
"""simple docstring"""

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
   import os

   # The slow tests are often failing with OOM error on GPU
   # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
   # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
   SCREAMING_SNAKE_CASE_         =  '''platform'''

   import jax
   import jax.numpy as jnp

   from transformers import AutoTokenizer
   from transformers.models.mbart.modeling_flax_mbart import (
       FlaxMBartForConditionalGeneration,
       FlaxMBartForQuestionAnswering,
       FlaxMBartForSequenceClassification,
       FlaxMBartModel,
       shift_tokens_right,
   )




def       lowercase__  (   lowerCAmelCase			:   Tuple	,			lowerCAmelCase			:   str	,			lowerCAmelCase			:   List[Any]=None	,			lowerCAmelCase			:   Dict=None	,			lowerCAmelCase			:   Optional[int]=None	,			lowerCAmelCase			:   Dict=None	,			lowerCAmelCase			:   Optional[Any]=None	,			lowerCAmelCase			:   Dict=None	,			)	-> List[Any]:






    """simple docstring"""




    if attention_mask is None:
        UpperCAmelCase       =   np.where(input_ids != config.pad_token_id	,			1	,			0	)
    if decoder_attention_mask is None:
        UpperCAmelCase       =   np.where(decoder_input_ids != config.pad_token_id	,			1	,			0	)
    if head_mask is None:
        UpperCAmelCase       =   np.ones((config.encoder_layers, config.encoder_attention_heads)	)
    if decoder_head_mask is None:
        UpperCAmelCase       =   np.ones((config.decoder_layers, config.decoder_attention_heads)	)
    if cross_attn_head_mask is None:
        UpperCAmelCase       =   np.ones((config.decoder_layers, config.decoder_attention_heads)	)
    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": decoder_attention_mask,
    }
class        _UpperCAmelCase							(						unittest.TestCase							):




      def __init__(							self				,    lowercase_				,    lowercase_=1_3				,    lowercase_=7				,    lowercase_=True				,    lowercase_=False				,    lowercase_=9_9				,    lowercase_=1_6				,    lowercase_=2				,    lowercase_=4				,    lowercase_=4				,    lowercase_="gelu"				,    lowercase_=0.1				,    lowercase_=0.1				,    lowercase_=3_2				,    lowercase_=2				,    lowercase_=1				,    lowercase_=0				,    lowercase_=2				,    lowercase_=0.0_2				,    )  ->   Any:
          UpperCAmelCase       =   parent
          UpperCAmelCase       =   batch_size
          UpperCAmelCase       =   seq_length
          UpperCAmelCase       =   is_training
          UpperCAmelCase       =   use_labels
          UpperCAmelCase       =   vocab_size
          UpperCAmelCase       =   hidden_size
          UpperCAmelCase       =   num_hidden_layers
          UpperCAmelCase       =   num_attention_heads
          UpperCAmelCase       =   intermediate_size
          UpperCAmelCase       =   hidden_act
          UpperCAmelCase       =   hidden_dropout_prob
          UpperCAmelCase       =   attention_probs_dropout_prob
          UpperCAmelCase       =   max_position_embeddings
          UpperCAmelCase       =   eos_token_id
          UpperCAmelCase       =   pad_token_id
          UpperCAmelCase       =   bos_token_id
          UpperCAmelCase       =   decoder_start_token_id
          UpperCAmelCase       =   initializer_range




      def       a_ (							self							)  ->   Optional[Any]:
          UpperCAmelCase       =   np.clip(ids_tensor([self.batch_size, self.seq_length - 1]				,    self.vocab_size							)				,    3				,    self.vocab_size							)
          UpperCAmelCase       =   np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)				,    dtype=np.intaa							))				,    -1							)

          UpperCAmelCase       =   shift_tokens_right(lowercase_				,    1							)

