

import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   str     					=				(PNDMScheduler,)
	_lowercase		:   List[Any]     					=				(('''num_inference_steps''', 50),)




	def        lowerCAmelCase_       (     self						:     str					,      **__A						:     Optional[int]							):
		__A :			List[str]		    =			{
		    """num_train_timesteps""": 1000,
		    """beta_start""": 0.0_0_0_1,
		    """beta_end""": 0.0_2,
		    """beta_schedule""": """linear""",
		}

		config.update(**__A							)
		return config




	def        lowerCAmelCase_       (     self						:     List[str]					,      __A						:     str=0					,      **__A						:     List[str]							):
		__A :			Tuple		    =			dict(self.forward_default_kwargs							)
		__A :			str		    =			kwargs.pop("""num_inference_steps"""					,      __A							)
		__A :			str		    =			self.dummy_sample
		__A :			Any		    =			0.1 * sample
		__A :			List[str]		    =			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

		for scheduler_class in self.scheduler_classes:
			__A :			Dict		    =			self.get_scheduler_config(**__A							)
			__A :			Optional[Any]		    =			scheduler_class(**__A							)
			scheduler.set_timesteps(__A							)
			# copy over dummy past residuals
			__A :			Union[str, Any]		    =			dummy_past_residuals[:]

			with tempfile.TemporaryDirectory() as tmpdirname:
				scheduler.save_config(__A							)
				__A :			str		    =			scheduler_class.from_pretrained(__A							)
				new_scheduler.set_timesteps(__A							)
				# copy over dummy past residuals
				__A :			Any		    =			dummy_past_residuals[:]

			__A :			List[str]		    =			scheduler.step_prk(__A					,      __A					,      __A					,      **__A							).prev_sample
			__A :			int		    =			new_scheduler.step_prk(__A					,      __A					,      __A					,      **__A							).prev_sample

			assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"

			__A :			int		    =			scheduler.step_plms(__A					,      __A					,      __A					,      **__A							).prev_sample
			__A :			List[str]		    =			new_scheduler.step_plms(__A					,      __A					,      __A					,      **__A							).prev_sample

			assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"




	def        lowerCAmelCase_       (     self						:     str							):
		pass




	def        lowerCAmelCase_       (     self						:     Optional[int]					,      __A						:     Dict=0					,      **__A						:     str							):
		__A :			List[str]		    =			dict(self.forward_default_kwargs							)
		__A :			Dict		    =			kwargs.pop("""num_inference_steps"""					,      __A							)
		__A :			int		    =			self.dummy_sample
		__A :			Tuple		    =			0.1 * sample
		__A :			Optional[int]		    =			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

		for scheduler_class in self.scheduler_classes:
			__A :			Any		    =			self.get_scheduler_config()
			__A :			Union[str, Any]		    =			scheduler_class(**__A							)
			scheduler.set_timesteps(__A							)

			# copy over dummy past residuals (must be after setting timesteps)
			__A :			Dict		    =			dummy_past_residuals[:]

			with tempfile.TemporaryDirectory() as tmpdirname:
				scheduler.save_config(__A							)
				__A :			List[str]		    =			scheduler_class.from_pretrained(__A							)
				# copy over dummy past residuals
				new_scheduler.set_timesteps(__A							)

				# copy over dummy past residual (must be after setting timesteps)
				__A :			List[Any]		    =			dummy_past_residuals[:]

			__A :			List[str]		    =			scheduler.step_prk(__A					,      __A					,      __A					,      **__A							).prev_sample
			__A :			Any		    =			new_scheduler.step_prk(__A					,      __A					,      __A					,      **__A							).prev_sample

			assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"

			__A :			Tuple		    =			scheduler.step_plms(__A					,      __A					,      __A					,      **__A							).prev_sample
			__A :			Tuple		    =			new_scheduler.step_plms(__A					,      __A					,      __A					,      **__A							).prev_sample

			assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"




	def        lowerCAmelCase_       (     self						:     Union[str, Any]					,      **__A						:     Optional[Any]							):
		__A :			int		    =			self.scheduler_classes[0]
		__A :			List[Any]		    =			self.get_scheduler_config(**__A							)
		__A :			List[Any]		    =			scheduler_class(**__A							)

