




"""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     lowercase (      unittest.TestCase):







    def __init__(							self				:   List[str] ,       _lowerCamelCase				:   List[str] ,       _lowerCamelCase				:   Optional[Any]=7 ,       _lowerCamelCase				:   Dict=3 ,       _lowerCamelCase				:   Dict=18 ,       _lowerCamelCase				:   List[str]=30 ,       _lowerCamelCase				:   Optional[int]=4_00 ,       _lowerCamelCase				:   int=True ,       _lowerCamelCase				:   Tuple=None ,       _lowerCamelCase				:   Dict=True ,       _lowerCamelCase				:   Tuple=None ,       _lowerCamelCase				:   List[Any]=True ,       _lowerCamelCase				:   Optional[int]=[0.48145466, 0.4578275, 0.40821073] ,       _lowerCamelCase				:   Optional[int]=[0.26862954, 0.26130258, 0.27577711] ,       _lowerCamelCase				:   Tuple=True ,       ):



     """simple docstring"""

     A_					:			List[str]						=						parent
     A_					:			Optional[int]						=						batch_size
     A_					:			Optional[Any]						=						num_channels
     A_					:			Any						=						image_size
     A_					:			Union[str, Any]						=						min_resolution
     A_					:			str						=						max_resolution
     A_					:			int						=						do_resize
     A_					:			Optional[int]						=						size if size is not None else {'height': 18, 'width': 18}
     A_					:			List[Any]						=						do_center_crop
     A_					:			Tuple						=						crop_size if crop_size is not None else {'height': 18, 'width': 18}
     A_					:			str						=						do_normalize
     A_					:			Union[str, Any]						=						image_mean
     A_					:			List[str]						=						image_std
     A_					:			List[str]						=						do_convert_rgb







    def    a_					(							self				:   Dict    ):



     """simple docstring"""

     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     lowercase (      lowerCAmelCase_       ,		unittest.TestCase):
    __lowerCAmelCase      :      int					    =				OwlViTImageProcessor if is_vision_available() else None







    def    a_					(							self				:   Tuple    ):



     """simple docstring"""

     A_					:			Union[str, Any]						=						OwlViTImageProcessingTester(self    )







    @property
    def    a_					(							self				:   str    ):



     """simple docstring"""

     return self.image_processor_tester.prepare_image_processor_dict()







    def    a_					(							self				:   Dict    ):



     """simple docstring"""

     A_					:			Tuple						=						self.image_processing_class(**self.image_processor_dict    )
     self.assertTrue(hasattr(_snake_case ,       '''do_resize'''    )    )
     self.assertTrue(hasattr(_snake_case ,       '''size'''    )    )
     self.assertTrue(hasattr(_snake_case ,       '''do_center_crop'''    )    )
     self.assertTrue(hasattr(_snake_case ,       '''center_crop'''    )    )
     self.assertTrue(hasattr(_snake_case ,       '''do_normalize'''    )    )
     self.assertTrue(hasattr(_snake_case ,       '''image_mean'''    )    )
     self.assertTrue(hasattr(_snake_case ,       '''image_std'''    )    )
     self.assertTrue(hasattr(_snake_case ,       '''do_convert_rgb'''    )    )







    def    a_					(							self				:   int    ):



     """simple docstring"""

     A_					:			int						=						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}    )

     A_					:			List[Any]						=						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    a_					(							self				:   Dict    ):



     """simple docstring"""

     A_					:			Tuple						=						self.image_processing_class(**self.image_processor_dict    )
     # create random PIL images
     A_					:			Any						=						prepare_image_inputs(self.image_processor_tester ,       equal_resolution=_snake_case    )

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

     # Test not batched input
     A_					:			Union[str, Any]						=						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
     A_					:			Optional[Any]						=						image_processing(_snake_case ,       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    a_					(							self				:   Optional[Any]    ):



     """simple docstring"""

     A_					:			Optional[Any]						=						self.image_processing_class(**self.image_processor_dict    )
     # create random numpy tensors
     A_					:			List[Any]						=						prepare_image_inputs(self.image_processor_tester ,       equal_resolution=_snake_case ,       numpify=_snake_case    )
     for image in image_inputs:
      self.assertIsInstance(_snake_case ,       np.ndarray    )

