






"""simple docstring"""




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


_snake_case          =   logging.get_logger(__name__)






class        UpperCamelCase			(	__snake_case      ):
 UpperCamelCase					:       List[str]			=					['''pixel_values''']


 def __init__(     self		:						Tuple							,						UpperCAmelCase__		:						Any = True							,						UpperCAmelCase__		:						List[str] = None							,						UpperCAmelCase__		:						Dict = None							,						UpperCAmelCase__		:						Any = PILImageResampling.BILINEAR							,						UpperCAmelCase__		:						List[str] = True							,						UpperCAmelCase__		:						Dict = 1 / 255							,						UpperCAmelCase__		:						Any = True							,						UpperCAmelCase__		:						Union[str, Any] = None							,						UpperCAmelCase__		:						int = None							,						**UpperCAmelCase__		:						List[str]							,						)					->       None:
   super().__init__(**UpperCAmelCase__							)
   _a  :       str             =	size if size is not None else {'''shortest_edge''': 384}
   _a  :       Tuple             =	get_size_dict(UpperCAmelCase__							,						default_to_square=UpperCAmelCase__							)

   _a  :       str             =	do_resize
   _a  :       List[Any]             =	size
   # Default value set here for backwards compatibility where the value in config is None
   _a  :       List[Any]             =	crop_pct if crop_pct is not None else 224 / 256
   _a  :       Optional[int]             =	resample
   _a  :       Union[str, Any]             =	do_rescale
   _a  :       List[str]             =	rescale_factor
   _a  :       Union[str, Any]             =	do_normalize
   _a  :       Any             =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
   _a  :       Optional[int]             =	image_std if image_std is not None else IMAGENET_STANDARD_STD


 def _lowercase	(     self		:						Tuple							,						UpperCAmelCase__		:						int							,						UpperCAmelCase__		:						Tuple							,						UpperCAmelCase__		:						int							,						UpperCAmelCase__		:						Tuple = PILImageResampling.BICUBIC							,						UpperCAmelCase__		:						Any = None							,						**UpperCAmelCase__		:						Tuple							,						)					->       np.ndarray:
   _a  :       str             =	get_size_dict(UpperCAmelCase__							,						default_to_square=UpperCAmelCase__							)
   if "shortest_edge" not in size:
     raise ValueError(f"""Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}"""							)
   _a  :       Any             =	size['''shortest_edge''']

   if shortest_edge < 384:
     # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
     _a  :       Dict             =	int(shortest_edge / crop_pct							)
     _a  :       str             =	get_resize_output_image_size(UpperCAmelCase__							,						size=UpperCAmelCase__							,						default_to_square=UpperCAmelCase__							)
     _a  :       int             =	resize(image=UpperCAmelCase__							,						size=UpperCAmelCase__							,						resample=UpperCAmelCase__							,						data_format=UpperCAmelCase__							,						**UpperCAmelCase__							)
     # then crop to (shortest_edge, shortest_edge)
     return center_crop(image=UpperCAmelCase__							,						size=(shortest_edge, shortest_edge)							,						data_format=UpperCAmelCase__							,						**UpperCAmelCase__							)
   else:
     # warping (no cropping) when evaluated at 384 or larger
     return resize(
         UpperCAmelCase__							,						size=(shortest_edge, shortest_edge)							,						resample=UpperCAmelCase__							,						data_format=UpperCAmelCase__							,						**UpperCAmelCase__							)


 def _lowercase	(     self		:						Tuple							,						UpperCAmelCase__		:						Dict							,						UpperCAmelCase__		:						str							,						UpperCAmelCase__		:						Union[str, Any] = None							,						**UpperCAmelCase__		:						Union[str, Any]							,						)					->       List[str]:
   return rescale(UpperCAmelCase__							,						scale=UpperCAmelCase__							,						data_format=UpperCAmelCase__							,						**UpperCAmelCase__							)