		__A :			Optional[int]		    =			10
		__A :			Optional[Any]		    =			self.dummy_model()
		__A :			Optional[Any]		    =			self.dummy_sample_deter
		scheduler.set_timesteps(__A							)

		for i, t in enumerate(scheduler.prk_timesteps							):
			__A :			Tuple		    =			model(__A					,      __A							)
			__A :			List[str]		    =			scheduler.step_prk(__A					,      __A					,      __A							).prev_sample

		for i, t in enumerate(scheduler.plms_timesteps							):
			__A :			Optional[Any]		    =			model(__A					,      __A							)
			__A :			Tuple		    =			scheduler.step_plms(__A					,      __A					,      __A							).prev_sample

		return sample




	def        lowerCAmelCase_       (     self						:     str							):
		__A :			Tuple		    =			dict(self.forward_default_kwargs							)

		__A :			Optional[int]		    =			kwargs.pop("""num_inference_steps"""					,      __A							)

		for scheduler_class in self.scheduler_classes:
			__A :			str		    =			self.get_scheduler_config()
			__A :			List[str]		    =			scheduler_class(**__A							)

			__A :			Any		    =			self.dummy_sample
			__A :			str		    =			0.1 * sample

			if num_inference_steps is not None and hasattr(__A					,      """set_timesteps"""							):
				scheduler.set_timesteps(__A							)
			elif num_inference_steps is not None and not hasattr(__A					,      """set_timesteps"""							):
				__A :			Union[str, Any]		    =			num_inference_steps

			# copy over dummy past residuals (must be done after set_timesteps)
			__A :			Optional[int]		    =			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
			__A :			Any		    =			dummy_past_residuals[:]

			__A :			Any		    =			scheduler.step_prk(__A					,      0					,      __A					,      **__A							).prev_sample
			__A :			Tuple		    =			scheduler.step_prk(__A					,      1					,      __A					,      **__A							).prev_sample

			self.assertEqual(output_a.shape					,      sample.shape							)
			self.assertEqual(output_a.shape					,      output_a.shape							)

			__A :			Dict		    =			scheduler.step_plms(__A					,      0					,      __A					,      **__A							).prev_sample
			__A :			List[Any]		    =			scheduler.step_plms(__A					,      1					,      __A					,      **__A							).prev_sample

			self.assertEqual(output_a.shape					,      sample.shape							)
			self.assertEqual(output_a.shape					,      output_a.shape							)




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		for timesteps in [100, 1000]:
			self.check_over_configs(num_train_timesteps=__A							)




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		for steps_offset in [0, 1]:
			self.check_over_configs(steps_offset=__A							)

		__A :			int		    =			self.scheduler_classes[0]
		__A :			Any		    =			self.get_scheduler_config(steps_offset=1							)
		__A :			List[str]		    =			scheduler_class(**__A							)
		scheduler.set_timesteps(10							)
		assert torch.equal(
		    scheduler.timesteps					,      torch.LongTensor(
		        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]							)					,      )




	def        lowerCAmelCase_       (     self						:     Any							):
		for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]					,      [0.0_0_2, 0.0_2]							):
			self.check_over_configs(beta_start=__A					,      beta_end=__A							)




	def        lowerCAmelCase_       (     self						:     str							):
		for schedule in ["linear", "squaredcos_cap_v2"]:
			self.check_over_configs(beta_schedule=__A							)




	def        lowerCAmelCase_       (     self						:     List[str]							):
		for prediction_type in ["epsilon", "v_prediction"]:
			self.check_over_configs(prediction_type=__A							)




	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		for t in [1, 5, 10]:
			self.check_over_forward(time_step=__A							)




	def        lowerCAmelCase_       (     self						:     Any							):
		for t, num_inference_steps in zip([1, 5, 10]					,      [10, 50, 100]							):
			self.check_over_forward(num_inference_steps=__A							)




	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
		__A :			Dict		    =			27

		for scheduler_class in self.scheduler_classes:
			__A :			Tuple		    =			self.dummy_sample
			__A :			List[str]		    =			0.1 * sample

			__A :			List[Any]		    =			self.get_scheduler_config()
			__A :			int		    =			scheduler_class(**__A							)

			scheduler.set_timesteps(__A							)

