

from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


if is_tf_available():
      import numpy as np
      import tensorflow as tf

      from transformers import (
          TF_MODEL_FOR_CAUSAL_LM_MAPPING,
          TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
          TF_MODEL_FOR_MASKED_LM_MAPPING,
          TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
          TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
          TF_MODEL_FOR_PRETRAINING_MAPPING,
          TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
          TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
          TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
          TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
          TFSharedEmbeddings,
      )

      if _tf_gpu_memory_limit is not None:
            _SCREAMING_SNAKE_CASE          = tf.config.list_physical_devices('''GPU''')
            for gpu in gpus:
                  # Restrict TensorFlow to only allocate x GB of memory on the GPUs
                  try:
                        tf.config.set_logical_device_configuration(
                            gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
                        )
                        _SCREAMING_SNAKE_CASE          = tf.config.list_logical_devices('''GPU''')
                        print('''Logical GPUs''', logical_gpus)
                  except RuntimeError as e:
                        # Virtual devices must be set before GPUs have been initialized
                        print(e)


@require_tf
class     lowerCamelCase_   :
 _lowerCAmelCase       		=  None
 _lowerCAmelCase       		=  ()
 _lowerCAmelCase       		=  ()
 _lowerCAmelCase       		=  True
 _lowerCAmelCase       		=  True
 _lowerCAmelCase       		=  True
 _lowerCAmelCase       		=  False




 def 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase=False       ):
   __UpperCAmelCase  :     Optional[int]		=    copy.deepcopy(_UpperCAmelCase       )

   if model_class in get_values(_UpperCAmelCase       ):
     __UpperCAmelCase  :     str		=    {
         k: tf.tile(tf.expand_dims(_UpperCAmelCase     ,     1       )     ,     (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)       )
         if isinstance(_UpperCAmelCase     ,     tf.Tensor       ) and v.ndim > 0
         else v
         for k, v in inputs_dict.items()
     }

   if return_labels:
     if model_class in get_values(_UpperCAmelCase       ):
       __UpperCAmelCase  :     Optional[Any]		=    tf.ones(self.model_tester.batch_size     ,     dtype=tf.intaa       )
     elif model_class in get_values(_UpperCAmelCase       ):
       __UpperCAmelCase  :     int		=    tf.zeros(self.model_tester.batch_size     ,     dtype=tf.intaa       )
       __UpperCAmelCase  :     Any		=    tf.zeros(self.model_tester.batch_size     ,     dtype=tf.intaa       )
     elif model_class in [
         *get_values(_UpperCAmelCase       ),
         *get_values(_UpperCAmelCase       ),
     ]:
       __UpperCAmelCase  :     Any		=    tf.zeros(self.model_tester.batch_size     ,     dtype=tf.intaa       )
     elif model_class in get_values(_UpperCAmelCase       ):
       __UpperCAmelCase  :     Dict		=    tf.zeros(self.model_tester.batch_size     ,     dtype=tf.intaa       )
     elif model_class in [
         *get_values(_UpperCAmelCase       ),
         *get_values(_UpperCAmelCase       ),
         *get_values(_UpperCAmelCase       ),
         *get_values(_UpperCAmelCase       ),
         *get_values(_UpperCAmelCase       ),
     ]:
       __UpperCAmelCase  :     Union[str, Any]		=    tf.zeros(
           (self.model_tester.batch_size, self.model_tester.seq_length)     ,     dtype=tf.intaa       )
   return inputs_dict




 @slow
 def 						a					( self       ):
   __UpperCAmelCase, __UpperCAmelCase  :     Optional[int]		=    self.model_tester.prepare_config_and_inputs_for_common()
   for model_class in self.all_model_classes[:2]:
     __UpperCAmelCase  :     Tuple		=    self._prepare_for_class(_UpperCAmelCase     ,     _UpperCAmelCase       )
     __UpperCAmelCase  :     Union[str, Any]		=    model_class(_UpperCAmelCase       )

