

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 SegformerImageProcessor







class 					__lowerCamelCase							(      unittest.TestCase      ):







      def __init__(	self				,    lowerCamelCase				,    lowerCamelCase=7				,    lowerCamelCase=3				,    lowerCamelCase=30				,    lowerCamelCase=400				,    lowerCamelCase=True				,    lowerCamelCase=None				,    lowerCamelCase=True				,    lowerCamelCase=[0.5, 0.5, 0.5]				,    lowerCamelCase=[0.5, 0.5, 0.5]				,    lowerCamelCase=False				,    )		->			Any:
        snake_case_					= size if size is not None else {"""height""": 30, """width""": 30}
        snake_case_					= parent
        snake_case_					= batch_size
        snake_case_					= num_channels
        snake_case_					= min_resolution
        snake_case_					= max_resolution
        snake_case_					= do_resize
        snake_case_					= size
        snake_case_					= do_normalize
        snake_case_					= image_mean
        snake_case_					= image_std
        snake_case_					= do_reduce_labels







      def       lowerCAmelCase_       (	self  )		->			int:
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_reduce_labels": self.do_reduce_labels,
        }



def   UpperCamelCase(   )      ->							Optional[Any]:





  '''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   UpperCamelCase(   )      ->							int:





  '''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"""]				)
  snake_case_					= Image.open(dataset[2]["""file"""]				)
  snake_case_					= Image.open(dataset[3]["""file"""]				)

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







@require_torch
@require_vision
class 					__lowerCamelCase							(      __snake_case    ,				unittest.TestCase      ):
      lowerCamelCase_      : Any  			=   SegformerImageProcessor if is_vision_available() else None







      def       lowerCAmelCase_       (	self  )		->			List[str]:
        snake_case_					= SegformerImageProcessingTester(self  )







      @property
      def       lowerCAmelCase_       (	self  )		->			Dict:
        return self.image_processor_tester.prepare_image_processor_dict()







      def       lowerCAmelCase_       (	self  )		->			Any:
        snake_case_					= self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(lowerCamelCase				,    """do_resize"""  )  )
        self.assertTrue(hasattr(lowerCamelCase				,    """size"""  )  )
        self.assertTrue(hasattr(lowerCamelCase				,    """do_normalize"""  )  )
        self.assertTrue(hasattr(lowerCamelCase				,    """image_mean"""  )  )
        self.assertTrue(hasattr(lowerCamelCase				,    """image_std"""  )  )
        self.assertTrue(hasattr(lowerCamelCase				,    """do_reduce_labels"""  )  )







      def       lowerCAmelCase_       (	self  )		->			List[str]:
        snake_case_					= self.image_processing_class.from_dict(self.image_processor_dict  )
        self.assertEqual(image_processor.size				,    {"""height""": 30, """width""": 30}  )
        self.assertEqual(image_processor.do_reduce_labels				,    lowerCamelCase  )

        snake_case_					= self.image_processing_class.from_dict(self.image_processor_dict				,    size=42				,    reduce_labels=lowerCamelCase  )
        self.assertEqual(image_processor.size				,    {"""height""": 42, """width""": 42}  )
        self.assertEqual(image_processor.do_reduce_labels				,    lowerCamelCase  )







      def       lowerCAmelCase_       (	self  )		->			Optional[int]:
        pass







      def       lowerCAmelCase_       (	self  )		->			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=lowerCamelCase  )
        for image in image_inputs:
          self.assertIsInstance(lowerCamelCase				,    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.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )				,    )

        # Test batched
        snake_case_					= image_processing(lowerCamelCase				,    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"""],
            )				,    )







      def       lowerCAmelCase_       (	self  )		->			Union[str, Any]:
        # 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=lowerCamelCase				,    numpify=lowerCamelCase  )
        for image in image_inputs:
          self.assertIsInstance(lowerCamelCase				,    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.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )				,    )

        # Test batched
        snake_case_					= image_processing(lowerCamelCase				,    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"""],
            )				,    )







      def       lowerCAmelCase_       (	self  )		->			int:
        # 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=lowerCamelCase				,    torchify=lowerCamelCase  )
        for image in image_inputs:
          self.assertIsInstance(lowerCamelCase				,    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.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )				,    )

