






"""simple docstring"""







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

import flax
import jax.numpy as jnp
from scipy import integrate

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


@flax.struct.dataclass
class     __UpperCAmelCase:




						"""simple docstring"""




						__lowerCamelCase				       =					42

						# setable values
						__lowerCamelCase				       =					42
						__lowerCamelCase				       =					42
						__lowerCamelCase				       =					42
						__lowerCamelCase				       =					None

						# running values
						__lowerCamelCase				       =					None




						@classmethod
						def 		UpperCAmelCase_       (   cls       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__			):
												'''simple docstring'''
												return cls(common=snake_case__       ,  init_noise_sigma=snake_case__       ,  timesteps=snake_case__       ,  sigmas=snake_case__			)


@dataclass
class     __UpperCAmelCase(   SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						__lowerCamelCase				       =					42


class     __UpperCAmelCase(   SCREAMING_SNAKE_CASE__  ,  SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						__lowerCamelCase				       =					[e.name for e in FlaxKarrasDiffusionSchedulers]

						__lowerCamelCase				       =					42




						@property
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												return True




						@register_to_config
						def __init__(   self       ,  snake_case__ = 1000       ,  snake_case__ = 0.00_01       ,  snake_case__ = 0.02       ,  snake_case__ = "linear"       ,  snake_case__ = None       ,  snake_case__ = "epsilon"       ,  snake_case__ = jnp.floataa       ,  ):
												'''simple docstring'''
												lowercase__				:      Optional[Any]=       dtype




						def 		UpperCAmelCase_       (   self       ,  snake_case__ = None			):
												'''simple docstring'''
												if common is None:
																		lowercase__				:      Tuple=       CommonSchedulerState.create(self			)

												lowercase__				:      int=       jnp.arange(0       ,  self.config.num_train_timesteps			).round()[::-1]
												lowercase__				:      Optional[Any]=       ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

												# standard deviation of the initial noise distribution
												lowercase__				:      str=       sigmas.max()

												return LMSDiscreteSchedulerState.create(
												    common=snake_case__       ,  init_noise_sigma=snake_case__       ,  timesteps=snake_case__       ,  sigmas=snake_case__       ,  )




						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__       ,  snake_case__			):
												'''simple docstring'''
												((lowercase__),	)				:      int=       jnp.where(state.timesteps == timestep       ,  size=1			)
												lowercase__				:      Any=       step_index[0]

												lowercase__				:      Any=       state.sigmas[step_index]
												lowercase__				:      Optional[Any]=       sample / ((sigma**2 + 1) ** 0.5)
												return sample




						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__			):
												'''simple docstring'''

												def lms_derivative(snake_case__			):
																		lowercase__				:      Dict=       1.0
																		for k in range(snake_case__			):
																								if current_order == k:
																														continue
																								prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
																		return prod

												lowercase__				:      Any=       integrate.quad(snake_case__       ,  state.sigmas[t]       ,  state.sigmas[t + 1]       ,  epsrel=1e-4			)[0]

												return integrated_coeff




						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__       ,  snake_case__ = ()			):
												'''simple docstring'''
												lowercase__				:      Any=       jnp.linspace(self.config.num_train_timesteps - 1       ,  0       ,  snake_case__       ,  dtype=self.dtype			)

												lowercase__				:      Union[str, Any]=       jnp.floor(snake_case__			).astype(jnp.intaa			)
												lowercase__				:      Tuple=       jnp.ceil(snake_case__			).astype(jnp.intaa			)

												lowercase__				:      List[Any]=       jnp.mod(snake_case__       ,  1.0			)

												lowercase__				:      List[str]=       ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
												lowercase__				:      List[Any]=       (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
												lowercase__				:      Tuple=       jnp.concatenate([sigmas, jnp.array([0.0]       ,  dtype=self.dtype			)]			)

												lowercase__				:      Any=       timesteps.astype(jnp.intaa			)

												# initial running values
												lowercase__				:      List[Any]=       jnp.zeros((0,) + shape       ,  dtype=self.dtype			)

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




						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__ = 4       ,  snake_case__ = True       ,  ):
												'''simple docstring'''
												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"			)

												lowercase__				:      List[Any]=       state.sigmas[timestep]

												# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
												if self.config.prediction_type == "epsilon":
																		lowercase__				:      List[str]=       sample - sigma * model_output
												elif self.config.prediction_type == "v_prediction":
																		# * c_out + input * c_skip
																		lowercase__				:      Any=       model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
												else:
																		raise ValueError(
																		    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''			)

												# 2. Convert to an ODE derivative
												lowercase__				:      Optional[int]=       (sample - pred_original_sample) / sigma
												lowercase__				:      Union[str, Any]=       state.replace(derivatives=jnp.append(state.derivatives       ,  snake_case__			)			)
												if len(state.derivatives			) > order:
																		lowercase__				:      Tuple=       state.replace(derivatives=jnp.delete(state.derivatives       ,  0			)			)

												# 3. Compute linear multistep coefficients
												lowercase__				:      Union[str, Any]=       min(timestep + 1       ,  snake_case__			)
												lowercase__				:      List[str]=       [self.get_lms_coefficient(snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__			) for curr_order in range(snake_case__			)]

												# 4. Compute previous sample based on the derivatives path
												lowercase__				:      List[Any]=       sample + sum(
												    coeff * derivative for coeff, derivative in zip(snake_case__       ,  reversed(state.derivatives			)			)			)

												if not return_dict:
																		return (prev_sample, state)

												return FlaxLMSSchedulerOutput(prev_sample=snake_case__       ,  state=snake_case__			)




						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  ):
												'''simple docstring'''
												lowercase__				:      Union[str, Any]=       state.sigmas[timesteps].flatten()
												lowercase__				:      List[str]=       broadcast_to_shape_from_left(snake_case__       ,  noise.shape			)

												lowercase__				:      int=       original_samples + noise * sigma

												return noisy_samples




						def __len__(   self			):
												'''simple docstring'''
												return self.config.num_train_timesteps














"""simple docstring"""







import unittest

from transformers import SpeechaTextaConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers.models.speech_to_text_a.modeling_speech_to_text_a import (
			    SpeechaTextaDecoder,
			    SpeechaTextaForCausalLM,
			)


@require_torch
class     __UpperCAmelCase:




						"""simple docstring"""




						def __init__(   self       ,  snake_case__       ,  snake_case__=99       ,  snake_case__=13       ,  snake_case__=16       ,  snake_case__=7       ,  snake_case__=True       ,  snake_case__=True       ,  snake_case__=True       ,  snake_case__=False       ,  snake_case__=True       ,  snake_case__=2       ,  snake_case__=32       ,  snake_case__=4       ,  snake_case__=4       ,  snake_case__=30       ,  snake_case__=0       ,  snake_case__=1       ,  snake_case__=2       ,  snake_case__=None       ,  ):
												'''simple docstring'''
												lowercase__				:      List[str]=       parent
												lowercase__				:      Dict=       batch_size
												lowercase__				:      List[str]=       decoder_seq_length
												# For common tests
												lowercase__				:      List[str]=       self.decoder_seq_length
												lowercase__				:      int=       is_training
												lowercase__				:      Any=       use_attention_mask
												lowercase__				:      List[str]=       use_labels

												lowercase__				:      Dict=       vocab_size
												lowercase__				:      Dict=       d_model
												lowercase__				:      Union[str, Any]=       d_model
												lowercase__				:      List[str]=       decoder_layers
												lowercase__				:      Optional[Any]=       decoder_layers
												lowercase__				:      Optional[Any]=       decoder_ffn_dim
												lowercase__				:      Union[str, Any]=       decoder_attention_heads
												lowercase__				:      Tuple=       decoder_attention_heads
												lowercase__				:      Optional[Any]=       eos_token_id
												lowercase__				:      List[Any]=       bos_token_id
												lowercase__				:      Any=       pad_token_id
												lowercase__				:      Union[str, Any]=       decoder_start_token_id
												lowercase__				:      Optional[Any]=       use_cache
												lowercase__				:      List[Any]=       max_position_embeddings

												lowercase__				:      Tuple=       None
												lowercase__				:      Optional[Any]=       decoder_seq_length
												lowercase__				:      Optional[int]=       2
												lowercase__				:      Tuple=       1




