





from typing import Dict, List, Optional, Tuple, Union

import numpy as np

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


if is_vision_available():
	import PIL

if is_torch_available():
	import torch


lowerCAmelCase_        =							logging.get_logger(__name__)



class    __lowerCAmelCase			(     _a	):
			lowerCamelCase_	:	Optional[Any]			     = ['''pixel_values''']






			def __init__(self   ,				__magic_name__ = True   ,				__magic_name__ = None   ,				__magic_name__ = PILImageResampling.BILINEAR   ,				__magic_name__ = True   ,				__magic_name__ = 1 / 255   ,				__magic_name__ = True   ,				__magic_name__ = None   ,				__magic_name__ = True   ,				**__magic_name__   ,				)	->  None:


							'''simple docstring'''



							super().__init__(**__magic_name__       )
							snake_case_		:       Dict           =    size if size is not None else {'''shortest_edge''': 224}
							snake_case_		:       List[Any]           =    get_size_dict(__magic_name__   ,				default_to_square=__magic_name__       )
							snake_case_		:       Optional[Any]           =    crop_size if crop_size is not None else {'''height''': 256, '''width''': 256}
							snake_case_		:       Union[str, Any]           =    get_size_dict(__magic_name__   ,				param_name='''crop_size'''       )

							snake_case_		:       List[str]           =    do_resize
							snake_case_		:       List[Any]           =    size
							snake_case_		:       List[str]           =    resample
							snake_case_		:       int           =    do_rescale
							snake_case_		:       Tuple           =    rescale_factor
							snake_case_		:       Any           =    do_center_crop
							snake_case_		:       Union[str, Any]           =    crop_size
							snake_case_		:       int           =    do_flip_channel_order






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__ = PIL.Image.BILINEAR   ,				__magic_name__ = None   ,				**__magic_name__   ,				)	->  np.ndarray:


							'''simple docstring'''



							snake_case_		:       Optional[Any]           =    get_size_dict(__magic_name__   ,				default_to_square=__magic_name__       )
							if "shortest_edge" not in size:
											raise ValueError(F'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'''       )
							snake_case_		:       Dict           =    get_resize_output_image_size(__magic_name__   ,				size=size['''shortest_edge''']   ,				default_to_square=__magic_name__       )
							return resize(__magic_name__   ,				size=__magic_name__   ,				resample=__magic_name__   ,				data_format=__magic_name__   ,				**__magic_name__       )






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__ = None   ,				**__magic_name__   ,				)	->  np.ndarray:


							'''simple docstring'''



							snake_case_		:       Optional[Any]           =    get_size_dict(__magic_name__       )
							if "height" not in size or "width" not in size:
											raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''       )
							return center_crop(__magic_name__   ,				size=(size['''height'''], size['''width'''])   ,				data_format=__magic_name__   ,				**__magic_name__       )






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__ = None   ,				**__magic_name__   ,				)	->  Optional[int]:


							'''simple docstring'''



							return rescale(__magic_name__   ,				scale=__magic_name__   ,				data_format=__magic_name__   ,				**__magic_name__       )






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__ = None       )	->  np.ndarray:


							'''simple docstring'''



							return flip_channel_order(__magic_name__   ,				data_format=__magic_name__       )






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__ = None   ,				__magic_name__ = None   ,				__magic_name__ = None   ,				__magic_name__ = None   ,				__magic_name__ = None   ,				__magic_name__ = None   ,				__magic_name__ = None   ,				__magic_name__ = None   ,				__magic_name__ = None   ,				__magic_name__ = ChannelDimension.FIRST   ,				**__magic_name__   ,				)	->  PIL.Image.Image:


							'''simple docstring'''



							snake_case_		:       Dict           =    do_resize if do_resize is not None else self.do_resize
							snake_case_		:       Any           =    resample if resample is not None else self.resample
							snake_case_		:       Any           =    do_rescale if do_rescale is not None else self.do_rescale
							snake_case_		:       Optional[int]           =    rescale_factor if rescale_factor is not None else self.rescale_factor
							snake_case_		:       str           =    do_center_crop if do_center_crop is not None else self.do_center_crop
							snake_case_		:       List[str]           =    (
							    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
							)

							snake_case_		:       Any           =    size if size is not None else self.size
							snake_case_		:       Optional[int]           =    get_size_dict(__magic_name__   ,				default_to_square=__magic_name__       )
							snake_case_		:       Tuple           =    crop_size if crop_size is not None else self.crop_size
							snake_case_		:       Tuple           =    get_size_dict(__magic_name__   ,				param_name='''crop_size'''       )

							snake_case_		:       Tuple           =    make_list_of_images(__magic_name__       )

							if not valid_images(__magic_name__       ):
											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:
											raise ValueError('''Size must be specified if do_resize is True.'''       )

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

							if do_center_crop and crop_size is None:
											raise ValueError('''Crop size must be specified if do_center_crop is True.'''       )

							# All transformations expect numpy arrays.
							snake_case_		:       Tuple           =    [to_numpy_array(__magic_name__       ) for image in images]

							if do_resize:
											snake_case_		:       List[str]           =    [self.resize(image=__magic_name__   ,				size=__magic_name__   ,				resample=__magic_name__       ) for image in images]

							if do_center_crop:
											snake_case_		:       Dict           =    [self.center_crop(image=__magic_name__   ,				size=__magic_name__       ) for image in images]

