


'''simple docstring'''

import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
	import torch

if is_soundfile_availble():
	import soundfile as sf

if is_vision_available():
	from PIL import Image

def 							__magic_name__(						lowerCamelCase=""):
						__lowerCAmelCase							       =				tempfile.mkdtemp()
						return os.path.join(__lowerCAmelCase,				str(uuid.uuida()) + suffix)


@require_soundfile
@require_torch
class       a__				( unittest.TestCase      ):







				"""simple docstring"""



				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				torch.rand(12       ,			dtype=torch.floataa		) - 0.5
										__lowerCAmelCase							       =				AgentAudio(lowerCamelCase__		)
										__lowerCAmelCase							       =				str(agent_type.to_string()		)

										# Ensure that the tensor and the agent_type's tensor are the same
										self.assertTrue(torch.allclose(lowerCamelCase__       ,			agent_type.to_raw()       ,			atol=1e-4		)		)

										del agent_type

										# Ensure the path remains even after the object deletion
										self.assertTrue(os.path.exists(lowerCamelCase__		)		)

										# Ensure that the file contains the same value as the original tensor
										__lowerCAmelCase							       =				sf.read(lowerCamelCase__		)
										self.assertTrue(torch.allclose(lowerCamelCase__       ,			torch.tensor(lowerCamelCase__		)       ,			atol=1e-4		)		)




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				torch.rand(12       ,			dtype=torch.floataa		) - 0.5
										__lowerCAmelCase							       =				get_new_path(suffix='''.wav'''		)
										sf.write(lowerCamelCase__       ,			lowerCamelCase__       ,			1_60_00		)

										__lowerCAmelCase							       =				AgentAudio(lowerCamelCase__		)

										self.assertTrue(torch.allclose(lowerCamelCase__       ,			agent_type.to_raw()       ,			atol=1e-4		)		)
										self.assertEqual(agent_type.to_string()       ,			lowerCamelCase__		)


@require_vision
@require_torch
class       a__				( unittest.TestCase      ):







				"""simple docstring"""



				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				torch.randint(0       ,			2_56       ,			(64, 64, 3)		)
										__lowerCAmelCase							       =				AgentImage(lowerCamelCase__		)
										__lowerCAmelCase							       =				str(agent_type.to_string()		)

										# Ensure that the tensor and the agent_type's tensor are the same
										self.assertTrue(torch.allclose(lowerCamelCase__       ,			agent_type._tensor       ,			atol=1e-4		)		)

										self.assertIsInstance(agent_type.to_raw()       ,			Image.Image		)

										# Ensure the path remains even after the object deletion
										del agent_type
										self.assertTrue(os.path.exists(lowerCamelCase__		)		)




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				Path(get_tests_dir('''fixtures/tests_samples/COCO'''		)		) / "000000039769.png"
										__lowerCAmelCase							       =				Image.open(lowerCamelCase__		)
										__lowerCAmelCase							       =				AgentImage(lowerCamelCase__		)

										self.assertTrue(path.samefile(agent_type.to_string()		)		)
										self.assertTrue(image == agent_type.to_raw()		)

										# Ensure the path remains even after the object deletion
										del agent_type
										self.assertTrue(os.path.exists(lowerCamelCase__		)		)




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				Path(get_tests_dir('''fixtures/tests_samples/COCO'''		)		) / "000000039769.png"
										__lowerCAmelCase							       =				Image.open(lowerCamelCase__		)
										__lowerCAmelCase							       =				AgentImage(lowerCamelCase__		)

										self.assertFalse(path.samefile(agent_type.to_string()		)		)
										self.assertTrue(image == agent_type.to_raw()		)

										# Ensure the path remains even after the object deletion
										del agent_type
										self.assertTrue(os.path.exists(lowerCamelCase__		)		)


class       a__				( unittest.TestCase      ):







				"""simple docstring"""



