
import unittest

import numpy as np
from datasets import load_dataset

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 BeitImageProcessor







class   lowerCamelCase	(						unittest.TestCase						):





     def __init__(self   :					Any       ,					_A   :					Optional[Any]       ,					_A   :					Union[str, Any]=7       ,					_A   :					Any=3       ,					_A   :					Optional[Any]=1_8       ,					_A   :					Dict=3_0       ,					_A   :					Tuple=4_0_0       ,					_A   :					Optional[int]=True       ,					_A   :					Union[str, Any]=None       ,					_A   :					Union[str, Any]=True       ,					_A   :					List[str]=None       ,					_A   :					List[str]=True       ,					_A   :					Tuple=[0.5, 0.5, 0.5]       ,					_A   :					Optional[Any]=[0.5, 0.5, 0.5]       ,					_A   :					List[Any]=False       ,					)		->		Tuple:
         snake_case				  =      size if size is not None else {"height": 2_0, "width": 2_0}
         snake_case				  =      crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
         snake_case				  =      parent
         snake_case				  =      batch_size
         snake_case				  =      num_channels
         snake_case				  =      image_size
         snake_case				  =      min_resolution
         snake_case				  =      max_resolution
         snake_case				  =      do_resize
         snake_case				  =      size
         snake_case				  =      do_center_crop
         snake_case				  =      crop_size
         snake_case				  =      do_normalize
         snake_case				  =      image_mean
         snake_case				  =      image_std
         snake_case				  =      do_reduce_labels





     def 	UpperCAmelCase(self   :					str     )		->		str:
         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_reduce_labels": self.do_reduce_labels,
         }



def 		lowercase_    (     )							->							Tuple:
    """simple docstring"""

    snake_case				  =      load_dataset("hf-internal-testing/fixtures_ade20k"				,	split="test"	)

    snake_case				  =      Image.open(dataset[0]["file"]	)
    snake_case				  =      Image.open(dataset[1]["file"]	)

    return image, map



def 		lowercase_    (     )							->							Union[str, Any]:
    """simple docstring"""

    snake_case				  =      load_dataset("hf-internal-testing/fixtures_ade20k"				,	split="test"	)

    snake_case				  =      Image.open(ds[0]["file"]	)
    snake_case				  =      Image.open(ds[1]["file"]	)
    snake_case				  =      Image.open(ds[2]["file"]	)
    snake_case				  =      Image.open(ds[3]["file"]	)

    return [imagea, imagea], [mapa, mapa]







@require_torch
@require_vision
class   lowerCamelCase	(						A_							,				unittest.TestCase						):
     UpperCAmelCase__       :							str     =	BeitImageProcessor if is_vision_available() else None





     def 	UpperCAmelCase(self   :					Dict     )		->		List[Any]:
         snake_case				  =      BeitImageProcessingTester(self     )





     @property
     def 	UpperCAmelCase(self   :					str     )		->		List[str]:
         return self.image_processor_tester.prepare_image_processor_dict()





     def 	UpperCAmelCase(self   :					int     )		->		Dict:
         snake_case				  =      self.image_processing_class(**self.image_processor_dict     )
         self.assertTrue(hasattr(_A       ,					"do_resize"     )     )
         self.assertTrue(hasattr(_A       ,					"size"     )     )
         self.assertTrue(hasattr(_A       ,					"do_center_crop"     )     )
         self.assertTrue(hasattr(_A       ,					"center_crop"     )     )
         self.assertTrue(hasattr(_A       ,					"do_normalize"     )     )
         self.assertTrue(hasattr(_A       ,					"image_mean"     )     )
         self.assertTrue(hasattr(_A       ,					"image_std"     )     )





     def 	UpperCAmelCase(self   :					Tuple     )		->		int:
         snake_case				  =      self.image_processing_class.from_dict(self.image_processor_dict     )
         self.assertEqual(image_processor.size       ,					{"height": 2_0, "width": 2_0}     )
         self.assertEqual(image_processor.crop_size       ,					{"height": 1_8, "width": 1_8}     )
         self.assertEqual(image_processor.do_reduce_labels       ,					_A     )

         snake_case				  =      self.image_processing_class.from_dict(
             self.image_processor_dict       ,					size=4_2       ,					crop_size=8_4       ,					reduce_labels=_A     )
         self.assertEqual(image_processor.size       ,					{"height": 4_2, "width": 4_2}     )
         self.assertEqual(image_processor.crop_size       ,					{"height": 8_4, "width": 8_4}     )
         self.assertEqual(image_processor.do_reduce_labels       ,					_A     )





     def 	UpperCAmelCase(self   :					Optional[Any]     )		->		Tuple:
         pass





     def 	UpperCAmelCase(self   :					List[str]     )		->		Any:
         # Initialize image_processing
         snake_case				  =      self.image_processing_class(**self.image_processor_dict     )
         # create random PIL images
         snake_case				  =      prepare_image_inputs(self.image_processor_tester       ,					equal_resolution=_A     )
         for image in image_inputs:
             self.assertIsInstance(_A       ,					Image.Image     )

         # Test not batched input
         snake_case				  =      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
         snake_case				  =      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.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )       ,					)





     def 	UpperCAmelCase(self   :					List[Any]     )		->		int:
         # Initialize image_processing
         snake_case				  =      self.image_processing_class(**self.image_processor_dict     )
         # create random numpy tensors
         snake_case				  =      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
         snake_case				  =      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
         snake_case				  =      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.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )       ,					)





     def 	UpperCAmelCase(self   :					List[Any]     )		->		Union[str, Any]:
         # Initialize image_processing
         snake_case				  =      self.image_processing_class(**self.image_processor_dict     )
         # create random PyTorch tensors
         snake_case				  =      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
         snake_case				  =      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
         snake_case				  =      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.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )       ,					)