        # Test batched
        snake_case_					= image_processing(lowerCamelCase				,    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"""],
            )				,    )







      def       lowerCAmelCase_       (	self  )		->			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=lowerCamelCase				,    torchify=lowerCamelCase  )
        snake_case_					= []
        for image in image_inputs:
          self.assertIsInstance(lowerCamelCase				,    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.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )				,    )
        self.assertEqual(
            encoding["""labels"""].shape				,    (
                1,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.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
        snake_case_					= image_processing(lowerCamelCase				,    lowerCamelCase				,    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.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )				,    )
        self.assertEqual(
            encoding["""labels"""].shape				,    (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.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)
        snake_case_		,							snake_case_					= prepare_semantic_single_inputs()

        snake_case_					= image_processing(lowerCamelCase				,    lowerCamelCase				,    return_tensors="""pt"""  )
        self.assertEqual(
            encoding["""pixel_values"""].shape				,    (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )				,    )
        self.assertEqual(
            encoding["""labels"""].shape				,    (
                1,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.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)
        snake_case_		,							snake_case_					= prepare_semantic_batch_inputs()

        snake_case_					= image_processing(lowerCamelCase				,    lowerCamelCase				,    return_tensors="""pt"""  )
        self.assertEqual(
            encoding["""pixel_values"""].shape				,    (
                2,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )				,    )
        self.assertEqual(
            encoding["""labels"""].shape				,    (
                2,
                self.image_processor_tester.size["""height"""],
                self.image_processor_tester.size["""width"""],
            )				,    )
        self.assertEqual(encoding["""labels"""].dtype				,    torch.long  )
        self.assertTrue(encoding["""labels"""].min().item() >= 0  )
        self.assertTrue(encoding["""labels"""].max().item() <= 255  )







      def       lowerCAmelCase_       (	self  )		->			Optional[int]:
        # 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(lowerCamelCase				,    lowerCamelCase				,    return_tensors="""pt"""  )
        self.assertTrue(encoding["""labels"""].min().item() >= 0  )
        self.assertTrue(encoding["""labels"""].max().item() <= 150  )

        snake_case_					= True
        snake_case_					= image_processing(lowerCamelCase				,    lowerCamelCase				,    return_tensors="""pt"""  )
        self.assertTrue(encoding["""labels"""].min().item() >= 0  )
        self.assertTrue(encoding["""labels"""].max().item() <= 255  )


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)







@flax.struct.dataclass
class 					__lowerCamelCase							:
      lowerCamelCase_      : CommonSchedulerState

      # setable values
      lowerCamelCase_      : jnp.ndarray
      lowerCamelCase_      : jnp.ndarray
      lowerCamelCase_      : jnp.ndarray
      lowerCamelCase_      : Optional[int]  			=   None

      # running values
      lowerCamelCase_      : Optional[jnp.ndarray]  			=   None







      @classmethod
      def       lowerCAmelCase_       (	cls				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )		->			Union[str, Any]:
        return cls(common=lowerCamelCase				,    init_noise_sigma=lowerCamelCase				,    timesteps=lowerCamelCase				,    sigmas=lowerCamelCase  )







@dataclass
class 					__lowerCamelCase							(      __snake_case      ):
      lowerCamelCase_      : LMSDiscreteSchedulerState







class 					__lowerCamelCase							(      __snake_case    ,				__snake_case      ):
      lowerCamelCase_      : str  			=   [e.name for e in FlaxKarrasDiffusionSchedulers]

      lowerCamelCase_      : jnp.dtype







      @property
      def       lowerCAmelCase_       (	self  )		->			List[str]:
        return True







      @register_to_config
      def __init__(	self				,    lowerCamelCase = 1000				,    lowerCamelCase = 0.0001				,    lowerCamelCase = 0.02				,    lowerCamelCase = "linear"				,    lowerCamelCase = None				,    lowerCamelCase = "epsilon"				,    lowerCamelCase = jnp.floataa				,    )		->			Optional[Any]:
        snake_case_					= dtype







      def       lowerCAmelCase_       (	self				,    lowerCamelCase = None  )		->			LMSDiscreteSchedulerState:
        if common is None:
          snake_case_					= CommonSchedulerState.create(self  )

        snake_case_					= jnp.arange(0				,    self.config.num_train_timesteps  ).round()[::-1]
        snake_case_					= ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