				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				"Hey!"
										__lowerCAmelCase							       =				AgentText(lowerCamelCase__		)

										self.assertEqual(lowerCamelCase__       ,			agent_type.to_string()		)
										self.assertEqual(lowerCamelCase__       ,			agent_type.to_raw()		)
										self.assertEqual(lowerCamelCase__       ,			lowerCamelCase__		)





'''simple docstring'''

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				: Optional[Any]							    =				logging.getLogger(__name__)


class       a__				( __A      ):







				"""simple docstring"""



				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase=None       ,			__lowercase=None		):
										__lowerCAmelCase							       =				self.layer[current_layer](__lowercase       ,			__lowercase       ,			head_mask[current_layer]		)

										__lowerCAmelCase							       =				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.'							,						__A							,						)
class       a__				( __A      ):







				"""simple docstring"""



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

										__lowerCAmelCase							       =				BertEncoderWithPabee(__lowercase		)

										self.init_weights()
										__lowerCAmelCase							       =				0
										__lowerCAmelCase							       =				0
										__lowerCAmelCase							       =				0

										__lowerCAmelCase							       =				0




				def 					_snake_case  (self       ,			__lowercase		):
										__lowerCAmelCase							       =				threshold




				def 					_snake_case  (self       ,			__lowercase		):
										__lowerCAmelCase							       =				patience




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				0
										__lowerCAmelCase							       =				0




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				self.inference_layers_num / self.inference_instances_num
										__lowerCAmelCase							       =				(
										    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(__lowercase		)




				@add_start_docstrings_to_model_forward(__lowercase		)
				def 					_snake_case  (self       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=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:
																__lowerCAmelCase							       =				input_ids.size()
										elif inputs_embeds is not None:
																__lowerCAmelCase							       =				inputs_embeds.size()[:-1]
										else:
																raise ValueError('''You have to specify either input_ids or inputs_embeds'''		)

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

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

										# 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.
										__lowerCAmelCase							       =				self.get_extended_attention_mask(__lowercase       ,			__lowercase       ,			__lowercase		)

										# 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:
																__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase							       =				encoder_hidden_states.size()
																__lowerCAmelCase							       =				(encoder_batch_size, encoder_sequence_length)
																if encoder_attention_mask is None:
																						__lowerCAmelCase							       =				torch.ones(__lowercase       ,			device=__lowercase		)
																__lowerCAmelCase							       =				self.invert_attention_mask(__lowercase		)
										else:
																__lowerCAmelCase							       =				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]
										__lowerCAmelCase							       =				self.get_head_mask(__lowercase       ,			self.config.num_hidden_layers		)

										__lowerCAmelCase							       =				self.embeddings(
										    input_ids=__lowercase       ,			position_ids=__lowercase       ,			token_type_ids=__lowercase       ,			inputs_embeds=__lowercase		)
										__lowerCAmelCase							       =				embedding_output

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

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

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

																						__lowerCAmelCase							       =				logits
																						if patient_counter == self.patience:
																												break
																__lowerCAmelCase							       =				[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. '							,						__A							,						)
class       a__				( __A      ):







				"""simple docstring"""



				def __init__(self       ,			__lowercase		):
										super().__init__(__lowercase		)
										__lowerCAmelCase							       =				config.num_labels

										__lowerCAmelCase							       =				BertModelWithPabee(__lowercase		)
										__lowerCAmelCase							       =				nn.Dropout(config.hidden_dropout_prob		)
										__lowerCAmelCase							       =				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(__lowercase		)
				def 					_snake_case  (self       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase=None       ,			):
										__lowerCAmelCase							       =				self.bert(
										    input_ids=__lowercase       ,			attention_mask=__lowercase       ,			token_type_ids=__lowercase       ,			position_ids=__lowercase       ,			head_mask=__lowercase       ,			inputs_embeds=__lowercase       ,			output_dropout=self.dropout       ,			output_layers=self.classifiers       ,			regression=self.num_labels == 1       ,			)

										__lowerCAmelCase							       =				(logits[-1],)

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

										return outputs

