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

import numpy as np
import torch

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

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class        _UpperCamelCase				(					_snake_case  ):
			__UpperCamelCase      :						torch.FloatTensor
			__UpperCamelCase      :						Optional[torch.FloatTensor]					      = None







def        A__					(						lowerCamelCase     ,    lowerCamelCase=0.999     ,    lowerCamelCase="cosine"     ,    )  ->	str:







				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}'''						)

				UpperCamelCase_:							int	=						[]
				for i in range(__UpperCAmelCase						):
								UpperCamelCase_:							Optional[Any]	=						i / num_diffusion_timesteps
								UpperCamelCase_:							List[Any]	=						(i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(__UpperCAmelCase						) / alpha_bar_fn(__UpperCAmelCase						)     ,    __UpperCAmelCase						)						)
				return torch.tensor(__UpperCAmelCase     ,    dtype=torch.floataa						)





class        _UpperCamelCase				(					_snake_case     ,       _snake_case  ):

			@register_to_config
			def __init__(       self :       Optional[int]						,	snake_case_ :       int = 1000						,	snake_case_ :       str = "fixed_small_log"						,	snake_case_ :       bool = True						,	snake_case_ :       Optional[float] = 1.0						,	snake_case_ :       str = "epsilon"						,	snake_case_ :       str = "squaredcos_cap_v2"						,	):

							if beta_schedule != "squaredcos_cap_v2":
											raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"""      )

							UpperCamelCase_:							Optional[Any]	=						betas_for_alpha_bar(snake_case_      )

							UpperCamelCase_:							Union[str, Any]	=						1.0 - self.betas
							UpperCamelCase_:							Optional[int]	=						torch.cumprod(self.alphas						,	dim=0      )
							UpperCamelCase_:							Tuple	=						torch.tensor(1.0      )

							# standard deviation of the initial noise distribution
							UpperCamelCase_:							Optional[Any]	=						1.0

							# setable values
							UpperCamelCase_:							Optional[Any]	=						None
							UpperCamelCase_:							Dict	=						torch.from_numpy(np.arange(0						,	snake_case_      )[::-1].copy()      )

							UpperCamelCase_:							int	=						variance_type

			def       lowerCAmelCase__	(       self :       List[str]						,	snake_case_ :       torch.FloatTensor						,	snake_case_ :       Optional[int] = None      ):
							return sample

			def       lowerCAmelCase__	(       self :       List[Any]						,	snake_case_ :       int						,	snake_case_ :       Union[str, torch.device] = None      ):
							UpperCamelCase_:							List[Any]	=						num_inference_steps
							UpperCamelCase_:							Optional[int]	=						(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
							UpperCamelCase_:							Union[str, Any]	=						(np.arange(0						,	snake_case_      ) * step_ratio).round()[::-1].copy().astype(np.intaa      )
							UpperCamelCase_:							Optional[int]	=						torch.from_numpy(snake_case_      ).to(snake_case_      )

			def       lowerCAmelCase__	(       self :       Optional[int]						,	snake_case_ :       Any						,	snake_case_ :       List[str]=None						,	snake_case_ :       Optional[int]=None						,	snake_case_ :       Any=None      ):

							if prev_timestep is None:
											UpperCamelCase_:							List[Any]	=						t - 1

							UpperCamelCase_:							List[str]	=						self.alphas_cumprod[t]
							UpperCamelCase_:							Any	=						self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
							UpperCamelCase_:							Optional[int]	=						1 - alpha_prod_t
							UpperCamelCase_:							Optional[int]	=						1 - alpha_prod_t_prev

							if prev_timestep == t - 1:
											UpperCamelCase_:							Dict	=						self.betas[t]
							else:
											UpperCamelCase_:							List[str]	=						1 - alpha_prod_t / alpha_prod_t_prev

							# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
							# and sample from it to get previous sample
							# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
							UpperCamelCase_:							Any	=						beta_prod_t_prev / beta_prod_t * beta

							if variance_type is None:
											UpperCamelCase_:							int	=						self.config.variance_type

							# hacks - were probably added for training stability
							if variance_type == "fixed_small_log":
											UpperCamelCase_:							Union[str, Any]	=						torch.log(torch.clamp(snake_case_						,	min=1e-20      )      )
											UpperCamelCase_:							Tuple	=						torch.exp(0.5 * variance      )
							elif variance_type == "learned_range":
											# NOTE difference with DDPM scheduler
											UpperCamelCase_:							Any	=						variance.log()
											UpperCamelCase_:							int	=						beta.log()

											UpperCamelCase_:							str	=						(predicted_variance + 1) / 2
											UpperCamelCase_:							List[str]	=						frac * max_log + (1 - frac) * min_log

							return variance

			def       lowerCAmelCase__	(       self :       List[Any]						,	snake_case_ :       torch.FloatTensor						,	snake_case_ :       int						,	snake_case_ :       torch.FloatTensor						,	snake_case_ :       Optional[int] = None						,	snake_case_ :       List[str]=None						,	snake_case_ :       bool = True						,	):
							UpperCamelCase_:							List[str]	=						timestep

							if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
											UpperCamelCase_,   UpperCamelCase_:							List[str]	=						torch.split(snake_case_						,	sample.shape[1]						,	dim=1      )
							else:
											UpperCamelCase_:							Union[str, Any]	=						None

