

'''simple docstring'''







import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel
def       UpperCAmelCase__    (      UpperCAmelCase__=32,       UpperCAmelCase__=10,       UpperCAmelCase__=1_00,       UpperCAmelCase__=10_26,       UpperCAmelCase__=True,       UpperCAmelCase__="data/tokenized_stories_train_wikitext103.jbl",       UpperCAmelCase__="igf_context_pairs.jbl",       )			->			List[str]:
       set_seed(3				)
       # generate train_data and objective_set
       A_					,			A_						=      generate_datasets(
           UpperCAmelCase__,       UpperCAmelCase__,       number=UpperCAmelCase__,       min_len=10_26,       trim=UpperCAmelCase__				)
       # keeps model same across runs
       set_seed(4				)
       # model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
       # can we train on GPU?
       A_						=      torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""				)

       # load pretrained model
       A_						=      load_gpta("""gpt2"""				).to(UpperCAmelCase__				)
       print("""computing perplexity on objective set"""				)
       A_						=      compute_perplexity(UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				).item()
       print("""perplexity on objective set:""",       UpperCAmelCase__				)

       # collect igf pairs and save to file demo.jbl
       collect_objective_set(UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)

       # clean up, delete model and data we don't need anymore
       del model, train_data, objective_set
       torch.cuda.empty_cache()
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__=15,       UpperCAmelCase__=1_28,       UpperCAmelCase__=1_00,       UpperCAmelCase__="igf_model.pt",       )			->			Union[str, Any]:
       set_seed(42				)

       # Load pre-trained model
       A_						=      GPTaLMHeadModel.from_pretrained("""gpt2"""				)

       # Initialize secondary learner to use embedding weights of model
       A_						=      SecondaryLearner(UpperCAmelCase__				)

       # Train secondary learner
       A_						=      train_secondary_learner(
           UpperCAmelCase__,       UpperCAmelCase__,       max_epochs=UpperCAmelCase__,       batch_size=UpperCAmelCase__,       eval_freq=1_00,       igf_model_path=UpperCAmelCase__,       )

       del model, secondary_learner_train_data
       torch.cuda.empty_cache()

       return secondary_learner
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__=32,       UpperCAmelCase__=10_00,       UpperCAmelCase__=16,       UpperCAmelCase__=1.0,       UpperCAmelCase__=recopy_gpta,       UpperCAmelCase__=None,       UpperCAmelCase__=10,       UpperCAmelCase__="gpt2_finetuned.pt",       )			->			int:
       A_						=      torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""				)
       A_						=      RandomSampler(UpperCAmelCase__				)
       A_						=      DataLoader(UpperCAmelCase__,       sampler=UpperCAmelCase__				)

       A_						=      max_steps // (len(UpperCAmelCase__				)) + 1
       A_						=      0
       A_						=      torch.zeros((1, context_len),       dtype=torch.long,       device=UpperCAmelCase__				)
       A_					,			A_					,			A_						=      recopy_model(UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)

       model.train()
       if secondary_learner is not None:
              secondary_learner.to(UpperCAmelCase__				)
              secondary_learner.eval()
       A_						=      []
       A_						=      0

       A_						=      []
       A_						=      []

       # Compute the performance of the transformer model at the beginning
       A_						=      compute_perplexity(UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)
       test_perps.append(UpperCAmelCase__				)
       print("""Test perplexity, step""",       UpperCAmelCase__,       """:""",       UpperCAmelCase__				)
       for epoch in range(int(UpperCAmelCase__				)				):
              for step, example in enumerate(UpperCAmelCase__				):
                     torch.cuda.empty_cache()
                     A_						=      random.randint(0,       example.size(2				) - context_len - 1				)
                     A_						=      example[0, 0, start : start + context_len]
                     lm_optimizer.zero_grad()
                     A_						=      model(UpperCAmelCase__,       labels=UpperCAmelCase__				)
                     A_						=      True

                     if secondary_learner is not None:
                            A_						=      secondary_learner.forward(
                                torch.tensor(UpperCAmelCase__,       dtype=torch.long,       device=UpperCAmelCase__				).unsqueeze(0				)				)[0].item()
                            observed_qs.append(float(UpperCAmelCase__				)				)

                            # Here we implement the simple non-constant threshold for the predicted IG(X) value
                            # We will decay the selectivity of our secondary learner filter from
                            # 1 standard deviation above average to 1 below average after 10 batches.

