





'''simple docstring'''






import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
		import torch


A :			List[str]							   =      logging.get_logger(__name__)







class       lowerCamelCase       :


					def __init__(       self						:	str							,  __snake_case						:	Dict							,  __snake_case						:	Optional[int]=21							,  __snake_case						:	Optional[Any]=[]							,  __snake_case						:	Union[str, Any]=None							):





											'''simple docstring'''





											_snake_case: Tuple	       =		model
											_snake_case: Optional[Any]	       =		trace_batch_nums
											_snake_case: Dict	       =		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: Optional[int]	       =		collections.deque([]							,  __lowercase							)
											_snake_case: int	       =		[]
											_snake_case: List[str]	       =		0
											_snake_case: Tuple	       =		0
											_snake_case: Union[str, Any]	       =		False
											_snake_case: Tuple	       =		'''                 '''

											self.analyse_model()

											self.register_forward_hook()


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							,  __snake_case						:	Optional[int]=None							):





											'''simple docstring'''





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


					def  SCREAMING_SNAKE_CASE_		(       self						:	int							,  __snake_case						:	List[Any]							):





											'''simple docstring'''





											self.frame.append(__lowercase							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							):





											'''simple docstring'''





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


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





											_snake_case: Tuple	       =		[]


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							):





											'''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: Tuple	       =		[]


					def  SCREAMING_SNAKE_CASE_		(       self						:	str							):





											'''simple docstring'''





											_snake_case: Tuple	       =		{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  SCREAMING_SNAKE_CASE_		(       self						:	int							,  __snake_case						:	List[Any]							,  __snake_case						:	Dict							):





											'''simple docstring'''





											if torch.is_tensor(__lowercase							):
																	self.expand_frame(get_abs_min_max(__lowercase							,  __lowercase							)							)
																	if detect_overflow(__lowercase							,  __lowercase							):
																							_snake_case: Optional[int]	       =		True
											elif var is None:
																	self.expand_frame(f'''{"None":>17} {ctx}'''							)
											else:
																	self.expand_frame(f'''{"not a tensor":>17} {ctx}'''							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							):





											'''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  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							):





											'''simple docstring'''





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


					def  SCREAMING_SNAKE_CASE_		(       self						:	int							,  __snake_case						:	Any							,  __snake_case						:	str							,  __snake_case						:	List[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=__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  SCREAMING_SNAKE_CASE_		(       self						:	int							):





											'''simple docstring'''





											self.model.apply(self._register_forward_hook							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	str							,  __snake_case						:	Tuple							):





											'''simple docstring'''





											module.register_forward_hook(self.forward_hook							)




					def  SCREAMING_SNAKE_CASE_		(       self						:	int							,  __snake_case						:	Optional[int]							,  __snake_case						:	Optional[int]							,  __snake_case						:	Any							):





											'''simple docstring'''





											_snake_case: Any	       =		False

											_snake_case: Any	       =		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: int	       =		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    lowercase_   (		lowercase__					,	lowercase__    ) ->str:
						_snake_case: Tuple	       =		var.abs()
						return F'''{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'''





def    lowercase_   (		lowercase__					,	lowercase__    ) ->Any:
						_snake_case: Dict	       =		False
						if torch.isnan(SCREAMING_SNAKE_CASE_    ).any().item():
												_snake_case: List[Any]	       =		True
												print(F'''{ctx} has nans'''    )
						if torch.isinf(SCREAMING_SNAKE_CASE_    ).any().item():
												_snake_case: Any	       =		True
												print(F'''{ctx} has infs'''    )

						# if needed to monitor large elements can enable the following
						if 0:  # and detected:
												_snake_case: Dict	       =		var[torch.ge(var.abs()					,	100    )]
												if naaa.numel() > 0:
																		print(F'''{ctx}:  n100={naaa.numel()}'''    )
												_snake_case: List[Any]	       =		var[torch.ge(var.abs()					,	1000    )]
												if naaaa.numel() > 0:
																		print(F'''{ctx}: n1000={naaaa.numel()}'''    )
												_snake_case: int	       =		var[torch.ge(var.abs()					,	1_0000    )]
												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       lowerCamelCase       (    _A	):
					_SCREAMING_SNAKE_CASE				 =     "underflow_overflow"
					_SCREAMING_SNAKE_CASE				 =     "tpu_metrics_debug"












import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  List[Any]:
		# Initialise PyTorch model
		lowerCamelCase :			Dict    		=FNetConfig.from_json_file(SCREAMING_SNAKE_CASE_		)
		print(F"Building PyTorch model from configuration: {config}"		)
		lowerCamelCase :			Tuple    		=FNetForPreTraining(SCREAMING_SNAKE_CASE_		)

		lowerCamelCase :			List[Any]    		=restore_checkpoint(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			List[str]    		=checkpoint_dict['''target''']

