'''simple docstring'''

import copy
import logging
import random

import joblib
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup


_lowerCamelCase :				Any														= logging.getLogger(__name__)



def  _lowerCAmelCase				(				__a      )  ->      Tuple:
     '''simple docstring'''







     random.seed(__a      )
     np.random.seed(__a      )
     torch.manual_seed(__a      )
     torch.cuda.manual_seed_all(__a      )



def  _lowerCAmelCase				(				__a			,      __a			,      __a      )  ->      Union[str, Any]:
     '''simple docstring'''







     model.eval()
     _UpperCamelCase  :Any       =next(model.parameters()      ).device
     _UpperCamelCase  :Tuple       =1
     _UpperCamelCase  :Union[str, Any]       =torch.zeros((eval_batch_size, context_len)			,      dtype=torch.long			,      device=__a      )
     _UpperCamelCase  :Optional[Any]       =DataLoader(__a			,      shuffle=__a			,      batch_size=__a      )
     _UpperCamelCase  :List[Any]       =torch.zeros(1			,      device=__a      )
     _UpperCamelCase  :Any       =0
     for batch in eval_dataloader:
          batch.to(__a      )
          # pad
          context.zero_()
          for i in range(__a      ):
               _UpperCamelCase  :Tuple       =batch[i]
          _UpperCamelCase  :Dict       =model(__a			,      labels=__a      )
          eval_loss += outputs[0].sum().item()
          nb_eval_examples += batch.size(0      )
     _UpperCamelCase  :Dict       =eval_loss / nb_eval_examples
     _UpperCamelCase  :Any       =torch.exp(__a      )
     model.train()
     return perplexity



def  _lowerCAmelCase				(				__a="gpt2"      )  ->      int:
     '''simple docstring'''







     _UpperCamelCase  :Tuple       =GPTaLMHeadModel.from_pretrained(__a			,      output_hidden_states=__a      )
     torch.save(model.state_dict()			,      model_name + """local.pt"""      )
     return model



def  _lowerCAmelCase				(				__a			,      __a			,      __a      )  ->      List[Any]:
     '''simple docstring'''







     _UpperCamelCase  :Any       =copy.deepcopy(__a      )
     model.to(__a      )

     _UpperCamelCase  :List[Any]       =["""bias""", """LayerNorm.weight"""]
     _UpperCamelCase  :Dict       =[
         {
             """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay      )],
             """weight_decay""": 0.0,
         },
         {"""params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay      )], """weight_decay""": 0.0},
     ]
     _UpperCamelCase  :List[str]       =AdamW(__a			,      lr=5e-5			,      eps=1e-8      )
     _UpperCamelCase  :List[Any]       =get_linear_schedule_with_warmup(__a			,      0			,      __a      )
     torch.cuda.empty_cache()
     return model, lm_optimizer, lm_scheduler



def  _lowerCAmelCase				(				__a			,      __a			,      __a			,      __a      )  ->      Tuple:
     '''simple docstring'''







     _UpperCamelCase  :List[str]       =np.array(__a      ).mean()
     _UpperCamelCase  :Dict       =np.array(__a      ).std()
     _UpperCamelCase  :Tuple       =(real_perps - avg) / std
     _UpperCamelCase  :Optional[Any]       =list(zip(__a			,      __a			,      __a      )      )
     joblib.dump(__a			,      __a      )



def  _lowerCAmelCase				(				__a			,      __a			,      __a			,      __a			,      __a			,      __a			,      __a			,      __a="dev.jbl"			,      __a=recopy_gpta			,      )  ->      List[Any]:
     '''simple docstring'''







     _UpperCamelCase  :List[Any]       =[]
     _UpperCamelCase  :List[str]       =[]
     _UpperCamelCase  :Dict       =[]

     # Initialize the transformer model
     _UpperCamelCase  :Any       =copy.deepcopy(__a      )
     orig_model.to(device="""cpu"""      )
     torch.cuda.empty_cache()

