import logging

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

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


UpperCAmelCase__     = logging.getLogger(__name__)







class     __lowerCAmelCase					(    UpperCamelCase_		):



			def 					_lowerCamelCase (		self     :						Dict				,	A     :						Optional[Any]				,	A     :						List[str]				,	A     :						Optional[Any]=None				,	A     :						Tuple=None)     ->					Tuple:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.layer[current_layer](A				,	A				,	head_mask[current_layer])

						_UpperCAmelCase	 =     layer_outputs[0]

						return hidden_states







@add_start_docstrings(
    '''The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.'''       ,		UpperCamelCase_       ,		)
class     __lowerCAmelCase					(    UpperCamelCase_		):



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



						"""simple docstring"""






						super().__init__(A)

						_UpperCAmelCase	 =     BertEncoderWithPabee(A)

						self.init_weights()
						_UpperCAmelCase	 =     0
						_UpperCAmelCase	 =     0
						_UpperCAmelCase	 =     0

						_UpperCAmelCase	 =     0



			def 					_lowerCamelCase (		self     :						Union[str, Any]				,	A     :						List[Any])     ->					Tuple:



						"""simple docstring"""






						_UpperCAmelCase	 =     threshold



			def 					_lowerCamelCase (		self     :						Optional[Any]				,	A     :						List[str])     ->					Dict:



						"""simple docstring"""






						_UpperCAmelCase	 =     patience



			def 					_lowerCamelCase (		self     :						Any)     ->					Any:



						"""simple docstring"""






						_UpperCAmelCase	 =     0
						_UpperCAmelCase	 =     0



			def 					_lowerCamelCase (		self     :						str)     ->					List[str]:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.inference_layers_num / self.inference_instances_num
						_UpperCAmelCase	 =     (
						    F"*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ="
						    F" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"
						)
						print(A)



			@add_start_docstrings_to_model_forward(A)
			def 					_lowerCamelCase (		self     :						List[Any]				,	A     :						List[str]=None				,	A     :						Optional[Any]=None				,	A     :						Optional[int]=None				,	A     :						List[Any]=None				,	A     :						List[Any]=None				,	A     :						str=None				,	A     :						Any=None				,	A     :						Union[str, Any]=None				,	A     :						Optional[Any]=None				,	A     :						int=None				,	A     :						Optional[Any]=False				,	)     ->					Union[str, Any]:



						"""simple docstring"""






						if input_ids is not None and inputs_embeds is not None:
									raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time')
						elif input_ids is not None:
									_UpperCAmelCase	 =     input_ids.size()
						elif inputs_embeds is not None:
									_UpperCAmelCase	 =     inputs_embeds.size()[:-1]
						else:
									raise ValueError('You have to specify either input_ids or inputs_embeds')

						_UpperCAmelCase	 =     input_ids.device if input_ids is not None else inputs_embeds.device

						if attention_mask is None:
									_UpperCAmelCase	 =     torch.ones(A				,	device=A)
						if token_type_ids is None:
									_UpperCAmelCase	 =     torch.zeros(A				,	dtype=torch.long				,	device=A)

						# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
						# ourselves in which case we just need to make it broadcastable to all heads.
						_UpperCAmelCase	 =     self.get_extended_attention_mask(A				,	A				,	A)

						# If a 2D ou 3D attention mask is provided for the cross-attention
						# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
						if self.config.is_decoder and encoder_hidden_states is not None:
									_UpperCAmelCase	 =     encoder_hidden_states.size()
									_UpperCAmelCase	 =     (encoder_batch_size, encoder_sequence_length)
									if encoder_attention_mask is None:
												_UpperCAmelCase	 =     torch.ones(A				,	device=A)
									_UpperCAmelCase	 =     self.invert_attention_mask(A)
						else:
									_UpperCAmelCase	 =     None

						# Prepare head mask if needed
						# 1.0 in head_mask indicate we keep the head
						# attention_probs has shape bsz x n_heads x N x N
						# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
						# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
						_UpperCAmelCase	 =     self.get_head_mask(A				,	self.config.num_hidden_layers)

