



import math
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class 	__snake_case (				_A):







				'''simple docstring'''






				UpperCamelCase__			: Dict             =   4_2
				UpperCamelCase__			: int             =   None




def      A_ (		__a       :			List[Any]	,						__a       :			List[Any]=0.9_9_9	,						__a       :			List[Any]="cosine"	,						):

				"""simple docstring"""





				if alpha_transform_type == "cosine":

								def alpha_bar_fn(__a       :			str						):
												return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2						) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(__a       :			Optional[int]						):
												return math.exp(t * -1_2.0						)

				else:
								raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''						)

				a__			 =						[]
				for i in range(A_						):
								a__			 =						i / num_diffusion_timesteps
								a__			 =						(i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(A_						) / alpha_bar_fn(A_						)	,						A_						)						)
				return torch.tensor(A_	,						dtype=torch.floataa						)




class 	__snake_case (				_A     ,_A):







				'''simple docstring'''






				UpperCamelCase__			: List[Any]             =   [e.name for e in KarrasDiffusionSchedulers]
				UpperCamelCase__			: List[str]             =   1


				@register_to_config
				def __init__(							self					,					a_ = 1_000					,					a_ = 0.0_001					,					a_ = 0.02					,					a_ = "linear"					,					a_ = None					,					a_ = False					,					a_ = "epsilon"					,					a_ = "linspace"					,					a_ = 0					,					):






								if trained_betas is not None:
												a__			 =						torch.tensor(UpperCamelCase__					,					dtype=torch.floataa						)
								elif beta_schedule == "linear":
												a__			 =						torch.linspace(UpperCamelCase__					,					UpperCamelCase__					,					UpperCamelCase__					,					dtype=torch.floataa						)
								elif beta_schedule == "scaled_linear":
												# this schedule is very specific to the latent diffusion model.
												a__			 =						(
												    torch.linspace(beta_start**0.5					,					beta_end**0.5					,					UpperCamelCase__					,					dtype=torch.floataa						) ** 2
												)
								elif beta_schedule == "squaredcos_cap_v2":
												# Glide cosine schedule
												a__			 =						betas_for_alpha_bar(UpperCamelCase__						)
								else:
												raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''						)

								a__			 =						1.0 - self.betas
								a__			 =						torch.cumprod(self.alphas					,					dim=0						)

								a__			 =						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
								a__			 =						np.concatenate([sigmas[::-1], [0.0]]						).astype(np.floataa						)
								a__			 =						torch.from_numpy(UpperCamelCase__						)

								# setable values
								a__			 =						None
								a__			 =						use_karras_sigmas
								self.set_timesteps(UpperCamelCase__					,					UpperCamelCase__						)
								a__			 =						[]
								a__			 =						False


				@property
				def _a (							self						):






								if self.config.timestep_spacing in ["linspace", "trailing"]:
												return self.sigmas.max()

								return (self.sigmas.max() ** 2 + 1) ** 0.5


				def _a (							self					,					a_					,					a_						):






								if isinstance(UpperCamelCase__					,					torch.Tensor						):
												a__			 =						timestep.to(self.timesteps.device						)
								a__			 =						(self.timesteps == timestep).nonzero().item()
								a__			 =						self.sigmas[step_index]
								a__			 =						sample / ((sigma**2 + 1) ** 0.5)
								a__			 =						True
								return sample


				def _a (							self					,					a_					,					a_					,					a_						):






								def lms_derivative(a_						):
												a__			 =						1.0
												for k in range(UpperCamelCase__						):
																if current_order == k:
																				continue
																prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
												return prod

								a__			 =						integrate.quad(UpperCamelCase__					,					self.sigmas[t]					,					self.sigmas[t + 1]					,					epsrel=1E-4						)[0]

								return integrated_coeff


				def _a (							self					,					a_					,					a_ = None						):
								a__			 =						num_inference_steps

								# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
								if self.config.timestep_spacing == "linspace":
												a__			 =						np.linspace(0					,					self.config.num_train_timesteps - 1					,					UpperCamelCase__					,					dtype=UpperCamelCase__						)[
												    ::-1
												].copy()
								elif self.config.timestep_spacing == "leading":
												a__			 =						self.config.num_train_timesteps // self.num_inference_steps
												# creates integer timesteps by multiplying by ratio
												# casting to int to avoid issues when num_inference_step is power of 3
												a__			 =						(np.arange(0					,					UpperCamelCase__						) * step_ratio).round()[::-1].copy().astype(UpperCamelCase__						)
												timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
												a__			 =						self.config.num_train_timesteps / self.num_inference_steps
												# creates integer timesteps by multiplying by ratio
												# casting to int to avoid issues when num_inference_step is power of 3
												a__			 =						(np.arange(self.config.num_train_timesteps					,					0					,					-step_ratio						)).round().copy().astype(UpperCamelCase__						)
												timesteps -= 1
								else:
												raise ValueError(
												    F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''						)