        # standard deviation of the initial noise distribution
        snake_case_					= sigmas.max()

        return LMSDiscreteSchedulerState.create(
            common=lowerCamelCase				,    init_noise_sigma=lowerCamelCase				,    timesteps=lowerCamelCase				,    sigmas=lowerCamelCase				,    )







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )		->			jnp.ndarray:
        ((snake_case_)		,							)					= jnp.where(state.timesteps == timestep				,    size=1  )
        snake_case_					= step_index[0]

        snake_case_					= state.sigmas[step_index]
        snake_case_					= sample / ((sigma**2 + 1) ** 0.5)
        return sample







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )		->			Any:

        def lms_derivative(lowerCamelCase  ):
          snake_case_					= 1.0
          for k in range(lowerCamelCase  ):
            if current_order == k:
              continue
            prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
          return prod

        snake_case_					= integrate.quad(lowerCamelCase				,    state.sigmas[t]				,    state.sigmas[t + 1]				,    epsrel=1e-4  )[0]

        return integrated_coeff







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase = ()  )		->			LMSDiscreteSchedulerState:
        snake_case_					= jnp.linspace(self.config.num_train_timesteps - 1				,    0				,    lowerCamelCase				,    dtype=self.dtype  )

        snake_case_					= jnp.floor(lowerCamelCase  ).astype(jnp.intaa  )
        snake_case_					= jnp.ceil(lowerCamelCase  ).astype(jnp.intaa  )

        snake_case_					= jnp.mod(lowerCamelCase				,    1.0  )

        snake_case_					= ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
        snake_case_					= (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
        snake_case_					= jnp.concatenate([sigmas, jnp.array([0.0]				,    dtype=self.dtype  )]  )

        snake_case_					= timesteps.astype(jnp.intaa  )

        # initial running values
        snake_case_					= jnp.zeros((0,) + shape				,    dtype=self.dtype  )

        return state.replace(
            timesteps=lowerCamelCase				,    sigmas=lowerCamelCase				,    num_inference_steps=lowerCamelCase				,    derivatives=lowerCamelCase				,    )







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase = 4				,    lowerCamelCase = True				,    )		->			Union[FlaxLMSSchedulerOutput, Tuple]:
        if state.num_inference_steps is None:
          raise ValueError(
              """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""  )

        snake_case_					= state.sigmas[timestep]

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
          snake_case_					= sample - sigma * model_output
        elif self.config.prediction_type == "v_prediction":
          # * c_out + input * c_skip
          snake_case_					= model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
        else:
          raise ValueError(
              f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''  )

        # 2. Convert to an ODE derivative
        snake_case_					= (sample - pred_original_sample) / sigma
        snake_case_					= state.replace(derivatives=jnp.append(state.derivatives				,    lowerCamelCase  )  )
        if len(state.derivatives  ) > order:
          snake_case_					= state.replace(derivatives=jnp.delete(state.derivatives				,    0  )  )

        # 3. Compute linear multistep coefficients
        snake_case_					= min(timestep + 1				,    lowerCamelCase  )
        snake_case_					= [self.get_lms_coefficient(lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  ) for curr_order in range(lowerCamelCase  )]

        # 4. Compute previous sample based on the derivatives path
        snake_case_					= sample + sum(
            coeff * derivative for coeff, derivative in zip(lowerCamelCase				,    reversed(state.derivatives  )  )  )

        if not return_dict:
          return (prev_sample, state)

        return FlaxLMSSchedulerOutput(prev_sample=lowerCamelCase				,    state=lowerCamelCase  )







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    )		->			jnp.ndarray:
        snake_case_					= state.sigmas[timesteps].flatten()
        snake_case_					= broadcast_to_shape_from_left(lowerCamelCase				,    noise.shape  )

        snake_case_					= original_samples + noise * sigma

        return noisy_samples







      def __len__(	self  )		->			Optional[Any]:
        return self.config.num_train_timesteps