"""simple docstring"""






import unittest

import numpy as np

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 PIL import Image

						from transformers import OwlViTImageProcessor
class     snake_case	(			unittest.TestCase    ):




					"""simple docstring"""

					def __init__(    self       :     Dict   ,lowerCamelCase__       :     Dict   ,lowerCamelCase__       :     Dict=7   ,lowerCamelCase__       :     Union[str, Any]=3   ,lowerCamelCase__       :     List[Any]=18   ,lowerCamelCase__       :     str=30   ,lowerCamelCase__       :     Dict=400   ,lowerCamelCase__       :     Optional[int]=True   ,lowerCamelCase__       :     Union[str, Any]=None   ,lowerCamelCase__       :     int=True   ,lowerCamelCase__       :     Optional[int]=None   ,lowerCamelCase__       :     str=True   ,lowerCamelCase__       :     Union[str, Any]=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]   ,lowerCamelCase__       :     Optional[Any]=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]   ,lowerCamelCase__       :     Union[str, Any]=True   ,):
									UpperCAmelCase__					=							parent
									UpperCAmelCase__					=							batch_size
									UpperCAmelCase__					=							num_channels
									UpperCAmelCase__					=							image_size
									UpperCAmelCase__					=							min_resolution
									UpperCAmelCase__					=							max_resolution
									UpperCAmelCase__					=							do_resize
									UpperCAmelCase__					=							size if size is not None else {"height": 18, "width": 18}
									UpperCAmelCase__					=							do_center_crop
									UpperCAmelCase__					=							crop_size if crop_size is not None else {"height": 18, "width": 18}
									UpperCAmelCase__					=							do_normalize
									UpperCAmelCase__					=							image_mean
									UpperCAmelCase__					=							image_std
									UpperCAmelCase__					=							do_convert_rgb







					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_center_crop": self.do_center_crop,
									    "crop_size": self.crop_size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_convert_rgb": self.do_convert_rgb,
									}






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




					"""simple docstring"""

					snake_case__       =			OwlViTImageProcessor if is_vision_available() else None







					def 		__lowerCAmelCase						(    self       :     int      ):
									UpperCAmelCase__					=							OwlViTImageProcessingTester(self      )







					@property
					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									return self.image_processor_tester.prepare_image_processor_dict()







					def 		__lowerCAmelCase						(    self       :     List[Any]      ):
									UpperCAmelCase__					=							self.image_processing_class(**self.image_processor_dict      )
									self.assertTrue(hasattr(_UpperCAmelCase   ,'do_resize'      )      )
									self.assertTrue(hasattr(_UpperCAmelCase   ,'size'      )      )
									self.assertTrue(hasattr(_UpperCAmelCase   ,'do_center_crop'      )      )
									self.assertTrue(hasattr(_UpperCAmelCase   ,'center_crop'      )      )
									self.assertTrue(hasattr(_UpperCAmelCase   ,'do_normalize'      )      )
									self.assertTrue(hasattr(_UpperCAmelCase   ,'image_mean'      )      )
									self.assertTrue(hasattr(_UpperCAmelCase   ,'image_std'      )      )
									self.assertTrue(hasattr(_UpperCAmelCase   ,'do_convert_rgb'      )      )







					def 		__lowerCAmelCase						(    self       :     Any      ):
									UpperCAmelCase__					=							self.image_processing_class.from_dict(self.image_processor_dict      )
									self.assertEqual(image_processor.size   ,{'height': 18, 'width': 18}      )
									self.assertEqual(image_processor.crop_size   ,{'height': 18, 'width': 18}      )

									UpperCAmelCase__					=							self.image_processing_class.from_dict(self.image_processor_dict   ,size=42   ,crop_size=84      )
									self.assertEqual(image_processor.size   ,{'height': 42, 'width': 42}      )
									self.assertEqual(image_processor.crop_size   ,{'height': 84, 'width': 84}      )







					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									# Initialize image_processing
									UpperCAmelCase__					=							self.image_processing_class(**self.image_processor_dict      )
									# create random PIL images
									UpperCAmelCase__					=							prepare_image_inputs(self.image_processor_tester   ,equal_resolution=_UpperCAmelCase      )

									for image in image_inputs:
													self.assertIsInstance(_UpperCAmelCase   ,Image.Image      )

									# Test not batched input
									UpperCAmelCase__					=							image_processing(image_inputs[0]   ,return_tensors='pt'      ).pixel_values
									self.assertEqual(
									    encoded_images.shape   ,(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )   ,)

									# Test batched
									UpperCAmelCase__					=							image_processing(_UpperCAmelCase   ,return_tensors='pt'      ).pixel_values
									self.assertEqual(
									    encoded_images.shape   ,(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )   ,)







					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									# Initialize image_processing
									UpperCAmelCase__					=							self.image_processing_class(**self.image_processor_dict      )
									# create random numpy tensors
									UpperCAmelCase__					=							prepare_image_inputs(self.image_processor_tester   ,equal_resolution=_UpperCAmelCase   ,numpify=_UpperCAmelCase      )
									for image in image_inputs:
													self.assertIsInstance(_UpperCAmelCase   ,np.ndarray      )