								a__			 =						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
								a__			 =						np.log(UpperCamelCase__						)
								a__			 =						np.interp(UpperCamelCase__					,					np.arange(0					,					len(UpperCamelCase__						)						)					,					UpperCamelCase__						)

								if self.use_karras_sigmas:
												a__			 =						self._convert_to_karras(in_sigmas=UpperCamelCase__						)
												a__			 =						np.array([self._sigma_to_t(UpperCamelCase__					,					UpperCamelCase__						) for sigma in sigmas]						)

								a__			 =						np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)

								a__			 =						torch.from_numpy(UpperCamelCase__						).to(device=UpperCamelCase__						)
								if str(UpperCamelCase__						).startswith("""mps"""						):
												# mps does not support float64
												a__			 =						torch.from_numpy(UpperCamelCase__						).to(UpperCamelCase__					,					dtype=torch.floataa						)
								else:
												a__			 =						torch.from_numpy(UpperCamelCase__						).to(device=UpperCamelCase__						)

								a__			 =						[]


				def _a (							self					,					a_					,					a_						):
								a__			 =						np.log(UpperCamelCase__						)

								# get distribution
								a__			 =						log_sigma - log_sigmas[:, np.newaxis]

								# get sigmas range
								a__			 =						np.cumsum((dists >= 0)					,					axis=0						).argmax(axis=0						).clip(max=log_sigmas.shape[0] - 2						)
								a__			 =						low_idx + 1

								a__			 =						log_sigmas[low_idx]
								a__			 =						log_sigmas[high_idx]

								# interpolate sigmas
								a__			 =						(low - log_sigma) / (low - high)
								a__			 =						np.clip(UpperCamelCase__					,					0					,					1						)

								# transform interpolation to time range
								a__			 =						(1 - w) * low_idx + w * high_idx
								a__			 =						t.reshape(sigma.shape						)
								return t


				def _a (							self					,					a_						):
								a__			 =						in_sigmas[-1].item()
								a__			 =						in_sigmas[0].item()

								a__			 =						7.0  # 7.0 is the value used in the paper
								a__			 =						np.linspace(0					,					1					,					self.num_inference_steps						)
								a__			 =						sigma_min ** (1 / rho)
								a__			 =						sigma_max ** (1 / rho)
								a__			 =						(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
								return sigmas


				def _a (							self					,					a_					,					a_					,					a_					,					a_ = 4					,					a_ = True					,					):






								if not self.is_scale_input_called:
												warnings.warn(
												    """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
												    """See `StableDiffusionPipeline` for a usage example."""						)

								if isinstance(UpperCamelCase__					,					torch.Tensor						):
												a__			 =						timestep.to(self.timesteps.device						)
								a__			 =						(self.timesteps == timestep).nonzero().item()
								a__			 =						self.sigmas[step_index]

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								if self.config.prediction_type == "epsilon":
												a__			 =						sample - sigma * model_output
								elif self.config.prediction_type == "v_prediction":
												# * c_out + input * c_skip
												a__			 =						model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
								elif self.config.prediction_type == "sample":
												a__			 =						model_output
								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
								a__			 =						(sample - pred_original_sample) / sigma
								self.derivatives.append(UpperCamelCase__						)
								if len(self.derivatives						) > order:
												self.derivatives.pop(0						)

								# 3. Compute linear multistep coefficients
								a__			 =						min(step_index + 1					,					UpperCamelCase__						)
								a__			 =						[self.get_lms_coefficient(UpperCamelCase__					,					UpperCamelCase__					,					UpperCamelCase__						) for curr_order in range(UpperCamelCase__						)]

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

								if not return_dict:
												return (prev_sample,)

								return LMSDiscreteSchedulerOutput(prev_sample=UpperCamelCase__					,					pred_original_sample=UpperCamelCase__						)


				def _a (							self					,					a_					,					a_					,					a_					,					):
								a__			 =						self.sigmas.to(device=original_samples.device					,					dtype=original_samples.dtype						)
								if original_samples.device.type == "mps" and torch.is_floating_point(UpperCamelCase__						):
												# mps does not support float64
												a__			 =						self.timesteps.to(original_samples.device					,					dtype=torch.floataa						)
												a__			 =						timesteps.to(original_samples.device					,					dtype=torch.floataa						)
								else:
												a__			 =						self.timesteps.to(original_samples.device						)
												a__			 =						timesteps.to(original_samples.device						)

								a__			 =						[(schedule_timesteps == t).nonzero().item() for t in timesteps]

								a__			 =						sigmas[step_indices].flatten()
								while len(sigma.shape						) < len(original_samples.shape						):
												a__			 =						sigma.unsqueeze(-1						)

								a__			 =						original_samples + noise * sigma
								return noisy_samples







				def __len__(							self						):
								return self.config.num_train_timesteps






import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast

@require_vision
class 	__snake_case (				unittest.TestCase):







