
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,
)


snake_case :		Dict							      =  logging.getLogger(__name__)





class       _snake_case					(     snake_case				):







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a=None						,   _a=None		):
				__magic_name__		:		List[Any]	   =     self.layer[current_layer](_a						,   _a						,   head_mask[current_layer]		)

				__magic_name__		:		Optional[Any]	   =     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.'				,						snake_case				,						)
class       _snake_case					(     snake_case				):







			def __init__(				self						,   _a		):
				super().__init__(_a		)

				__magic_name__		:		Tuple	   =     BertEncoderWithPabee(_a		)

				self.init_weights()
				__magic_name__		:		Optional[Any]	   =     0
				__magic_name__		:		Dict	   =     0
				__magic_name__		:		Optional[Any]	   =     0

				__magic_name__		:		Optional[Any]	   =     0







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				__magic_name__		:		Optional[Any]	   =     threshold







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				__magic_name__		:		List[str]	   =     patience







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Any	   =     0
				__magic_name__		:		Optional[int]	   =     0







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		str	   =     self.inference_layers_num / self.inference_instances_num
				__magic_name__		:		Any	   =     (
				    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 			SCREAMING_SNAKE_CASE    (				self						,   _a=None						,   _a=None						,   _a=None						,   _a=None						,   _a=None						,   _a=None						,   _a=None						,   _a=None						,   _a=None						,   _a=None						,   _a=False						,   ):

				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:
					__magic_name__		:		Dict	   =     input_ids.size()
				elif inputs_embeds is not None:
					__magic_name__		:		Optional[Any]	   =     inputs_embeds.size()[:-1]
				else:
					raise ValueError("You have to specify either input_ids or inputs_embeds"		)

				__magic_name__		:		Optional[Any]	   =     input_ids.device if input_ids is not None else inputs_embeds.device

				if attention_mask is None:
					__magic_name__		:		Optional[int]	   =     torch.ones(_a						,   device=_a		)
				if token_type_ids is None:
					__magic_name__		:		List[str]	   =     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.
				__magic_name__		:		torch.Tensor	   =     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:
					__magic_name__   ,    __magic_name__   ,    __magic_name__		:		Optional[int]	   =     encoder_hidden_states.size()
					__magic_name__		:		Tuple	   =     (encoder_batch_size, encoder_sequence_length)
					if encoder_attention_mask is None:
						__magic_name__		:		Dict	   =     torch.ones(_a						,   device=_a		)
					__magic_name__		:		Dict	   =     self.invert_attention_mask(_a		)
				else:
					__magic_name__		:		Any	   =     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]
				__magic_name__		:		str	   =     self.get_head_mask(_a						,   self.config.num_hidden_layers		)

				__magic_name__		:		List[Any]	   =     self.embeddings(
				    input_ids=_a						,   position_ids=_a						,   token_type_ids=_a						,   inputs_embeds=_a		)
				__magic_name__		:		Union[str, Any]	   =     embedding_output

				if self.training:
					__magic_name__		:		List[Any]	   =     []
					for i in range(self.config.num_hidden_layers		):
						__magic_name__		:		Any	   =     self.encoder.adaptive_forward(
						    _a						,   current_layer=_a						,   attention_mask=_a						,   head_mask=_a		)

						__magic_name__		:		Tuple	   =     self.pooler(_a		)
						__magic_name__		:		List[str]	   =     output_layers[i](output_dropout(_a		)		)
						res.append(_a		)
				elif self.patience == 0:  # Use all layers for inference
					__magic_name__		:		List[str]	   =     self.encoder(
					    _a						,   attention_mask=_a						,   head_mask=_a						,   encoder_hidden_states=_a						,   encoder_attention_mask=_a						,   )
					__magic_name__		:		Optional[int]	   =     self.pooler(encoder_outputs[0]		)
					__magic_name__		:		str	   =     [output_layers[self.config.num_hidden_layers - 1](_a		)]
				else:
					__magic_name__		:		Union[str, Any]	   =     0
					__magic_name__		:		Optional[Any]	   =     None
					__magic_name__		:		Any	   =     0
					for i in range(self.config.num_hidden_layers		):
						calculated_layer_num += 1
						__magic_name__		:		List[str]	   =     self.encoder.adaptive_forward(
						    _a						,   current_layer=_a						,   attention_mask=_a						,   head_mask=_a		)

						__magic_name__		:		Union[str, Any]	   =     self.pooler(_a		)
						__magic_name__		:		Union[str, Any]	   =     output_layers[i](_a		)
						if regression:
							__magic_name__		:		List[Any]	   =     logits.detach()
							if patient_result is not None:
								__magic_name__		:		Dict	   =     patient_result.detach()
							if (patient_result is not None) and torch.abs(patient_result - labels		) < self.regression_threshold:
								patient_counter += 1
							else:
								__magic_name__		:		str	   =     0
						else:
							__magic_name__		:		str	   =     logits.detach().argmax(dim=1		)
							if patient_result is not None:
								__magic_name__		:		Dict	   =     patient_result.detach().argmax(dim=1		)
							if (patient_result is not None) and torch.all(labels.eq(_a		)		):
								patient_counter += 1
							else:
								__magic_name__		:		Tuple	   =     0

