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

									# Test batched
									A__   ,						A__					:       List[Any]     =self.image_processor_tester.get_expected_values(lowerCAmelCase_		,      batched=lowerCAmelCase_			)

									A__					:       List[Any]     =image_processing(lowerCAmelCase_		,      return_tensors="""pt"""			).pixel_values
									self.assertEqual(
									    encoded_images.shape		,      (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )		,      )


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





									'''simple docstring'''
									# Initialize image_processing
									A__					:       Tuple     =self.image_processing_class(**self.image_processor_dict			)
									# create random numpy tensors
									A__					:       int     =prepare_image_inputs(self.image_processor_tester		,      equal_resolution=lowerCAmelCase_		,      numpify=lowerCAmelCase_			)
									for image in image_inputs:
															self.assertIsInstance(lowerCAmelCase_		,      np.ndarray			)

									# Test not batched input
									A__					:       int     =image_processing(image_inputs[0]		,      return_tensors="""pt"""			).pixel_values

									A__   ,						A__					:       str     =self.image_processor_tester.get_expected_values(lowerCAmelCase_			)

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

									# Test batched
									A__					:       Tuple     =image_processing(lowerCAmelCase_		,      return_tensors="""pt"""			).pixel_values

									A__   ,						A__					:       Dict     =self.image_processor_tester.get_expected_values(lowerCAmelCase_		,      batched=lowerCAmelCase_			)

									self.assertEqual(
									    encoded_images.shape		,      (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )		,      )


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





									'''simple docstring'''
									# Initialize image_processing
									A__					:       int     =self.image_processing_class(**self.image_processor_dict			)
									# create random PyTorch tensors
									A__					:       Any     =prepare_image_inputs(self.image_processor_tester		,      equal_resolution=lowerCAmelCase_		,      torchify=lowerCAmelCase_			)
									for image in image_inputs:
															self.assertIsInstance(lowerCAmelCase_		,      torch.Tensor			)

									# Test not batched input
									A__					:       int     =image_processing(image_inputs[0]		,      return_tensors="""pt"""			).pixel_values

									A__   ,						A__					:       List[Any]     =self.image_processor_tester.get_expected_values(lowerCAmelCase_			)

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

									# Test batched
									A__					:       Any     =image_processing(lowerCAmelCase_		,      return_tensors="""pt"""			).pixel_values

									A__   ,						A__					:       Optional[int]     =self.image_processor_tester.get_expected_values(lowerCAmelCase_		,      batched=lowerCAmelCase_			)

									self.assertEqual(
									    encoded_images.shape		,      (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )		,      )


			@slow
			def lowercase__						(				self	:						Tuple			)      ->		Any:





									'''simple docstring'''
									# prepare image and target
									A__					:       List[str]     =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
									with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""		,      """r"""			) as f:
															A__					:       Union[str, Any]     =json.loads(f.read()			)

									A__					:       Tuple     ={"""image_id""": 3_97_69, """annotations""": target}

									# encode them
									A__					:       int     =ConditionalDetrImageProcessor.from_pretrained("""microsoft/conditional-detr-resnet-50"""			)
									A__					:       Union[str, Any]     =image_processing(images=lowerCAmelCase_		,      annotations=lowerCAmelCase_		,      return_tensors="""pt"""			)

									# verify pixel values
									A__					:       List[str]     =torch.Size([1, 3, 8_00, 10_66]			)
									self.assertEqual(encoding["""pixel_values"""].shape		,      lowerCAmelCase_			)

									A__					:       str     =torch.tensor([0.2796, 0.3138, 0.3481]			)
									self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]		,      lowerCAmelCase_		,      atol=1e-4			)			)

									# verify area
									A__					:       Optional[Any]     =torch.tensor([5887.9600, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]			)
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]		,      lowerCAmelCase_			)			)
									# verify boxes
									A__					:       Any     =torch.Size([6, 4]			)
									self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape		,      lowerCAmelCase_			)
									A__					:       str     =torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]			)
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]		,      lowerCAmelCase_		,      atol=1e-3			)			)
									# verify image_id
									A__					:       Dict     =torch.tensor([3_97_69]			)
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]		,      lowerCAmelCase_			)			)
									# verify is_crowd
									A__					:       int     =torch.tensor([0, 0, 0, 0, 0, 0]			)
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]		,      lowerCAmelCase_			)			)
									# verify class_labels
									A__					:       List[str]     =torch.tensor([75, 75, 63, 65, 17, 17]			)
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]		,      lowerCAmelCase_			)			)
									# verify orig_size
									A__					:       Any     =torch.tensor([4_80, 6_40]			)
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]		,      lowerCAmelCase_			)			)
									# verify size
									A__					:       Any     =torch.tensor([8_00, 10_66]			)
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]		,      lowerCAmelCase_			)			)



			@slow
			def lowercase__						(				self	:						Tuple			)      ->		Any:





									'''simple docstring'''
									# prepare image, target and masks_path
									A__					:       Dict     =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
									with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""		,      """r"""			) as f:
															A__					:       int     =json.loads(f.read()			)

									A__					:       Dict     ={"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}

									A__					:       Optional[Any]     =pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""			)

									# encode them
									A__					:       Union[str, Any]     =ConditionalDetrImageProcessor(format="""coco_panoptic"""			)
									A__					:       Optional[Any]     =image_processing(images=lowerCAmelCase_		,      annotations=lowerCAmelCase_		,      masks_path=lowerCAmelCase_		,      return_tensors="""pt"""			)

									# verify pixel values
									A__					:       Dict     =torch.Size([1, 3, 8_00, 10_66]			)
									self.assertEqual(encoding["""pixel_values"""].shape		,      lowerCAmelCase_			)

									A__					:       List[Any]     =torch.tensor([0.2796, 0.3138, 0.3481]			)
									self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]		,      lowerCAmelCase_		,      atol=1e-4			)			)

									# verify area
									A__					:       int     =torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 5879.6562, 7634.1147]			)
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]		,      lowerCAmelCase_			)			)
									# verify boxes
									A__					:       Tuple     =torch.Size([6, 4]			)
									self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape		,      lowerCAmelCase_			)
									A__					:       Any     =torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]			)
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]		,      lowerCAmelCase_		,      atol=1e-3			)			)
									# verify image_id
									A__					:       Dict     =torch.tensor([3_97_69]			)
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]		,      lowerCAmelCase_			)			)
									# verify is_crowd
									A__					:       int     =torch.tensor([0, 0, 0, 0, 0, 0]			)
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]		,      lowerCAmelCase_			)			)
									# verify class_labels
									A__					:       Optional[int]     =torch.tensor([17, 17, 63, 75, 75, 93]			)
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]		,      lowerCAmelCase_			)			)
									# verify masks
									A__					:       str     =82_28_73
									self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()		,      lowerCAmelCase_			)
									# verify orig_size
									A__					:       Optional[Any]     =torch.tensor([4_80, 6_40]			)
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]		,      lowerCAmelCase_			)			)
									# verify size
									A__					:       Dict     =torch.tensor([8_00, 10_66]			)
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]		,      lowerCAmelCase_			)			)




'''simple docstring'''



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


__snake_case			:     Union[str, Any]										=      logging.get_logger(__name__)

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



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			__snake_case        =	'gpt_bigcode'
			__snake_case        =	['past_key_values']
			__snake_case        =	{
			    'hidden_size': 'n_embd',
			    'max_position_embeddings': 'n_positions',
			    'num_attention_heads': 'n_head',
			    'num_hidden_layers': 'n_layer',
			}


			def __init__(				self	:						Optional[Any]		,      lowerCAmelCase_	:						List[str]=5_02_57		,      lowerCAmelCase_	:						str=10_24		,      lowerCAmelCase_	:						str=7_68		,      lowerCAmelCase_	:						str=12		,      lowerCAmelCase_	:						int=12		,      lowerCAmelCase_	:						Dict=None		,      lowerCAmelCase_	:						List[Any]="gelu_pytorch_tanh"		,      lowerCAmelCase_	:						Union[str, Any]=0.1		,      lowerCAmelCase_	:						Optional[Any]=0.1		,      lowerCAmelCase_	:						Optional[int]=0.1		,      lowerCAmelCase_	:						Dict=1e-5		,      lowerCAmelCase_	:						str=0.02		,      lowerCAmelCase_	:						List[Any]=True		,      lowerCAmelCase_	:						str=True		,      lowerCAmelCase_	:						str=5_02_56		,      lowerCAmelCase_	:						Dict=5_02_56		,      lowerCAmelCase_	:						Union[str, Any]=True		,      lowerCAmelCase_	:						str=True		,      lowerCAmelCase_	:						Optional[Any]=True		,      **lowerCAmelCase_	:						Optional[Any]		,      )      ->		Tuple:





									'''simple docstring'''
									A__					:       Optional[Any]     =vocab_size
									A__					:       Optional[Any]     =n_positions
									A__					:       List[str]     =n_embd
									A__					:       str     =n_layer
									A__					:       Optional[int]     =n_head
									A__					:       Optional[int]     =n_inner
									A__					:       int     =activation_function
									A__					:       int     =resid_pdrop
									A__					:       int     =embd_pdrop
									A__					:       Dict     =attn_pdrop
									A__					:       Any     =layer_norm_epsilon
									A__					:       List[Any]     =initializer_range
									A__					:       Dict     =scale_attn_weights
									A__					:       Any     =use_cache
									A__					:       List[Any]     =attention_softmax_in_fpaa
									A__					:       Optional[int]     =scale_attention_softmax_in_fpaa
									A__					:       Dict     =multi_query

									A__					:       List[str]     =bos_token_id
									A__					:       Any     =eos_token_id

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