							'''simple docstring'''






							def _a (							self						):
											a__			 =						tempfile.mkdtemp()

											a__			 =						CLIPImageProcessor()
											a__			 =						BertTokenizer.from_pretrained(
											    """hf-internal-testing/tiny-random-BertModel"""					,					model_input_names=["""input_ids""", """attention_mask"""]						)

											a__			 =						GitProcessor(a_					,					a_						)

											processor.save_pretrained(self.tmpdirname						)


							def _a (							self					,					**a_						):
											return AutoProcessor.from_pretrained(self.tmpdirname					,					**a_						).tokenizer


							def _a (							self					,					**a_						):
											return AutoProcessor.from_pretrained(self.tmpdirname					,					**a_						).image_processor


							def _a (							self						):
											shutil.rmtree(self.tmpdirname						)


							def _a (							self						):
											a__			 =						[np.random.randint(255					,					size=(3, 30, 400)					,					dtype=np.uinta						)]

											a__			 =						[Image.fromarray(np.moveaxis(a_					,					0					,					-1						)						) for x in image_inputs]

											return image_inputs


							def _a (							self						):
											a__			 =						GitProcessor(tokenizer=self.get_tokenizer()					,					image_processor=self.get_image_processor()						)
											processor.save_pretrained(self.tmpdirname						)

											a__			 =						self.get_tokenizer(bos_token="""(BOS)"""					,					eos_token="""(EOS)"""						)
											a__			 =						self.get_image_processor(do_normalize=a_					,					padding_value=1.0						)

											a__			 =						GitProcessor.from_pretrained(
											    self.tmpdirname					,					bos_token="""(BOS)"""					,					eos_token="""(EOS)"""					,					do_normalize=a_					,					padding_value=1.0						)

											self.assertEqual(processor.tokenizer.get_vocab()					,					tokenizer_add_kwargs.get_vocab()						)
											self.assertIsInstance(processor.tokenizer					,					a_						)

											self.assertEqual(processor.image_processor.to_json_string()					,					image_processor_add_kwargs.to_json_string()						)
											self.assertIsInstance(processor.image_processor					,					a_						)


							def _a (							self						):
											a__			 =						self.get_image_processor()
											a__			 =						self.get_tokenizer()

											a__			 =						GitProcessor(tokenizer=a_					,					image_processor=a_						)

											a__			 =						self.prepare_image_inputs()

											a__			 =						image_processor(a_					,					return_tensors="""np"""						)
											a__			 =						processor(images=a_					,					return_tensors="""np"""						)

											for key in input_feat_extract.keys():
															self.assertAlmostEqual(input_feat_extract[key].sum()					,					input_processor[key].sum()					,					delta=1E-2						)


							def _a (							self						):
											a__			 =						self.get_image_processor()
											a__			 =						self.get_tokenizer()

											a__			 =						GitProcessor(tokenizer=a_					,					image_processor=a_						)

											a__			 =						"""lower newer"""

											a__			 =						processor(text=a_						)

											a__			 =						tokenizer(a_					,					return_token_type_ids=a_						)

											for key in encoded_tok.keys():
															self.assertListEqual(encoded_tok[key]					,					encoded_processor[key]						)


							def _a (							self						):
											a__			 =						self.get_image_processor()
											a__			 =						self.get_tokenizer()

											a__			 =						GitProcessor(tokenizer=a_					,					image_processor=a_						)

											a__			 =						"""lower newer"""
											a__			 =						self.prepare_image_inputs()

											a__			 =						processor(text=a_					,					images=a_						)

											self.assertListEqual(list(inputs.keys()						)					,					["""input_ids""", """attention_mask""", """pixel_values"""]						)

											# test if it raises when no input is passed
											with pytest.raises(a_						):
															processor()


							def _a (							self						):
											a__			 =						self.get_image_processor()
											a__			 =						self.get_tokenizer()

											a__			 =						GitProcessor(tokenizer=a_					,					image_processor=a_						)

											a__			 =						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											a__			 =						processor.batch_decode(a_						)
											a__			 =						tokenizer.batch_decode(a_						)

											self.assertListEqual(a_					,					a_						)







							def _a (							self						):
											a__			 =						self.get_image_processor()
											a__			 =						self.get_tokenizer()

											a__			 =						GitProcessor(tokenizer=a_					,					image_processor=a_						)

											a__			 =						"""lower newer"""
											a__			 =						self.prepare_image_inputs()

											a__			 =						processor(text=a_					,					images=a_						)

											# For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
											self.assertListEqual(list(inputs.keys()						)					,					["""input_ids""", """attention_mask""", """pixel_values"""]						)

