



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 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""
	__A							=							42
	__A							=							None






def     a(      A       :    List[Any] ,     A       :    List[Any]=0.999 ,     A       :    Optional[int]="cosine" ,     )		->       List[Any]:
					"""simple docstring"""





					if alpha_transform_type == "cosine":

										def alpha_bar_fn(A       :    List[Any]       ):
															return math.cos((t + 0.008) / 1.008 * math.pi / 2       ) ** 2

					elif alpha_transform_type == "exp":

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

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

					a					     =   []
					for i in range(A       ):
										a					     =   i / num_diffusion_timesteps
										a					     =   (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 	_lowercase							(   lowerCAmelCase,      lowerCAmelCase	):





	"""simple docstring"""




	@register_to_config
	def __init__(self		,       lowerCamelCase_ = 1000		,       lowerCamelCase_ = "fixed_small_log"		,       lowerCamelCase_ = True		,       lowerCamelCase_ = 1.0		,       lowerCamelCase_ = "epsilon"		,       lowerCamelCase_ = "squaredcos_cap_v2"		,       ):
						"""simple docstring"""

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

						a					     =   betas_for_alpha_bar(lowerCamelCase_		)

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

						# standard deviation of the initial noise distribution
						a					     =   1.0

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

						a					     =   variance_type




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_ = None		):
						"""simple docstring"""

						return sample




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_ = None		):
						"""simple docstring"""

						a					     =   num_inference_steps
						a					     =   (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
						a					     =   (np.arange(0		,       lowerCamelCase_		) * step_ratio).round()[::-1].copy().astype(np.intaa		)
						a					     =   torch.from_numpy(lowerCamelCase_		).to(lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=None		,       lowerCamelCase_=None		,       lowerCamelCase_=None		):
						"""simple docstring"""

						if prev_timestep is None:
											a					     =   t - 1

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

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

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

						# hacks - were probably added for training stability
						if variance_type == "fixed_small_log":
											a					     =   torch.log(torch.clamp(lowerCamelCase_		,       min=1E-2_0		)		)
											a					     =   torch.exp(0.5 * variance		)
						elif variance_type == "learned_range":
											# NOTE difference with DDPM scheduler
											a					     =   variance.log()
											a					     =   beta.log()

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

						return variance




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_ = None		,       lowerCamelCase_=None		,       lowerCamelCase_ = True		,       ):
						"""simple docstring"""

						a					     =   timestep

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

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

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

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

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

											a					     =   self._get_variance(
											    lowerCamelCase_		,       predicted_variance=lowerCamelCase_		,       prev_timestep=lowerCamelCase_		,       )

											if self.variance_type == "fixed_small_log":
																a					     =   variance
											elif self.variance_type == "learned_range":
																a					     =   (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."		)

											a					     =   variance * variance_noise

						a					     =   pred_prev_sample + variance

						if not return_dict:
											return (pred_prev_sample,)

						return UnCLIPSchedulerOutput(prev_sample=lowerCamelCase_		,       pred_original_sample=lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       ):
						"""simple docstring"""

						a					     =   self.alphas_cumprod.to(device=original_samples.device		,       dtype=original_samples.dtype		)
						a					     =   timesteps.to(original_samples.device		)

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

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

						a					     =   sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
						return noisy_samples





import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_lowercase: Optional[int]      			=		get_tests_dir("fixtures/test_sentencepiece.model")



@require_sentencepiece
class 	_lowercase							(   lowerCAmelCase,      unittest.TestCase	):





	"""simple docstring"""
	__A							=							XLMProphetNetTokenizer
	__A							=							False
	__A							=							True




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						super().setUp()

						# We have a SentencePiece fixture for testing
						a					     =   XLMProphetNetTokenizer(lowerCamelCase_		,       keep_accents=lowerCamelCase_		)
						tokenizer.save_pretrained(self.tmpdirname		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   "[PAD]"
						a					     =   0

						self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase_		)		,       lowerCamelCase_		)
						self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase_		)		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   list(self.get_tokenizer().get_vocab().keys()		)

						self.assertEqual(vocab_keys[0]		,       "[PAD]"		)
						self.assertEqual(vocab_keys[1]		,       "[CLS]"		)
						self.assertEqual(vocab_keys[-1]		,       "j"		)
						self.assertEqual(len(lowerCamelCase_		)		,       1012		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						self.assertEqual(self.get_tokenizer().vocab_size		,       1012		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   XLMProphetNetTokenizer(lowerCamelCase_		,       keep_accents=lowerCamelCase_		)

						a					     =   tokenizer.tokenize("This is a test"		)
						self.assertListEqual(lowerCamelCase_		,       ["▁This", "▁is", "▁a", "▁t", "est"]		)

