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

import numpy as np
import torch

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


__magic_name__           =					logging.get_logger(__name__)  # pylint: disable=invalid-name




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class    _SCREAMING_SNAKE_CASE					( lowercase__	):
      _A  :						torch.FloatTensor
      _A  :						Optional[torch.FloatTensor]      		=       None





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase=0.999      ,						__lowerCAmelCase="cosine"      ,						):

    if alpha_transform_type == "cosine":

        def alpha_bar_fn(__lowerCAmelCase							):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2							) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(__lowerCAmelCase							):
            return math.exp(t * -12.0							)

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

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





class    _SCREAMING_SNAKE_CASE					( lowercase__    ,   lowercase__	):
      _A  :						Dict      		=       [e.name for e in KarrasDiffusionSchedulers]
      _A  :						str      		=       1
      @register_to_config
      def __init__(     self						,		lowerCamelCase = 10_00						,		lowerCamelCase = 0.0_0_0_1						,		lowerCamelCase = 0.0_2						,		lowerCamelCase = "linear"						,		lowerCamelCase = None						,		lowerCamelCase = "epsilon"						,		lowerCamelCase = "linear"						,		lowerCamelCase = False						,		lowerCamelCase = "linspace"						,		lowerCamelCase = 0						,		):
          if trained_betas is not None:
              snake_case__  =			torch.tensor(lowerCAmelCase__						,		dtype=torch.floataa	)
          elif beta_schedule == "linear":
              snake_case__  =			torch.linspace(lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__						,		dtype=torch.floataa	)
          elif beta_schedule == "scaled_linear":
              # this schedule is very specific to the latent diffusion model.
              snake_case__  =			(
                  torch.linspace(beta_start**0.5						,		beta_end**0.5						,		lowerCAmelCase__						,		dtype=torch.floataa	) ** 2
              )
          elif beta_schedule == "squaredcos_cap_v2":
              # Glide cosine schedule
              snake_case__  =			betas_for_alpha_bar(lowerCAmelCase__	)
          else:
              raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""	)

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

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

          # setable values
          snake_case__  =			None
          snake_case__  =			np.linspace(0						,		num_train_timesteps - 1						,		lowerCAmelCase__						,		dtype=lowerCAmelCase__	)[::-1].copy()
          snake_case__  =			torch.from_numpy(lowerCAmelCase__	)
          snake_case__  =			False
          snake_case__  =			use_karras_sigmas
      @property
      def A_     (     self	):
          # standard deviation of the initial noise distribution
          if self.config.timestep_spacing in ["linspace", "trailing"]:
              return self.sigmas.max()

          return (self.sigmas.max() ** 2 + 1) ** 0.5
      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase	):
          if isinstance(lowerCAmelCase__						,		torch.Tensor	):
              snake_case__  =			timestep.to(self.timesteps.device	)
          snake_case__  =			(self.timesteps == timestep).nonzero().item()
          snake_case__  =			self.sigmas[step_index]

          snake_case__  =			sample / ((sigma**2 + 1) ** 0.5)

          snake_case__  =			True
          return sample
      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase = None	):
          snake_case__  =			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":
              snake_case__  =			np.linspace(0						,		self.config.num_train_timesteps - 1						,		lowerCAmelCase__						,		dtype=lowerCAmelCase__	)[
                  ::-1
              ].copy()
          elif self.config.timestep_spacing == "leading":
              snake_case__  =			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
              snake_case__  =			(np.arange(0						,		lowerCAmelCase__	) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase__	)
              timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
              snake_case__  =			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
              snake_case__  =			(np.arange(self.config.num_train_timesteps						,		0						,		-step_ratio	)).round().copy().astype(lowerCAmelCase__	)
              timesteps -= 1
          else:
              raise ValueError(
                  F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'."""	)

          snake_case__  =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
          snake_case__  =			np.log(lowerCAmelCase__	)

          if self.config.interpolation_type == "linear":
              snake_case__  =			np.interp(lowerCAmelCase__						,		np.arange(0						,		len(lowerCAmelCase__	)	)						,		lowerCAmelCase__	)
          elif self.config.interpolation_type == "log_linear":
              snake_case__  =			torch.linspace(np.log(sigmas[-1]	)						,		np.log(sigmas[0]	)						,		num_inference_steps + 1	).exp()
          else:
              raise ValueError(
                  F"""{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either"""
                  " \'linear\' or \'log_linear\'"	)