     # Compute perplexity of initial transformer model for comparison
     model.train()
     _UpperCamelCase       ,      _UpperCamelCase       ,      _UpperCamelCase  :Optional[Any]       =recopy_model(__a			,      __a			,      __a      )

     for step in tqdm(range(__a      )      ):
          _UpperCamelCase  :List[Any]       =torch.zeros((1, context_len)			,      dtype=torch.long			,      device=__a      )
          _UpperCamelCase  :str       =random.choice(__a      )
          _UpperCamelCase  :List[Any]       =random.randint(0			,      len(story[0]      ) - context_len - 1      )
          _UpperCamelCase  :str       =story[0][start : start + context_len]
          lm_optimizer.zero_grad()
          _UpperCamelCase  :Optional[Any]       =model(__a			,      labels=__a      )
          _UpperCamelCase  :Union[str, Any]       =outputs[0]
          _UpperCamelCase  :Optional[Any]       =compute_perplexity(__a			,      __a			,      __a      )
          model.train()
          lm_loss.backward()
          # Do LM backprop
          torch.nn.utils.clip_grad_norm_(model.parameters()			,      3.0      )
          lm_optimizer.step()
          lm_scheduler.step()  # Update learning rate schedule

          # Compute perplexity after back-propagating on the selected context
          _UpperCamelCase  :str       =compute_perplexity(__a			,      __a			,      __a      )

          # Periodically save the stored (X, IG(X)) pairs
          if step % 10_00 == 0 and step > 1:
               intermittent_save(__a			,      __a			,      __a			,      __a      )

          # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
          _UpperCamelCase       ,      _UpperCamelCase       ,      _UpperCamelCase  :Dict       =recopy_model(__a			,      __a			,      __a      )

          past_perps.append(past_perp.item()      )
          real_perps.append(orig_perp - real_perp.item()      )
          contexts.append(np.array(context.cpu()      )      )

     intermittent_save(__a			,      __a			,      __a			,      __a      )



def  _lowerCAmelCase				(				__a			,      __a="data/tokenized_stories_train_wikitext103.jbl"			,      __a=1_00			,      __a=10_26			,      __a=True      )  ->      Optional[int]:
     '''simple docstring'''







     _UpperCamelCase  :Dict       =joblib.load(__a      )
     print("""data loaded"""      )
     _UpperCamelCase  :List[str]       =[]
     if trim:
          for i, example in enumerate(__a      ):
               if len(example[0]      ) > min_len:
                    _UpperCamelCase  :Optional[int]       =random.randint(0			,      len(example[0]      ) - context_len - 1      )
                    objective_set.append(example[0, start : start + context_len]      )
               if len(__a      ) >= number:
                    break
          _UpperCamelCase  :str       =[]
          for j in range(i + 1			,      len(__a      )      ):
               if len(data[j][0]      ) > min_len:
                    train_data.append(data[j]      )
     else:
          _UpperCamelCase  :int       =data[0:number]
          _UpperCamelCase  :Tuple       =data[number:]

     joblib.dump(__a			,      """objective_set.jbl"""      )
     print("""objective set saved"""      )
     return train_data, objective_set



def  _lowerCAmelCase				(				__a			,      __a			,      __a			,      __a			,      __a=50			,      __a="secondary_learner.pt"      )  ->      int:
     '''simple docstring'''







     _UpperCamelCase  :List[str]       =torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""      )
     # We will use the first 512 pairs from our dataset as a test set for
     # our secondary learner and the rest to train
     _UpperCamelCase  :Any       =train_dataset[:5_12]
     _UpperCamelCase  :List[str]       =train_dataset[5_12:]
     _UpperCamelCase  :int       =DataLoader(__a			,      shuffle=__a			,      batch_size=__a      )
     _UpperCamelCase  :Tuple       =DataLoader(__a			,      shuffle=__a			,      batch_size=__a      )

