






import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin





class       __a  (   lowerCamelCase__	,       unittest.TestCase	):
	_lowerCAmelCase	:	Optional[Any]				=			CanineTokenizer
	_lowerCAmelCase	:	Optional[int]				=			False







	def  __lowercase (		self :       Dict						):


						'''simple docstring'''






						super().setUp()
						UpperCamelCase__      :       Optional[Any]	    =  CanineTokenizer()
						tokenizer.save_pretrained(self.tmpdirname						)







	@cached_property
	def  __lowercase (		self :       Union[str, Any]						):


						'''simple docstring'''






						return CanineTokenizer.from_pretrained("google/canine-s"						)







	def  __lowercase (		self :       str							,       **SCREAMING_SNAKE_CASE :       Optional[Any]						):


						'''simple docstring'''






						UpperCamelCase__      :       Union[str, Any]	    =  self.tokenizer_class.from_pretrained(self.tmpdirname							,       **lowercase__						)
						UpperCamelCase__      :       Dict	    =  10_24
						return tokenizer







	@require_torch
	def  __lowercase (		self :       Union[str, Any]						):


						'''simple docstring'''






						UpperCamelCase__      :       str	    =  self.canine_tokenizer
						UpperCamelCase__      :       Optional[Any]	    =  ["Life is like a box of chocolates.", "You never know what you\'re gonna get."]
						# fmt: off
						UpperCamelCase__      :       Tuple	    =  [5_73_44, 76, 1_05, 1_02, 1_01, 32, 1_05, 1_15, 32, 1_08, 1_05, 1_07, 1_01, 32, 97, 32, 98, 1_11, 1_20, 32, 1_11, 1_02, 32, 99, 1_04, 1_11, 99, 1_11, 1_08, 97, 1_16, 1_01, 1_15, 46, 5_73_45, 0, 0, 0, 0]
						# fmt: on
						UpperCamelCase__      :       Optional[Any]	    =  tokenizer(lowercase__							,       padding=lowercase__							,       return_tensors="pt"						)
						self.assertIsInstance(lowercase__							,       lowercase__						)

						UpperCamelCase__      :       Optional[int]	    =  list(batch.input_ids.numpy()[0]						)

						self.assertListEqual(lowercase__							,       lowercase__						)

						self.assertEqual((2, 39)							,       batch.input_ids.shape						)
						self.assertEqual((2, 39)							,       batch.attention_mask.shape						)







	@require_torch
	def  __lowercase (		self :       Dict						):


						'''simple docstring'''






						UpperCamelCase__      :       Any	    =  self.canine_tokenizer
						UpperCamelCase__      :       Union[str, Any]	    =  ["Once there was a man.", "He wrote a test in HuggingFace Tranformers."]
						UpperCamelCase__      :       Any	    =  tokenizer(lowercase__							,       padding=lowercase__							,       return_tensors="pt"						)
						# check if input_ids, attention_mask and token_type_ids are returned
						self.assertIn("input_ids"							,       lowercase__						)
						self.assertIn("attention_mask"							,       lowercase__						)
						self.assertIn("token_type_ids"							,       lowercase__						)







	@require_torch
	def  __lowercase (		self :       Dict						):


						'''simple docstring'''






						UpperCamelCase__      :       Optional[int]	    =  self.canine_tokenizer
						UpperCamelCase__      :       Optional[Any]	    =  [
						    "What\'s the weater?",
						    "It\'s about 25 degrees.",
						]
						UpperCamelCase__      :       str	    =  tokenizer(
						    text_target=lowercase__							,       max_length=32							,       padding="max_length"							,       truncation=lowercase__							,       return_tensors="pt"						)
						self.assertEqual(32							,       targets["input_ids"].shape[1]						)







	def  __lowercase (		self :       List[str]						):


						'''simple docstring'''






						UpperCamelCase__      :       List[Any]	    =  self.get_tokenizers()
						for tokenizer in tokenizers:
											with self.subTest(F'{tokenizer.__class__.__name__}'						):
																self.assertNotEqual(tokenizer.model_max_length							,       42						)

																   # Now let's start the test
						UpperCamelCase__      :       List[str]	    =  self.get_tokenizers()
						for tokenizer in tokenizers:
											with self.subTest(F'{tokenizer.__class__.__name__}'						):
																# Isolate this from the other tests because we save additional tokens/etc
																UpperCamelCase__      :       List[Any]	    =  tempfile.mkdtemp()