     @tf.function
     def run_in_graph_mode():
       return model(_UpperCAmelCase       )

     __UpperCAmelCase  :     str		=    run_in_graph_mode()
     self.assertIsNotNone(_UpperCAmelCase       )




 @slow
 def 						a					( self       ):
   __UpperCAmelCase, __UpperCAmelCase  :     List[str]		=    self.model_tester.prepare_config_and_inputs_for_common()
   for model_class in self.all_model_classes[:2]:
     __UpperCAmelCase  :     List[str]		=    self._prepare_for_class(_UpperCAmelCase     ,     _UpperCAmelCase       )
     __UpperCAmelCase  :     Optional[int]		=    model_class(_UpperCAmelCase       )

     @tf.function(experimental_compile=_UpperCAmelCase       )
     def run_in_graph_mode():
       return model(_UpperCAmelCase       )

     __UpperCAmelCase  :     List[str]		=    run_in_graph_mode()
     self.assertIsNotNone(_UpperCAmelCase       )




 @slow
 def 						a					( self       ):
   # This is a copy of the test_keras_fit method, but we use XLA compilation instead of eager
   __UpperCAmelCase, __UpperCAmelCase  :     Union[str, Any]		=    self.model_tester.prepare_config_and_inputs_for_common()
   for model_class in self.all_model_classes[:2]:
     __UpperCAmelCase  :     List[str]		=    model_class(_UpperCAmelCase       )
     if getattr(_UpperCAmelCase     ,     'hf_compute_loss'     ,     _UpperCAmelCase       ):
       # Test that model correctly compute the loss with kwargs
       __UpperCAmelCase  :     Dict		=    self._prepare_for_class(inputs_dict.copy()     ,     _UpperCAmelCase     ,     return_labels=_UpperCAmelCase       )
       # Is there a better way to remove these decoder inputs?
       __UpperCAmelCase  :     Any		=    {
           key: val
           for key, val in prepared_for_class.items()
           if key not in ('head_mask', 'decoder_head_mask', 'cross_attn_head_mask', 'decoder_input_ids')
       }

       __UpperCAmelCase  :     Tuple		=    {
           'labels',
           'label',
           'label_ids',
           'start_positions',
           'start_position',
           'end_positions',
           'end_position',
           'next_sentence_label',
       }
       __UpperCAmelCase  :     str		=    possible_label_cols.intersection(set(_UpperCAmelCase       )       )
       self.assertGreater(len(_UpperCAmelCase       )     ,     0     ,     msg='No matching label names found!'       )
       __UpperCAmelCase  :     List[str]		=    {key: val for key, val in prepared_for_class.items() if key in label_names}
       __UpperCAmelCase  :     Tuple		=    {key: val for key, val in prepared_for_class.items() if key not in label_names}
       self.assertGreater(len(_UpperCAmelCase       )     ,     0       )

       # Make sure it works with XLA!
       model.compile(optimizer=tf.keras.optimizers.SGD(0.0       )     ,     jit_compile=_UpperCAmelCase       )
       # Make sure the model fits without crashing regardless of where we pass the labels
       __UpperCAmelCase  :     Any		=    model.fit(
           _UpperCAmelCase     ,     validation_data=_UpperCAmelCase     ,     steps_per_epoch=1     ,     validation_steps=1     ,     shuffle=_UpperCAmelCase     ,     verbose=0     ,     )
       __UpperCAmelCase  :     Optional[int]		=    history.history['loss'][0]
       self.assertTrue(not isnan(_UpperCAmelCase       )       )
       __UpperCAmelCase  :     Dict		=    history.history['val_loss'][0]
       self.assertTrue(not isnan(_UpperCAmelCase       )       )

       # Now test it with separate labels, to make sure that path works in XLA too.
       __UpperCAmelCase  :     Dict		=    model_class(_UpperCAmelCase       )
       model.compile(optimizer=tf.keras.optimizers.SGD(0.0       )     ,     jit_compile=_UpperCAmelCase       )
       __UpperCAmelCase  :     str		=    model.fit(
           _UpperCAmelCase     ,     _UpperCAmelCase     ,     validation_data=(inputs_minus_labels, labels)     ,     steps_per_epoch=1     ,     validation_steps=1     ,     shuffle=_UpperCAmelCase     ,     verbose=0     ,     )

