



"""simple docstring"""






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 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							42
					_a             =							None






def 					snake_case__       (		__lowerCamelCase    :   int							,		__lowerCamelCase    :   Tuple=0.9_99							,		__lowerCamelCase    :   int="cosine"							,		):
			"""simple docstring"""
			if alpha_transform_type == "cosine":

						def alpha_bar_fn(__lowerCamelCase    :   Optional[Any]     ):
									return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2     ) ** 2

			elif alpha_transform_type == "exp":

						def alpha_bar_fn(__lowerCamelCase    :   Optional[Any]     ):
									return math.exp(t * -12.0     )

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

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

class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_		,       lowerCAmelCase_       ):


					'''simple docstring'''





					@register_to_config
					def __init__(	self							:  str,  lowerCamelCase							:  int = 1000,  lowerCamelCase							:  str = "fixed_small_log",  lowerCamelCase							:  bool = True,  lowerCamelCase							:  Optional[float] = 1.0,  lowerCamelCase							:  str = "epsilon",  lowerCamelCase							:  str = "squaredcos_cap_v2",  )-> Dict:
								if beta_schedule != "squaredcos_cap_v2":
											raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''''					)

								lowerCamelCase__		:		Tuple		     =betas_for_alpha_bar(lowerCamelCase					)

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

								# standard deviation of the initial noise distribution
								lowerCamelCase__		:		Any		     =1.0

								# setable values
								lowerCamelCase__		:		Any		     =None
								lowerCamelCase__		:		List[str]		     =torch.from_numpy(np.arange(0,  lowerCamelCase					)[::-1].copy()					)

								lowerCamelCase__		:		Optional[Any]		     =variance_type



					def 		snake_case	(	self							:  Union[str, Any],  lowerCamelCase							:  torch.FloatTensor,  lowerCamelCase							:  Optional[int] = None					)-> torch.FloatTensor:
								return sample



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



					def 		snake_case	(	self							:  List[str],  lowerCamelCase							:  Dict,  lowerCamelCase							:  Tuple=None,  lowerCamelCase							:  List[str]=None,  lowerCamelCase							:  List[str]=None					)-> Union[str, Any]:
								if prev_timestep is None:
											lowerCamelCase__		:		Union[str, Any]		     =t - 1

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

								if prev_timestep == t - 1:
											lowerCamelCase__		:		str		     =self.betas[t]
								else:
											lowerCamelCase__		:		Union[str, Any]		     =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
								lowerCamelCase__		:		Any		     =beta_prod_t_prev / beta_prod_t * beta

								if variance_type is None:
											lowerCamelCase__		:		Any		     =self.config.variance_type

								# hacks - were probably added for training stability
								if variance_type == "fixed_small_log":
											lowerCamelCase__		:		Tuple		     =torch.log(torch.clamp(lowerCamelCase,  min=1E-20					)					)
											lowerCamelCase__		:		Dict		     =torch.exp(0.5 * variance					)
								elif variance_type == "learned_range":
											# NOTE difference with DDPM scheduler
											lowerCamelCase__		:		int		     =variance.log()
											lowerCamelCase__		:		int		     =beta.log()

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

								return variance



					def 		snake_case	(	self							:  List[Any],  lowerCamelCase							:  torch.FloatTensor,  lowerCamelCase							:  int,  lowerCamelCase							:  torch.FloatTensor,  lowerCamelCase							:  Optional[int] = None,  lowerCamelCase							:  int=None,  lowerCamelCase							:  bool = True,  )-> Union[UnCLIPSchedulerOutput, Tuple]:
								lowerCamelCase__		:		Optional[int]		     =timestep

								if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
											lowerCamelCase__     ,       lowerCamelCase__		:		Dict		     =torch.split(lowerCamelCase,  sample.shape[1],  dim=1					)
								else:
											lowerCamelCase__		:		int		     =None