																UpperCamelCase__      :       Tuple	    =  " He is very happy, UNwant\u00E9d,running"
																UpperCamelCase__      :       List[str]	    =  tokenizer.encode(lowercase__							,       add_special_tokens=lowercase__						)
																tokenizer.save_pretrained(lowercase__						)

																UpperCamelCase__      :       Optional[Any]	    =  tokenizer.__class__.from_pretrained(lowercase__						)
																UpperCamelCase__      :       str	    =  after_tokenizer.encode(lowercase__							,       add_special_tokens=lowercase__						)
																self.assertListEqual(lowercase__							,       lowercase__						)

																shutil.rmtree(lowercase__						)

						UpperCamelCase__      :       List[str]	    =  self.get_tokenizers(model_max_length=42						)
						for tokenizer in tokenizers:
											with self.subTest(F'{tokenizer.__class__.__name__}'						):
																# Isolate this from the other tests because we save additional tokens/etc
																UpperCamelCase__      :       str	    =  tempfile.mkdtemp()

																UpperCamelCase__      :       Optional[int]	    =  " He is very happy, UNwant\u00E9d,running"

																UpperCamelCase__      :       str	    =  tokenizer.additional_special_tokens

																# We can add a new special token for Canine as follows:
																UpperCamelCase__      :       Optional[Any]	    =  chr(0Xe0_07						)
																additional_special_tokens.append(lowercase__						)
																tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}						)
																UpperCamelCase__      :       Dict	    =  tokenizer.encode(lowercase__							,       add_special_tokens=lowercase__						)
																tokenizer.save_pretrained(lowercase__						)

																UpperCamelCase__      :       List[Any]	    =  tokenizer.__class__.from_pretrained(lowercase__						)
																UpperCamelCase__      :       Optional[int]	    =  after_tokenizer.encode(lowercase__							,       add_special_tokens=lowercase__						)
																self.assertListEqual(lowercase__							,       lowercase__						)
																self.assertIn(lowercase__							,       after_tokenizer.additional_special_tokens						)
																self.assertEqual(after_tokenizer.model_max_length							,       42						)

																UpperCamelCase__      :       Tuple	    =  tokenizer.__class__.from_pretrained(lowercase__							,       model_max_length=43						)
																self.assertEqual(tokenizer.model_max_length							,       43						)

																shutil.rmtree(lowercase__						)







	def  __lowercase (		self :       Optional[Any]						):


						'''simple docstring'''






						UpperCamelCase__      :       Dict	    =  self.get_tokenizers(do_lower_case=lowercase__						)
						for tokenizer in tokenizers:
											with self.subTest(F'{tokenizer.__class__.__name__}'						):
																UpperCamelCase__    ,					UpperCamelCase__      :       Union[str, Any]	    =  self.get_clean_sequence(lowercase__						)

																# a special token for Canine can be defined as follows:
																UpperCamelCase__      :       int	    =  0Xe0_05
																UpperCamelCase__      :       Union[str, Any]	    =  chr(lowercase__						)

																tokenizer.add_special_tokens({"cls_token": special_token}						)
																UpperCamelCase__      :       Any	    =  tokenizer.encode(lowercase__							,       add_special_tokens=lowercase__						)
																self.assertEqual(len(lowercase__						)							,       1						)

																UpperCamelCase__      :       Tuple	    =  tokenizer.decode(ids + encoded_special_token							,       clean_up_tokenization_spaces=lowercase__						)
																UpperCamelCase__      :       Union[str, Any]	    =  tokenizer.encode(lowercase__							,       add_special_tokens=lowercase__						)

																UpperCamelCase__      :       str	    =  tokenizer.encode(lowercase__							,       add_special_tokens=lowercase__						)
																UpperCamelCase__      :       Dict	    =  tokenizer.encode(lowercase__							,       add_special_tokens=lowercase__						)
																self.assertEqual(lowercase__							,       input_encoded + special_token_id						)

																UpperCamelCase__      :       Dict	    =  tokenizer.decode(lowercase__							,       skip_special_tokens=lowercase__						)
																self.assertTrue(special_token not in decoded						)







	def  __lowercase (		self :       Dict						):


						'''simple docstring'''






