
import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
							import torch


SCREAMING_SNAKE_CASE__				=							random.Random()




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: List[Any]       ,				SCREAMING_SNAKE_CASE			: Tuple=1.0       ,				SCREAMING_SNAKE_CASE			: Tuple=None       ,				SCREAMING_SNAKE_CASE			: Optional[Any]=None     )	->		Tuple:
				if rng is None:
								__lowercase								=   global_rng

				__lowercase								=   []
				for batch_idx in range(shape[0]     ):
								values.append([]     )
								for _ in range(shape[1]     ):
												values[-1].append(rng.random() * scale     )

				return values
@require_torch
class       A__  (		unittest.TestCase							):





							def __init__(					self    :  int	,					_UpperCAmelCase    :  str	,					_UpperCAmelCase    :  Any=7	,					_UpperCAmelCase    :  Any=4_00	,					_UpperCAmelCase    :  Optional[int]=20_00	,					_UpperCAmelCase    :  Dict=1	,					_UpperCAmelCase    :  List[str]=0.0	,					_UpperCAmelCase    :  Optional[int]=2_40_00	,					_UpperCAmelCase    :  Optional[int]=True	,					)		->						Dict:



											"""simple docstring"""


											__lowercase								=   parent
											__lowercase								=   batch_size
											__lowercase								=   min_seq_length
											__lowercase								=   max_seq_length
											__lowercase								=   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
											__lowercase								=   feature_size
											__lowercase								=   padding_value
											__lowercase								=   sampling_rate
											__lowercase								=   return_attention_mask





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



											"""simple docstring"""


											return {
											    "feature_size": self.feature_size,
											    "padding_value": self.padding_value,
											    "sampling_rate": self.sampling_rate,
											    "return_attention_mask": self.return_attention_mask,
											}





							def      a__					(					self    :  Optional[int]	,					_UpperCAmelCase    :  str=False	,					_UpperCAmelCase    :  Dict=False							)		->						Tuple:



											"""simple docstring"""


											def _flatten(_UpperCAmelCase    :  Optional[int]							):
															return list(itertools.chain(*_UpperCAmelCase							)							)

											if equal_length:
															__lowercase								=   floats_list((self.batch_size, self.max_seq_length)							)
											else:
															# make sure that inputs increase in size
															__lowercase								=   [
															    _flatten(floats_list((x, self.feature_size)							)							)
															    for x in range(self.min_seq_length	,					self.max_seq_length	,					self.seq_length_diff							)
															]

											if numpify:
															__lowercase								=   [np.asarray(_UpperCAmelCase							) for x in audio_inputs]

											return audio_inputs






@require_torch
class       A__  (		lowerCAmelCase__       ,		unittest.TestCase							):
							lowerCAmelCase__						:		List[str]            =				EncodecFeatureExtractor





							def      a__					(					self    :  Tuple							)		->						Any:



											"""simple docstring"""


											__lowercase								=   EnCodecFeatureExtractionTester(self							)





							def      a__					(					self    :  str							)		->						Dict:



											"""simple docstring"""


											__lowercase								=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
											# create three inputs of length 800, 1000, and 1200
											__lowercase								=   [floats_list((1, x)							)[0] for x in range(8_00	,					14_00	,					2_00							)]
											__lowercase								=   [np.asarray(_UpperCAmelCase							) for audio_input in audio_inputs]

											# Test not batched input
											__lowercase								=   feat_extract(audio_inputs[0]	,					return_tensors='np'							).input_values
											__lowercase								=   feat_extract(np_audio_inputs[0]	,					return_tensors='np'							).input_values
											self.assertTrue(np.allclose(_UpperCAmelCase	,					_UpperCAmelCase	,					atol=1e-3							)							)

											# Test batched
											__lowercase								=   feat_extract(_UpperCAmelCase	,					padding=_UpperCAmelCase	,					return_tensors='np'							).input_values
											__lowercase								=   feat_extract(_UpperCAmelCase	,					padding=_UpperCAmelCase	,					return_tensors='np'							).input_values
											for enc_seq_a, enc_seq_a in zip(_UpperCAmelCase	,					_UpperCAmelCase							):
															self.assertTrue(np.allclose(_UpperCAmelCase	,					_UpperCAmelCase	,					atol=1e-3							)							)





							def      a__					(					self    :  Tuple							)		->						Tuple:



											"""simple docstring"""


