
'''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


UpperCamelCase_            =    logging.getLogger(__name__)


def 		_UpperCAmelCase			(					_lowerCamelCase  :    int						)      ->  List[str]:
  random.seed(_lowerCamelCase						)
  np.random.seed(_lowerCamelCase						)
  torch.manual_seed(_lowerCamelCase						)
  torch.cuda.manual_seed_all(_lowerCamelCase						)


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


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Optional[Any]="gpt2"						)      ->  str:
  _lowerCAmelCase    :					Any							=   GPTaLMHeadModel.from_pretrained(_lowerCamelCase					, output_hidden_states=_lowerCamelCase						)
  torch.save(model.state_dict()					, model_name + """local.pt"""						)
  return model


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Dict					, _lowerCamelCase  :    Union[str, Any]					, _lowerCamelCase  :    Tuple						)      ->  Optional[int]:
  _lowerCAmelCase    :					Any							=   copy.deepcopy(_lowerCamelCase						)
  model.to(_lowerCamelCase						)

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


def 		_UpperCAmelCase			(					_lowerCamelCase  :    List[Any]					, _lowerCamelCase  :    Optional[int]					, _lowerCamelCase  :    int					, _lowerCamelCase  :    Dict						)      ->  Optional[Any]:
  _lowerCAmelCase    :					int							=   np.array(_lowerCamelCase						).mean()
  _lowerCAmelCase    :					Union[str, Any]							=   np.array(_lowerCamelCase						).std()
  _lowerCAmelCase    :					Optional[Any]							=   (real_perps - avg) / std
  _lowerCAmelCase    :					List[Any]							=   list(zip(_lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase						)						)
  joblib.dump(_lowerCamelCase					, _lowerCamelCase						)


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Dict					, _lowerCamelCase  :    Optional[int]					, _lowerCamelCase  :    Dict					, _lowerCamelCase  :    str					, _lowerCamelCase  :    str					, _lowerCamelCase  :    Any					, _lowerCamelCase  :    Tuple					, _lowerCamelCase  :    Optional[Any]="dev.jbl"					, _lowerCamelCase  :    Optional[Any]=recopy_gpta					, )      ->  List[Any]:
  _lowerCAmelCase    :					str							=   []
  _lowerCAmelCase    :					List[str]							=   []
  _lowerCAmelCase    :					Any							=   []

  # Initialize the transformer model
  _lowerCAmelCase    :					int							=   copy.deepcopy(_lowerCamelCase						)
  orig_model.to(device="""cpu"""						)
  torch.cuda.empty_cache()

  # Compute perplexity of initial transformer model for comparison
  model.train()
  _lowerCAmelCase    :					Tuple							=   recopy_model(_lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase						)

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

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

    # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
    _lowerCAmelCase    :					List[str]							=   recopy_model(_lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase						)

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

  intermittent_save(_lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase						)


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Tuple					, _lowerCamelCase  :    Tuple="data/tokenized_stories_train_wikitext103.jbl"					, _lowerCamelCase  :    int=1_00					, _lowerCamelCase  :    Any=10_26					, _lowerCamelCase  :    List[str]=True						)      ->  Union[str, Any]:
  _lowerCAmelCase    :					int							=   joblib.load(_lowerCamelCase						)
  print("""data loaded"""						)
  _lowerCAmelCase    :					List[Any]							=   []
  if trim:
    for i, example in enumerate(_lowerCamelCase						):
      if len(example[0]						) > min_len:
        _lowerCAmelCase    :					Dict							=   random.randint(0					, len(example[0]						) - context_len - 1						)
        objective_set.append(example[0, start : start + context_len]						)
      if len(_lowerCamelCase						) >= number:
        break
    _lowerCAmelCase    :					Any							=   []
    for j in range(i + 1					, len(_lowerCamelCase						)						):
      if len(data[j][0]						) > min_len:
        train_data.append(data[j]						)
  else:
    _lowerCAmelCase    :					List[str]							=   data[0:number]
    _lowerCAmelCase    :					Optional[int]							=   data[number:]

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


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Optional[Any]					, _lowerCamelCase  :    List[str]					, _lowerCamelCase  :    Optional[int]					, _lowerCamelCase  :    str					, _lowerCamelCase  :    Any=50					, _lowerCamelCase  :    str="secondary_learner.pt"						)      ->  int:
  _lowerCAmelCase    :					Optional[int]							=   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
  _lowerCAmelCase    :					List[Any]							=   train_dataset[:5_12]
  _lowerCAmelCase    :					List[str]							=   train_dataset[5_12:]
  _lowerCAmelCase    :					List[Any]							=   DataLoader(_lowerCamelCase					, shuffle=_lowerCamelCase					, batch_size=_lowerCamelCase						)
  _lowerCAmelCase    :					List[Any]							=   DataLoader(_lowerCamelCase					, shuffle=_lowerCamelCase					, batch_size=_lowerCamelCase						)