									# Test not batched input
									UpperCAmelCase__					=							image_processing(image_inputs[0]   ,return_tensors='pt'      ).pixel_values
									self.assertEqual(
									    encoded_images.shape   ,(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )   ,)

									# Test batched
									UpperCAmelCase__					=							image_processing(_UpperCAmelCase   ,return_tensors='pt'      ).pixel_values
									self.assertEqual(
									    encoded_images.shape   ,(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )   ,)







					def 		__lowerCAmelCase						(    self       :     Dict      ):
									# Initialize image_processing
									UpperCAmelCase__					=							self.image_processing_class(**self.image_processor_dict      )
									# create random PyTorch tensors
									UpperCAmelCase__					=							prepare_image_inputs(self.image_processor_tester   ,equal_resolution=_UpperCAmelCase   ,torchify=_UpperCAmelCase      )
									for image in image_inputs:
													self.assertIsInstance(_UpperCAmelCase   ,torch.Tensor      )

									# Test not batched input
									UpperCAmelCase__					=							image_processing(image_inputs[0]   ,return_tensors='pt'      ).pixel_values
									self.assertEqual(
									    encoded_images.shape   ,(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )   ,)

									# Test batched
									UpperCAmelCase__					=							image_processing(_UpperCAmelCase   ,return_tensors='pt'      ).pixel_values
									self.assertEqual(
									    encoded_images.shape   ,(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )   ,)




"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowerCAmelCase__  :  str											=     logging.get_logger(__name__)

lowerCAmelCase__  :  Union[str, Any]											=     {
    'uclanlp/plbart-base': 'https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}
class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					snake_case__       =			"plbart"
					snake_case__       =			["past_key_values"]
					snake_case__       =			{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







					def __init__(    self       :     str   ,lowerCamelCase__       :     List[Any]=50_005   ,lowerCamelCase__       :     Optional[Any]=1_024   ,lowerCamelCase__       :     List[str]=6   ,lowerCamelCase__       :     Tuple=3_072   ,lowerCamelCase__       :     Optional[int]=12   ,lowerCamelCase__       :     Optional[int]=6   ,lowerCamelCase__       :     Optional[int]=3_072   ,lowerCamelCase__       :     Any=12   ,lowerCamelCase__       :     str=0.0   ,lowerCamelCase__       :     str=0.0   ,lowerCamelCase__       :     Union[str, Any]=True   ,lowerCamelCase__       :     str=True   ,lowerCamelCase__       :     Optional[int]="gelu"   ,lowerCamelCase__       :     Optional[Any]=768   ,lowerCamelCase__       :     int=0.1   ,lowerCamelCase__       :     int=0.1   ,lowerCamelCase__       :     Union[str, Any]=0.0   ,lowerCamelCase__       :     List[str]=0.0_2   ,lowerCamelCase__       :     Optional[int]=0.0   ,lowerCamelCase__       :     Dict=True   ,lowerCamelCase__       :     List[Any]=1   ,lowerCamelCase__       :     List[str]=0   ,lowerCamelCase__       :     Any=2   ,lowerCamelCase__       :     Any=2   ,**lowerCamelCase__       :     Dict   ,):
									UpperCAmelCase__					=							vocab_size
									UpperCAmelCase__					=							max_position_embeddings
									UpperCAmelCase__					=							d_model
									UpperCAmelCase__					=							encoder_ffn_dim
									UpperCAmelCase__					=							encoder_layers
									UpperCAmelCase__					=							encoder_attention_heads
									UpperCAmelCase__					=							decoder_ffn_dim
									UpperCAmelCase__					=							decoder_layers
									UpperCAmelCase__					=							decoder_attention_heads
									UpperCAmelCase__					=							dropout
									UpperCAmelCase__					=							attention_dropout
									UpperCAmelCase__					=							activation_dropout
									UpperCAmelCase__					=							activation_function
									UpperCAmelCase__					=							init_std
									UpperCAmelCase__					=							encoder_layerdrop
									UpperCAmelCase__					=							decoder_layerdrop
									UpperCAmelCase__					=							classifier_dropout
									UpperCAmelCase__					=							use_cache
									UpperCAmelCase__					=							encoder_layers
									UpperCAmelCase__					=							scale_embedding  # scale factor will be sqrt(d_model) if True
									super().__init__(
									    pad_token_id=lowerCamelCase__   ,bos_token_id=lowerCamelCase__   ,eos_token_id=lowerCamelCase__   ,is_encoder_decoder=lowerCamelCase__   ,forced_eos_token_id=lowerCamelCase__   ,**lowerCamelCase__   ,)






class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					@property
					def 		__lowerCAmelCase						(    self       :     int      ):
									return OrderedDict(
									    [
									        ('input_ids', {0: 'batch', 1: 'sequence'}),
									        ('attention_mask', {0: 'batch', 1: 'sequence'}),
									    ]      )







					@property
					def 		__lowerCAmelCase						(    self       :     Any      ):
									if self.use_past:
													return OrderedDict(
													    [
													        ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
													        ('past_keys', {0: 'batch', 2: 'sequence'}),
													        ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
													    ]      )
									else:
													return OrderedDict(
													    [
													        ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
													        ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
													    ]      )