          UpperCAmelCase       =   MBartConfig(
              vocab_size=self.vocab_size				,    d_model=self.hidden_size				,    encoder_layers=self.num_hidden_layers				,    decoder_layers=self.num_hidden_layers				,    encoder_attention_heads=self.num_attention_heads				,    decoder_attention_heads=self.num_attention_heads				,    encoder_ffn_dim=self.intermediate_size				,    decoder_ffn_dim=self.intermediate_size				,    dropout=self.hidden_dropout_prob				,    attention_dropout=self.attention_probs_dropout_prob				,    max_position_embeddings=self.max_position_embeddings				,    eos_token_id=self.eos_token_id				,    bos_token_id=self.bos_token_id				,    pad_token_id=self.pad_token_id				,    decoder_start_token_id=self.decoder_start_token_id				,    initializer_range=self.initializer_range				,    use_cache=lowercase_				,    )
          UpperCAmelCase       =   prepare_mbart_inputs_dict(lowercase_				,    lowercase_				,    lowercase_							)
          return config, inputs_dict




      def       a_ (							self							)  ->   List[Any]:
          UpperCAmelCase     ,							UpperCAmelCase       =   self.prepare_config_and_inputs()
          return config, inputs_dict




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_							)  ->   Union[str, Any]:
          UpperCAmelCase       =   2_0
          UpperCAmelCase       =   model_class_name(lowercase_							)

          UpperCAmelCase       =   model.encode(inputs_dict['input_ids']							)

          UpperCAmelCase     ,							UpperCAmelCase       =   (
              inputs_dict['decoder_input_ids'],
              inputs_dict['decoder_attention_mask'],
          )

          UpperCAmelCase       =   model.init_cache(decoder_input_ids.shape[0]				,    lowercase_				,    lowercase_							)
          UpperCAmelCase       =   jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,    dtype='i4'							)

          UpperCAmelCase       =   jnp.broadcast_to(
              jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]				,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,    )
          UpperCAmelCase       =   model.decode(
              decoder_input_ids[:, :-1]				,    lowercase_				,    decoder_attention_mask=lowercase_				,    past_key_values=lowercase_				,    decoder_position_ids=lowercase_				,    )

          UpperCAmelCase       =   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,    dtype='i4'							)
          UpperCAmelCase       =   model.decode(
              decoder_input_ids[:, -1:]				,    lowercase_				,    decoder_attention_mask=lowercase_				,    past_key_values=outputs_cache.past_key_values				,    decoder_position_ids=lowercase_				,    )

          UpperCAmelCase       =   model.decode(lowercase_				,    lowercase_							)

          UpperCAmelCase       =   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
          self.parent.assertTrue(diff < 1E-3				,    msg=F"Max diff is {diff}"							)




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_							)  ->   Optional[Any]:
          UpperCAmelCase       =   2_0
          UpperCAmelCase       =   model_class_name(lowercase_							)

          UpperCAmelCase       =   model.encode(inputs_dict['input_ids']							)

          UpperCAmelCase     ,							UpperCAmelCase       =   (
              inputs_dict['decoder_input_ids'],
              inputs_dict['decoder_attention_mask'],
          )

          UpperCAmelCase       =   jnp.concatenate(
              [
                  decoder_attention_mask,
                  jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])							),
              ]				,    axis=-1				,    )

          UpperCAmelCase       =   model.init_cache(decoder_input_ids.shape[0]				,    lowercase_				,    lowercase_							)
          UpperCAmelCase       =   jnp.broadcast_to(
              jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]				,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,    )

          UpperCAmelCase       =   model.decode(
              decoder_input_ids[:, :-1]				,    lowercase_				,    decoder_attention_mask=lowercase_				,    past_key_values=lowercase_				,    decoder_position_ids=lowercase_				,    )
          UpperCAmelCase       =   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,    dtype='i4'							)
          UpperCAmelCase       =   model.decode(
              decoder_input_ids[:, -1:]				,    lowercase_				,    past_key_values=outputs_cache.past_key_values				,    decoder_attention_mask=lowercase_				,    decoder_position_ids=lowercase_				,    )

