



"""simple docstring"""






import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


__snake_case				:  int =       logging.get_logger(__name__)


__snake_case				:  List[str] =       R'\n    Args:\n        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax\n            or scores for each vocabulary token after SoftMax.\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional stopping criteria specific kwargs.\n\n    Return:\n        `bool`. `False` indicates we should continue, `True` indicates we should stop.\n\n'



class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	@add_start_docstrings(_SCREAMING_SNAKE_CASE)
	def __call__(	self:						Dict			,     _SCREAMING_SNAKE_CASE:						torch.LongTensor			,     _SCREAMING_SNAKE_CASE:						torch.FloatTensor			,     **_SCREAMING_SNAKE_CASE:						Dict)							->			bool:

				"""simple docstring"""

				raise NotImplementedError("StoppingCriteria needs to be subclassed")






class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	def __init__(	self:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						Optional[int] = None)							->			int:

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			max_length
				__lowerCAmelCase :    Optional[int]				=			max_position_embeddings







	@add_start_docstrings(_SCREAMING_SNAKE_CASE)
	def __call__(	self:						Any			,     _SCREAMING_SNAKE_CASE:						torch.LongTensor			,     _SCREAMING_SNAKE_CASE:						torch.FloatTensor			,     **_SCREAMING_SNAKE_CASE:						List[Any])							->			bool:

				"""simple docstring"""

				__lowerCAmelCase :    List[Any]				=			input_ids.shape[-1]
				__lowerCAmelCase :    str				=			cur_len >= self.max_length
				if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
							logger.warning_once(
							    "This is a friendly reminder - the current text generation call will exceed the model's predefined "
							    F"""maximum length ({self.max_position_embeddings}). Depending on the model, you may observe """
							    "exceptions, performance degradation, or nothing at all.")
				return is_done






class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	def __init__(	self:						Tuple			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int)							->			Optional[Any]:

				"""simple docstring"""

				warnings.warn(
				    "The class `MaxNewTokensCriteria` is deprecated. "
				    F"""Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` """
				    "with `max_length = start_length + max_new_tokens` instead."			,     _SCREAMING_SNAKE_CASE			,     )
				__lowerCAmelCase :    int				=			start_length
				__lowerCAmelCase :    Optional[int]				=			max_new_tokens
				__lowerCAmelCase :    List[Any]				=			start_length + max_new_tokens







	@add_start_docstrings(_SCREAMING_SNAKE_CASE)
	def __call__(	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						torch.LongTensor			,     _SCREAMING_SNAKE_CASE:						torch.FloatTensor			,     **_SCREAMING_SNAKE_CASE:						Tuple)							->			bool:

				"""simple docstring"""

				return input_ids.shape[-1] >= self.max_length






class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	def __init__(	self:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						float			,     _SCREAMING_SNAKE_CASE:						Optional[float] = None)							->			Any:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[int]				=			max_time
				__lowerCAmelCase :    str				=			time.time() if initial_timestamp is None else initial_timestamp







	@add_start_docstrings(_SCREAMING_SNAKE_CASE)
	def __call__(	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						torch.LongTensor			,     _SCREAMING_SNAKE_CASE:						torch.FloatTensor			,     **_SCREAMING_SNAKE_CASE:						Optional[Any])							->			bool:

				"""simple docstring"""

				return time.time() - self.initial_timestamp > self.max_time






class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	@add_start_docstrings(_SCREAMING_SNAKE_CASE)
	def __call__(	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						torch.LongTensor			,     _SCREAMING_SNAKE_CASE:						torch.FloatTensor			,     **_SCREAMING_SNAKE_CASE:						int)							->			bool:

				"""simple docstring"""

				return any(criteria(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE) for criteria in self)







	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						int)							->			Optional[int]:

				"""simple docstring"""

				for stopping_criterium in self:
							if isinstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE):
										return stopping_criterium.max_length
							elif isinstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE):
										return stopping_criterium.max_length
				return None






def      _lowercase							(    __snake_case   ,__snake_case      )		->  StoppingCriteriaList:
			__lowerCAmelCase :    Optional[int]				=			stopping_criteria.max_length
			__lowerCAmelCase :    Any				=			deepcopy(__snake_case      )
			if stopping_max_length is not None and stopping_max_length != max_length:
						warnings.warn("You set different `max_length` for stopping criteria and `max_length` parameter"   ,__snake_case      )
			elif stopping_max_length is None:
						new_stopping_criteria.append(MaxLengthCriteria(max_length=__snake_case      )      )
			return new_stopping_criteria




"""simple docstring"""






import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
	__snake_case				:  Optional[Any] =       argparse.ArgumentParser(
	    description=(
	        'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
	        ' Distillation'
	    )
	)
	parser.add_argument('--model_type', default='bert', choices=['bert'])
	parser.add_argument('--model_name', default='bert-base-uncased', type=str)
	parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
	parser.add_argument('--vocab_transform', action='store_true')
	__snake_case				:  List[Any] =       parser.parse_args()

	if args.model_type == "bert":
		__snake_case				:  int =       BertForMaskedLM.from_pretrained(args.model_name)
		__snake_case				:  Tuple =       'bert'
	else:
		raise ValueError('args.model_type should be "bert".')

	__snake_case				:  List[Any] =       model.state_dict()
	__snake_case				:  Any =       {}

	for w in ["word_embeddings", "position_embeddings"]:
		__snake_case				:  List[str] =       state_dict[F"""{prefix}.embeddings.{w}.weight"""]
	for w in ["weight", "bias"]:
		__snake_case				:  Optional[int] =       state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""]

	__snake_case				:  List[str] =       0
	for teacher_idx in [0, 2, 4, 7, 9, 11]:
		for w in ["weight", "bias"]:
			__snake_case				:  str =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
			]
			__snake_case				:  Tuple =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
			]
			__snake_case				:  str =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
			]

			__snake_case				:  Any =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
			]
			__snake_case				:  Union[str, Any] =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
			]

			__snake_case				:  Union[str, Any] =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
			]
			__snake_case				:  Any =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
			]
			__snake_case				:  Optional[int] =       state_dict[
			    F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
			]
		std_idx += 1

	__snake_case				:  Union[str, Any] =       state_dict['cls.predictions.decoder.weight']
	__snake_case				:  Union[str, Any] =       state_dict['cls.predictions.bias']
	if args.vocab_transform:
		for w in ["weight", "bias"]:
			__snake_case				:  Union[str, Any] =       state_dict[F"""cls.predictions.transform.dense.{w}"""]
			__snake_case				:  List[str] =       state_dict[F"""cls.predictions.transform.LayerNorm.{w}"""]

	print(F"""N layers selected for distillation: {std_idx}""")
	print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

	print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
	torch.save(compressed_sd, args.dump_checkpoint)