




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

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)




@flax.struct.dataclass
class 	UpperCamelCase__			:

    '''simple docstring'''



    lowerCamelCase_   :			CommonSchedulerState

    # setable values
    lowerCamelCase_   :			jnp.ndarray
    lowerCamelCase_   :			jnp.ndarray
    lowerCamelCase_   :			jnp.ndarray
    lowerCamelCase_   :			Optional[int]			  =   None

    # running values
    lowerCamelCase_   :			Optional[jnp.ndarray]			  =   None







    @classmethod
    def 					_lowercase  (					cls  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  )  ->   Tuple:
           return cls(common=UpperCamelCase__  ,						init_noise_sigma=UpperCamelCase__  ,						timesteps=UpperCamelCase__  ,						sigmas=UpperCamelCase__  )




@dataclass
class 	UpperCamelCase__			(lowerCAmelCase__  ):

    '''simple docstring'''



    lowerCamelCase_   :			LMSDiscreteSchedulerState




class 	UpperCamelCase__			(lowerCAmelCase__					,       lowerCAmelCase__  ):

    '''simple docstring'''



    lowerCamelCase_   :			Tuple			  =   [e.name for e in FlaxKarrasDiffusionSchedulers]

    lowerCamelCase_   :			jnp.dtype







    @property
    def 					_lowercase  (					self  )  ->   Dict:
           return True







    @register_to_config
    def __init__(					self  ,						UpperCamelCase__ = 1000  ,						UpperCamelCase__ = 0.0001  ,						UpperCamelCase__ = 0.02  ,						UpperCamelCase__ = "linear"  ,						UpperCamelCase__ = None  ,						UpperCamelCase__ = "epsilon"  ,						UpperCamelCase__ = jnp.floataa  ,						)  ->   Optional[int]:
           lowerCamelCase     :   Dict    				=      dtype







    def 					_lowercase  (					self  ,						UpperCamelCase__ = None  )  ->   LMSDiscreteSchedulerState:
           if common is None:
                  lowerCamelCase     :   Optional[int]    				=      CommonSchedulerState.create(self  )

           lowerCamelCase     :   Union[str, Any]    				=      jnp.arange(0  ,						self.config.num_train_timesteps  ).round()[::-1]
           lowerCamelCase     :   Optional[Any]    				=      ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

           # standard deviation of the initial noise distribution
           lowerCamelCase     :   Optional[int]    				=      sigmas.max()

           return LMSDiscreteSchedulerState.create(
               common=UpperCamelCase__  ,						init_noise_sigma=UpperCamelCase__  ,						timesteps=UpperCamelCase__  ,						sigmas=UpperCamelCase__  ,						)







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  )  ->   jnp.ndarray:
           (lowerCamelCase   )     :   int    				=      jnp.where(state.timesteps == timestep  ,						size=1  )
           lowerCamelCase     :   int    				=      step_index[0]

           lowerCamelCase     :   List[Any]    				=      state.sigmas[step_index]
           lowerCamelCase     :   Tuple    				=      sample / ((sigma**2 + 1) ** 0.5)
           return sample







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  )  ->   str:

           def lms_derivative(UpperCamelCase__  ):
                  lowerCamelCase     :   Optional[int]    				=      1.0
                  for k in range(UpperCamelCase__  ):
                         if current_order == k:
                                continue
                         prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
                  return prod

           lowerCamelCase     :   List[str]    				=      integrate.quad(UpperCamelCase__  ,						state.sigmas[t]  ,						state.sigmas[t + 1]  ,						epsrel=1e-4  )[0]

           return integrated_coeff







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ = ()  )  ->   LMSDiscreteSchedulerState:
           lowerCamelCase     :   Tuple    				=      jnp.linspace(self.config.num_train_timesteps - 1  ,						0  ,						UpperCamelCase__  ,						dtype=self.dtype  )

           lowerCamelCase     :   Optional[int]    				=      jnp.floor(UpperCamelCase__  ).astype(jnp.intaa  )
           lowerCamelCase     :   Optional[int]    				=      jnp.ceil(UpperCamelCase__  ).astype(jnp.intaa  )

           lowerCamelCase     :   Optional[Any]    				=      jnp.mod(UpperCamelCase__  ,						1.0  )

           lowerCamelCase     :   Dict    				=      ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
           lowerCamelCase     :   List[Any]    				=      (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
           lowerCamelCase     :   List[Any]    				=      jnp.concatenate([sigmas, jnp.array([0.0]  ,						dtype=self.dtype  )]  )

           lowerCamelCase     :   Optional[Any]    				=      timesteps.astype(jnp.intaa  )

