





'''simple docstring'''

import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     =       {
    'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}






class     a_						(      lowerCamelCase    ):
  lowercase							=				"""sew-d"""

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE=32      ,		_SCREAMING_SNAKE_CASE=768      ,		_SCREAMING_SNAKE_CASE=12      ,		_SCREAMING_SNAKE_CASE=12      ,		_SCREAMING_SNAKE_CASE=3072      ,		_SCREAMING_SNAKE_CASE=2      ,		_SCREAMING_SNAKE_CASE=512      ,		_SCREAMING_SNAKE_CASE=256      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=("p2c", "c2p")      ,		_SCREAMING_SNAKE_CASE="layer_norm"      ,		_SCREAMING_SNAKE_CASE="gelu_python"      ,		_SCREAMING_SNAKE_CASE=0.1      ,		_SCREAMING_SNAKE_CASE=0.1      ,		_SCREAMING_SNAKE_CASE=0.1      ,		_SCREAMING_SNAKE_CASE=0.0      ,		_SCREAMING_SNAKE_CASE=0.1      ,		_SCREAMING_SNAKE_CASE=0.0_2      ,		_SCREAMING_SNAKE_CASE=1e-7      ,		_SCREAMING_SNAKE_CASE=1e-5      ,		_SCREAMING_SNAKE_CASE="group"      ,		_SCREAMING_SNAKE_CASE="gelu"      ,		_SCREAMING_SNAKE_CASE=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)      ,		_SCREAMING_SNAKE_CASE=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)      ,		_SCREAMING_SNAKE_CASE=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)      ,		_SCREAMING_SNAKE_CASE=False      ,		_SCREAMING_SNAKE_CASE=128      ,		_SCREAMING_SNAKE_CASE=16      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=0.0_5      ,		_SCREAMING_SNAKE_CASE=10      ,		_SCREAMING_SNAKE_CASE=2      ,		_SCREAMING_SNAKE_CASE=0.0      ,		_SCREAMING_SNAKE_CASE=10      ,		_SCREAMING_SNAKE_CASE=0      ,		_SCREAMING_SNAKE_CASE="mean"      ,		_SCREAMING_SNAKE_CASE=False      ,		_SCREAMING_SNAKE_CASE=False      ,		_SCREAMING_SNAKE_CASE=256      ,		_SCREAMING_SNAKE_CASE=0      ,		_SCREAMING_SNAKE_CASE=1      ,		_SCREAMING_SNAKE_CASE=2      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				List[Any]:



        """simple docstring"""






        super().__init__(**_SCREAMING_SNAKE_CASE      ,		pad_token_id=_SCREAMING_SNAKE_CASE      ,		bos_token_id=_SCREAMING_SNAKE_CASE      ,		eos_token_id=_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = hidden_size
        UpperCamelCase      = feat_extract_norm
        UpperCamelCase      = feat_extract_activation
        UpperCamelCase      = list(_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = list(_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = list(_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = conv_bias
        UpperCamelCase      = num_conv_pos_embeddings
        UpperCamelCase      = num_conv_pos_embedding_groups
        UpperCamelCase      = len(self.conv_dim				)
        UpperCamelCase      = num_hidden_layers
        UpperCamelCase      = intermediate_size
        UpperCamelCase      = squeeze_factor
        UpperCamelCase      = max_position_embeddings
        UpperCamelCase      = position_buckets
        UpperCamelCase      = share_att_key
        UpperCamelCase      = relative_attention
        UpperCamelCase      = norm_rel_ebd
        UpperCamelCase      = list(_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = hidden_act
        UpperCamelCase      = num_attention_heads
        UpperCamelCase      = hidden_dropout
        UpperCamelCase      = attention_dropout
        UpperCamelCase      = activation_dropout
        UpperCamelCase      = feat_proj_dropout
        UpperCamelCase      = final_dropout
        UpperCamelCase      = layer_norm_eps
        UpperCamelCase      = feature_layer_norm_eps
        UpperCamelCase      = initializer_range
        UpperCamelCase      = vocab_size

        if (
            (len(self.conv_stride				) != self.num_feat_extract_layers)
            or (len(self.conv_kernel				) != self.num_feat_extract_layers)
            or (len(self.conv_dim				) != self.num_feat_extract_layers)
        ):
              raise ValueError(
                  """Configuration for convolutional layers is incorrect."""
                  """It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"""
                  F"but is `len(config.conv_dim) = {len(self.conv_dim				)}`, `len(config.conv_stride)"
                  F"= {len(self.conv_stride				)}`, `len(config.conv_kernel) = {len(self.conv_kernel				)}`."				)

