
'''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


UpperCamelCase__      :		Dict       							=  8
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=BITS						)     ->     Union[str, Any]:




			"""simple docstring"""
			_SCREAMING_SNAKE_CASE      =				x.device

			_SCREAMING_SNAKE_CASE      =				(x * 2_55).int().clamp(0    ,      2_55						)

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

			_SCREAMING_SNAKE_CASE      =				((x & mask) != 0).float()
			_SCREAMING_SNAKE_CASE      =				rearrange(lowerCAmelCase__    ,      """b c d h w -> b (c d) h w"""						)
			_SCREAMING_SNAKE_CASE      =				bits * 2 - 1
			return bits
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=BITS						)     ->     str:




			"""simple docstring"""
			_SCREAMING_SNAKE_CASE      =				x.device

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

			_SCREAMING_SNAKE_CASE      =				rearrange(lowerCAmelCase__    ,      """d -> d 1 1"""						)
			_SCREAMING_SNAKE_CASE      =				rearrange(lowerCAmelCase__    ,      """b (c d) h w -> b c d h w"""    ,      d=8						)
			_SCREAMING_SNAKE_CASE      =				reduce(x * mask    ,      """b c d h w -> b c h w"""    ,      """sum"""						)
			return (dec / 2_55).clamp(0.0    ,      1.0						)
def    lowerCAmelCase_	(				self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = 0.0    ,      SCREAMING_SNAKE_CASE_ = True    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_ = True    ,      )     ->     Union[str, Any]:




			"""simple docstring"""
			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)
			_SCREAMING_SNAKE_CASE      =				timestep - self.config.num_train_timesteps // self.num_inference_steps

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

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

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

			# 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)
			_SCREAMING_SNAKE_CASE      =				self._get_variance(lowerCAmelCase__    ,      lowerCAmelCase__						)
			_SCREAMING_SNAKE_CASE      =				eta * variance ** 0.5

			if use_clipped_model_output:
						# the model_output is always re-derived from the clipped x_0 in Glide
						_SCREAMING_SNAKE_CASE      =				(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
			_SCREAMING_SNAKE_CASE      =				(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
			_SCREAMING_SNAKE_CASE      =				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
						_SCREAMING_SNAKE_CASE      =				model_output.device if torch.is_tensor(lowerCAmelCase__						) else """cpu"""
						_SCREAMING_SNAKE_CASE      =				torch.randn(model_output.shape    ,      dtype=model_output.dtype    ,      generator=lowerCAmelCase__						).to(lowerCAmelCase__						)
						_SCREAMING_SNAKE_CASE      =				self._get_variance(lowerCAmelCase__    ,      lowerCAmelCase__						) ** 0.5 * eta * noise

						_SCREAMING_SNAKE_CASE      =				prev_sample + variance

			if not return_dict:
						return (prev_sample,)

			return DDIMSchedulerOutput(prev_sample=lowerCAmelCase__    ,      pred_original_sample=lowerCAmelCase__						)
def    lowerCAmelCase_	(				self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_="epsilon"    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_ = True    ,      )     ->     Optional[int]:




			"""simple docstring"""
			_SCREAMING_SNAKE_CASE      =				timestep

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

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

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

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

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

			_SCREAMING_SNAKE_CASE      =				pred_prev_sample + variance

			if not return_dict:
						return (pred_prev_sample,)

			return DDPMSchedulerOutput(prev_sample=lowerCAmelCase__    ,      pred_original_sample=lowerCAmelCase__						)







class    _a				(_lowerCamelCase):





							"""simple docstring"""




							def __init__(							self       ,							A__       ,							A__       ,							A__ = 1.0       ,							)    ->			str:
										super().__init__()
										_SCREAMING_SNAKE_CASE      =				bit_scale
										_SCREAMING_SNAKE_CASE      =				(
										    ddim_bit_scheduler_step if isinstance(_UpperCAmelCase       ,							_UpperCAmelCase      ) else ddpm_bit_scheduler_step
										)

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






							@torch.no_grad()
							def __call__(							self       ,							A__ = 2_56       ,							A__ = 2_56       ,							A__ = 50       ,							A__ = None       ,							A__ = 1       ,							A__ = "pil"       ,							A__ = True       ,							**A__       ,							)    ->			Union[Tuple, ImagePipelineOutput]:
										_SCREAMING_SNAKE_CASE      =				torch.randn(
										    (batch_size, self.unet.config.in_channels, height, width)       ,							generator=_UpperCAmelCase       ,							)
										_SCREAMING_SNAKE_CASE      =				decimal_to_bits(_UpperCAmelCase      ) * self.bit_scale
										_SCREAMING_SNAKE_CASE      =				latents.to(self.device      )

										self.scheduler.set_timesteps(_UpperCAmelCase      )

