






'''simple docstring'''
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


UpperCamelCase__			=      logging.get_logger(__name__)




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase=None    ,		_UpperCamelCase=None				):


   """simple docstring"""




   return field(default_factory=lambda: default    ,		metadata=_UpperCamelCase				)





@dataclass
class 							_UpperCAmelCase    :
    __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=snake_case					,       metadata={'help': 'Whether to benchmark inference of model. Inference can be disabled via --no-inference.'}					,       )
    __lowerCamelCase: bool									=     field(
        default=snake_case					,       metadata={'help': 'Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'}					,       )
    __lowerCamelCase: bool									=     field(
        default=snake_case					,       metadata={'help': 'Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'}      )
    __lowerCamelCase: bool									=     field(default=snake_case					,       metadata={'help': 'Use FP16 to accelerate inference.'}      )
    __lowerCamelCase: bool									=     field(default=snake_case					,       metadata={'help': 'Benchmark training of model'}      )
    __lowerCamelCase: bool									=     field(default=snake_case					,       metadata={'help': 'Verbose memory tracing'}      )
    __lowerCamelCase: bool									=     field(
        default=snake_case					,       metadata={'help': 'Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'}					,       )
    __lowerCamelCase: bool									=     field(
        default=snake_case					,       metadata={
            'help': 'Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'
        }					,       )
    __lowerCamelCase: bool									=     field(default=snake_case					,       metadata={'help': 'Trace memory line by line'}      )
    __lowerCamelCase: bool									=     field(default=snake_case					,       metadata={'help': 'Save result to a CSV file'}      )
    __lowerCamelCase: bool									=     field(default=snake_case					,       metadata={'help': 'Save all print statements in a log file'}      )
    __lowerCamelCase: bool									=     field(default=snake_case					,       metadata={'help': 'Whether to print environment information'}      )
    __lowerCamelCase: bool									=     field(
        default=snake_case					,       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=snake_case					,       metadata={
            'help': (
                'Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'
                ' model weights.'
            )
        }					,       )




    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






       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."	,  a	,  )




    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       return json.dumps(dataclasses.asdict(self      )	,  indent=2      )




    @property
    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






       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 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       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












'''simple docstring'''
import flax.linen as nn
import jax
import jax.numpy as jnp


class 							_UpperCAmelCase    (		nn.Module      ):
    __lowerCamelCase: int
    __lowerCamelCase: jnp.dtype									=     jnp.floataa




    def 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  nn.Conv(
           self.out_channels	,  kernel_size=(3, 3)	,  strides=(1, 1)	,  padding=((1, 1), (1, 1))	,  dtype=self.dtype	,  )





    def __call__(      self	:							List[str]	,  a	:							Optional[int]      ):






       '''simple docstring'''






       lowercase_     ,		lowercase_     ,		lowercase_     ,		lowercase_						:     Optional[Any]							     =  hidden_states.shape
       lowercase_						:     Tuple							     =  jax.image.resize(
           a	,  shape=(batch, height * 2, width * 2, channels)	,  method="nearest"	,  )
       lowercase_						:     List[Any]							     =  self.conv(a      )
       return hidden_states


class 							_UpperCAmelCase    (		nn.Module      ):
    __lowerCamelCase: int
    __lowerCamelCase: jnp.dtype									=     jnp.floataa




    def 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






       lowercase_						:     Any							     =  nn.Conv(
           self.out_channels	,  kernel_size=(3, 3)	,  strides=(2, 2)	,  padding=((1, 1), (1, 1))	,  dtype=self.dtype	,  )





    def __call__(      self	:							Any	,  a	:							int      ):






       '''simple docstring'''






       lowercase_						:     Any							     =  self.conv(a      )
       return hidden_states


class 							_UpperCAmelCase    (		nn.Module      ):
    __lowerCamelCase: int
    __lowerCamelCase: int									=     None
    __lowerCamelCase: float									=     0.0
    __lowerCamelCase: bool									=     None
    __lowerCamelCase: jnp.dtype									=     jnp.floataa




    def 					lowerCAmelCase__					(      self	:							Optional[int]      ):






       '''simple docstring'''






       lowercase_						:     Union[str, Any]							     =  self.in_channels if self.out_channels is None else self.out_channels

       lowercase_						:     Optional[int]							     =  nn.GroupNorm(num_groups=3_2	,  epsilon=1e-5      )
       lowercase_						:     Tuple							     =  nn.Conv(
           a	,  kernel_size=(3, 3)	,  strides=(1, 1)	,  padding=((1, 1), (1, 1))	,  dtype=self.dtype	,  )

       lowercase_						:     List[str]							     =  nn.Dense(a	,  dtype=self.dtype      )

       lowercase_						:     Optional[int]							     =  nn.GroupNorm(num_groups=3_2	,  epsilon=1e-5      )
       lowercase_						:     Any							     =  nn.Dropout(self.dropout_prob      )
       lowercase_						:     Dict							     =  nn.Conv(
           a	,  kernel_size=(3, 3)	,  strides=(1, 1)	,  padding=((1, 1), (1, 1))	,  dtype=self.dtype	,  )

       lowercase_						:     Tuple							     =  self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

       lowercase_						:     Optional[Any]							     =  None
       if use_nin_shortcut:
          lowercase_						:     Union[str, Any]							     =  nn.Conv(
              a	,  kernel_size=(1, 1)	,  strides=(1, 1)	,  padding="VALID"	,  dtype=self.dtype	,  )





    def __call__(      self	:							List[str]	,  a	:							str	,  a	:							Dict	,  a	:							List[str]=True      ):






       '''simple docstring'''






       lowercase_						:     Dict							     =  hidden_states
       lowercase_						:     int							     =  self.norma(a      )
       lowercase_						:     List[Any]							     =  nn.swish(a      )
       lowercase_						:     Dict							     =  self.conva(a      )

       lowercase_						:     Optional[int]							     =  self.time_emb_proj(nn.swish(a      )      )
       lowercase_						:     Tuple							     =  jnp.expand_dims(jnp.expand_dims(a	,  1      )	,  1      )
       lowercase_						:     List[str]							     =  hidden_states + temb

       lowercase_						:     Optional[Any]							     =  self.norma(a      )
       lowercase_						:     Any							     =  nn.swish(a      )
       lowercase_						:     List[str]							     =  self.dropout(a	,  a      )
       lowercase_						:     int							     =  self.conva(a      )

       if self.conv_shortcut is not None:
          lowercase_						:     List[str]							     =  self.conv_shortcut(a      )

       return hidden_states + residual




