





# 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 					lowercase__ :

   '''simple docstring'''


   a		:							CommonSchedulerState

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




   @classmethod
   def    UpperCamelCase__						(					cls,	__magic_name__,	__magic_name__,	__magic_name__							)				->   Optional[int]:






       """simple docstring"""
       return cls(common=__magic_name__,	init_noise_sigma=__magic_name__,	timesteps=__magic_name__							)






@dataclass
class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							DDPMSchedulerState






class 					lowercase__ (			__lowerCamelCase    ,       __lowerCamelCase       ):

   '''simple docstring'''


   a		:							str     	=	[e.name for e in FlaxKarrasDiffusionSchedulers]

   a		:							jnp.dtype




   @property
   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       return True




   @register_to_config
   def __init__(					self,	__magic_name__ = 1000,	__magic_name__ = 0.0001,	__magic_name__ = 0.02,	__magic_name__ = "linear",	__magic_name__ = None,	__magic_name__ = "fixed_small",	__magic_name__ = True,	__magic_name__ = "epsilon",	__magic_name__ = jnp.floataa,	)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       Union[str, Any]      =   dtype




   def    UpperCamelCase__						(					self,	__magic_name__ = None							)				->   DDPMSchedulerState:






       """simple docstring"""
       if common is None:
           UpperCamelCase__							:       List[str]      =   CommonSchedulerState.create(self							)

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

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

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




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__ = None							)				->   jnp.ndarray:






       """simple docstring"""
       return sample




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__ = ()							)				->   DDPMSchedulerState:






       """simple docstring"""
       UpperCamelCase__							:       List[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__							:       str      =   (jnp.arange(0,	__magic_name__							) * step_ratio).round()[::-1]

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




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__=None,	__magic_name__=None							)				->   Dict:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   state.common.alphas_cumprod[t]
       UpperCamelCase__							:       int      =   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__							:       Dict      =   (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

       if variance_type is None:
           UpperCamelCase__							:       Dict      =   self.config.variance_type

       # hacks - were probably added for training stability
       if variance_type == "fixed_small":
           UpperCamelCase__							:       Dict      =   jnp.clip(__magic_name__,	a_min=1E-20							)
       # for rl-diffuser https://arxiv.org/abs/2205.09991
       elif variance_type == "fixed_small_log":
           UpperCamelCase__							:       List[Any]      =   jnp.log(jnp.clip(__magic_name__,	a_min=1E-20							)							)
       elif variance_type == "fixed_large":
           UpperCamelCase__							:       Optional[int]      =   state.common.betas[t]
       elif variance_type == "fixed_large_log":
           # Glide max_log
           UpperCamelCase__							:       List[Any]      =   jnp.log(state.common.betas[t]							)
       elif variance_type == "learned":
           return predicted_variance
       elif variance_type == "learned_range":
           UpperCamelCase__							:       Union[str, Any]      =   variance
           UpperCamelCase__							:       int      =   state.common.betas[t]
           UpperCamelCase__							:       Optional[Any]      =   (predicted_variance + 1) / 2
           UpperCamelCase__							:       List[str]      =   frac * max_log + (1 - frac) * min_log

       return variance




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__ = None,	__magic_name__ = True,	)				->   Union[FlaxDDPMSchedulerOutput, Tuple]:






       """simple docstring"""
       UpperCamelCase__							:       Union[str, Any]      =   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__							:       Any      =   jnp.split(__magic_name__,	sample.shape[1],	axis=1							)
       else:
           UpperCamelCase__							:       Any      =   None

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

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

       UpperCamelCase__							:       Optional[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=__magic_name__,	state=__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	)				->   jnp.ndarray:






       """simple docstring"""
       return add_noise_common(state.common,	__magic_name__,	__magic_name__,	__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	)				->   jnp.ndarray:






       """simple docstring"""
       return get_velocity_common(state.common,	__magic_name__,	__magic_name__,	__magic_name__							)




   def __len__(					self							)				->   str:






       """simple docstring"""
       return self.config.num_train_timesteps








import unittest

from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  import jax

  from transformers.models.big_bird.modeling_flax_big_bird import (
      FlaxBigBirdForCausalLM,
      FlaxBigBirdForMaskedLM,
      FlaxBigBirdForMultipleChoice,
      FlaxBigBirdForPreTraining,
      FlaxBigBirdForQuestionAnswering,
      FlaxBigBirdForSequenceClassification,
      FlaxBigBirdForTokenClassification,
      FlaxBigBirdModel,
  )






class 					lowercase__ (			unittest.TestCase       ):

   '''simple docstring'''


   def __init__(					self,	__magic_name__,	__magic_name__=2,	__magic_name__=56,	__magic_name__=True,	__magic_name__=True,	__magic_name__=True,	__magic_name__=True,	__magic_name__=99,	__magic_name__=32,	__magic_name__=2,	__magic_name__=2,	__magic_name__=7,	__magic_name__="gelu_new",	__magic_name__=0.1,	__magic_name__=0.1,	__magic_name__=512,	__magic_name__=16,	__magic_name__=2,	__magic_name__=0.02,	__magic_name__=4,	__magic_name__="block_sparse",	__magic_name__=True,	__magic_name__=False,	__magic_name__=2,	__magic_name__=3,	)				->   Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:       List[str]      =   parent
       UpperCamelCase__							:       List[Any]      =   batch_size
       UpperCamelCase__							:       str      =   seq_length
       UpperCamelCase__							:       List[str]      =   is_training
       UpperCamelCase__							:       List[Any]      =   use_attention_mask
       UpperCamelCase__							:       Any      =   use_token_type_ids
       UpperCamelCase__							:       Tuple      =   use_labels
       UpperCamelCase__							:       Any      =   vocab_size
       UpperCamelCase__							:       Optional[int]      =   hidden_size
       UpperCamelCase__							:       Union[str, Any]      =   num_hidden_layers
       UpperCamelCase__							:       Optional[Any]      =   num_attention_heads
       UpperCamelCase__							:       List[str]      =   intermediate_size
       UpperCamelCase__							:       List[str]      =   hidden_act
       UpperCamelCase__							:       Union[str, Any]      =   hidden_dropout_prob
       UpperCamelCase__							:       Union[str, Any]      =   attention_probs_dropout_prob
       UpperCamelCase__							:       int      =   max_position_embeddings
       UpperCamelCase__							:       List[Any]      =   type_vocab_size
       UpperCamelCase__							:       Tuple      =   type_sequence_label_size
       UpperCamelCase__							:       Optional[Any]      =   initializer_range
       UpperCamelCase__							:       Dict      =   num_choices

       UpperCamelCase__							:       Dict      =   rescale_embeddings
       UpperCamelCase__							:       Tuple      =   attention_type
       UpperCamelCase__							:       Union[str, Any]      =   use_bias
       UpperCamelCase__							:       Union[str, Any]      =   block_size
       UpperCamelCase__							:       Optional[int]      =   num_random_blocks




   def    UpperCamelCase__						(					self							)				->   int:






       """simple docstring"""
       UpperCamelCase__							:       int      =   ids_tensor([self.batch_size, self.seq_length],	self.vocab_size							)

       UpperCamelCase__							:       int      =   None
       if self.use_attention_mask:
           UpperCamelCase__							:       List[str]      =   random_attention_mask([self.batch_size, self.seq_length]							)

       UpperCamelCase__							:       Optional[int]      =   None
       if self.use_token_type_ids:
           UpperCamelCase__							:       List[str]      =   ids_tensor([self.batch_size, self.seq_length],	self.type_vocab_size							)

