


"""simple docstring"""
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin





@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class      __A     (snake_case__):





							'''simple docstring'''

							__lowercase:		torch.FloatTensor
							__lowercase:		Optional[torch.FloatTensor]							      =    None







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE=0.999				, _SCREAMING_SNAKE_CASE="cosine"				, )    ->				Optional[int]:
							if alpha_transform_type == "cosine":

														def alpha_bar_fn(_SCREAMING_SNAKE_CASE				):
																					return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

							elif alpha_transform_type == "exp":

														def alpha_bar_fn(_SCREAMING_SNAKE_CASE				):
																					return math.exp(t * -12.0				)

							else:
														raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""				)

							snake_case_           =		[]
							for i in range(_SCREAMING_SNAKE_CASE				):
														snake_case_           =		i / num_diffusion_timesteps
														snake_case_           =		(i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar_fn(_SCREAMING_SNAKE_CASE				) / alpha_bar_fn(_SCREAMING_SNAKE_CASE				)				, _SCREAMING_SNAKE_CASE				)				)
							return torch.tensor(_SCREAMING_SNAKE_CASE				, dtype=torch.floataa				)





class      __A     (snake_case__						,		snake_case__):





							'''simple docstring'''

							@register_to_config
							def __init__(				self							:      Union[str, Any]		,       UpperCAmelCase_							:      int = 1_000		,       UpperCAmelCase_							:      str = "fixed_small_log"		,       UpperCAmelCase_							:      bool = True		,       UpperCAmelCase_							:      Optional[float] = 1.0		,       UpperCAmelCase_							:      str = "epsilon"		,       UpperCAmelCase_							:      str = "squaredcos_cap_v2"		,       )					->Optional[int]:



														"""simple docstring"""



														if beta_schedule != "squaredcos_cap_v2":
																					raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"""       )

														snake_case_           =		betas_for_alpha_bar(UpperCAmelCase_       )

														snake_case_           =		1.0 - self.betas
														snake_case_           =		torch.cumprod(self.alphas		,       dim=0       )
														snake_case_           =		torch.tensor(1.0       )

														# standard deviation of the initial noise distribution
														snake_case_           =		1.0

														# setable values
														snake_case_           =		None
														snake_case_           =		torch.from_numpy(np.arange(0		,       UpperCAmelCase_       )[::-1].copy()       )

														snake_case_           =		variance_type



							def 		lowerCAmelCase       (				self							:      List[Any]		,       UpperCAmelCase_							:      torch.FloatTensor		,       UpperCAmelCase_							:      Optional[int] = None       )					->torch.FloatTensor:



														"""simple docstring"""



														return sample



							def 		lowerCAmelCase       (				self							:      Optional[int]		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      Union[str, torch.device] = None       )					->Tuple:



														"""simple docstring"""



														snake_case_           =		num_inference_steps
														snake_case_           =		(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
														snake_case_           =		(np.arange(0		,       UpperCAmelCase_       ) * step_ratio).round()[::-1].copy().astype(np.intaa       )
														snake_case_           =		torch.from_numpy(UpperCAmelCase_       ).to(UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      List[str]		,       UpperCAmelCase_							:      Optional[int]		,       UpperCAmelCase_							:      List[str]=None		,       UpperCAmelCase_							:      Optional[Any]=None		,       UpperCAmelCase_							:      List[str]=None       )					->str:



														"""simple docstring"""



														if prev_timestep is None:
																					snake_case_           =		t - 1

														snake_case_           =		self.alphas_cumprod[t]
														snake_case_           =		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
														snake_case_           =		1 - alpha_prod_t
														snake_case_           =		1 - alpha_prod_t_prev

														if prev_timestep == t - 1:
																					snake_case_           =		self.betas[t]
														else:
																					snake_case_           =		1 - alpha_prod_t / alpha_prod_t_prev

														# 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
														snake_case_           =		beta_prod_t_prev / beta_prod_t * beta

														if variance_type is None:
																					snake_case_           =		self.config.variance_type