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

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

										_SCREAMING_SNAKE_CASE      =				bits_to_decimal(_UpperCAmelCase      )

										if output_type == "pil":
													_SCREAMING_SNAKE_CASE      =				self.numpy_to_pil(_UpperCAmelCase      )

										if not return_dict:
													return (image,)

										return ImagePipelineOutput(images=_UpperCAmelCase      )








'''simple docstring'''



import logging
import os

import quant_trainer
import torch
from torch.utils.data import DataLoader

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput


UpperCamelCase__      :		Tuple       							=  logging.getLogger(__name__)

if is_torch_tpu_available(check_device=False):
     import torch_xla.core.xla_model as xm
     import torch_xla.debug.metrics as met

class    _a				(_lowerCamelCase):





       """simple docstring"""




       def __init__(							self       ,							*A__       ,							A__=None       ,							A__=None       ,							A__=None       ,							**A__      )    ->			Optional[int]:
          super().__init__(*A__       ,							**A__      )
          _SCREAMING_SNAKE_CASE      =				eval_examples
          _SCREAMING_SNAKE_CASE      =				post_process_function
          _SCREAMING_SNAKE_CASE      =				quant_trainer_args
          _SCREAMING_SNAKE_CASE      =				1_28  # default number of calibration samples






       def        UpperCamelCase				(							self       ,							A__=None      )    ->			Union[str, Any]:
          if calib_dataset is None and self.calib_dataset is None:
             raise ValueError("""Trainer: calibration requires an calib_dataset."""      )
          _SCREAMING_SNAKE_CASE      =				calib_dataset if calib_dataset is not None else self.calib_dataset

          _SCREAMING_SNAKE_CASE      =				self._remove_unused_columns(A__       ,							description="""Calibration"""      )

          return DataLoader(
              A__       ,							batch_size=self.args.eval_batch_size       ,							collate_fn=self.data_collator       ,							drop_last=self.args.dataloader_drop_last       ,							num_workers=self.args.dataloader_num_workers       ,							pin_memory=self.args.dataloader_pin_memory       ,							shuffle=A__       ,							)






       def        UpperCamelCase				(							self       ,							A__=None      )    ->			str:
          _SCREAMING_SNAKE_CASE      =				self.train_dataset if calib_dataset is None else calib_dataset
          _SCREAMING_SNAKE_CASE      =				self.get_calib_dataloader(A__      )

          _SCREAMING_SNAKE_CASE      =				self.model
          quant_trainer.configure_model(A__       ,							self.quant_trainer_args       ,							calib=A__      )
          model.eval()
          quant_trainer.enable_calibration(A__      )

          logger.info("""***** Running calibration *****"""      )
          logger.info(F"  Num examples = {self.calib_num}"      )
          logger.info(F"  Batch size = {calib_dataloader.batch_size}"      )

          for step, inputs in enumerate(A__      ):
             # Prediction step
             _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				self.prediction_step(A__       ,							A__       ,							prediction_loss_only=A__      )
             if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
                break

          quant_trainer.finish_calibration(A__       ,							self.quant_trainer_args      )
          _SCREAMING_SNAKE_CASE      =				model






       def        UpperCamelCase				(							self       ,							A__=None       ,							A__=None       ,							A__=None       ,							A__ = "eval"      )    ->			List[Any]:
          _SCREAMING_SNAKE_CASE      =				self.eval_dataset if eval_dataset is None else eval_dataset
          _SCREAMING_SNAKE_CASE      =				self.get_eval_dataloader(A__      )
          _SCREAMING_SNAKE_CASE      =				self.eval_examples if eval_examples is None else eval_examples

          # Temporarily disable metric computation, we will do it in the loop here.
          _SCREAMING_SNAKE_CASE      =				self.compute_metrics
          _SCREAMING_SNAKE_CASE      =				None
          _SCREAMING_SNAKE_CASE      =				self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
          try:
             _SCREAMING_SNAKE_CASE      =				eval_loop(
                 A__       ,							description="""Evaluation"""       ,							prediction_loss_only=True if compute_metrics is None else None       ,							ignore_keys=A__       ,							)
          finally:
             _SCREAMING_SNAKE_CASE      =				compute_metrics

          if self.post_process_function is not None and self.compute_metrics is not None:
             _SCREAMING_SNAKE_CASE      =				self.post_process_function(A__       ,							A__       ,							output.predictions      )
             _SCREAMING_SNAKE_CASE      =				self.compute_metrics(A__      )