     # secondary learner model set up
     _UpperCamelCase  :Any       =nn.MSELoss()
     _UpperCamelCase  :List[Any]       =nn.MSELoss(reduction="""sum"""      )
     secondary_learner.to(__a      )
     _UpperCamelCase  :int       =torch.optim.Adam(secondary_learner.parameters()			,      lr=0.0_00_01      )
     secondary_learner.train()

     # TODO in original code this is written as number of actual batches seen
     # not number of items seen but other places it is number of items instead.
     # improve consistency! changed this to epochs for clarity
     _UpperCamelCase  :Union[str, Any]       =float("""inf"""      )
     # Iterate through batches until we've used max_steps batches
     for epoch in range(int(__a      )      ):
          _UpperCamelCase  :int       =0.0
          secondary_learner.train()
          for step, batch in enumerate(__a      ):
               _UpperCamelCase  :List[Any]       =batch[0].to(__a      )
               _UpperCamelCase  :List[str]       =batch[1].to(__a      )
               _UpperCamelCase  :str       =secondary_learner(__a      )
               q_optimizer.zero_grad()
               _UpperCamelCase  :Tuple       =loss(__a			,      real_q.float()      )
               q_loss.backward()
               q_optimizer.step()
               tr_q_loss += q_loss.item()

               # model trains fairly quickly so we won't wait for a full epoch
               # eval is triggered at eval_freq and end of epochs
               if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(__a      )):
                    _UpperCamelCase  :List[Any]       =tr_q_loss / (step + 1)

                    secondary_learner.eval()
                    _UpperCamelCase  :str       =0.0
                    _UpperCamelCase  :List[Any]       =0.0
                    _UpperCamelCase  :Optional[int]       =[]
                    _UpperCamelCase  :str       =[]
                    # Compute performance of the secondary learner after this batch
                    for stepa, batcha in enumerate(__a      ):
                         _UpperCamelCase  :str       =batcha[0].to(__a      )
                         _UpperCamelCase  :List[Any]       =batcha[1].to(__a      )
                         _UpperCamelCase  :Tuple       =secondary_learner(__a      )
                         q_lossa += test_loss(__a			,      __a      ).item()
                         sum_qa += torch.sum(__a      ).item()
                         for ei, i in enumerate(predicted_qa.cpu().detach().numpy()      ):
                              predicted.append(i.item()      )
                         for ei, i in enumerate(real_qa.cpu().detach().numpy()      ):
                              actual.append(i.item()      )

                    q_lossa /= len(__a      )
                    print(
                        """Epoch: """			,      __a			,      """step: """			,      __a			,      """Avg. q:"""			,      sum_qa / len(__a      )			,      """Train Loss: """			,      __a			,      """Test Loss: """			,      __a			,      )
                    if q_lossa < best_test_loss:
                         joblib.dump((predicted, actual)			,      """pred_vs_actual.jbl"""      )
                         torch.save(secondary_learner.state_dict()			,      __a      )
                         _UpperCamelCase  :List[str]       =q_lossa

               secondary_learner.train()
     return secondary_learner




class    lowerCamelCase__  (   nn.Module     ):



   def __init__(    self					,      lowerCAmelCase__     )				->     Tuple:
        """simple docstring"""





        super(lowerCAmelCase__					,      self     ).__init__()
        _UpperCamelCase  :Optional[Any]       =model.transformer.wte
        _UpperCamelCase  :Any       =copy.deepcopy(model.transformer.wte.weight     )
        _UpperCamelCase  :int       =nn.Convad(self.embeddings.weight.size(1     )					,      256					,      3					,      padding=1     )
        _UpperCamelCase  :int       =nn.Sequential(nn.Linear(256					,      32     )					,      nn.Dropout(p=0.1     )					,      nn.Linear(32					,      32     )					,      nn.Linear(32					,      1     )     )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__     )				->     Optional[int]:
        """simple docstring"""





