
import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
    from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
    import torch_xla.core.xla_model as xm


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):

      '''simple docstring'''





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


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ = True		):

      '''simple docstring'''





      UpperCamelCase__    :   Optional[Any]					=				(torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

      UpperCamelCase__    :   Optional[Any]					=				is_compiled_module(UpperCamelCase__		)
      if is_compiled:
            UpperCamelCase__    :   Any					=				model
            UpperCamelCase__    :   Dict					=				model._orig_mod

      if is_deepspeed_available():
            options += (DeepSpeedEngine,)

      while isinstance(UpperCamelCase__ ,		UpperCamelCase__		):
            UpperCamelCase__    :   Union[str, Any]					=				model.module

      if not keep_fpaa_wrapper:
            UpperCamelCase__    :   str					=				getattr(UpperCamelCase__ ,		'''forward'''		)
            UpperCamelCase__    :   int					=				model.__dict__.pop('''_original_forward''' ,		UpperCamelCase__		)
            if original_forward is not None:
                  while hasattr(UpperCamelCase__ ,		'''__wrapped__'''		):
                        UpperCamelCase__    :   List[str]					=				forward.__wrapped__
                        if forward == original_forward:
                              break
                  UpperCamelCase__    :   Tuple					=				forward
            if getattr(UpperCamelCase__ ,		'''_converted_to_transformer_engine''' ,		UpperCamelCase__		):
                  convert_model(UpperCamelCase__ ,		to_transformer_engine=UpperCamelCase__		)

      if is_compiled:
            UpperCamelCase__    :   Union[str, Any]					=				model
            UpperCamelCase__    :   str					=				compiled_model

      return model


def 							SCREAMING_SNAKE_CASE_      (			):

      '''simple docstring'''





      PartialState().wait_for_everyone()


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):

      '''simple docstring'''





      if PartialState().distributed_type == DistributedType.TPU:
            xm.save(UpperCamelCase__ ,		UpperCamelCase__		)
      elif PartialState().local_process_index == 0:
            torch.save(UpperCamelCase__ ,		UpperCamelCase__		)


@contextmanager
def 							SCREAMING_SNAKE_CASE_      (			**UpperCamelCase__		):

      '''simple docstring'''





      for key, value in kwargs.items():
            UpperCamelCase__    :   List[Any]					=				str(UpperCamelCase__		)

      yield

      for key in kwargs:
            if key.upper() in os.environ:
                  del os.environ[key.upper()]


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):

      '''simple docstring'''





      if not hasattr(UpperCamelCase__ ,		'''__qualname__'''		) and not hasattr(UpperCamelCase__ ,		'''__name__'''		):
            UpperCamelCase__    :   Tuple					=				getattr(UpperCamelCase__ ,		'''__class__''' ,		UpperCamelCase__		)
      if hasattr(UpperCamelCase__ ,		'''__qualname__'''		):
            return obj.__qualname__
      if hasattr(UpperCamelCase__ ,		'''__name__'''		):
            return obj.__name__
      return str(UpperCamelCase__		)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):

      '''simple docstring'''





      for key, value in source.items():
            if isinstance(UpperCamelCase__ ,		UpperCamelCase__		):
                  UpperCamelCase__    :   Optional[Any]					=				destination.setdefault(UpperCamelCase__ ,		{}		)
                  merge_dicts(UpperCamelCase__ ,		UpperCamelCase__		)
            else:
                  UpperCamelCase__    :   int					=				value

      return destination


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ = None		):

      '''simple docstring'''





      if port is None:
            UpperCamelCase__    :   List[Any]					=				2_9_5_0_0
      with socket.socket(socket.AF_INET ,		socket.SOCK_STREAM		) as s:
            return s.connect_ex(('''localhost''', port)		) == 0




from typing import TYPE_CHECKING

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


lowerCamelCase      ={
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =["BlenderbotTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =[
        "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "BlenderbotForCausalLM",
        "BlenderbotForConditionalGeneration",
        "BlenderbotModel",
        "BlenderbotPreTrainedModel",
    ]


try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =[
        "TFBlenderbotForConditionalGeneration",
        "TFBlenderbotModel",
        "TFBlenderbotPreTrainedModel",
    ]


try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =[
        "FlaxBlenderbotForConditionalGeneration",
        "FlaxBlenderbotModel",
        "FlaxBlenderbotPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_blenderbot import (
        BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        BlenderbotConfig,
        BlenderbotOnnxConfig,
    )
    from .tokenization_blenderbot import BlenderbotTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_blenderbot import (
            BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
            BlenderbotForCausalLM,
            BlenderbotForConditionalGeneration,
            BlenderbotModel,
            BlenderbotPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_blenderbot import (
            TFBlenderbotForConditionalGeneration,
            TFBlenderbotModel,
            TFBlenderbotPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_blenderbot import (
            FlaxBlenderbotForConditionalGeneration,
            FlaxBlenderbotModel,
            FlaxBlenderbotPreTrainedModel,
        )

else:
    import sys

    lowerCamelCase      =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


