"""simple docstring"""







# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)

@flax.struct.dataclass
class 	UpperCamelCase    :
				UpperCAmelCase       :   CommonSchedulerState

				# setable values
				UpperCAmelCase       :   jnp.ndarray
				UpperCAmelCase       :   jnp.ndarray
				UpperCAmelCase       :   Optional[int]					=	None



				@classmethod
				def       _lowercase  (cls       :   Any			,						_A       :   CommonSchedulerState			,						_A       :   jnp.ndarray			,						_A       :   jnp.ndarray)					->			Optional[int]:
					return cls(common=_A			,						init_noise_sigma=_A			,						timesteps=_A)







@dataclass
class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   DDPMSchedulerState







class 	UpperCamelCase    (					lowercase   ,			lowercase		):
				UpperCAmelCase       :   List[Any]					=	[e.name for e in FlaxKarrasDiffusionSchedulers]

				UpperCAmelCase       :   jnp.dtype



				@property
				def       _lowercase  (self       :   Union[str, Any])					->			Tuple:
					return True



				@register_to_config
				def __init__(self       :   Union[str, Any]			,						_A       :   int = 10_00			,						_A       :   float = 0.0_001			,						_A       :   float = 0.02			,						_A       :   str = "linear"			,						_A       :   Optional[jnp.ndarray] = None			,						_A       :   str = "fixed_small"			,						_A       :   bool = True			,						_A       :   str = "epsilon"			,						_A       :   jnp.dtype = jnp.floataa			,						)					->			List[Any]:
					__snake_case    :							Any   	=				dtype



				def       _lowercase  (self       :   Optional[int]			,						_A       :   Optional[CommonSchedulerState] = None)					->			DDPMSchedulerState:
					if common is None:
						__snake_case    :							Any   	=				CommonSchedulerState.create(self)

					# standard deviation of the initial noise distribution
					__snake_case    :							str   	=				jnp.array(1.0			,						dtype=self.dtype)

					__snake_case    :							Optional[int]   	=				jnp.arange(0			,						self.config.num_train_timesteps).round()[::-1]

					return DDPMSchedulerState.create(
					    common=_A			,						init_noise_sigma=_A			,						timesteps=_A			,						)



				def       _lowercase  (self       :   Union[str, Any]			,						_A       :   DDPMSchedulerState			,						_A       :   jnp.ndarray			,						_A       :   Optional[int] = None)					->			jnp.ndarray:
					return sample



				def       _lowercase  (self       :   Union[str, Any]			,						_A       :   DDPMSchedulerState			,						_A       :   int			,						_A       :   Tuple = ())					->			DDPMSchedulerState:
					__snake_case    :							List[str]   	=				self.config.num_train_timesteps // num_inference_steps
					# creates integer timesteps by multiplying by ratio
					# rounding to avoid issues when num_inference_step is power of 3
					__snake_case    :							Tuple   	=				(jnp.arange(0			,						_A) * step_ratio).round()[::-1]

					return state.replace(
					    num_inference_steps=_A			,						timesteps=_A			,						)



				def       _lowercase  (self       :   List[Any]			,						_A       :   DDPMSchedulerState			,						_A       :   int			,						_A       :   Union[str, Any]=None			,						_A       :   List[str]=None)					->			List[str]:
					__snake_case    :							Tuple   	=				state.common.alphas_cumprod[t]
					__snake_case    :							str   	=				jnp.where(t > 0			,						state.common.alphas_cumprod[t - 1]			,						jnp.array(1.0			,						dtype=self.dtype))

					# 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
					__snake_case    :							str   	=				(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

					if variance_type is None:
						__snake_case    :							List[Any]   	=				self.config.variance_type

					# hacks - were probably added for training stability
					if variance_type == "fixed_small":
						__snake_case    :							List[Any]   	=				jnp.clip(_A			,						a_min=1E-20)
					# for rl-diffuser https://arxiv.org/abs/2205.09991
					elif variance_type == "fixed_small_log":
						__snake_case    :							Tuple   	=				jnp.log(jnp.clip(_A			,						a_min=1E-20))
					elif variance_type == "fixed_large":
						__snake_case    :							str   	=				state.common.betas[t]
					elif variance_type == "fixed_large_log":
						# Glide max_log
						__snake_case    :							Any   	=				jnp.log(state.common.betas[t])
					elif variance_type == "learned":
						return predicted_variance
					elif variance_type == "learned_range":
						__snake_case    :							int   	=				variance
						__snake_case    :							Optional[Any]   	=				state.common.betas[t]
						__snake_case    :							Union[str, Any]   	=				(predicted_variance + 1) / 2
						__snake_case    :							int   	=				frac * max_log + (1 - frac) * min_log

