"""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


lowercase__   			=      logging.getLogger(__name__)







def 		__a  (      _SCREAMING_SNAKE_CASE  )      ->Any:
  random.seed(_SCREAMING_SNAKE_CASE  )
  np.random.seed(_SCREAMING_SNAKE_CASE  )
  torch.manual_seed(_SCREAMING_SNAKE_CASE  )
  torch.cuda.manual_seed_all(_SCREAMING_SNAKE_CASE  )







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







def 		__a  (      _SCREAMING_SNAKE_CASE="gpt2"  )      ->List[str]:
  a__:	List[Any]     				=      GPTaLMHeadModel.from_pretrained(_SCREAMING_SNAKE_CASE						,	output_hidden_states=_SCREAMING_SNAKE_CASE  )
  torch.save(model.state_dict()						,	model_name + 'local.pt'  )
  return model







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->Any:
  a__:	Optional[int]     				=      copy.deepcopy(_SCREAMING_SNAKE_CASE  )
  model.to(_SCREAMING_SNAKE_CASE  )

  a__:	Optional[int]     				=      ['bias', 'LayerNorm.weight']
  a__:	int     				=      [
      {
          '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},
  ]
  a__:	Any     				=      AdamW(_SCREAMING_SNAKE_CASE						,	lr=5e-5						,	eps=1e-8  )
  a__:	List[Any]     				=      get_linear_schedule_with_warmup(_SCREAMING_SNAKE_CASE						,	0						,	_SCREAMING_SNAKE_CASE  )
  torch.cuda.empty_cache()
  return model, lm_optimizer, lm_scheduler







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->Tuple:
  a__:	Dict     				=      np.array(_SCREAMING_SNAKE_CASE  ).mean()
  a__:	Optional[int]     				=      np.array(_SCREAMING_SNAKE_CASE  ).std()
  a__:	Optional[int]     				=      (real_perps - avg) / std
  a__:	int     				=      list(zip(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )  )
  joblib.dump(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE="dev.jbl"						,	_SCREAMING_SNAKE_CASE=recopy_gpta						,	)      ->Tuple:
  a__:	int     				=      []
  a__:	str     				=      []
  a__:	List[Any]     				=      []

  # Initialize the transformer model
  a__:	Optional[int]     				=      copy.deepcopy(_SCREAMING_SNAKE_CASE  )
  orig_model.to(device='cpu'  )
  torch.cuda.empty_cache()

  # Compute perplexity of initial transformer model for comparison
  model.train()
  a__				,       a__				,       a__:	Union[str, Any]     				=      recopy_model(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )

  for step in tqdm(range(_SCREAMING_SNAKE_CASE  )  ):
    a__:	Any     				=      torch.zeros((1, context_len)						,	dtype=torch.long						,	device=_SCREAMING_SNAKE_CASE  )
    a__:	Optional[int]     				=      random.choice(_SCREAMING_SNAKE_CASE  )
    a__:	Tuple     				=      random.randint(0						,	len(story[0]  ) - context_len - 1  )
    a__:	Optional[int]     				=      story[0][start : start + context_len]
    lm_optimizer.zero_grad()
    a__:	Dict     				=      model(_SCREAMING_SNAKE_CASE						,	labels=_SCREAMING_SNAKE_CASE  )
    a__:	List[Any]     				=      outputs[0]
    a__:	int     				=      compute_perplexity(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
    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
    a__:	str     				=      compute_perplexity(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )

    # Periodically save the stored (X, IG(X)) pairs
    if step % 1000 == 0 and step > 1:
      intermittent_save(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )

    # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
    a__				,       a__				,       a__:	Tuple     				=      recopy_model(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )

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

  intermittent_save(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE="data/tokenized_stories_train_wikitext103.jbl"						,	_SCREAMING_SNAKE_CASE=100						,	_SCREAMING_SNAKE_CASE=1026						,	_SCREAMING_SNAKE_CASE=True  )      ->Optional[Any]:
  a__:	Optional[int]     				=      joblib.load(_SCREAMING_SNAKE_CASE  )
  print('data loaded'  )
  a__:	List[Any]     				=      []
  if trim:
    for i, example in enumerate(_SCREAMING_SNAKE_CASE  ):
      if len(example[0]  ) > min_len:
        a__:	Optional[Any]     				=      random.randint(0						,	len(example[0]  ) - context_len - 1  )
        objective_set.append(example[0, start : start + context_len]  )
      if len(_SCREAMING_SNAKE_CASE  ) >= number:
        break
    a__:	int     				=      []
    for j in range(i + 1						,	len(_SCREAMING_SNAKE_CASE  )  ):
      if len(data[j][0]  ) > min_len:
        train_data.append(data[j]  )
  else:
    a__:	Tuple     				=      data[0:number]
    a__:	int     				=      data[number:]

  joblib.dump(_SCREAMING_SNAKE_CASE						,	'objective_set.jbl'  )
  print('objective set saved'  )
  return train_data, objective_set







