






import unittest

from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_detectrona,
    require_pytesseract,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
 from PIL import Image

 from transformers.image_utils import load_image
else:





 class       __a  :







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


       '''simple docstring'''






       pass







 def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     Dict:
      return None


# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
lowerCamelCase     :							Any    =(
    '''https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'''
)





@is_pipeline_test
@require_torch
@require_vision
class       __a  (   unittest.TestCase	):
 _lowerCAmelCase	:	int				=			MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING







 @require_pytesseract
 @require_vision
 def  __lowercase (		self :       int							,       SCREAMING_SNAKE_CASE :       List[str]							,       SCREAMING_SNAKE_CASE :       int							,       SCREAMING_SNAKE_CASE :       int						):


      '''simple docstring'''






      UpperCamelCase__      :       Any	    =  pipeline(
          "document-question-answering"							,       model=SCREAMING_SNAKE_CASE							,       tokenizer=SCREAMING_SNAKE_CASE							,       image_processor=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       str	    =  INVOICE_URL
      UpperCamelCase__      :       Dict	    =  list(zip(*apply_tesseract(load_image(SCREAMING_SNAKE_CASE						)							,       SCREAMING_SNAKE_CASE							,       ""						)						)						)
      UpperCamelCase__      :       Dict	    =  "What is the placebo?"
      UpperCamelCase__      :       str	    =  [
          {
              "image": load_image(SCREAMING_SNAKE_CASE						),
              "question": question,
          },
          {
              "image": image,
              "question": question,
          },
          {
              "image": image,
              "question": question,
              "word_boxes": word_boxes,
          },
      ]
      return dqa_pipeline, examples







 def  __lowercase (		self :       Optional[int]							,       SCREAMING_SNAKE_CASE :       int							,       SCREAMING_SNAKE_CASE :       Tuple						):


      '''simple docstring'''






      UpperCamelCase__      :       Any	    =  dqa_pipeline(SCREAMING_SNAKE_CASE							,       top_k=2						)
      self.assertEqual(
          SCREAMING_SNAKE_CASE							,       [
              [
                  {"score": ANY(SCREAMING_SNAKE_CASE						), "answer": ANY(SCREAMING_SNAKE_CASE						), "start": ANY(SCREAMING_SNAKE_CASE						), "end": ANY(SCREAMING_SNAKE_CASE						)},
                  {"score": ANY(SCREAMING_SNAKE_CASE						), "answer": ANY(SCREAMING_SNAKE_CASE						), "start": ANY(SCREAMING_SNAKE_CASE						), "end": ANY(SCREAMING_SNAKE_CASE						)},
              ]
          ]
          * 3							,       )







 @require_torch
 @require_detectrona
 @require_pytesseract
 def  __lowercase (		self :       int						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[Any]	    =  pipeline("document-question-answering"							,       model="hf-internal-testing/tiny-random-layoutlmv2"						)
      UpperCamelCase__      :       Optional[int]	    =  INVOICE_URL
      UpperCamelCase__      :       int	    =  "How many cats are there?"

      UpperCamelCase__      :       Dict	    =  [
          {"score": 0.0_0_0_1, "answer": "oy 2312/2019", "start": 38, "end": 39},
          {"score": 0.0_0_0_1, "answer": "oy 2312/2019 DUE", "start": 38, "end": 40},
      ]
      UpperCamelCase__      :       List[str]	    =  dqa_pipeline(image=SCREAMING_SNAKE_CASE							,       question=SCREAMING_SNAKE_CASE							,       top_k=2						)
      self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE							,       decimals=4						)							,       SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       Tuple	    =  dqa_pipeline({"image": image, "question": question}							,       top_k=2						)
      self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE							,       decimals=4						)							,       SCREAMING_SNAKE_CASE						)

      # This image does not detect ANY text in it, meaning layoutlmv2 should fail.
      # Empty answer probably
      UpperCamelCase__      :       str	    =  "./tests/fixtures/tests_samples/COCO/000000039769.png"
      UpperCamelCase__      :       Tuple	    =  dqa_pipeline(image=SCREAMING_SNAKE_CASE							,       question=SCREAMING_SNAKE_CASE							,       top_k=2						)
      self.assertEqual(SCREAMING_SNAKE_CASE							,       []						)

      # We can optionnally pass directly the words and bounding boxes
      UpperCamelCase__      :       List[str]	    =  "./tests/fixtures/tests_samples/COCO/000000039769.png"
      UpperCamelCase__      :       List[Any]	    =  []
      UpperCamelCase__      :       Dict	    =  []
      UpperCamelCase__      :       Union[str, Any]	    =  dqa_pipeline(image=SCREAMING_SNAKE_CASE							,       question=SCREAMING_SNAKE_CASE							,       words=SCREAMING_SNAKE_CASE							,       boxes=SCREAMING_SNAKE_CASE							,       top_k=2						)
      self.assertEqual(SCREAMING_SNAKE_CASE							,       []						)







 @slow
 @require_torch
 @require_detectrona
 @require_pytesseract
 def  __lowercase (		self :       Tuple						):


      '''simple docstring'''






      UpperCamelCase__      :       str	    =  pipeline(
          "document-question-answering"							,       model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa"							,       revision="9977165"							,       )
      UpperCamelCase__      :       Tuple	    =  INVOICE_URL
      UpperCamelCase__      :       int	    =  "What is the invoice number?"

