






"""simple docstring"""



import unittest

import numpy as np

from transformers import RoFormerConfig, 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.numpy as jnp

						from transformers.models.roformer.modeling_flax_roformer import (
						    FlaxRoFormerForMaskedLM,
						    FlaxRoFormerForMultipleChoice,
						    FlaxRoFormerForQuestionAnswering,
						    FlaxRoFormerForSequenceClassification,
						    FlaxRoFormerForTokenClassification,
						    FlaxRoFormerModel,
						)







class        lowercase__      (							unittest.TestCase       ):


			def __init__(				self					:  Union[str, Any]       ,	snake_case__					:  Dict       ,	snake_case__					:  str=13       ,	snake_case__					:  Optional[Any]=7       ,	snake_case__					:  Optional[Any]=True       ,	snake_case__					:  Optional[int]=True       ,	snake_case__					:  Any=True       ,	snake_case__					:  Dict=True       ,	snake_case__					:  List[str]=99       ,	snake_case__					:  int=32       ,	snake_case__					:  Union[str, Any]=5       ,	snake_case__					:  Optional[int]=4       ,	snake_case__					:  List[Any]=37       ,	snake_case__					:  Dict="gelu"       ,	snake_case__					:  Optional[Any]=0.1       ,	snake_case__					:  Any=0.1       ,	snake_case__					:  Tuple=512       ,	snake_case__					:  int=16       ,	snake_case__					:  List[str]=2       ,	snake_case__					:  Any=0.02       ,	snake_case__					:  List[Any]=4       ,	):
										lowerCamelCase_	:							Tuple									=parent
										lowerCamelCase_	:							List[Any]									=batch_size
										lowerCamelCase_	:							Dict									=seq_length
										lowerCamelCase_	:							Optional[int]									=is_training
										lowerCamelCase_	:							List[Any]									=use_attention_mask
										lowerCamelCase_	:							Union[str, Any]									=use_token_type_ids
										lowerCamelCase_	:							List[str]									=use_labels
										lowerCamelCase_	:							Any									=vocab_size
										lowerCamelCase_	:							Any									=hidden_size
										lowerCamelCase_	:							Any									=num_hidden_layers
										lowerCamelCase_	:							Optional[Any]									=num_attention_heads
										lowerCamelCase_	:							Any									=intermediate_size
										lowerCamelCase_	:							int									=hidden_act
										lowerCamelCase_	:							List[Any]									=hidden_dropout_prob
										lowerCamelCase_	:							Tuple									=attention_probs_dropout_prob
										lowerCamelCase_	:							int									=max_position_embeddings
										lowerCamelCase_	:							List[str]									=type_vocab_size
										lowerCamelCase_	:							int									=type_sequence_label_size
										lowerCamelCase_	:							Any									=initializer_range
										lowerCamelCase_	:							Dict									=num_choices


			def      UpperCAmelCase__						(				self					:  Union[str, Any] ):
										lowerCamelCase_	:							Any									=ids_tensor([self.batch_size, self.seq_length]       ,	self.vocab_size )

										lowerCamelCase_	:							Optional[int]									=None
										if self.use_attention_mask:
																	lowerCamelCase_	:							List[Any]									=random_attention_mask([self.batch_size, self.seq_length] )

										lowerCamelCase_	:							Union[str, Any]									=None
										if self.use_token_type_ids:
																	lowerCamelCase_	:							Tuple									=ids_tensor([self.batch_size, self.seq_length]       ,	self.type_vocab_size )

										lowerCamelCase_	:							str									=RoFormerConfig(
										    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=snake_case__       ,	initializer_range=self.initializer_range       ,	)

										return config, input_ids, token_type_ids, attention_mask


			def      UpperCAmelCase__						(				self					:  List[str] ):
										lowerCamelCase_	:							Optional[int]									=self.prepare_config_and_inputs()
										lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_	:							int									=config_and_inputs
										lowerCamelCase_	:							Any									={"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
										return config, inputs_dict







@require_flax
class        lowercase__      (							snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:Union[str, Any]             =			True

			_UpperCAmelCase		:Optional[Any]             =			(
			    (
			        FlaxRoFormerModel,
			        FlaxRoFormerForMaskedLM,
			        FlaxRoFormerForSequenceClassification,
			        FlaxRoFormerForTokenClassification,
			        FlaxRoFormerForMultipleChoice,
			        FlaxRoFormerForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)


			def      UpperCAmelCase__						(				self					:  int ):
										lowerCamelCase_	:							Optional[int]									=FlaxRoFormerModelTester(self )