														# hacks - were probably added for training stability
														if variance_type == "fixed_small_log":
																					snake_case_           =		torch.log(torch.clamp(UpperCAmelCase_		,       min=1E-20       )       )
																					snake_case_           =		torch.exp(0.5 * variance       )
														elif variance_type == "learned_range":
																					# NOTE difference with DDPM scheduler
																					snake_case_           =		variance.log()
																					snake_case_           =		beta.log()

																					snake_case_           =		(predicted_variance + 1) / 2
																					snake_case_           =		frac * max_log + (1 - frac) * min_log

														return variance



							def 		lowerCAmelCase       (				self							:      Union[str, Any]		,       UpperCAmelCase_							:      torch.FloatTensor		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      torch.FloatTensor		,       UpperCAmelCase_							:      Optional[int] = None		,       UpperCAmelCase_							:      Any=None		,       UpperCAmelCase_							:      bool = True		,       )					->Union[UnCLIPSchedulerOutput, Tuple]:



														"""simple docstring"""



														snake_case_           =		timestep

														if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
																					snake_case_       ,						snake_case_           =		torch.split(UpperCAmelCase_		,       sample.shape[1]		,       dim=1       )
														else:
																					snake_case_           =		None

														# 1. compute alphas, betas
														if prev_timestep is None:
																					snake_case_           =		t - 1

														snake_case_           =		self.alphas_cumprod[t]
														snake_case_           =		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
														snake_case_           =		1 - alpha_prod_t
														snake_case_           =		1 - alpha_prod_t_prev

														if prev_timestep == t - 1:
																					snake_case_           =		self.betas[t]
																					snake_case_           =		self.alphas[t]
														else:
																					snake_case_           =		1 - alpha_prod_t / alpha_prod_t_prev
																					snake_case_           =		1 - beta

														# 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":
																					snake_case_           =		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
														elif self.config.prediction_type == "sample":
																					snake_case_           =		model_output
														else:
																					raise ValueError(
																					    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
																					    """ for the UnCLIPScheduler."""       )

														# 3. Clip "predicted x_0"
														if self.config.clip_sample:
																					snake_case_           =		torch.clamp(
																					    UpperCAmelCase_		,       -self.config.clip_sample_range		,       self.config.clip_sample_range       )

														# 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
														snake_case_           =		(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
														snake_case_           =		alpha ** 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
														snake_case_           =		pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

														# 6. Add noise
														snake_case_           =		0
														if t > 0:
																					snake_case_           =		randn_tensor(
																					    model_output.shape		,       dtype=model_output.dtype		,       generator=UpperCAmelCase_		,       device=model_output.device       )

																					snake_case_           =		self._get_variance(
																					    UpperCAmelCase_		,       predicted_variance=UpperCAmelCase_		,       prev_timestep=UpperCAmelCase_		,       )

																					if self.variance_type == "fixed_small_log":
																												snake_case_           =		variance
																					elif self.variance_type == "learned_range":
																												snake_case_           =		(0.5 * variance).exp()
																					else:
																												raise ValueError(
																												    F"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
																												    """ for the UnCLIPScheduler."""       )

																					snake_case_           =		variance * variance_noise

														snake_case_           =		pred_prev_sample + variance

														if not return_dict:
																					return (pred_prev_sample,)

														return UnCLIPSchedulerOutput(prev_sample=UpperCAmelCase_		,       pred_original_sample=UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      int		,       UpperCAmelCase_							:      torch.FloatTensor		,       UpperCAmelCase_							:      torch.FloatTensor		,       UpperCAmelCase_							:      torch.IntTensor		,       )					->torch.FloatTensor:



														"""simple docstring"""



														snake_case_           =		self.alphas_cumprod.to(device=original_samples.device		,       dtype=original_samples.dtype       )
														snake_case_           =		timesteps.to(original_samples.device       )

														snake_case_           =		alphas_cumprod[timesteps] ** 0.5
														snake_case_           =		sqrt_alpha_prod.flatten()
														while len(sqrt_alpha_prod.shape       ) < len(original_samples.shape       ):
																					snake_case_           =		sqrt_alpha_prod.unsqueeze(-1       )

														snake_case_           =		(1 - alphas_cumprod[timesteps]) ** 0.5
														snake_case_           =		sqrt_one_minus_alpha_prod.flatten()
														while len(sqrt_one_minus_alpha_prod.shape       ) < len(original_samples.shape       ):
																					snake_case_           =		sqrt_one_minus_alpha_prod.unsqueeze(-1       )

														snake_case_           =		sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
														return noisy_samples







"""simple docstring"""
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


__SCREAMING_SNAKE_CASE							:    Union[str, Any]		=    Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

__SCREAMING_SNAKE_CASE							:    Dict		=    {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

__SCREAMING_SNAKE_CASE							:    Dict		=    'zero2'
__SCREAMING_SNAKE_CASE							:    List[Any]		=    'zero3'
__SCREAMING_SNAKE_CASE							:    int		=    [ZEROa, ZEROa]