        _UpperCamelCase  :Optional[Any]       =torch.max(self.conv(self.embeddings(lowerCAmelCase__     ).squeeze(1     ).transpose(1					,      2     )     )					,      2     )[0]
        _UpperCamelCase  :List[Any]       =self.fc(lowerCAmelCase__     )
        return qs.squeeze(1     )




   @classmethod
   def  _UpperCamelCase		(    cls					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     Optional[int]:
        """simple docstring"""





        _UpperCamelCase  :Optional[int]       =cls(lowerCAmelCase__     )  # this calls __init__
        _UpperCamelCase  :Optional[Any]       =torch.load(lowerCAmelCase__     )
        secondary_learner.load_state_dict(lowerCAmelCase__     )
        _UpperCamelCase  :Any       =model.transformer.wte
        _UpperCamelCase  :Tuple       =copy.deepcopy(model.transformer.wte.weight     )
        return secondary_learner
"""simple docstring"""




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, 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, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
				from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import PoolFormerImageProcessor
class      UpperCamelCase__(      __A			):





						def 					snake_case__						(							self  )       ->					int:
													A__						  =				self.config_class(**self.inputs_dict  )
													self.parent.assertTrue(hasattr(__UpperCAmelCase		,'hidden_sizes'  )  )
													self.parent.assertTrue(hasattr(__UpperCAmelCase		,'num_encoder_blocks'  )  )






class      UpperCamelCase__:





						def __init__(							self		,__UpperCAmelCase		,__UpperCAmelCase=13		,__UpperCAmelCase=64		,__UpperCAmelCase=3		,__UpperCAmelCase=4		,__UpperCAmelCase=[2, 2, 2, 2]		,__UpperCAmelCase=[8, 4, 2, 1]		,__UpperCAmelCase=[16, 32, 64, 1_28]		,__UpperCAmelCase=[1, 4, 8, 16]		,__UpperCAmelCase=False		,__UpperCAmelCase=True		,__UpperCAmelCase="gelu"		,__UpperCAmelCase=0.1		,__UpperCAmelCase=0.0_2		,__UpperCAmelCase=3		,__UpperCAmelCase=None		,)       ->					Tuple:
													A__						  =				parent
													A__						  =				batch_size
													A__						  =				image_size
													A__						  =				num_channels
													A__						  =				num_encoder_blocks
													A__						  =				sr_ratios
													A__						  =				depths
													A__						  =				hidden_sizes
													A__						  =				downsampling_rates
													A__						  =				is_training
													A__						  =				use_labels
													A__						  =				hidden_act
													A__						  =				hidden_dropout_prob
													A__						  =				initializer_range
													A__						  =				num_labels
													A__						  =				scope





						def 					snake_case__						(							self  )       ->					Tuple:
													A__						  =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

													A__						  =				PoolFormerConfig(
													    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		,hidden_act=self.hidden_act		,hidden_dropout_prob=self.hidden_dropout_prob		,initializer_range=self.initializer_range		,)

													return config, pixel_values, labels





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					Union[str, Any]:
													A__						  =				PoolFormerModel(config=__UpperCAmelCase  )
													model.to(__UpperCAmelCase  )
													model.eval()
													A__						  =				model(__UpperCAmelCase  )
													A__						  =				A__						  =				self.image_size // 3_2.0
													self.parent.assertEqual(
													    result.last_hidden_state.shape		,(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)  )





						def 					snake_case__						(							self  )       ->					Any:
													A__						  =				self.prepare_config_and_inputs()
													A__  ,	A__  ,	A__						  =				config_and_inputs
													A__						  =				{'pixel_values': pixel_values}
													return config, inputs_dict