          UpperCAmelCase       =   model.decode(lowercase_				,    lowercase_				,    decoder_attention_mask=lowercase_							)

          UpperCAmelCase       =   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
          self.parent.assertTrue(diff < 1E-3				,    msg=F"Max diff is {diff}"							)

@require_flax
class        _UpperCAmelCase							(						unittest.TestCase							):
      __SCREAMING_SNAKE_CASE				:							List[Any]							 =		9_9




      def       a_ (							self							)  ->   int:
          UpperCAmelCase       =   np.array(
              [
                  [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
                  [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
                  [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
                  [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
                  [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
                  [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
                  [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
                  [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
                  [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
                  [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
                  [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
                  [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
                  [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
              ]				,    dtype=np.intaa				,    )

          UpperCAmelCase       =   input_ids.shape[0]
          UpperCAmelCase       =   MBartConfig(
              vocab_size=self.vocab_size				,    d_model=2_4				,    encoder_layers=2				,    decoder_layers=2				,    encoder_attention_heads=2				,    decoder_attention_heads=2				,    encoder_ffn_dim=3_2				,    decoder_ffn_dim=3_2				,    max_position_embeddings=4_8				,    eos_token_id=2				,    pad_token_id=1				,    bos_token_id=0				,    )
          return config, input_ids, batch_size




      def       a_ (							self							)  ->   List[Any]:
          UpperCAmelCase     ,							UpperCAmelCase     ,							UpperCAmelCase       =   self._get_config_and_data()
          UpperCAmelCase       =   FlaxMBartForSequenceClassification(lowercase_							)
          UpperCAmelCase       =   model(input_ids=lowercase_				,    decoder_input_ids=lowercase_							)
          UpperCAmelCase       =   (batch_size, config.num_labels)
          self.assertEqual(outputs['logits'].shape				,    lowercase_							)




      def       a_ (							self							)  ->   Optional[int]:
          UpperCAmelCase     ,							UpperCAmelCase     ,							UpperCAmelCase       =   self._get_config_and_data()
          UpperCAmelCase       =   FlaxMBartForQuestionAnswering(lowercase_							)
          UpperCAmelCase       =   model(input_ids=lowercase_							)

          self.assertEqual(outputs['start_logits'].shape				,    input_ids.shape							)
          self.assertEqual(outputs['end_logits'].shape				,    input_ids.shape							)




      def       a_ (							self							)  ->   int:
          UpperCAmelCase     ,							UpperCAmelCase     ,							UpperCAmelCase       =   self._get_config_and_data()
          UpperCAmelCase       =   FlaxMBartForConditionalGeneration(lowercase_							)
          UpperCAmelCase       =   lm_model(input_ids=lowercase_							)
          UpperCAmelCase       =   (batch_size, input_ids.shape[1], config.vocab_size)
          self.assertEqual(outputs['logits'].shape				,    lowercase_							)




      def       a_ (							self							)  ->   Dict:
          UpperCAmelCase       =   MBartConfig(
              vocab_size=self.vocab_size				,    d_model=1_4				,    encoder_layers=2				,    decoder_layers=2				,    encoder_attention_heads=2				,    decoder_attention_heads=2				,    encoder_ffn_dim=8				,    decoder_ffn_dim=8				,    max_position_embeddings=4_8				,    )
          UpperCAmelCase       =   FlaxMBartForConditionalGeneration(lowercase_							)
          UpperCAmelCase       =   np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]				,    dtype=np.intaa							)
          UpperCAmelCase       =   np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]				,    dtype=np.intaa							)
          UpperCAmelCase       =   lm_model(input_ids=lowercase_				,    decoder_input_ids=lowercase_							)
          UpperCAmelCase       =   (*summary.shape, config.vocab_size)
          self.assertEqual(outputs['logits'].shape				,    lowercase_							)




      def       a_ (							self							)  ->   int:
          UpperCAmelCase       =   np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]				,    dtype=np.intaa							)
          UpperCAmelCase       =   shift_tokens_right(lowercase_				,    1							)
          UpperCAmelCase       =   np.equal(lowercase_				,    1							).astype(np.floataa							).sum()
          UpperCAmelCase       =   np.equal(lowercase_				,    1							).astype(np.floataa							).sum()
          self.assertEqual(shifted.shape				,    input_ids.shape							)
          self.assertEqual(lowercase_				,    n_pad_before - 1							)
          self.assertTrue(np.equal(shifted[:, 0]				,    2							).all()							)

