


import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
       import transformers



def  _a			(						__UpperCamelCase ):
     random.seed(__UpperCamelCase )
     np.random.seed(__UpperCamelCase )
     torch.manual_seed(__UpperCamelCase )
     torch.cuda.manual_seed_all(__UpperCamelCase )
     # ^^ safe to call this function even if cuda is not available


class   a__	:

       def __init__(   self      :     List[Any]      ,							lowerCamelCase_      :     Iterable[torch.nn.Parameter]      ,							lowerCamelCase_      :     float = 0.9_9_9_9      ,							lowerCamelCase_      :     float = 0.0      ,							lowerCamelCase_      :     int = 0      ,							lowerCamelCase_      :     bool = False      ,							lowerCamelCase_      :     Union[float, int] = 1.0      ,							lowerCamelCase_      :     Union[float, int] = 2 / 3      ,							lowerCamelCase_      :     Optional[Any] = None      ,							lowerCamelCase_      :     Dict[str, Any] = None      ,							**lowerCamelCase_      :     str      ,							):

            if isinstance(lowerCamelCase_      ,							torch.nn.Module    ):
                 a_    :     Dict										=    (
                     """Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. """
                     """Please pass the parameters of the module instead."""
                 )
                 deprecate(
                     """passing a `torch.nn.Module` to `ExponentialMovingAverage`"""      ,							"""1.0.0"""      ,							lowerCamelCase_      ,							standard_warn=lowerCamelCase_      ,							)
                 a_    :     Optional[Any]										=    parameters.parameters()

                 # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
                 a_    :     Dict										=    True

            if kwargs.get("""max_value"""      ,							lowerCamelCase_    ) is not None:
                 a_    :     Union[str, Any]										=    """The `max_value` argument is deprecated. Please use `decay` instead."""
                 deprecate("""max_value"""      ,							"""1.0.0"""      ,							lowerCamelCase_      ,							standard_warn=lowerCamelCase_    )
                 a_    :     Tuple										=    kwargs["""max_value"""]

            if kwargs.get("""min_value"""      ,							lowerCamelCase_    ) is not None:
                 a_    :     Any										=    """The `min_value` argument is deprecated. Please use `min_decay` instead."""
                 deprecate("""min_value"""      ,							"""1.0.0"""      ,							lowerCamelCase_      ,							standard_warn=lowerCamelCase_    )
                 a_    :     List[str]										=    kwargs["""min_value"""]

            a_    :     int										=    list(lowerCamelCase_    )
            a_    :     Any										=    [p.clone().detach() for p in parameters]

            if kwargs.get("""device"""      ,							lowerCamelCase_    ) is not None:
                 a_    :     Union[str, Any]										=    """The `device` argument is deprecated. Please use `to` instead."""
                 deprecate("""device"""      ,							"""1.0.0"""      ,							lowerCamelCase_      ,							standard_warn=lowerCamelCase_    )
                 self.to(device=kwargs["""device"""]    )

            a_    :     str										=    None

            a_    :     List[Any]										=    decay
            a_    :     str										=    min_decay
            a_    :     Any										=    update_after_step
            a_    :     List[str]										=    use_ema_warmup
            a_    :     Tuple										=    inv_gamma
            a_    :     Any										=    power
            a_    :     str										=    0
            a_    :     Union[str, Any]										=    None  # set in `step()`

            a_    :     Dict										=    model_cls
            a_    :     Dict										=    model_config

