
'''simple docstring'''







import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class  lowerCAmelCase__   (  lowerCamelCase_    ,						unittest.TestCase     ):
					lowerCAmelCase_        =     RobertaTokenizer
					lowerCAmelCase_        =     RobertaTokenizerFast
					lowerCAmelCase_        =     True
					lowerCAmelCase_        =     {'''cls_token''': '''<s>'''}






					def 	_snake_case    (   self		):


											"""simple docstring"""





											super().setUp()

											# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
											lowercase_	:     Any													=       [
											    '''l''',
											    '''o''',
											    '''w''',
											    '''e''',
											    '''r''',
											    '''s''',
											    '''t''',
											    '''i''',
											    '''d''',
											    '''n''',
											    '''\u0120''',
											    '''\u0120l''',
											    '''\u0120n''',
											    '''\u0120lo''',
											    '''\u0120low''',
											    '''er''',
											    '''\u0120lowest''',
											    '''\u0120newer''',
											    '''\u0120wider''',
											    '''<unk>''',
											]
											lowercase_	:     List[str]													=       dict(zip(__SCREAMING_SNAKE_CASE				,							range(len(__SCREAMING_SNAKE_CASE		)		)		)		)
											lowercase_	:     List[Any]													=       ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
											lowercase_	:     Union[str, Any]													=       {'''unk_token''': '''<unk>'''}

											lowercase_	:     Optional[int]													=       os.path.join(self.tmpdirname				,							VOCAB_FILES_NAMES['''vocab_file''']		)
											lowercase_	:     Optional[Any]													=       os.path.join(self.tmpdirname				,							VOCAB_FILES_NAMES['''merges_file''']		)
											with open(self.vocab_file				,							'''w'''				,							encoding='''utf-8'''		) as fp:
																	fp.write(json.dumps(__SCREAMING_SNAKE_CASE		) + '''\n'''		)
											with open(self.merges_file				,							'''w'''				,							encoding='''utf-8'''		) as fp:
																	fp.write('''\n'''.join(__SCREAMING_SNAKE_CASE		)		)






					def 	_snake_case    (   self				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											kwargs.update(self.special_tokens_map		)
											return self.tokenizer_class.from_pretrained(self.tmpdirname				,							**__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											kwargs.update(self.special_tokens_map		)
											return RobertaTokenizerFast.from_pretrained(self.tmpdirname				,							**__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     List[Any]													=       '''lower newer'''
											lowercase_	:     Dict													=       '''lower newer'''
											return input_text, output_text






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       self.tokenizer_class(self.vocab_file				,							self.merges_file				,							**self.special_tokens_map		)
											lowercase_	:     List[Any]													=       '''lower newer'''
											lowercase_	:     Union[str, Any]													=       ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
											lowercase_	:     Union[str, Any]													=       tokenizer.tokenize(__SCREAMING_SNAKE_CASE		)  # , add_prefix_space=True)
											self.assertListEqual(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Optional[int]													=       tokens + [tokenizer.unk_token]
											lowercase_	:     Tuple													=       [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE		)				,							__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     List[str]													=       self.get_tokenizer()

											self.assertListEqual(tokenizer.encode('''Hello world!'''				,							add_special_tokens=__SCREAMING_SNAKE_CASE		)				,							[0, 3_14_14, 2_32, 3_28, 2]		)
											self.assertListEqual(
											    tokenizer.encode('''Hello world! cécé herlolip 418'''				,							add_special_tokens=__SCREAMING_SNAKE_CASE		)				,							[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]				,							)






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     List[Any]													=       self.tokenizer_class.from_pretrained('''roberta-base'''		)

											lowercase_	:     Optional[Any]													=       tokenizer.encode('''sequence builders'''				,							add_special_tokens=__SCREAMING_SNAKE_CASE		)
											lowercase_	:     List[Any]													=       tokenizer.encode('''multi-sequence build'''				,							add_special_tokens=__SCREAMING_SNAKE_CASE		)

											lowercase_	:     str													=       tokenizer.encode(
											    '''sequence builders'''				,							add_special_tokens=__SCREAMING_SNAKE_CASE				,							add_prefix_space=__SCREAMING_SNAKE_CASE		)
											lowercase_	:     str													=       tokenizer.encode(
											    '''sequence builders'''				,							'''multi-sequence build'''				,							add_special_tokens=__SCREAMING_SNAKE_CASE				,							add_prefix_space=__SCREAMING_SNAKE_CASE		)

											lowercase_	:     str													=       tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)

											assert encoded_sentence == encoded_text_from_decode
											assert encoded_pair == encoded_pair_from_decode