       __UpperCAmelCase  :     Optional[int]		=    history.history['loss'][0]
       self.assertTrue(not isnan(_UpperCAmelCase       )       )
       __UpperCAmelCase  :     Union[str, Any]		=    history.history['val_loss'][0]
       self.assertTrue(not isnan(_UpperCAmelCase       )       )




 @slow
 def 						a					( self       ):
   __UpperCAmelCase, __UpperCAmelCase  :     List[str]		=    self.model_tester.prepare_config_and_inputs_for_common()
   __UpperCAmelCase  :     str		=    True
   __UpperCAmelCase  :     Union[str, Any]		=    True

   if hasattr(_UpperCAmelCase     ,     'use_cache'       ):
     __UpperCAmelCase  :     str		=    True

   __UpperCAmelCase  :     str		=    getattr(self.model_tester     ,     'encoder_seq_length'     ,     self.model_tester.seq_length       )
   __UpperCAmelCase  :     Optional[int]		=    getattr(self.model_tester     ,     'key_length'     ,     _UpperCAmelCase       )

   for model_class in self.all_model_classes[:2]:
     __UpperCAmelCase  :     Tuple		=    self._prepare_for_class(_UpperCAmelCase     ,     _UpperCAmelCase       )
     __UpperCAmelCase  :     Dict		=    model_class(_UpperCAmelCase       )
     model.build()
     __UpperCAmelCase  :     Any		=    len(model(_UpperCAmelCase       )       )

     for key in list(class_inputs_dict.keys()       ):
       # Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
       if key not in model.input_signature:
         del class_inputs_dict[key]
       # Check it's a tensor, in case the inputs dict has some bools in it too
       elif isinstance(class_inputs_dict[key]     ,     tf.Tensor       ) and class_inputs_dict[key].dtype.is_integer:
         __UpperCAmelCase  :     Any		=    tf.cast(class_inputs_dict[key]     ,     tf.intaa       )

     if set(class_inputs_dict.keys()       ) != set(model.input_signature.keys()       ):
       continue  # Some models have inputs that the preparation functions don't create, we skip those

     with tempfile.TemporaryDirectory() as tmpdirname:
       model.save_pretrained(_UpperCAmelCase     ,     saved_model=_UpperCAmelCase       )
       __UpperCAmelCase  :     List[str]		=    os.path.join(_UpperCAmelCase     ,     'saved_model'     ,     '1'       )
       __UpperCAmelCase  :     List[Any]		=    tf.keras.models.load_model(_UpperCAmelCase       )
       __UpperCAmelCase  :     Union[str, Any]		=    model(_UpperCAmelCase       )

       if self.is_encoder_decoder:
         __UpperCAmelCase  :     Dict		=    outputs['encoder_hidden_states']
         __UpperCAmelCase  :     Tuple		=    outputs['encoder_attentions']
       else:
         __UpperCAmelCase  :     Optional[int]		=    outputs['hidden_states']
         __UpperCAmelCase  :     Dict		=    outputs['attentions']

       self.assertEqual(len(_UpperCAmelCase       )     ,     _UpperCAmelCase       )

       __UpperCAmelCase  :     Any		=    getattr(
           self.model_tester     ,     'expected_num_hidden_layers'     ,     self.model_tester.num_hidden_layers + 1       )

       self.assertEqual(len(_UpperCAmelCase       )     ,     _UpperCAmelCase       )
       self.assertListEqual(
           list(output_hidden_states[0].shape[-2:]       )     ,     [self.model_tester.seq_length, self.model_tester.hidden_size]     ,     )

       self.assertEqual(len(_UpperCAmelCase       )     ,     self.model_tester.num_hidden_layers       )
       self.assertListEqual(
           list(output_attentions[0].shape[-3:]       )     ,     [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]     ,     )