           # initial running values
           lowerCamelCase     :   int    				=      jnp.zeros((0,) + shape  ,						dtype=self.dtype  )

           return state.replace(
               timesteps=UpperCamelCase__  ,						sigmas=UpperCamelCase__  ,						num_inference_steps=UpperCamelCase__  ,						derivatives=UpperCamelCase__  ,						)







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ = 4  ,						UpperCamelCase__ = True  ,						)  ->   Union[FlaxLMSSchedulerOutput, Tuple]:
           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"  )

           lowerCamelCase     :   Optional[int]    				=      state.sigmas[timestep]

           # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
           if self.config.prediction_type == "epsilon":
                  lowerCamelCase     :   str    				=      sample - sigma * model_output
           elif self.config.prediction_type == "v_prediction":
                  # * c_out + input * c_skip
                  lowerCamelCase     :   Any    				=      model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
           else:
                  raise ValueError(
                      F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''  )

           # 2. Convert to an ODE derivative
           lowerCamelCase     :   Any    				=      (sample - pred_original_sample) / sigma
           lowerCamelCase     :   List[str]    				=      state.replace(derivatives=jnp.append(state.derivatives  ,						UpperCamelCase__  )  )
           if len(state.derivatives  ) > order:
                  lowerCamelCase     :   Union[str, Any]    				=      state.replace(derivatives=jnp.delete(state.derivatives  ,						0  )  )

           # 3. Compute linear multistep coefficients
           lowerCamelCase     :   Union[str, Any]    				=      min(timestep + 1  ,						UpperCamelCase__  )
           lowerCamelCase     :   Dict    				=      [self.get_lms_coefficient(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ) for curr_order in range(UpperCamelCase__  )]

           # 4. Compute previous sample based on the derivatives path
           lowerCamelCase     :   int    				=      sample + sum(
               coeff * derivative for coeff, derivative in zip(UpperCamelCase__  ,						reversed(state.derivatives  )  )  )

           if not return_dict:
                  return (prev_sample, state)

           return FlaxLMSSchedulerOutput(prev_sample=UpperCamelCase__  ,						state=UpperCamelCase__  )







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						)  ->   jnp.ndarray:
           lowerCamelCase     :   List[str]    				=      state.sigmas[timesteps].flatten()
           lowerCamelCase     :   Any    				=      broadcast_to_shape_from_left(UpperCamelCase__  ,						noise.shape  )

           lowerCamelCase     :   Any    				=      original_samples + noise * sigma

           return noisy_samples







    def __len__(					self  )  ->   Optional[Any]:
           return self.config.num_train_timesteps











import timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
       import torch

       from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
       import pyanvml.pyanvml as nvml


SCREAMING_SNAKE_CASE__ :   List[Any]						=     logging.get_logger(__name__)




class 	UpperCamelCase__			(lowerCAmelCase__  ):

    '''simple docstring'''



    lowerCamelCase_   :			PyTorchBenchmarkArguments
    lowerCamelCase_   :			PretrainedConfig
    lowerCamelCase_   :			str			  =   "PyTorch"







    @property
    def 					_lowercase  (					self  )  ->   Optional[int]:
           return torch.__version__







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  )  ->   float:
           lowerCamelCase     :   List[Any]    				=      self._prepare_inference_func(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  )
           return self._measure_speed(_inference  )







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  )  ->   [Memory, Optional[MemorySummary]]:
           lowerCamelCase     :   List[Any]    				=      self._prepare_inference_func(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  )
           return self._measure_memory(_inference  )







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  )  ->   float:
           lowerCamelCase     :   Dict    				=      self._prepare_train_func(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  )
           return self._measure_speed(_train  )







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  )  ->   [Memory, Optional[MemorySummary]]:
           lowerCamelCase     :   Optional[Any]    				=      self._prepare_train_func(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  )
           return self._measure_memory(_train  )