		# Embeddings
		# Position IDs
		lowerCamelCase :			Optional[Any]    		=fnet_pretraining_model.state_dict()

		lowerCamelCase :			Tuple    		=state_dict['''fnet.embeddings.position_ids''']
		lowerCamelCase :			str    		={'''fnet.embeddings.position_ids''': position_ids}
		# Embedding Layers
		lowerCamelCase :			int    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''word''']['''embedding''']		)
		lowerCamelCase :			int    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''position''']['''embedding'''][0]		)
		lowerCamelCase :			str    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''type''']['''embedding''']		)
		lowerCamelCase :			str    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''hidden_mapping_in''']['''kernel''']		).T
		lowerCamelCase :			List[Any]    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''hidden_mapping_in''']['''bias''']		)
		lowerCamelCase :			List[str]    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''layer_norm''']['''scale''']		)
		lowerCamelCase :			Tuple    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''layer_norm''']['''bias''']		)

		# Encoder Layers
		for layer in range(config.num_hidden_layers		):
				lowerCamelCase :			int    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"encoder_{layer}"]['''mixing_layer_norm''']['''scale''']		)
				lowerCamelCase :			int    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"encoder_{layer}"]['''mixing_layer_norm''']['''bias''']		)

				lowerCamelCase :			List[Any]    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"feed_forward_{layer}"]['''intermediate''']['''kernel''']		).T
				lowerCamelCase :			List[Any]    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"feed_forward_{layer}"]['''intermediate''']['''bias''']		)

				lowerCamelCase :			str    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"feed_forward_{layer}"]['''output''']['''kernel''']		).T
				lowerCamelCase :			Tuple    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"feed_forward_{layer}"]['''output''']['''bias''']		)

				lowerCamelCase :			List[Any]    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"encoder_{layer}"]['''output_layer_norm''']['''scale''']		)
				lowerCamelCase :			Optional[Any]    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"encoder_{layer}"]['''output_layer_norm''']['''bias''']		)

		# Pooler Layers
		lowerCamelCase :			Any    		=torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''kernel''']		).T
		lowerCamelCase :			str    		=torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''bias''']		)

		# Masked LM Layers
		lowerCamelCase :			Optional[int]    		=torch.tensor(
		    pretrained_model_params['''predictions_dense''']['''kernel''']		).T
		lowerCamelCase :			str    		=torch.tensor(
		    pretrained_model_params['''predictions_dense''']['''bias''']		)
		lowerCamelCase :			Optional[Any]    		=torch.tensor(
		    pretrained_model_params['''predictions_layer_norm''']['''scale''']		)
		lowerCamelCase :			Union[str, Any]    		=torch.tensor(
		    pretrained_model_params['''predictions_layer_norm''']['''bias''']		)
		lowerCamelCase :			Optional[int]    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''word''']['''embedding''']		)
		lowerCamelCase :			Union[str, Any]    		=torch.tensor(
		    pretrained_model_params['''predictions_output''']['''output_bias''']		)
		lowerCamelCase :			Tuple    		=torch.tensor(pretrained_model_params['''predictions_output''']['''output_bias''']		)

		# Seq Relationship Layers
		lowerCamelCase :			Union[str, Any]    		=torch.tensor(
		    pretrained_model_params['''classification''']['''output_kernel''']		)
		lowerCamelCase :			Any    		=torch.tensor(
		    pretrained_model_params['''classification''']['''output_bias''']		)

		# Load State Dict
		fnet_pretraining_model.load_state_dict(SCREAMING_SNAKE_CASE_		)

		# Save PreTrained
		print(F"Saving pretrained model to {save_path}"		)
		fnet_pretraining_model.save_pretrained(SCREAMING_SNAKE_CASE_		)


if __name__ == "__main__":
				snake_case_				=      argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--flax_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
				)
				parser.add_argument(
				    '''--fnet_config_file''',
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        '''The config json file corresponding to the pre-trained FNet model. \n'''
				        '''This specifies the model architecture.'''
				    ),
				)
				parser.add_argument('''--save_path''', default=None, type=str, required=True, help='''Path to the output model.''')
				snake_case_				=      parser.parse_args()
				convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)