 @slow
 def 						a					( self       ):
   tf.keras.mixed_precision.set_global_policy('mixed_float16'       )

   # try/finally block to ensure subsequent tests run in float32
   try:
     __UpperCAmelCase, __UpperCAmelCase  :     Optional[int]		=    self.model_tester.prepare_config_and_inputs_for_common()
     for model_class in self.all_model_classes[:2]:
       __UpperCAmelCase  :     Tuple		=    self._prepare_for_class(_UpperCAmelCase     ,     _UpperCAmelCase       )
       __UpperCAmelCase  :     int		=    model_class(_UpperCAmelCase       )
       __UpperCAmelCase  :     str		=    model(_UpperCAmelCase       )

       self.assertIsNotNone(_UpperCAmelCase       )
   finally:
     tf.keras.mixed_precision.set_global_policy('float32'       )




 @slow
 def 						a					( self       ):
   __UpperCAmelCase, __UpperCAmelCase  :     Optional[int]		=    self.model_tester.prepare_config_and_inputs_for_common()
   # head_mask and decoder_head_mask has different shapes than other input args
   if "head_mask" in inputs_dict:
     del inputs_dict["head_mask"]
   if "decoder_head_mask" in inputs_dict:
     del inputs_dict["decoder_head_mask"]
   if "cross_attn_head_mask" in inputs_dict:
     del inputs_dict["cross_attn_head_mask"]
   __UpperCAmelCase  :     Union[str, Any]		=    {
       module_member
       for model_class in self.all_model_classes
       for module in (import_module(model_class.__module__       ),)
       for module_member_name in dir(_UpperCAmelCase       )
       if module_member_name.endswith('MainLayer'       )
       for module_member in (getattr(_UpperCAmelCase     ,     _UpperCAmelCase       ),)
       if isinstance(_UpperCAmelCase     ,     _UpperCAmelCase       )
       and tf.keras.layers.Layer in module_member.__bases__
       and getattr(_UpperCAmelCase     ,     '_keras_serializable'     ,     _UpperCAmelCase       )
   }

   for main_layer_class in tf_main_layer_classes:
     # T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
     if "T5" in main_layer_class.__name__:
       # Take the same values than in TFT5ModelTester for this shared layer
       __UpperCAmelCase  :     Optional[int]		=    TFSharedEmbeddings(self.model_tester.vocab_size     ,     self.model_tester.hidden_size     ,     name='shared'       )
       __UpperCAmelCase  :     int		=    False
       __UpperCAmelCase  :     Optional[Any]		=    main_layer_class(_UpperCAmelCase     ,     embed_tokens=_UpperCAmelCase       )
     else:
       __UpperCAmelCase  :     List[str]		=    main_layer_class(_UpperCAmelCase       )

     __UpperCAmelCase  :     Optional[int]		=    {
         name: tf.keras.Input(tensor.shape[1:]     ,     dtype=tensor.dtype       ) for name, tensor in inputs_dict.items()
     }

     if hasattr(self.model_tester     ,     'num_labels'       ):
       __UpperCAmelCase  :     Union[str, Any]		=    self.model_tester.num_labels
     else:
       __UpperCAmelCase  :     List[str]		=    2

     __UpperCAmelCase  :     List[Any]		=    tf.data.Dataset.from_tensor_slices(
         (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)       ))       ).batch(1       )