  # secondary learner model set up
  _lowerCAmelCase    :					int							=   nn.MSELoss()
  _lowerCAmelCase    :					int							=   nn.MSELoss(reduction="""sum"""						)
  secondary_learner.to(_lowerCamelCase						)
  _lowerCAmelCase    :					Optional[Any]							=   torch.optim.Adam(secondary_learner.parameters()					, lr=0.0_0001						)
  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
  _lowerCAmelCase    :					str							=   float("""inf"""						)
  # Iterate through batches until we've used max_steps batches
  for epoch in range(int(_lowerCamelCase						)						):
    _lowerCAmelCase    :					Optional[int]							=   0.0
    secondary_learner.train()
    for step, batch in enumerate(_lowerCamelCase						):
      _lowerCAmelCase    :					List[str]							=   batch[0].to(_lowerCamelCase						)
      _lowerCAmelCase    :					str							=   batch[1].to(_lowerCamelCase						)
      _lowerCAmelCase    :					Any							=   secondary_learner(_lowerCamelCase						)
      q_optimizer.zero_grad()
      _lowerCAmelCase    :					Optional[Any]							=   loss(_lowerCamelCase					, 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(_lowerCamelCase						)):
        _lowerCAmelCase    :					int							=   tr_q_loss / (step + 1)

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

      secondary_learner.train()
  return secondary_learner



class 					a_					(nn.Module      ):




       def __init__(       self				,				snake_case_					):
         super(snake_case_				,				self					).__init__()
         _lowerCAmelCase    :					Union[str, Any]							=   model.transformer.wte
         _lowerCAmelCase    :					Union[str, Any]							=   copy.deepcopy(model.transformer.wte.weight					)
         _lowerCAmelCase    :					Optional[int]							=   nn.Convad(self.embeddings.weight.size(1					)				,				2_5_6				,				3				,				padding=1					)
         _lowerCAmelCase    :					Dict							=   nn.Sequential(nn.Linear(2_5_6				,				3_2					)				,				nn.Dropout(p=0.1					)				,				nn.Linear(3_2				,				3_2					)				,				nn.Linear(3_2				,				1					)					)




       def 							__UpperCamelCase    (       self				,				snake_case_					):
         _lowerCAmelCase    :					Tuple							=   torch.max(self.conv(self.embeddings(snake_case_					).squeeze(1					).transpose(1				,				2					)					)				,				2					)[0]
         _lowerCAmelCase    :					List[str]							=   self.fc(snake_case_					)
         return qs.squeeze(1					)







       @classmethod
       def 							__UpperCamelCase    (       cls				,				snake_case_				,				snake_case_					):
         _lowerCAmelCase    :					List[str]							=   cls(snake_case_					)  # this calls __init__
         _lowerCAmelCase    :					Union[str, Any]							=   torch.load(snake_case_					)
         secondary_learner.load_state_dict(snake_case_					)
         _lowerCAmelCase    :					int							=   model.transformer.wte
         _lowerCAmelCase    :					Tuple							=   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


UpperCamelCase_            =    logging.get_logger(__name__)

UpperCamelCase_            =    {
    """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 					a_					(_a      ):
       __lowerCAmelCase      :			List[str]      = """mobilevit"""




       def __init__(       self				,				snake_case_=3				,				snake_case_=2_5_6				,				snake_case_=2				,				snake_case_=[1_4_4, 1_9_2, 2_4_0]				,				snake_case_=[1_6, 3_2, 6_4, 9_6, 1_2_8, 1_6_0, 6_4_0]				,				snake_case_=4				,				snake_case_=2.0				,				snake_case_=4.0				,				snake_case_="silu"				,				snake_case_=3				,				snake_case_=3_2				,				snake_case_=0.1				,				snake_case_=0.0				,				snake_case_=0.1				,				snake_case_=0.02				,				snake_case_=1E-5				,				snake_case_=True				,				snake_case_=2_5_6				,				snake_case_=[6, 1_2, 1_8]				,				snake_case_=0.1				,				snake_case_=2_5_5				,				**snake_case_				,				):
         super().__init__(**snake_case_					)

         _lowerCAmelCase    :					Optional[Any]							=   num_channels
         _lowerCAmelCase    :					Optional[int]							=   image_size
         _lowerCAmelCase    :					Union[str, Any]							=   patch_size
         _lowerCAmelCase    :					Any							=   hidden_sizes
         _lowerCAmelCase    :					List[Any]							=   neck_hidden_sizes
         _lowerCAmelCase    :					Any							=   num_attention_heads
         _lowerCAmelCase    :					List[Any]							=   mlp_ratio
         _lowerCAmelCase    :					Optional[Any]							=   expand_ratio
         _lowerCAmelCase    :					List[Any]							=   hidden_act
         _lowerCAmelCase    :					Dict							=   conv_kernel_size
         _lowerCAmelCase    :					Tuple							=   output_stride
         _lowerCAmelCase    :					Dict							=   hidden_dropout_prob
         _lowerCAmelCase    :					Tuple							=   attention_probs_dropout_prob
         _lowerCAmelCase    :					Optional[int]							=   classifier_dropout_prob
         _lowerCAmelCase    :					int							=   initializer_range
         _lowerCAmelCase    :					List[Any]							=   layer_norm_eps
         _lowerCAmelCase    :					List[Any]							=   qkv_bias

         # decode head attributes for semantic segmentation
         _lowerCAmelCase    :					str							=   aspp_out_channels
         _lowerCAmelCase    :					Optional[Any]							=   atrous_rates
         _lowerCAmelCase    :					Any							=   aspp_dropout_prob
         _lowerCAmelCase    :					Union[str, Any]							=   semantic_loss_ignore_index



class 					a_					(_a      ):
       __lowerCAmelCase      :			Optional[int]      = version.parse("""1.11"""      )




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




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







       @property
       def 							__UpperCamelCase    (       self					):
         return 1E-4