@require_flax
class        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_       ,     unittest.TestCase       ,     SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE				:							Any							 =		True
      __SCREAMING_SNAKE_CASE				:							Optional[int]							 =		(
          (
              FlaxMBartModel,
              FlaxMBartForConditionalGeneration,
              FlaxMBartForSequenceClassification,
              FlaxMBartForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )
      __SCREAMING_SNAKE_CASE				:							List[Any]							 =		(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()




      def       a_ (							self							)  ->   Dict:
          UpperCAmelCase       =   FlaxMBartModelTester(self							)




      def       a_ (							self							)  ->   Tuple:
          UpperCAmelCase     ,							UpperCAmelCase       =   self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
              self.model_tester.check_use_cache_forward(lowercase_				,    lowercase_				,    lowercase_							)




      def       a_ (							self							)  ->   Any:
          UpperCAmelCase     ,							UpperCAmelCase       =   self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
              self.model_tester.check_use_cache_forward_with_attn_mask(lowercase_				,    lowercase_				,    lowercase_							)




      def       a_ (							self							)  ->   Tuple:
          UpperCAmelCase     ,							UpperCAmelCase       =   self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__							):
                  UpperCAmelCase       =   self._prepare_for_class(lowercase_				,    lowercase_							)
                  UpperCAmelCase       =   model_class(lowercase_							)

                  @jax.jit
                  def encode_jitted(lowercase_				,    lowercase_=None				,    **lowercase_							):
                      return model.encode(input_ids=lowercase_				,    attention_mask=lowercase_							)

                  with self.subTest('JIT Enabled'							):
                      UpperCAmelCase       =   encode_jitted(**lowercase_							).to_tuple()

                  with self.subTest('JIT Disabled'							):
                      with jax.disable_jit():
                          UpperCAmelCase       =   encode_jitted(**lowercase_							).to_tuple()

                  self.assertEqual(len(lowercase_							)				,    len(lowercase_							)							)
                  for jitted_output, output in zip(lowercase_				,    lowercase_							):
                      self.assertEqual(jitted_output.shape				,    output.shape							)




      def       a_ (							self							)  ->   List[Any]:
          UpperCAmelCase     ,							UpperCAmelCase       =   self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__							):
                  UpperCAmelCase       =   model_class(lowercase_							)
                  UpperCAmelCase       =   model.encode(inputs_dict['input_ids']				,    inputs_dict['attention_mask']							)

                  UpperCAmelCase       =   {
                      'decoder_input_ids': inputs_dict['decoder_input_ids'],
                      'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
                      'encoder_outputs': encoder_outputs,
                  }

                  @jax.jit
                  def decode_jitted(lowercase_				,    lowercase_				,    lowercase_							):
                      return model.decode(
                          decoder_input_ids=lowercase_				,    decoder_attention_mask=lowercase_				,    encoder_outputs=lowercase_				,    )

                  with self.subTest('JIT Enabled'							):
                      UpperCAmelCase       =   decode_jitted(**lowercase_							).to_tuple()

                  with self.subTest('JIT Disabled'							):
                      with jax.disable_jit():
                          UpperCAmelCase       =   decode_jitted(**lowercase_							).to_tuple()

                  self.assertEqual(len(lowercase_							)				,    len(lowercase_							)							)
                  for jitted_output, output in zip(lowercase_				,    lowercase_							):
                      self.assertEqual(jitted_output.shape				,    output.shape							)




