

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


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






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class _UpperCamelCase							(							__A				):
			'''simple docstring'''







			lowerCamelCase__          =42
			lowerCamelCase__          =None

def 	lowerCamelCase__	(   _a							,     _a=0.999							,     _a="cosine"							,     ):
	if alpha_transform_type == "cosine":

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

	elif alpha_transform_type == "exp":

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

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

	SCREAMING_SNAKE_CASE				: List[Any]			       =    []
	for i in range(_a):
		SCREAMING_SNAKE_CASE				: Dict			       =    i / num_diffusion_timesteps
		SCREAMING_SNAKE_CASE				: Any			       =    (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 _UpperCamelCase							(							__A							,		__A				):
			'''simple docstring'''







			lowerCamelCase__          =[e.name for e in KarrasDiffusionSchedulers]
			lowerCamelCase__          =1
			@register_to_config
			def __init__(     self							:	int , a							:	int = 1000 , a							:	float = 0.0001 , a							:	float = 0.02 , a							:	str = "linear" , a							:	Optional[Union[np.ndarray, List[float]]] = None , a							:	str = "epsilon" , a							:	str = "linspace" , a							:	int = 0 , )							->    Optional[Any]:

				"""simple docstring"""

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

				SCREAMING_SNAKE_CASE				: Optional[int]			       =    1.0 - self.betas
				SCREAMING_SNAKE_CASE				: Dict			       =    torch.cumprod(self.alphas , dim=0		)

				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5		)
				SCREAMING_SNAKE_CASE				: Tuple			       =    np.concatenate([sigmas[::-1], [0.0]]		).astype(np.floataa		)
				SCREAMING_SNAKE_CASE				: Tuple			       =    torch.from_numpy(a		)

				# setable values
				SCREAMING_SNAKE_CASE				: str			       =    None
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    np.linspace(0 , num_train_timesteps - 1 , a , dtype=a		)[::-1].copy()
				SCREAMING_SNAKE_CASE				: int			       =    torch.from_numpy(a		)
				SCREAMING_SNAKE_CASE				: str			       =    False
			@property
			def 							__UpperCamelCase	(     self							:	Optional[Any]		)							->    int:

				"""simple docstring"""

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

				return (self.sigmas.max() ** 2 + 1) ** 0.5
			def 							__UpperCamelCase	(     self							:	Dict , a							:	torch.FloatTensor , a							:	Union[float, torch.FloatTensor]		)							->    torch.FloatTensor:

				"""simple docstring"""

				if isinstance(a , torch.Tensor		):
					SCREAMING_SNAKE_CASE				: Optional[Any]			       =    timestep.to(self.timesteps.device		)
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    (self.timesteps == timestep).nonzero().item()
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    self.sigmas[step_index]
				SCREAMING_SNAKE_CASE				: str			       =    sample / ((sigma**2 + 1) ** 0.5)
				SCREAMING_SNAKE_CASE				: Dict			       =    True
				return sample
			def 							__UpperCamelCase	(     self							:	int , a							:	int , a							:	Union[str, torch.device] = None		)							->    int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Any			       =    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":
					SCREAMING_SNAKE_CASE				: List[str]			       =    np.linspace(0 , self.config.num_train_timesteps - 1 , a , dtype=a		)[
					    ::-1
					].copy()
				elif self.config.timestep_spacing == "leading":
					SCREAMING_SNAKE_CASE				: Dict			       =    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
					SCREAMING_SNAKE_CASE				: Tuple			       =    (np.arange(0 , a		) * step_ratio).round()[::-1].copy().astype(a		)
					timesteps += self.config.steps_offset
				elif self.config.timestep_spacing == "trailing":
					SCREAMING_SNAKE_CASE				: Tuple			       =    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
					SCREAMING_SNAKE_CASE				: Optional[int]			       =    (np.arange(self.config.num_train_timesteps , 0 , -step_ratio		)).round().copy().astype(a		)
					timesteps -= 1
				else:
					raise ValueError(
					    F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."		)

				SCREAMING_SNAKE_CASE				: List[str]			       =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5		)
				SCREAMING_SNAKE_CASE				: str			       =    np.interp(a , np.arange(0 , len(a		)		) , a		)
				SCREAMING_SNAKE_CASE				: str			       =    np.concatenate([sigmas, [0.0]]		).astype(np.floataa		)
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    torch.from_numpy(a		).to(device=a		)
				if str(a		).startswith("mps"		):
					# mps does not support float64
					SCREAMING_SNAKE_CASE				: Tuple			       =    torch.from_numpy(a		).to(a , dtype=torch.floataa		)
				else:
					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    torch.from_numpy(a		).to(device=a		)
			def 							__UpperCamelCase	(     self							:	str , a							:	torch.FloatTensor , a							:	Union[float, torch.FloatTensor] , a							:	torch.FloatTensor , a							:	Optional[torch.Generator] = None , a							:	bool = True , )							->    Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