                            if global_step == 10:
                                   A_						=      -1
                            if predicted_q < threshold:
                                   A_						=      False

            # If we passed the filter, add the context to the batch!
                     if do_backprop:
                            contexts.append(np.array(context.cpu()				)				)
                            A_						=      outputs[0]
                            lm_loss.backward()
                            examples += 1

                     del outputs

                     # Once the batch is filled with enough contexts, backprop on the batch.
                     if examples == batch_size:
                            torch.cuda.empty_cache()
                            A_						=      0
                            # Do LM backprop
                            torch.nn.utils.clip_grad_norm_(model.parameters(),       3.0				)
                            lm_optimizer.step()
                            lm_scheduler.step()  # Update learning rate schedule
                            global_step += 1
                            # Compute the performance of the transformer model at this batch
                            if global_step % eval_interval == 0:
                                   A_						=      compute_perplexity(UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)
                                   test_perps.append(UpperCAmelCase__				)

                                   print("""Test perplexity, step""",       UpperCAmelCase__,       """:""",       UpperCAmelCase__				)
            # Break out of the loop after 60 batches
                     if max_steps > 0 and global_step > 60:
                            break
              if max_steps > 0 and global_step > 60:
                     break

    # save finetuned transformer model
       torch.save(model.state_dict(),       UpperCAmelCase__				)
       torch.cuda.empty_cache()
       # Do some cleaning up so we can reinitialize for the next run of this function
       del lm_optimizer
       del lm_scheduler
       return model
def       UpperCAmelCase__    (      )			->			List[str]:
       A_						=      argparse.ArgumentParser(description="""Fine-tune a transformer model with IGF on a language modeling task"""				)

       # Required parameters
       parser.add_argument(
           """--data_dir""",       default=UpperCAmelCase__,       type=UpperCAmelCase__,       required=UpperCAmelCase__,       help="""The input data dir. Should contain data files for WikiText.""",       )
       parser.add_argument(
           """--model_name_or_path""",       default=UpperCAmelCase__,       type=UpperCAmelCase__,       required=UpperCAmelCase__,       help="""Path to pretrained model or model identifier from huggingface.co/models""",       )
       parser.add_argument(
           """--data_file""",       type=UpperCAmelCase__,       default=UpperCAmelCase__,       help=(
               """A jbl file containing tokenized data which can be split as objective dataset, """
               """train_dataset and test_dataset."""
           ),       )

       parser.add_argument(
           """--igf_data_file""",       type=UpperCAmelCase__,       default=UpperCAmelCase__,       help="""A jbl file containing the context and information gain pairs to train secondary learner.""",       )

       parser.add_argument(
           """--output_dir""",       default=UpperCAmelCase__,       type=UpperCAmelCase__,       required=UpperCAmelCase__,       help="""The output directory where the final fine-tuned model is stored.""",       )

       parser.add_argument(
           """--tokenizer_name""",       default=UpperCAmelCase__,       type=UpperCAmelCase__,       help="""Pretrained tokenizer name or path if not the same as model_name""",       )
       parser.add_argument("""--seed""",       type=UpperCAmelCase__,       default=UpperCAmelCase__,       help="""A seed for reproducible training."""				)

       parser.add_argument(
           """--context_len""",       default=32,       type=UpperCAmelCase__,       help=(
               """The maximum total input sequence length after tokenization. Sequences longer """
               """than this will be truncated, sequences shorter will be padded."""
           ),       )

       parser.add_argument(
           """--size_objective_set""",       default=1_00,       type=UpperCAmelCase__,       help="""number of articles that are long enough to be used as our objective set""",       )
       parser.add_argument(
           """--eval_freq""",       default=1_00,       type=UpperCAmelCase__,       help="""secondary model evaluation is triggered at eval_freq"""				)

