



'''simple docstring'''



from typing import TYPE_CHECKING

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


_lowercase       :						Any						={
    "configuration_bart": ["BART_PRETRAINED_CONFIG_ARCHIVE_MAP", "BartConfig", "BartOnnxConfig"],
    "tokenization_bart": ["BartTokenizer"],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase       :						List[str]						=["BartTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase       :						Union[str, Any]						=[
				    "BART_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "BartForCausalLM",
				    "BartForConditionalGeneration",
				    "BartForQuestionAnswering",
				    "BartForSequenceClassification",
				    "BartModel",
				    "BartPretrainedModel",
				    "PretrainedBartModel",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase       :						Tuple						=[
				    "TFBartForConditionalGeneration",
				    "TFBartForSequenceClassification",
				    "TFBartModel",
				    "TFBartPretrainedModel",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase       :						str						=[
				    "FlaxBartDecoderPreTrainedModel",
				    "FlaxBartForCausalLM",
				    "FlaxBartForConditionalGeneration",
				    "FlaxBartForQuestionAnswering",
				    "FlaxBartForSequenceClassification",
				    "FlaxBartModel",
				    "FlaxBartPreTrainedModel",
				]

if TYPE_CHECKING:
				from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
				from .tokenization_bart import BartTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_bart_fast import BartTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bart import (
								    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BartForCausalLM,
								    BartForConditionalGeneration,
								    BartForQuestionAnswering,
								    BartForSequenceClassification,
								    BartModel,
								    BartPretrainedModel,
								    PretrainedBartModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_bart import (
								    TFBartForConditionalGeneration,
								    TFBartForSequenceClassification,
								    TFBartModel,
								    TFBartPretrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_bart import (
								    FlaxBartDecoderPreTrainedModel,
								    FlaxBartForCausalLM,
								    FlaxBartForConditionalGeneration,
								    FlaxBartForQuestionAnswering,
								    FlaxBartForSequenceClassification,
								    FlaxBartModel,
								    FlaxBartPreTrainedModel,
								)

else:
				import sys

				_lowercase       :						Optional[Any]						=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






'''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      )

