




"""simple docstring"""




from typing import TYPE_CHECKING

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


a_        =       {
    'configuration_blenderbot_small': [
        'BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlenderbotSmallConfig',
        'BlenderbotSmallOnnxConfig',
    ],
    'tokenization_blenderbot_small': ['BlenderbotSmallTokenizer'],
}

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

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_        =       [
			    'BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'BlenderbotSmallForCausalLM',
			    'BlenderbotSmallForConditionalGeneration',
			    'BlenderbotSmallModel',
			    'BlenderbotSmallPreTrainedModel',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_        =       [
			    'TFBlenderbotSmallForConditionalGeneration',
			    'TFBlenderbotSmallModel',
			    'TFBlenderbotSmallPreTrainedModel',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_        =       [
			    'FlaxBlenderbotSmallForConditionalGeneration',
			    'FlaxBlenderbotSmallModel',
			    'FlaxBlenderbotSmallPreTrainedModel',
			]

if TYPE_CHECKING:
			from .configuration_blenderbot_small import (
			    BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    BlenderbotSmallConfig,
			    BlenderbotSmallOnnxConfig,
			)
			from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_blenderbot_small import (
						    BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BlenderbotSmallForCausalLM,
						    BlenderbotSmallForConditionalGeneration,
						    BlenderbotSmallModel,
						    BlenderbotSmallPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_blenderbot_small import (
						    TFBlenderbotSmallForConditionalGeneration,
						    TFBlenderbotSmallModel,
						    TFBlenderbotSmallPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_blenderbot_small import (
						    FlaxBlenderbotSmallForConditionalGeneration,
						    FlaxBlenderbotSmallModel,
						    FlaxBlenderbotSmallPreTrainedModel,
						)

else:
			import sys

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




import logging
import os

from .state import PartialState
class     lowerCAmelCase_     (       logging.LoggerAdapter ):







							@staticmethod
							def  UpperCamelCase_						(   _A      :     Any		):
												_UpperCamelCase			     =     PartialState()
												return not main_process_only or (main_process_only and state.is_main_process)







							def  UpperCamelCase_						(   self      :     Union[str, Any] ,			_A      :     Optional[Any] ,			_A      :     str ,			*_A      :     int ,			**_A      :     List[Any]		):
												if PartialState._shared_state == {}:
																	raise RuntimeError(
																	    '''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'''		)
												_UpperCamelCase			     =     kwargs.pop('''main_process_only''' ,			_A		)
												_UpperCamelCase			     =     kwargs.pop('''in_order''' ,			_A		)

												if self.isEnabledFor(_A		):
																	if self._should_log(_A		):
																						_UpperCamelCase     ,    _UpperCamelCase			     =     self.process(_A ,			_A		)
																						self.logger.log(_A ,			_A ,			*_A ,			**_A		)

																	elif in_order:
																						_UpperCamelCase			     =     PartialState()
																						for i in range(state.num_processes		):
																											if i == state.process_index:
																																_UpperCamelCase     ,    _UpperCamelCase			     =     self.process(_A ,			_A		)
																																self.logger.log(_A ,			_A ,			*_A ,			**_A		)
																											state.wait_for_everyone()




def 					_snake_case	(  __snake_case  ,     __snake_case = None						):
					if log_level is None:
										_UpperCamelCase			     =     os.environ.get('''ACCELERATE_LOG_LEVEL'''  ,     __snake_case						)
					_UpperCamelCase			     =     logging.getLogger(__snake_case						)
					if log_level is not None:
										logger.setLevel(log_level.upper()						)
										logger.root.setLevel(log_level.upper()						)
					return MultiProcessAdapter(__snake_case  ,     {}						)