     # Test not batched input
     A_					:			Optional[int]						=						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
     A_					:			Optional[Any]						=						image_processing(_snake_case ,       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    a_					(							self				:   List[str]    ):



     """simple docstring"""

     A_					:			Optional[Any]						=						self.image_processing_class(**self.image_processor_dict    )
     # create random PyTorch tensors
     A_					:			Optional[Any]						=						prepare_image_inputs(self.image_processor_tester ,       equal_resolution=_snake_case ,       torchify=_snake_case    )
     for image in image_inputs:
      self.assertIsInstance(_snake_case ,       torch.Tensor    )

     # Test not batched input
     A_					:			List[Any]						=						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
     A_					:			Optional[int]						=						image_processing(_snake_case ,       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					:     List[Any]				       =    logging.get_logger(__name__)

_lowerCamelCase					:     Dict				       =    {
    '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     lowercase (      __UpperCAmelCase):
				__lowerCAmelCase      :      List[str]					    =				"""plbart"""
				__lowerCAmelCase      :      int					    =				["""past_key_values"""]
				__lowerCAmelCase      :      Union[str, Any]					    =				{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







				def __init__(							self				:   Any ,       _lowerCamelCase				:   Any=5_00_05 ,       _lowerCamelCase				:   List[str]=10_24 ,       _lowerCamelCase				:   Union[str, Any]=6 ,       _lowerCamelCase				:   Any=30_72 ,       _lowerCamelCase				:   List[str]=12 ,       _lowerCamelCase				:   Tuple=6 ,       _lowerCamelCase				:   Dict=30_72 ,       _lowerCamelCase				:   Optional[int]=12 ,       _lowerCamelCase				:   Optional[int]=0.0 ,       _lowerCamelCase				:   Optional[int]=0.0 ,       _lowerCamelCase				:   Tuple=True ,       _lowerCamelCase				:   Optional[int]=True ,       _lowerCamelCase				:   str="gelu" ,       _lowerCamelCase				:   int=7_68 ,       _lowerCamelCase				:   Union[str, Any]=0.1 ,       _lowerCamelCase				:   Optional[Any]=0.1 ,       _lowerCamelCase				:   Tuple=0.0 ,       _lowerCamelCase				:   Optional[int]=0.02 ,       _lowerCamelCase				:   Dict=0.0 ,       _lowerCamelCase				:   Dict=True ,       _lowerCamelCase				:   List[str]=1 ,       _lowerCamelCase				:   str=0 ,       _lowerCamelCase				:   int=2 ,       _lowerCamelCase				:   str=2 ,       **_lowerCamelCase				:   Dict ,       ):



					"""simple docstring"""

					A_					:			int						=						vocab_size
					A_					:			Tuple						=						max_position_embeddings
					A_					:			Optional[Any]						=						d_model
					A_					:			Optional[int]						=						encoder_ffn_dim
					A_					:			Optional[int]						=						encoder_layers
					A_					:			List[str]						=						encoder_attention_heads
					A_					:			Dict						=						decoder_ffn_dim
					A_					:			List[str]						=						decoder_layers
					A_					:			str						=						decoder_attention_heads
					A_					:			List[Any]						=						dropout
					A_					:			int						=						attention_dropout
					A_					:			Dict						=						activation_dropout
					A_					:			int						=						activation_function
					A_					:			List[str]						=						init_std
					A_					:			int						=						encoder_layerdrop
					A_					:			int						=						decoder_layerdrop
					A_					:			Optional[int]						=						classifier_dropout
					A_					:			Optional[int]						=						use_cache
					A_					:			Dict						=						encoder_layers
					A_					:			int						=						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     lowercase (      __UpperCAmelCase):







				@property
				def    a_					(							self				:   Any    ):



					"""simple docstring"""

					return OrderedDict(
					    [
					        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
					        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
					    ]    )







				@property
				def    a_					(							self				:   Dict    ):



					"""simple docstring"""

					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'''}),
						    ]    )