							# 1. compute alphas, betas
							if prev_timestep is None:
											UpperCamelCase_:							List[Any]	=						t - 1

							UpperCamelCase_:							Optional[int]	=						self.alphas_cumprod[t]
							UpperCamelCase_:							List[str]	=						self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
							UpperCamelCase_:							Optional[Any]	=						1 - alpha_prod_t
							UpperCamelCase_:							List[str]	=						1 - alpha_prod_t_prev

							if prev_timestep == t - 1:
											UpperCamelCase_:							Union[str, Any]	=						self.betas[t]
											UpperCamelCase_:							str	=						self.alphas[t]
							else:
											UpperCamelCase_:							Dict	=						1 - alpha_prod_t / alpha_prod_t_prev
											UpperCamelCase_:							Optional[int]	=						1 - beta

							# 2. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
							if self.config.prediction_type == "epsilon":
											UpperCamelCase_:							Dict	=						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif self.config.prediction_type == "sample":
											UpperCamelCase_:							List[str]	=						model_output
							else:
											raise ValueError(
											    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'''
											    """ for the UnCLIPScheduler."""      )

							# 3. Clip "predicted x_0"
							if self.config.clip_sample:
											UpperCamelCase_:							str	=						torch.clamp(
											    snake_case_						,	-self.config.clip_sample_range						,	self.config.clip_sample_range      )

							# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							UpperCamelCase_:							Optional[int]	=						(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
							UpperCamelCase_:							int	=						alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

							# 5. Compute predicted previous sample µ_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							UpperCamelCase_:							Tuple	=						pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

							# 6. Add noise
							UpperCamelCase_:							Union[str, Any]	=						0
							if t > 0:
											UpperCamelCase_:							Optional[int]	=						randn_tensor(
											    model_output.shape						,	dtype=model_output.dtype						,	generator=snake_case_						,	device=model_output.device      )

											UpperCamelCase_:							Optional[int]	=						self._get_variance(
											    snake_case_						,	predicted_variance=snake_case_						,	prev_timestep=snake_case_						,	)

											if self.variance_type == "fixed_small_log":
															UpperCamelCase_:							Optional[int]	=						variance
											elif self.variance_type == "learned_range":
															UpperCamelCase_:							int	=						(0.5 * variance).exp()
											else:
															raise ValueError(
															    f'''variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'''
															    """ for the UnCLIPScheduler."""      )

											UpperCamelCase_:							Any	=						variance * variance_noise

							UpperCamelCase_:							Union[str, Any]	=						pred_prev_sample + variance

							if not return_dict:
											return (pred_prev_sample,)

							return UnCLIPSchedulerOutput(prev_sample=snake_case_						,	pred_original_sample=snake_case_      )




			def       lowerCAmelCase__	(       self :       str						,	snake_case_ :       torch.FloatTensor						,	snake_case_ :       torch.FloatTensor						,	snake_case_ :       torch.IntTensor						,	):
							UpperCamelCase_:							int	=						self.alphas_cumprod.to(device=original_samples.device						,	dtype=original_samples.dtype      )
							UpperCamelCase_:							int	=						timesteps.to(original_samples.device      )

							UpperCamelCase_:							int	=						alphas_cumprod[timesteps] ** 0.5
							UpperCamelCase_:							Optional[int]	=						sqrt_alpha_prod.flatten()
							while len(sqrt_alpha_prod.shape      ) < len(original_samples.shape      ):
											UpperCamelCase_:							Optional[int]	=						sqrt_alpha_prod.unsqueeze(-1      )

							UpperCamelCase_:							Dict	=						(1 - alphas_cumprod[timesteps]) ** 0.5
							UpperCamelCase_:							Optional[Any]	=						sqrt_one_minus_alpha_prod.flatten()
							while len(sqrt_one_minus_alpha_prod.shape      ) < len(original_samples.shape      ):
											UpperCamelCase_:							Optional[Any]	=						sqrt_one_minus_alpha_prod.unsqueeze(-1      )

							UpperCamelCase_:							Dict	=						sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
							return noisy_samples








# 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

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase_						: List[str]         =  {
    """configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
    """processing_mgp_str""": ["""MgpstrProcessor"""],
    """tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Optional[Any]         =  [
							    """MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MgpstrModel""",
							    """MgpstrPreTrainedModel""",
							    """MgpstrForSceneTextRecognition""",
							]

if TYPE_CHECKING:
							from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
							from .processing_mgp_str import MgpstrProcessor
							from .tokenization_mgp_str import MgpstrTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mgp_str import (
														    MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MgpstrForSceneTextRecognition,
														    MgpstrModel,
														    MgpstrPreTrainedModel,
														)
else:
							import sys

							lowerCamelCase_						: int         =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