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      List[Any]=       ids_tensor([self.batch_size, self.decoder_seq_length]       ,  self.vocab_size			)

												lowercase__				:      Tuple=       None
												if self.use_attention_mask:
																		lowercase__				:      List[str]=       ids_tensor([self.batch_size, self.decoder_seq_length]       ,  vocab_size=2			)

												lowercase__				:      Any=       None
												if self.use_labels:
																		lowercase__				:      str=       ids_tensor([self.batch_size, self.decoder_seq_length]       ,  self.vocab_size			)

												lowercase__				:      Dict=       SpeechaTextaConfig(
												    vocab_size=self.vocab_size       ,  d_model=self.d_model       ,  decoder_layers=self.decoder_layers       ,  decoder_ffn_dim=self.decoder_ffn_dim       ,  decoder_attention_heads=self.decoder_attention_heads       ,  eos_token_id=self.eos_token_id       ,  bos_token_id=self.bos_token_id       ,  use_cache=self.use_cache       ,  pad_token_id=self.pad_token_id       ,  decoder_start_token_id=self.decoder_start_token_id       ,  max_position_embeddings=self.max_position_embeddings       ,  )

												return (
												    config,
												    input_ids,
												    attention_mask,
												    lm_labels,
												)




						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  ):
												'''simple docstring'''
												lowercase__				:      Union[str, Any]=       True
												lowercase__				:      List[Any]=       SpeechaTextaDecoder(config=snake_case__			).to(snake_case__			).eval()
												lowercase__				:      Any=       input_ids[:2]

												input_ids[input_ids == 0] += 1
												# first forward pass
												lowercase__				:      List[str]=       model(snake_case__       ,  use_cache=snake_case__			)
												lowercase__				:      Optional[Any]=       model(snake_case__			)
												lowercase__				:      List[Any]=       model(snake_case__       ,  use_cache=snake_case__			)

												self.parent.assertTrue(len(snake_case__			) == len(snake_case__			)			)
												self.parent.assertTrue(len(snake_case__			) == len(snake_case__			) + 1			)

												lowercase__				:      List[Any]=       outputs["past_key_values"]

												# create hypothetical next token and extent to next_input_ids
												lowercase__				:      List[Any]=       ids_tensor((2, 1)       ,  config.vocab_size - 1			) + 1

												# append to next input_ids and
												lowercase__				:      Tuple=       torch.cat([input_ids, next_tokens]       ,  dim=-1			)

												print(snake_case__			)

												lowercase__				:      int=       model(snake_case__			)["last_hidden_state"]
												lowercase__				:      int=       model(snake_case__       ,  past_key_values=snake_case__			)["last_hidden_state"]

												# select random slice
												lowercase__				:      str=       ids_tensor((1,)       ,  output_from_past.shape[-1]			).item()
												lowercase__				:      Any=       output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
												lowercase__				:      int=       output_from_past[:, 0, random_slice_idx].detach()

												# test that outputs are equal for slice
												assert torch.allclose(snake_case__       ,  snake_case__       ,  atol=1e-3			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      str=       self.prepare_config_and_inputs()
												(
												    (
												    lowercase__
												),	(
												    lowercase__
												),	(
												    lowercase__
												),	(
												    lowercase__
												),	
												)				:      Dict=       config_and_inputs

												lowercase__				:      Optional[int]=       {
												    "input_ids": input_ids,
												    "attention_mask": attention_mask,
												}
												return config, inputs_dict


@require_torch
class     __UpperCAmelCase(   SCREAMING_SNAKE_CASE__  ,  SCREAMING_SNAKE_CASE__  ,  SCREAMING_SNAKE_CASE__  ,  unittest.TestCase							):




						"""simple docstring"""




						__lowerCamelCase				       =					(SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
						__lowerCamelCase				       =					(SpeechaTextaForCausalLM,) if is_torch_available() else ()
						__lowerCamelCase				       =					{"text-generation": SpeechaTextaForCausalLM} if is_torch_available() else {}
						__lowerCamelCase				       =					True
						__lowerCamelCase				       =					False




						def 		UpperCAmelCase_       (   self       ,  ):
												'''simple docstring'''
												lowercase__				:      List[str]=       SpeechaTextaStandaloneDecoderModelTester(self       ,  is_training=snake_case__			)
												lowercase__				:      int=       ConfigTester(self       ,  config_class=snake_case__			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												pass




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												pass




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												pass




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												self.config_tester.run_common_tests()




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Optional[int]=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_decoder_model_past(*snake_case__			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												return






