




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
					from .tokenization_fnet import FNetTokenizer
else:
					__A        =    None

__A        =    logging.get_logger(__name__)
__A        =    {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

__A        =    {
    "vocab_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json",
    },
}

__A        =    {
    "google/fnet-base": 5_12,
    "google/fnet-large": 5_12,
}

__A        =    "▁"







class     __lowerCAmelCase (       __magic_name__ ):

			"""simple docstring"""







			snake_case_   		=       VOCAB_FILES_NAMES
			snake_case_   		=       PRETRAINED_VOCAB_FILES_MAP
			snake_case_   		=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case_   		=       ['''input_ids''', '''token_type_ids''']
			snake_case_   		=       FNetTokenizer


			def __init__(      self			,						lowerCamelCase__=None			,						lowerCamelCase__=None			,						lowerCamelCase__=False			,						lowerCamelCase__=True			,						lowerCamelCase__=True			,						lowerCamelCase__="<unk>"			,						lowerCamelCase__="[SEP]"			,						lowerCamelCase__="<pad>"			,						lowerCamelCase__="[CLS]"			,						lowerCamelCase__="[MASK]"			,						**lowerCamelCase__			,						)    ->      Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    (
							    AddedToken(__a			,						lstrip=__a			,						rstrip=__a			,						normalized=__a							)
							    if isinstance(__a			,						__a							)
							    else mask_token
							)

							super().__init__(
							    __a			,						tokenizer_file=__a			,						do_lower_case=__a			,						remove_space=__a			,						keep_accents=__a			,						unk_token=__a			,						sep_token=__a			,						pad_token=__a			,						cls_token=__a			,						mask_token=__a			,						**__a			,						)

							__lowerCamelCase		       =    do_lower_case
							__lowerCamelCase		       =    remove_space
							__lowerCamelCase		       =    keep_accents
							__lowerCamelCase		       =    vocab_file
							__lowerCamelCase		       =    False if not self.vocab_file else True


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__ = None							)    ->      Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    [self.sep_token_id]
							__lowerCamelCase		       =    [self.cls_token_id]
							if token_ids_a is None:
											return cls + token_ids_a + sep
							return cls + token_ids_a + sep + token_ids_a + sep


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__ = None							)    ->      List[Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    [self.sep_token_id]
							__lowerCamelCase		       =    [self.cls_token_id]

							if token_ids_a is None:
											return len(cls + token_ids_a + sep							) * [0]
							return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]






			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__ = None							)    ->      List[str]:



							'''simple docstring'''

							if not os.path.isdir(__a							):
											logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
											return
							__lowerCamelCase		       =    os.path.join(
							    __a			,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)

							if os.path.abspath(self.vocab_file							) != os.path.abspath(__a							):
											copyfile(self.vocab_file			,						__a							)

							return (out_vocab_file,)







from __future__ import absolute_import, division, print_function, unicode_literals

from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy



@add_start_docstrings(
    'The RoBERTa Model transformer with early exiting (DeeRoBERTa). '   ,					a   ,					)
class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		RobertaConfig
							lowerCamelCase__   			=		'roberta'







							def __init__(   self,			__a):

														'''simple docstring'''

														super().__init__(__a)

														_lowerCAmelCase     :							Optional[Any]    =    RobertaEmbeddings(__a)
														self.init_weights()



@add_start_docstrings(
    'RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n    also takes care of multi-layer training. '   ,					a   ,					)
class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		RobertaConfig
							lowerCamelCase__   			=		'roberta'







							def __init__(   self,			__a):

														'''simple docstring'''

														super().__init__(__a)
														_lowerCAmelCase     :							Optional[int]    =    config.num_labels
														_lowerCAmelCase     :							Optional[int]    =    config.num_hidden_layers

														_lowerCAmelCase     :							Optional[int]    =    DeeRobertaModel(__a)
														_lowerCAmelCase     :							Union[str, Any]    =    nn.Dropout(config.hidden_dropout_prob)
														_lowerCAmelCase     :							List[str]    =    nn.Linear(config.hidden_size,			self.config.num_labels)







							@add_start_docstrings_to_model_forward(__a)
							def    snake_case__      (   self,			__a=None,			__a=None,			__a=None,			__a=None,			__a=None,			__a=None,			__a=None,			__a=-1,			__a=False,			):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    self.num_layers
														try:
																					_lowerCAmelCase     :							List[Any]    =    self.roberta(
																					    __a,			attention_mask=__a,			token_type_ids=__a,			position_ids=__a,			head_mask=__a,			inputs_embeds=__a,			)

																					_lowerCAmelCase     :							List[Any]    =    outputs[1]

																					_lowerCAmelCase     :							Dict    =    self.dropout(__a)
																					_lowerCAmelCase     :							Dict    =    self.classifier(__a)
																					_lowerCAmelCase     :							Optional[Any]    =    (logits,) + outputs[2:]  # add hidden states and attention if they are here
														except HighwayException as e:
																					_lowerCAmelCase     :							Tuple    =    e.message
																					_lowerCAmelCase     :							Union[str, Any]    =    e.exit_layer
																					_lowerCAmelCase     :							List[Any]    =    outputs[0]

														if not self.training:
																					_lowerCAmelCase     :							int    =    entropy(__a)
																					_lowerCAmelCase     :							List[Any]    =    []
																					_lowerCAmelCase     :							str    =    []
														if labels is not None:
																					if self.num_labels == 1:
																												#  We are doing regression
																												_lowerCAmelCase     :							Optional[Any]    =    MSELoss()
																												_lowerCAmelCase     :							int    =    loss_fct(logits.view(-1),			labels.view(-1))
																					else:
																												_lowerCAmelCase     :							Optional[Any]    =    CrossEntropyLoss()
																												_lowerCAmelCase     :							Optional[Any]    =    loss_fct(logits.view(-1,			self.num_labels),			labels.view(-1))

																					# work with highway exits
																					_lowerCAmelCase     :							Optional[int]    =    []
																					for highway_exit in outputs[-1]:
																												_lowerCAmelCase     :							Any    =    highway_exit[0]
																												if not self.training:
																																			highway_logits_all.append(__a)
																																			highway_entropy.append(highway_exit[2])
																												if self.num_labels == 1:
																																			#  We are doing regression
																																			_lowerCAmelCase     :							List[str]    =    MSELoss()
																																			_lowerCAmelCase     :							List[Any]    =    loss_fct(highway_logits.view(-1),			labels.view(-1))
																												else:
																																			_lowerCAmelCase     :							Dict    =    CrossEntropyLoss()
																																			_lowerCAmelCase     :							Optional[Any]    =    loss_fct(highway_logits.view(-1,			self.num_labels),			labels.view(-1))
																												highway_losses.append(__a)

																					if train_highway:
																												_lowerCAmelCase     :							int    =    (sum(highway_losses[:-1]),) + outputs
																												# exclude the final highway, of course
																					else:
																												_lowerCAmelCase     :							Any    =    (loss,) + outputs
														if not self.training:
																					_lowerCAmelCase     :							Optional[Any]    =    outputs + ((original_entropy, highway_entropy), exit_layer)
																					if output_layer >= 0:
																												_lowerCAmelCase     :							Optional[Any]    =    (
																												    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
																												)  # use the highway of the last layer

														return outputs  # (loss), logits, (hidden_states), (attentions), entropy
