





import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
				from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import GLPNImageProcessor







class 	_UpperCamelCase	(							__lowerCamelCase	):







					'''simple docstring'''

					def 							SCREAMING_SNAKE_CASE				(    self     :	Dict		):






						'''simple docstring'''







						UpperCAmelCase_		    =  self.config_class(**self.inputs_dict		)
						self.parent.assertTrue(hasattr(UpperCamelCase_			,       """hidden_sizes"""		)		)
						self.parent.assertTrue(hasattr(UpperCamelCase_			,       """num_attention_heads"""		)		)
						self.parent.assertTrue(hasattr(UpperCamelCase_			,       """num_encoder_blocks"""		)		)







class 	_UpperCamelCase	:







					'''simple docstring'''

					def __init__(    self     :	Optional[Any]			,       __lowercase     :	List[Any]			,       __lowercase     :	str=13			,       __lowercase     :	Optional[Any]=64			,       __lowercase     :	Optional[int]=3			,       __lowercase     :	Dict=4			,       __lowercase     :	Optional[Any]=[2, 2, 2, 2]			,       __lowercase     :	str=[8, 4, 2, 1]			,       __lowercase     :	List[Any]=[16, 32, 64, 1_28]			,       __lowercase     :	Dict=[1, 4, 8, 16]			,       __lowercase     :	List[str]=[1, 2, 4, 8]			,       __lowercase     :	Optional[int]=True			,       __lowercase     :	Optional[int]=True			,       __lowercase     :	str="gelu"			,       __lowercase     :	List[str]=0.1			,       __lowercase     :	int=0.1			,       __lowercase     :	Union[str, Any]=0.02			,       __lowercase     :	str=16			,       __lowercase     :	int=3			,       __lowercase     :	Union[str, Any]=None			,       ):






						'''simple docstring'''







						UpperCAmelCase_		    =  parent
						UpperCAmelCase_		    =  batch_size
						UpperCAmelCase_		    =  image_size
						UpperCAmelCase_		    =  num_channels
						UpperCAmelCase_		    =  num_encoder_blocks
						UpperCAmelCase_		    =  sr_ratios
						UpperCAmelCase_		    =  depths
						UpperCAmelCase_		    =  hidden_sizes
						UpperCAmelCase_		    =  downsampling_rates
						UpperCAmelCase_		    =  num_attention_heads
						UpperCAmelCase_		    =  is_training
						UpperCAmelCase_		    =  use_labels
						UpperCAmelCase_		    =  hidden_act
						UpperCAmelCase_		    =  hidden_dropout_prob
						UpperCAmelCase_		    =  attention_probs_dropout_prob
						UpperCAmelCase_		    =  initializer_range
						UpperCAmelCase_		    =  decoder_hidden_size
						UpperCAmelCase_		    =  num_labels
						UpperCAmelCase_		    =  scope





					def 							SCREAMING_SNAKE_CASE				(    self     :	int		):






						'''simple docstring'''







						UpperCAmelCase_		    =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

						UpperCAmelCase_		    =  None
						if self.use_labels:
							UpperCAmelCase_		    =  ids_tensor([self.batch_size, self.image_size, self.image_size]			,       self.num_labels		)

						UpperCAmelCase_		    =  self.get_config()
						return config, pixel_values, labels





					def 							SCREAMING_SNAKE_CASE				(    self     :	Dict		):






						'''simple docstring'''







						return GLPNConfig(
						    image_size=self.image_size			,       num_channels=self.num_channels			,       num_encoder_blocks=self.num_encoder_blocks			,       depths=self.depths			,       hidden_sizes=self.hidden_sizes			,       num_attention_heads=self.num_attention_heads			,       hidden_act=self.hidden_act			,       hidden_dropout_prob=self.hidden_dropout_prob			,       attention_probs_dropout_prob=self.attention_probs_dropout_prob			,       initializer_range=self.initializer_range			,       decoder_hidden_size=self.decoder_hidden_size			,       )





					def 							SCREAMING_SNAKE_CASE				(    self     :	int			,       __lowercase     :	List[str]			,       __lowercase     :	Optional[int]			,       __lowercase     :	int		):






						'''simple docstring'''







