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        lowerCamelCase__				(   __SCREAMING_SNAKE_CASE				):
			UpperCamelCase__    							=42
			UpperCamelCase__    							=None







def _lowerCamelCase  (		snake_case     , snake_case=0.9_99     , snake_case="cosine"     , ):
							if alpha_transform_type == "cosine":

														def alpha_bar_fn(snake_case  ):
																					return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2  ) ** 2

							elif alpha_transform_type == "exp":

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

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

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



class        lowerCamelCase__				(   __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE				):





			@register_to_config
			def __init__(       self	:	List[Any]  ,  lowercase__	:	Optional[Any] = 10_00  ,  lowercase__	:	Dict = "fixed_small_log"  ,  lowercase__	:	Tuple = True  ,  lowercase__	:	Tuple = 1.0  ,  lowercase__	:	Optional[Any] = "epsilon"  ,  lowercase__	:	int = "squaredcos_cap_v2"  ,  ):







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

										_lowerCAmelCase							=							betas_for_alpha_bar(_A						)

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

										# standard deviation of the initial noise distribution
										_lowerCAmelCase							=							1.0

										# setable values
										_lowerCAmelCase							=							None
										_lowerCAmelCase							=							torch.from_numpy(np.arange(0  ,  _A						)[::-1].copy()						)

										_lowerCAmelCase							=							variance_type





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]  ,  lowercase__	:	Tuple  ,  lowercase__	:	Optional[Any] = None						):
										return sample





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]  ,  lowercase__	:	Optional[Any]  ,  lowercase__	:	Union[str, Any] = None						):
										_lowerCAmelCase							=							num_inference_steps
										_lowerCAmelCase							=							(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
										_lowerCAmelCase							=							(np.arange(0  ,  _A						) * step_ratio).round()[::-1].copy().astype(np.intaa						)
										_lowerCAmelCase							=							torch.from_numpy(_A						).to(_A						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]  ,  lowercase__	:	Dict  ,  lowercase__	:	Optional[int]=None  ,  lowercase__	:	str=None  ,  lowercase__	:	Dict=None						):







										if prev_timestep is None:
																	_lowerCAmelCase							=							t - 1

										_lowerCAmelCase							=							self.alphas_cumprod[t]
										_lowerCAmelCase							=							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
										_lowerCAmelCase							=							1 - alpha_prod_t
										_lowerCAmelCase							=							1 - alpha_prod_t_prev

										if prev_timestep == t - 1:
																	_lowerCAmelCase							=							self.betas[t]
										else:
																	_lowerCAmelCase							=							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							=							beta_prod_t_prev / beta_prod_t * beta

										if variance_type is None:
																	_lowerCAmelCase							=							self.config.variance_type

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

																	_lowerCAmelCase							=							(predicted_variance + 1) / 2
																	_lowerCAmelCase							=							frac * max_log + (1 - frac) * min_log

										return variance





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]  ,  lowercase__	:	Optional[Any]  ,  lowercase__	:	Tuple  ,  lowercase__	:	int  ,  lowercase__	:	Any = None  ,  lowercase__	:	Optional[int]=None  ,  lowercase__	:	str = True  ,  ):
										_lowerCAmelCase							=							timestep

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

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

										_lowerCAmelCase							=							self.alphas_cumprod[t]
										_lowerCAmelCase							=							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
										_lowerCAmelCase							=							1 - alpha_prod_t
										_lowerCAmelCase							=							1 - alpha_prod_t_prev

										if prev_timestep == t - 1:
																	_lowerCAmelCase							=							self.betas[t]
																	_lowerCAmelCase							=							self.alphas[t]
										else:
																	_lowerCAmelCase							=							1 - alpha_prod_t / alpha_prod_t_prev
																	_lowerCAmelCase							=							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							=							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
										elif self.config.prediction_type == "sample":
																	_lowerCAmelCase							=							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							=							torch.clamp(
																	    _A  ,  -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							=							(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
										_lowerCAmelCase							=							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							=							pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

										# 6. Add noise
										_lowerCAmelCase							=							0
										if t > 0:
																	_lowerCAmelCase							=							randn_tensor(
																	    model_output.shape  ,  dtype=model_output.dtype  ,  generator=_A  ,  device=model_output.device						)

																	_lowerCAmelCase							=							self._get_variance(
																	    _A  ,  predicted_variance=_A  ,  prev_timestep=_A  ,  )

																	if self.variance_type == "fixed_small_log":
																								_lowerCAmelCase							=							variance
																	elif self.variance_type == "learned_range":
																								_lowerCAmelCase							=							(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							=							variance * variance_noise

										_lowerCAmelCase							=							pred_prev_sample + variance

										if not return_dict:
																	return (pred_prev_sample,)

										return UnCLIPSchedulerOutput(prev_sample=_A  ,  pred_original_sample=_A						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]  ,  lowercase__	:	Union[str, Any]  ,  lowercase__	:	Dict  ,  lowercase__	:	Union[str, Any]  ,  ):
										_lowerCAmelCase							=							self.alphas_cumprod.to(device=original_samples.device  ,  dtype=original_samples.dtype						)
										_lowerCAmelCase							=							timesteps.to(original_samples.device						)

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

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

										_lowerCAmelCase							=							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_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__magic_name__       :					Dict						=			{
    """configuration_layoutlmv3""": [
        """LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """LayoutLMv3Config""",
        """LayoutLMv3OnnxConfig""",
    ],
    """processing_layoutlmv3""": ["""LayoutLMv3Processor"""],
    """tokenization_layoutlmv3""": ["""LayoutLMv3Tokenizer"""],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					Optional[Any]						=			["""LayoutLMv3TokenizerFast"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					int						=			[
		    """LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """LayoutLMv3ForQuestionAnswering""",
		    """LayoutLMv3ForSequenceClassification""",
		    """LayoutLMv3ForTokenClassification""",
		    """LayoutLMv3Model""",
		    """LayoutLMv3PreTrainedModel""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					Tuple						=			[
		    """TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFLayoutLMv3ForQuestionAnswering""",
		    """TFLayoutLMv3ForSequenceClassification""",
		    """TFLayoutLMv3ForTokenClassification""",
		    """TFLayoutLMv3Model""",
		    """TFLayoutLMv3PreTrainedModel""",
		]

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					Union[str, Any]						=			["""LayoutLMv3FeatureExtractor"""]
		__magic_name__       :					Any						=			["""LayoutLMv3ImageProcessor"""]


if TYPE_CHECKING:
		from .configuration_layoutlmva import (
		    LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    LayoutLMvaConfig,
		    LayoutLMvaOnnxConfig,
		)
		from .processing_layoutlmva import LayoutLMvaProcessor
		from .tokenization_layoutlmva import LayoutLMvaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_layoutlmva import (
				    LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LayoutLMvaForQuestionAnswering,
				    LayoutLMvaForSequenceClassification,
				    LayoutLMvaForTokenClassification,
				    LayoutLMvaModel,
				    LayoutLMvaPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_layoutlmva import (
				    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFLayoutLMvaForQuestionAnswering,
				    TFLayoutLMvaForSequenceClassification,
				    TFLayoutLMvaForTokenClassification,
				    TFLayoutLMvaModel,
				    TFLayoutLMvaPreTrainedModel,
				)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
				from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
		import sys

		__magic_name__       :					Union[str, Any]						=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