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE=50						,	_SCREAMING_SNAKE_CASE="secondary_learner.pt"  )      ->Any:
  a__:	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
  a__:	Dict     				=      train_dataset[:512]
  a__:	Union[str, Any]     				=      train_dataset[512:]
  a__:	Optional[int]     				=      DataLoader(_SCREAMING_SNAKE_CASE						,	shuffle=_SCREAMING_SNAKE_CASE						,	batch_size=_SCREAMING_SNAKE_CASE  )
  a__:	str     				=      DataLoader(_SCREAMING_SNAKE_CASE						,	shuffle=_SCREAMING_SNAKE_CASE						,	batch_size=_SCREAMING_SNAKE_CASE  )

  # secondary learner model set up
  a__:	Tuple     				=      nn.MSELoss()
  a__:	Any     				=      nn.MSELoss(reduction='sum'  )
  secondary_learner.to(_SCREAMING_SNAKE_CASE  )
  a__:	List[Any]     				=      torch.optim.Adam(secondary_learner.parameters()						,	lr=0.00_001  )
  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
  a__:	Dict     				=      float('inf'  )
  # Iterate through batches until we've used max_steps batches
  for epoch in range(int(_SCREAMING_SNAKE_CASE  )  ):
    a__:	int     				=      0.0
    secondary_learner.train()
    for step, batch in enumerate(_SCREAMING_SNAKE_CASE  ):
      a__:	Any     				=      batch[0].to(_SCREAMING_SNAKE_CASE  )
      a__:	Any     				=      batch[1].to(_SCREAMING_SNAKE_CASE  )
      a__:	int     				=      secondary_learner(_SCREAMING_SNAKE_CASE  )
      q_optimizer.zero_grad()
      a__:	Tuple     				=      loss(_SCREAMING_SNAKE_CASE						,	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(_SCREAMING_SNAKE_CASE  )):
        a__:	Union[str, Any]     				=      tr_q_loss / (step + 1)

        secondary_learner.eval()
        a__:	Optional[int]     				=      0.0
        a__:	Tuple     				=      0.0
        a__:	List[Any]     				=      []
        a__:	List[str]     				=      []
        # Compute performance of the secondary learner after this batch
        for stepa, batcha in enumerate(_SCREAMING_SNAKE_CASE  ):
          a__:	Dict     				=      batcha[0].to(_SCREAMING_SNAKE_CASE  )
          a__:	int     				=      batcha[1].to(_SCREAMING_SNAKE_CASE  )
          a__:	str     				=      secondary_learner(_SCREAMING_SNAKE_CASE  )
          q_lossa += test_loss(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  ).item()
          sum_qa += torch.sum(_SCREAMING_SNAKE_CASE  ).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(_SCREAMING_SNAKE_CASE  )
        print(
            'Epoch: '						,	_SCREAMING_SNAKE_CASE						,	'step: '						,	_SCREAMING_SNAKE_CASE						,	'Avg. q:'						,	sum_qa / len(_SCREAMING_SNAKE_CASE  )						,	'Train Loss: '						,	_SCREAMING_SNAKE_CASE						,	'Test Loss: '						,	_SCREAMING_SNAKE_CASE						,	)
        if q_lossa < best_test_loss:
          joblib.dump((predicted, actual)						,	'pred_vs_actual.jbl'  )
          torch.save(secondary_learner.state_dict()						,	_SCREAMING_SNAKE_CASE  )
          a__:	Optional[int]     				=      q_lossa

      secondary_learner.train()
  return secondary_learner




class   __snake_case			(	nn.Module							):



      def __init__(		self       ,     lowercase)      ->     int:





        '''simple docstring'''





        super(lowercase       ,     self).__init__()
        a__:	int     				=      model.transformer.wte
        a__:	int     				=      copy.deepcopy(model.transformer.wte.weight)
        a__:	int     				=      nn.Convad(self.embeddings.weight.size(1)       ,     2_56       ,     3       ,     padding=1)
        a__:	Optional[int]     				=      nn.Sequential(nn.Linear(2_56       ,     32)       ,     nn.Dropout(p=0.1)       ,     nn.Linear(32       ,     32)       ,     nn.Linear(32       ,     1))



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Dict:





        '''simple docstring'''





        a__:	Union[str, Any]     				=      torch.max(self.conv(self.embeddings(lowercase).squeeze(1).transpose(1       ,     2))       ,     2)[0]
        a__:	List[str]     				=      self.fc(lowercase)
        return qs.squeeze(1)



      @classmethod
      def     lowerCamelCase_  (		cls       ,     lowercase       ,     lowercase)      ->     List[Any]:





        '''simple docstring'''





        a__:	Optional[Any]     				=      cls(lowercase)  # this calls __init__
        a__:	int     				=      torch.load(lowercase)
        secondary_learner.load_state_dict(lowercase)
        a__:	Optional[int]     				=      model.transformer.wte
        a__:	Any     				=      copy.deepcopy(model.transformer.wte.weight)
        return secondary_learner



"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase__    :    List[Any]			=      logging.get_logger(__name__)

lowerCAmelCase__    :    Dict			=      {
    'apple/mobilevit-small': 'https://huggingface.co/apple/mobilevit-small/resolve/main/config.json',
    'apple/mobilevit-x-small': 'https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json',
    'apple/mobilevit-xx-small': 'https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json',
    'apple/deeplabv3-mobilevit-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json'
    ),
    'apple/deeplabv3-mobilevit-x-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json'
    ),
    'apple/deeplabv3-mobilevit-xx-small': (
        'https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json'
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}
class 							snake_case			(					__UpperCAmelCase							):




					"""simple docstring"""

					snake_case__							       =					"mobilevit"







					def __init__(							self  :      Union[str, Any]       ,lowerCamelCase__  :      Any=3       ,lowerCamelCase__  :      str=256       ,lowerCamelCase__  :      Optional[int]=2       ,lowerCamelCase__  :      Union[str, Any]=[144, 192, 240]       ,lowerCamelCase__  :      Optional[int]=[16, 32, 64, 96, 128, 160, 640]       ,lowerCamelCase__  :      Dict=4       ,lowerCamelCase__  :      int=2.0       ,lowerCamelCase__  :      Optional[Any]=4.0       ,lowerCamelCase__  :      Optional[int]="silu"       ,lowerCamelCase__  :      List[str]=3       ,lowerCamelCase__  :      int=32       ,lowerCamelCase__  :      int=0.1       ,lowerCamelCase__  :      List[str]=0.0       ,lowerCamelCase__  :      List[str]=0.1       ,lowerCamelCase__  :      Tuple=0.0_2       ,lowerCamelCase__  :      Union[str, Any]=1e-5       ,lowerCamelCase__  :      Tuple=True       ,lowerCamelCase__  :      Dict=256       ,lowerCamelCase__  :      Any=[6, 12, 18]       ,lowerCamelCase__  :      Optional[int]=0.1       ,lowerCamelCase__  :      List[str]=255       ,**lowerCamelCase__  :      List[Any]       ,):
									super().__init__(**lowerCamelCase__     )

									UpperCAmelCase__			 =				num_channels
									UpperCAmelCase__			 =				image_size
									UpperCAmelCase__			 =				patch_size
									UpperCAmelCase__			 =				hidden_sizes
									UpperCAmelCase__			 =				neck_hidden_sizes
									UpperCAmelCase__			 =				num_attention_heads
									UpperCAmelCase__			 =				mlp_ratio
									UpperCAmelCase__			 =				expand_ratio
									UpperCAmelCase__			 =				hidden_act
									UpperCAmelCase__			 =				conv_kernel_size
									UpperCAmelCase__			 =				output_stride
									UpperCAmelCase__			 =				hidden_dropout_prob
									UpperCAmelCase__			 =				attention_probs_dropout_prob
									UpperCAmelCase__			 =				classifier_dropout_prob
									UpperCAmelCase__			 =				initializer_range
									UpperCAmelCase__			 =				layer_norm_eps
									UpperCAmelCase__			 =				qkv_bias

									# decode head attributes for semantic segmentation
									UpperCAmelCase__			 =				aspp_out_channels
									UpperCAmelCase__			 =				atrous_rates
									UpperCAmelCase__			 =				aspp_dropout_prob
									UpperCAmelCase__			 =				semantic_loss_ignore_index






class 							snake_case			(					__UpperCAmelCase							):




					"""simple docstring"""

					snake_case__							       =					version.parse("1.11"							)







					@property
					def 						__lowerCAmelCase		(							self  :      str     ):
									return OrderedDict([('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'})]     )







					@property
					def 						__lowerCAmelCase		(							self  :      Dict     ):
									if self.task == "image-classification":
													return OrderedDict([('logits', {0: 'batch'})]     )
									else:
													return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]     )







					@property
					def 						__lowerCAmelCase		(							self  :      str     ):
									return 1e-4