											__lowercase								=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
											__lowercase								=   np.random.rand(1_00							).astype(np.floataa							)
											__lowercase								=   np_audio_inputs.tolist()

											for inputs in [py_audio_inputs, np_audio_inputs]:
															__lowercase								=   feature_extractor.pad([{'input_values': inputs}]	,					return_tensors='np'							)
															self.assertTrue(np_processed.input_values.dtype == np.floataa							)
															__lowercase								=   feature_extractor.pad([{'input_values': inputs}]	,					return_tensors='pt'							)
															self.assertTrue(pt_processed.input_values.dtype == torch.floataa							)





							def      a__					(					self    :  Dict	,					_UpperCAmelCase    :  Tuple							)		->						List[str]:



											"""simple docstring"""


											from datasets import load_dataset

											__lowercase								=   load_dataset('hf-internal-testing/librispeech_asr_dummy'	,					'clean'	,					split='validation'							)
											# automatic decoding with librispeech
											__lowercase								=   ds.sort('id'							).select(range(_UpperCAmelCase							)							)[:num_samples]['audio']

											return [x["array"] for x in audio_samples]





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



											"""simple docstring"""


											__lowercase								=   torch.tensor(
											    [2.3_8_0_4e-0_3, 2.0_7_5_2e-0_3, 1.9_8_3_6e-0_3, 2.1_0_5_7e-0_3, 1.6_1_7_4e-0_3,
											     3.0_5_1_8e-0_4, 9.1_5_5_3e-0_5, 3.3_5_6_9e-0_4, 9.7_6_5_6e-0_4, 1.8_3_1_1e-0_3,
											     2.0_1_4_2e-0_3, 2.1_0_5_7e-0_3, 1.7_3_9_5e-0_3, 4.5_7_7_6e-0_4, -3.9_6_7_3e-0_4,
											     4.5_7_7_6e-0_4, 1.0_0_7_1e-0_3, 9.1_5_5_3e-0_5, 4.8_8_2_8e-0_4, 1.1_5_9_7e-0_3,
											     7.3_2_4_2e-0_4, 9.4_6_0_4e-0_4, 1.8_0_0_5e-0_3, 1.8_3_1_1e-0_3, 8.8_5_0_1e-0_4,
											     4.2_7_2_5e-0_4, 4.8_8_2_8e-0_4, 7.3_2_4_2e-0_4, 1.0_9_8_6e-0_3, 2.1_0_5_7e-0_3]							)
											# fmt: on
											__lowercase								=   self._load_datasamples(1							)
											__lowercase								=   EncodecFeatureExtractor()
											__lowercase								=   feature_extractor(_UpperCAmelCase	,					return_tensors='pt'							).input_values
											self.assertEquals(input_values.shape	,					(1, 1, 9_36_80)							)
											self.assertTrue(torch.allclose(input_values[0, 0, :30]	,					_UpperCAmelCase	,					atol=1e-6							)							)





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



											"""simple docstring"""


											__lowercase								=   torch.tensor(
											    [2.3_8_0_4e-0_3, 2.0_7_5_2e-0_3, 1.9_8_3_6e-0_3, 2.1_0_5_7e-0_3, 1.6_1_7_4e-0_3,
											     3.0_5_1_8e-0_4, 9.1_5_5_3e-0_5, 3.3_5_6_9e-0_4, 9.7_6_5_6e-0_4, 1.8_3_1_1e-0_3,
											     2.0_1_4_2e-0_3, 2.1_0_5_7e-0_3, 1.7_3_9_5e-0_3, 4.5_7_7_6e-0_4, -3.9_6_7_3e-0_4,
											     4.5_7_7_6e-0_4, 1.0_0_7_1e-0_3, 9.1_5_5_3e-0_5, 4.8_8_2_8e-0_4, 1.1_5_9_7e-0_3,
											     7.3_2_4_2e-0_4, 9.4_6_0_4e-0_4, 1.8_0_0_5e-0_3, 1.8_3_1_1e-0_3, 8.8_5_0_1e-0_4,
											     4.2_7_2_5e-0_4, 4.8_8_2_8e-0_4, 7.3_2_4_2e-0_4, 1.0_9_8_6e-0_3, 2.1_0_5_7e-0_3]							)
											# fmt: on
											__lowercase								=   self._load_datasamples(1							)
											__lowercase								=   [np.tile(input_audio[0][None]	,					reps=(2, 1)							)]
											input_audio[0][1] *= 0.5
											__lowercase								=   EncodecFeatureExtractor(feature_size=2							)
											__lowercase								=   feature_extractor(_UpperCAmelCase	,					return_tensors='pt'							).input_values
											self.assertEquals(input_values.shape	,					(1, 2, 9_36_80)							)
											self.assertTrue(torch.allclose(input_values[0, 0, :30]	,					_UpperCAmelCase	,					atol=1e-6							)							)
											self.assertTrue(torch.allclose(input_values[0, 1, :30]	,					EXPECTED_INPUT_VALUES * 0.5	,					atol=1e-6							)							)





