






'''simple docstring'''





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 	_UpperCAmelCase  (  unittest.TestCase		):




      """simple docstring"""



      def __init__(   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase=7    ,	_lowerCAmelCase=3    ,	_lowerCAmelCase=18    ,	_lowerCAmelCase=30    ,	_lowerCAmelCase=400    ,	_lowerCAmelCase=True    ,	_lowerCAmelCase=None    ,	_lowerCAmelCase=True    ,	_lowerCAmelCase=None    ,	_lowerCAmelCase=True    ,	_lowerCAmelCase=[0.5, 0.5, 0.5]    ,	_lowerCAmelCase=[0.5, 0.5, 0.5]    ,	_lowerCAmelCase=False    ,	):



          '''simple docstring'''
          lowerCAmelCase__   :Optional[Any]      							=				size if size is not None else {"height": 20, "width": 20}
          lowerCAmelCase__   :Tuple      							=				crop_size if crop_size is not None else {"height": 18, "width": 18}
          lowerCAmelCase__   :str      							=				parent
          lowerCAmelCase__   :Any      							=				batch_size
          lowerCAmelCase__   :Optional[int]      							=				num_channels
          lowerCAmelCase__   :Tuple      							=				image_size
          lowerCAmelCase__   :str      							=				min_resolution
          lowerCAmelCase__   :List[str]      							=				max_resolution
          lowerCAmelCase__   :Any      							=				do_resize
          lowerCAmelCase__   :Optional[Any]      							=				size
          lowerCAmelCase__   :str      							=				do_center_crop
          lowerCAmelCase__   :Tuple      							=				crop_size
          lowerCAmelCase__   :Tuple      							=				do_normalize
          lowerCAmelCase__   :str      							=				image_mean
          lowerCAmelCase__   :Tuple      							=				image_std
          lowerCAmelCase__   :Optional[Any]      							=				do_reduce_labels



      def 			snake_case_      (   self		):



          '''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_reduce_labels": self.do_reduce_labels,
          }





def 				snake_case__							(							):
    lowerCAmelCase__   :List[str]      							=				load_dataset("hf-internal-testing/fixtures_ade20k"    ,			split="test"       )

    lowerCAmelCase__   :Tuple      							=				Image.open(dataset[0]["file"]       )
    lowerCAmelCase__   :Optional[int]      							=				Image.open(dataset[1]["file"]       )

    return image, map


def 				snake_case__							(							):
    lowerCAmelCase__   :str      							=				load_dataset("hf-internal-testing/fixtures_ade20k"    ,			split="test"       )

    lowerCAmelCase__   :int      							=				Image.open(ds[0]["file"]       )
    lowerCAmelCase__   :Any      							=				Image.open(ds[1]["file"]       )
    lowerCAmelCase__   :Dict      							=				Image.open(ds[2]["file"]       )
    lowerCAmelCase__   :Tuple      							=				Image.open(ds[3]["file"]       )

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







@require_torch
@require_vision
class 	_UpperCAmelCase  (  __UpperCAmelCase				,							unittest.TestCase		):




      """simple docstring"""



      A          =    BeitImageProcessor if is_vision_available() else None
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Dict      							=				BeitImageProcessingTester(self		)
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return self.image_processor_tester.prepare_image_processor_dict()
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Dict      							=				self.image_processing_class(**self.image_processor_dict		)
          self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE    ,	"do_resize"		)		)
          self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE    ,	"size"		)		)
          self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE    ,	"do_center_crop"		)		)
          self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE    ,	"center_crop"		)		)
          self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE    ,	"do_normalize"		)		)
          self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE    ,	"image_mean"		)		)
          self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE    ,	"image_std"		)		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :int      							=				self.image_processing_class.from_dict(self.image_processor_dict		)
          self.assertEqual(image_processor.size    ,	{"height": 20, "width": 20}		)
          self.assertEqual(image_processor.crop_size    ,	{"height": 18, "width": 18}		)
          self.assertEqual(image_processor.do_reduce_labels    ,	__SCREAMING_SNAKE_CASE		)

          lowerCAmelCase__   :Tuple      							=				self.image_processing_class.from_dict(
              self.image_processor_dict    ,	size=42    ,	crop_size=84    ,	reduce_labels=__SCREAMING_SNAKE_CASE		)
          self.assertEqual(image_processor.size    ,	{"height": 42, "width": 42}		)
          self.assertEqual(image_processor.crop_size    ,	{"height": 84, "width": 84}		)
          self.assertEqual(image_processor.do_reduce_labels    ,	__SCREAMING_SNAKE_CASE		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          pass
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Union[str, Any]      							=				self.image_processing_class(**self.image_processor_dict		)
          # create random PIL images
          lowerCAmelCase__   :Optional[int]      							=				prepare_image_inputs(self.image_processor_tester    ,	equal_resolution=__SCREAMING_SNAKE_CASE		)
          for image in image_inputs:
              self.assertIsInstance(__SCREAMING_SNAKE_CASE    ,	Image.Image		)