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(lowerCamelCase_		)		,       [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]		,       )

						a					     =   tokenizer.tokenize("I was born in 92000, and this is falsé."		)
						self.assertListEqual(
						    lowerCamelCase_		,       [
						        SPIECE_UNDERLINE + "I",
						        SPIECE_UNDERLINE + "was",
						        SPIECE_UNDERLINE + "b",
						        "or",
						        "n",
						        SPIECE_UNDERLINE + "in",
						        SPIECE_UNDERLINE + "",
						        "9",
						        "2",
						        "0",
						        "0",
						        "0",
						        ",",
						        SPIECE_UNDERLINE + "and",
						        SPIECE_UNDERLINE + "this",
						        SPIECE_UNDERLINE + "is",
						        SPIECE_UNDERLINE + "f",
						        "al",
						        "s",
						        "é",
						        ".",
						    ]		,       )
						a					     =   tokenizer.convert_tokens_to_ids(lowerCamelCase_		)
						self.assertListEqual(
						    lowerCamelCase_		,       [
						        value + tokenizer.fairseq_offset
						        for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
						    ]		,       )

						a					     =   tokenizer.convert_ids_to_tokens(lowerCamelCase_		)
						self.assertListEqual(
						    lowerCamelCase_		,       [
						        SPIECE_UNDERLINE + "I",
						        SPIECE_UNDERLINE + "was",
						        SPIECE_UNDERLINE + "b",
						        "or",
						        "n",
						        SPIECE_UNDERLINE + "in",
						        SPIECE_UNDERLINE + "",
						        "[UNK]",
						        "2",
						        "0",
						        "0",
						        "0",
						        ",",
						        SPIECE_UNDERLINE + "and",
						        SPIECE_UNDERLINE + "this",
						        SPIECE_UNDERLINE + "is",
						        SPIECE_UNDERLINE + "f",
						        "al",
						        "s",
						        "[UNK]",
						        ".",
						    ]		,       )




	@cached_property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return XLMProphetNetTokenizer.from_pretrained("microsoft/xprophetnet-large-wiki100-cased"		)




	@slow
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   "Hello World!"
						a					     =   [35389, 6672, 49, 2]
						self.assertListEqual(lowerCamelCase_		,       self.big_tokenizer.encode(lowerCamelCase_		)		)




	@slow
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   {"input_ids": [[11073, 82783, 18, 26, 82783, 549, 51540, 248, 17209, 1301, 217, 20, 215186, 1325, 147, 17209, 1301, 217, 20, 56370, 53, 122020, 20, 16477, 27, 87355, 4548, 20, 4728, 78392, 17, 159969, 18, 26, 24491, 629, 15, 538, 22704, 5439, 15, 2788, 24491, 9885, 15, 43534, 605, 15, 814, 18403, 33200, 29, 15, 43534, 24458, 12410, 111, 24966, 83669, 9637, 144068, 26, 850, 22346, 27, 147, 24966, 83669, 83490, 26, 39113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 122020, 115785, 34, 816, 1339, 46887, 18, 147, 53905, 1951, 42238, 41170, 17732, 834, 436, 15, 27523, 98733, 217, 147, 5542, 4981, 930, 17347, 16, 2], [20091, 629, 94, 82786, 58, 490, 20, 1528, 84, 53905, 344, 80592, 110128, 18822, 5267, 1306, 62, 152537, 308, 7997, 401, 124427, 549, 35442, 225, 109, 15055, 25748, 147, 7119, 43712, 34, 767, 135366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63784, 119466, 17, 147808, 88214, 18, 656, 81, 32, 3296, 10280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
						# fmt: on

						self.tokenizer_integration_test_util(
						    expected_encoding=lowerCamelCase_		,       model_name="microsoft/xprophetnet-large-wiki100-cased"		,       revision="1acad1643ddd54a44df6a1b797ada8373685d90e"		,       )