					return variance



				def       _lowercase  (self       :   str			,						_A       :   DDPMSchedulerState			,						_A       :   jnp.ndarray			,						_A       :   int			,						_A       :   jnp.ndarray			,						_A       :   Optional[jax.random.KeyArray] = None			,						_A       :   bool = True			,						)					->			Union[FlaxDDPMSchedulerOutput, Tuple]:
					__snake_case    :							Optional[int]   	=				timestep

					if key is None:
						__snake_case    :							Optional[Any]   	=				jax.random.PRNGKey(0)

					if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
						__snake_case	,       __snake_case    :							Dict   	=				jnp.split(_A			,						sample.shape[1]			,						axis=1)
					else:
						__snake_case    :							Optional[int]   	=				None

					# 1. compute alphas, betas
					__snake_case    :							Optional[int]   	=				state.common.alphas_cumprod[t]
					__snake_case    :							Dict   	=				jnp.where(t > 0			,						state.common.alphas_cumprod[t - 1]			,						jnp.array(1.0			,						dtype=self.dtype))
					__snake_case    :							Dict   	=				1 - alpha_prod_t
					__snake_case    :							List[Any]   	=				1 - alpha_prod_t_prev

					# 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":
						__snake_case    :							Optional[int]   	=				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
					elif self.config.prediction_type == "sample":
						__snake_case    :							Dict   	=				model_output
					elif self.config.prediction_type == "v_prediction":
						__snake_case    :							str   	=				(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
					else:
						raise ValueError(
						    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` "
						    ' for the FlaxDDPMScheduler.')

					# 3. Clip "predicted x_0"
					if self.config.clip_sample:
						__snake_case    :							str   	=				jnp.clip(_A			,						-1			,						1)

					# 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
					__snake_case    :							Dict   	=				(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
					__snake_case    :							Optional[int]   	=				state.common.alphas[t] ** 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
					__snake_case    :							str   	=				pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

					# 6. Add noise
					def random_variance():
						__snake_case    :							List[Any]   	=				jax.random.split(_A			,						num=1)
						__snake_case    :							Optional[int]   	=				jax.random.normal(_A			,						shape=model_output.shape			,						dtype=self.dtype)
						return (self._get_variance(_A			,						_A			,						predicted_variance=_A) ** 0.5) * noise

					__snake_case    :							Optional[int]   	=				jnp.where(t > 0			,						random_variance()			,						jnp.zeros(model_output.shape			,						dtype=self.dtype))

					__snake_case    :							Tuple   	=				pred_prev_sample + variance

					if not return_dict:
						return (pred_prev_sample, state)

					return FlaxDDPMSchedulerOutput(prev_sample=_A			,						state=_A)



				def       _lowercase  (self       :   Union[str, Any]			,						_A       :   DDPMSchedulerState			,						_A       :   jnp.ndarray			,						_A       :   jnp.ndarray			,						_A       :   jnp.ndarray			,						)					->			jnp.ndarray:
					return add_noise_common(state.common			,						_A			,						_A			,						_A)



				def       _lowercase  (self       :   int			,						_A       :   DDPMSchedulerState			,						_A       :   jnp.ndarray			,						_A       :   jnp.ndarray			,						_A       :   jnp.ndarray			,						)					->			jnp.ndarray:
					return get_velocity_common(state.common			,						_A			,						_A			,						_A)






				def __len__(self       :   Any)					->			str:
					return self.config.num_train_timesteps






"""simple docstring"""







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


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

@require_sentencepiece
class 	UpperCamelCase    (					lowercase   ,			unittest.TestCase		):
				UpperCAmelCase       :   List[str]					=	XLMProphetNetTokenizer
				UpperCAmelCase       :   int					=	False
				UpperCAmelCase       :   List[Any]					=	True



				def       _lowercase  (self       :   Dict)					->			Dict:
					super().setUp()

					# We have a SentencePiece fixture for testing
					__snake_case    :							Tuple   	=				XLMProphetNetTokenizer(_A			,						keep_accents=_A)
					tokenizer.save_pretrained(self.tmpdirname)



				def       _lowercase  (self       :   str)					->			Optional[int]:
					__snake_case    :							List[str]   	=				'[PAD]'
					__snake_case    :							str   	=				0