						UpperCamelCase__      :       Optional[int]	    =  self.get_tokenizers(do_lower_case=lowercase__						)
						for tokenizer in tokenizers:
											with self.subTest(F'{tokenizer.__class__.__name__}'						):
																UpperCamelCase__      :       int	    =  chr(0Xe0_05						)
																UpperCamelCase__      :       Tuple	    =  chr(0Xe0_06						)

																# `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
																tokenizer.add_tokens([SPECIAL_TOKEN_1]							,       special_tokens=lowercase__						)
																# `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
																# which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
																tokenizer.add_special_tokens({"additional_special_tokens": [SPECIAL_TOKEN_2]}						)

																UpperCamelCase__      :       Optional[int]	    =  tokenizer.tokenize(lowercase__						)
																UpperCamelCase__      :       Optional[int]	    =  tokenizer.tokenize(lowercase__						)

																self.assertEqual(len(lowercase__						)							,       1						)
																self.assertEqual(len(lowercase__						)							,       1						)
																self.assertEqual(token_a[0]							,       lowercase__						)
																self.assertEqual(token_a[0]							,       lowercase__						)







	@require_tokenizers
	def  __lowercase (		self :       Tuple						):


						'''simple docstring'''






						UpperCamelCase__      :       Dict	    =  self.get_tokenizers(do_lower_case=lowercase__						)
						for tokenizer in tokenizers:
											with self.subTest(F'{tokenizer.__class__.__name__}'						):
																# a special token for Canine can be defined as follows:
																UpperCamelCase__      :       str	    =  0Xe0_06
																UpperCamelCase__      :       int	    =  chr(lowercase__						)

																UpperCamelCase__      :       str	    =  AddedToken(lowercase__							,       lstrip=lowercase__						)
																tokenizer.add_special_tokens({"additional_special_tokens": [new_token]}						)

																with tempfile.TemporaryDirectory() as tmp_dir_name:
																					tokenizer.save_pretrained(lowercase__						)
																					tokenizer.from_pretrained(lowercase__						)







	def  __lowercase (		self :       Optional[int]						):


						'''simple docstring'''






						UpperCamelCase__      :       List[Any]	    =  []
						if self.test_slow_tokenizer:
											tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())						)

						if self.test_rust_tokenizer:
											tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())						)

						for tokenizer_class, tokenizer_utils in tokenizer_list:
											with tempfile.TemporaryDirectory() as tmp_dir:
																tokenizer_utils.save_pretrained(lowercase__						)

																with open(os.path.join(lowercase__							,       "special_tokens_map.json"						)							,       encoding="utf-8"						) as json_file:
																					UpperCamelCase__      :       Any	    =  json.load(lowercase__						)

																with open(os.path.join(lowercase__							,       "tokenizer_config.json"						)							,       encoding="utf-8"						) as json_file:
																					UpperCamelCase__      :       Optional[int]	    =  json.load(lowercase__						)

																# a special token for Canine can be defined as follows:
																UpperCamelCase__      :       str	    =  0Xe0_06
																UpperCamelCase__      :       Dict	    =  chr(lowercase__						)

																UpperCamelCase__      :       Union[str, Any]	    =  [new_token_a]
																UpperCamelCase__      :       Optional[int]	    =  [new_token_a]

																with open(os.path.join(lowercase__							,       "special_tokens_map.json"						)							,       "w"							,       encoding="utf-8"						) as outfile:
																					json.dump(lowercase__							,       lowercase__						)
																with open(os.path.join(lowercase__							,       "tokenizer_config.json"						)							,       "w"							,       encoding="utf-8"						) as outfile:
																					json.dump(lowercase__							,       lowercase__						)

																# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
																# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
																# "special_tokens_map.json" files
																UpperCamelCase__      :       Any	    =  tokenizer_class.from_pretrained(lowercase__							,       extra_ids=0						)
																self.assertIn(lowercase__							,       tokenizer_without_change_in_init.additional_special_tokens						)
																# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
																self.assertEqual(
																    [new_token_a]							,       tokenizer_without_change_in_init.convert_ids_to_tokens(
																        tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]						)						)							,       )

