
'''simple docstring'''

import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
    import tensorflow as tf
    from tensorflow.python.framework.errors_impl import ResourceExhaustedError

    from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
    import pyanvml.pyanvml as nvml

__snake_case:  Any								=			logging.get_logger(__name__)






def 	_snake_case						(      A_       :    bool     ,		A_       :    bool   ):
  """simple docstring"""







  def run_func(A_       :    Union[str, Any]   ):
    @wraps(A_   )
    def run_in_eager_mode(*A_       :    Tuple     ,		**A_       :    Optional[int]   ):
      return func(*A_     ,		**A_   )

    @wraps(A_   )
    @tf.function(experimental_compile=A_   )
    def run_in_graph_mode(*A_       :    Union[str, Any]     ,		**A_       :    str   ):
      return func(*A_     ,		**A_   )

    if do_eager_mode is True:
      if use_xla is not False:
        raise ValueError(
            """Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`."""   )
      return run_in_eager_mode
    else:
      return run_in_graph_mode

  return run_func






def 	_snake_case						(      A_       :    int     ,		A_       :    int     ,		A_       :    int   ):
  """simple docstring"""







  a_      :	Optional[Any]      =							random.Random()
  a_      :	Union[str, Any]      =							[rng.randint(0     ,		vocab_size - 1   ) for i in range(batch_size * sequence_length   )]
  return tf.constant(A_     ,		shape=(batch_size, sequence_length)     ,		dtype=tf.intaa   )




class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					42
     a_    	=					42
     a_    	=					"TensorFlow"


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return tf.__version__


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

       '''simple docstring'''

       a_      :	Any      =							self.args.strategy
       if strategy is None:
         raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
       a_      :	str      =							self._prepare_inference_func(lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ )
       return self._measure_speed(_inference )


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

       '''simple docstring'''

       a_      :	Union[str, Any]      =							self.args.strategy
       if strategy is None:
         raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
       a_      :	List[str]      =							self._prepare_train_func(lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ )
       return self._measure_speed(_train )


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

       '''simple docstring'''

       if self.args.is_gpu:
         tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]							,		lowerCAmelCase_ )
       a_      :	Optional[int]      =							self.args.strategy
       if strategy is None:
         raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
       a_      :	Union[str, Any]      =							self._prepare_inference_func(lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ )
       return self._measure_memory(_inference )


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

       '''simple docstring'''

       if self.args.is_gpu:
         tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]							,		lowerCAmelCase_ )
       a_      :	Dict      =							self.args.strategy
       if strategy is None:
         raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )

       a_      :	List[str]      =							self._prepare_train_func(lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ )
       return self._measure_memory(_train )


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

       '''simple docstring'''

       a_      :	Optional[int]      =							self.config_dict[model_name]

       if self.args.fpaa:
         raise NotImplementedError("""Mixed precision is currently not supported.""" )

       a_      :	List[str]      =							(
           hasattr(lowerCAmelCase_							,		"""architectures""" )
           and isinstance(config.architectures							,		lowerCAmelCase_ )
           and len(config.architectures ) > 0
       )
       if not self.args.only_pretrain_model and has_model_class_in_config:
         try:
           a_      :	Optional[Any]      =							"""TF""" + config.architectures[0]  # prepend 'TF' for tensorflow model
           a_      :	Optional[int]      =							__import__("""transformers"""							,		fromlist=[model_class] )
           a_      :	List[str]      =							getattr(lowerCAmelCase_							,		lowerCAmelCase_ )
           a_      :	Any      =							model_cls(lowerCAmelCase_ )
         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:
         a_      :	Optional[Any]      =							TF_MODEL_MAPPING[config.__class__](lowerCAmelCase_ )

       # encoder-decoder has vocab size saved differently
       a_      :	Optional[int]      =							config.vocab_size if hasattr(lowerCAmelCase_							,		"""vocab_size""" ) else config.encoder.vocab_size
       a_      :	List[str]      =							random_input_ids(lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ )

       @run_with_tf_optimizations(self.args.eager_mode							,		self.args.use_xla )
       def encoder_decoder_forward():
         return model(lowerCAmelCase_							,		decoder_input_ids=lowerCAmelCase_							,		training=lowerCAmelCase_ )

       @run_with_tf_optimizations(self.args.eager_mode							,		self.args.use_xla )
       def encoder_forward():
         return model(lowerCAmelCase_							,		training=lowerCAmelCase_ )

       a_      :	Union[str, Any]      =							encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

       return _inference


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

       '''simple docstring'''

       a_      :	Any      =							self.config_dict[model_name]

       if self.args.eager_mode is not False:
         raise ValueError("""Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.""" )

       if self.args.fpaa:
         raise NotImplementedError("""Mixed precision is currently not supported.""" )

       a_      :	List[str]      =							(
           hasattr(lowerCAmelCase_							,		"""architectures""" )
           and isinstance(config.architectures							,		lowerCAmelCase_ )
           and len(config.architectures ) > 0
       )
       if not self.args.only_pretrain_model and has_model_class_in_config:
         try:
           a_      :	List[str]      =							"""TF""" + config.architectures[0]  # prepend 'TF' for tensorflow model
           a_      :	Dict      =							__import__("""transformers"""							,		fromlist=[model_class] )
           a_      :	int      =							getattr(lowerCAmelCase_							,		lowerCAmelCase_ )
           a_      :	int      =							model_cls(lowerCAmelCase_ )
         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:
         a_      :	int      =							TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCAmelCase_ )

       # encoder-decoder has vocab size saved differently
       a_      :	Dict      =							config.vocab_size if hasattr(lowerCAmelCase_							,		"""vocab_size""" ) else config.encoder.vocab_size
       a_      :	Dict      =							random_input_ids(lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ )

       @run_with_tf_optimizations(self.args.eager_mode							,		self.args.use_xla )
       def encoder_decoder_train():
         a_      :	Optional[Any]      =							model(lowerCAmelCase_							,		decoder_input_ids=lowerCAmelCase_							,		labels=lowerCAmelCase_							,		training=lowerCAmelCase_ )[0]
         a_      :	Optional[int]      =							tf.gradients(lowerCAmelCase_							,		model.trainable_variables )
         return gradients

       @run_with_tf_optimizations(self.args.eager_mode							,		self.args.use_xla )
       def encoder_train():
         a_      :	Optional[int]      =							model(lowerCAmelCase_							,		labels=lowerCAmelCase_							,		training=lowerCAmelCase_ )[0]
         a_      :	str      =							tf.gradients(lowerCAmelCase_							,		model.trainable_variables )
         return gradients

       a_      :	Optional[int]      =							encoder_decoder_train if config.is_encoder_decoder else encoder_train

       return _train


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       with self.args.strategy.scope():
         try:
           if self.args.is_tpu or self.args.use_xla:
             # run additional 10 times to stabilize compilation for tpu
             logger.info("""Do inference on TPU. Running model 5 times to stabilize compilation""" )
             timeit.repeat(lowerCAmelCase_							,		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
           a_      :	Dict      =							timeit.repeat(
               lowerCAmelCase_							,		repeat=self.args.repeat							,		number=10							,		)

           return min(lowerCAmelCase_ ) / 10.0
         except ResourceExhaustedError as e:
           self.print_fn(f'''Doesn\'t fit on GPU. {e}''' )






