
'''simple docstring'''
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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
		import torch


lowercase__											=logging.get_logger(__name__)







class   a_ (      lowercase_					):
			lowerCamelCase__	:			str       =    ['pixel_values']




			def __init__(       self					,   UpperCAmelCase = True					,   UpperCAmelCase = None					,   UpperCAmelCase = PILImageResampling.BILINEAR					,   UpperCAmelCase = True					,   UpperCAmelCase = None					,   UpperCAmelCase = True					,   UpperCAmelCase = 1 / 2_55					,   UpperCAmelCase = True					,   UpperCAmelCase = None					,   UpperCAmelCase = None					,   **UpperCAmelCase					,   ):
				super().__init__(**_lowercase	)
				a_      				=						size if size is not None else {"""shortest_edge""": 2_56}
				a_      				=						get_size_dict(_lowercase					,   default_to_square=_lowercase	)
				a_      				=						crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
				a_      				=						get_size_dict(_lowercase					,   param_name="""crop_size"""	)
				a_      				=						do_resize
				a_      				=						size
				a_      				=						resample
				a_      				=						do_center_crop
				a_      				=						crop_size
				a_      				=						do_rescale
				a_      				=						rescale_factor
				a_      				=						do_normalize
				a_      				=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
				a_      				=						image_std if image_std is not None else IMAGENET_STANDARD_STD




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase = PILImageResampling.BICUBIC					,   UpperCAmelCase = None					,   **UpperCAmelCase					,   ):
				a_      				=						get_size_dict(_lowercase					,   default_to_square=_lowercase	)
				if "shortest_edge" not in size:
					raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''	)
				a_      				=						get_resize_output_image_size(_lowercase					,   size=size["""shortest_edge"""]					,   default_to_square=_lowercase	)
				return resize(_lowercase					,   size=_lowercase					,   resample=_lowercase					,   data_format=_lowercase					,   **_lowercase	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase = None					,   **UpperCAmelCase					,   ):
				a_      				=						get_size_dict(_lowercase	)
				if "height" not in size or "width" not in size:
					raise ValueError(f'''The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'''	)
				return center_crop(_lowercase					,   size=(size["""height"""], size["""width"""])					,   data_format=_lowercase					,   **_lowercase	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase = None					,   **UpperCAmelCase	):
				return rescale(_lowercase					,   scale=_lowercase					,   data_format=_lowercase					,   **_lowercase	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase = None					,   **UpperCAmelCase					,   ):
				return normalize(_lowercase					,   mean=_lowercase					,   std=_lowercase					,   data_format=_lowercase					,   **_lowercase	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase = None					,   UpperCAmelCase = None					,   UpperCAmelCase = None					,   UpperCAmelCase = None					,   UpperCAmelCase = None					,   UpperCAmelCase = None					,   UpperCAmelCase = None					,   UpperCAmelCase = None					,   UpperCAmelCase = None					,   UpperCAmelCase = None					,   UpperCAmelCase = None					,   UpperCAmelCase = ChannelDimension.FIRST					,   **UpperCAmelCase					,   ):
				a_      				=						do_resize if do_resize is not None else self.do_resize
				a_      				=						size if size is not None else self.size
				a_      				=						get_size_dict(_lowercase					,   default_to_square=_lowercase	)
				a_      				=						resample if resample is not None else self.resample
				a_      				=						do_center_crop if do_center_crop is not None else self.do_center_crop
				a_      				=						crop_size if crop_size is not None else self.crop_size
				a_      				=						get_size_dict(_lowercase					,   param_name="""crop_size"""	)
				a_      				=						do_rescale if do_rescale is not None else self.do_rescale
				a_      				=						rescale_factor if rescale_factor is not None else self.rescale_factor
				a_      				=						do_normalize if do_normalize is not None else self.do_normalize
				a_      				=						image_mean if image_mean is not None else self.image_mean
				a_      				=						image_std if image_std is not None else self.image_std

				a_      				=						make_list_of_images(_lowercase	)

				if not valid_images(_lowercase	):
					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_center_crop and crop_size is None:
					raise ValueError("""Crop size must be specified if do_center_crop is True."""	)

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

				if do_normalize and (image_mean is None or image_std is None):
					raise ValueError("""Image mean and std must be specified if do_normalize is True."""	)