						UpperCAmelCase_		    =  GLPNModel(config=UpperCamelCase_		)
						model.to(UpperCamelCase_		)
						model.eval()
						UpperCAmelCase_		    =  model(UpperCamelCase_		)
						UpperCAmelCase_		    =  UpperCAmelCase_		    =  self.image_size // (self.downsampling_rates[-1] * 2)
						self.parent.assertEqual(
						    result.last_hidden_state.shape			,       (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	Union[str, Any]			,       __lowercase     :	Optional[int]			,       __lowercase     :	List[str]			,       __lowercase     :	Optional[int]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  self.num_labels
						UpperCAmelCase_		    =  GLPNForDepthEstimation(UpperCamelCase_		)
						model.to(UpperCamelCase_		)
						model.eval()
						UpperCAmelCase_		    =  model(UpperCamelCase_		)
						self.parent.assertEqual(result.predicted_depth.shape			,       (self.batch_size, self.image_size, self.image_size)		)
						UpperCAmelCase_		    =  model(UpperCamelCase_			,       labels=UpperCamelCase_		)
						self.parent.assertEqual(result.predicted_depth.shape			,       (self.batch_size, self.image_size, self.image_size)		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	str		):






						'''simple docstring'''







						UpperCAmelCase_		    =  self.prepare_config_and_inputs()
						UpperCAmelCase_    ,						UpperCAmelCase_    ,						UpperCAmelCase_		    =  config_and_inputs
						UpperCAmelCase_		    =  {"""pixel_values""": pixel_values}
						return config, inputs_dict







@require_torch
class 	_UpperCamelCase	(							__lowerCamelCase    ,							__lowerCamelCase    ,							unittest.TestCase	):







					'''simple docstring'''

					lowerCamelCase  :  Any           =							(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
					lowerCamelCase  :  int           =							(
					    {'depth-estimation': GLPNForDepthEstimation, 'feature-extraction': GLPNModel} if is_torch_available() else {}
					)

					lowerCamelCase  :  str           =							False
					lowerCamelCase  :  Union[str, Any]           =							False
					lowerCamelCase  :  Tuple           =							False





					def 							SCREAMING_SNAKE_CASE				(    self     :	Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  GLPNModelTester(self		)
						UpperCAmelCase_		    =  GLPNConfigTester(self			,       config_class=UpperCamelCase_		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	Dict		):






						'''simple docstring'''







						self.config_tester.run_common_tests()





					def 							SCREAMING_SNAKE_CASE				(    self     :	Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*UpperCamelCase_		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	Union[str, Any]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_depth_estimation(*UpperCamelCase_		)





					@unittest.skip("""GLPN does not use inputs_embeds"""		)
					def 							SCREAMING_SNAKE_CASE				(    self     :	Optional[Any]		):






						'''simple docstring'''







						pass





					@unittest.skip("""GLPN does not have get_input_embeddings method and get_output_embeddings methods"""		)
					def 							SCREAMING_SNAKE_CASE				(    self     :	Union[str, Any]		):






						'''simple docstring'''







						pass





					def 							SCREAMING_SNAKE_CASE				(    self     :	Union[str, Any]		):






						'''simple docstring'''







						UpperCAmelCase_    ,						UpperCAmelCase_		    =  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							UpperCAmelCase_		    =  model_class(UpperCamelCase_		)
							UpperCAmelCase_		    =  inspect.signature(model.forward		)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							UpperCAmelCase_		    =  [*signature.parameters.keys()]

							UpperCAmelCase_		    =  ["""pixel_values"""]
							self.assertListEqual(arg_names[:1]			,       UpperCamelCase_		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	Tuple		):






						'''simple docstring'''







						UpperCAmelCase_    ,						UpperCAmelCase_		    =  self.model_tester.prepare_config_and_inputs_for_common()
						UpperCAmelCase_		    =  True

						for model_class in self.all_model_classes:
							UpperCAmelCase_		    =  True
							UpperCAmelCase_		    =  False
							UpperCAmelCase_		    =  True
							UpperCAmelCase_		    =  model_class(UpperCamelCase_		)
							model.to(UpperCamelCase_		)
							model.eval()
							with torch.no_grad():
								UpperCAmelCase_		    =  model(**self._prepare_for_class(UpperCamelCase_			,       UpperCamelCase_		)		)
							UpperCAmelCase_		    =  outputs.attentions

							UpperCAmelCase_		    =  sum(self.model_tester.depths		)
							self.assertEqual(len(UpperCamelCase_		)			,       UpperCamelCase_		)

							# check that output_attentions also work using config
							del inputs_dict["output_attentions"]
							UpperCAmelCase_		    =  True
							UpperCAmelCase_		    =  model_class(UpperCamelCase_		)
							model.to(UpperCamelCase_		)
							model.eval()
							with torch.no_grad():
								UpperCAmelCase_		    =  model(**self._prepare_for_class(UpperCamelCase_			,       UpperCamelCase_		)		)
							UpperCAmelCase_		    =  outputs.attentions

							self.assertEqual(len(UpperCamelCase_		)			,       UpperCamelCase_		)

							# verify the first attentions (first block, first layer)
							UpperCAmelCase_		    =  (self.model_tester.image_size // 4) ** 2
							UpperCAmelCase_		    =  (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
							self.assertListEqual(
							    list(attentions[0].shape[-3:]		)			,       [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]			,       )