			# before power of 3 fix, would error on first step, so we only need to do two
			for i, t in enumerate(scheduler.prk_timesteps[:2]							):
				__A :			Any		    =			scheduler.step_prk(__A					,      __A					,      __A							).prev_sample




	def        lowerCAmelCase_       (     self						:     List[Any]							):
		with self.assertRaises(__A							):
			__A :			Optional[int]		    =			self.scheduler_classes[0]
			__A :			int		    =			self.get_scheduler_config()
			__A :			Tuple		    =			scheduler_class(**__A							)

			scheduler.step_plms(self.dummy_sample					,      1					,      self.dummy_sample							).prev_sample




	def        lowerCAmelCase_       (     self						:     Dict							):
		__A :			Dict		    =			self.full_loop()
		__A :			int		    =			torch.sum(torch.abs(__A							)							)
		__A :			str		    =			torch.mean(torch.abs(__A							)							)

		assert abs(result_sum.item() - 1_9_8.1_3_1_8							) < 1e-2
		assert abs(result_mean.item() - 0.2_5_8_0							) < 1e-3




	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		__A :			Union[str, Any]		    =			self.full_loop(prediction_type="""v_prediction"""							)
		__A :			Tuple		    =			torch.sum(torch.abs(__A							)							)
		__A :			List[Any]		    =			torch.mean(torch.abs(__A							)							)

		assert abs(result_sum.item() - 6_7.3_9_8_6							) < 1e-2
		assert abs(result_mean.item() - 0.0_8_7_8							) < 1e-3




	def        lowerCAmelCase_       (     self						:     str							):
		# We specify different beta, so that the first alpha is 0.99
		__A :			Optional[Any]		    =			self.full_loop(set_alpha_to_one=__A					,      beta_start=0.0_1							)
		__A :			List[Any]		    =			torch.sum(torch.abs(__A							)							)
		__A :			Optional[int]		    =			torch.mean(torch.abs(__A							)							)

		assert abs(result_sum.item() - 2_3_0.0_3_9_9							) < 1e-2
		assert abs(result_mean.item() - 0.2_9_9_5							) < 1e-3




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		# We specify different beta, so that the first alpha is 0.99
		__A :			Optional[int]		    =			self.full_loop(set_alpha_to_one=__A					,      beta_start=0.0_1							)
		__A :			Optional[int]		    =			torch.sum(torch.abs(__A							)							)
		__A :			List[Any]		    =			torch.mean(torch.abs(__A							)							)

		assert abs(result_sum.item() - 1_8_6.9_4_8_2							) < 1e-2
		assert abs(result_mean.item() - 0.2_4_3_4							) < 1e-3








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


UpperCAmelCase_							:					Optional[int]		 =   logging.get_logger(__name__)






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   List[str]     					=				['''pixel_values''']




	def __init__(     self						:     Dict					,      __A						:     bool = True					,      __A						:     Optional[Dict[str, int]] = None					,      __A						:     PILImageResampling = PILImageResampling.BILINEAR					,      __A						:     bool = True					,      __A						:     Dict[str, int] = None					,      __A						:     bool = True					,      __A						:     Union[int, float] = 1 / 255					,      __A						:     bool = True					,      __A						:     Optional[Union[float, List[float]]] = None					,      __A						:     Optional[Union[float, List[float]]] = None					,      **__A						:     int					,      ):
		super().__init__(**__A							)
		__A :			Union[str, Any]		    =			size if size is not None else {"""shortest_edge""": 256}
		__A :			Dict		    =			get_size_dict(__A					,      default_to_square=__A							)
		__A :			str		    =			crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
		__A :			int		    =			get_size_dict(__A					,      param_name="""crop_size"""							)
		__A :			str		    =			do_resize
		__A :			Dict		    =			size
		__A :			Any		    =			resample
		__A :			Optional[Any]		    =			do_center_crop
		__A :			List[str]		    =			crop_size
		__A :			Optional[int]		    =			do_rescale
		__A :			int		    =			rescale_factor
		__A :			Union[str, Any]		    =			do_normalize
		__A :			int		    =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
		__A :			Dict		    =			image_std if image_std is not None else IMAGENET_STANDARD_STD