@require_torch
class      UpperCamelCase__(      __A		,    __A		,    unittest.TestCase			):
						lowerCAmelCase__	:    Optional[int]    				=		(PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
						lowerCAmelCase__	:    Optional[Any]    				=		(
						    {'feature-extraction': PoolFormerModel, 'image-classification': PoolFormerForImageClassification}
						    if is_torch_available()
						    else {}
						)

						lowerCAmelCase__	:    Any    				=		False
						lowerCAmelCase__	:    str    				=		False
						lowerCAmelCase__	:    Any    				=		False
						lowerCAmelCase__	:    str    				=		False
						lowerCAmelCase__	:    Optional[int]    				=		False





						def 					snake_case__						(							self  )       ->					Any:
													A__						  =				PoolFormerModelTester(self  )
													A__						  =				PoolFormerConfigTester(self		,config_class=__UpperCAmelCase  )





						def 					snake_case__						(							self  )       ->					int:
													self.config_tester.run_common_tests()





						def 					snake_case__						(							self  )       ->					Tuple:
													A__						  =				self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*__UpperCAmelCase  )





						@unittest.skip('PoolFormer does not use inputs_embeds'  )
						def 					snake_case__						(							self  )       ->					int:
													pass





						@unittest.skip('PoolFormer does not have get_input_embeddings method and get_output_embeddings methods'  )
						def 					snake_case__						(							self  )       ->					List[str]:
													pass





						def 					snake_case__						(							self  )       ->					List[Any]:
													def check_hidden_states_output(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  ):
																				A__						  =				model_class(__UpperCAmelCase  )
																				model.to(__UpperCAmelCase  )
																				model.eval()

																				with torch.no_grad():
																											A__						  =				model(**self._prepare_for_class(__UpperCAmelCase		,__UpperCAmelCase  )  )

																				A__						  =				outputs.hidden_states

																				A__						  =				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,
																				    ]		,)

													A__  ,	A__						  =				self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				A__						  =				True
																				check_hidden_states_output(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )

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

																				check_hidden_states_output(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )





						def 					snake_case__						(							self  )       ->					Union[str, Any]:
													if not self.model_tester.is_training:
																				return

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

													for model_class in self.all_model_classes:
																				if model_class in get_values(__UpperCAmelCase  ):
																											continue
																				A__						  =				model_class(__UpperCAmelCase  )
																				model.to(__UpperCAmelCase  )
																				model.train()
																				A__						  =				self._prepare_for_class(__UpperCAmelCase		,__UpperCAmelCase		,return_labels=__UpperCAmelCase  )
																				A__						  =				model(**__UpperCAmelCase  ).loss
																				loss.backward()





						def 					snake_case__						(							self  )       ->					Union[str, Any]:
													A__  ,	A__						  =				self.model_tester.prepare_config_and_inputs_for_common()

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

																				A__						  =				['pixel_values']
																				self.assertListEqual(arg_names[:1]		,__UpperCAmelCase  )





						@slow
						def 					snake_case__						(							self  )       ->					Union[str, Any]:
													for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				A__						  =				PoolFormerModel.from_pretrained(__UpperCAmelCase  )
																				self.assertIsNotNone(__UpperCAmelCase  )






def 	UpperCAmelCase       (						):






							"""simple docstring"""





							A__						  =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
							return image


@require_torch
class      UpperCamelCase__(      unittest.TestCase			):





						@slow
						def 					snake_case__						(							self  )       ->					Union[str, Any]:
													A__						  =				PoolFormerImageProcessor()
													A__						  =				PoolFormerForImageClassification.from_pretrained('sail/poolformer_s12'  ).to(__UpperCAmelCase  )

													A__						  =				image_processor(images=prepare_img()		,return_tensors='pt'  ).to(__UpperCAmelCase  )

													# forward pass
													with torch.no_grad():
																				A__						  =				model(**__UpperCAmelCase  )

													# verify the logits
													A__						  =				torch.Size((1, 10_00)  )
													self.assertEqual(outputs.logits.shape		,__UpperCAmelCase  )

													A__						  =				torch.tensor([-0.6_1_1_3, 0.1_6_8_5, -0.0_4_9_2]  ).to(__UpperCAmelCase  )
													self.assertTrue(torch.allclose(outputs.logits[0, :3]		,__UpperCAmelCase		,atol=1e-4  )  )