							# verify the last attentions (last block, last layer)
							UpperCAmelCase_		    =  (self.model_tester.image_size // 32) ** 2
							UpperCAmelCase_		    =  (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
							self.assertListEqual(
							    list(attentions[-1].shape[-3:]		)			,       [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]			,       )
							UpperCAmelCase_		    =  len(UpperCamelCase_		)

							# Check attention is always last and order is fine
							UpperCAmelCase_		    =  True
							UpperCAmelCase_		    =  True
							UpperCAmelCase_		    =  model_class(UpperCamelCase_		)
							model.to(UpperCamelCase_		)
							model.eval()
							with torch.no_grad():
								UpperCAmelCase_		    =  model(**self._prepare_for_class(UpperCamelCase_			,       UpperCamelCase_		)		)

							self.assertEqual(out_len + 1			,       len(UpperCamelCase_		)		)

							UpperCAmelCase_		    =  outputs.attentions

							self.assertEqual(len(UpperCamelCase_		)			,       UpperCamelCase_		)
							# verify the first attentions (first block, first layer)
							UpperCAmelCase_		    =  (self.model_tester.image_size // 4) ** 2
							UpperCAmelCase_		    =  (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
							self.assertListEqual(
							    list(self_attentions[0].shape[-3:]		)			,       [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]			,       )





					def 							SCREAMING_SNAKE_CASE				(    self     :	List[Any]		):






						'''simple docstring'''







						def check_hidden_states_output(__lowercase     :	List[str]			,       __lowercase     :	Any			,       __lowercase     :	List[str]		):
							UpperCAmelCase_		    =  model_class(UpperCamelCase_		)
							model.to(UpperCamelCase_		)
							model.eval()

							with torch.no_grad():
								UpperCAmelCase_		    =  model(**self._prepare_for_class(UpperCamelCase_			,       UpperCamelCase_		)		)

							UpperCAmelCase_		    =  outputs.hidden_states

							UpperCAmelCase_		    =  self.model_tester.num_encoder_blocks
							self.assertEqual(len(UpperCamelCase_		)			,       UpperCamelCase_		)

							# verify the first hidden states (first block)
							self.assertListEqual(
							    list(hidden_states[0].shape[-3:]		)			,       [
							        self.model_tester.hidden_sizes[0],
							        self.model_tester.image_size // 4,
							        self.model_tester.image_size // 4,
							    ]			,       )

						UpperCAmelCase_    ,						UpperCAmelCase_		    =  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							UpperCAmelCase_		    =  True
							check_hidden_states_output(UpperCamelCase_			,       UpperCamelCase_			,       UpperCamelCase_		)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							UpperCAmelCase_		    =  True

							check_hidden_states_output(UpperCamelCase_			,       UpperCamelCase_			,       UpperCamelCase_		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	Dict		):






						'''simple docstring'''







						if not self.model_tester.is_training:
							return

						UpperCAmelCase_    ,						UpperCAmelCase_		    =  self.model_tester.prepare_config_and_inputs_for_common()
						UpperCAmelCase_		    =  True

						for model_class in self.all_model_classes:
							if model_class in get_values(UpperCamelCase_		):
								continue
							# TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
							# this can then be incorporated into _prepare_for_class in test_modeling_common.py
							if model_class.__name__ == "GLPNForDepthEstimation":
								UpperCAmelCase_    ,						UpperCAmelCase_    ,						UpperCAmelCase_    ,						UpperCAmelCase_		    =  inputs_dict["""pixel_values"""].shape
								UpperCAmelCase_		    =  torch.zeros(
								    [self.model_tester.batch_size, height, width]			,       device=UpperCamelCase_		).long()
							UpperCAmelCase_		    =  model_class(UpperCamelCase_		)
							model.to(UpperCamelCase_		)
							model.train()
							UpperCAmelCase_		    =  self._prepare_for_class(UpperCamelCase_			,       UpperCamelCase_			,       return_labels=UpperCamelCase_		)
							UpperCAmelCase_		    =  model(**UpperCamelCase_		).loss
							loss.backward()





					@slow
					def 							SCREAMING_SNAKE_CASE				(    self     :	Tuple		):






						'''simple docstring'''







						for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							UpperCAmelCase_		    =  GLPNModel.from_pretrained(UpperCamelCase_		)
							self.assertIsNotNone(UpperCamelCase_		)




def  A_(      ):
	UpperCAmelCase_		    =  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
	return image







@require_torch
@require_vision
@slow
class 	_UpperCamelCase	(							unittest.TestCase	):







					'''simple docstring'''