							def      a__					(					self    :  Tuple							)		->						Dict:



											"""simple docstring"""


											__lowercase								=   self._load_datasamples(2							)
											# would be easier if the stride was like
											__lowercase								=   EncodecFeatureExtractor(feature_size=1	,					chunk_length_s=1	,					overlap=0.01							)

											# pad and trunc raise an error ?
											with self.assertRaisesRegex(
											    _UpperCAmelCase	,					'^Both padding and truncation were set. Make sure you only set one.$'	,					):
															__lowercase								=   feature_extractor(
															    _UpperCAmelCase	,					padding='max_length'	,					truncation=_UpperCAmelCase	,					return_tensors='pt'							).input_values

											# truncate to chunk
											__lowercase								=   feature_extractor(_UpperCAmelCase	,					truncation=_UpperCAmelCase	,					return_tensors='pt'							).input_values
											self.assertEquals(truncated_outputs.shape	,					(2, 1, 7_15_20)							)  # 2 chunks

											# force truncate to max_length
											__lowercase								=   feature_extractor(
											    _UpperCAmelCase	,					truncation=_UpperCAmelCase	,					max_length=4_80_00	,					return_tensors='pt'							).input_values
											self.assertEquals(truncated_outputs.shape	,					(2, 1, 4_80_00)							)

											# pad to chunk
											__lowercase								=   feature_extractor(_UpperCAmelCase	,					padding=_UpperCAmelCase	,					return_tensors='pt'							).input_values
											self.assertEquals(padded_outputs.shape	,					(2, 1, 9_52_80)							)

											# pad to chunk
											__lowercase								=   feature_extractor(_UpperCAmelCase	,					return_tensors='pt'							).input_values
											self.assertEquals(truncated_outputs.shape	,					(2, 1, 9_52_80)							)

											# force pad to max length
											__lowercase								=   feature_extractor(
											    _UpperCAmelCase	,					padding='max_length'	,					max_length=10_00_00	,					return_tensors='pt'							).input_values
											self.assertEquals(truncated_outputs.shape	,					(2, 1, 10_00_00)							)

											# force no pad
											with self.assertRaisesRegex(
											    _UpperCAmelCase	,					'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'	,					):
															__lowercase								=   feature_extractor(_UpperCAmelCase	,					padding=_UpperCAmelCase	,					return_tensors='pt'							).input_values

											__lowercase								=   feature_extractor(input_audio[0]	,					padding=_UpperCAmelCase	,					return_tensors='pt'							).input_values
											self.assertEquals(truncated_outputs.shape	,					(1, 1, 9_36_80)							)

											# no pad if no chunk_length_s
											__lowercase								=   None
											with self.assertRaisesRegex(
											    _UpperCAmelCase	,					'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'	,					):
															__lowercase								=   feature_extractor(_UpperCAmelCase	,					padding=_UpperCAmelCase	,					return_tensors='pt'							).input_values

											__lowercase								=   feature_extractor(input_audio[0]	,					padding=_UpperCAmelCase	,					return_tensors='pt'							).input_values
											self.assertEquals(truncated_outputs.shape	,					(1, 1, 9_36_80)							)

											# no pad if no overlap
											__lowercase								=   2
											__lowercase								=   None
											with self.assertRaisesRegex(
											    _UpperCAmelCase	,					'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'	,					):
															__lowercase								=   feature_extractor(_UpperCAmelCase	,					padding=_UpperCAmelCase	,					return_tensors='pt'							).input_values

											__lowercase								=   feature_extractor(input_audio[0]	,					padding=_UpperCAmelCase	,					return_tensors='pt'							).input_values
											self.assertEquals(truncated_outputs.shape	,					(1, 1, 9_36_80)							)







# 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