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  )  ->   Callable[[], None]:
           lowerCamelCase     :   Optional[Any]    				=      self.config_dict[model_name]

           if self.args.torchscript:
                  lowerCamelCase     :   Optional[Any]    				=      True

           lowerCamelCase     :   Dict    				=      (
               hasattr(UpperCamelCase__  ,						"architectures"  )
               and isinstance(config.architectures  ,						UpperCamelCase__  )
               and len(config.architectures  ) > 0
           )
           if not self.args.only_pretrain_model and has_model_class_in_config:
                  try:
                         lowerCamelCase     :   Optional[Any]    				=      config.architectures[0]
                         lowerCamelCase     :   Optional[int]    				=      __import__("transformers"  ,						fromlist=[model_class]  )
                         lowerCamelCase     :   Optional[int]    				=      getattr(UpperCamelCase__  ,						UpperCamelCase__  )
                         lowerCamelCase     :   List[str]    				=      model_cls(UpperCamelCase__  )
                  except ImportError:
                         raise ImportError(
                             F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
                             " set `--only_pretrain_model` or `args.only_pretrain_model=True`."  )
           else:
                  lowerCamelCase     :   Tuple    				=      MODEL_MAPPING[config.__class__](UpperCamelCase__  )

           model.eval()
           model.to(self.args.device  )

           # encoder-decoder has vocab size saved differently
           lowerCamelCase     :   str    				=      config.vocab_size if hasattr(UpperCamelCase__  ,						"vocab_size"  ) else config.encoder.vocab_size
           lowerCamelCase     :   int    				=      torch.randint(UpperCamelCase__  ,						(batch_size, sequence_length)  ,						dtype=torch.long  ,						device=self.args.device  )

           if self.args.fpaa:
                  logger.info("Running training in Mixed Precision..."  )
                  if not self.args.is_gpu:
                         raise ValueError("Mixed precision is possible only for GPU."  )
                  # amp seems to have memory leaks so that memory usage
                  # is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
                  model.half()

           if self.args.torchscript:
                  with torch.no_grad():
                         lowerCamelCase     :   Optional[int]    				=      torch.jit.trace(UpperCamelCase__  ,						UpperCamelCase__  )
           else:
                  lowerCamelCase     :   Dict    				=      model

           def encoder_decoder_forward():
                  with torch.no_grad():
                         lowerCamelCase     :   Dict    				=      inference_model(UpperCamelCase__  ,						decoder_input_ids=UpperCamelCase__  )
                  return outputs

           def encoder_forward():
                  with torch.no_grad():
                         lowerCamelCase     :   Union[str, Any]    				=      inference_model(UpperCamelCase__  )
                  return outputs

           lowerCamelCase     :   Dict    				=      encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
           return _forward







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  )  ->   Callable[[], None]:
           lowerCamelCase     :   Optional[int]    				=      self.config_dict[model_name]

           lowerCamelCase     :   List[Any]    				=      (
               hasattr(UpperCamelCase__  ,						"architectures"  )
               and isinstance(config.architectures  ,						UpperCamelCase__  )
               and len(config.architectures  ) > 0
           )
           if not self.args.only_pretrain_model and has_model_class_in_config:
                  try:
                         lowerCamelCase     :   Union[str, Any]    				=      config.architectures[0]
                         lowerCamelCase     :   int    				=      __import__("transformers"  ,						fromlist=[model_class]  )
                         lowerCamelCase     :   Tuple    				=      getattr(UpperCamelCase__  ,						UpperCamelCase__  )
                         lowerCamelCase     :   Tuple    				=      model_cls(UpperCamelCase__  )
                  except ImportError:
                         raise ImportError(
                             F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
                             " set `--only_pretrain_model` or `args.only_pretrain_model=True`."  )
           else:
                  lowerCamelCase     :   List[str]    				=      MODEL_WITH_LM_HEAD_MAPPING[config.__class__](UpperCamelCase__  )

           if self.args.torchscript:
                  raise NotImplementedError("Training for torchscript is currently not implemented"  )
           else:
                  lowerCamelCase     :   Dict    				=      model

           model.train()
           model.to(self.args.device  )