      UpperCamelCase__      :       Optional[Any]	    =  dqa_pipeline(image=SCREAMING_SNAKE_CASE							,       question=SCREAMING_SNAKE_CASE							,       top_k=2						)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,       decimals=4						)							,       [
              {"score": 0.9_9_4_4, "answer": "us-001", "start": 16, "end": 16},
              {"score": 0.0_0_0_9, "answer": "us-001", "start": 16, "end": 16},
          ]							,       )

      UpperCamelCase__      :       Any	    =  dqa_pipeline({"image": image, "question": question}							,       top_k=2						)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,       decimals=4						)							,       [
              {"score": 0.9_9_4_4, "answer": "us-001", "start": 16, "end": 16},
              {"score": 0.0_0_0_9, "answer": "us-001", "start": 16, "end": 16},
          ]							,       )

      UpperCamelCase__      :       Any	    =  dqa_pipeline(
          [{"image": image, "question": question}, {"image": image, "question": question}]							,       top_k=2						)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,       decimals=4						)							,       [
              [
                  {"score": 0.9_9_4_4, "answer": "us-001", "start": 16, "end": 16},
                  {"score": 0.0_0_0_9, "answer": "us-001", "start": 16, "end": 16},
              ],
          ]
          * 2							,       )







 @slow
 @require_torch
 @require_detectrona
 @require_pytesseract
 def  __lowercase (		self :       List[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       List[str]	    =  pipeline(
          "document-question-answering"							,       model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa"							,       revision="9977165"							,       max_seq_len=50							,       )
      UpperCamelCase__      :       Union[str, Any]	    =  INVOICE_URL
      UpperCamelCase__      :       int	    =  "What is the invoice number?"

      UpperCamelCase__      :       List[str]	    =  dqa_pipeline(image=SCREAMING_SNAKE_CASE							,       question=SCREAMING_SNAKE_CASE							,       top_k=2						)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,       decimals=4						)							,       [
              {"score": 0.9_9_7_4, "answer": "1110212019", "start": 23, "end": 23},
              {"score": 0.9_9_4_8, "answer": "us-001", "start": 16, "end": 16},
          ]							,       )

      UpperCamelCase__      :       Any	    =  dqa_pipeline({"image": image, "question": question}							,       top_k=2						)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,       decimals=4						)							,       [
              {"score": 0.9_9_7_4, "answer": "1110212019", "start": 23, "end": 23},
              {"score": 0.9_9_4_8, "answer": "us-001", "start": 16, "end": 16},
          ]							,       )

      UpperCamelCase__      :       List[Any]	    =  dqa_pipeline(
          [{"image": image, "question": question}, {"image": image, "question": question}]							,       top_k=2						)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,       decimals=4						)							,       [
              [
                  {"score": 0.9_9_7_4, "answer": "1110212019", "start": 23, "end": 23},
                  {"score": 0.9_9_4_8, "answer": "us-001", "start": 16, "end": 16},
              ]
          ]
          * 2							,       )







 @slow
 @require_torch
 @require_pytesseract
 @require_vision
 def  __lowercase (		self :       List[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       Dict	    =  AutoTokenizer.from_pretrained(
          "impira/layoutlm-document-qa"							,       revision="3dc6de3"							,       add_prefix_space=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       Any	    =  pipeline(
          "document-question-answering"							,       model="impira/layoutlm-document-qa"							,       tokenizer=SCREAMING_SNAKE_CASE							,       revision="3dc6de3"							,       )
      UpperCamelCase__      :       int	    =  INVOICE_URL
      UpperCamelCase__      :       List[str]	    =  "What is the invoice number?"