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        UpperCamelCase      = apply_spec_augment
        UpperCamelCase      = mask_time_prob
        UpperCamelCase      = mask_time_length
        UpperCamelCase      = mask_time_min_masks
        UpperCamelCase      = mask_feature_prob
        UpperCamelCase      = mask_feature_length
        UpperCamelCase      = mask_feature_min_masks

        # ctc loss
        UpperCamelCase      = ctc_loss_reduction
        UpperCamelCase      = ctc_zero_infinity

        # sequence classification
        UpperCamelCase      = use_weighted_layer_sum
        UpperCamelCase      = classifier_proj_size


  @property
  def   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






        return functools.reduce(operator.mul      ,		self.conv_stride      ,		1				)








'''simple docstring'''

import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


SCREAMING_SNAKE_CASE__     =       logging.get_logger(__name__)







def    lowercase__			(				)->			Tuple:
      # Get the sagemaker specific mp parameters from smp_options variable.
      UpperCamelCase      = os.getenv("""SM_HP_MP_PARAMETERS"""		,						"""{}"""   )
      try:
            # Parse it and check the field "partitions" is included, it is required for model parallel.
            UpperCamelCase      = json.loads(__UpperCamelCase   )
            if "partitions" not in smp_options:
                  return False
      except json.JSONDecodeError:
            return False

      # Get the sagemaker specific framework parameters from mpi_options variable.
      UpperCamelCase      = os.getenv("""SM_FRAMEWORK_PARAMS"""		,						"""{}"""   )
      try:
            # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
            UpperCamelCase      = json.loads(__UpperCamelCase   )
            if not mpi_options.get("""sagemaker_mpi_enabled"""		,						__UpperCamelCase   ):
                  return False
      except json.JSONDecodeError:
            return False
      # Lastly, check if the `smdistributed` module is present.
      return importlib.util.find_spec("""smdistributed"""   ) is not None


if is_sagemaker_model_parallel_available():
     import smdistributed.modelparallel.torch as smp

     smp.init()






@dataclass
class     a_						(      lowerCamelCase    ):
  lowercase							=				field(
      default=""""""     ,	metadata={"""help""": """Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"""}     ,	)

  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        super().__post_init__()
        warnings.warn(
            """`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use """
            """`TrainingArguments` instead."""      ,		_SCREAMING_SNAKE_CASE      ,		)

  @cached_property
  def   A__						(				self				)  ->				"torch.device":



        """simple docstring"""






        logger.info("""PyTorch: setting up devices"""				)
        if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
              logger.warning(
                  """torch.distributed process group is initialized, but local_rank == -1. """
                  """In order to use Torch DDP, launch your script with `python -m torch.distributed.launch"""				)
        if self.no_cuda:
              UpperCamelCase      = torch.device("""cpu"""				)
              UpperCamelCase      = 0
        elif is_sagemaker_model_parallel_available():
              UpperCamelCase      = smp.local_rank()
              UpperCamelCase      = torch.device("""cuda"""      ,		_SCREAMING_SNAKE_CASE				)
              UpperCamelCase      = 1
        elif is_sagemaker_dp_enabled():
              import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

              torch.distributed.init_process_group(backend="""smddp"""      ,		timeout=self.ddp_timeout_delta				)
              UpperCamelCase      = int(os.getenv("""SMDATAPARALLEL_LOCAL_RANK"""				)				)
              UpperCamelCase      = torch.device("""cuda"""      ,		self.local_rank				)
              UpperCamelCase      = 1
        elif self.local_rank == -1:
              # if n_gpu is > 1 we'll use nn.DataParallel.
              # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
              # Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
              # trigger an error that a device index is missing. Index 0 takes into account the
              # GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
              # will use the first GPU in that env, i.e. GPU#1
              UpperCamelCase      = torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""				)
              # Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
              # the default value.
              UpperCamelCase      = torch.cuda.device_count()
        else:
              # Here, we'll use torch.distributed.
              # Initializes the distributed backend which will take care of synchronizing nodes/GPUs
              if not torch.distributed.is_initialized():
                    torch.distributed.init_process_group(backend="""nccl"""      ,		timeout=self.ddp_timeout_delta				)
              UpperCamelCase      = torch.device("""cuda"""      ,		self.local_rank				)
              UpperCamelCase      = 1

        if device.type == "cuda":
              torch.cuda.set_device(_SCREAMING_SNAKE_CASE				)

        return device

  @property
  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        if is_sagemaker_model_parallel_available():
              return smp.dp_size()

        return super().world_size

  @property
  def   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






        return not is_sagemaker_model_parallel_available()


  @property
  def   A__						(				self				)  ->				str:



        """simple docstring"""






        return False