					@slow
					def 							SCREAMING_SNAKE_CASE				(    self     :	Union[str, Any]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
						UpperCAmelCase_		    =  GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(UpperCamelCase_		)

						UpperCAmelCase_		    =  prepare_img()
						UpperCAmelCase_		    =  image_processor(images=UpperCamelCase_			,       return_tensors="""pt"""		).to(UpperCamelCase_		)

						# forward pass
						with torch.no_grad():
							UpperCAmelCase_		    =  model(**UpperCamelCase_		)

						# verify the predicted depth
						UpperCAmelCase_		    =  torch.Size([1, 4_80, 6_40]		)
						self.assertEqual(outputs.predicted_depth.shape			,       UpperCamelCase_		)

						UpperCAmelCase_		    =  torch.tensor(
						    [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]		).to(UpperCamelCase_		)

						self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]			,       UpperCamelCase_			,       atol=1e-4		)		)








# Copyright (c) 2019 Uber Technologies, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import csv
import json
import math
import time

import numpy as np
import torch
import torch.optim as optim
import torch.utils.data as data
from nltk.tokenize.treebank import TreebankWordDetokenizer
from pplm_classification_head import ClassificationHead
from torch import nn
from torchtext import data as torchtext_data
from torchtext import datasets
from tqdm import tqdm, trange

from transformers import GPTaLMHeadModel, GPTaTokenizer


torch.manual_seed(0)
np.random.seed(0)
UpperCAmelCase_   							=      1E-10
UpperCAmelCase_   							=      'This is incredible! I love it, this is the best chicken I have ever had.'
UpperCAmelCase_   							=      100





class        lowerCamelCase__(   nn.Module):
      def __init__(							self:					Optional[Any]       ,			UpperCamelCase_:					int       ,			UpperCamelCase_:					Any="gpt2-medium"       ,			UpperCamelCase_:					Any=False       ,			UpperCamelCase_:					List[Any]="cpu"  ):
             super().__init__()
             __lowerCamelCase  =		GPTaTokenizer.from_pretrained(UpperCamelCase_  )
             __lowerCamelCase  =		GPTaLMHeadModel.from_pretrained(UpperCamelCase_  )
             __lowerCamelCase  =		self.encoder.transformer.config.hidden_size
             __lowerCamelCase  =		ClassificationHead(class_size=UpperCamelCase_       ,			embed_size=self.embed_size  )
             __lowerCamelCase  =		cached_mode
             __lowerCamelCase  =		device
      def      lowerCAmelCase__		(							self:					List[Any]  ):
             return self.classifier_head
      def      lowerCAmelCase__		(							self:					int  ):
             for param in self.encoder.parameters():
                    __lowerCamelCase  =		False
             self.classifier_head.train()
      def      lowerCAmelCase__		(							self:					Optional[Any]       ,			UpperCamelCase_:					Optional[int]  ):
             __lowerCamelCase  =		x.ne(0  ).unsqueeze(2  ).repeat(1       ,			1       ,			self.embed_size  ).float().to(self.device  ).detach()
             __lowerCamelCase  =		self.encoder.transformer(UpperCamelCase_  )["""last_hidden_state"""]
             __lowerCamelCase  =		hidden * mask
             __lowerCamelCase  =		torch.sum(UpperCamelCase_       ,			dim=1  ) / (torch.sum(UpperCamelCase_       ,			dim=1  ).detach() + EPSILON)
             return avg_hidden




      def      lowerCAmelCase__		(							self:					Dict       ,			UpperCamelCase_:					Optional[int]  ):
             if self.cached_mode:
                    __lowerCamelCase  =		x.to(self.device  )
             else:
                    __lowerCamelCase  =		self.avg_representation(x.to(self.device  )  )

             __lowerCamelCase  =		self.classifier_head(UpperCamelCase_  )
             __lowerCamelCase  =		nn.functional.log_softmax(UpperCamelCase_       ,			dim=-1  )

             return probs





class        lowerCamelCase__(   data.Dataset):
      def __init__(							self:					List[Any]       ,			UpperCamelCase_:					Optional[Any]       ,			UpperCamelCase_:					Optional[int]  ):
             __lowerCamelCase  =		X
             __lowerCamelCase  =		y
      def __len__(							self:					Dict  ):
             return len(self.X  )




      def __getitem__(							self:					Dict       ,			UpperCamelCase_:					Dict  ):
             __lowerCamelCase  =		{}
             __lowerCamelCase  =		self.X[index]
             __lowerCamelCase  =		self.y[index]
             return data




def    lowerCamelCase__			(		A__			:   List[Any]      ):





       '''simple docstring'''



       def pad_sequences(A__			:   List[str]      ):
              __lowerCamelCase  =		[len(A__      ) for seq in sequences]

