


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 ):
					snake_case_		:					str    					=						RobertaConfig
					snake_case_		:					List[str]    					=						"""roberta"""


					def __init__(    self						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		Any    )      ->						List[Any]:




											'''simple docstring'''

											super().__init__(SCREAMING_SNAKE_CASE    )

											__snake_case				=		RobertaEmbeddings(SCREAMING_SNAKE_CASE    )
											self.init_weights()






@add_start_docstrings(
    """RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
    also takes care of multi-layer training. """  ,				_a  ,				)
class      UpperCamelCase(	_a ):
					snake_case_		:					Union[str, Any]    					=						RobertaConfig
					snake_case_		:					List[Any]    					=						"""roberta"""


					def __init__(    self						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]    )      ->						Optional[Any]:




											'''simple docstring'''

											super().__init__(SCREAMING_SNAKE_CASE    )
											__snake_case				=		config.num_labels
											__snake_case				=		config.num_hidden_layers

											__snake_case				=		DeeRobertaModel(SCREAMING_SNAKE_CASE    )
											__snake_case				=		nn.Dropout(config.hidden_dropout_prob    )
											__snake_case				=		nn.Linear(config.hidden_size  ,     self.config.num_labels    )






					@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE    )
					def 				SCREAMING_SNAKE_CASE_	(    self						:		Any  ,     SCREAMING_SNAKE_CASE						:		Any=None  ,     SCREAMING_SNAKE_CASE						:		str=None  ,     SCREAMING_SNAKE_CASE						:		Tuple=None  ,     SCREAMING_SNAKE_CASE						:		Dict=None  ,     SCREAMING_SNAKE_CASE						:		Any=None  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]=None  ,     SCREAMING_SNAKE_CASE						:		int=None  ,     SCREAMING_SNAKE_CASE						:		Optional[int]=-1  ,     SCREAMING_SNAKE_CASE						:		Tuple=False  ,     )      ->						Tuple:




											'''simple docstring'''

											__snake_case				=		self.num_layers
											try:
																	__snake_case				=		self.roberta(
																	    SCREAMING_SNAKE_CASE  ,     attention_mask=SCREAMING_SNAKE_CASE  ,     token_type_ids=SCREAMING_SNAKE_CASE  ,     position_ids=SCREAMING_SNAKE_CASE  ,     head_mask=SCREAMING_SNAKE_CASE  ,     inputs_embeds=SCREAMING_SNAKE_CASE  ,     )

																	__snake_case				=		outputs[1]

																	__snake_case				=		self.dropout(SCREAMING_SNAKE_CASE    )
																	__snake_case				=		self.classifier(SCREAMING_SNAKE_CASE    )
																	__snake_case				=		(logits,) + outputs[2:]  # add hidden states and attention if they are here
											except HighwayException as e:
																	__snake_case				=		e.message
																	__snake_case				=		e.exit_layer
																	__snake_case				=		outputs[0]

											if not self.training:
																	__snake_case				=		entropy(SCREAMING_SNAKE_CASE    )
																	__snake_case				=		[]
																	__snake_case				=		[]
											if labels is not None:
																	if self.num_labels == 1:
																							#  We are doing regression
																							__snake_case				=		MSELoss()
																							__snake_case				=		loss_fct(logits.view(-1    )  ,     labels.view(-1    )    )
																	else:
																							__snake_case				=		CrossEntropyLoss()
																							__snake_case				=		loss_fct(logits.view(-1  ,     self.num_labels    )  ,     labels.view(-1    )    )

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

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

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









from __future__ import annotations






class      UpperCamelCase:


					def __init__(    self						:		Optional[int]  ,     SCREAMING_SNAKE_CASE						:		str  ,     SCREAMING_SNAKE_CASE						:		str    )      ->						str:




											'''simple docstring'''

											__snake_case		,       __snake_case				=		text, pattern
											__snake_case		,       __snake_case				=		len(SCREAMING_SNAKE_CASE    ), len(SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]  ,     SCREAMING_SNAKE_CASE						:		str    )      ->						int:




											'''simple docstring'''

											for i in range(self.patLen - 1  ,     -1  ,     -1    ):
																	if char == self.pattern[i]:
																							return i
											return -1


					def 				SCREAMING_SNAKE_CASE_	(    self						:		int  ,     SCREAMING_SNAKE_CASE						:		int    )      ->						int:




											'''simple docstring'''

											for i in range(self.patLen - 1  ,     -1  ,     -1    ):
																	if self.pattern[i] != self.text[current_pos + i]:
																							return current_pos + i
											return -1






					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[Any]    )      ->						list[int]:




											'''simple docstring'''

											__snake_case				=		[]
											for i in range(self.textLen - self.patLen + 1    ):
																	__snake_case				=		self.mismatch_in_text(SCREAMING_SNAKE_CASE    )
																	if mismatch_index == -1:
																							positions.append(SCREAMING_SNAKE_CASE    )
																	else:
																							__snake_case				=		self.match_in_pattern(self.text[mismatch_index]    )
																							__snake_case				=		(
																							    mismatch_index - match_index
																							)  # shifting index lgtm [py/multiple-definition]
											return positions




A				:       str      =  'ABAABA'
A				:       Optional[Any]      =  'AB'
A				:       Any      =  BoyerMooreSearch(text, pattern)
A				:       Tuple      =  bms.bad_character_heuristic()

if len(positions) == 0:
	print('No match found')
else:
	print('Pattern found in following positions: ')
	print(positions)





