




from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE:    Optional[Any]											=   {
    '''configuration_flava''': [
        '''FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''FlavaConfig''',
        '''FlavaImageCodebookConfig''',
        '''FlavaImageConfig''',
        '''FlavaMultimodalConfig''',
        '''FlavaTextConfig''',
    ],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:    List[Any]											=   ['''FlavaFeatureExtractor''']
						SCREAMING_SNAKE_CASE:    Any											=   ['''FlavaImageProcessor''']
						SCREAMING_SNAKE_CASE:    List[Any]											=   ['''FlavaProcessor''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:    Any											=   [
						    '''FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''FlavaForPreTraining''',
						    '''FlavaImageCodebook''',
						    '''FlavaImageModel''',
						    '''FlavaModel''',
						    '''FlavaMultimodalModel''',
						    '''FlavaPreTrainedModel''',
						    '''FlavaTextModel''',
						]

if TYPE_CHECKING:
						from .configuration_flava import (
						    FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    FlavaConfig,
						    FlavaImageCodebookConfig,
						    FlavaImageConfig,
						    FlavaMultimodalConfig,
						    FlavaTextConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_flava import FlavaFeatureExtractor
												from .image_processing_flava import FlavaImageProcessor
												from .processing_flava import FlavaProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flava import (
												    FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FlavaForPreTraining,
												    FlavaImageCodebook,
												    FlavaImageModel,
												    FlavaModel,
												    FlavaMultimodalModel,
												    FlavaPreTrainedModel,
												    FlavaTextModel,
												)

else:
						import sys

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





import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
						import torch


SCREAMING_SNAKE_CASE:    Any											=   logging.get_logger(__name__)



class       lowercase_		:



						def __init__( self  :      Union[str, Any]				,       snake_case__  :      List[str]				,       snake_case__  :      Dict=21				,       snake_case__  :      List[str]=[]				,       snake_case__  :      List[str]=None     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       model
								SCREAMING_SNAKE_CASE_							=       trace_batch_nums
								SCREAMING_SNAKE_CASE_							=       abort_after_batch_num

								# keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
								SCREAMING_SNAKE_CASE_							=       collections.deque([]				,       snake_case__     )
								SCREAMING_SNAKE_CASE_							=       []
								SCREAMING_SNAKE_CASE_							=       0
								SCREAMING_SNAKE_CASE_							=       0
								SCREAMING_SNAKE_CASE_							=       False
								SCREAMING_SNAKE_CASE_							=       '                 '

								self.analyse_model()

								self.register_forward_hook()



						def   __a				( self  :      Union[str, Any]				,       snake_case__  :      Optional[int]=None     ):

								"""simple docstring"""

								if frame is not None:
										self.expand_frame(snake_case__     )
								self.frames.append('\n'.join(self.frame     )     )
								SCREAMING_SNAKE_CASE_							=       []  # start a new frame



						def   __a				( self  :      Union[str, Any]				,       snake_case__  :      Any     ):

								"""simple docstring"""

								self.frame.append(snake_case__     )



						def   __a				( self  :      Optional[int]     ):

								"""simple docstring"""

								print('\n'.join(self.frames     )     )
								SCREAMING_SNAKE_CASE_							=       []



						def   __a				( self  :      Dict     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       []



						def   __a				( self  :      int     ):

								"""simple docstring"""

								print(f'''\nDetected inf/nan during batch_number={self.batch_number}'''     )
								print(f'''Last {len(self.frames     )} forward frames:'''     )
								print(f'''{"abs min":8} {"abs max":8} metadata'''     )
								print('\n'.join(self.frames     )     )
								print('\n\n'     )
								SCREAMING_SNAKE_CASE_							=       []



						def   __a				( self  :      str     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       {m: name for name, m in self.model.named_modules()}
								# self.longest_module_name = max(len(v) for v in self.module_names.values())



						def   __a				( self  :      List[str]				,       snake_case__  :      List[str]				,       snake_case__  :      Union[str, Any]     ):

								"""simple docstring"""

								if torch.is_tensor(snake_case__     ):
										self.expand_frame(get_abs_min_max(snake_case__				,       snake_case__     )     )
										if detect_overflow(snake_case__				,       snake_case__     ):
												SCREAMING_SNAKE_CASE_							=       True
								elif var is None:
										self.expand_frame(f'''{"None":>17} {ctx}'''     )
								else:
										self.expand_frame(f'''{"not a tensor":>17} {ctx}'''     )



						def   __a				( self  :      str     ):

								"""simple docstring"""

								self.expand_frame(f'''\n\n{self.prefix} *** Starting batch number={self.batch_number} ***'''     )
								self.expand_frame(f'''{"abs min":8} {"abs max":8} metadata'''     )



						def   __a				( self  :      List[str]     ):