				# All transformations expect numpy arrays.
				a_      				=						[to_numpy_array(_lowercase	) for image in images]

				if do_resize:
					a_      				=						[self.resize(image=_lowercase					,   size=_lowercase					,   resample=_lowercase	) for image in images]

				if do_center_crop:
					a_      				=						[self.center_crop(image=_lowercase					,   size=_lowercase	) for image in images]

				if do_rescale:
					a_      				=						[self.rescale(image=_lowercase					,   scale=_lowercase	) for image in images]

				if do_normalize:
					a_      				=						[self.normalize(image=_lowercase					,   mean=_lowercase					,   std=_lowercase	) for image in images]

				a_      				=						[to_channel_dimension_format(_lowercase					,   _lowercase	) for image in images]

				a_      				=						{"""pixel_values""": images}
				return BatchFeature(data=_lowercase					,   tensor_type=_lowercase	)






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase = None	):
				a_      				=						outputs.logits

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

					if is_torch_tensor(_lowercase	):
						a_      				=						target_sizes.numpy()

					a_      				=						[]

					for idx in range(len(_lowercase	)	):
						a_      				=						torch.nn.functional.interpolate(
						    logits[idx].unsqueeze(dim=0	)					,   size=target_sizes[idx]					,   mode="""bilinear"""					,   align_corners=_lowercase	)
						a_      				=						resized_logits[0].argmax(dim=0	)
						semantic_segmentation.append(_lowercase	)
				else:
					a_      				=						logits.argmax(dim=1	)
					a_      				=						[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]	)]

				return semantic_segmentation



'''simple docstring'''

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin






@flax.struct.dataclass
class    snake_case     :






				"""simple docstring"""
				# setable values
				_a    	=     None
				_a    	=     None
				_a    	=     None  # sigma(t_i)



				@classmethod
				def   a__					(    cls       )     ->							Dict:
											return cls()






@dataclass
class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     42
				_a    	=     42
				_a    	=     42






class    snake_case     (      lowercase_,						lowercase_		):






				"""simple docstring"""



				@property
				def   a__					(    self       )     ->							int:
											return True



				@register_to_config
				def __init__(    self,     _lowercase = 0.02,     _lowercase = 100,     _lowercase = 1.007,     _lowercase = 80,     _lowercase = 0.05,     _lowercase = 50,     )     ->							List[Any]:
											pass



				def   a__					(    self       )     ->							List[Any]:
											return KarrasVeSchedulerState.create()



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase = ()       )     ->							KarrasVeSchedulerState:
											SCREAMING_SNAKE_CASE_				=				jnp.arange(0,     _lowercase       )[::-1].copy()
											SCREAMING_SNAKE_CASE_				=				[
											    (
											        self.config.sigma_max**2
											        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
											    )
											    for i in timesteps
											]

											return state.replace(
											    num_inference_steps=_lowercase,     schedule=jnp.array(_lowercase,     dtype=jnp.floataa       ),     timesteps=_lowercase,     )



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     )     ->							Tuple[jnp.ndarray, float]:
											if self.config.s_min <= sigma <= self.config.s_max:
																		SCREAMING_SNAKE_CASE_				=				min(self.config.s_churn / state.num_inference_steps,     2**0.5 - 1       )
											else:
																		SCREAMING_SNAKE_CASE_				=				0

											# sample eps ~ N(0, S_noise^2 * I)
											SCREAMING_SNAKE_CASE_				=				random.split(_lowercase,     num=1       )
											SCREAMING_SNAKE_CASE_				=				self.config.s_noise * random.normal(key=_lowercase,     shape=sample.shape       )
											SCREAMING_SNAKE_CASE_				=				sigma + gamma * sigma
											SCREAMING_SNAKE_CASE_				=				sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

											return sample_hat, sigma_hat



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase = True,     )     ->							Union[FlaxKarrasVeOutput, Tuple]:
											SCREAMING_SNAKE_CASE_				=				sample_hat + sigma_hat * model_output
											SCREAMING_SNAKE_CASE_				=				(sample_hat - pred_original_sample) / sigma_hat
											SCREAMING_SNAKE_CASE_				=				sample_hat + (sigma_prev - sigma_hat) * derivative

											if not return_dict:
																		return (sample_prev, derivative, state)

											return FlaxKarrasVeOutput(prev_sample=_lowercase,     derivative=_lowercase,     state=_lowercase       )



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase = True,     )     ->							Union[FlaxKarrasVeOutput, Tuple]:
											SCREAMING_SNAKE_CASE_				=				sample_prev + sigma_prev * model_output
											SCREAMING_SNAKE_CASE_				=				(sample_prev - pred_original_sample) / sigma_prev
											SCREAMING_SNAKE_CASE_				=				sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

											if not return_dict:
																		return (sample_prev, derivative, state)

											return FlaxKarrasVeOutput(prev_sample=_lowercase,     derivative=_lowercase,     state=_lowercase       )





				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase       )     ->							List[Any]:
											raise NotImplementedError()





