


import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class       __lowerCAmelCase       (	a	,					unittest.TestCase   ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE						      =    UnCLIPPipeline
  _SCREAMING_SNAKE_CASE						      =    TEXT_TO_IMAGE_PARAMS - {
      'negative_prompt',
      'height',
      'width',
      'negative_prompt_embeds',
      'guidance_scale',
      'prompt_embeds',
      'cross_attention_kwargs',
  }
  _SCREAMING_SNAKE_CASE						      =    TEXT_TO_IMAGE_BATCH_PARAMS
  _SCREAMING_SNAKE_CASE						      =    [
      'generator',
      'return_dict',
      'prior_num_inference_steps',
      'decoder_num_inference_steps',
      'super_res_num_inference_steps',
  ]
  _SCREAMING_SNAKE_CASE						      =    False



  @property
  def    lowerCAmelCase__					(       self    :    Any					)				->	str:




     """simple docstring"""
     return 3_2



  @property
  def    lowerCAmelCase__					(       self    :    int					)				->	Tuple:




     """simple docstring"""
     return 3_2



  @property
  def    lowerCAmelCase__					(       self    :    Optional[int]					)				->	str:




     """simple docstring"""
     return self.time_input_dim



  @property
  def    lowerCAmelCase__					(       self    :    str					)				->	Optional[int]:




     """simple docstring"""
     return self.time_input_dim * 4



  @property
  def    lowerCAmelCase__					(       self    :    Dict					)				->	Optional[int]:




     """simple docstring"""
     return 1_0_0



  @property
  def    lowerCAmelCase__					(       self    :    Any					)				->	List[Any]:




     """simple docstring"""
     snake_case_         =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)
     return tokenizer



  @property
  def    lowerCAmelCase__					(       self    :    List[Any]					)				->	Tuple:




     """simple docstring"""
     torch.manual_seed(0					)
     snake_case_         =   CLIPTextConfig(
         bos_token_id=0      ,			eos_token_id=2      ,			hidden_size=self.text_embedder_hidden_size      ,			projection_dim=self.text_embedder_hidden_size      ,			intermediate_size=3_7      ,			layer_norm_eps=1e-05      ,			num_attention_heads=4      ,			num_hidden_layers=5      ,			pad_token_id=1      ,			vocab_size=1_0_0_0      ,			)
     return CLIPTextModelWithProjection(_lowerCAmelCase					)



  @property
  def    lowerCAmelCase__					(       self    :    str					)				->	Dict:




     """simple docstring"""
     torch.manual_seed(0					)

     snake_case_         =   {
         "num_attention_heads": 2,
         "attention_head_dim": 1_2,
         "embedding_dim": self.text_embedder_hidden_size,
         "num_layers": 1,
     }

     snake_case_         =   PriorTransformer(**_lowerCAmelCase					)
     return model



  @property
  def    lowerCAmelCase__					(       self    :    int					)				->	List[Any]:




     """simple docstring"""
     torch.manual_seed(0					)

     snake_case_         =   {
         "clip_embeddings_dim": self.text_embedder_hidden_size,
         "time_embed_dim": self.time_embed_dim,
         "cross_attention_dim": self.cross_attention_dim,
     }

     snake_case_         =   UnCLIPTextProjModel(**_lowerCAmelCase					)
     return model



  @property
  def    lowerCAmelCase__					(       self    :    List[str]					)				->	Any:




     """simple docstring"""
     torch.manual_seed(0					)

     snake_case_         =   {
         "sample_size": 3_2,
         # RGB in channels
         "in_channels": 3,
         # Out channels is double in channels because predicts mean and variance
         "out_channels": 6,
         "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
         "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
         "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
         "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
         "layers_per_block": 1,
         "cross_attention_dim": self.cross_attention_dim,
         "attention_head_dim": 4,
         "resnet_time_scale_shift": "scale_shift",
         "class_embed_type": "identity",
     }

     snake_case_         =   UNetaDConditionModel(**_lowerCAmelCase					)
     return model



  @property
  def    lowerCAmelCase__					(       self    :    List[Any]					)				->	List[str]:




     """simple docstring"""
     return {
         "sample_size": 6_4,
         "layers_per_block": 1,
         "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
         "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
         "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
         "in_channels": 6,
         "out_channels": 3,
     }



  @property
  def    lowerCAmelCase__					(       self    :    Optional[int]					)				->	Any:




     """simple docstring"""
     torch.manual_seed(0					)

     snake_case_         =   UNetaDModel(**self.dummy_super_res_kwargs					)
     return model



  @property
  def    lowerCAmelCase__					(       self    :    str					)				->	Optional[int]:




     """simple docstring"""
     # seeded differently to get different unet than `self.dummy_super_res_first`
     torch.manual_seed(1					)

     snake_case_         =   UNetaDModel(**self.dummy_super_res_kwargs					)
     return model



  def    lowerCAmelCase__					(       self    :    Dict					)				->	List[Any]:




     """simple docstring"""
     snake_case_         =   self.dummy_prior
     snake_case_         =   self.dummy_decoder
     snake_case_         =   self.dummy_text_proj
     snake_case_         =   self.dummy_text_encoder
     snake_case_         =   self.dummy_tokenizer
     snake_case_         =   self.dummy_super_res_first
     snake_case_         =   self.dummy_super_res_last

     snake_case_         =   UnCLIPScheduler(
         variance_type="fixed_small_log"      ,			prediction_type="sample"      ,			num_train_timesteps=1_0_0_0      ,			clip_sample_range=5.0      ,			)

     snake_case_         =   UnCLIPScheduler(
         variance_type="learned_range"      ,			prediction_type="epsilon"      ,			num_train_timesteps=1_0_0_0      ,			)

     snake_case_         =   UnCLIPScheduler(
         variance_type="fixed_small_log"      ,			prediction_type="epsilon"      ,			num_train_timesteps=1_0_0_0      ,			)

     snake_case_         =   {
         "prior": prior,
         "decoder": decoder,
         "text_proj": text_proj,
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "super_res_first": super_res_first,
         "super_res_last": super_res_last,
         "prior_scheduler": prior_scheduler,
         "decoder_scheduler": decoder_scheduler,
         "super_res_scheduler": super_res_scheduler,
     }

     return components



  def    lowerCAmelCase__					(       self    :    Tuple      ,			_lowerCAmelCase    :    str      ,			_lowerCAmelCase    :    Dict=0					)				->	str:




     """simple docstring"""
     if str(_lowerCAmelCase					).startswith("mps"					):
        snake_case_         =   torch.manual_seed(_lowerCAmelCase					)
     else:
        snake_case_         =   torch.Generator(device=_lowerCAmelCase					).manual_seed(_lowerCAmelCase					)
     snake_case_         =   {
         "prompt": "horse",
         "generator": generator,
         "prior_num_inference_steps": 2,
         "decoder_num_inference_steps": 2,
         "super_res_num_inference_steps": 2,
         "output_type": "numpy",
     }
     return inputs



  def    lowerCAmelCase__					(       self    :    Dict					)				->	Any:




     """simple docstring"""
     snake_case_         =   "cpu"

     snake_case_         =   self.get_dummy_components()

     snake_case_         =   self.pipeline_class(**_lowerCAmelCase					)
     snake_case_         =   pipe.to(_lowerCAmelCase					)

     pipe.set_progress_bar_config(disable=_lowerCAmelCase					)

     snake_case_         =   pipe(**self.get_dummy_inputs(_lowerCAmelCase					)					)
     snake_case_         =   output.images

     snake_case_         =   pipe(
         **self.get_dummy_inputs(_lowerCAmelCase					)      ,			return_dict=_lowerCAmelCase      ,			)[0]

     snake_case_         =   image[0, -3:, -3:, -1]
     snake_case_         =   image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 6_4, 6_4, 3)

     snake_case_         =   np.array(
         [
             0.9_997,
             0.9_988,
             0.0_028,
             0.9_997,
             0.9_984,
             0.9_965,
             0.0_029,
             0.9_986,
             0.0_025,
         ]					)

     assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2



  def    lowerCAmelCase__					(       self    :    List[str]					)				->	Optional[Any]:




     """simple docstring"""
     snake_case_         =   torch.device("cpu"					)


     class       __lowerCAmelCase       :






       """simple docstring"""





       _SCREAMING_SNAKE_CASE						      =    1






     snake_case_         =   self.get_dummy_components()

     snake_case_         =   self.pipeline_class(**_lowerCAmelCase					)
     snake_case_         =   pipe.to(_lowerCAmelCase					)

     snake_case_         =   components["prior"]
     snake_case_         =   components["decoder"]
     snake_case_         =   components["super_res_first"]
     snake_case_         =   components["tokenizer"]
     snake_case_         =   components["text_encoder"]

     snake_case_         =   torch.Generator(device=_lowerCAmelCase					).manual_seed(0					)
     snake_case_         =   prior.dtype
     snake_case_         =   1

     snake_case_         =   (batch_size, prior.config.embedding_dim)
     snake_case_         =   pipe.prepare_latents(
         _lowerCAmelCase      ,			dtype=_lowerCAmelCase      ,			device=_lowerCAmelCase      ,			generator=_lowerCAmelCase      ,			latents=_lowerCAmelCase      ,			scheduler=DummyScheduler()					)
     snake_case_         =   (batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
     snake_case_         =   pipe.prepare_latents(
         _lowerCAmelCase      ,			dtype=_lowerCAmelCase      ,			device=_lowerCAmelCase      ,			generator=_lowerCAmelCase      ,			latents=_lowerCAmelCase      ,			scheduler=DummyScheduler()					)

     snake_case_         =   (
         batch_size,
         super_res_first.config.in_channels // 2,
         super_res_first.config.sample_size,
         super_res_first.config.sample_size,
     )
     snake_case_         =   pipe.prepare_latents(
         _lowerCAmelCase      ,			dtype=_lowerCAmelCase      ,			device=_lowerCAmelCase      ,			generator=_lowerCAmelCase      ,			latents=_lowerCAmelCase      ,			scheduler=DummyScheduler()					)

     pipe.set_progress_bar_config(disable=_lowerCAmelCase					)

     snake_case_         =   "this is a prompt example"

     snake_case_         =   torch.Generator(device=_lowerCAmelCase					).manual_seed(0					)
     snake_case_         =   pipe(
         [prompt]      ,			generator=_lowerCAmelCase      ,			prior_num_inference_steps=2      ,			decoder_num_inference_steps=2      ,			super_res_num_inference_steps=2      ,			prior_latents=_lowerCAmelCase      ,			decoder_latents=_lowerCAmelCase      ,			super_res_latents=_lowerCAmelCase      ,			output_type="np"      ,			)
     snake_case_         =   output.images

     snake_case_         =   tokenizer(
         _lowerCAmelCase      ,			padding="max_length"      ,			max_length=tokenizer.model_max_length      ,			return_tensors="pt"      ,			)
     snake_case_         =   text_encoder(text_inputs.input_ids					)
     snake_case_         =   text_inputs.attention_mask

     snake_case_         =   torch.Generator(device=_lowerCAmelCase					).manual_seed(0					)
     snake_case_         =   pipe(
         generator=_lowerCAmelCase      ,			prior_num_inference_steps=2      ,			decoder_num_inference_steps=2      ,			super_res_num_inference_steps=2      ,			prior_latents=_lowerCAmelCase      ,			decoder_latents=_lowerCAmelCase      ,			super_res_latents=_lowerCAmelCase      ,			text_model_output=_lowerCAmelCase      ,			text_attention_mask=_lowerCAmelCase      ,			output_type="np"      ,			)[0]

     # make sure passing text embeddings manually is identical
     assert np.abs(image - image_from_text					).max() < 1e-4



  @skip_mps
  def    lowerCAmelCase__					(       self    :    List[Any]					)				->	Any:




     """simple docstring"""
     snake_case_         =   torch_device == "cpu"

     self._test_attention_slicing_forward_pass(test_max_difference=_lowerCAmelCase      ,			expected_max_diff=0.01					)



  @skip_mps
  def    lowerCAmelCase__					(       self    :    List[str]					)				->	Any:




     """simple docstring"""
     snake_case_         =   torch_device == "cpu"
     snake_case_         =   True
     snake_case_         =   [
         "prior_num_inference_steps",
         "decoder_num_inference_steps",
         "super_res_num_inference_steps",
     ]

     self._test_inference_batch_single_identical(
         test_max_difference=_lowerCAmelCase      ,			relax_max_difference=_lowerCAmelCase      ,			additional_params_copy_to_batched_inputs=_lowerCAmelCase      ,			)



  def    lowerCAmelCase__					(       self    :    Optional[Any]					)				->	Any:




     """simple docstring"""
     snake_case_         =   [
         "prior_num_inference_steps",
         "decoder_num_inference_steps",
         "super_res_num_inference_steps",
     ]

     if torch_device == "mps":
        # TODO: MPS errors with larger batch sizes
        snake_case_         =   [2, 3]
        self._test_inference_batch_consistent(
            batch_sizes=_lowerCAmelCase      ,			additional_params_copy_to_batched_inputs=_lowerCAmelCase      ,			)
     else:
        self._test_inference_batch_consistent(
            additional_params_copy_to_batched_inputs=_lowerCAmelCase					)



  @skip_mps
  def    lowerCAmelCase__					(       self    :    Optional[int]					)				->	int:




     """simple docstring"""
     return super().test_dict_tuple_outputs_equivalent()



  @skip_mps
  def    lowerCAmelCase__					(       self    :    Any					)				->	Optional[int]:




     """simple docstring"""
     return super().test_save_load_local()



  @skip_mps
  def    lowerCAmelCase__					(       self    :    str					)				->	str:




     """simple docstring"""
     return super().test_save_load_optional_components()






@nightly
class       __lowerCAmelCase       (	unittest.TestCase   ):






  """simple docstring"""





  def    lowerCAmelCase__					(       self    :    int					)				->	Tuple:




     """simple docstring"""
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()



  def    lowerCAmelCase__					(       self    :    Any					)				->	int:




     """simple docstring"""
     snake_case_         =   load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/unclip/karlo_v1_alpha_horse_cpu.npy"					)

     snake_case_         =   UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"					)
     pipeline.set_progress_bar_config(disable=_lowerCAmelCase					)

     snake_case_         =   torch.manual_seed(0					)
     snake_case_         =   pipeline(
         "horse"      ,			num_images_per_prompt=1      ,			generator=_lowerCAmelCase      ,			output_type="np"      ,			)

     snake_case_         =   output.images[0]

     assert image.shape == (2_5_6, 2_5_6, 3)
     assert np.abs(expected_image - image					).max() < 1e-1






@slow
@require_torch_gpu
class       __lowerCAmelCase       (	unittest.TestCase   ):






  """simple docstring"""





  def    lowerCAmelCase__					(       self    :    int					)				->	List[str]:




     """simple docstring"""
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()



  def    lowerCAmelCase__					(       self    :    List[str]					)				->	Dict:




     """simple docstring"""
     snake_case_         =   load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/unclip/karlo_v1_alpha_horse_fp16.npy"					)

     snake_case_         =   UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"      ,			torch_dtype=torch.floataa					)
     snake_case_         =   pipeline.to(_lowerCAmelCase					)
     pipeline.set_progress_bar_config(disable=_lowerCAmelCase					)

     snake_case_         =   torch.Generator(device="cpu"					).manual_seed(0					)
     snake_case_         =   pipeline(
         "horse"      ,			generator=_lowerCAmelCase      ,			output_type="np"      ,			)

     snake_case_         =   output.images[0]

     assert image.shape == (2_5_6, 2_5_6, 3)

     assert_mean_pixel_difference(_lowerCAmelCase      ,			_lowerCAmelCase					)



  def    lowerCAmelCase__					(       self    :    Dict					)				->	List[Any]:




     """simple docstring"""
     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     snake_case_         =   UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"      ,			torch_dtype=torch.floataa					)
     snake_case_         =   pipe.to(_lowerCAmelCase					)
     pipe.set_progress_bar_config(disable=_lowerCAmelCase					)
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     snake_case_         =   pipe(
         "horse"      ,			num_images_per_prompt=1      ,			prior_num_inference_steps=2      ,			decoder_num_inference_steps=2      ,			super_res_num_inference_steps=2      ,			output_type="np"      ,			)

     snake_case_         =   torch.cuda.max_memory_allocated()
     # make sure that less than 7 GB is allocated
     assert mem_bytes < 7 * 1_0**9








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		:Dict											=       logging.getLogger(__name__)



def      _lowerCAmelCase  (							lowerCAmelCase_							:List[str]							)->str:
   '''simple docstring'''






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



def      _lowerCAmelCase  (							lowerCAmelCase_							:Optional[int]						,  lowerCAmelCase_							:Union[str, Any]						,  lowerCAmelCase_							:Optional[Any]							)->Dict:
   '''simple docstring'''






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



def      _lowerCAmelCase  (							lowerCAmelCase_							:Union[str, Any]="gpt2"							)->Union[str, Any]:
   '''simple docstring'''






   snake_case_         =   GPTaLMHeadModel.from_pretrained(lowerCAmelCase_						,  output_hidden_states=lowerCAmelCase_							)
   torch.save(model.state_dict()						,  model_name + "local.pt"							)
   return model



def      _lowerCAmelCase  (							lowerCAmelCase_							:List[str]						,  lowerCAmelCase_							:List[str]						,  lowerCAmelCase_							:List[Any]							)->int:
   '''simple docstring'''






   snake_case_         =   copy.deepcopy(lowerCAmelCase_							)
   model.to(lowerCAmelCase_							)

   snake_case_         =   ["bias", "LayerNorm.weight"]
   snake_case_         =   [
       {
           "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},
   ]
   snake_case_         =   AdamW(lowerCAmelCase_						,  lr=5e-5						,  eps=1e-8							)
   snake_case_         =   get_linear_schedule_with_warmup(lowerCAmelCase_						,  0						,  lowerCAmelCase_							)
   torch.cuda.empty_cache()
   return model, lm_optimizer, lm_scheduler



def      _lowerCAmelCase  (							lowerCAmelCase_							:Tuple						,  lowerCAmelCase_							:List[str]						,  lowerCAmelCase_							:Optional[int]						,  lowerCAmelCase_							:str							)->Tuple:
   '''simple docstring'''






   snake_case_         =   np.array(lowerCAmelCase_							).mean()
   snake_case_         =   np.array(lowerCAmelCase_							).std()
   snake_case_         =   (real_perps - avg) / std
   snake_case_         =   list(zip(lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_							)							)
   joblib.dump(lowerCAmelCase_						,  lowerCAmelCase_							)



def      _lowerCAmelCase  (							lowerCAmelCase_							:Optional[int]						,  lowerCAmelCase_							:List[str]						,  lowerCAmelCase_							:Optional[Any]						,  lowerCAmelCase_							:str						,  lowerCAmelCase_							:Optional[int]						,  lowerCAmelCase_							:Any						,  lowerCAmelCase_							:Any						,  lowerCAmelCase_							:str="dev.jbl"						,  lowerCAmelCase_							:int=recopy_gpta						,  )->Tuple:
   '''simple docstring'''






   snake_case_         =   []
   snake_case_         =   []
   snake_case_         =   []

   # Initialize the transformer model
   snake_case_         =   copy.deepcopy(lowerCAmelCase_							)
   orig_model.to(device="cpu"							)
   torch.cuda.empty_cache()

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

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

      # Periodically save the stored (X, IG(X)) pairs
      if step % 1_000 == 0 and step > 1:
         intermittent_save(lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_							)

      # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
      snake_case_      ,      snake_case_      ,      snake_case_         =   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      _lowerCAmelCase  (							lowerCAmelCase_							:List[Any]						,  lowerCAmelCase_							:Union[str, Any]="data/tokenized_stories_train_wikitext103.jbl"						,  lowerCAmelCase_							:Tuple=100						,  lowerCAmelCase_							:int=1_026						,  lowerCAmelCase_							:int=True							)->List[Any]:
   '''simple docstring'''