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Union[str, Any]:
							# customize the test name generator function as we want both params to appear in the sub-test
							# name, as by default it shows only the first param
							snake_case_           =		parameterized.to_safe_name("""_""".join(str(_SCREAMING_SNAKE_CASE				) for x in param.args				)				)
							return f"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
__SCREAMING_SNAKE_CASE							:    Dict		=    list(itertools.product(stages, models.keys()))





@slow
@require_deepspeed
@require_torch_gpu
class      __A     (snake_case__):





							'''simple docstring'''

							@parameterized.expand(UpperCAmelCase_		,       name_func=UpperCAmelCase_       )
							def 		lowerCAmelCase       (				self							:      Union[str, Any]		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      Union[str, Any]       )					->Any:



														"""simple docstring"""



														self.run_and_check(
														    stage=UpperCAmelCase_		,       model=UpperCAmelCase_		,       distributed=UpperCAmelCase_		,       fpaa=UpperCAmelCase_		,       )



							@require_torch_multi_gpu
							@parameterized.expand(UpperCAmelCase_		,       name_func=UpperCAmelCase_       )
							def 		lowerCAmelCase       (				self							:      Optional[int]		,       UpperCAmelCase_							:      List[Any]		,       UpperCAmelCase_							:      Optional[Any]       )					->Optional[Any]:



														"""simple docstring"""



														self.run_and_check(
														    stage=UpperCAmelCase_		,       model=UpperCAmelCase_		,       distributed=UpperCAmelCase_		,       fpaa=UpperCAmelCase_		,       )



							@parameterized.expand(UpperCAmelCase_		,       name_func=UpperCAmelCase_       )
							def 		lowerCAmelCase       (				self							:      Tuple		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      Optional[int]       )					->List[str]:



														"""simple docstring"""



														self.run_and_check(
														    stage=UpperCAmelCase_		,       model=UpperCAmelCase_		,       distributed=UpperCAmelCase_		,       fpaa=UpperCAmelCase_		,       )



							@require_torch_multi_gpu
							@parameterized.expand(UpperCAmelCase_		,       name_func=UpperCAmelCase_       )
							def 		lowerCAmelCase       (				self							:      Dict		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      List[Any]       )					->Optional[int]:



														"""simple docstring"""



														self.run_and_check(
														    stage=UpperCAmelCase_		,       model=UpperCAmelCase_		,       distributed=UpperCAmelCase_		,       fpaa=UpperCAmelCase_		,       )



							def 		lowerCAmelCase       (				self							:      Optional[int]		,       UpperCAmelCase_							:      Union[str, Any]       )					->Optional[int]:



														"""simple docstring"""



														pass



							def 		lowerCAmelCase       (				self							:      Any		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      int = 10		,       UpperCAmelCase_							:      bool = True		,       UpperCAmelCase_							:      bool = True		,       UpperCAmelCase_							:      bool = True		,       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		models[model]

														snake_case_           =		self.run_trainer(
														    stage=UpperCAmelCase_		,       model_name=UpperCAmelCase_		,       eval_steps=UpperCAmelCase_		,       num_train_epochs=1		,       distributed=UpperCAmelCase_		,       fpaa=UpperCAmelCase_		,       )

														self.do_checks(UpperCAmelCase_       )

														return output_dir



							def 		lowerCAmelCase       (				self							:      Union[str, Any]		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      int = 10		,       UpperCAmelCase_							:      int = 1		,       UpperCAmelCase_							:      bool = True		,       UpperCAmelCase_							:      bool = True		,       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		self.get_auto_remove_tmp_dir("""./xxx"""		,       after=UpperCAmelCase_       )
														snake_case_           =		F"""
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(UpperCAmelCase_       )}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        """.split()

														if fpaa:
																					args.extend(["""--fp16"""]       )

														# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
														# hence the separate config files
														snake_case_           =		F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
														snake_case_           =		[F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
														snake_case_           =		self.get_launcher(UpperCAmelCase_       )

														snake_case_           =		launcher + script + args + ds_args
														# keep for quick debug
														# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
														execute_subprocess_async(UpperCAmelCase_		,       env=self.get_env()       )

														return output_dir



							def 		lowerCAmelCase       (				self							:      Union[str, Any]		,       UpperCAmelCase_							:      Any=False       )					->Tuple:



														"""simple docstring"""



														snake_case_           =		min(2		,       get_gpu_count()       ) if distributed else 1
														return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()