						_UpperCAmelCase	 =     self.embeddings(
						    input_ids=A				,	position_ids=A				,	token_type_ids=A				,	inputs_embeds=A)
						_UpperCAmelCase	 =     embedding_output

						if self.training:
									_UpperCAmelCase	 =     []
									for i in range(self.config.num_hidden_layers):
												_UpperCAmelCase	 =     self.encoder.adaptive_forward(
												    A				,	current_layer=A				,	attention_mask=A				,	head_mask=A)

												_UpperCAmelCase	 =     self.pooler(A)
												_UpperCAmelCase	 =     output_layers[i](output_dropout(A))
												res.append(A)
						elif self.patience == 0:  # Use all layers for inference
									_UpperCAmelCase	 =     self.encoder(
									    A				,	attention_mask=A				,	head_mask=A				,	encoder_hidden_states=A				,	encoder_attention_mask=A				,	)
									_UpperCAmelCase	 =     self.pooler(encoder_outputs[0])
									_UpperCAmelCase	 =     [output_layers[self.config.num_hidden_layers - 1](A)]
						else:
									_UpperCAmelCase	 =     0
									_UpperCAmelCase	 =     None
									_UpperCAmelCase	 =     0
									for i in range(self.config.num_hidden_layers):
												calculated_layer_num += 1
												_UpperCAmelCase	 =     self.encoder.adaptive_forward(
												    A				,	current_layer=A				,	attention_mask=A				,	head_mask=A)

												_UpperCAmelCase	 =     self.pooler(A)
												_UpperCAmelCase	 =     output_layers[i](A)
												if regression:
															_UpperCAmelCase	 =     logits.detach()
															if patient_result is not None:
																		_UpperCAmelCase	 =     patient_result.detach()
															if (patient_result is not None) and torch.abs(patient_result - labels) < self.regression_threshold:
																		patient_counter += 1
															else:
																		_UpperCAmelCase	 =     0
												else:
															_UpperCAmelCase	 =     logits.detach().argmax(dim=1)
															if patient_result is not None:
																		_UpperCAmelCase	 =     patient_result.detach().argmax(dim=1)
															if (patient_result is not None) and torch.all(labels.eq(A)):
																		patient_counter += 1
															else:
																		_UpperCAmelCase	 =     0

												_UpperCAmelCase	 =     logits
												if patient_counter == self.patience:
															break
									_UpperCAmelCase	 =     [patient_result]
									self.inference_layers_num += calculated_layer_num
									self.inference_instances_num += 1

						return res







@add_start_docstrings(
    '''Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n    the pooled output) e.g. for GLUE tasks. '''       ,		UpperCamelCase_       ,		)
class     __lowerCAmelCase					(    UpperCamelCase_		):



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



						"""simple docstring"""






						super().__init__(A)
						_UpperCAmelCase	 =     config.num_labels

						_UpperCAmelCase	 =     BertModelWithPabee(A)
						_UpperCAmelCase	 =     nn.Dropout(config.hidden_dropout_prob)
						_UpperCAmelCase	 =     nn.ModuleList(
						    [nn.Linear(config.hidden_size				,	self.config.num_labels) for _ in range(config.num_hidden_layers)])

						self.init_weights()



			@add_start_docstrings_to_model_forward(A)
			def 					_lowerCamelCase (		self     :						Any				,	A     :						str=None				,	A     :						Optional[Any]=None				,	A     :						Union[str, Any]=None				,	A     :						List[str]=None				,	A     :						List[str]=None				,	A     :						Any=None				,	A     :						Optional[Any]=None				,	)     ->					Dict:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.bert(
						    input_ids=A				,	attention_mask=A				,	token_type_ids=A				,	position_ids=A				,	head_mask=A				,	inputs_embeds=A				,	output_dropout=self.dropout				,	output_layers=self.classifiers				,	regression=self.num_labels == 1				,	)