          # Test not batched input
          lowerCAmelCase__   :Tuple      							=				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
          lowerCAmelCase__   :Dict      							=				image_processing(__SCREAMING_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 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Union[str, Any]      							=				self.image_processing_class(**self.image_processor_dict		)
          # create random numpy tensors
          lowerCAmelCase__   :int      							=				prepare_image_inputs(self.image_processor_tester    ,	equal_resolution=__SCREAMING_SNAKE_CASE    ,	numpify=__SCREAMING_SNAKE_CASE		)
          for image in image_inputs:
              self.assertIsInstance(__SCREAMING_SNAKE_CASE    ,	np.ndarray		)

          # Test not batched input
          lowerCAmelCase__   :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
          lowerCAmelCase__   :Optional[int]      							=				image_processing(__SCREAMING_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 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :List[str]      							=				self.image_processing_class(**self.image_processor_dict		)
          # create random PyTorch tensors
          lowerCAmelCase__   :Tuple      							=				prepare_image_inputs(self.image_processor_tester    ,	equal_resolution=__SCREAMING_SNAKE_CASE    ,	torchify=__SCREAMING_SNAKE_CASE		)
          for image in image_inputs:
              self.assertIsInstance(__SCREAMING_SNAKE_CASE    ,	torch.Tensor		)

          # Test not batched input
          lowerCAmelCase__   :Optional[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
          lowerCAmelCase__   :Optional[Any]      							=				image_processing(__SCREAMING_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 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Dict      							=				self.image_processing_class(**self.image_processor_dict		)
          # create random PyTorch tensors
          lowerCAmelCase__   :Dict      							=				prepare_image_inputs(self.image_processor_tester    ,	equal_resolution=__SCREAMING_SNAKE_CASE    ,	torchify=__SCREAMING_SNAKE_CASE		)
          lowerCAmelCase__   :List[str]      							=				[]
          for image in image_inputs:
              self.assertIsInstance(__SCREAMING_SNAKE_CASE    ,	torch.Tensor		)
              maps.append(torch.zeros(image.shape[-2:]		).long()		)

          # Test not batched input
          lowerCAmelCase__   :Any      							=				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() <= 255		)

          # Test batched
          lowerCAmelCase__   :int      							=				image_processing(__SCREAMING_SNAKE_CASE    ,	__SCREAMING_SNAKE_CASE    ,	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() <= 255		)

          # Test not batched input (PIL images)
          lowerCAmelCase__							,lowerCAmelCase__   :Optional[Any]      							=				prepare_semantic_single_inputs()

          lowerCAmelCase__   :int      							=				image_processing(__SCREAMING_SNAKE_CASE    ,	__SCREAMING_SNAKE_CASE    ,	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() <= 255		)

          # Test batched input (PIL images)
          lowerCAmelCase__							,lowerCAmelCase__   :List[str]      							=				prepare_semantic_batch_inputs()

          lowerCAmelCase__   :int      							=				image_processing(__SCREAMING_SNAKE_CASE    ,	__SCREAMING_SNAKE_CASE    ,	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() <= 255		)



      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Optional[int]      							=				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
          lowerCAmelCase__							,lowerCAmelCase__   :List[Any]      							=				prepare_semantic_single_inputs()
          lowerCAmelCase__   :List[str]      							=				image_processing(__SCREAMING_SNAKE_CASE    ,	__SCREAMING_SNAKE_CASE    ,	return_tensors="pt"		)
          self.assertTrue(encoding["labels"].min().item() >= 0		)
          self.assertTrue(encoding["labels"].max().item() <= 150		)

          lowerCAmelCase__   :List[Any]      							=				True
          lowerCAmelCase__   :Union[str, Any]      							=				image_processing(__SCREAMING_SNAKE_CASE    ,	__SCREAMING_SNAKE_CASE    ,	return_tensors="pt"		)
          self.assertTrue(encoding["labels"].min().item() >= 0		)
          self.assertTrue(encoding["labels"].max().item() <= 255		)











from __future__ import annotations

import math
from collections.abc import Callable


def 				snake_case__							(							UpperCAmelCase							:  Callable[[int | float], int | float]    ,			UpperCAmelCase							:  int | float    ,			UpperCAmelCase							:  int | float    ,			UpperCAmelCase							:  int = 1_0_0    ,			):
    lowerCAmelCase__   :int      							=				x_start
    lowerCAmelCase__   :int      							=				fnc(UpperCAmelCase       )
    lowerCAmelCase__   :List[Any]      							=				0.0

    for _ in range(UpperCAmelCase       ):
        # Approximates curve as a sequence of linear lines and sums their length
        lowerCAmelCase__   :Dict      							=				(x_end - x_start) / steps + xa
        lowerCAmelCase__   :Optional[Any]      							=				fnc(UpperCAmelCase       )
        length += math.hypot(xa - xa    ,			fxa - fxa       )

        # Increment step
        lowerCAmelCase__   :Union[str, Any]      							=				xa
        lowerCAmelCase__   :str      							=				fxa

    return length


if __name__ == "__main__":


 def 				snake_case__							(							UpperCAmelCase							:  int       ):
     return math.sin(1_0 * x       )

 print("""f(x) = sin(10 * x)""")
 print("""The length of the curve from x = -10 to x = 10 is:""")
 _a    :   Any        =			10
 while i <= 10_0000:
  print(f"""With {i} steps: {line_length(f, -10, 10, i)}""")
  i *= 10



