
'''simple docstring'''

# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
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  _UpperCAmelCase :

     """simple docstring"""

     a_    	=					42

     # setable values
     a_    	=					42
     a_    	=					42
     a_    	=					None


     @classmethod
     def 							_lowerCAmelCase  (      cls							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       return cls(common=lowerCAmelCase_							,		init_noise_sigma=lowerCAmelCase_							,		timesteps=lowerCAmelCase_ )







@dataclass
class  _UpperCAmelCase ( __UpperCAmelCase    ):

     """simple docstring"""

     a_    	=					42







class  _UpperCAmelCase ( __UpperCAmelCase	,__UpperCAmelCase    ):

     """simple docstring"""

     a_    	=					[e.name for e in FlaxKarrasDiffusionSchedulers]

     a_    	=					42


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return True


     @register_to_config
     def __init__(      self							,		lowerCAmelCase_ = 10_00							,		lowerCAmelCase_ = 0.0001							,		lowerCAmelCase_ = 0.02							,		lowerCAmelCase_ = "linear"							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = "fixed_small"							,		lowerCAmelCase_ = True							,		lowerCAmelCase_ = "epsilon"							,		lowerCAmelCase_ = jnp.floataa							,		):

       '''simple docstring'''

       a_      :	Optional[int]      =							dtype


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ = None ):

       '''simple docstring'''

       if common is None:
         a_      :	Optional[Any]      =							CommonSchedulerState.create(self )

       # standard deviation of the initial noise distribution
       a_      :	Tuple      =							jnp.array(1.0							,		dtype=self.dtype )

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

       return DDPMSchedulerState.create(
           common=lowerCAmelCase_							,		init_noise_sigma=lowerCAmelCase_							,		timesteps=lowerCAmelCase_							,		)


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ = None ):

       '''simple docstring'''

       return sample


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ = () ):

       '''simple docstring'''

       a_      :	Any      =							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
       a_      :	Union[str, Any]      =							(jnp.arange(0							,		lowerCAmelCase_ ) * step_ratio).round()[::-1]

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


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_=None							,		lowerCAmelCase_=None ):

       '''simple docstring'''

       a_      :	List[Any]      =							state.common.alphas_cumprod[t]
       a_      :	str      =							jnp.where(t > 0							,		state.common.alphas_cumprod[t - 1]							,		jnp.array(1.0							,		dtype=self.dtype ) )

       # 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
       a_      :	Optional[Any]      =							(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

       if variance_type is None:
         a_      :	List[Any]      =							self.config.variance_type

       # hacks - were probably added for training stability
       if variance_type == "fixed_small":
         a_      :	Tuple      =							jnp.clip(lowerCAmelCase_							,		a_min=1E-2_0 )
       # for rl-diffuser https://arxiv.org/abs/2205.09991
       elif variance_type == "fixed_small_log":
         a_      :	Dict      =							jnp.log(jnp.clip(lowerCAmelCase_							,		a_min=1E-2_0 ) )
       elif variance_type == "fixed_large":
         a_      :	Dict      =							state.common.betas[t]
       elif variance_type == "fixed_large_log":
         # Glide max_log
         a_      :	Optional[Any]      =							jnp.log(state.common.betas[t] )
       elif variance_type == "learned":
         return predicted_variance
       elif variance_type == "learned_range":
         a_      :	Dict      =							variance
         a_      :	List[Any]      =							state.common.betas[t]
         a_      :	int      =							(predicted_variance + 1) / 2
         a_      :	str      =							frac * max_log + (1 - frac) * min_log

       return variance


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = True							,		):

       '''simple docstring'''

       a_      :	Any      =							timestep

       if key is None:
         a_      :	Optional[Any]      =							jax.random.PRNGKey(0 )

       if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
         a_      , a_      :	str      =							jnp.split(lowerCAmelCase_							,		sample.shape[1]							,		axis=1 )
       else:
         a_      :	List[Any]      =							None