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Tuple													=       self.get_tokenizer()

											lowercase_	:     Optional[Any]													=       '''Encode this sequence.'''
											lowercase_	:     Optional[Any]													=       tokenizer.byte_encoder[''' '''.encode('''utf-8'''		)[0]]

											# Testing encoder arguments
											lowercase_	:     int													=       tokenizer.encode(__SCREAMING_SNAKE_CASE				,							add_special_tokens=__SCREAMING_SNAKE_CASE				,							add_prefix_space=__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Tuple													=       tokenizer.convert_ids_to_tokens(encoded[0]		)[0]
											self.assertNotEqual(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Optional[int]													=       tokenizer.encode(__SCREAMING_SNAKE_CASE				,							add_special_tokens=__SCREAMING_SNAKE_CASE				,							add_prefix_space=__SCREAMING_SNAKE_CASE		)
											lowercase_	:     List[Any]													=       tokenizer.convert_ids_to_tokens(encoded[0]		)[0]
											self.assertEqual(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)

											tokenizer.add_special_tokens({'''bos_token''': '''<s>'''}		)
											lowercase_	:     List[str]													=       tokenizer.encode(__SCREAMING_SNAKE_CASE				,							add_special_tokens=__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Any													=       tokenizer.convert_ids_to_tokens(encoded[1]		)[0]
											self.assertNotEqual(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)

											# Testing spaces after special tokens
											lowercase_	:     Any													=       '''<mask>'''
											tokenizer.add_special_tokens(
											    {'''mask_token''': AddedToken(__SCREAMING_SNAKE_CASE				,							lstrip=__SCREAMING_SNAKE_CASE				,							rstrip=__SCREAMING_SNAKE_CASE		)}		)  # mask token has a left space
											lowercase_	:     List[Any]													=       tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE		)

											lowercase_	:     int													=       '''Encode <mask> sequence'''
											lowercase_	:     List[Any]													=       '''Encode <mask>sequence'''

											lowercase_	:     Tuple													=       tokenizer.encode(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Dict													=       encoded.index(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[int]													=       tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]		)[0]
											self.assertEqual(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)

											lowercase_	:     List[str]													=       tokenizer.encode(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       encoded.index(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     str													=       tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]		)[0]
											self.assertNotEqual(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											pass






					def 	_snake_case    (   self		):


											"""simple docstring"""





											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''		):
																							lowercase_	:     Optional[Any]													=       self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)
																							lowercase_	:     Union[str, Any]													=       self.tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)
																							lowercase_	:     str													=       '''A, <mask> AllenNLP sentence.'''
																							lowercase_	:     Optional[int]													=       tokenizer_r.encode_plus(__SCREAMING_SNAKE_CASE				,							add_special_tokens=__SCREAMING_SNAKE_CASE				,							return_token_type_ids=__SCREAMING_SNAKE_CASE		)
																							lowercase_	:     Optional[int]													=       tokenizer_p.encode_plus(__SCREAMING_SNAKE_CASE				,							add_special_tokens=__SCREAMING_SNAKE_CASE				,							return_token_type_ids=__SCREAMING_SNAKE_CASE		)

																							# token_type_ids should put 0 everywhere
																							self.assertEqual(sum(tokens_r['''token_type_ids''']		)				,							sum(tokens_p['''token_type_ids''']		)		)

																							# attention_mask should put 1 everywhere, so sum over length should be 1
																							self.assertEqual(
																							    sum(tokens_r['''attention_mask''']		) / len(tokens_r['''attention_mask''']		)				,							sum(tokens_p['''attention_mask''']		) / len(tokens_p['''attention_mask''']		)				,							)

																							lowercase_	:     Union[str, Any]													=       tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']		)
																							lowercase_	:     List[Any]													=       tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']		)