              __lowerCamelCase  =		torch.zeros(len(A__      )    ,     max(A__      )      ).long()  # padding value = 0

              for i, seq in enumerate(A__      ):
                     __lowerCamelCase  =		lengths[i]
                     __lowerCamelCase  =		seq[:end]

              return padded_sequences, lengths

       __lowerCamelCase  =		{}
       for key in data[0].keys():
              __lowerCamelCase  =		[d[key] for d in data]

       __lowerCamelCase,  __lowerCamelCase  =		pad_sequences(item_info["""X"""]      )
       __lowerCamelCase  =		torch.tensor(item_info["""y"""]    ,     dtype=torch.long      )

       return x_batch, y_batch




def    lowerCamelCase__			(		A__			:   Optional[int]      ):





       '''simple docstring'''



       __lowerCamelCase  =		{}
       for key in data[0].keys():
              __lowerCamelCase  =		[d[key] for d in data]

       __lowerCamelCase  =		torch.cat(item_info["""X"""]    ,     0      )
       __lowerCamelCase  =		torch.tensor(item_info["""y"""]    ,     dtype=torch.long      )

       return x_batch, y_batch




def    lowerCamelCase__			(		A__			:   List[Any]    ,     A__			:   Optional[Any]    ,     A__			:   List[str]    ,     A__			:   int=0    ,     A__			:   Optional[Any]=10    ,     A__			:   List[str]="cpu"      ):





       '''simple docstring'''



       __lowerCamelCase  =		0
       discriminator.train_custom()
       for batch_idx, (input_t, target_t) in enumerate(A__      ):
              __lowerCamelCase,  __lowerCamelCase  =		input_t.to(A__      ), target_t.to(A__      )

              optimizer.zero_grad()

              __lowerCamelCase  =		discriminator(A__      )
              __lowerCamelCase  =		nn.functional.nll_loss(A__    ,     A__      )
              loss.backward(retain_graph=A__      )
              optimizer.step()

              samples_so_far += len(A__      )

              if batch_idx % log_interval == 0:
                     print(
                         """Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}""".format(
                             epoch + 1    ,     A__    ,     len(data_loader.dataset      )    ,     100 * samples_so_far / len(data_loader.dataset      )    ,     loss.item()    ,     )      )




def    lowerCamelCase__			(		A__			:   Tuple    ,     A__			:   str    ,     A__			:   int="cpu"      ):





       '''simple docstring'''



       discriminator.eval()
       __lowerCamelCase  =		0
       __lowerCamelCase  =		0
       with torch.no_grad():
              for input_t, target_t in data_loader:
                     __lowerCamelCase,  __lowerCamelCase  =		input_t.to(A__      ), target_t.to(A__      )
                     __lowerCamelCase  =		discriminator(A__      )
                     # sum up batch loss
                     test_loss += nn.functional.nll_loss(A__    ,     A__    ,     reduction="""sum"""      ).item()
                     # get the index of the max log-probability
                     __lowerCamelCase  =		output_t.argmax(dim=1    ,     keepdim=A__      )
                     correct += pred_t.eq(target_t.view_as(A__      )      ).sum().item()

       test_loss /= len(data_loader.dataset      )

       print(
           """Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)""".format(
               A__    ,     A__    ,     len(data_loader.dataset      )    ,     100.0 * correct / len(data_loader.dataset      )      )      )




def    lowerCamelCase__			(		A__			:   Tuple    ,     A__			:   str    ,     A__			:   str    ,     A__			:   List[str]=False    ,     A__			:   Optional[Any]="cpu"      ):





       '''simple docstring'''



       __lowerCamelCase  =		model.tokenizer.encode(A__      )
       __lowerCamelCase  =		torch.tensor([input_t]    ,     dtype=torch.long    ,     device=A__      )
       if cached:
              __lowerCamelCase  =		model.avg_representation(A__      )

       __lowerCamelCase  =		model(A__      ).data.cpu().numpy().flatten().tolist()
       print("""Input sentence:"""    ,     A__      )
       print(
           """Predictions:"""    ,     """, """.join("""{}: {:.4f}""".format(A__    ,     math.exp(A__      )      ) for c, log_prob in zip(A__    ,     A__      )      )    ,     )




def    lowerCamelCase__			(		A__			:   Tuple    ,     A__			:   Any    ,     A__			:   Tuple    ,     A__			:   List[str]=False    ,     A__			:   Union[str, Any]="cpu"      ):





       '''simple docstring'''



