


"""simple docstring"""





from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


lowerCamelCase       	=						8
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__=BITS				):
      UpperCAmelCase_      			=							x.device

      UpperCAmelCase_      			=							(x * 255).int().clamp(0     ,      255				)

      UpperCAmelCase_      			=							2 ** torch.arange(bits - 1     ,      -1     ,      -1     ,      device=snake_case_				)
      UpperCAmelCase_      			=							rearrange(snake_case_     ,      "d -> d 1 1"				)
      UpperCAmelCase_      			=							rearrange(snake_case_     ,      "b c h w -> b c 1 h w"				)

      UpperCAmelCase_      			=							((x & mask) != 0).float()
      UpperCAmelCase_      			=							rearrange(snake_case_     ,      "b c d h w -> b (c d) h w"				)
      UpperCAmelCase_      			=							bits * 2 - 1
      return bits
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__=BITS				):
      UpperCAmelCase_      			=							x.device

      UpperCAmelCase_      			=							(x > 0).int()
      UpperCAmelCase_      			=							2 ** torch.arange(bits - 1     ,      -1     ,      -1     ,      device=snake_case_     ,      dtype=torch.intaa				)

      UpperCAmelCase_      			=							rearrange(snake_case_     ,      "d -> d 1 1"				)
      UpperCAmelCase_      			=							rearrange(snake_case_     ,      "b (c d) h w -> b c d h w"     ,      d=8				)
      UpperCAmelCase_      			=							reduce(x * mask     ,      "b c d h w -> b c h w"     ,      "sum"				)
      return (dec / 255).clamp(0.0     ,      1.0				)
def        a__     (				self     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__ = 0.0     ,      lowerCAmelCase__ = True     ,      lowerCAmelCase__=None     ,      lowerCAmelCase__ = True     ,      ):




      if self.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)
      UpperCAmelCase_      			=							timestep - self.config.num_train_timesteps // self.num_inference_steps

      # 2. compute alphas, betas
      UpperCAmelCase_      			=							self.alphas_cumprod[timestep]
      UpperCAmelCase_      			=							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

      UpperCAmelCase_      			=							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
      UpperCAmelCase_      			=							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

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

      # 5. 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)
      UpperCAmelCase_      			=							self._get_variance(snake_case_     ,      snake_case_				)
      UpperCAmelCase_      			=							eta * variance ** 0.5

      if use_clipped_model_output:
            # the model_output is always re-derived from the clipped x_0 in Glide
            UpperCAmelCase_      			=							(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

      # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      UpperCAmelCase_      			=							(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

      # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      UpperCAmelCase_      			=							alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

      if eta > 0:
            # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
            UpperCAmelCase_      			=							model_output.device if torch.is_tensor(snake_case_				) else "cpu"
            UpperCAmelCase_      			=							torch.randn(model_output.shape     ,      dtype=model_output.dtype     ,      generator=snake_case_				).to(snake_case_				)
            UpperCAmelCase_      			=							self._get_variance(snake_case_     ,      snake_case_				) ** 0.5 * eta * noise

            UpperCAmelCase_      			=							prev_sample + variance

      if not return_dict:
            return (prev_sample,)

      return DDIMSchedulerOutput(prev_sample=snake_case_     ,      pred_original_sample=snake_case_				)
def        a__     (				self     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__="epsilon"     ,      lowerCAmelCase__=None     ,      lowerCAmelCase__ = True     ,      ):
      UpperCAmelCase_      			=							timestep

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

      # 1. compute alphas, betas
      UpperCAmelCase_      			=							self.alphas_cumprod[t]
      UpperCAmelCase_      			=							self.alphas_cumprod[t - 1] if t > 0 else self.one
      UpperCAmelCase_      			=							1 - alpha_prod_t
      UpperCAmelCase_      			=							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 prediction_type == "epsilon":
            UpperCAmelCase_      			=							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
      elif prediction_type == "sample":
            UpperCAmelCase_      			=							model_output
      else:
            raise ValueError(f"""Unsupported prediction_type {prediction_type}."""				)

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

      # 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
      UpperCAmelCase_      			=							(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
      UpperCAmelCase_      			=							self.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
      UpperCAmelCase_      			=							pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

      # 6. Add noise
      UpperCAmelCase_      			=							0
      if t > 0:
            UpperCAmelCase_      			=							torch.randn(
                model_output.size()     ,      dtype=model_output.dtype     ,      layout=model_output.layout     ,      generator=snake_case_				).to(model_output.device				)
            UpperCAmelCase_      			=							(self._get_variance(snake_case_     ,      predicted_variance=snake_case_				) ** 0.5) * noise

      UpperCAmelCase_      			=							pred_prev_sample + variance

      if not return_dict:
            return (pred_prev_sample,)

      return DDPMSchedulerOutput(prev_sample=snake_case_     ,      pred_original_sample=snake_case_				)


class 				lowercase__       (   UpperCamelCase__				):







       '''simple docstring'''


       def __init__(							self      :  List[str]		,    _UpperCAmelCase      :  UNetaDConditionModel		,    _UpperCAmelCase      :  Union[DDIMScheduler, DDPMScheduler]		,    _UpperCAmelCase      :  Optional[float] = 1.0		,    )			->						List[str]:


             '''simple docstring'''




             super().__init__()
             UpperCAmelCase_      			=							bit_scale
             UpperCAmelCase_      			=							(
                 ddim_bit_scheduler_step if isinstance(__a		,    __a   ) else ddpm_bit_scheduler_step
             )

             self.register_modules(unet=__a		,    scheduler=__a   )







       @torch.no_grad()
       def __call__(							self      :  List[Any]		,    _UpperCAmelCase      :  Optional[int] = 256		,    _UpperCAmelCase      :  Optional[int] = 256		,    _UpperCAmelCase      :  Optional[int] = 50		,    _UpperCAmelCase      :  Optional[torch.Generator] = None		,    _UpperCAmelCase      :  Optional[int] = 1		,    _UpperCAmelCase      :  Optional[str] = "pil"		,    _UpperCAmelCase      :  bool = True		,    **_UpperCAmelCase      :  Union[str, Any]		,    )			->						List[Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							torch.randn(
                 (batch_size, self.unet.config.in_channels, height, width)		,    generator=__a		,    )
             UpperCAmelCase_      			=							decimal_to_bits(__a   ) * self.bit_scale
             UpperCAmelCase_      			=							latents.to(self.device   )

             self.scheduler.set_timesteps(__a   )

             for t in self.progress_bar(self.scheduler.timesteps   ):
                   # predict the noise residual
                   UpperCAmelCase_      			=							self.unet(__a		,    __a   ).sample

                   # compute the previous noisy sample x_t -> x_t-1
                   UpperCAmelCase_      			=							self.scheduler.step(__a		,    __a		,    __a   ).prev_sample

             UpperCAmelCase_      			=							bits_to_decimal(__a   )

             if output_type == "pil":
                   UpperCAmelCase_      			=							self.numpy_to_pil(__a   )

             if not return_dict:
                   return (image,)

             return ImagePipelineOutput(images=__a   )









"""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


lowerCamelCase       	=						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)

lowerCamelCase       	=						{"""base""": """patrickvonplaten/wav2vec2_tiny_random""", """robust""": """patrickvonplaten/wav2vec2_tiny_random_robust"""}

lowerCamelCase       	=						"""zero2"""
lowerCamelCase       	=						"""zero3"""
lowerCamelCase       	=						[ZEROa, ZEROa]
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__				):
      # 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
      UpperCAmelCase_      			=							parameterized.to_safe_name("_".join(str(lowerCAmelCase__				) for x in param.args				)				)
      return f"""{func.__name__}_{param_based_name}"""


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


@slow
@require_deepspeed
@require_torch_gpu
class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       @parameterized.expand(_UpperCAmelCase		,    name_func=_UpperCAmelCase   )
       def 					lowercase__    (							self      :  Dict		,    _UpperCAmelCase      :  int		,    _UpperCAmelCase      :  Any   )			->						Optional[int]:


             '''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 					lowercase__    (							self      :  Any		,    _UpperCAmelCase      :  Optional[Any]		,    _UpperCAmelCase      :  str   )			->						Any:


             '''simple docstring'''




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



       @parameterized.expand(_UpperCAmelCase		,    name_func=_UpperCAmelCase   )
       def 					lowercase__    (							self      :  Optional[int]		,    _UpperCAmelCase      :  str		,    _UpperCAmelCase      :  List[str]   )			->						Union[str, 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 					lowercase__    (							self      :  int		,    _UpperCAmelCase      :  int		,    _UpperCAmelCase      :  Optional[int]   )			->						Optional[int]:


             '''simple docstring'''




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



       def 					lowercase__    (							self      :  Optional[Any]		,    _UpperCAmelCase      :  Tuple   )			->						Optional[int]:


             '''simple docstring'''




             pass



       def 					lowercase__    (							self      :  Optional[Any]		,    _UpperCAmelCase      :  str		,    _UpperCAmelCase      :  str		,    _UpperCAmelCase      :  int = 10		,    _UpperCAmelCase      :  bool = True		,    _UpperCAmelCase      :  bool = True		,    _UpperCAmelCase      :  bool = True		,    )			->						Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							models[model]

             UpperCAmelCase_      			=							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 					lowercase__    (							self      :  Union[str, Any]		,    _UpperCAmelCase      :  str		,    _UpperCAmelCase      :  str		,    _UpperCAmelCase      :  int = 10		,    _UpperCAmelCase      :  int = 1		,    _UpperCAmelCase      :  bool = True		,    _UpperCAmelCase      :  bool = True		,    )			->						Tuple:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.get_auto_remove_tmp_dir("./xxx"		,    after=_UpperCAmelCase   )
             UpperCAmelCase_      			=							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
             UpperCAmelCase_      			=							F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
             UpperCAmelCase_      			=							[F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
             UpperCAmelCase_      			=							self.get_launcher(_UpperCAmelCase   )

             UpperCAmelCase_      			=							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 					lowercase__    (							self      :  List[Any]		,    _UpperCAmelCase      :  int=False   )			->						Optional[Any]:


             '''simple docstring'''




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