																							# Rust correctly handles the space before the mask while python doesnt
																							self.assertSequenceEqual(tokens_p['''input_ids''']				,							[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]		)
																							self.assertSequenceEqual(tokens_r['''input_ids''']				,							[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]		)

																							self.assertSequenceEqual(
																							    __SCREAMING_SNAKE_CASE				,							['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']		)
																							self.assertSequenceEqual(
																							    __SCREAMING_SNAKE_CASE				,							['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											for trim_offsets, add_prefix_space in itertools.product([True, False]				,							repeat=2		):
																	lowercase_	:     List[str]													=       self.rust_tokenizer_class.from_pretrained(
																	    self.tmpdirname				,							use_fast=__SCREAMING_SNAKE_CASE				,							add_prefix_space=__SCREAMING_SNAKE_CASE				,							trim_offsets=__SCREAMING_SNAKE_CASE		)

																	lowercase_	:     List[str]													=       json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()		)
																	lowercase_	:     List[Any]													=       json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()		)

																	self.assertEqual(pre_tokenizer_state['''add_prefix_space''']				,							__SCREAMING_SNAKE_CASE		)

																	self.assertEqual(post_processor_state['''add_prefix_space''']				,							__SCREAMING_SNAKE_CASE		)
																	self.assertEqual(post_processor_state['''trim_offsets''']				,							__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''		):
																							lowercase_	:     Optional[Any]													=       '''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
																							lowercase_	:     int													=       F'''{text_of_1_token} {text_of_1_token}'''

																							lowercase_	:     Dict													=       self.rust_tokenizer_class.from_pretrained(
																							    __SCREAMING_SNAKE_CASE				,							use_fast=__SCREAMING_SNAKE_CASE				,							add_prefix_space=__SCREAMING_SNAKE_CASE				,							trim_offsets=__SCREAMING_SNAKE_CASE		)
																							lowercase_	:     Tuple													=       tokenizer_r(__SCREAMING_SNAKE_CASE				,							return_offsets_mapping=__SCREAMING_SNAKE_CASE				,							add_special_tokens=__SCREAMING_SNAKE_CASE		)
																							self.assertEqual(encoding.offset_mapping[0]				,							(0, len(__SCREAMING_SNAKE_CASE		))		)
																							self.assertEqual(
																							    encoding.offset_mapping[1]				,							(len(__SCREAMING_SNAKE_CASE		) + 1, len(__SCREAMING_SNAKE_CASE		) + 1 + len(__SCREAMING_SNAKE_CASE		))				,							)

																							lowercase_	:     Optional[int]													=       self.rust_tokenizer_class.from_pretrained(
																							    __SCREAMING_SNAKE_CASE				,							use_fast=__SCREAMING_SNAKE_CASE				,							add_prefix_space=__SCREAMING_SNAKE_CASE				,							trim_offsets=__SCREAMING_SNAKE_CASE		)
																							lowercase_	:     List[str]													=       tokenizer_r(__SCREAMING_SNAKE_CASE				,							return_offsets_mapping=__SCREAMING_SNAKE_CASE				,							add_special_tokens=__SCREAMING_SNAKE_CASE		)
																							self.assertEqual(encoding.offset_mapping[0]				,							(0, len(__SCREAMING_SNAKE_CASE		))		)
																							self.assertEqual(
																							    encoding.offset_mapping[1]				,							(len(__SCREAMING_SNAKE_CASE		) + 1, len(__SCREAMING_SNAKE_CASE		) + 1 + len(__SCREAMING_SNAKE_CASE		))				,							)

																							lowercase_	:     Optional[Any]													=       self.rust_tokenizer_class.from_pretrained(
																							    __SCREAMING_SNAKE_CASE				,							use_fast=__SCREAMING_SNAKE_CASE				,							add_prefix_space=__SCREAMING_SNAKE_CASE				,							trim_offsets=__SCREAMING_SNAKE_CASE		)
																							lowercase_	:     Dict													=       tokenizer_r(__SCREAMING_SNAKE_CASE				,							return_offsets_mapping=__SCREAMING_SNAKE_CASE				,							add_special_tokens=__SCREAMING_SNAKE_CASE		)
																							self.assertEqual(encoding.offset_mapping[0]				,							(0, len(__SCREAMING_SNAKE_CASE		))		)
																							self.assertEqual(
																							    encoding.offset_mapping[1]				,							(len(__SCREAMING_SNAKE_CASE		), len(__SCREAMING_SNAKE_CASE		) + 1 + len(__SCREAMING_SNAKE_CASE		))				,							)

																							lowercase_	:     Union[str, Any]													=       self.rust_tokenizer_class.from_pretrained(
																							    __SCREAMING_SNAKE_CASE				,							use_fast=__SCREAMING_SNAKE_CASE				,							add_prefix_space=__SCREAMING_SNAKE_CASE				,							trim_offsets=__SCREAMING_SNAKE_CASE		)
																							lowercase_	:     Dict													=       tokenizer_r(__SCREAMING_SNAKE_CASE				,							return_offsets_mapping=__SCREAMING_SNAKE_CASE				,							add_special_tokens=__SCREAMING_SNAKE_CASE		)
																							self.assertEqual(encoding.offset_mapping[0]				,							(0, len(__SCREAMING_SNAKE_CASE		))		)
																							self.assertEqual(
																							    encoding.offset_mapping[1]				,							(len(__SCREAMING_SNAKE_CASE		), len(__SCREAMING_SNAKE_CASE		) + 1 + len(__SCREAMING_SNAKE_CASE		))				,							)

																							lowercase_	:     List[str]													=       F''' {text}'''