      UpperCamelCase__      :       Tuple	    =  dqa_pipeline(image=SCREAMING_SNAKE_CASE							,       question=SCREAMING_SNAKE_CASE							,       top_k=2						)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,       decimals=4						)							,       [
              {"score": 0.4_2_5_1, "answer": "us-001", "start": 16, "end": 16},
              {"score": 0.0_8_1_9, "answer": "1110212019", "start": 23, "end": 23},
          ]							,       )

      UpperCamelCase__      :       str	    =  dqa_pipeline({"image": image, "question": question}							,       top_k=2						)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,       decimals=4						)							,       [
              {"score": 0.4_2_5_1, "answer": "us-001", "start": 16, "end": 16},
              {"score": 0.0_8_1_9, "answer": "1110212019", "start": 23, "end": 23},
          ]							,       )

      UpperCamelCase__      :       List[Any]	    =  dqa_pipeline(
          [{"image": image, "question": question}, {"image": image, "question": question}]							,       top_k=2						)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,       decimals=4						)							,       [
              [
                  {"score": 0.4_2_5_1, "answer": "us-001", "start": 16, "end": 16},
                  {"score": 0.0_8_1_9, "answer": "1110212019", "start": 23, "end": 23},
              ]
          ]
          * 2							,       )

      UpperCamelCase__      :       Optional[int]	    =  list(zip(*apply_tesseract(load_image(SCREAMING_SNAKE_CASE						)							,       SCREAMING_SNAKE_CASE							,       ""						)						)						)

      # This model should also work if `image` is set to None
      UpperCamelCase__      :       Dict	    =  dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question}							,       top_k=2						)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,       decimals=4						)							,       [
              {"score": 0.4_2_5_1, "answer": "us-001", "start": 16, "end": 16},
              {"score": 0.0_8_1_9, "answer": "1110212019", "start": 23, "end": 23},
          ]							,       )







 @slow
 @require_torch
 @require_pytesseract
 @require_vision
 def  __lowercase (		self :       Dict						):


      '''simple docstring'''






      UpperCamelCase__      :       str	    =  AutoTokenizer.from_pretrained(
          "impira/layoutlm-document-qa"							,       revision="3dc6de3"							,       add_prefix_space=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       Union[str, Any]	    =  pipeline(
          "document-question-answering"							,       model="impira/layoutlm-document-qa"							,       tokenizer=SCREAMING_SNAKE_CASE							,       revision="3dc6de3"							,       max_seq_len=50							,       )
      UpperCamelCase__      :       Dict	    =  INVOICE_URL
      UpperCamelCase__      :       Tuple	    =  "What is the invoice number?"

      UpperCamelCase__      :       Optional[Any]	    =  dqa_pipeline(image=SCREAMING_SNAKE_CASE							,       question=SCREAMING_SNAKE_CASE							,       top_k=2						)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,       decimals=4						)							,       [
              {"score": 0.9_9_9_9, "answer": "us-001", "start": 16, "end": 16},
              {"score": 0.9_9_9_8, "answer": "us-001", "start": 16, "end": 16},
          ]							,       )

      UpperCamelCase__      :       Optional[int]	    =  dqa_pipeline(
          [{"image": image, "question": question}, {"image": image, "question": question}]							,       top_k=2						)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,       decimals=4						)							,       [
              [
                  {"score": 0.9_9_9_9, "answer": "us-001", "start": 16, "end": 16},
                  {"score": 0.9_9_9_8, "answer": "us-001", "start": 16, "end": 16},
              ]
          ]
          * 2							,       )

      UpperCamelCase__      :       Any	    =  list(zip(*apply_tesseract(load_image(SCREAMING_SNAKE_CASE						)							,       SCREAMING_SNAKE_CASE							,       ""						)						)						)

      # This model should also work if `image` is set to None
      UpperCamelCase__      :       Any	    =  dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question}							,       top_k=2						)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,       decimals=4						)							,       [
              {"score": 0.9_9_9_9, "answer": "us-001", "start": 16, "end": 16},
              {"score": 0.9_9_9_8, "answer": "us-001", "start": 16, "end": 16},
          ]							,       )







 @slow
 @require_torch
 def  __lowercase (		self :       int						):


      '''simple docstring'''






      UpperCamelCase__      :       str	    =  pipeline(
          "document-question-answering"							,       model="naver-clova-ix/donut-base-finetuned-docvqa"							,       tokenizer=AutoTokenizer.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa"						)							,       feature_extractor="naver-clova-ix/donut-base-finetuned-docvqa"							,       )

      UpperCamelCase__      :       Optional[Any]	    =  INVOICE_URL
      UpperCamelCase__      :       Optional[int]	    =  "What is the invoice number?"
      UpperCamelCase__      :       Optional[int]	    =  dqa_pipeline(image=SCREAMING_SNAKE_CASE							,       question=SCREAMING_SNAKE_CASE							,       top_k=2						)
      self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE							,       decimals=4						)							,       [{"answer": "us-001"}]						)







 @require_tf
 @unittest.skip("Document question answering not implemented in TF"						)
 def  __lowercase (		self :       Optional[int]						):


      '''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