								# 1. compute alphas, betas
								if prev_timestep is None:
											lowerCamelCase__		:		str		     =t - 1

								lowerCamelCase__		:		Tuple		     =self.alphas_cumprod[t]
								lowerCamelCase__		:		Tuple		     =self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
								lowerCamelCase__		:		Optional[Any]		     =1 - alpha_prod_t
								lowerCamelCase__		:		Optional[Any]		     =1 - alpha_prod_t_prev

								if prev_timestep == t - 1:
											lowerCamelCase__		:		Optional[int]		     =self.betas[t]
											lowerCamelCase__		:		Dict		     =self.alphas[t]
								else:
											lowerCamelCase__		:		Optional[Any]		     =1 - alpha_prod_t / alpha_prod_t_prev
											lowerCamelCase__		:		Any		     =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":
											lowerCamelCase__		:		str		     =(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
								elif self.config.prediction_type == "sample":
											lowerCamelCase__		:		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:
											lowerCamelCase__		:		Dict		     =torch.clamp(
											    lowerCamelCase,  -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
								lowerCamelCase__		:		List[Any]		     =(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
								lowerCamelCase__		:		List[str]		     =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
								lowerCamelCase__		:		int		     =pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

								# 6. Add noise
								lowerCamelCase__		:		List[Any]		     =0
								if t > 0:
											lowerCamelCase__		:		Union[str, Any]		     =randn_tensor(
											    model_output.shape,  dtype=model_output.dtype,  generator=lowerCamelCase,  device=model_output.device					)

											lowerCamelCase__		:		Any		     =self._get_variance(
											    lowerCamelCase,  predicted_variance=lowerCamelCase,  prev_timestep=lowerCamelCase,  )

											if self.variance_type == "fixed_small_log":
														lowerCamelCase__		:		int		     =variance
											elif self.variance_type == "learned_range":
														lowerCamelCase__		:		Tuple		     =(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.'''					)

											lowerCamelCase__		:		Optional[Any]		     =variance * variance_noise

								lowerCamelCase__		:		Tuple		     =pred_prev_sample + variance

								if not return_dict:
											return (pred_prev_sample,)

								return UnCLIPSchedulerOutput(prev_sample=lowerCamelCase,  pred_original_sample=lowerCamelCase					)




					def 		snake_case	(	self							:  Optional[Any],  lowerCamelCase							:  torch.FloatTensor,  lowerCamelCase							:  torch.FloatTensor,  lowerCamelCase							:  torch.IntTensor,  )-> torch.FloatTensor:
								# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
								lowerCamelCase__		:		List[str]		     =self.alphas_cumprod.to(device=original_samples.device,  dtype=original_samples.dtype					)
								lowerCamelCase__		:		List[str]		     =timesteps.to(original_samples.device					)

								lowerCamelCase__		:		str		     =alphas_cumprod[timesteps] ** 0.5
								lowerCamelCase__		:		Dict		     =sqrt_alpha_prod.flatten()
								while len(sqrt_alpha_prod.shape					) < len(original_samples.shape					):
											lowerCamelCase__		:		Union[str, Any]		     =sqrt_alpha_prod.unsqueeze(-1					)

								lowerCamelCase__		:		Tuple		     =(1 - alphas_cumprod[timesteps]) ** 0.5
								lowerCamelCase__		:		Union[str, Any]		     =sqrt_one_minus_alpha_prod.flatten()
								while len(sqrt_one_minus_alpha_prod.shape					) < len(original_samples.shape					):
											lowerCamelCase__		:		Union[str, Any]		     =sqrt_one_minus_alpha_prod.unsqueeze(-1					)

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









"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


_lowercase			:			Any										=						{
    "configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig"],
    "processing_trocr": ["TrOCRProcessor"],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase			:			List[Any]										=						[
		    "TROCR_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TrOCRForCausalLM",
		    "TrOCRPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
		from .processing_trocr import TrOCRProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
		import sys

		_lowercase			:			Tuple										=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