     __UpperCAmelCase  :     str		=    main_layer(_UpperCAmelCase       )[0]
     __UpperCAmelCase  :     Optional[int]		=    tf.keras.layers.Dense(_UpperCAmelCase     ,     activation='softmax'     ,     name='outputs'       )(_UpperCAmelCase       )
     __UpperCAmelCase  :     Tuple		=    tf.keras.models.Model(inputs=_UpperCAmelCase     ,     outputs=[outputs]       )

     model.compile(loss='binary_crossentropy'     ,     optimizer='adam'     ,     metrics=['binary_accuracy']       )
     model.fit(_UpperCAmelCase     ,     epochs=1       )

     with tempfile.TemporaryDirectory() as tmpdirname:
       __UpperCAmelCase  :     Union[str, Any]		=    os.path.join(_UpperCAmelCase     ,     'keras_model.h5'       )
       model.save(_UpperCAmelCase       )
       if "T5" in main_layer_class.__name__:
         __UpperCAmelCase  :     Union[str, Any]		=    tf.keras.models.load_model(
             _UpperCAmelCase     ,     custom_objects={
                 main_layer_class.__name__: main_layer_class,
                 'TFSharedEmbeddings': TFSharedEmbeddings,
             }     ,     )
       else:
         __UpperCAmelCase  :     Optional[int]		=    tf.keras.models.load_model(
             _UpperCAmelCase     ,     custom_objects={main_layer_class.__name__: main_layer_class}       )
       assert isinstance(_UpperCAmelCase     ,     tf.keras.Model       )
       model(_UpperCAmelCase       )




 @slow
 def 						a					( self       ):
   __UpperCAmelCase, __UpperCAmelCase  :     Dict		=    self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes[:2]:
     __UpperCAmelCase  :     Any		=    model_class(_UpperCAmelCase       )

     __UpperCAmelCase  :     List[str]		=    copy.deepcopy(_UpperCAmelCase       )

     if not self.is_encoder_decoder:
       __UpperCAmelCase  :     int		=    inputs['input_ids']
       del inputs["input_ids"]
     else:
       __UpperCAmelCase  :     int		=    inputs['input_ids']
       __UpperCAmelCase  :     List[Any]		=    inputs.get('decoder_input_ids'     ,     _UpperCAmelCase       )
       del inputs["input_ids"]
       inputs.pop('decoder_input_ids'     ,     _UpperCAmelCase       )

     if not self.is_encoder_decoder:
       __UpperCAmelCase  :     Any		=    model.get_input_embeddings()(_UpperCAmelCase       )
     else:
       __UpperCAmelCase  :     Tuple		=    model.get_input_embeddings()(_UpperCAmelCase       )
       __UpperCAmelCase  :     Any		=    model.get_input_embeddings()(_UpperCAmelCase       )

     __UpperCAmelCase  :     Optional[int]		=    self._prepare_for_class(_UpperCAmelCase     ,     _UpperCAmelCase       )

     @tf.function
     def run_in_graph_mode():
       return model(_UpperCAmelCase       )

     __UpperCAmelCase  :     int		=    run_in_graph_mode()
     self.assertIsNotNone(_UpperCAmelCase       )




 def 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase       ):
   # special tokens cannot be bad tokens
   __UpperCAmelCase  :     Union[str, Any]		=    []
   if model.config.bos_token_id is not None:
     special_tokens.append(model.config.bos_token_id       )
   if model.config.pad_token_id is not None:
     special_tokens.append(model.config.pad_token_id       )
   if model.config.eos_token_id is not None:
     special_tokens.append(model.config.eos_token_id       )

   # create random bad tokens that are not special tokens
   __UpperCAmelCase  :     Optional[Any]		=    []
   while len(_UpperCAmelCase       ) < num_bad_tokens:
     __UpperCAmelCase  :     Tuple		=    tf.squeeze(ids_tensor((1, 1)     ,     self.model_tester.vocab_size       )     ,     0       ).numpy()[0]
     if token not in special_tokens:
       bad_tokens.append(_UpperCAmelCase       )
   return bad_tokens




 def 						a					( self     ,     _UpperCAmelCase       ):
   for token_id in output_ids[0].numpy().tolist():
     self.assertGreaterEqual(_UpperCAmelCase     ,     0       )
     self.assertLess(_UpperCAmelCase     ,     self.model_tester.vocab_size       )




 def 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase       ):
   # for all bad word tokens
   for bad_word_ids in bad_words_ids:
     # for all slices in batch
     for generated_ids_slice in generated_ids:
       # for all word idx
       for i in range(len(_UpperCAmelCase       )     ,     len(_UpperCAmelCase       )       ):
         # if tokens match
         if generated_ids_slice[i - len(_UpperCAmelCase       ) : i] == bad_word_ids:
           return True
   return False