	def        lowerCAmelCase_       (     self						:     Optional[Any]					,      __A						:     np.ndarray					,      __A						:     Dict[str, int]					,      __A						:     PILImageResampling = PILImageResampling.BICUBIC					,      __A						:     Optional[Union[str, ChannelDimension]] = None					,      **__A						:     List[Any]					,      ):
		__A :			str		    =			get_size_dict(__A					,      default_to_square=__A							)
		if "shortest_edge" not in size:
			raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""							)
		__A :			Dict		    =			get_resize_output_image_size(__A					,      size=size["""shortest_edge"""]					,      default_to_square=__A							)
		return resize(__A					,      size=__A					,      resample=__A					,      data_format=__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     Tuple					,      __A						:     np.ndarray					,      __A						:     Dict[str, int]					,      __A						:     Optional[Union[str, ChannelDimension]] = None					,      **__A						:     str					,      ):
		__A :			str		    =			get_size_dict(__A							)
		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(__A					,      size=(size["""height"""], size["""width"""])					,      data_format=__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     List[str]					,      __A						:     np.ndarray					,      __A						:     float					,      __A						:     Optional[Union[str, ChannelDimension]] = None					,      **__A						:     Optional[int]							):
		return rescale(__A					,      scale=__A					,      data_format=__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     Any					,      __A						:     np.ndarray					,      __A						:     Union[float, List[float]]					,      __A						:     Union[float, List[float]]					,      __A						:     Optional[Union[str, ChannelDimension]] = None					,      **__A						:     Tuple					,      ):
		return normalize(__A					,      mean=__A					,      std=__A					,      data_format=__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     int					,      __A						:     ImageInput					,      __A						:     Optional[bool] = None					,      __A						:     Dict[str, int] = None					,      __A						:     PILImageResampling = None					,      __A						:     bool = None					,      __A						:     Dict[str, int] = None					,      __A						:     Optional[bool] = None					,      __A						:     Optional[float] = None					,      __A						:     Optional[bool] = None					,      __A						:     Optional[Union[float, List[float]]] = None					,      __A						:     Optional[Union[float, List[float]]] = None					,      __A						:     Optional[Union[str, TensorType]] = None					,      __A						:     Union[str, ChannelDimension] = ChannelDimension.FIRST					,      **__A						:     Optional[int]					,      ):
		__A :			List[str]		    =			do_resize if do_resize is not None else self.do_resize
		__A :			Any		    =			size if size is not None else self.size
		__A :			Union[str, Any]		    =			get_size_dict(__A					,      default_to_square=__A							)
		__A :			Tuple		    =			resample if resample is not None else self.resample
		__A :			Tuple		    =			do_center_crop if do_center_crop is not None else self.do_center_crop
		__A :			List[Any]		    =			crop_size if crop_size is not None else self.crop_size
		__A :			int		    =			get_size_dict(__A					,      param_name="""crop_size"""							)
		__A :			Tuple		    =			do_rescale if do_rescale is not None else self.do_rescale
		__A :			Optional[Any]		    =			rescale_factor if rescale_factor is not None else self.rescale_factor
		__A :			Optional[int]		    =			do_normalize if do_normalize is not None else self.do_normalize
		__A :			Optional[int]		    =			image_mean if image_mean is not None else self.image_mean
		__A :			List[str]		    =			image_std if image_std is not None else self.image_std

		__A :			Union[str, Any]		    =			make_list_of_images(__A							)

		if not valid_images(__A							):
			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 :			Union[str, Any]		    =			[to_numpy_array(__A							) for image in images]

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

		if do_center_crop:
			__A :			Optional[Any]		    =			[self.center_crop(image=__A					,      size=__A							) for image in images]

		if do_rescale:
			__A :			List[Any]		    =			[self.rescale(image=__A					,      scale=__A							) for image in images]

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

		__A :			int		    =			[to_channel_dimension_format(__A					,      __A							) for image in images]

		__A :			Tuple		    =			{"""pixel_values""": images}
		return BatchFeature(data=__A					,      tensor_type=__A							)




	def        lowerCAmelCase_       (     self						:     int					,      __A						:     List[str]					,      __A						:     List[Tuple] = None							):
		__A :			Union[str, Any]		    =			outputs.logits

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

			if is_torch_tensor(__A							):
				__A :			str		    =			target_sizes.numpy()

			__A :			int		    =			[]

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

		return semantic_segmentation