																							# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
																							#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
																							# )
																							# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
																							# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
																							# self.assertEqual(
																							#     encoding.offset_mapping[1],
																							#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
																							# )

																							lowercase_	:     str													=       self.rust_tokenizer_class.from_pretrained(
																							    __SCREAMING_SNAKE_CASE				,							use_fast=__SCREAMING_SNAKE_CASE				,							add_prefix_space=__SCREAMING_SNAKE_CASE				,							trim_offsets=__SCREAMING_SNAKE_CASE		)
																							lowercase_	:     Optional[Any]													=       tokenizer_r(__SCREAMING_SNAKE_CASE				,							return_offsets_mapping=__SCREAMING_SNAKE_CASE				,							add_special_tokens=__SCREAMING_SNAKE_CASE		)
																							self.assertEqual(encoding.offset_mapping[0]				,							(1, 1 + len(__SCREAMING_SNAKE_CASE		))		)
																							self.assertEqual(
																							    encoding.offset_mapping[1]				,							(1 + len(__SCREAMING_SNAKE_CASE		) + 1, 1 + len(__SCREAMING_SNAKE_CASE		) + 1 + len(__SCREAMING_SNAKE_CASE		))				,							)

																							lowercase_	:     Any													=       self.rust_tokenizer_class.from_pretrained(
																							    __SCREAMING_SNAKE_CASE				,							use_fast=__SCREAMING_SNAKE_CASE				,							add_prefix_space=__SCREAMING_SNAKE_CASE				,							trim_offsets=__SCREAMING_SNAKE_CASE		)
																							lowercase_	:     str													=       tokenizer_r(__SCREAMING_SNAKE_CASE				,							return_offsets_mapping=__SCREAMING_SNAKE_CASE				,							add_special_tokens=__SCREAMING_SNAKE_CASE		)
																							self.assertEqual(encoding.offset_mapping[0]				,							(0, 1 + len(__SCREAMING_SNAKE_CASE		))		)
																							self.assertEqual(
																							    encoding.offset_mapping[1]				,							(1 + len(__SCREAMING_SNAKE_CASE		), 1 + len(__SCREAMING_SNAKE_CASE		) + 1 + len(__SCREAMING_SNAKE_CASE		))				,							)

																							lowercase_	:     List[str]													=       self.rust_tokenizer_class.from_pretrained(
																							    __SCREAMING_SNAKE_CASE				,							use_fast=__SCREAMING_SNAKE_CASE				,							add_prefix_space=__SCREAMING_SNAKE_CASE				,							trim_offsets=__SCREAMING_SNAKE_CASE		)
																							lowercase_	:     Dict													=       tokenizer_r(__SCREAMING_SNAKE_CASE				,							return_offsets_mapping=__SCREAMING_SNAKE_CASE				,							add_special_tokens=__SCREAMING_SNAKE_CASE		)
																							self.assertEqual(encoding.offset_mapping[0]				,							(0, 1 + len(__SCREAMING_SNAKE_CASE		))		)
																							self.assertEqual(
																							    encoding.offset_mapping[1]				,							(1 + len(__SCREAMING_SNAKE_CASE		), 1 + len(__SCREAMING_SNAKE_CASE		) + 1 + len(__SCREAMING_SNAKE_CASE		))				,							)





'''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  lowerCAmelCase__   :
					lowerCAmelCase_        =     42

					# setable values
					lowerCAmelCase_        =     42
					lowerCAmelCase_        =     42
					lowerCAmelCase_        =     None






					@classmethod
					def 	_snake_case    (   cls				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return cls(common=__SCREAMING_SNAKE_CASE				,							init_noise_sigma=__SCREAMING_SNAKE_CASE				,							timesteps=__SCREAMING_SNAKE_CASE		)