       __lowerCamelCase  =		torch.utils.data.DataLoader(dataset=A__    ,     batch_size=A__    ,     collate_fn=A__      )

       __lowerCamelCase  =		[]
       __lowerCamelCase  =		[]
       for batch_idx, (x, y) in enumerate(tqdm(A__    ,     ascii=A__      )      ):
              with torch.no_grad():
                     __lowerCamelCase  =		x.to(A__      )
                     __lowerCamelCase  =		discriminator.avg_representation(A__      ).cpu().detach()
                     __lowerCamelCase  =		torch.unbind(avg_rep.unsqueeze(1      )      )
                     xs += avg_rep_list
                     ys += y.cpu().numpy().tolist()

       __lowerCamelCase  =		torch.utils.data.DataLoader(
           dataset=Dataset(A__    ,     A__      )    ,     batch_size=A__    ,     shuffle=A__    ,     collate_fn=A__      )

       return data_loader




def    lowerCamelCase__			(		A__			:   List[Any]    ,     A__			:   Union[str, Any]=None    ,     A__			:   List[str]="gpt2-medium"    ,     A__			:   Union[str, Any]=10    ,     A__			:   str=64    ,     A__			:   str=10    ,     A__			:   Tuple=False    ,     A__			:   Optional[int]=False    ,     A__			:   str=False    ,     ):





       '''simple docstring'''



       __lowerCamelCase  =		"""cuda""" if torch.cuda.is_available() and not no_cuda else """cpu"""

       print("""Preprocessing {} dataset...""".format(A__      )      )
       __lowerCamelCase  =		time.time()

       if dataset == "SST":
              __lowerCamelCase  =		["""positive""", """negative""", """very positive""", """very negative""", """neutral"""]
              __lowerCamelCase  =		{c: i for i, c in enumerate(A__      )}

              __lowerCamelCase  =		Discriminator(
                  class_size=len(A__      )    ,     pretrained_model=A__    ,     cached_mode=A__    ,     device=A__      ).to(A__      )

              __lowerCamelCase  =		torchtext_data.Field()
              __lowerCamelCase  =		torchtext_data.Field(sequential=A__      )
              __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase  =		datasets.SST.splits(
                  A__    ,     A__    ,     fine_grained=A__    ,     train_subtrees=A__    ,     )

              __lowerCamelCase  =		[]
              __lowerCamelCase  =		[]
              for i in trange(len(A__      )    ,     ascii=A__      ):
                     __lowerCamelCase  =		TreebankWordDetokenizer().detokenize(vars(train_data[i]      )["""text"""]      )
                     __lowerCamelCase  =		discriminator.tokenizer.encode(A__      )
                     __lowerCamelCase  =		torch.tensor([50256] + seq    ,     device=A__    ,     dtype=torch.long      )
                     x.append(A__      )
                     y.append(classaidx[vars(train_data[i]      )["""label"""]]      )
              __lowerCamelCase  =		Dataset(A__    ,     A__      )

              __lowerCamelCase  =		[]
              __lowerCamelCase  =		[]
              for i in trange(len(A__      )    ,     ascii=A__      ):
                     __lowerCamelCase  =		TreebankWordDetokenizer().detokenize(vars(test_data[i]      )["""text"""]      )
                     __lowerCamelCase  =		discriminator.tokenizer.encode(A__      )
                     __lowerCamelCase  =		torch.tensor([50256] + seq    ,     device=A__    ,     dtype=torch.long      )
                     test_x.append(A__      )
                     test_y.append(classaidx[vars(test_data[i]      )["""label"""]]      )
              __lowerCamelCase  =		Dataset(A__    ,     A__      )

              __lowerCamelCase  =		{
                  """class_size""": len(A__      ),
                  """embed_size""": discriminator.embed_size,
                  """pretrained_model""": pretrained_model,
                  """class_vocab""": classaidx,
                  """default_class""": 2,
              }

       elif dataset == "clickbait":
              __lowerCamelCase  =		["""non_clickbait""", """clickbait"""]
              __lowerCamelCase  =		{c: i for i, c in enumerate(A__      )}

              __lowerCamelCase  =		Discriminator(
                  class_size=len(A__      )    ,     pretrained_model=A__    ,     cached_mode=A__    ,     device=A__      ).to(A__      )

              with open("""datasets/clickbait/clickbait_train_prefix.txt"""      ) as f:
                     __lowerCamelCase  =		[]
                     for i, line in enumerate(A__      ):
                            try:
                                   data.append(eval(A__      )      )
                            except Exception:
                                   print("""Error evaluating line {}: {}""".format(A__    ,     A__      )      )
                                   continue
              __lowerCamelCase  =		[]
              __lowerCamelCase  =		[]
              with open("""datasets/clickbait/clickbait_train_prefix.txt"""      ) as f:
                     for i, line in enumerate(tqdm(A__    ,     ascii=A__      )      ):
                            try:
                                   __lowerCamelCase  =		eval(A__      )
                                   __lowerCamelCase  =		discriminator.tokenizer.encode(d["""text"""]      )