       UpperCamelCase__							:       str      =   BigBirdConfig(
           vocab_size=self.vocab_size,	hidden_size=self.hidden_size,	num_hidden_layers=self.num_hidden_layers,	num_attention_heads=self.num_attention_heads,	intermediate_size=self.intermediate_size,	hidden_act=self.hidden_act,	hidden_dropout_prob=self.hidden_dropout_prob,	attention_probs_dropout_prob=self.attention_probs_dropout_prob,	max_position_embeddings=self.max_position_embeddings,	type_vocab_size=self.type_vocab_size,	is_decoder=__magic_name__,	initializer_range=self.initializer_range,	attention_type=self.attention_type,	block_size=self.block_size,	num_random_blocks=self.num_random_blocks,	use_bias=self.use_bias,	rescale_embeddings=self.rescale_embeddings,	)

       return config, input_ids, token_type_ids, attention_mask




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       List[str]      =   self.prepare_config_and_inputs()
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       Tuple      =   config_and_inputs
       UpperCamelCase__							:       Union[str, Any]      =   {
           '''input_ids''': input_ids,
           '''token_type_ids''': token_type_ids,
           '''attention_mask''': attention_mask,
       }
       return config, inputs_dict






@require_flax
class 					lowercase__ (			__lowerCamelCase    ,       unittest.TestCase       ):

   '''simple docstring'''


   a		:							Any     	=	(
       (
           FlaxBigBirdForCausalLM,
           FlaxBigBirdModel,
           FlaxBigBirdForPreTraining,
           FlaxBigBirdForMaskedLM,
           FlaxBigBirdForMultipleChoice,
           FlaxBigBirdForQuestionAnswering,
           FlaxBigBirdForSequenceClassification,
           FlaxBigBirdForTokenClassification,
       )
       if is_flax_available()
       else ()
   )

   a		:							List[str]     	=	False
   a		:							str     	=	False




   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       Union[str, Any]      =   FlaxBigBirdModelTester(self							)




   @slow
   # copied from `test_modeling_flax_common` because it takes much longer than other models
   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       super().test_from_pretrained_save_pretrained()




   @slow
   # copied from `test_modeling_flax_common` because it takes much longer than other models
   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       super().test_from_pretrained_with_no_automatic_init()




   @slow
   # copied from `test_modeling_flax_common` because it takes much longer than other models
   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       super().test_no_automatic_init()




   @slow
   # copied from `test_modeling_flax_common` because it takes much longer than other models
   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       super().test_hidden_states_output()




   @slow
   def    UpperCamelCase__						(					self							)				->   List[str]:






       """simple docstring"""
       for model_class_name in self.all_model_classes:
           UpperCamelCase__							:       List[str]      =   model_class_name.from_pretrained('''google/bigbird-roberta-base'''							)
           self.assertIsNotNone(__magic_name__							)




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       if self.test_attn_probs:
           super().test_attention_outputs()




   @slow
   # copied from `test_modeling_flax_common` because it takes much longer than other models
   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__							:       Dict      =   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__							):
               UpperCamelCase__							:       Union[str, Any]      =   self._prepare_for_class(__magic_name__,	__magic_name__							)
               UpperCamelCase__							:       Tuple      =   model_class(__magic_name__							)

               @jax.jit
               def model_jitted(__magic_name__,	__magic_name__=None,	**__magic_name__							):
                   return model(input_ids=__magic_name__,	attention_mask=__magic_name__,	**__magic_name__							)

               with self.subTest('''JIT Enabled'''							):
                   UpperCamelCase__							:       str      =   model_jitted(**__magic_name__							).to_tuple()

               with self.subTest('''JIT Disabled'''							):
                   with jax.disable_jit():
                       UpperCamelCase__							:       Dict      =   model_jitted(**__magic_name__							).to_tuple()

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




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__=1E-5,	__magic_name__="outputs",	__magic_name__=None							)				->   List[Any]:






       """simple docstring"""
       # `bigbird_block_sparse_attention` in `FlaxBigBird` returns `attention_probs = None`, while in PyTorch version,
       # an effort was done to return `attention_probs` (yet to be verified).
       if name.startswith('''outputs.attentions'''							):
           return
       else:
           super().check_pt_flax_outputs(__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)