     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		str:
         # Initialize image_processing
         snake_case				  =      self.image_processing_class(**self.image_processor_dict     )
         # create random PyTorch tensors
         snake_case				  =      prepare_image_inputs(self.image_processor_tester       ,					equal_resolution=_A       ,					torchify=_A     )
         snake_case				  =      []
         for image in image_inputs:
             self.assertIsInstance(_A       ,					torch.Tensor     )
             maps.append(torch.zeros(image.shape[-2:]     ).long()     )

         # Test not batched input
         snake_case				  =      image_processing(image_inputs[0]       ,					maps[0]       ,					return_tensors="pt"     )
         self.assertEqual(
             encoding["pixel_values"].shape       ,					(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )       ,					)
         self.assertEqual(
             encoding["labels"].shape       ,					(
                 1,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )       ,					)
         self.assertEqual(encoding["labels"].dtype       ,					torch.long     )
         self.assertTrue(encoding["labels"].min().item() >= 0     )
         self.assertTrue(encoding["labels"].max().item() <= 2_5_5     )

         # Test batched
         snake_case				  =      image_processing(_A       ,					_A       ,					return_tensors="pt"     )
         self.assertEqual(
             encoding["pixel_values"].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"],
             )       ,					)
         self.assertEqual(
             encoding["labels"].shape       ,					(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )       ,					)
         self.assertEqual(encoding["labels"].dtype       ,					torch.long     )
         self.assertTrue(encoding["labels"].min().item() >= 0     )
         self.assertTrue(encoding["labels"].max().item() <= 2_5_5     )

         # Test not batched input (PIL images)
         snake_case  ,							snake_case				  =      prepare_semantic_single_inputs()

         snake_case				  =      image_processing(_A       ,					_A       ,					return_tensors="pt"     )
         self.assertEqual(
             encoding["pixel_values"].shape       ,					(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )       ,					)
         self.assertEqual(
             encoding["labels"].shape       ,					(
                 1,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )       ,					)
         self.assertEqual(encoding["labels"].dtype       ,					torch.long     )
         self.assertTrue(encoding["labels"].min().item() >= 0     )
         self.assertTrue(encoding["labels"].max().item() <= 2_5_5     )

         # Test batched input (PIL images)
         snake_case  ,							snake_case				  =      prepare_semantic_batch_inputs()

         snake_case				  =      image_processing(_A       ,					_A       ,					return_tensors="pt"     )
         self.assertEqual(
             encoding["pixel_values"].shape       ,					(
                 2,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )       ,					)
         self.assertEqual(
             encoding["labels"].shape       ,					(
                 2,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )       ,					)
         self.assertEqual(encoding["labels"].dtype       ,					torch.long     )
         self.assertTrue(encoding["labels"].min().item() >= 0     )
         self.assertTrue(encoding["labels"].max().item() <= 2_5_5     )





     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		List[Any]:
         # Initialize image_processing
         snake_case				  =      self.image_processing_class(**self.image_processor_dict     )

         # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
         snake_case  ,							snake_case				  =      prepare_semantic_single_inputs()
         snake_case				  =      image_processing(_A       ,					_A       ,					return_tensors="pt"     )
         self.assertTrue(encoding["labels"].min().item() >= 0     )
         self.assertTrue(encoding["labels"].max().item() <= 1_5_0     )

         snake_case				  =      True
         snake_case				  =      image_processing(_A       ,					_A       ,					return_tensors="pt"     )
         self.assertTrue(encoding["labels"].min().item() >= 0     )
         self.assertTrue(encoding["labels"].max().item() <= 2_5_5     )



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


_A						      =				logging.get_logger(__name__)

_A						      =				{
    "sail/poolformer_s12": "https://huggingface.co/sail/poolformer_s12/resolve/main/config.json",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							Any     =	"poolformer"





     def __init__(self   :					Optional[int]       ,					_A   :					Optional[Any]=3       ,					_A   :					Optional[int]=1_6       ,					_A   :					Dict=1_6       ,					_A   :					Tuple=3       ,					_A   :					Tuple=4.0       ,					_A   :					int=[2, 2, 6, 2]       ,					_A   :					Dict=[6_4, 1_2_8, 3_2_0, 5_1_2]       ,					_A   :					int=[7, 3, 3, 3]       ,					_A   :					List[str]=[4, 2, 2, 2]       ,					_A   :					str=[2, 1, 1, 1]       ,					_A   :					List[Any]=4       ,					_A   :					Any=0.0       ,					_A   :					Optional[Any]="gelu"       ,					_A   :					Optional[Any]=True       ,					_A   :					List[str]=1E-5       ,					_A   :					List[str]=0.02       ,					**_A   :					str       ,					)		->		Tuple:
         snake_case				  =      num_channels
         snake_case				  =      patch_size
         snake_case				  =      stride
         snake_case				  =      padding
         snake_case				  =      pool_size
         snake_case				  =      hidden_sizes
         snake_case				  =      mlp_ratio
         snake_case				  =      depths
         snake_case				  =      patch_sizes
         snake_case				  =      strides
         snake_case				  =      num_encoder_blocks
         snake_case				  =      drop_path_rate
         snake_case				  =      hidden_act
         snake_case				  =      use_layer_scale
         snake_case				  =      layer_scale_init_value
         snake_case				  =      initializer_range
         super().__init__(**_A     )







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							Tuple     =	version.parse("1.11"						)





     @property
     def 	UpperCAmelCase(self   :					int     )		->		Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
             ]     )





     @property
     def 	UpperCAmelCase(self   :					int     )		->		float:
         return 2E-3