      @slow
      def       a_ (							self							)  ->   Tuple:
          for model_class_name in self.all_model_classes:
              UpperCAmelCase       =   model_class_name.from_pretrained('facebook/mbart-large-cc25'				,    from_pt=lowercase_							)
              # FlaxMBartForSequenceClassification expects eos token in input_ids
              UpperCAmelCase       =   np.ones((1, 1)							) * model.config.eos_token_id
              UpperCAmelCase       =   model(lowercase_							)
              self.assertIsNotNone(lowercase_							)

@require_flax
@require_sentencepiece
@require_tokenizers
class        _UpperCAmelCase							(						unittest.TestCase							):
      __SCREAMING_SNAKE_CASE				:							Union[str, Any]							 =		[
          " UN Chief Says There Is No Military Solution in Syria",
      ]
      __SCREAMING_SNAKE_CASE				:							List[Any]							 =		[
          "Şeful ONU declară că nu există o soluţie militară în Siria",
      ]
      __SCREAMING_SNAKE_CASE				:							str							 =		"facebook/mbart-large-en-ro"




      @cached_property
      def       a_ (							self							)  ->   Optional[int]:
          return AutoTokenizer.from_pretrained(self.model_name							)




      @cached_property
      def       a_ (							self							)  ->   Optional[int]:
          UpperCAmelCase       =   FlaxMBartForConditionalGeneration.from_pretrained(self.model_name				,    from_pt=lowercase_							)
          return model




      def       a_ (							self				,    **lowercase_							)  ->   str:
          UpperCAmelCase       =   self.translate_src_text(**lowercase_							)
          self.assertListEqual(self.expected_text				,    lowercase_							)




      def       a_ (							self				,    **lowercase_							)  ->   Tuple:
          UpperCAmelCase       =   self.tokenizer(self.src_text				,    **lowercase_				,    return_tensors='np'							)
          UpperCAmelCase       =   self.model.generate(
              model_inputs.input_ids				,    attention_mask=model_inputs.attention_mask				,    decoder_start_token_id=self.tokenizer.lang_code_to_id['ro_RO']				,    early_stopping=lowercase_				,    num_beams=2				,    ).sequences
          UpperCAmelCase       =   self.tokenizer.batch_decode(lowercase_				,    skip_special_tokens=lowercase_							)
          return generated_words




      @slow
      def       a_ (							self							)  ->   Tuple:
          self._assert_generated_batch_equal_expected()





"""simple docstring"""

# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)
@flax.struct.dataclass
class        _UpperCAmelCase							:
      __SCREAMING_SNAKE_CASE				:							CommonSchedulerState
      __SCREAMING_SNAKE_CASE				:							jnp.ndarray

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




      @classmethod
      def       a_ (							cls				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    )  ->   Dict:
          return cls(
              common=lowercase_				,    final_alpha_cumprod=lowercase_				,    init_noise_sigma=lowercase_				,    timesteps=lowercase_				,    )

@dataclass
class        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE				:							DDIMSchedulerState

class        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE				:							Optional[Any]							 =		[e.name for e in FlaxKarrasDiffusionSchedulers]

      __SCREAMING_SNAKE_CASE				:							jnp.dtype




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




      @register_to_config
      def __init__(							self				,    lowercase_ = 1_0_0_0				,    lowercase_ = 0.0_0_0_1				,    lowercase_ = 0.0_2				,    lowercase_ = "linear"				,    lowercase_ = None				,    lowercase_ = True				,    lowercase_ = 0				,    lowercase_ = "epsilon"				,    lowercase_ = jnp.floataa				,    )  ->   Optional[Any]:
          UpperCAmelCase       =   dtype




      def       a_ (							self				,    lowercase_ = None							)  ->   DDIMSchedulerState:
          if common is None:
              UpperCAmelCase       =   CommonSchedulerState.create(self							)