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

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



def      _lowerCAmelCase  (							lowerCAmelCase_							:Union[str, Any]						,  lowerCAmelCase_							:Tuple						,  lowerCAmelCase_							:List[Any]						,  lowerCAmelCase_							:Optional[Any]						,  lowerCAmelCase_							:Dict=50						,  lowerCAmelCase_							:Dict="secondary_learner.pt"							)->Union[str, Any]:
   '''simple docstring'''






   snake_case_         =   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
   snake_case_         =   train_dataset[:512]
   snake_case_         =   train_dataset[512:]
   snake_case_         =   DataLoader(lowerCAmelCase_						,  shuffle=lowerCAmelCase_						,  batch_size=lowerCAmelCase_							)
   snake_case_         =   DataLoader(lowerCAmelCase_						,  shuffle=lowerCAmelCase_						,  batch_size=lowerCAmelCase_							)

   # secondary learner model set up
   snake_case_         =   nn.MSELoss()
   snake_case_         =   nn.MSELoss(reduction="sum"							)
   secondary_learner.to(lowerCAmelCase_							)
   snake_case_         =   torch.optim.Adam(secondary_learner.parameters()						,  lr=0.0_0_0_0_1							)
   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
   snake_case_         =   float("inf"							)
   # Iterate through batches until we've used max_steps batches
   for epoch in range(int(lowerCAmelCase_							)							):
      snake_case_         =   0.0
      secondary_learner.train()
      for step, batch in enumerate(lowerCAmelCase_							):
         snake_case_         =   batch[0].to(lowerCAmelCase_							)
         snake_case_         =   batch[1].to(lowerCAmelCase_							)
         snake_case_         =   secondary_learner(lowerCAmelCase_							)
         q_optimizer.zero_grad()
         snake_case_         =   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_							)):
            snake_case_         =   tr_q_loss / (step + 1)

            secondary_learner.eval()
            snake_case_         =   0.0
            snake_case_         =   0.0
            snake_case_         =   []
            snake_case_         =   []
            # Compute performance of the secondary learner after this batch
            for stepa, batcha in enumerate(lowerCAmelCase_							):
               snake_case_         =   batcha[0].to(lowerCAmelCase_							)
               snake_case_         =   batcha[1].to(lowerCAmelCase_							)
               snake_case_         =   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_							)
               snake_case_         =   q_lossa

         secondary_learner.train()
   return secondary_learner



class       __lowerCAmelCase       (	nn.Module   ):






  """simple docstring"""





  def __init__(       self    :    Optional[int]      ,			_lowerCAmelCase    :    Dict					)				->	List[Any]:




     """simple docstring"""
     super(_lowerCAmelCase      ,			self					).__init__()
     snake_case_         =   model.transformer.wte
     snake_case_         =   copy.deepcopy(model.transformer.wte.weight					)
     snake_case_         =   nn.Convad(self.embeddings.weight.size(1					)      ,			2_5_6      ,			3      ,			padding=1					)
     snake_case_         =   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    lowerCAmelCase__					(       self    :    Union[str, Any]      ,			_lowerCAmelCase    :    Optional[int]					)				->	List[Any]:




     """simple docstring"""
     snake_case_         =   torch.max(self.conv(self.embeddings(_lowerCAmelCase					).squeeze(1					).transpose(1      ,			2					)					)      ,			2					)[0]
     snake_case_         =   self.fc(_lowerCAmelCase					)
     return qs.squeeze(1					)



  @classmethod
  def    lowerCAmelCase__					(       cls    :    List[Any]      ,			_lowerCAmelCase    :    List[Any]      ,			_lowerCAmelCase    :    Dict					)				->	Dict:




     """simple docstring"""
     snake_case_         =   cls(_lowerCAmelCase					)  # this calls __init__
     snake_case_         =   torch.load(_lowerCAmelCase					)
     secondary_learner.load_state_dict(_lowerCAmelCase					)
     snake_case_         =   model.transformer.wte
     snake_case_         =   copy.deepcopy(model.transformer.wte.weight					)
     return secondary_learner