								"""simple docstring"""

								self.expand_frame(f'''{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'''     )



						def   __a				( self  :      Optional[int]				,       snake_case__  :      int				,       snake_case__  :      List[Any]				,       snake_case__  :      str     ):

								"""simple docstring"""

								self.expand_frame(f'''{self.prefix} {self.module_names[module]} {module.__class__.__name__}'''     )

								# params
								for name, p in module.named_parameters(recurse=snake_case__     ):
										self.analyse_variable(snake_case__				,       snake_case__     )

								# inputs
								if isinstance(snake_case__				,       snake_case__     ):
										for i, x in enumerate(snake_case__     ):
												self.analyse_variable(snake_case__				,       f'''input[{i}]'''     )
								else:
										self.analyse_variable(snake_case__				,       'input'     )

								# outputs
								if isinstance(snake_case__				,       snake_case__     ):
										for i, x in enumerate(snake_case__     ):
												# possibly a tuple of tuples
												if isinstance(snake_case__				,       snake_case__     ):
														for j, y in enumerate(snake_case__     ):
																self.analyse_variable(snake_case__				,       f'''output[{i}][{j}]'''     )
												else:
														self.analyse_variable(snake_case__				,       f'''output[{i}]'''     )
								else:
										self.analyse_variable(snake_case__				,       'output'     )

								self.save_frame()



						def   __a				( self  :      Tuple     ):

								"""simple docstring"""

								self.model.apply(self._register_forward_hook     )



						def   __a				( self  :      int				,       snake_case__  :      Optional[int]     ):

								"""simple docstring"""

								module.register_forward_hook(self.forward_hook     )






						def   __a				( self  :      Optional[Any]				,       snake_case__  :      str				,       snake_case__  :      Optional[Any]				,       snake_case__  :      Dict     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       False

								SCREAMING_SNAKE_CASE_							=       True if self.batch_number in self.trace_batch_nums else False
								if trace_mode:
										self.reset_saved_frames()

								if self.total_calls == 0:
										self.batch_start_frame()
								self.total_calls += 1

								# count batch numbers - the very first forward hook of the batch will be called when the
								# batch completes - i.e. it gets called very last - we know this batch has finished
								if module == self.model:
										self.batch_number += 1
										SCREAMING_SNAKE_CASE_							=       True

								self.create_frame(snake_case__				,       snake_case__				,       snake_case__     )

								# if last_frame_of_batch:
								#     self.batch_end_frame()

								if trace_mode:
										self.trace_frames()

								if last_frame_of_batch:
										self.batch_start_frame()

								if self.detected_overflow and not trace_mode:
										self.dump_saved_frames()

										# now we can abort, as it's pointless to continue running
										raise ValueError(
										    'DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. '
										    'Please scroll up above this traceback to see the activation values prior to this event.'     )

								# abort after certain batch if requested to do so
								if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
										raise ValueError(
										    f'''DebugUnderflowOverflow: aborting after {self.batch_number} batches due to'''
										    f''' `abort_after_batch_num={self.abort_after_batch_num}` arg'''     )


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  Union[str, Any]:
		SCREAMING_SNAKE_CASE_							=       var.abs()
		return F'''{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'''


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  int:
		SCREAMING_SNAKE_CASE_							=       False
		if torch.isnan(lowerCAmelCase   ).any().item():
				SCREAMING_SNAKE_CASE_							=       True
				print(F'''{ctx} has nans'''   )
		if torch.isinf(lowerCAmelCase   ).any().item():
				SCREAMING_SNAKE_CASE_							=       True
				print(F'''{ctx} has infs'''   )

		# if needed to monitor large elements can enable the following
		if 0:  # and detected:
				SCREAMING_SNAKE_CASE_							=       var[torch.ge(var.abs()						,	100   )]
				if naaa.numel() > 0:
						print(F'''{ctx}:  n100={naaa.numel()}'''   )
				SCREAMING_SNAKE_CASE_							=       var[torch.ge(var.abs()						,	1000   )]
				if naaaa.numel() > 0:
						print(F'''{ctx}: n1000={naaaa.numel()}'''   )
				SCREAMING_SNAKE_CASE_							=       var[torch.ge(var.abs()						,	10000   )]
				if naaaaa.numel() > 0:
						print(F'''{ctx}: n10000={naaaaa.numel()}'''   )

		if 0:
				print(F'''min={var.min():9.2e} max={var.max():9.2e}'''   )

		if 0:
				print(F'''min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})'''   )

		return detected




class       lowercase_		(SCREAMING_SNAKE_CASE__   ):
						lowerCAmelCase__ 			="underflow_overflow"
						lowerCAmelCase__ 			="tpu_metrics_debug"