
import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
 import transformer_engine.pytorch as te


def     a__			(    snake_case   ,	snake_case=True   ,	snake_case=True   ,	snake_case=True    ):





  """simple docstring"""
  if not is_fpa_available():
    raise ImportError('''Using `convert_model` requires transformer_engine to be installed.'''    )
  for name, module in model.named_children():
    if isinstance(_lowerCAmelCase   ,	nn.Linear    ) and to_transformer_engine and _convert_linear:
      # Return early if the linear layer weights are not multiples of 16
      if any(p % 16 != 0 for p in module.weight.shape    ):
        return
      __SCREAMING_SNAKE_CASE     :					int			=						module.bias is not None
      __SCREAMING_SNAKE_CASE     :					int			=						te.Linear(
          module.in_features   ,	module.out_features   ,	bias=_lowerCAmelCase   ,	params_dtype=module.weight.dtype    )
      __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						module.weight.data.clone()
      if has_bias:
        __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						module.bias.data.clone()

      setattr(_lowerCAmelCase   ,	_lowerCAmelCase   ,	_lowerCAmelCase    )
    elif isinstance(_lowerCAmelCase   ,	nn.LayerNorm    ) and to_transformer_engine and _convert_ln:
      __SCREAMING_SNAKE_CASE     :					Optional[int]			=						te.LayerNorm(module.normalized_shape[0]   ,	eps=module.eps   ,	params_dtype=module.weight.dtype    )
      __SCREAMING_SNAKE_CASE     :					Any			=						module.weight.data.clone()
      __SCREAMING_SNAKE_CASE     :					Optional[int]			=						module.bias.data.clone()

      setattr(_lowerCAmelCase   ,	_lowerCAmelCase   ,	_lowerCAmelCase    )
    elif isinstance(_lowerCAmelCase   ,	te.Linear    ) and not to_transformer_engine and _convert_linear:
      __SCREAMING_SNAKE_CASE     :					Dict			=						module.bias is not None
      __SCREAMING_SNAKE_CASE     :					Optional[int]			=						nn.Linear(
          module.in_features   ,	module.out_features   ,	bias=_lowerCAmelCase   ,	params_dtype=module.weight.dtype    )
      __SCREAMING_SNAKE_CASE     :					int			=						module.weight.data.clone()
      if has_bias:
        __SCREAMING_SNAKE_CASE     :					str			=						module.bias.data.clone()

      setattr(_lowerCAmelCase   ,	_lowerCAmelCase   ,	_lowerCAmelCase    )
    elif isinstance(_lowerCAmelCase   ,	te.LayerNorm    ) and not to_transformer_engine and _convert_ln:
      __SCREAMING_SNAKE_CASE     :					Tuple			=						nn.LayerNorm(module.normalized_shape[0]   ,	eps=module.eps   ,	params_dtype=module.weight.dtype    )
      __SCREAMING_SNAKE_CASE     :					Optional[int]			=						module.weight.data.clone()
      __SCREAMING_SNAKE_CASE     :					Dict			=						module.bias.data.clone()

      setattr(_lowerCAmelCase   ,	_lowerCAmelCase   ,	_lowerCAmelCase    )
    else:
      convert_model(
          _lowerCAmelCase   ,	to_transformer_engine=_lowerCAmelCase   ,	_convert_linear=_convert_linear   ,	_convert_ln=_convert_ln   ,	)


def     a__			(    snake_case    ):





  """simple docstring"""
  if not is_fpa_available():
    raise ImportError('''Using `has_transformer_engine_layers` requires transformer_engine to be installed.'''    )
  for m in model.modules():
    if isinstance(_lowerCAmelCase   ,	(te.LayerNorm, te.Linear)    ):
      return True
  return False


from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


lowercase_											=	[
    """AutomaticSpeechRecognition""",
    """AudioClassification""",
    """ImageClassification""",
    """LanguageModeling""",
    """QuestionAnsweringExtractive""",
    """Summarization""",
    """TaskTemplate""",
    """TextClassification""",
]

lowercase_											=	get_logger(__name__)


lowercase_											=	{
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}


def     a__			(    snake_case    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					str			=						task_template_dict.get('''task'''    )
  if task_name is None:
    logger.warning(F'''Couldn\'t find template for task \'{task_name}\'. Available templates: {list(snake_case    )}'''    )
    return None
  __SCREAMING_SNAKE_CASE     :					Any			=						NAMEaTEMPLATE.get(snake_case    )
  return template.from_dict(snake_case    )