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

          snake_case__  =			np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
          snake_case__  =			torch.from_numpy(lowerCAmelCase__	).to(device=lowerCAmelCase__	)
          if str(lowerCAmelCase__	).startswith("mps"	):
              # mps does not support float64
              snake_case__  =			torch.from_numpy(lowerCAmelCase__	).to(lowerCAmelCase__						,		dtype=torch.floataa	)
          else:
              snake_case__  =			torch.from_numpy(lowerCAmelCase__	).to(device=lowerCAmelCase__	)
      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase	):
          snake_case__  =			np.log(lowerCAmelCase__	)

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

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

          snake_case__  =			log_sigmas[low_idx]
          snake_case__  =			log_sigmas[high_idx]

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

          # transform interpolation to time range
          snake_case__  =			(1 - w) * low_idx + w * high_idx
          snake_case__  =			t.reshape(sigma.shape	)
          return t
      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase	):
          snake_case__  =			in_sigmas[-1].item()
          snake_case__  =			in_sigmas[0].item()

          snake_case__  =			7.0  # 7.0 is the value used in the paper
          snake_case__  =			np.linspace(0						,		1						,		lowerCAmelCase__	)
          snake_case__  =			sigma_min ** (1 / rho)
          snake_case__  =			sigma_max ** (1 / rho)
          snake_case__  =			(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
          return sigmas
      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase = 0.0						,		lowerCamelCase = 0.0						,		lowerCamelCase = float("inf"	)						,		lowerCamelCase = 1.0						,		lowerCamelCase = None						,		lowerCamelCase = True						,		):

          if (
              isinstance(lowerCAmelCase__						,		lowerCAmelCase__	)
              or isinstance(lowerCAmelCase__						,		torch.IntTensor	)
              or isinstance(lowerCAmelCase__						,		torch.LongTensor	)
          ):
              raise ValueError(
                  (
                      "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
                      " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
                      " one of the `scheduler.timesteps` as a timestep."
                  )						,		)

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

          if isinstance(lowerCAmelCase__						,		torch.Tensor	):
              snake_case__  =			timestep.to(self.timesteps.device	)

          snake_case__  =			(self.timesteps == timestep).nonzero().item()
          snake_case__  =			self.sigmas[step_index]

          snake_case__  =			min(s_churn / (len(self.sigmas	) - 1)						,		2**0.5 - 1	) if s_tmin <= sigma <= s_tmax else 0.0

          snake_case__  =			randn_tensor(
              model_output.shape						,		dtype=model_output.dtype						,		device=model_output.device						,		generator=lowerCAmelCase__	)

          snake_case__  =			noise * s_noise
          snake_case__  =			sigma * (gamma + 1)

          if gamma > 0:
              snake_case__  =			sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          # NOTE: "original_sample" should not be an expected prediction_type but is left in for
          # backwards compatibility
          if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
              snake_case__  =			model_output
          elif self.config.prediction_type == "epsilon":
              snake_case__  =			sample - sigma_hat * 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_hat

          snake_case__  =			self.sigmas[step_index + 1] - sigma_hat

          snake_case__  =			sample + derivative * dt

          if not return_dict:
              return (prev_sample,)

          return EulerDiscreteSchedulerOutput(prev_sample=lowerCAmelCase__						,		pred_original_sample=lowerCAmelCase__	)
      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		):
          snake_case__  =			self.sigmas.to(device=original_samples.device						,		dtype=original_samples.dtype	)
          if original_samples.device.type == "mps" and torch.is_floating_point(lowerCAmelCase__	):
              # mps does not support float64
              snake_case__  =			self.timesteps.to(original_samples.device						,		dtype=torch.floataa	)
              snake_case__  =			timesteps.to(original_samples.device						,		dtype=torch.floataa	)
          else:
              snake_case__  =			self.timesteps.to(original_samples.device	)
              snake_case__  =			timesteps.to(original_samples.device	)

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

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

          snake_case__  =			original_samples + noise * sigma
          return noisy_samples



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




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__magic_name__           =					{
    '''configuration_convnextv2''': [
        '''CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ConvNextV2Config''',
    ]
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__           =					[
         '''CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''ConvNextV2ForImageClassification''',
         '''ConvNextV2Model''',
         '''ConvNextV2PreTrainedModel''',
         '''ConvNextV2Backbone''',
     ]


if TYPE_CHECKING:
     from .configuration_convnextva import (
         CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
         ConvNextVaConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_convnextva import (
              CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
              ConvNextVaBackbone,
              ConvNextVaForImageClassification,
              ConvNextVaModel,
              ConvNextVaPreTrainedModel,
          )

else:
     import sys

     __magic_name__           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure)



