

'''simple docstring'''






from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
      import torch

snake_case_       =       logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
      from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):


      def 					__lowerCamelCase			(      cls     :	List[str]    )							->   List[Any]:
            """simple docstring"""

            return cls


def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Union[Tuple, List]  ,  SCREAMING_SNAKE_CASE_     :	Optional[Union[List["torch.Generator"], "torch.Generator"]] = None  ,  SCREAMING_SNAKE_CASE_     :	Optional["torch.device"] = None  ,  SCREAMING_SNAKE_CASE_     :	Optional["torch.dtype"] = None  ,  SCREAMING_SNAKE_CASE_     :	Optional["torch.layout"] = None  ,  )							->   List[Any]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			List[Any]     =       device
      SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       shape[0]

      SCREAMING_SNAKE_CASE_      :			Tuple     =       layout or torch.strided
      SCREAMING_SNAKE_CASE_      :			Tuple     =       device or torch.device("cpu"    )

      if generator is not None:
            SCREAMING_SNAKE_CASE_      :			Optional[int]     =       generator.device.type if not isinstance(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_    ) else generator[0].device.type
            if gen_device_type != device.type and gen_device_type == "cpu":
                  SCREAMING_SNAKE_CASE_      :			List[str]     =       "cpu"
                  if device != "mps":
                        logger.info(
                            F"The passed generator was created on 'cpu' even though a tensor on {device} was expected."
                            F" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"
                            F" slighly speed up this function by passing a generator that was created on the {device} device."    )
            elif gen_device_type != device.type and gen_device_type == "cuda":
                  raise ValueError(F"Cannot generate a {device} tensor from a generator of type {gen_device_type}."    )

      if isinstance(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_    ):
            SCREAMING_SNAKE_CASE_      :			List[Any]     =       (1,) + shape[1:]
            SCREAMING_SNAKE_CASE_      :			str     =       [
                torch.randn(SCREAMING_SNAKE_CASE_  ,  generator=generator[i]  ,  device=SCREAMING_SNAKE_CASE_  ,  dtype=SCREAMING_SNAKE_CASE_  ,  layout=SCREAMING_SNAKE_CASE_    )
                for i in range(SCREAMING_SNAKE_CASE_    )
            ]
            SCREAMING_SNAKE_CASE_      :			str     =       torch.cat(SCREAMING_SNAKE_CASE_  ,  dim=0    ).to(SCREAMING_SNAKE_CASE_    )
      else:
            SCREAMING_SNAKE_CASE_      :			Tuple     =       torch.randn(SCREAMING_SNAKE_CASE_  ,  generator=SCREAMING_SNAKE_CASE_  ,  device=SCREAMING_SNAKE_CASE_  ,  dtype=SCREAMING_SNAKE_CASE_  ,  layout=SCREAMING_SNAKE_CASE_    ).to(SCREAMING_SNAKE_CASE_    )

      return latents





def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	str    )							->   str:
      """simple docstring"""

      if is_torch_version("<"  ,  "2.0.0"    ) or not hasattr(SCREAMING_SNAKE_CASE_  ,  "_dynamo"    ):
            return False
      return isinstance(SCREAMING_SNAKE_CASE_  ,  torch._dynamo.eval_frame.OptimizedModule    )




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_    =					{
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_    =					['''MPNetTokenizerFast''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_    =					[
        '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''MPNetForMaskedLM''',
        '''MPNetForMultipleChoice''',
        '''MPNetForQuestionAnswering''',
        '''MPNetForSequenceClassification''',
        '''MPNetForTokenClassification''',
        '''MPNetLayer''',
        '''MPNetModel''',
        '''MPNetPreTrainedModel''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_    =					[
        '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TFMPNetEmbeddings''',
        '''TFMPNetForMaskedLM''',
        '''TFMPNetForMultipleChoice''',
        '''TFMPNetForQuestionAnswering''',
        '''TFMPNetForSequenceClassification''',
        '''TFMPNetForTokenClassification''',
        '''TFMPNetMainLayer''',
        '''TFMPNetModel''',
        '''TFMPNetPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
    from .tokenization_mpnet import MPNetTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mpnet_fast import MPNetTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mpnet import (
            MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            MPNetForMaskedLM,
            MPNetForMultipleChoice,
            MPNetForQuestionAnswering,
            MPNetForSequenceClassification,
            MPNetForTokenClassification,
            MPNetLayer,
            MPNetModel,
            MPNetPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mpnet import (
            TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFMPNetEmbeddings,
            TFMPNetForMaskedLM,
            TFMPNetForMultipleChoice,
            TFMPNetForQuestionAnswering,
            TFMPNetForSequenceClassification,
            TFMPNetForTokenClassification,
            TFMPNetMainLayer,
            TFMPNetModel,
            TFMPNetPreTrainedModel,
        )

else:
    import sys

    lowerCamelCase_    =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)