				"""simple docstring"""

				if (
				    isinstance(a , a		)
				    or isinstance(a , torch.IntTensor		)
				    or isinstance(a , 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(a , torch.Tensor		):
					SCREAMING_SNAKE_CASE				: Any			       =    timestep.to(self.timesteps.device		)

				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    (self.timesteps == timestep).nonzero().item()
				SCREAMING_SNAKE_CASE				: int			       =    self.sigmas[step_index]

				# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
				if self.config.prediction_type == "epsilon":
					SCREAMING_SNAKE_CASE				: Any			       =    sample - sigma * model_output
				elif self.config.prediction_type == "v_prediction":
					# * c_out + input * c_skip
					SCREAMING_SNAKE_CASE				: int			       =    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
				elif self.config.prediction_type == "sample":
					raise NotImplementedError("prediction_type not implemented yet: sample"		)
				else:
					raise ValueError(
					    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"		)

				SCREAMING_SNAKE_CASE				: int			       =    self.sigmas[step_index]
				SCREAMING_SNAKE_CASE				: Tuple			       =    self.sigmas[step_index + 1]
				SCREAMING_SNAKE_CASE				: List[Any]			       =    (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    (sigma_to**2 - sigma_up**2) ** 0.5

				# 2. Convert to an ODE derivative
				SCREAMING_SNAKE_CASE				: List[Any]			       =    (sample - pred_original_sample) / sigma

				SCREAMING_SNAKE_CASE				: Any			       =    sigma_down - sigma

				SCREAMING_SNAKE_CASE				: Any			       =    sample + derivative * dt

				SCREAMING_SNAKE_CASE				: List[str]			       =    model_output.device
				SCREAMING_SNAKE_CASE				: str			       =    randn_tensor(model_output.shape , dtype=model_output.dtype , device=a , generator=a		)

				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    prev_sample + noise * sigma_up

				if not return_dict:
					return (prev_sample,)

				return EulerAncestralDiscreteSchedulerOutput(
				    prev_sample=a , pred_original_sample=a		)
			def 							__UpperCamelCase	(     self							:	str , a							:	torch.FloatTensor , a							:	torch.FloatTensor , a							:	torch.FloatTensor , )							->    torch.FloatTensor:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    self.sigmas.to(device=original_samples.device , dtype=original_samples.dtype		)
				if original_samples.device.type == "mps" and torch.is_floating_point(a		):
					# mps does not support float64
					SCREAMING_SNAKE_CASE				: List[str]			       =    self.timesteps.to(original_samples.device , dtype=torch.floataa		)
					SCREAMING_SNAKE_CASE				: Dict			       =    timesteps.to(original_samples.device , dtype=torch.floataa		)
				else:
					SCREAMING_SNAKE_CASE				: Tuple			       =    self.timesteps.to(original_samples.device		)
					SCREAMING_SNAKE_CASE				: Any			       =    timesteps.to(original_samples.device		)

				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    [(schedule_timesteps == t).nonzero().item() for t in timesteps]

				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    sigmas[step_indices].flatten()
				while len(sigma.shape		) < len(original_samples.shape		):
					SCREAMING_SNAKE_CASE				: Tuple			       =    sigma.unsqueeze(-1		)

				SCREAMING_SNAKE_CASE				: Tuple			       =    original_samples + noise * sigma
				return noisy_samples






			def __len__(     self							:	List[Any]		)							->    Dict:

				"""simple docstring"""

				return self.config.num_train_timesteps


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a_         =      {
    'configuration_codegen': ['CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CodeGenConfig', 'CodeGenOnnxConfig'],
    'tokenization_codegen': ['CodeGenTokenizer'],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_         =      ['CodeGenTokenizerFast']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_         =      [
			    'CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'CodeGenForCausalLM',
			    'CodeGenModel',
			    'CodeGenPreTrainedModel',
			]

if TYPE_CHECKING:
			from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
			from .tokenization_codegen import CodeGenTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_codegen_fast import CodeGenTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_codegen import (
						    CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CodeGenForCausalLM,
						    CodeGenModel,
						    CodeGenPreTrainedModel,
						)

else:
			import sys

			a_         =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)