"""simple docstring"""







import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


_a  :  Optional[int]=				"scheduler_config.json"

class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   Tuple					=	1
				UpperCAmelCase       :   Any					=	2
				UpperCAmelCase       :   Optional[Any]					=	3
				UpperCAmelCase       :   str					=	4
				UpperCAmelCase       :   Any					=	5







@dataclass
class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   jnp.ndarray







class 	UpperCamelCase    :
				UpperCAmelCase       :   str					=	SCHEDULER_CONFIG_NAME
				UpperCAmelCase       :   Optional[Any]					=	["""dtype"""]
				UpperCAmelCase       :   Optional[Any]					=	[]
				UpperCAmelCase       :   Tuple					=	True



				@classmethod
				def       _lowercase  (cls       :   Optional[Any]			,						_A       :   Dict[str, Any] = None			,						_A       :   Optional[str] = None			,						_A       :   List[str]=False			,						**_A       :   List[Any]			,						)					->			int:
					__snake_case	,       __snake_case    :							List[str]   	=				cls.load_config(
					    pretrained_model_name_or_path=_A			,						subfolder=_A			,						return_unused_kwargs=_A			,						**_A			,						)
					__snake_case	,       __snake_case    :							Union[str, Any]   	=				cls.from_config(_A			,						return_unused_kwargs=_A			,						**_A)

					if hasattr(_A			,						'create_state') and getattr(_A			,						'has_state'			,						_A):
						__snake_case    :							List[Any]   	=				scheduler.create_state()

					if return_unused_kwargs:
						return scheduler, state, unused_kwargs

					return scheduler, state



				def       _lowercase  (self       :   Optional[int]			,						_A       :   Union[str, os.PathLike]			,						_A       :   bool = False			,						**_A       :   List[str])					->			Union[str, Any]:
					self.save_config(save_directory=_A			,						push_to_hub=_A			,						**_A)



				@property
				def       _lowercase  (self       :   Tuple)					->			Tuple:
					return self._get_compatibles()






				@classmethod
				def       _lowercase  (cls       :   Tuple)					->			Optional[int]:
					__snake_case    :							str   	=				list(set([cls.__name__] + cls._compatibles))
					__snake_case    :							List[str]   	=				importlib.import_module(__name__.split('.')[0])
					__snake_case    :							Dict   	=				[
					    getattr(_A			,						_A) for c in compatible_classes_str if hasattr(_A			,						_A)
					]
					return compatible_classes







def 						__UpperCAmelCase       (					UpperCAmelCase_					:       jnp.ndarray    ,							UpperCAmelCase_					:       Tuple[int]						)							->    jnp.ndarray:

	'''simple docstring'''





	assert len(UpperCAmelCase_						) >= x.ndim
	return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(UpperCAmelCase_						) - x.ndim)						)    ,							UpperCAmelCase_						)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       int    ,							UpperCAmelCase_					:       Optional[int]=0.999    ,							UpperCAmelCase_					:       Tuple=jnp.floataa						)							->    jnp.ndarray:

	'''simple docstring'''





	def alpha_bar(UpperCAmelCase_					:       Optional[Any]						):
		return math.cos((time_step + 0.008) / 1.008 * math.pi / 2						) ** 2

	__snake_case    :							int   	=				[]
	for i in range(UpperCAmelCase_						):
		__snake_case    :							Optional[int]   	=				i / num_diffusion_timesteps
		__snake_case    :							Dict   	=				(i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar(UpperCAmelCase_						) / alpha_bar(UpperCAmelCase_						)    ,							UpperCAmelCase_						)						)
	return jnp.array(UpperCAmelCase_    ,							dtype=UpperCAmelCase_						)

@flax.struct.dataclass
class 	UpperCamelCase    :
				UpperCAmelCase       :   jnp.ndarray
				UpperCAmelCase       :   jnp.ndarray
				UpperCAmelCase       :   jnp.ndarray



				@classmethod
				def       _lowercase  (cls       :   str			,						_A       :   Tuple)					->			Optional[int]:
					__snake_case    :							Dict   	=				scheduler.config

					if config.trained_betas is not None:
						__snake_case    :							Tuple   	=				jnp.asarray(config.trained_betas			,						dtype=scheduler.dtype)
					elif config.beta_schedule == "linear":
						__snake_case    :							Tuple   	=				jnp.linspace(config.beta_start			,						config.beta_end			,						config.num_train_timesteps			,						dtype=scheduler.dtype)
					elif config.beta_schedule == "scaled_linear":
						# this schedule is very specific to the latent diffusion model.
						__snake_case    :							List[str]   	=				(
						    jnp.linspace(
						        config.beta_start**0.5			,						config.beta_end**0.5			,						config.num_train_timesteps			,						dtype=scheduler.dtype)
						    ** 2
						)
					elif config.beta_schedule == "squaredcos_cap_v2":
						# Glide cosine schedule
						__snake_case    :							Optional[int]   	=				betas_for_alpha_bar(config.num_train_timesteps			,						dtype=scheduler.dtype)
					else:
						raise NotImplementedError(
						    f"beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}")

					__snake_case    :							Any   	=				1.0 - betas

					__snake_case    :							str   	=				jnp.cumprod(_A			,						axis=0)

					return cls(
					    alphas=_A			,						betas=_A			,						alphas_cumprod=_A			,						)







def 						__UpperCAmelCase       (					UpperCAmelCase_					:       CommonSchedulerState    ,							UpperCAmelCase_					:       jnp.ndarray    ,							UpperCAmelCase_					:       jnp.ndarray    ,							UpperCAmelCase_					:       jnp.ndarray						)							->    List[str]:

	'''simple docstring'''





	__snake_case    :							Any   	=				state.alphas_cumprod

	__snake_case    :							Tuple   	=				alphas_cumprod[timesteps] ** 0.5
	__snake_case    :							Optional[int]   	=				sqrt_alpha_prod.flatten()
	__snake_case    :							Union[str, Any]   	=				broadcast_to_shape_from_left(UpperCAmelCase_    ,							original_samples.shape						)

	__snake_case    :							Union[str, Any]   	=				(1 - alphas_cumprod[timesteps]) ** 0.5
	__snake_case    :							Dict   	=				sqrt_one_minus_alpha_prod.flatten()
	__snake_case    :							List[str]   	=				broadcast_to_shape_from_left(UpperCAmelCase_    ,							original_samples.shape						)

	return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       CommonSchedulerState    ,							UpperCAmelCase_					:       jnp.ndarray    ,							UpperCAmelCase_					:       jnp.ndarray    ,							UpperCAmelCase_					:       jnp.ndarray						)							->    List[Any]:

	'''simple docstring'''





	__snake_case	,       __snake_case    :							Any   	=				get_sqrt_alpha_prod(UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_						)
	__snake_case    :							Dict   	=				sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
	return noisy_samples

def 						__UpperCAmelCase       (					UpperCAmelCase_					:       CommonSchedulerState    ,							UpperCAmelCase_					:       jnp.ndarray    ,							UpperCAmelCase_					:       jnp.ndarray    ,							UpperCAmelCase_					:       jnp.ndarray						)							->    Optional[int]:

	'''simple docstring'''





	__snake_case	,       __snake_case    :							Optional[Any]   	=				get_sqrt_alpha_prod(UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_    ,							UpperCAmelCase_						)
	__snake_case    :							str   	=				sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
	return velocity






"""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'			,						)