             # Prefix all keys with metric_key_prefix + '_'
             for key in list(metrics.keys()      ):
                if not key.startswith(F"{metric_key_prefix}_"      ):
                   _SCREAMING_SNAKE_CASE      =				metrics.pop(A__      )

             self.log(A__      )
          else:
             _SCREAMING_SNAKE_CASE      =				{}

          if self.args.tpu_metrics_debug or self.args.debug:
             # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
             xm.master_print(met.metrics_report()      )

          _SCREAMING_SNAKE_CASE      =				self.callback_handler.on_evaluate(self.args       ,							self.state       ,							self.control       ,							A__      )
          return metrics






       def        UpperCamelCase				(							self       ,							A__       ,							A__       ,							A__=None       ,							A__ = "test"      )    ->			List[str]:
          _SCREAMING_SNAKE_CASE      =				self.get_test_dataloader(A__      )

          # Temporarily disable metric computation, we will do it in the loop here.
          _SCREAMING_SNAKE_CASE      =				self.compute_metrics
          _SCREAMING_SNAKE_CASE      =				None
          _SCREAMING_SNAKE_CASE      =				self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
          try:
             _SCREAMING_SNAKE_CASE      =				eval_loop(
                 A__       ,							description="""Prediction"""       ,							prediction_loss_only=True if compute_metrics is None else None       ,							ignore_keys=A__       ,							)
          finally:
             _SCREAMING_SNAKE_CASE      =				compute_metrics

          if self.post_process_function is None or self.compute_metrics is None:
             return output

          _SCREAMING_SNAKE_CASE      =				self.post_process_function(A__       ,							A__       ,							output.predictions       ,							"""predict"""      )
          _SCREAMING_SNAKE_CASE      =				self.compute_metrics(A__      )

          # Prefix all keys with metric_key_prefix + '_'
          for key in list(metrics.keys()      ):
             if not key.startswith(F"{metric_key_prefix}_"      ):
                _SCREAMING_SNAKE_CASE      =				metrics.pop(A__      )

          return PredictionOutput(predictions=predictions.predictions       ,							label_ids=predictions.label_ids       ,							metrics=A__      )






       def        UpperCamelCase				(							self       ,							A__="./"      )    ->			Tuple:
          _SCREAMING_SNAKE_CASE      =				self.eval_dataset
          _SCREAMING_SNAKE_CASE      =				self.get_eval_dataloader(A__      )

          _SCREAMING_SNAKE_CASE      =				next(iter(A__      )      )

          # saving device - to make it consistent
          _SCREAMING_SNAKE_CASE      =				torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""      )

          # convert to tuple
          _SCREAMING_SNAKE_CASE      =				tuple(v.to(A__      ) for k, v in batch.items()      )

          logger.info("""Converting model to be onnx compatible"""      )
          from pytorch_quantization.nn import TensorQuantizer

          _SCREAMING_SNAKE_CASE      =				True

          _SCREAMING_SNAKE_CASE      =				self.model.to(A__      )

          model.eval()
          model.float()

          _SCREAMING_SNAKE_CASE      =				model.module if hasattr(A__       ,							"""module"""      ) else model
          quant_trainer.configure_model(A__       ,							self.quant_trainer_args      )

          _SCREAMING_SNAKE_CASE      =				os.path.join(A__       ,							"""model.onnx"""      )
          logger.info(F"exporting model to {output_model_file}"      )

          _SCREAMING_SNAKE_CASE      =				{0: """batch_size""", 1: """seq_len"""}

          torch.onnx.export(
              A__       ,							A__       ,							A__       ,							export_params=A__       ,							opset_version=13       ,							do_constant_folding=A__       ,							input_names=["""input_ids""", """attention_mask""", """token_type_ids"""]       ,							output_names=["""output_start_logits""", """output_end_logits"""]       ,							dynamic_axes={
                  """input_ids""": axes,
                  """attention_mask""": axes,
                  """token_type_ids""": axes,
                  """output_start_logits""": axes,
                  """output_end_logits""": axes,
              }       ,							verbose=A__       ,							)
          logger.info("""onnx export finished"""      )