          # At every step in ddim, we are looking into the previous alphas_cumprod
          # For the final step, there is no previous alphas_cumprod because we are already at 0
          # `set_alpha_to_one` decides whether we set this parameter simply to one or
          # whether we use the final alpha of the "non-previous" one.
          UpperCAmelCase       =   (
              jnp.array(1.0				,    dtype=self.dtype							) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
          )

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

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

          return DDIMSchedulerState.create(
              common=lowercase_				,    final_alpha_cumprod=lowercase_				,    init_noise_sigma=lowercase_				,    timesteps=lowercase_				,    )




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_ = None							)  ->   jnp.ndarray:
          return sample




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_ = ()							)  ->   DDIMSchedulerState:
          UpperCAmelCase       =   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       =   (jnp.arange(0				,    lowercase_							) * step_ratio).round()[::-1] + self.config.steps_offset

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




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_							)  ->   Union[str, Any]:
          UpperCAmelCase       =   state.common.alphas_cumprod[timestep]
          UpperCAmelCase       =   jnp.where(
              prev_timestep >= 0				,    state.common.alphas_cumprod[prev_timestep]				,    state.final_alpha_cumprod							)
          UpperCAmelCase       =   1 - alpha_prod_t
          UpperCAmelCase       =   1 - alpha_prod_t_prev

          UpperCAmelCase       =   (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

          return variance




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_ = 0.0				,    lowercase_ = True				,    )  ->   Union[FlaxDDIMSchedulerOutput, Tuple]:
          if state.num_inference_steps is None:
              raise ValueError(
                  'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'							)

          # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
          # Ideally, read DDIM paper in-detail understanding

          # Notation (<variable name> -> <name in paper>
          # - pred_noise_t -> e_theta(x_t, t)
          # - pred_original_sample -> f_theta(x_t, t) or x_0
          # - std_dev_t -> sigma_t
          # - eta -> η
          # - pred_sample_direction -> "direction pointing to x_t"
          # - pred_prev_sample -> "x_t-1"

          # 1. get previous step value (=t-1)
          UpperCAmelCase       =   timestep - self.config.num_train_timesteps // state.num_inference_steps

          UpperCAmelCase       =   state.common.alphas_cumprod
          UpperCAmelCase       =   state.final_alpha_cumprod

          # 2. compute alphas, betas
          UpperCAmelCase       =   alphas_cumprod[timestep]
          UpperCAmelCase       =   jnp.where(prev_timestep >= 0				,    alphas_cumprod[prev_timestep]				,    lowercase_							)

          UpperCAmelCase       =   1 - alpha_prod_t

          # 3. compute predicted original sample from predicted noise also called
          # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          if self.config.prediction_type == "epsilon":
              UpperCAmelCase       =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
              UpperCAmelCase       =   model_output
          elif self.config.prediction_type == "sample":
              UpperCAmelCase       =   model_output
              UpperCAmelCase       =   (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
          elif self.config.prediction_type == "v_prediction":
              UpperCAmelCase       =   (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
              UpperCAmelCase       =   (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
          else:
              raise ValueError(
                  F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
                  ' `v_prediction`'							)

          # 4. compute variance: "sigma_t(η)" -> see formula (16)
          # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
          UpperCAmelCase       =   self._get_variance(lowercase_				,    lowercase_				,    lowercase_							)
          UpperCAmelCase       =   eta * variance ** 0.5

          # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          UpperCAmelCase       =   (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

          # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          UpperCAmelCase       =   alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

          if not return_dict:
              return (prev_sample, state)

          return FlaxDDIMSchedulerOutput(prev_sample=lowercase_				,    state=lowercase_							)




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    )  ->   jnp.ndarray:
          return add_noise_common(state.common				,    lowercase_				,    lowercase_				,    lowercase_							)




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    )  ->   jnp.ndarray:
          return get_velocity_common(state.common				,    lowercase_				,    lowercase_				,    lowercase_							)




      def __len__(							self							)  ->   Optional[Any]:
          return self.config.num_train_timesteps