     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       logger.info(
           """Note that TensorFlow allocates more memory than """
           """it might need to speed up computation. """
           """The memory reported here corresponds to the memory """
           """reported by `nvidia-smi`, which can vary depending """
           """on total available memory on the GPU that is used.""" )
       with self.args.strategy.scope():
         try:
           if self.args.trace_memory_line_by_line:
             if not self.args.eager_mode:
               raise ValueError(
                   """`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"""
                   """ consumption line by line.""" )
             a_      :	Tuple      =							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 `args.memory=False`""" )
           elif self.args.is_gpu:
             # 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.""" )
               a_      :	Tuple      =							"""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()
               a_      :	Tuple      =							nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
               a_      :	Dict      =							nvml.nvmlDeviceGetMemoryInfo(lowerCAmelCase_ )
               a_      :	Any      =							meminfo.used
               a_      :	List[Any]      =							Memory(lowerCAmelCase_ )
               # shutdown nvml
               nvml.nvmlShutdown()
           else:
             # cpu
             if self.args.trace_memory_line_by_line:
               logger.info(
                   """When enabling line by line tracing, the max peak memory for CPU is inaccurate in"""
                   """ TensorFlow.""" )
               a_      :	Optional[Any]      =							None
             else:
               a_      :	Tuple      =							measure_peak_memory_cpu(lowerCAmelCase_ )
               a_      :	Optional[int]      =							Memory(lowerCAmelCase_ ) if isinstance(lowerCAmelCase_							,		lowerCAmelCase_ ) else memory_bytes
           if self.args.trace_memory_line_by_line:
             a_      :	Tuple      =							stop_memory_tracing(lowerCAmelCase_ )
             if memory is None:
               a_      :	List[Any]      =							summary.total
           else:
             a_      :	Any      =							None

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




'''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  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					42
     a_    	=					None







def 	_snake_case						(      A_       :    int     ,		A_       :    List[str]=0.999     ,		A_       :    List[Any]="cosine"     ,		):
  """simple docstring"""







  if alpha_transform_type == "cosine":

    def alpha_bar_fn(A_       :    List[Any]   ):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2   ) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(A_       :    List[str]   ):
      return math.exp(t * -12.0   )

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

  a_      :	int      =							[]
  for i in range(A_   ):
    a_      :	Optional[Any]      =							i / num_diffusion_timesteps
    a_      :	List[Any]      =							(i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(A_   ) / alpha_bar_fn(A_   )     ,		A_   )   )
  return torch.tensor(A_     ,		dtype=torch.floataa   )




class  _UpperCAmelCase ( lowerCAmelCase__	,lowerCAmelCase__    ):

     """simple docstring"""

     @register_to_config
     def __init__(      self							,		lowerCAmelCase_ = 10_00							,		lowerCAmelCase_ = "fixed_small_log"							,		lowerCAmelCase_ = True							,		lowerCAmelCase_ = 1.0							,		lowerCAmelCase_ = "epsilon"							,		lowerCAmelCase_ = "squaredcos_cap_v2"							,		):

       '''simple docstring'''

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

       a_      :	Optional[Any]      =							betas_for_alpha_bar(lowerCAmelCase_ )

       a_      :	Optional[Any]      =							1.0 - self.betas
       a_      :	List[str]      =							torch.cumprod(self.alphas							,		dim=0 )
       a_      :	Any      =							torch.tensor(1.0 )

       # standard deviation of the initial noise distribution
       a_      :	Any      =							1.0

       # setable values
       a_      :	List[Any]      =							None
       a_      :	Any      =							torch.from_numpy(np.arange(0							,		lowerCAmelCase_ )[::-1].copy() )

       a_      :	Any      =							variance_type


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

       '''simple docstring'''

       return sample


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

       '''simple docstring'''

       a_      :	List[str]      =							num_inference_steps
       a_      :	Dict      =							(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
       a_      :	Optional[Any]      =							(np.arange(0							,		lowerCAmelCase_ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
       a_      :	List[Any]      =							torch.from_numpy(lowerCAmelCase_ ).to(lowerCAmelCase_ )