                                   if len(A__      ) < max_length_seq:
                                          __lowerCamelCase  =		torch.tensor([50256] + seq    ,     device=A__    ,     dtype=torch.long      )
                                   else:
                                          print("""Line {} is longer than maximum length {}""".format(A__    ,     A__      )      )
                                          continue
                                   x.append(A__      )
                                   y.append(d["""label"""]      )
                            except Exception:
                                   print("""Error evaluating / tokenizing line {}, skipping it""".format(A__      )      )
                                   pass

              __lowerCamelCase  =		Dataset(A__    ,     A__      )
              __lowerCamelCase  =		int(0.9 * len(A__      )      )
              __lowerCamelCase  =		len(A__      ) - train_size
              __lowerCamelCase,  __lowerCamelCase  =		torch.utils.data.random_split(A__    ,     [train_size, test_size]      )

              __lowerCamelCase  =		{
                  """class_size""": len(A__      ),
                  """embed_size""": discriminator.embed_size,
                  """pretrained_model""": pretrained_model,
                  """class_vocab""": classaidx,
                  """default_class""": 1,
              }

       elif dataset == "toxic":
              __lowerCamelCase  =		["""non_toxic""", """toxic"""]
              __lowerCamelCase  =		{c: i for i, c in enumerate(A__      )}

              __lowerCamelCase  =		Discriminator(
                  class_size=len(A__      )    ,     pretrained_model=A__    ,     cached_mode=A__    ,     device=A__      ).to(A__      )

              __lowerCamelCase  =		[]
              __lowerCamelCase  =		[]
              with open("""datasets/toxic/toxic_train.txt"""      ) as f:
                     for i, line in enumerate(tqdm(A__    ,     ascii=A__      )      ):
                            try:
                                   __lowerCamelCase  =		eval(A__      )
                                   __lowerCamelCase  =		discriminator.tokenizer.encode(d["""text"""]      )

                                   if len(A__      ) < max_length_seq:
                                          __lowerCamelCase  =		torch.tensor([50256] + seq    ,     device=A__    ,     dtype=torch.long      )
                                   else:
                                          print("""Line {} is longer than maximum length {}""".format(A__    ,     A__      )      )
                                          continue
                                   x.append(A__      )
                                   y.append(int(np.sum(d["""label"""]      ) > 0      )      )
                            except Exception:
                                   print("""Error evaluating / tokenizing line {}, skipping it""".format(A__      )      )
                                   pass

              __lowerCamelCase  =		Dataset(A__    ,     A__      )
              __lowerCamelCase  =		int(0.9 * len(A__      )      )
              __lowerCamelCase  =		len(A__      ) - train_size
              __lowerCamelCase,  __lowerCamelCase  =		torch.utils.data.random_split(A__    ,     [train_size, test_size]      )

              __lowerCamelCase  =		{
                  """class_size""": len(A__      ),
                  """embed_size""": discriminator.embed_size,
                  """pretrained_model""": pretrained_model,
                  """class_vocab""": classaidx,
                  """default_class""": 0,
              }

       else:  # if dataset == "generic":
              # This assumes the input dataset is a TSV with the following structure:
              # class \t text

              if dataset_fp is None:
                     raise ValueError("""When generic dataset is selected, dataset_fp needs to be specified aswell."""      )

              __lowerCamelCase  =		set()
              with open(A__      ) as f:
                     __lowerCamelCase  =		csv.reader(A__    ,     delimiter="""\t"""      )
                     for row in tqdm(A__    ,     ascii=A__      ):
                            if row:
                                   classes.add(row[0]      )

              __lowerCamelCase  =		sorted(A__      )
              __lowerCamelCase  =		{c: i for i, c in enumerate(A__      )}

              __lowerCamelCase  =		Discriminator(
                  class_size=len(A__      )    ,     pretrained_model=A__    ,     cached_mode=A__    ,     device=A__      ).to(A__      )

              __lowerCamelCase  =		[]
              __lowerCamelCase  =		[]
              with open(A__      ) as f:
                     __lowerCamelCase  =		csv.reader(A__    ,     delimiter="""\t"""      )
                     for i, row in enumerate(tqdm(A__    ,     ascii=A__      )      ):
                            if row:
                                   __lowerCamelCase  =		row[0]
                                   __lowerCamelCase  =		row[1]

                                   try:
                                          __lowerCamelCase  =		discriminator.tokenizer.encode(A__      )
                                          if len(A__      ) < max_length_seq:
                                                 __lowerCamelCase  =		torch.tensor([50256] + seq    ,     device=A__    ,     dtype=torch.long      )