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

							# the pretrained checkpoints assume images are BGR, not RGB
							if do_flip_channel_order:
											snake_case_		:       str           =    [self.flip_channel_order(image=__magic_name__       ) for image in images]

							snake_case_		:       Tuple           =    [to_channel_dimension_format(__magic_name__   ,				__magic_name__       ) for image in images]

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






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__ = None       )	->  Optional[int]:


							'''simple docstring'''



							snake_case_		:       Tuple           =    outputs.logits

							# Resize logits and compute semantic segmentation maps
							if target_sizes is not None:
											if len(__magic_name__       ) != len(__magic_name__       ):
															raise ValueError(
															    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''       )

											if is_torch_tensor(__magic_name__       ):
															snake_case_		:       List[str]           =    target_sizes.numpy()

											snake_case_		:       Any           =    []

											for idx in range(len(__magic_name__       )       ):
															snake_case_		:       Any           =    torch.nn.functional.interpolate(
															    logits[idx].unsqueeze(dim=0       )   ,				size=target_sizes[idx]   ,				mode='''bilinear'''   ,				align_corners=__magic_name__       )
															snake_case_		:       Union[str, Any]           =    resized_logits[0].argmax(dim=0       )
															semantic_segmentation.append(__magic_name__       )
							else:
											snake_case_		:       str           =    logits.argmax(dim=1       )
											snake_case_		:       int           =    [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]       )]

							return semantic_segmentation




"""simple docstring"""


import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
	from PIL import Image
else:
	class     _UpperCamelCase    :
								'''simple docstring'''







								@staticmethod
								def      snake_case							(	*__a       ,  **__a      ):
											pass
@is_pipeline_test
@require_torch
@require_vision
class     _UpperCamelCase    (  unittest.TestCase						):
							'''simple docstring'''







							__UpperCAmelCase		:       str							 =MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     pipeline("visual-question-answering"       ,  model="hf-internal-testing/tiny-vilt-random-vqa"      )
										__lowerCAmelCase							       =     [
										    {
										        "image": Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      ),
										        "question": "How many cats are there?",
										    },
										    {
										        "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
										        "question": "How many cats are there?",
										    },
										]
										return vqa_pipeline, examples

							def      snake_case							(	self       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     vqa_pipeline(__a       ,  top_k=1      )
										self.assertEqual(
										    __a       ,  [
										        [{"score": ANY(__a      ), "answer": ANY(__a      )}],
										        [{"score": ANY(__a      ), "answer": ANY(__a      )}],
										    ]       ,  )

							@require_torch
							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     pipeline("visual-question-answering"       ,  model="hf-internal-testing/tiny-vilt-random-vqa"      )
										__lowerCAmelCase							       =     "./tests/fixtures/tests_samples/COCO/000000039769.png"
										__lowerCAmelCase							       =     "How many cats are there?"

										__lowerCAmelCase							       =     vqa_pipeline(image=__a       ,  question="How many cats are there?"       ,  top_k=2      )
										self.assertEqual(
										    __a       ,  [{"score": ANY(__a      ), "answer": ANY(__a      )}, {"score": ANY(__a      ), "answer": ANY(__a      )}]      )

										__lowerCAmelCase							       =     vqa_pipeline({"image": image, "question": question}       ,  top_k=2      )
										self.assertEqual(
										    __a       ,  [{"score": ANY(__a      ), "answer": ANY(__a      )}, {"score": ANY(__a      ), "answer": ANY(__a      )}]      )

							@slow
							@require_torch
							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     pipeline("visual-question-answering"       ,  model="dandelin/vilt-b32-finetuned-vqa"      )
										__lowerCAmelCase							       =     "./tests/fixtures/tests_samples/COCO/000000039769.png"
										__lowerCAmelCase							       =     "How many cats are there?"

										__lowerCAmelCase							       =     vqa_pipeline(image=__a       ,  question=__a       ,  top_k=2      )
										self.assertEqual(
										    nested_simplify(__a       ,  decimals=4      )       ,  [{"score": 0.8_7_9_9, "answer": "2"}, {"score": 0.2_9_6, "answer": "1"}]      )

										__lowerCAmelCase							       =     vqa_pipeline({"image": image, "question": question}       ,  top_k=2      )
										self.assertEqual(
										    nested_simplify(__a       ,  decimals=4      )       ,  [{"score": 0.8_7_9_9, "answer": "2"}, {"score": 0.2_9_6, "answer": "1"}]      )

										__lowerCAmelCase							       =     vqa_pipeline(
										    [{"image": image, "question": question}, {"image": image, "question": question}]       ,  top_k=2      )
										self.assertEqual(
										    nested_simplify(__a       ,  decimals=4      )       ,  [[{"score": 0.8_7_9_9, "answer": "2"}, {"score": 0.2_9_6, "answer": "1"}]] * 2       ,  )



							@require_tf
							@unittest.skip("Visual question answering not implemented in TF"      )
							def      snake_case							(	self      ):
										pass