						__magic_name__		:		Dict	   =     logits
						if patient_counter == self.patience:
							break
					__magic_name__		:		List[Any]	   =     [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. '				,						snake_case				,						)
class       _snake_case					(     snake_case				):







			def __init__(				self						,   _a		):
				super().__init__(_a		)
				__magic_name__		:		Optional[Any]	   =     config.num_labels

				__magic_name__		:		Optional[int]	   =     BertModelWithPabee(_a		)
				__magic_name__		:		Union[str, Any]	   =     nn.Dropout(config.hidden_dropout_prob		)
				__magic_name__		:		List[str]	   =     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 			SCREAMING_SNAKE_CASE    (				self						,   _a=None						,   _a=None						,   _a=None						,   _a=None						,   _a=None						,   _a=None						,   _a=None						,   ):
				__magic_name__		:		Optional[Any]	   =     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						,   )

				__magic_name__		:		Optional[Any]	   =     (logits[-1],)

				if labels is not None:
					__magic_name__		:		str	   =     None
					__magic_name__		:		str	   =     0
					for ix, logits_item in enumerate(_a		):
						if self.num_labels == 1:
							#  We are doing regression
							__magic_name__		:		List[Any]	   =     MSELoss()
							__magic_name__		:		Optional[int]	   =     loss_fct(logits_item.view(-1		)						,   labels.view(-1		)		)
						else:
							__magic_name__		:		List[str]	   =     CrossEntropyLoss()
							__magic_name__		:		Dict	   =     loss_fct(logits_item.view(-1						,   self.num_labels		)						,   labels.view(-1		)		)
						if total_loss is None:
							__magic_name__		:		List[Any]	   =     loss
						else:
							total_loss += loss * (ix + 1)
						total_weights += ix + 1
					__magic_name__		:		List[str]	   =     (total_loss / total_weights,) + outputs

				return outputs


snake_case :		Optional[int]							      =  "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"






def   lowerCAmelCase_						(					_snake_case      :  bytes   )				->		bytes:







	'''simple docstring'''


	if not isinstance(_snake_case      ,   _snake_case   ):
		__magic_name__		:		Tuple	   =     F'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
		raise TypeError(_snake_case   )

	__magic_name__		:		Optional[int]	   =     "".join(bin(_snake_case   )[2:].zfill(8   ) for byte in data   )

	__magic_name__		:		List[Any]	   =     len(_snake_case   ) % 6 != 0

	if padding_needed:
		# The padding that will be added later
		__magic_name__		:		List[str]	   =     B"=" * ((6 - len(_snake_case   ) % 6) // 2)

		# Append binary_stream with arbitrary binary digits (0's by default) to make its
		# length a multiple of 6.
		binary_stream += "0" * (6 - len(_snake_case   ) % 6)
	else:
		__magic_name__		:		List[str]	   =     B""

	# Encode every 6 binary digits to their corresponding Base64 character
	return (
	    "".join(
	        B64_CHARSET[int(binary_stream[index : index + 6]      ,   2   )]
	        for index in range(0      ,   len(_snake_case   )      ,   6   )   ).encode()
	    + padding
	)






def   lowerCAmelCase_						(					_snake_case      :  str   )				->		bytes:







	'''simple docstring'''


	if not isinstance(_snake_case      ,   _snake_case   ) and not isinstance(_snake_case      ,   _snake_case   ):
		__magic_name__		:		List[str]	   =     (
		    "argument should be a bytes-like object or ASCII string, "
		    F'''not \'{encoded_data.__class__.__name__}\''''
		)
		raise TypeError(_snake_case   )

	# In case encoded_data is a bytes-like object, make sure it contains only
	# ASCII characters so we convert it to a string object
	if isinstance(_snake_case      ,   _snake_case   ):
		try:
			__magic_name__		:		List[Any]	   =     encoded_data.decode("utf-8"   )
		except UnicodeDecodeError:
			raise ValueError("base64 encoded data should only contain ASCII characters"   )

	__magic_name__		:		List[str]	   =     encoded_data.count("="   )

	# Check if the encoded string contains non base64 characters
	if padding:
		assert all(
		    char in B64_CHARSET for char in encoded_data[:-padding]   ), "Invalid base64 character(s) found."
	else:
		assert all(
		    char in B64_CHARSET for char in encoded_data   ), "Invalid base64 character(s) found."

	# Check the padding
	assert len(_snake_case   ) % 4 == 0 and padding < 3, "Incorrect padding"

	if padding:
		# Remove padding if there is one
		__magic_name__		:		Optional[int]	   =     encoded_data[:-padding]

		__magic_name__		:		Dict	   =     "".join(
		    bin(B64_CHARSET.index(_snake_case   )   )[2:].zfill(6   ) for char in encoded_data   )[: -padding * 2]
	else:
		__magic_name__		:		Union[str, Any]	   =     "".join(
		    bin(B64_CHARSET.index(_snake_case   )   )[2:].zfill(6   ) for char in encoded_data   )

	__magic_name__		:		List[Any]	   =     [
	    int(binary_stream[index : index + 8]      ,   2   )
	    for index in range(0      ,   len(_snake_case   )      ,   8   )
	]

	return bytes(_snake_case   )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