@dataclass
class  lowerCAmelCase__   (  lowerCamelCase_     ):
					lowerCAmelCase_        =     42







class  lowerCAmelCase__   (  lowerCamelCase_    ,						lowerCamelCase_     ):
					lowerCAmelCase_        =     [e.name for e in FlaxKarrasDiffusionSchedulers]

					lowerCAmelCase_        =     42






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return True






					@register_to_config
					def __init__(   self				,							__SCREAMING_SNAKE_CASE = 10_00				,							__SCREAMING_SNAKE_CASE = 0.0_001				,							__SCREAMING_SNAKE_CASE = 0.02				,							__SCREAMING_SNAKE_CASE = "linear"				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = "fixed_small"				,							__SCREAMING_SNAKE_CASE = True				,							__SCREAMING_SNAKE_CASE = "epsilon"				,							__SCREAMING_SNAKE_CASE = jnp.floataa				,							):


											"""simple docstring"""





											lowercase_	:     Dict													=       dtype






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											if common is None:
																	lowercase_	:     Tuple													=       CommonSchedulerState.create(self		)

											# standard deviation of the initial noise distribution
											lowercase_	:     Union[str, Any]													=       jnp.array(1.0				,							dtype=self.dtype		)

											lowercase_	:     List[Any]													=       jnp.arange(0				,							self.config.num_train_timesteps		).round()[::-1]

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






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											return sample






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = ()		):


											"""simple docstring"""





											lowercase_	:     Optional[Any]													=       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
											lowercase_	:     int													=       (jnp.arange(0				,							__SCREAMING_SNAKE_CASE		) * step_ratio).round()[::-1]

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






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE=None		):


											"""simple docstring"""





											lowercase_	:     List[Any]													=       state.common.alphas_cumprod[t]
											lowercase_	:     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
											lowercase_	:     int													=       (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

											if variance_type is None:
																	lowercase_	:     str													=       self.config.variance_type

											# hacks - were probably added for training stability
											if variance_type == "fixed_small":
																	lowercase_	:     int													=       jnp.clip(__SCREAMING_SNAKE_CASE				,							a_min=1E-2_0		)
											# for rl-diffuser https://arxiv.org/abs/2205.09991
											elif variance_type == "fixed_small_log":
																	lowercase_	:     List[str]													=       jnp.log(jnp.clip(__SCREAMING_SNAKE_CASE				,							a_min=1E-2_0		)		)
											elif variance_type == "fixed_large":
																	lowercase_	:     List[Any]													=       state.common.betas[t]
											elif variance_type == "fixed_large_log":
																	# Glide max_log
																	lowercase_	:     List[Any]													=       jnp.log(state.common.betas[t]		)
											elif variance_type == "learned":
																	return predicted_variance
											elif variance_type == "learned_range":
																	lowercase_	:     Optional[Any]													=       variance
																	lowercase_	:     Union[str, Any]													=       state.common.betas[t]
																	lowercase_	:     Union[str, Any]													=       (predicted_variance + 1) / 2
																	lowercase_	:     Any													=       frac * max_log + (1 - frac) * min_log

											return variance






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = True				,							):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       timestep

											if key is None:
																	lowercase_	:     int													=       jax.random.PRNGKey(0		)

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

											# 1. compute alphas, betas
											lowercase_	:     Any													=       state.common.alphas_cumprod[t]
											lowercase_	:     Optional[int]													=       jnp.where(t > 0				,							state.common.alphas_cumprod[t - 1]				,							jnp.array(1.0				,							dtype=self.dtype		)		)
											lowercase_	:     int													=       1 - alpha_prod_t
											lowercase_	:     str													=       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":
																	lowercase_	:     Tuple													=       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
											elif self.config.prediction_type == "sample":
																	lowercase_	:     Any													=       model_output
											elif self.config.prediction_type == "v_prediction":
																	lowercase_	:     List[Any]													=       (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:
																	lowercase_	:     Optional[Any]													=       jnp.clip(__SCREAMING_SNAKE_CASE				,							-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
											lowercase_	:     List[Any]													=       (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
											lowercase_	:     Optional[Any]													=       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
											lowercase_	:     Optional[Any]													=       pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

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

											lowercase_	:     Any													=       pred_prev_sample + variance

											if not return_dict:
																	return (pred_prev_sample, state)

											return FlaxDDPMSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE				,							state=__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											return add_noise_common(state.common				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											return get_velocity_common(state.common				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)






					def __len__(   self		):


											"""simple docstring"""





											return self.config.num_train_timesteps