																UpperCamelCase__      :       int	    =  0Xe0_07
																UpperCamelCase__      :       Tuple	    =  chr(lowercase__						)
																# Now we test that we can change the value of additional_special_tokens in the from_pretrained
																UpperCamelCase__      :       Any	    =  [AddedToken(lowercase__							,       lstrip=lowercase__						)]
																UpperCamelCase__      :       str	    =  tokenizer_class.from_pretrained(
																    lowercase__							,       additional_special_tokens=lowercase__							,       extra_ids=0						)

																self.assertIn(lowercase__							,       tokenizer.additional_special_tokens						)
																# self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
																self.assertEqual(
																    [new_token_a]							,       tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]						)						)						)







	@require_tokenizers
	def  __lowercase (		self :       Tuple						):


						'''simple docstring'''






						UpperCamelCase__      :       List[Any]	    =  self.get_tokenizers(do_lower_case=lowercase__						)
						for tokenizer in tokenizers:
											with self.subTest(F'{tokenizer.__class__.__name__}'						):
																UpperCamelCase__      :       Union[str, Any]	    =  "hello world"
																if self.space_between_special_tokens:
																					UpperCamelCase__      :       List[str]	    =  "[CLS] hello world [SEP]"
																else:
																					UpperCamelCase__      :       List[Any]	    =  input
																UpperCamelCase__      :       Any	    =  tokenizer.encode(lowercase__							,       add_special_tokens=lowercase__						)
																UpperCamelCase__      :       str	    =  tokenizer.decode(lowercase__							,       spaces_between_special_tokens=self.space_between_special_tokens						)
																self.assertIn(lowercase__							,       [output, output.lower()]						)







	def  __lowercase (		self :       Optional[Any]						):


						'''simple docstring'''






						UpperCamelCase__      :       Any	    =  self.get_tokenizers()
						for tokenizer in tokenizers:
											with self.subTest(F'{tokenizer.__class__.__name__}'						):
																UpperCamelCase__      :       Dict	    =  [
																    "bos_token",
																    "eos_token",
																    "unk_token",
																    "sep_token",
																    "pad_token",
																    "cls_token",
																    "mask_token",
																]

						UpperCamelCase__      :       List[Any]	    =  "a"
						UpperCamelCase__      :       Optional[int]	    =  ord(lowercase__						)

						for attr in attributes_list:
											setattr(lowercase__							,       attr + "_id"							,       lowercase__						)
											self.assertEqual(getattr(lowercase__							,       lowercase__						)							,       lowercase__						)
											self.assertEqual(getattr(lowercase__							,       attr + "_id"						)							,       lowercase__						)

											setattr(lowercase__							,       attr + "_id"							,       lowercase__						)
											self.assertEqual(getattr(lowercase__							,       lowercase__						)							,       lowercase__						)
											self.assertEqual(getattr(lowercase__							,       attr + "_id"						)							,       lowercase__						)

						setattr(lowercase__							,       "additional_special_tokens_ids"							,       []						)
						self.assertListEqual(getattr(lowercase__							,       "additional_special_tokens"						)							,       []						)
						self.assertListEqual(getattr(lowercase__							,       "additional_special_tokens_ids"						)							,       []						)

						UpperCamelCase__      :       int	    =  0Xe0_06
						UpperCamelCase__      :       Any	    =  chr(lowercase__						)
						setattr(lowercase__							,       "additional_special_tokens_ids"							,       [additional_special_token_id]						)
						self.assertListEqual(getattr(lowercase__							,       "additional_special_tokens"						)							,       [additional_special_token]						)
						self.assertListEqual(getattr(lowercase__							,       "additional_special_tokens_ids"						)							,       [additional_special_token_id]						)







	def  __lowercase (		self :       Dict						):


						'''simple docstring'''






						pass







	def  __lowercase (		self :       Dict						):


						'''simple docstring'''






						pass







	def  __lowercase (		self :       str						):


						'''simple docstring'''






						pass







	def  __lowercase (		self :       Union[str, Any]						):


						'''simple docstring'''






						pass







	def  __lowercase (		self :       Optional[int]						):


						'''simple docstring'''






						pass







	def  __lowercase (		self :       Any						):


						'''simple docstring'''






						pass







	def  __lowercase (		self :       List[str]						):


						'''simple docstring'''






						pass







	def  __lowercase (		self :       str						):


						'''simple docstring'''






						pass







# 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       __a  :
 _lowerCAmelCase	:	CommonSchedulerState

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







 @classmethod
 def  __lowercase (		cls :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       CommonSchedulerState							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       jnp.ndarray						):


      '''simple docstring'''