       @classmethod
       def 						UpperCAmelCase(   cls      :     Optional[Any]      ,							lowerCamelCase_      :     str      ,							lowerCamelCase_      :     List[Any]    ):
            a_			,			a_    :     Union[str, Any]										=    model_cls.load_config(lowerCamelCase_      ,							return_unused_kwargs=lowerCamelCase_    )
            a_    :     List[str]										=    model_cls.from_pretrained(lowerCamelCase_    )

            a_    :     Tuple										=    cls(model.parameters()      ,							model_cls=lowerCamelCase_      ,							model_config=model.config    )

            ema_model.load_state_dict(lowerCamelCase_    )
            return ema_model

       def 						UpperCAmelCase(   self      :     int      ,							lowerCamelCase_      :     Optional[int]    ):
            if self.model_cls is None:
                 raise ValueError("""`save_pretrained` can only be used if `model_cls` was defined at __init__."""    )

            if self.model_config is None:
                 raise ValueError("""`save_pretrained` can only be used if `model_config` was defined at __init__."""    )

            a_    :     str										=    self.model_cls.from_config(self.model_config    )
            a_    :     Tuple										=    self.state_dict()
            state_dict.pop("""shadow_params"""      ,							lowerCamelCase_    )

            model.register_to_config(**lowerCamelCase_    )
            self.copy_to(model.parameters()    )
            model.save_pretrained(lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     List[Any]      ,							lowerCamelCase_      :     int    ):
            a_    :     Union[str, Any]										=    max(0      ,							optimization_step - self.update_after_step - 1    )

            if step <= 0:
                 return 0.0

            if self.use_ema_warmup:
                 a_    :     str										=    1 - (1 + step / self.inv_gamma) ** -self.power
            else:
                 a_    :     Union[str, Any]										=    (1 + step) / (1_0 + step)

            a_    :     List[Any]										=    min(lowerCamelCase_      ,							self.decay    )
            # make sure decay is not smaller than min_decay
            a_    :     Any										=    max(lowerCamelCase_      ,							self.min_decay    )
            return cur_decay_value

       @torch.no_grad()
       def 						UpperCAmelCase(   self      :     Dict      ,							lowerCamelCase_      :     Iterable[torch.nn.Parameter]    ):
            if isinstance(lowerCamelCase_      ,							torch.nn.Module    ):
                 a_    :     Dict										=    (
                     """Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. """
                     """Please pass the parameters of the module instead."""
                 )
                 deprecate(
                     """passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"""      ,							"""1.0.0"""      ,							lowerCamelCase_      ,							standard_warn=lowerCamelCase_      ,							)
                 a_    :     Optional[Any]										=    parameters.parameters()

            a_    :     Optional[Any]										=    list(lowerCamelCase_    )

            self.optimization_step += 1

            # Compute the decay factor for the exponential moving average.
            a_    :     Tuple										=    self.get_decay(self.optimization_step    )
            a_    :     Tuple										=    decay
            a_    :     Optional[Any]										=    1 - decay

            a_    :     Tuple										=    contextlib.nullcontext
            if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                 import deepspeed

            for s_param, param in zip(self.shadow_params      ,							lowerCamelCase_    ):
                 if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                      a_    :     List[str]										=    deepspeed.zero.GatheredParameters(lowerCamelCase_      ,							modifier_rank=lowerCamelCase_    )

                 with context_manager():
                      if param.requires_grad:
                           s_param.sub_(one_minus_decay * (s_param - param)    )
                      else:
                           s_param.copy_(lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Dict      ,							lowerCamelCase_      :     Iterable[torch.nn.Parameter]    ):
            a_    :     List[str]										=    list(lowerCamelCase_    )
            for s_param, param in zip(self.shadow_params      ,							lowerCamelCase_    ):
                 param.data.copy_(s_param.to(param.device    ).data    )

       def 						UpperCAmelCase(   self      :     Dict      ,							lowerCamelCase_      :     Union[str, Any]=None      ,							lowerCamelCase_      :     Union[str, Any]=None    ):
            a_    :     List[str]										=    [
                p.to(device=lowerCamelCase_      ,							dtype=lowerCamelCase_    ) if p.is_floating_point() else p.to(device=lowerCamelCase_    )
                for p in self.shadow_params
            ]

       def 						UpperCAmelCase(   self      :     List[str]    ):
            return {
                "decay": self.decay,
                "min_decay": self.min_decay,
                "optimization_step": self.optimization_step,
                "update_after_step": self.update_after_step,
                "use_ema_warmup": self.use_ema_warmup,
                "inv_gamma": self.inv_gamma,
                "power": self.power,
                "shadow_params": self.shadow_params,
            }

