"""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       a_      (     __UpperCamelCase			):
					UpperCamelCase_ :	torch.FloatTensor
					UpperCamelCase_ :	Optional[torch.FloatTensor]			=    None







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__=0.9_99			,  lowerCamelCase__="cosine"			,  ):





		"""simple docstring"""




		if alpha_transform_type == "cosine":

				def alpha_bar_fn(lowerCamelCase__     ):
						return math.cos((t + 0.0_08) / 1.0_08 * 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}"""     )

		lowerCAmelCase__        =   []
		for i in range(lowerCamelCase__     ):
				lowerCAmelCase__        =   i / num_diffusion_timesteps
				lowerCAmelCase__        =   (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       a_      (     __UpperCamelCase ,		__UpperCamelCase			):




					@register_to_config
					def __init__(	self						:     List[Any]  ,						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'"""     )

							lowerCAmelCase__        =   betas_for_alpha_bar(snake_case__     )

							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  ,						snake_case__     )[::-1].copy()     )

							lowerCAmelCase__        =   variance_type




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str  ,						snake_case__						:     torch.FloatTensor  ,						snake_case__						:     Optional[int] = None     ):
							return sample




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




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

							if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
									lowerCAmelCase__   ,					lowerCAmelCase__        =   torch.split(snake_case__  ,						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(
									    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
							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=snake_case__  ,						device=model_output.device     )

									lowerCAmelCase__        =   self._get_variance(
									    snake_case__  ,						predicted_variance=snake_case__  ,						prev_timestep=snake_case__  ,						)

									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=snake_case__  ,						pred_original_sample=snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						snake_case__						:     torch.FloatTensor  ,						snake_case__						:     torch.FloatTensor  ,						snake_case__						:     torch.IntTensor  ,						):
							# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
							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"""




import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class       a_      (     __UpperCamelCase ,		unittest.TestCase			):
					UpperCamelCase_ :	str			=    LayoutLMTokenizer
					UpperCamelCase_ :	List[Any]			=    LayoutLMTokenizerFast
					UpperCamelCase_ :	Dict			=    True
					UpperCamelCase_ :	Any			=    True




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							super().setUp()

							lowerCAmelCase__        =   [
							    """[UNK]""",
							    """[CLS]""",
							    """[SEP]""",
							    """want""",
							    """##want""",
							    """##ed""",
							    """wa""",
							    """un""",
							    """runn""",
							    """##ing""",
							    """,""",
							    """low""",
							    """lowest""",
							]
							lowerCAmelCase__        =   os.path.join(self.tmpdirname  ,						VOCAB_FILES_NAMES["""vocab_file"""]     )
							with open(self.vocab_file  ,						"""w"""  ,						encoding="""utf-8"""     ) as vocab_writer:
									vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]     )     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						**snake_case__						:     Union[str, Any]     ):
							return LayoutLMTokenizer.from_pretrained(self.tmpdirname  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						snake_case__						:     Tuple     ):
							lowerCAmelCase__        =   """UNwant\u00E9d,running"""
							lowerCAmelCase__        =   """unwanted, running"""
							return input_text, output_text




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							lowerCAmelCase__        =   self.tokenizer_class(self.vocab_file     )

							lowerCAmelCase__        =   tokenizer.tokenize("""UNwant\u00E9d,running"""     )
							self.assertListEqual(snake_case__  ,						["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]     )
							self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__     )  ,						[7, 4, 5, 10, 8, 9]     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							pass




