
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       A__  :





							def __init__(					self    :  List[str]	,					_UpperCAmelCase    :  Dict	,					_UpperCAmelCase    :  str=99	,					_UpperCAmelCase    :  str=13	,					_UpperCAmelCase    :  str=16	,					_UpperCAmelCase    :  Optional[Any]=7	,					_UpperCAmelCase    :  int=True	,					_UpperCAmelCase    :  List[Any]=True	,					_UpperCAmelCase    :  str=True	,					_UpperCAmelCase    :  Dict=False	,					_UpperCAmelCase    :  int=True	,					_UpperCAmelCase    :  Dict=2	,					_UpperCAmelCase    :  Dict=32	,					_UpperCAmelCase    :  List[Any]=4	,					_UpperCAmelCase    :  int=4	,					_UpperCAmelCase    :  Optional[int]=30	,					_UpperCAmelCase    :  str=0	,					_UpperCAmelCase    :  Optional[int]=1	,					_UpperCAmelCase    :  List[Any]=2	,					_UpperCAmelCase    :  str=None	,					)		->						Optional[int]:



											"""simple docstring"""


											__lowercase								=   parent
											__lowercase								=   batch_size
											__lowercase								=   decoder_seq_length
											# For common tests
											__lowercase								=   self.decoder_seq_length
											__lowercase								=   is_training
											__lowercase								=   use_attention_mask
											__lowercase								=   use_labels

											__lowercase								=   vocab_size
											__lowercase								=   d_model
											__lowercase								=   d_model
											__lowercase								=   decoder_layers
											__lowercase								=   decoder_layers
											__lowercase								=   decoder_ffn_dim
											__lowercase								=   decoder_attention_heads
											__lowercase								=   decoder_attention_heads
											__lowercase								=   eos_token_id
											__lowercase								=   bos_token_id
											__lowercase								=   pad_token_id
											__lowercase								=   decoder_start_token_id
											__lowercase								=   use_cache
											__lowercase								=   max_position_embeddings

											__lowercase								=   None
											__lowercase								=   decoder_seq_length
											__lowercase								=   2
											__lowercase								=   1





							def      a__					(					self    :  Tuple							)		->						List[Any]:



											"""simple docstring"""


											__lowercase								=   ids_tensor([self.batch_size, self.decoder_seq_length]	,					self.vocab_size							)

											__lowercase								=   None
											if self.use_attention_mask:
															__lowercase								=   ids_tensor([self.batch_size, self.decoder_seq_length]	,					vocab_size=2							)

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

											__lowercase								=   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      a__					(					self    :  Tuple	,					_UpperCAmelCase    :  Optional[int]	,					_UpperCAmelCase    :  List[Any]	,					_UpperCAmelCase    :  Any	,					_UpperCAmelCase    :  Dict	,					)		->						Optional[int]:



											"""simple docstring"""


											__lowercase								=   True
											__lowercase								=   SpeechaTextaDecoder(config=_UpperCAmelCase							).to(_UpperCAmelCase							).eval()
											__lowercase								=   input_ids[:2]

											input_ids[input_ids == 0] += 1
											# first forward pass
											__lowercase								=   model(_UpperCAmelCase	,					use_cache=_UpperCAmelCase							)
											__lowercase								=   model(_UpperCAmelCase							)
											__lowercase								=   model(_UpperCAmelCase	,					use_cache=_UpperCAmelCase							)

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

											__lowercase								=   outputs['past_key_values']

											# create hypothetical next token and extent to next_input_ids
											__lowercase								=   ids_tensor((2, 1)	,					config.vocab_size - 1							) + 1

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

											print(_UpperCAmelCase							)

											__lowercase								=   model(_UpperCAmelCase							)['last_hidden_state']
											__lowercase								=   model(_UpperCAmelCase	,					past_key_values=_UpperCAmelCase							)['last_hidden_state']

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

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





							def      a__					(					self    :  str							)		->						Tuple:



											"""simple docstring"""