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

       '''simple docstring'''

       if prev_timestep is None:
         a_      :	Tuple      =							t - 1

       a_      :	Optional[Any]      =							self.alphas_cumprod[t]
       a_      :	Union[str, Any]      =							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
       a_      :	Optional[Any]      =							1 - alpha_prod_t
       a_      :	str      =							1 - alpha_prod_t_prev

       if prev_timestep == t - 1:
         a_      :	int      =							self.betas[t]
       else:
         a_      :	Tuple      =							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
       a_      :	str      =							beta_prod_t_prev / beta_prod_t * beta

       if variance_type is None:
         a_      :	int      =							self.config.variance_type

       # hacks - were probably added for training stability
       if variance_type == "fixed_small_log":
         a_      :	List[Any]      =							torch.log(torch.clamp(lowerCAmelCase_							,		min=1E-2_0 ) )
         a_      :	List[Any]      =							torch.exp(0.5 * variance )
       elif variance_type == "learned_range":
         # NOTE difference with DDPM scheduler
         a_      :	Tuple      =							variance.log()
         a_      :	int      =							beta.log()

         a_      :	List[Any]      =							(predicted_variance + 1) / 2
         a_      :	Dict      =							frac * max_log + (1 - frac) * min_log

       return variance


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

       '''simple docstring'''

       a_      :	Tuple      =							timestep

       if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
         a_      , a_      :	Optional[Any]      =							torch.split(lowerCAmelCase_							,		sample.shape[1]							,		dim=1 )
       else:
         a_      :	Optional[int]      =							None

       # 1. compute alphas, betas
       if prev_timestep is None:
         a_      :	List[Any]      =							t - 1

       a_      :	int      =							self.alphas_cumprod[t]
       a_      :	List[str]      =							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
       a_      :	Any      =							1 - alpha_prod_t
       a_      :	Any      =							1 - alpha_prod_t_prev

       if prev_timestep == t - 1:
         a_      :	Optional[int]      =							self.betas[t]
         a_      :	Any      =							self.alphas[t]
       else:
         a_      :	List[Any]      =							1 - alpha_prod_t / alpha_prod_t_prev
         a_      :	List[str]      =							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":
         a_      :	List[str]      =							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
       elif self.config.prediction_type == "sample":
         a_      :	Optional[int]      =							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:
         a_      :	Any      =							torch.clamp(
             lowerCAmelCase_							,		-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
       a_      :	str      =							(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
       a_      :	Dict      =							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
       a_      :	Any      =							pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

       # 6. Add noise
       a_      :	List[Any]      =							0
       if t > 0:
         a_      :	List[Any]      =							randn_tensor(
             model_output.shape							,		dtype=model_output.dtype							,		generator=lowerCAmelCase_							,		device=model_output.device )

         a_      :	List[Any]      =							self._get_variance(
             lowerCAmelCase_							,		predicted_variance=lowerCAmelCase_							,		prev_timestep=lowerCAmelCase_							,		)

         if self.variance_type == "fixed_small_log":
           a_      :	Dict      =							variance
         elif self.variance_type == "learned_range":
           a_      :	Any      =							(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.""" )

         a_      :	List[str]      =							variance * variance_noise

       a_      :	Optional[Any]      =							pred_prev_sample + variance

       if not return_dict:
         return (pred_prev_sample,)

       return UnCLIPSchedulerOutput(prev_sample=lowerCAmelCase_							,		pred_original_sample=lowerCAmelCase_ )






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

       '''simple docstring'''

       a_      :	List[str]      =							self.alphas_cumprod.to(device=original_samples.device							,		dtype=original_samples.dtype )
       a_      :	Union[str, Any]      =							timesteps.to(original_samples.device )

       a_      :	List[Any]      =							alphas_cumprod[timesteps] ** 0.5
       a_      :	Optional[Any]      =							sqrt_alpha_prod.flatten()
       while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
         a_      :	int      =							sqrt_alpha_prod.unsqueeze(-1 )

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

       a_      :	int      =							sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
       return noisy_samples