       # 1. compute alphas, betas
       a_      :	Union[str, Any]      =							state.common.alphas_cumprod[t]
       a_      :	List[str]      =							jnp.where(t > 0							,		state.common.alphas_cumprod[t - 1]							,		jnp.array(1.0							,		dtype=self.dtype ) )
       a_      :	int      =							1 - alpha_prod_t
       a_      :	int      =							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 self.config.prediction_type == "epsilon":
         a_      :	Tuple      =							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
       elif self.config.prediction_type == "sample":
         a_      :	Optional[Any]      =							model_output
       elif self.config.prediction_type == "v_prediction":
         a_      :	List[Any]      =							(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
       else:
         raise ValueError(
             f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
             """ for the FlaxDDPMScheduler.""" )

       # 3. Clip "predicted x_0"
       if self.config.clip_sample:
         a_      :	int      =							jnp.clip(lowerCAmelCase_							,		-1							,		1 )

       # 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
       a_      :	List[Any]      =							(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
       a_      :	Dict      =							state.common.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
       a_      :	List[str]      =							pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

       # 6. Add noise
       def random_variance():
         a_      :	Optional[Any]      =							jax.random.split(lowerCAmelCase_							,		num=1 )
         a_      :	str      =							jax.random.normal(lowerCAmelCase_							,		shape=model_output.shape							,		dtype=self.dtype )
         return (self._get_variance(lowerCAmelCase_							,		lowerCAmelCase_							,		predicted_variance=lowerCAmelCase_ ) ** 0.5) * noise

       a_      :	Tuple      =							jnp.where(t > 0							,		random_variance()							,		jnp.zeros(model_output.shape							,		dtype=self.dtype ) )

       a_      :	Tuple      =							pred_prev_sample + variance

       if not return_dict:
         return (pred_prev_sample, state)

       return FlaxDDPMSchedulerOutput(prev_sample=lowerCAmelCase_							,		state=lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		):

       '''simple docstring'''

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


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		):

       '''simple docstring'''

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






     def __len__(      self ):

       '''simple docstring'''

       return self.config.num_train_timesteps




'''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


__snake_case:  Dict								=			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  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     def __init__(      self							,		*lowerCAmelCase_							,		lowerCAmelCase_=None							,		lowerCAmelCase_=None							,		lowerCAmelCase_=None							,		**lowerCAmelCase_ ):

       '''simple docstring'''

       super().__init__(*lowerCAmelCase_							,		**lowerCAmelCase_ )
       a_      :	Tuple      =							eval_examples
       a_      :	Optional[Any]      =							post_process_function
       a_      :	List[str]      =							quant_trainer_args
       a_      :	List[str]      =							1_28  # default number of calibration samples


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_=None ):

       '''simple docstring'''

       if calib_dataset is None and self.calib_dataset is None:
         raise ValueError("""Trainer: calibration requires an calib_dataset.""" )
       a_      :	str      =							calib_dataset if calib_dataset is not None else self.calib_dataset

       a_      :	Dict      =							self._remove_unused_columns(lowerCAmelCase_							,		description="""Calibration""" )

       return DataLoader(
           lowerCAmelCase_							,		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=lowerCAmelCase_							,		)


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_=None ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							self.train_dataset if calib_dataset is None else calib_dataset
       a_      :	int      =							self.get_calib_dataloader(lowerCAmelCase_ )

       a_      :	Dict      =							self.model
       quant_trainer.configure_model(lowerCAmelCase_							,		self.quant_trainer_args							,		calib=lowerCAmelCase_ )
       model.eval()
       quant_trainer.enable_calibration(lowerCAmelCase_ )

       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(lowerCAmelCase_ ):
         # Prediction step
         a_      , a_      , a_      :	Optional[int]      =							self.prediction_step(lowerCAmelCase_							,		lowerCAmelCase_							,		prediction_loss_only=lowerCAmelCase_ )
         if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
           break

       quant_trainer.finish_calibration(lowerCAmelCase_							,		self.quant_trainer_args )
       a_      :	Optional[int]      =							model


