
import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
			import torch


_lowerCamelCase :       Optional[int]     					=    logging.get_logger(__name__)




class      SCREAMING_SNAKE_CASE__  :





	'''simple docstring'''



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


				'''simple docstring'''


				_snake_case						=    model
				_snake_case						=    trace_batch_nums
				_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
				_snake_case						=    collections.deque([]    ,						lowercase			)
				_snake_case						=    []
				_snake_case						=    0
				_snake_case						=    0
				_snake_case						=    False
				_snake_case						=    '                 '

				self.analyse_model()

				self.register_forward_hook()



	def 				A							(						self			:					Union[str, Any]    ,						lowercase			:					Tuple=None			):


				'''simple docstring'''


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



	def 				A							(						self			:					List[Any]    ,						lowercase			:					Any			):


				'''simple docstring'''


				self.frame.append(lowercase			)



	def 				A							(						self			:					Optional[Any]			):


				'''simple docstring'''


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



	def 				A							(						self			:					str			):


				'''simple docstring'''


				_snake_case						=    []



	def 				A							(						self			:					Tuple			):


				'''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'			)
				_snake_case						=    []



	def 				A							(						self			:					Optional[Any]			):


				'''simple docstring'''


				_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			:					Dict    ,						lowercase			:					Dict    ,						lowercase			:					Union[str, Any]			):


				'''simple docstring'''


				if torch.is_tensor(lowercase			):
							self.expand_frame(get_abs_min_max(lowercase    ,						lowercase			)			)
							if detect_overflow(lowercase    ,						lowercase			):
										_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			:					Optional[int]			):


				'''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			:					str			):


				'''simple docstring'''


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



	def 				A							(						self			:					Any    ,						lowercase			:					int    ,						lowercase			:					List[Any]    ,						lowercase			:					Optional[Any]			):


				'''simple docstring'''


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

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

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

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

				self.save_frame()



	def 				A							(						self			:					Union[str, Any]			):


				'''simple docstring'''


				self.model.apply(self._register_forward_hook			)



	def 				A							(						self			:					List[Any]    ,						lowercase			:					Dict			):


				'''simple docstring'''


				module.register_forward_hook(self.forward_hook			)





	def 				A							(						self			:					Dict    ,						lowercase			:					Dict    ,						lowercase			:					str    ,						lowercase			:					List[Any]			):


				'''simple docstring'''


				_snake_case						=    False

				_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
							_snake_case						=    True

				self.create_frame(lowercase    ,						lowercase    ,						lowercase			)

				# 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_	( __lowercase     :			Optional[int] ,      __lowercase     :			int				)				->						Optional[int]:
			_snake_case						=    var.abs()
			return f'''{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'''






def  a_	( __lowercase     :			List[str] ,      __lowercase     :			List[Any]				)				->						str:
			_snake_case						=    False
			if torch.isnan(__lowercase				).any().item():
						_snake_case						=    True
						print(f'''{ctx} has nans'''				)
			if torch.isinf(__lowercase				).any().item():
						_snake_case						=    True
						print(f'''{ctx} has infs'''				)

			# if needed to monitor large elements can enable the following
			if 0:  # and detected:
						_snake_case						=    var[torch.ge(var.abs() ,      100				)]
						if naaa.numel() > 0:
									print(f'''{ctx}:  n100={naaa.numel()}'''				)
						_snake_case						=    var[torch.ge(var.abs() ,      1_000				)]
						if naaaa.numel() > 0:
									print(f'''{ctx}: n1000={naaaa.numel()}'''				)
						_snake_case						=    var[torch.ge(var.abs() ,      10_000				)]
						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      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	Dict					     =      "underflow_overflow"
	_UpperCAmelCase :	List[str]					     =      "tpu_metrics_debug"

# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

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


_lowerCamelCase :       int     					=    {
    '''configuration_tvlt''': ['''TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TvltConfig'''],
    '''feature_extraction_tvlt''': ['''TvltFeatureExtractor'''],
    '''processing_tvlt''': ['''TvltProcessor'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase :       str     					=    [
			    '''TVLT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TvltModel''',
			    '''TvltForPreTraining''',
			    '''TvltForAudioVisualClassification''',
			    '''TvltPreTrainedModel''',
			]

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase :       str     					=    ['''TvltImageProcessor''']


if TYPE_CHECKING:
			from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
			from .processing_tvlt import TvltProcessor
			from .feature_extraction_tvlt import TvltFeatureExtractor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tvlt import (
						    TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TvltForAudioVisualClassification,
						    TvltForPreTraining,
						    TvltModel,
						    TvltPreTrainedModel,
						)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_tvlt import TvltImageProcessor


else:
			import sys

			_lowerCamelCase :       Dict     					=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)