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


_SCREAMING_SNAKE_CASE          = logging.getLogger(__name__)





def    _snake_case		(snake_case_				:    Union[str, Any]     )       ->		str:
  random.seed(snake_case_     )
  np.random.seed(snake_case_     )
  torch.manual_seed(snake_case_     )
  torch.cuda.manual_seed_all(snake_case_     )





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





def    _snake_case		(snake_case_				:    Optional[int]="gpt2"     )       ->		Optional[Any]:
  __UpperCAmelCase  :     Dict		=    GPTaLMHeadModel.from_pretrained(snake_case_						,					output_hidden_states=snake_case_     )
  torch.save(model.state_dict()						,					model_name + 'local.pt'     )
  return model





def    _snake_case		(snake_case_				:    Any						,					snake_case_				:    Optional[int]						,					snake_case_				:    List[str]     )       ->		Dict:
  __UpperCAmelCase  :     List[str]		=    copy.deepcopy(snake_case_     )
  model.to(snake_case_     )

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





def    _snake_case		(snake_case_				:    List[str]						,					snake_case_				:    List[str]						,					snake_case_				:    Union[str, Any]						,					snake_case_				:    Tuple     )       ->		Tuple:
  __UpperCAmelCase  :     Optional[Any]		=    np.array(snake_case_     ).mean()
  __UpperCAmelCase  :     str		=    np.array(snake_case_     ).std()
  __UpperCAmelCase  :     List[str]		=    (real_perps - avg) / std
  __UpperCAmelCase  :     List[str]		=    list(zip(snake_case_						,					snake_case_						,					snake_case_     )     )
  joblib.dump(snake_case_						,					snake_case_     )





def    _snake_case		(snake_case_				:    str						,					snake_case_				:    Optional[Any]						,					snake_case_				:    Optional[int]						,					snake_case_				:    List[str]						,					snake_case_				:    Any						,					snake_case_				:    Dict						,					snake_case_				:    int						,					snake_case_				:    Optional[int]="dev.jbl"						,					snake_case_				:    Tuple=recopy_gpta						,					)       ->		Dict:
  __UpperCAmelCase  :     Dict		=    []
  __UpperCAmelCase  :     int		=    []
  __UpperCAmelCase  :     Optional[Any]		=    []

  # Initialize the transformer model
  __UpperCAmelCase  :     Tuple		=    copy.deepcopy(snake_case_     )
  orig_model.to(device='cpu'     )
  torch.cuda.empty_cache()

  # Compute perplexity of initial transformer model for comparison
  model.train()
  __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase  :     str		=    recopy_model(snake_case_						,					snake_case_						,					snake_case_     )

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

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

    # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
    __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase  :     int		=    recopy_model(snake_case_						,					snake_case_						,					snake_case_     )

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

  intermittent_save(snake_case_						,					snake_case_						,					snake_case_						,					snake_case_     )





def    _snake_case		(snake_case_				:    str						,					snake_case_				:    int="data/tokenized_stories_train_wikitext103.jbl"						,					snake_case_				:    Tuple=100						,					snake_case_				:    str=1026						,					snake_case_				:    Union[str, Any]=True     )       ->		List[str]:
  __UpperCAmelCase  :     Union[str, Any]		=    joblib.load(snake_case_     )
  print('data loaded'     )
  __UpperCAmelCase  :     Any		=    []
  if trim:
    for i, example in enumerate(snake_case_     ):
      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(snake_case_     ) >= number:
        break
    __UpperCAmelCase  :     Union[str, Any]		=    []
    for j in range(i + 1						,					len(snake_case_     )     ):
      if len(data[j][0]     ) > min_len:
        train_data.append(data[j]     )
  else:
    __UpperCAmelCase  :     Any		=    data[0:number]
    __UpperCAmelCase  :     int		=    data[number:]