       def 						UpperCAmelCase(   self      :     List[str]      ,							lowerCamelCase_      :     Iterable[torch.nn.Parameter]    ):
            a_    :     List[Any]										=    [param.detach().cpu().clone() for param in parameters]

       def 						UpperCAmelCase(   self      :     List[str]      ,							lowerCamelCase_      :     Iterable[torch.nn.Parameter]    ):
            if self.temp_stored_params is None:
                 raise RuntimeError("""This ExponentialMovingAverage has no `store()`ed weights """ """to `restore()`"""    )
            for c_param, param in zip(self.temp_stored_params      ,							lowerCamelCase_    ):
                 param.data.copy_(c_param.data    )

            # Better memory-wise.
            a_    :     Union[str, Any]										=    None

       def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     dict    ):
            a_    :     Optional[Any]										=    copy.deepcopy(lowerCamelCase_    )

            a_    :     Dict										=    state_dict.get("""decay"""      ,							self.decay    )
            if self.decay < 0.0 or self.decay > 1.0:
                 raise ValueError("""Decay must be between 0 and 1"""    )

            a_    :     Optional[Any]										=    state_dict.get("""min_decay"""      ,							self.min_decay    )
            if not isinstance(self.min_decay      ,							lowerCamelCase_    ):
                 raise ValueError("""Invalid min_decay"""    )

            a_    :     List[Any]										=    state_dict.get("""optimization_step"""      ,							self.optimization_step    )
            if not isinstance(self.optimization_step      ,							lowerCamelCase_    ):
                 raise ValueError("""Invalid optimization_step"""    )

            a_    :     Any										=    state_dict.get("""update_after_step"""      ,							self.update_after_step    )
            if not isinstance(self.update_after_step      ,							lowerCamelCase_    ):
                 raise ValueError("""Invalid update_after_step"""    )

            a_    :     Any										=    state_dict.get("""use_ema_warmup"""      ,							self.use_ema_warmup    )
            if not isinstance(self.use_ema_warmup      ,							lowerCamelCase_    ):
                 raise ValueError("""Invalid use_ema_warmup"""    )

            a_    :     str										=    state_dict.get("""inv_gamma"""      ,							self.inv_gamma    )
            if not isinstance(self.inv_gamma      ,							(float, int)    ):
                 raise ValueError("""Invalid inv_gamma"""    )

            a_    :     Dict										=    state_dict.get("""power"""      ,							self.power    )
            if not isinstance(self.power      ,							(float, int)    ):
                 raise ValueError("""Invalid power"""    )

            a_    :     List[Any]										=    state_dict.get("""shadow_params"""      ,							lowerCamelCase_    )
            if shadow_params is not None:
                 a_    :     Tuple										=    shadow_params
                 if not isinstance(self.shadow_params      ,							lowerCamelCase_    ):
                      raise ValueError("""shadow_params must be a list"""    )
                 if not all(isinstance(lowerCamelCase_      ,							torch.Tensor    ) for p in self.shadow_params    ):
                      raise ValueError("""shadow_params must all be Tensors"""    )







import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


__lowerCamelCase        =     logging.get_logger(__name__)



def  _a			(						__UpperCamelCase=None  ,  __UpperCamelCase=None ):
     return field(default_factory=lambda: default  ,  metadata=__UpperCamelCase )


@dataclass
class   a__	:
       lowerCamelCase__:     List[str]							    =     list_field(
           default=[]						,       metadata={
               """help""": (
                   """Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"""
                   """ of all available models"""
               )
           }						,       )

       lowerCamelCase__:     List[int]							    =     list_field(
           default=[8]						,       metadata={"""help""": """List of batch sizes for which memory and time performance will be evaluated"""}    )

       lowerCamelCase__:     List[int]							    =     list_field(
           default=[8, 32, 128, 512]						,       metadata={"""help""": """List of sequence lengths for which memory and time performance will be evaluated"""}						,       )