 def _lowercase	(     self		:						Union[str, Any]							,						UpperCAmelCase__		:						str							,						UpperCAmelCase__		:						Any							,						UpperCAmelCase__		:						Tuple							,						UpperCAmelCase__		:						Optional[Any] = None							,						**UpperCAmelCase__		:						str							,						)					->       np.ndarray:
   return normalize(UpperCAmelCase__							,						mean=UpperCAmelCase__							,						std=UpperCAmelCase__							,						data_format=UpperCAmelCase__							,						**UpperCAmelCase__							)







 def _lowercase	(     self		:						Optional[Any]							,						UpperCAmelCase__		:						Optional[int]							,						UpperCAmelCase__		:						Any = None							,						UpperCAmelCase__		:						Tuple = None							,						UpperCAmelCase__		:						str = None							,						UpperCAmelCase__		:						str = None							,						UpperCAmelCase__		:						Optional[Any] = None							,						UpperCAmelCase__		:						List[str] = None							,						UpperCAmelCase__		:						List[Any] = None							,						UpperCAmelCase__		:						Dict = None							,						UpperCAmelCase__		:						List[str] = None							,						UpperCAmelCase__		:						Any = None							,						UpperCAmelCase__		:						List[str] = ChannelDimension.FIRST							,						**UpperCAmelCase__		:						Tuple							,						)					->       PIL.Image.Image:
   _a  :       int             =	do_resize if do_resize is not None else self.do_resize
   _a  :       Tuple             =	crop_pct if crop_pct is not None else self.crop_pct
   _a  :       Optional[Any]             =	resample if resample is not None else self.resample
   _a  :       List[Any]             =	do_rescale if do_rescale is not None else self.do_rescale
   _a  :       List[Any]             =	rescale_factor if rescale_factor is not None else self.rescale_factor
   _a  :       Union[str, Any]             =	do_normalize if do_normalize is not None else self.do_normalize
   _a  :       Union[str, Any]             =	image_mean if image_mean is not None else self.image_mean
   _a  :       List[str]             =	image_std if image_std is not None else self.image_std

   _a  :       Union[str, Any]             =	size if size is not None else self.size
   _a  :       List[Any]             =	get_size_dict(UpperCAmelCase__							,						default_to_square=UpperCAmelCase__							)

   _a  :       Any             =	make_list_of_images(UpperCAmelCase__							)

   if not valid_images(UpperCAmelCase__							):
     raise ValueError(
         """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
         """torch.Tensor, tf.Tensor or jax.ndarray."""							)

   if do_resize and size is None or resample is None:
     raise ValueError("""Size and resample must be specified if do_resize is True."""							)

   if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
     raise ValueError("""crop_pct must be specified if size < 384."""							)

   if do_rescale and rescale_factor is None:
     raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

   if do_normalize and (image_mean is None or image_std is None):
     raise ValueError("""Image mean and std must be specified if do_normalize is True."""							)

   # All transformations expect numpy arrays.
   _a  :       Optional[int]             =	[to_numpy_array(UpperCAmelCase__							) for image in images]

   if do_resize:
     _a  :       Any             =	[self.resize(image=UpperCAmelCase__							,						size=UpperCAmelCase__							,						crop_pct=UpperCAmelCase__							,						resample=UpperCAmelCase__							) for image in images]

   if do_rescale:
     _a  :       str             =	[self.rescale(image=UpperCAmelCase__							,						scale=UpperCAmelCase__							) for image in images]

   if do_normalize:
     _a  :       Dict             =	[self.normalize(image=UpperCAmelCase__							,						mean=UpperCAmelCase__							,						std=UpperCAmelCase__							) for image in images]

   _a  :       Any             =	[to_channel_dimension_format(UpperCAmelCase__							,						UpperCAmelCase__							) for image in images]

   _a  :       Optional[int]             =	{'''pixel_values''': images}
   return BatchFeature(data=UpperCAmelCase__							,						tensor_type=UpperCAmelCase__							)



'''simple docstring'''




import os
import sys
import unittest


lowercase			:				Dict    		=			os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


lowercase			:				Any    		=			os.path.join('tests', 'models', 'bert', 'test_modeling_bert.py')
lowercase			:				Optional[int]    		=			os.path.join('tests', 'models', 'blip', 'test_modeling_blip.py')





class  A				(							unittest.TestCase						):





     def 				__lowerCAmelCase							(							self	) ->				List[str]:






         """simple docstring"""






         A       :   Tuple					=						get_test_to_tester_mapping(SCREAMING_SNAKE_CASE	)
         A       :   Any					=						get_test_to_tester_mapping(SCREAMING_SNAKE_CASE	)

         A       :   List[Any]					=						{'''BertModelTest''': '''BertModelTester'''}

         A       :   int					=						{
             '''BlipModelTest''': '''BlipModelTester''',
             '''BlipTextImageModelTest''': '''BlipTextImageModelsModelTester''',
             '''BlipTextModelTest''': '''BlipTextModelTester''',
             '''BlipTextRetrievalModelTest''': '''BlipTextRetrievalModelTester''',
             '''BlipVQAModelTest''': '''BlipVQAModelTester''',
             '''BlipVisionModelTest''': '''BlipVisionModelTester''',
         }

         self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE	)						,			SCREAMING_SNAKE_CASE	)
         self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE	)						,			SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         A       :   Tuple					=						get_model_to_test_mapping(SCREAMING_SNAKE_CASE	)
         A       :   Tuple					=						get_model_to_test_mapping(SCREAMING_SNAKE_CASE	)

         A       :   List[str]					=						{
             '''BertForMaskedLM''': ['''BertModelTest'''],
             '''BertForMultipleChoice''': ['''BertModelTest'''],
             '''BertForNextSentencePrediction''': ['''BertModelTest'''],
             '''BertForPreTraining''': ['''BertModelTest'''],
             '''BertForQuestionAnswering''': ['''BertModelTest'''],
             '''BertForSequenceClassification''': ['''BertModelTest'''],
             '''BertForTokenClassification''': ['''BertModelTest'''],
             '''BertLMHeadModel''': ['''BertModelTest'''],
             '''BertModel''': ['''BertModelTest'''],
         }

         A       :   Union[str, Any]					=						{
             '''BlipForConditionalGeneration''': ['''BlipTextImageModelTest'''],
             '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTest'''],
             '''BlipForQuestionAnswering''': ['''BlipVQAModelTest'''],
             '''BlipModel''': ['''BlipModelTest'''],
             '''BlipTextModel''': ['''BlipTextModelTest'''],
             '''BlipVisionModel''': ['''BlipVisionModelTest'''],
         }

         self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE	)						,			SCREAMING_SNAKE_CASE	)
         self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE	)						,			SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






         A       :   int					=						get_model_to_tester_mapping(SCREAMING_SNAKE_CASE	)
         A       :   Union[str, Any]					=						get_model_to_tester_mapping(SCREAMING_SNAKE_CASE	)

         A       :   Dict					=						{
             '''BertForMaskedLM''': ['''BertModelTester'''],
             '''BertForMultipleChoice''': ['''BertModelTester'''],
             '''BertForNextSentencePrediction''': ['''BertModelTester'''],
             '''BertForPreTraining''': ['''BertModelTester'''],
             '''BertForQuestionAnswering''': ['''BertModelTester'''],
             '''BertForSequenceClassification''': ['''BertModelTester'''],
             '''BertForTokenClassification''': ['''BertModelTester'''],
             '''BertLMHeadModel''': ['''BertModelTester'''],
             '''BertModel''': ['''BertModelTester'''],
         }

         A       :   str					=						{
             '''BlipForConditionalGeneration''': ['''BlipTextImageModelsModelTester'''],
             '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTester'''],
             '''BlipForQuestionAnswering''': ['''BlipVQAModelTester'''],
             '''BlipModel''': ['''BlipModelTester'''],
             '''BlipTextModel''': ['''BlipTextModelTester'''],
             '''BlipVisionModel''': ['''BlipVisionModelTester'''],
         }

         self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE	)						,			SCREAMING_SNAKE_CASE	)
         self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE	)						,			SCREAMING_SNAKE_CASE	)