           # encoder-decoder has vocab size saved differently
           lowerCamelCase     :   Union[str, Any]    				=      config.vocab_size if hasattr(UpperCamelCase__  ,						"vocab_size"  ) else config.encoder.vocab_size
           lowerCamelCase     :   List[str]    				=      torch.randint(UpperCamelCase__  ,						(batch_size, sequence_length)  ,						dtype=torch.long  ,						device=self.args.device  )

           if self.args.fpaa:
                  logger.info("Running training in Mixed Precision..."  )
                  if not self.args.is_gpu:
                         raise ValueError("Mixed precision is possible only for GPU."  )

                  # amp seems to have memory leaks so that memory usage
                  # is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
                  model.half()

           def compute_loss_and_backprob_encoder():
                  lowerCamelCase     :   Tuple    				=      train_model(UpperCamelCase__  ,						labels=UpperCamelCase__  )[0]
                  loss.backward()
                  return loss

           def compute_loss_and_backprob_encoder_decoder():
                  lowerCamelCase     :   Optional[int]    				=      train_model(UpperCamelCase__  ,						decoder_input_ids=UpperCamelCase__  ,						labels=UpperCamelCase__  )[0]
                  loss.backward()
                  return loss

           lowerCamelCase     :   Union[str, Any]    				=      (
               compute_loss_and_backprob_encoder_decoder
               if config.is_encoder_decoder
               else compute_loss_and_backprob_encoder
           )
           return _train







    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   float:
           try:
                  if self.args.is_tpu or self.args.torchscript:
                         # run additional 10 times to stabilize compilation for tpu and torchscript
                         logger.info("Do inference on TPU or torchscript. Running model 5 times to stabilize compilation"  )
                         timeit.repeat(
                             UpperCamelCase__  ,						repeat=1  ,						number=5  ,						)

                  # as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
                  lowerCamelCase     :   List[str]    				=      timeit.repeat(
                      UpperCamelCase__  ,						repeat=self.args.repeat  ,						number=10  ,						)

                  if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
                         import torch_xla.debug.metrics as met

                         self.print_fn(met.metrics_report()  )

                  return min(UpperCamelCase__  ) / 10.0
           except RuntimeError as e:
                  self.print_fn(F'''Doesn\'t fit on GPU. {e}'''  )
                  return "N/A"







    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   [Memory, MemorySummary]:
           try:
                  if self.args.trace_memory_line_by_line:
                         lowerCamelCase     :   str    				=      start_memory_tracing("transformers"  )

                  if self.args.is_tpu:
                         # tpu
                         raise NotImplementedError(
                             "Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with"
                             " `--no-memory` or `args.memory=False`"  )
                  elif self.args.is_gpu:
                         if not is_pyanvml_available():
                                logger.warning(
                                    "py3nvml not installed, we won't log GPU memory usage. "
                                    "Install py3nvml (pip install py3nvml) to log information about GPU."  )
                                lowerCamelCase     :   Optional[Any]    				=      "N/A"
                         else:
                                logger.info(
                                    "Measuring total GPU usage on GPU device. Make sure to not have additional processes running"
                                    " on the same GPU."  )
                                # init nvml
                                nvml.nvmlInit()
                                func()
                                lowerCamelCase     :   int    				=      nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx  )
                                lowerCamelCase     :   Dict    				=      nvml.nvmlDeviceGetMemoryInfo(UpperCamelCase__  )
                                lowerCamelCase     :   int    				=      meminfo.used
                                lowerCamelCase     :   Dict    				=      Memory(UpperCamelCase__  )
                                # shutdown nvml
                                nvml.nvmlShutdown()
                  else:
                         # cpu
                         lowerCamelCase     :   Union[str, Any]    				=      measure_peak_memory_cpu(UpperCamelCase__  )
                         lowerCamelCase     :   int    				=      Memory(UpperCamelCase__  ) if isinstance(UpperCamelCase__  ,						UpperCamelCase__  ) else memory_bytes

                  if self.args.trace_memory_line_by_line:
                         lowerCamelCase     :   List[str]    				=      stop_memory_tracing(UpperCamelCase__  )
                  else:
                         lowerCamelCase     :   int    				=      None

                  return memory, summary
           except RuntimeError as e:
                  self.print_fn(F'''Doesn\'t fit on GPU. {e}'''  )
                  return "N/A", None






