
'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCAmelCase_      =   {
    "configuration_esm": ["ESM_PRETRAINED_CONFIG_ARCHIVE_MAP", "EsmConfig"],
    "tokenization_esm": ["EsmTokenizer"],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase_      =   [
        "ESM_PRETRAINED_MODEL_ARCHIVE_LIST",
        "EsmForMaskedLM",
        "EsmForSequenceClassification",
        "EsmForTokenClassification",
        "EsmModel",
        "EsmPreTrainedModel",
    ]
    lowerCAmelCase_      =   ["EsmForProteinFolding", "EsmFoldPreTrainedModel"]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase_      =   [
        "TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFEsmForMaskedLM",
        "TFEsmForSequenceClassification",
        "TFEsmForTokenClassification",
        "TFEsmModel",
        "TFEsmPreTrainedModel",
    ]

if TYPE_CHECKING:
    from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
    from .tokenization_esm import EsmTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_esm import (
            ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
            EsmForMaskedLM,
            EsmForSequenceClassification,
            EsmForTokenClassification,
            EsmModel,
            EsmPreTrainedModel,
        )
        from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_esm import (
            TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFEsmForMaskedLM,
            TFEsmForSequenceClassification,
            TFEsmForTokenClassification,
            TFEsmModel,
            TFEsmPreTrainedModel,
        )


else:
    import sys

    lowerCAmelCase_      =   _LazyModule(__name__, globals()["__file__"], _import_structure)



'''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

lowerCAmelCase_      =   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    :  Dict      ):







           '''simple docstring'''






           return cls







def   lowerCAmelCase(  a__    :  Union[Tuple, List]      , a__    :  Optional[Union[List["torch.Generator"], "torch.Generator"]] = None      , a__    :  Optional["torch.device"] = None      , a__    :  Optional["torch.dtype"] = None      , a__    :  Optional["torch.layout"] = None      , ):







       '''simple docstring'''






       lowerCamelCase__				   =      device
       lowerCamelCase__				   =      shape[0]

       lowerCamelCase__				   =      layout or torch.strided
       lowerCamelCase__				   =      device or torch.device("cpu"      )

       if generator is not None:
              lowerCamelCase__				   =      generator.device.type if not isinstance(a__      , a__      ) else generator[0].device.type
              if gen_device_type != device.type and gen_device_type == "cpu":
                     lowerCamelCase__				   =      "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(a__      , a__      ):
              lowerCamelCase__				   =      (1,) + shape[1:]
              lowerCamelCase__				   =      [
                  torch.randn(a__      , generator=generator[i]      , device=a__      , dtype=a__      , layout=a__      )
                  for i in range(a__      )
              ]
              lowerCamelCase__				   =      torch.cat(a__      , dim=0      ).to(a__      )
       else:
              lowerCamelCase__				   =      torch.randn(a__      , generator=a__      , device=a__      , dtype=a__      , layout=a__      ).to(a__      )

       return latents







def   lowerCAmelCase(  a__    :  Union[str, Any]      ):







       '''simple docstring'''






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