											__lowercase								=   self.prepare_config_and_inputs()
											(
											    (
											    __lowercase
											)						,   (
											    __lowercase
											)						,   (
											    __lowercase
											)						,   (
											    __lowercase
											)						,   
											)								=   config_and_inputs

											__lowercase								=   {
											    'input_ids': input_ids,
											    'attention_mask': attention_mask,
											}
											return config, inputs_dict






@require_torch
class       A__  (		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		unittest.TestCase							):
							lowerCAmelCase__						:		Tuple            =				(SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
							lowerCAmelCase__						:		Optional[int]            =				(SpeechaTextaForCausalLM,) if is_torch_available() else ()
							lowerCAmelCase__						:		str            =				{"text-generation": SpeechaTextaForCausalLM} if is_torch_available() else {}
							lowerCAmelCase__						:		Union[str, Any]            =				True
							lowerCAmelCase__						:		Dict            =				False





							def      a__					(					self    :  Dict	,					)		->						Optional[int]:



											"""simple docstring"""


											__lowercase								=   SpeechaTextaStandaloneDecoderModelTester(self	,					is_training=_UpperCAmelCase							)
											__lowercase								=   ConfigTester(self	,					config_class=_UpperCAmelCase							)





							def      a__					(					self    :  Dict							)		->						Dict:



											"""simple docstring"""


											pass





							def      a__					(					self    :  Optional[int]							)		->						Any:



											"""simple docstring"""


											pass





							def      a__					(					self    :  Dict							)		->						Optional[int]:



											"""simple docstring"""


											pass





							def      a__					(					self    :  Any							)		->						Optional[Any]:



											"""simple docstring"""


											self.config_tester.run_common_tests()





							def      a__					(					self    :  Tuple							)		->						Union[str, Any]:



											"""simple docstring"""


											__lowercase								=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_decoder_model_past(*_UpperCAmelCase							)





							def      a__					(					self    :  Tuple							)		->						Dict:



											"""simple docstring"""


											return







# 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       A__  :
							lowerCAmelCase__						:		CommonSchedulerState
							lowerCAmelCase__						:		jnp.ndarray

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





							@classmethod
							def      a__					(					cls    :  Optional[Any]	,					_UpperCAmelCase    :  CommonSchedulerState	,					_UpperCAmelCase    :  jnp.ndarray	,					_UpperCAmelCase    :  jnp.ndarray	,					_UpperCAmelCase    :  jnp.ndarray	,					)		->						Any:



											"""simple docstring"""


											return cls(
											    common=_UpperCAmelCase	,					final_alpha_cumprod=_UpperCAmelCase	,					init_noise_sigma=_UpperCAmelCase	,					timesteps=_UpperCAmelCase	,					)






@dataclass
class       A__  (		lowerCAmelCase__							):
							lowerCAmelCase__						:		DDIMSchedulerState






class       A__  (		lowerCAmelCase__       ,		lowerCAmelCase__							):
							lowerCAmelCase__						:		Union[str, Any]            =				[e.name for e in FlaxKarrasDiffusionSchedulers]

							lowerCAmelCase__						:		jnp.dtype





							@property
							def      a__					(					self    :  List[str]							)		->						Union[str, Any]:



											"""simple docstring"""


											return True





							@register_to_config
							def __init__(					self    :  str	,					_UpperCAmelCase    :  int = 10_00	,					_UpperCAmelCase    :  float = 0.0_001	,					_UpperCAmelCase    :  float = 0.02	,					_UpperCAmelCase    :  str = "linear"	,					_UpperCAmelCase    :  Optional[jnp.ndarray] = None	,					_UpperCAmelCase    :  bool = True	,					_UpperCAmelCase    :  int = 0	,					_UpperCAmelCase    :  str = "epsilon"	,					_UpperCAmelCase    :  jnp.dtype = jnp.floataa	,					)		->						Any:



											"""simple docstring"""


											__lowercase								=   dtype





							def      a__					(					self    :  Tuple	,					_UpperCAmelCase    :  Optional[CommonSchedulerState] = None							)		->						DDIMSchedulerState:



											"""simple docstring"""


											if common is None:
															__lowercase								=   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.
											__lowercase								=   (
											    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
											__lowercase								=   jnp.array(1.0	,					dtype=self.dtype							)

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

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





							def      a__					(					self    :  Optional[Any]	,					_UpperCAmelCase    :  DDIMSchedulerState	,					_UpperCAmelCase    :  jnp.ndarray	,					_UpperCAmelCase    :  Optional[int] = None							)		->						jnp.ndarray:



											"""simple docstring"""


											return sample





							def      a__					(					self    :  int	,					_UpperCAmelCase    :  DDIMSchedulerState	,					_UpperCAmelCase    :  int	,					_UpperCAmelCase    :  Tuple = ()							)		->						DDIMSchedulerState:



											"""simple docstring"""


											__lowercase								=   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
											__lowercase								=   (jnp.arange(0	,					_UpperCAmelCase							) * step_ratio).round()[::-1] + self.config.steps_offset

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





							def      a__					(					self    :  Tuple	,					_UpperCAmelCase    :  DDIMSchedulerState	,					_UpperCAmelCase    :  Union[str, Any]	,					_UpperCAmelCase    :  int							)		->						int:



											"""simple docstring"""


											__lowercase								=   state.common.alphas_cumprod[timestep]
											__lowercase								=   jnp.where(
											    prev_timestep >= 0	,					state.common.alphas_cumprod[prev_timestep]	,					state.final_alpha_cumprod							)
											__lowercase								=   1 - alpha_prod_t
											__lowercase								=   1 - alpha_prod_t_prev

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

											return variance





							def      a__					(					self    :  List[Any]	,					_UpperCAmelCase    :  DDIMSchedulerState	,					_UpperCAmelCase    :  jnp.ndarray	,					_UpperCAmelCase    :  int	,					_UpperCAmelCase    :  jnp.ndarray	,					_UpperCAmelCase    :  float = 0.0	,					_UpperCAmelCase    :  bool = True	,					)		->						Union[FlaxDDIMSchedulerOutput, Tuple]:



											"""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'							)

											# 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)
											__lowercase								=   timestep - self.config.num_train_timesteps // state.num_inference_steps

											__lowercase								=   state.common.alphas_cumprod
											__lowercase								=   state.final_alpha_cumprod

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

											__lowercase								=   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":
															__lowercase								=   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
															__lowercase								=   model_output
											elif self.config.prediction_type == "sample":
															__lowercase								=   model_output
															__lowercase								=   (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
											elif self.config.prediction_type == "v_prediction":
															__lowercase								=   (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
															__lowercase								=   (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)
											__lowercase								=   self._get_variance(_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase							)
											__lowercase								=   eta * variance ** 0.5

											# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											__lowercase								=   (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
											__lowercase								=   alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

											if not return_dict:
															return (prev_sample, state)

											return FlaxDDIMSchedulerOutput(prev_sample=_UpperCAmelCase	,					state=_UpperCAmelCase							)





							def      a__					(					self    :  Union[str, Any]	,					_UpperCAmelCase    :  DDIMSchedulerState	,					_UpperCAmelCase    :  jnp.ndarray	,					_UpperCAmelCase    :  jnp.ndarray	,					_UpperCAmelCase    :  jnp.ndarray	,					)		->						jnp.ndarray:



											"""simple docstring"""


											return add_noise_common(state.common	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase							)





							def      a__					(					self    :  Optional[int]	,					_UpperCAmelCase    :  DDIMSchedulerState	,					_UpperCAmelCase    :  jnp.ndarray	,					_UpperCAmelCase    :  jnp.ndarray	,					_UpperCAmelCase    :  jnp.ndarray	,					)		->						jnp.ndarray:



											"""simple docstring"""


											return get_velocity_common(state.common	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase							)





							def __len__(					self    :  List[Any]							)		->						List[str]:



											"""simple docstring"""


											return self.config.num_train_timesteps