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





def    _snake_case		(snake_case_				:    Optional[Any]						,					snake_case_				:    Tuple						,					snake_case_				:    Optional[Any]						,					snake_case_				:    str						,					snake_case_				:    Dict=50						,					snake_case_				:    Any="secondary_learner.pt"     )       ->		Tuple:
  __UpperCAmelCase  :     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
  __UpperCAmelCase  :     int		=    train_dataset[:512]
  __UpperCAmelCase  :     Tuple		=    train_dataset[512:]
  __UpperCAmelCase  :     Any		=    DataLoader(snake_case_						,					shuffle=snake_case_						,					batch_size=snake_case_     )
  __UpperCAmelCase  :     str		=    DataLoader(snake_case_						,					shuffle=snake_case_						,					batch_size=snake_case_     )

  # secondary learner model set up
  __UpperCAmelCase  :     Any		=    nn.MSELoss()
  __UpperCAmelCase  :     Any		=    nn.MSELoss(reduction='sum'     )
  secondary_learner.to(snake_case_     )
  __UpperCAmelCase  :     Union[str, Any]		=    torch.optim.Adam(secondary_learner.parameters()						,					lr=0.00001     )
  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  :     List[Any]		=    float('inf'     )
  # Iterate through batches until we've used max_steps batches
  for epoch in range(int(snake_case_     )     ):
    __UpperCAmelCase  :     int		=    0.0
    secondary_learner.train()
    for step, batch in enumerate(snake_case_     ):
      __UpperCAmelCase  :     Any		=    batch[0].to(snake_case_     )
      __UpperCAmelCase  :     Union[str, Any]		=    batch[1].to(snake_case_     )
      __UpperCAmelCase  :     List[Any]		=    secondary_learner(snake_case_     )
      q_optimizer.zero_grad()
      __UpperCAmelCase  :     Union[str, Any]		=    loss(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(snake_case_     )):
        __UpperCAmelCase  :     Optional[int]		=    tr_q_loss / (step + 1)

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

      secondary_learner.train()
  return secondary_learner







class     lowerCamelCase_   ( nn.Module):




 def __init__( self     ,     _UpperCAmelCase       ):
   super(_UpperCAmelCase     ,     self       ).__init__()
   __UpperCAmelCase  :     List[str]		=    model.transformer.wte
   __UpperCAmelCase  :     Optional[int]		=    copy.deepcopy(model.transformer.wte.weight       )
   __UpperCAmelCase  :     str		=    nn.Convad(self.embeddings.weight.size(1       )     ,     256     ,     3     ,     padding=1       )
   __UpperCAmelCase  :     Dict		=    nn.Sequential(nn.Linear(256     ,     32       )     ,     nn.Dropout(p=0.1       )     ,     nn.Linear(32     ,     32       )     ,     nn.Linear(32     ,     1       )       )




 def 						a					( self     ,     _UpperCAmelCase       ):
   __UpperCAmelCase  :     Union[str, Any]		=    torch.max(self.conv(self.embeddings(_UpperCAmelCase       ).squeeze(1       ).transpose(1     ,     2       )       )     ,     2       )[0]
   __UpperCAmelCase  :     Dict		=    self.fc(_UpperCAmelCase       )
   return qs.squeeze(1       )




 @classmethod
 def 						a					( cls     ,     _UpperCAmelCase     ,     _UpperCAmelCase       ):
   __UpperCAmelCase  :     List[Any]		=    cls(_UpperCAmelCase       )  # this calls __init__
   __UpperCAmelCase  :     int		=    torch.load(_UpperCAmelCase       )
   secondary_learner.load_state_dict(_UpperCAmelCase       )
   __UpperCAmelCase  :     List[Any]		=    model.transformer.wte
   __UpperCAmelCase  :     List[Any]		=    copy.deepcopy(model.transformer.wte.weight       )
   return secondary_learner