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







@dataclass
class       __a  (   A__	):
 _lowerCAmelCase	:	DDPMSchedulerState







class       __a  (   A__	,       A__	):
 _lowerCAmelCase	:	Tuple				=			[e.name for e in FlaxKarrasDiffusionSchedulers]

 _lowerCAmelCase	:	jnp.dtype







 @property
 def  __lowercase (		self :       List[str]						):


      '''simple docstring'''






      return True







 @register_to_config
 def __init__(		self :       Dict							,       SCREAMING_SNAKE_CASE :       int = 10_00							,       SCREAMING_SNAKE_CASE :       float = 0.0_0_0_1							,       SCREAMING_SNAKE_CASE :       float = 0.0_2							,       SCREAMING_SNAKE_CASE :       str = "linear"							,       SCREAMING_SNAKE_CASE :       Optional[jnp.ndarray] = None							,       SCREAMING_SNAKE_CASE :       str = "fixed_small"							,       SCREAMING_SNAKE_CASE :       bool = True							,       SCREAMING_SNAKE_CASE :       str = "epsilon"							,       SCREAMING_SNAKE_CASE :       jnp.dtype = jnp.floataa							,       ):


      '''simple docstring'''






      UpperCamelCase__      :       int	    =  dtype







 def  __lowercase (		self :       List[Any]							,       SCREAMING_SNAKE_CASE :       Optional[CommonSchedulerState] = None						):


      '''simple docstring'''






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

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

      UpperCamelCase__      :       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  __lowercase (		self :       Tuple							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       Optional[int] = None						):


      '''simple docstring'''






      return sample







 def  __lowercase (		self :       Tuple							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       int							,       SCREAMING_SNAKE_CASE :       Tuple = ()						):


      '''simple docstring'''






      UpperCamelCase__      :       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
      UpperCamelCase__      :       Optional[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  __lowercase (		self :       str							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       List[str]							,       SCREAMING_SNAKE_CASE :       Dict=None							,       SCREAMING_SNAKE_CASE :       Optional[Any]=None						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[int]	    =  state.common.alphas_cumprod[t]
      UpperCamelCase__      :       Tuple	    =  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
      UpperCamelCase__      :       int	    =  (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

      # hacks - were probably added for training stability
      if variance_type == "fixed_small":
           UpperCamelCase__      :       Union[str, Any]	    =  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":
           UpperCamelCase__      :       Optional[Any]	    =  jnp.log(jnp.clip(SCREAMING_SNAKE_CASE							,       a_min=1e-2_0						)						)
      elif variance_type == "fixed_large":
           UpperCamelCase__      :       Dict	    =  state.common.betas[t]
      elif variance_type == "fixed_large_log":
           # Glide max_log
           UpperCamelCase__      :       List[str]	    =  jnp.log(state.common.betas[t]						)
      elif variance_type == "learned":
           return predicted_variance
      elif variance_type == "learned_range":
           UpperCamelCase__      :       Tuple	    =  variance
           UpperCamelCase__      :       int	    =  state.common.betas[t]
           UpperCamelCase__      :       Union[str, Any]	    =  (predicted_variance + 1) / 2
           UpperCamelCase__      :       List[str]	    =  frac * max_log + (1 - frac) * min_log

      return variance







 def  __lowercase (		self :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       int							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       Optional[jax.random.KeyArray] = None							,       SCREAMING_SNAKE_CASE :       bool = True							,       ):


      '''simple docstring'''






      UpperCamelCase__      :       str	    =  timestep

      if key is None:
           UpperCamelCase__      :       str	    =  jax.random.PRNGKey(0						)

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

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

      # 6. Add noise
      def random_variance():
           UpperCamelCase__      :       str	    =  jax.random.split(SCREAMING_SNAKE_CASE							,       num=1						)
           UpperCamelCase__      :       Tuple	    =  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

      UpperCamelCase__      :       Any	    =  jnp.where(t > 0							,       random_variance()							,       jnp.zeros(model_output.shape							,       dtype=self.dtype						)						)

      UpperCamelCase__      :       List[str]	    =  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  __lowercase (		self :       Any							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       ):


      '''simple docstring'''






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







 def  __lowercase (		self :       Dict							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       ):


      '''simple docstring'''






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







 def __len__(		self :       int						):


      '''simple docstring'''






      return self.config.num_train_timesteps