			@slow
			def      UpperCAmelCase__						(				self					:  Dict ):
										for model_class_name in self.all_model_classes:
																	lowerCamelCase_	:							Union[str, Any]									=model_class_name.from_pretrained("junnyu/roformer_chinese_small"       ,	from_pt=snake_case__ )
																	lowerCamelCase_	:							Any									=model(np.ones((1, 1) ) )
																	self.assertIsNotNone(snake_case__ )







@require_flax
class        lowercase__      (							unittest.TestCase       ):


			@slow
			def      UpperCAmelCase__						(				self					:  List[Any] ):
										lowerCamelCase_	:							List[str]									=FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base" )
										lowerCamelCase_	:							Any									=jnp.array([[0, 1, 2, 3, 4, 5]] )
										lowerCamelCase_	:							Optional[int]									=model(snake_case__ )[0]

										lowerCamelCase_	:							int									=5_0000

										lowerCamelCase_	:							Optional[Any]									=(1, 6, vocab_size)
										self.assertEqual(output.shape       ,	snake_case__ )

										lowerCamelCase_	:							Tuple									=jnp.array(
										    [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]] )

										self.assertTrue(jnp.allclose(output[:, :3, :3]       ,	snake_case__       ,	atol=1E-4 ) )











"""simple docstring"""



# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)







@flax.struct.dataclass
class        lowercase__      :
			_UpperCAmelCase		:CommonSchedulerState

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


			@classmethod
			def      UpperCAmelCase__						(				cls					:  int       ,	snake_case__					:  CommonSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray ):
										return cls(common=snake_case__       ,	init_noise_sigma=snake_case__       ,	timesteps=snake_case__ )







@dataclass
class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:DDPMSchedulerState







class        lowercase__      (							snake_case__,       snake_case__       ):
			_UpperCAmelCase		:Any             =			[e.name for e in FlaxKarrasDiffusionSchedulers]

			_UpperCAmelCase		:jnp.dtype


			@property
			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										return True


			@register_to_config
			def __init__(				self					:  Optional[int]       ,	snake_case__					:  int = 1000       ,	snake_case__					:  float = 0.0_001       ,	snake_case__					:  float = 0.02       ,	snake_case__					:  str = "linear"       ,	snake_case__					:  Optional[jnp.ndarray] = None       ,	snake_case__					:  str = "fixed_small"       ,	snake_case__					:  bool = True       ,	snake_case__					:  str = "epsilon"       ,	snake_case__					:  jnp.dtype = jnp.floataa       ,	):
										lowerCamelCase_	:							str									=dtype


			def      UpperCAmelCase__						(				self					:  List[str]       ,	snake_case__					:  Optional[CommonSchedulerState] = None ):
										if common is None:
																	lowerCamelCase_	:							int									=CommonSchedulerState.create(self )

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

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

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


			def      UpperCAmelCase__						(				self					:  Optional[int]       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  Optional[int] = None ):
										return sample


			def      UpperCAmelCase__						(				self					:  Optional[Any]       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  int       ,	snake_case__					:  Tuple = () ):
										lowerCamelCase_	:							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
										lowerCamelCase_	:							List[str]									=(jnp.arange(0       ,	snake_case__ ) * step_ratio).round()[::-1]

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


			def      UpperCAmelCase__						(				self					:  Union[str, Any]       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  Union[str, Any]       ,	snake_case__					:  List[Any]=None       ,	snake_case__					:  Any=None ):
										lowerCamelCase_	:							List[str]									=state.common.alphas_cumprod[t]
										lowerCamelCase_	:							Union[str, Any]									=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
										lowerCamelCase_	:							Tuple									=(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

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

										return variance


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  int       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  Optional[jax.random.KeyArray] = None       ,	snake_case__					:  bool = True       ,	):
										lowerCamelCase_	:							Union[str, Any]									=timestep

										if key is None:
																	lowerCamelCase_	:							Dict									=jax.random.PRNGKey(0 )

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

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

										# 5. Compute predicted previous sample µ_t
										# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
										lowerCamelCase_	:							Any									=pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

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

										lowerCamelCase_	:							str									=pred_prev_sample + variance

										if not return_dict:
																	return (pred_prev_sample, state)

										return FlaxDDPMSchedulerOutput(prev_sample=snake_case__       ,	state=snake_case__ )


			def      UpperCAmelCase__						(				self					:  Dict       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray       ,	):
										return add_noise_common(state.common       ,	snake_case__       ,	snake_case__       ,	snake_case__ )


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray       ,	):
										return get_velocity_common(state.common       ,	snake_case__       ,	snake_case__       ,	snake_case__ )


			def __len__(				self					:  Tuple ):
										return self.config.num_train_timesteps



