'''simple docstring'''





import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor




class 	__A				(			unittest.TestCase     ):



    def __init__(self   :   List[Any]       ,      __a   :   Any       ,      __a   :   Optional[Any]=7       ,      __a   :   Any=3       ,      __a   :   Union[str, Any]=18       ,      __a   :   Tuple=30       ,      __a   :   List[str]=400       ,      __a   :   Dict=True       ,      __a   :   List[str]=None       ,      __a   :   Optional[int]=True       ,      __a   :   Optional[int]=False       ,      __a   :   str=True       ,      __a   :   Any=True       ,      __a   :   List[Any]=[0.5, 0.5, 0.5]       ,      __a   :   Any=[0.5, 0.5, 0.5]       ,      ):
         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": 20}
         UpperCAmelCase_	   =       do_thumbnail
         UpperCAmelCase_	   =       do_align_axis
         UpperCAmelCase_	   =       do_pad
         UpperCAmelCase_	   =       do_normalize
         UpperCAmelCase_	   =       image_mean
         UpperCAmelCase_	   =       image_std






    def  _lowercase						(self   :   Dict			):
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_thumbnail": self.do_thumbnail,
             "do_align_long_axis": self.do_align_axis,
             "do_pad": self.do_pad,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
         }




@require_torch
@require_vision
class 	__A				(			UpperCamelCase__		,     unittest.TestCase     ):
    a__	:						List[str]	       =  DonutImageProcessor if is_vision_available() else None



    def  _lowercase						(self   :   int			):
         UpperCAmelCase_	   =       DonutImageProcessingTester(self			)



    @property
    def  _lowercase						(self   :   Union[str, Any]			):
         return self.image_processor_tester.prepare_image_processor_dict()



    def  _lowercase						(self   :   List[Any]			):
         UpperCAmelCase_	   =       self.image_processing_class(**self.image_processor_dict			)
         self.assertTrue(hasattr(__a       ,      "do_resize"			)			)
         self.assertTrue(hasattr(__a       ,      "size"			)			)
         self.assertTrue(hasattr(__a       ,      "do_thumbnail"			)			)
         self.assertTrue(hasattr(__a       ,      "do_align_long_axis"			)			)
         self.assertTrue(hasattr(__a       ,      "do_pad"			)			)
         self.assertTrue(hasattr(__a       ,      "do_normalize"			)			)
         self.assertTrue(hasattr(__a       ,      "image_mean"			)			)
         self.assertTrue(hasattr(__a       ,      "image_std"			)			)



    def  _lowercase						(self   :   Optional[int]			):
         UpperCAmelCase_	   =       self.image_processing_class.from_dict(self.image_processor_dict			)
         self.assertEqual(image_processor.size       ,      {"height": 18, "width": 20}			)

         UpperCAmelCase_	   =       self.image_processing_class.from_dict(self.image_processor_dict       ,      size=42			)
         self.assertEqual(image_processor.size       ,      {"height": 42, "width": 42}			)

         # Previous config had dimensions in (width, height) order
         UpperCAmelCase_	   =       self.image_processing_class.from_dict(self.image_processor_dict       ,      size=(42, 84)			)
         self.assertEqual(image_processor.size       ,      {"height": 84, "width": 42}			)



    def  _lowercase						(self   :   Optional[int]			):
         pass



    @is_flaky()
    def  _lowercase						(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=__a			)
         for image in image_inputs:
              self.assertIsInstance(__a       ,      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.size["height"],
                 self.image_processor_tester.size["width"],
             )       ,      )

         # Test batched
         UpperCAmelCase_	   =       image_processing(__a       ,      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.size["height"],
                 self.image_processor_tester.size["width"],
             )       ,      )



    @is_flaky()
    def  _lowercase						(self   :   List[str]			):
         # 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=__a       ,      numpify=__a			)
         for image in image_inputs:
              self.assertIsInstance(__a       ,      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.size["height"],
                 self.image_processor_tester.size["width"],
             )       ,      )

         # Test batched
         UpperCAmelCase_	   =       image_processing(__a       ,      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.size["height"],
                 self.image_processor_tester.size["width"],
             )       ,      )






    @is_flaky()
    def  _lowercase						(self   :   List[Any]			):
         # 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=__a       ,      torchify=__a			)
         for image in image_inputs:
              self.assertIsInstance(__a       ,      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.size["height"],
                 self.image_processor_tester.size["width"],
             )       ,      )

         # Test batched
         UpperCAmelCase_	   =       image_processing(__a       ,      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.size["height"],
                 self.image_processor_tester.size["width"],
             )       ,      )





'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


SCREAMING_SNAKE_CASE_:					List[str]  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:					Tuple  ={
    'microsoft/resnet-50': 'https://huggingface.co/microsoft/resnet-50/blob/main/config.json',
}




class 	__A				(			UpperCamelCase__		,     UpperCamelCase__     ):
    a__	:						Optional[Any]	       =  """resnet"""
    a__	:						Tuple	       =  ["""basic""", """bottleneck"""]



    def __init__(self   :   List[Any]       ,      __a   :   Any=3       ,      __a   :   Dict=64       ,      __a   :   Union[str, Any]=[256, 512, 1024, 2048]       ,      __a   :   str=[3, 4, 6, 3]       ,      __a   :   Optional[Any]="bottleneck"       ,      __a   :   Tuple="relu"       ,      __a   :   int=False       ,      __a   :   Optional[int]=None       ,      __a   :   str=None       ,      **__a   :   Dict       ,      ):
         super().__init__(**__a			)
         if layer_type not in self.layer_types:
              raise ValueError(f"""layer_type={layer_type} is not one of {",".join(self.layer_types			)}"""			)
         UpperCAmelCase_	   =       num_channels
         UpperCAmelCase_	   =       embedding_size
         UpperCAmelCase_	   =       hidden_sizes
         UpperCAmelCase_	   =       depths
         UpperCAmelCase_	   =       layer_type
         UpperCAmelCase_	   =       hidden_act
         UpperCAmelCase_	   =       downsample_in_first_stage
         UpperCAmelCase_	   =       ["stem"] + [f"""stage{idx}""" for idx in range(1       ,      len(__a			) + 1			)]
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       get_aligned_output_features_output_indices(
             out_features=__a       ,      out_indices=__a       ,      stage_names=self.stage_names			)




class 	__A				(			UpperCamelCase__     ):
    a__	:						int	       =  version.parse("""1.11"""     )



    @property
    def  _lowercase						(self   :   Optional[int]			):
         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
             ]			)






    @property
    def  _lowercase						(self   :   str			):
         return 1E-3