                                          else:
                                                 print("""Line {} is longer than maximum length {}""".format(A__    ,     A__      )      )
                                                 continue

                                          x.append(A__      )
                                          y.append(classaidx[label]      )

                                   except Exception:
                                          print("""Error tokenizing line {}, skipping it""".format(A__      )      )
                                          pass

              __lowerCamelCase  =		Dataset(A__    ,     A__      )
              __lowerCamelCase  =		int(0.9 * len(A__      )      )
              __lowerCamelCase  =		len(A__      ) - train_size
              __lowerCamelCase,  __lowerCamelCase  =		torch.utils.data.random_split(A__    ,     [train_size, test_size]      )

              __lowerCamelCase  =		{
                  """class_size""": len(A__      ),
                  """embed_size""": discriminator.embed_size,
                  """pretrained_model""": pretrained_model,
                  """class_vocab""": classaidx,
                  """default_class""": 0,
              }

       __lowerCamelCase  =		time.time()
       print("""Preprocessed {} data points""".format(len(A__      ) + len(A__      )      )      )
       print("""Data preprocessing took: {:.3f}s""".format(end - start      )      )

       if cached:
              print("""Building representation cache..."""      )

              __lowerCamelCase  =		time.time()

              __lowerCamelCase  =		get_cached_data_loader(A__    ,     A__    ,     A__    ,     shuffle=A__    ,     device=A__      )

              __lowerCamelCase  =		get_cached_data_loader(A__    ,     A__    ,     A__    ,     device=A__      )

              __lowerCamelCase  =		time.time()
              print("""Building representation cache took: {:.3f}s""".format(end - start      )      )

       else:
              __lowerCamelCase  =		torch.utils.data.DataLoader(
                  dataset=A__    ,     batch_size=A__    ,     shuffle=A__    ,     collate_fn=A__      )
              __lowerCamelCase  =		torch.utils.data.DataLoader(dataset=A__    ,     batch_size=A__    ,     collate_fn=A__      )

       if save_model:
              with open("""{}_classifier_head_meta.json""".format(A__      )    ,     """w"""      ) as meta_file:
                     json.dump(A__    ,     A__      )

       __lowerCamelCase  =		optim.Adam(discriminator.parameters()    ,     lr=0.0_001      )

       for epoch in range(A__      ):
              __lowerCamelCase  =		time.time()
              print("""\nEpoch"""    ,     epoch + 1      )

              train_epoch(
                  discriminator=A__    ,     data_loader=A__    ,     optimizer=A__    ,     epoch=A__    ,     log_interval=A__    ,     device=A__    ,     )
              evaluate_performance(data_loader=A__    ,     discriminator=A__    ,     device=A__      )

              __lowerCamelCase  =		time.time()
              print("""Epoch took: {:.3f}s""".format(end - start      )      )

              print("""\nExample prediction"""      )
              predict(A__    ,     A__    ,     A__    ,     cached=A__    ,     device=A__      )

              if save_model:
                     # torch.save(discriminator.state_dict(),
                     #           "{}_discriminator_{}.pt".format(
                     #               args.dataset, epoch + 1
                     #               ))
                     torch.save(
                         discriminator.get_classifier().state_dict()    ,     """{}_classifier_head_epoch_{}.pt""".format(A__    ,     epoch + 1      )    ,     )


if __name__ == "__main__":
       UpperCAmelCase_   							=      argparse.ArgumentParser(description='Train a discriminator on top of GPT-2 representations')
       parser.add_argument(
           '--dataset',
           type=str,
           default='SST',
           choices=('SST', 'clickbait', 'toxic', 'generic'),
           help=(
               'dataset to train the discriminator on.'
               'In case of generic, the dataset is expected'
               'to be a TSBV file with structure: class \\t text'
           ),
       )
       parser.add_argument(
           '--dataset_fp',
           type=str,
           default='',
           help='File path of the dataset to use. Needed only in case of generic datadset',
       )
       parser.add_argument(
           '--pretrained_model', type=str, default='gpt2-medium', help='Pretrained model to use as encoder'
       )
       parser.add_argument('--epochs', type=int, default=10, metavar='N', help='Number of training epochs')
       parser.add_argument(
           '--batch_size', type=int, default=64, metavar='N', help='input batch size for training (default: 64)'
       )
       parser.add_argument(
           '--log_interval',
           type=int,
           default=10,
           metavar='N',
           help='how many batches to wait before logging training status',
       )
       parser.add_argument('--save_model', action='store_true', help='whether to save the model')
       parser.add_argument('--cached', action='store_true', help='whether to cache the input representations')
       parser.add_argument('--no_cuda', action='store_true', help='use to turn off cuda')
       UpperCAmelCase_   							=      parser.parse_args()

       train_discriminator(**(vars(args)))