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



				def       _lowercase  (self       :   Dict)					->			str:
					__snake_case    :							int   	=				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(_A)			,						10_12)



				def       _lowercase  (self       :   Dict)					->			List[str]:
					self.assertEqual(self.get_tokenizer().vocab_size			,						10_12)



				def       _lowercase  (self       :   str)					->			Dict:
					__snake_case    :							int   	=				XLMProphetNetTokenizer(_A			,						keep_accents=_A)

					__snake_case    :							Tuple   	=				tokenizer.tokenize('This is a test')
					self.assertListEqual(_A			,						['▁This', '▁is', '▁a', '▁t', 'est'])

					self.assertListEqual(
					    tokenizer.convert_tokens_to_ids(_A)			,						[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]			,						)

					__snake_case    :							int   	=				tokenizer.tokenize('I was born in 92000, and this is falsé.')
					self.assertListEqual(
					    _A			,						[
					        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',
					        '.',
					    ]			,						)
					__snake_case    :							Tuple   	=				tokenizer.convert_tokens_to_ids(_A)
					self.assertListEqual(
					    _A			,						[
					        value + tokenizer.fairseq_offset
					        for value in [8, 21, 84, 55, 24, 19, 7, -9, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, -9, 4]
					    ]			,						)

					__snake_case    :							Optional[Any]   	=				tokenizer.convert_ids_to_tokens(_A)
					self.assertListEqual(
					    _A			,						[
					        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       _lowercase  (self       :   List[Any])					->			Tuple:
					return XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased')



				@slow
				def       _lowercase  (self       :   Tuple)					->			Any:
					__snake_case    :							Dict   	=				'Hello World!'
					__snake_case    :							List[str]   	=				[3_53_89, 66_72, 49, 2]
					self.assertListEqual(_A			,						self.big_tokenizer.encode(_A))






				@slow
				def       _lowercase  (self       :   Union[str, Any])					->			List[Any]:
					# fmt: off
					__snake_case    :							Any   	=				{'input_ids': [[1_10_73, 8_27_83, 18, 26, 8_27_83, 5_49, 5_15_40, 2_48, 1_72_09, 13_01, 2_17, 20, 21_51_86, 13_25, 1_47, 1_72_09, 13_01, 2_17, 20, 5_63_70, 53, 12_20_20, 20, 1_64_77, 27, 8_73_55, 45_48, 20, 47_28, 7_83_92, 17, 15_99_69, 18, 26, 2_44_91, 6_29, 15, 5_38, 2_27_04, 54_39, 15, 27_88, 2_44_91, 98_85, 15, 4_35_34, 6_05, 15, 8_14, 1_84_03, 3_32_00, 29, 15, 4_35_34, 2_44_58, 1_24_10, 1_11, 2_49_66, 8_36_69, 96_37, 14_40_68, 26, 8_50, 2_23_46, 27, 1_47, 2_49_66, 8_36_69, 8_34_90, 26, 3_91_13, 7_35, 27, 6_89, 6_56, 28_00, 13_39, 46_00, 53, 12_20_20, 11_57_85, 34, 8_16, 13_39, 4_68_87, 18, 1_47, 5_39_05, 19_51, 4_22_38, 4_11_70, 1_77_32, 8_34, 4_36, 15, 2_75_23, 9_87_33, 2_17, 1_47, 55_42, 49_81, 9_30, 1_73_47, 16, 2], [2_00_91, 6_29, 94, 8_27_86, 58, 4_90, 20, 15_28, 84, 5_39_05, 3_44, 8_05_92, 11_01_28, 1_88_22, 52_67, 13_06, 62, 15_25_37, 3_08, 79_97, 4_01, 12_44_27, 5_49, 3_54_42, 2_25, 1_09, 1_50_55, 2_57_48, 1_47, 71_19, 4_37_12, 34, 7_67, 13_53_66, 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], [5_92, 6_37_84, 11_94_66, 17, 14_78_08, 8_82_14, 18, 6_56, 81, 32, 32_96, 1_02_80, 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=_A			,						model_name='microsoft/xprophetnet-large-wiki100-cased'			,						revision='1acad1643ddd54a44df6a1b797ada8373685d90e'			,						)