       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={"""help""": """Whether to benchmark inference of model. Inference can be disabled via --no-inference."""}						,       )
       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={"""help""": """Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."""}						,       )
       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={"""help""": """Whether to run on available tpu devices. TPU can be disabled via --no-tpu."""}    )
       lowerCamelCase__:     bool							    =     field(default=lowerCAmelCase_						,       metadata={"""help""": """Use FP16 to accelerate inference."""}    )
       lowerCamelCase__:     bool							    =     field(default=lowerCAmelCase_						,       metadata={"""help""": """Benchmark training of model"""}    )
       lowerCamelCase__:     bool							    =     field(default=lowerCAmelCase_						,       metadata={"""help""": """Verbose memory tracing"""}    )
       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={"""help""": """Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."""}						,       )
       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={
               """help""": """Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"""
           }						,       )
       lowerCamelCase__:     bool							    =     field(default=lowerCAmelCase_						,       metadata={"""help""": """Trace memory line by line"""}    )
       lowerCamelCase__:     bool							    =     field(default=lowerCAmelCase_						,       metadata={"""help""": """Save result to a CSV file"""}    )
       lowerCamelCase__:     bool							    =     field(default=lowerCAmelCase_						,       metadata={"""help""": """Save all print statements in a log file"""}    )
       lowerCamelCase__:     bool							    =     field(default=lowerCAmelCase_						,       metadata={"""help""": """Whether to print environment information"""}    )
       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={
               """help""": (
                   """Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"""
                   """ multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"""
                   """ for debugging / testing and on TPU."""
               )
           }						,       )
       lowerCamelCase__:     str							    =     field(
           default=F"""inference_time_{round(time()    )}.csv"""						,       metadata={"""help""": """CSV filename used if saving time results to csv."""}						,       )
       lowerCamelCase__:     str							    =     field(
           default=F"""inference_memory_{round(time()    )}.csv"""						,       metadata={"""help""": """CSV filename used if saving memory results to csv."""}						,       )
       lowerCamelCase__:     str							    =     field(
           default=F"""train_time_{round(time()    )}.csv"""						,       metadata={"""help""": """CSV filename used if saving time results to csv for training."""}						,       )
       lowerCamelCase__:     str							    =     field(
           default=F"""train_memory_{round(time()    )}.csv"""						,       metadata={"""help""": """CSV filename used if saving memory results to csv for training."""}						,       )
       lowerCamelCase__:     str							    =     field(
           default=F"""env_info_{round(time()    )}.csv"""						,       metadata={"""help""": """CSV filename used if saving environment information."""}						,       )
       lowerCamelCase__:     str							    =     field(
           default=F"""log_{round(time()    )}.csv"""						,       metadata={"""help""": """Log filename used if print statements are saved in log."""}						,       )
       lowerCamelCase__:     int							    =     field(default=3						,       metadata={"""help""": """Times an experiment will be run."""}    )
       lowerCamelCase__:     bool							    =     field(
           default=lowerCAmelCase_						,       metadata={
               """help""": (
                   """Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"""
                   """ model weights."""
               )
           }						,       )

       def 						UpperCAmelCase(   self      :     Any    ):
            warnings.warn(
                F'''The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'''
                """ are deprecated in general and it is advised to use external Benchmarking libraries """
                """ to benchmark Transformer models."""      ,							lowerCamelCase_      ,							)

       def 						UpperCAmelCase(   self      :     Union[str, Any]    ):
            return json.dumps(dataclasses.asdict(self    )      ,							indent=2    )

       @property
       def 						UpperCAmelCase(   self      :     List[str]    ):
            if len(self.models    ) <= 0:
                 raise ValueError(
                     """Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
                     """ bert-base-cased` or `args.models = ['bert-base-cased']."""    )
            return self.models

       @property
       def 						UpperCAmelCase(   self      :     Union[str, Any]    ):
            if not self.multi_process:
                 return False
            elif self.is_tpu:
                 logger.info("""Multiprocessing is currently not possible on TPU."""    )
                 return False
            else:
                 return True