						_UpperCAmelCase	 =     (logits[-1],)

						if labels is not None:
									_UpperCAmelCase	 =     None
									_UpperCAmelCase	 =     0
									for ix, logits_item in enumerate(A):
												if self.num_labels == 1:
															#  We are doing regression
															_UpperCAmelCase	 =     MSELoss()
															_UpperCAmelCase	 =     loss_fct(logits_item.view(-1)				,	labels.view(-1))
												else:
															_UpperCAmelCase	 =     CrossEntropyLoss()
															_UpperCAmelCase	 =     loss_fct(logits_item.view(-1				,	self.num_labels)				,	labels.view(-1))
												if total_loss is None:
															_UpperCAmelCase	 =     loss
												else:
															total_loss += loss * (ix + 1)
												total_weights += ix + 1
									_UpperCAmelCase	 =     (total_loss / total_weights,) + outputs

						return outputs





import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class     __lowerCAmelCase					(    A       ,		unittest.TestCase		):
			UpperCamelCase           =  MgpstrTokenizer
			UpperCamelCase           =  False
			UpperCamelCase           =  {}
			UpperCamelCase           =  False



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



						"""simple docstring"""






						super().setUp()

						# fmt: off
						_UpperCAmelCase	 =     ['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
						# fmt: on
						_UpperCAmelCase	 =     dict(zip(A				,	range(len(A))))

						_UpperCAmelCase	 =     os.path.join(self.tmpdirname				,	VOCAB_FILES_NAMES['vocab_file'])
						with open(self.vocab_file				,	'w'				,	encoding='utf-8') as fp:
									fp.write(json.dumps(A) + '\n')



			def 					_lowerCamelCase (		self     :						Dict				,	**A     :						List[Any])     ->					Optional[Any]:



						"""simple docstring"""






						return MgpstrTokenizer.from_pretrained(self.tmpdirname				,	**A)



			def 					_lowerCamelCase (		self     :						List[str]				,	A     :						Optional[int])     ->					Dict:



						"""simple docstring"""






						_UpperCAmelCase	 =     'tester'
						_UpperCAmelCase	 =     'tester'
						return input_text, output_text



			@unittest.skip('MGP-STR always lower cases letters.')
			def 					_lowerCamelCase (		self     :						Optional[Any])     ->					int:



						"""simple docstring"""






						pass



			def 					_lowerCamelCase (		self     :						Optional[int])     ->					Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.get_tokenizers(do_lower_case=A)
						for tokenizer in tokenizers:
									with self.subTest(F"{tokenizer.__class__.__name__}"):
												_UpperCAmelCase	 =     '[SPECIAL_TOKEN]'

												tokenizer.add_special_tokens({'cls_token': special_token})
												_UpperCAmelCase	 =     tokenizer.encode([special_token]				,	add_special_tokens=A)
												self.assertEqual(len(A)				,	1)

												_UpperCAmelCase	 =     tokenizer.decode(A				,	skip_special_tokens=A)
												self.assertTrue(special_token not in decoded)



			def 					_lowerCamelCase (		self     :						Any)     ->					str:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.get_tokenizers()
						for tokenizer in tokenizers:
									with self.subTest(F"{tokenizer.__class__.__name__}"):
												_UpperCAmelCase ,  _UpperCAmelCase	 =     self.get_input_output_texts(A)

												_UpperCAmelCase	 =     tokenizer.tokenize(A)
												_UpperCAmelCase	 =     tokenizer.convert_tokens_to_ids(A)
												_UpperCAmelCase	 =     tokenizer.encode(A				,	add_special_tokens=A)
												self.assertListEqual(A				,	A)

												_UpperCAmelCase	 =     tokenizer.convert_ids_to_tokens(A)
												self.assertNotEqual(len(A)				,	0)
												_UpperCAmelCase	 =     tokenizer.decode(A)
												self.assertIsInstance(A				,	A)

												self.assertEqual(text_a.replace(' '				,	'')				,	A)



			@unittest.skip('MGP-STR tokenizer only handles one sequence.')
			def 					_lowerCamelCase (		self     :						str)     ->					List[str]:



						"""simple docstring"""






						pass



			@unittest.skip('inputs cannot be pretokenized in MgpstrTokenizer')
			def 					_lowerCamelCase (		self     :						Dict)     ->					Tuple:



						"""simple docstring"""






						pass