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_=None							,		lowerCAmelCase_=None							,		lowerCAmelCase_=None							,		lowerCAmelCase_ = "eval" ):

       '''simple docstring'''

       a_      :	str      =							self.eval_dataset if eval_dataset is None else eval_dataset
       a_      :	List[str]      =							self.get_eval_dataloader(lowerCAmelCase_ )
       a_      :	str      =							self.eval_examples if eval_examples is None else eval_examples

       # Temporarily disable metric computation, we will do it in the loop here.
       a_      :	List[str]      =							self.compute_metrics
       a_      :	Tuple      =							None
       a_      :	Optional[Any]      =							self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
       try:
         a_      :	List[str]      =							eval_loop(
             lowerCAmelCase_							,		description="""Evaluation"""							,		prediction_loss_only=True if compute_metrics is None else None							,		ignore_keys=lowerCAmelCase_							,		)
       finally:
         a_      :	int      =							compute_metrics

       if self.post_process_function is not None and self.compute_metrics is not None:
         a_      :	Optional[Any]      =							self.post_process_function(lowerCAmelCase_							,		lowerCAmelCase_							,		output.predictions )
         a_      :	Tuple      =							self.compute_metrics(lowerCAmelCase_ )

         # Prefix all keys with metric_key_prefix + '_'
         for key in list(metrics.keys() ):
           if not key.startswith(f'''{metric_key_prefix}_''' ):
             a_      :	Optional[int]      =							metrics.pop(lowerCAmelCase_ )

         self.log(lowerCAmelCase_ )
       else:
         a_      :	Any      =							{}

       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() )

       a_      :	Optional[Any]      =							self.callback_handler.on_evaluate(self.args							,		self.state							,		self.control							,		lowerCAmelCase_ )
       return metrics


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_=None							,		lowerCAmelCase_ = "test" ):

       '''simple docstring'''

       a_      :	List[Any]      =							self.get_test_dataloader(lowerCAmelCase_ )

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

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

       a_      :	Tuple      =							self.post_process_function(lowerCAmelCase_							,		lowerCAmelCase_							,		output.predictions							,		"""predict""" )
       a_      :	List[Any]      =							self.compute_metrics(lowerCAmelCase_ )

       # Prefix all keys with metric_key_prefix + '_'
       for key in list(metrics.keys() ):
         if not key.startswith(f'''{metric_key_prefix}_''' ):
           a_      :	Tuple      =							metrics.pop(lowerCAmelCase_ )

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






     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_="./" ):

       '''simple docstring'''

       a_      :	Any      =							self.eval_dataset
       a_      :	Tuple      =							self.get_eval_dataloader(lowerCAmelCase_ )

       a_      :	List[str]      =							next(iter(lowerCAmelCase_ ) )

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

       # convert to tuple
       a_      :	Optional[int]      =							tuple(v.to(lowerCAmelCase_ ) for k, v in batch.items() )

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

       a_      :	List[str]      =							True

       a_      :	str      =							self.model.to(lowerCAmelCase_ )

       model.eval()
       model.float()

       a_      :	Optional[int]      =							model.module if hasattr(lowerCAmelCase_							,		"""module""" ) else model
       quant_trainer.configure_model(lowerCAmelCase_							,		self.quant_trainer_args )

       a_      :	Union[str, Any]      =							os.path.join(lowerCAmelCase_							,		"""model.onnx""" )
       logger.info(f'''exporting model to {output_model_file}''' )

       a_      :	Dict      =							{0: """batch_size""", 1: """seq_len"""}

       torch.onnx.export(
           lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		export_params=lowerCAmelCase_							,		opset_version=13							,		do_constant_folding=lowerCAmelCase_							,		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=lowerCAmelCase_							,		)
       logger.info("""onnx export finished""" )