       parser.add_argument("""--max_steps""",       default=10_00,       type=UpperCAmelCase__,       help="""To calculate training epochs"""				)

       parser.add_argument(
           """--secondary_learner_batch_size""",       default=1_28,       type=UpperCAmelCase__,       help="""batch size of training data for secondary learner""",       )

       parser.add_argument(
           """--batch_size""",       default=16,       type=UpperCAmelCase__,       help="""batch size of training data of language model(gpt2) """				)

       parser.add_argument(
           """--eval_interval""",       default=10,       type=UpperCAmelCase__,       help=(
               """decay the selectivity of our secondary learner filter from"""
               """1 standard deviation above average to 1 below average after 10 batches"""
           ),       )

       parser.add_argument(
           """--number""",       default=1_00,       type=UpperCAmelCase__,       help="""The number of examples split to be used as objective_set/test_data"""				)

       parser.add_argument(
           """--min_len""",       default=10_26,       type=UpperCAmelCase__,       help="""The minimum length of the article to be used as objective set"""				)

       parser.add_argument(
           """--secondary_learner_max_epochs""",       default=15,       type=UpperCAmelCase__,       help="""number of epochs to train secondary learner"""				)

       parser.add_argument("""--trim""",       default=UpperCAmelCase__,       type=UpperCAmelCase__,       help="""truncate the example if it exceeds context length"""				)

       parser.add_argument(
           """--threshold""",       default=1.0,       type=UpperCAmelCase__,       help=(
               """The threshold value used by secondary learner to filter the train_data and allow only"""
               """ informative data as input to the model"""
           ),       )

       parser.add_argument("""--finetuned_model_name""",       default="""gpt2_finetuned.pt""",       type=UpperCAmelCase__,       help="""finetuned_model_name"""				)

       parser.add_argument(
           """--recopy_model""",       default=UpperCAmelCase__,       type=UpperCAmelCase__,       help="""Reset the model to the original pretrained GPT-2 weights after each iteration""",       )

       # function calls
       # Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
       generate_n_pairs(
           context_len=32,       max_steps=10,       size_objective_set=1_00,       min_len=10_26,       trim=UpperCAmelCase__,       data_file="""data/tokenized_stories_train_wikitext103.jbl""",       igf_data_file="""igf_context_pairs.jbl""",       )

       # Load train data for secondary learner
       A_						=      joblib.load("""data/IGF_values.jbl"""				)

       # Train secondary learner
       A_						=      training_secondary_learner(
           UpperCAmelCase__,       secondary_learner_max_epochs=15,       secondary_learner_batch_size=1_28,       eval_freq=1_00,       igf_model_path="""igf_model.pt""",       )

       # load pretrained gpt2 model
       A_						=      GPTaLMHeadModel.from_pretrained("""gpt2"""				)
       set_seed(42				)

       # Generate train and test data to train and evaluate gpt2 model
       A_					,			A_						=      generate_datasets(
           context_len=32,       file="""data/tokenized_stories_train_wikitext103.jbl""",       number=1_00,       min_len=10_26,       trim=UpperCAmelCase__				)

       # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
       finetune(
           UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       context_len=32,       max_steps=10_00,       batch_size=16,       threshold=1.0,       recopy_model=UpperCAmelCase__,       secondary_learner=UpperCAmelCase__,       eval_interval=10,       finetuned_model_name="""gpt2_finetuned.pt""",       )


if __name__ == "__main__":
      main()



'''simple docstring'''







from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block


@dataclass
class 					A__      (      _snake_case			):
    lowercase						=		42


class 					A__      (      nn.Module			):



    def __init__(					self		,				UpperCamelCase__=3		,				UpperCamelCase__=3		,				UpperCamelCase__=("DownEncoderBlock2D",)		,				UpperCamelCase__=(64,)		,				UpperCamelCase__=2		,				UpperCamelCase__=32		,				UpperCamelCase__="silu"		,				UpperCamelCase__=True		,				)						->      Union[str, Any]:





           '''simple docstring'''
           super().__init__()
           A_						=      layers_per_block

           A_						=      torch.nn.Convad(
               UpperCamelCase__		,				block_out_channels[0]		,				kernel_size=3		,				stride=1		,				padding=1		,				)

           A_						=      None
           A_						=      nn.ModuleList([]       )

           # down
           A_						=      block_out_channels[0]
           for i, down_block_type in enumerate(UpperCamelCase__       ):
                  A_						=      output_channel
                  A_						=      block_out_channels[i]
                  A_						=      i == len(UpperCamelCase__       ) - 1

                  A_						=      get_down_block(
                      UpperCamelCase__		,				num_layers=self.layers_per_block		,				in_channels=UpperCamelCase__		,				out_channels=UpperCamelCase__		,				add_downsample=not is_final_block		,				resnet_eps=1e-6		,				downsample_padding=0		,				resnet_act_fn=UpperCamelCase__		,				resnet_groups=UpperCamelCase__		,				attention_head_dim=UpperCamelCase__		,				temb_channels=UpperCamelCase__		,				)
                  self.down_blocks.append(UpperCamelCase__       )

           # mid
           A_						=      UNetMidBlockaD(
               in_channels=block_out_channels[-1]		,				resnet_eps=1e-6		,				resnet_act_fn=UpperCamelCase__		,				output_scale_factor=1		,				resnet_time_scale_shift="""default"""		,				attention_head_dim=block_out_channels[-1]		,				resnet_groups=UpperCamelCase__		,				temb_channels=UpperCamelCase__		,				)

           # out
           A_						=      nn.GroupNorm(num_channels=block_out_channels[-1]		,				num_groups=UpperCamelCase__		,				eps=1e-6       )
           A_						=      nn.SiLU()

           A_						=      2 * out_channels if double_z else out_channels
           A_						=      nn.Convad(block_out_channels[-1]		,				UpperCamelCase__		,				3		,				padding=1       )

           A_						=      False







    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      str:





           '''simple docstring'''
           A_						=      x
           A_						=      self.conv_in(UpperCamelCase__       )

           if self.training and self.gradient_checkpointing:

                  def create_custom_forward(UpperCamelCase__       ):
                         def custom_forward(*UpperCamelCase__       ):
                                return module(*UpperCamelCase__       )

                         return custom_forward

                  # down
                  if is_torch_version(""">="""		,				"""1.11.0"""       ):
                         for down_block in self.down_blocks:
                                A_						=      torch.utils.checkpoint.checkpoint(
                                    create_custom_forward(UpperCamelCase__       )		,				UpperCamelCase__		,				use_reentrant=UpperCamelCase__       )
                         # middle
                         A_						=      torch.utils.checkpoint.checkpoint(
                             create_custom_forward(self.mid_block       )		,				UpperCamelCase__		,				use_reentrant=UpperCamelCase__       )
                  else:
                         for down_block in self.down_blocks:
                                A_						=      torch.utils.checkpoint.checkpoint(create_custom_forward(UpperCamelCase__       )		,				UpperCamelCase__       )
                         # middle
                         A_						=      torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block       )		,				UpperCamelCase__       )

           else:
                  # down
                  for down_block in self.down_blocks:
                         A_						=      down_block(UpperCamelCase__       )

                  # middle
                  A_						=      self.mid_block(UpperCamelCase__       )

           # post-process
           A_						=      self.conv_norm_out(UpperCamelCase__       )
           A_						=      self.conv_act(UpperCamelCase__       )
           A_						=      self.conv_out(UpperCamelCase__       )

           return sample


class 					A__      (      nn.Module			):



    def __init__(					self		,				UpperCamelCase__=3		,				UpperCamelCase__=3		,				UpperCamelCase__=("UpDecoderBlock2D",)		,				UpperCamelCase__=(64,)		,				UpperCamelCase__=2		,				UpperCamelCase__=32		,				UpperCamelCase__="silu"		,				UpperCamelCase__="group"		,				)						->      List[Any]:





           '''simple docstring'''
           super().__init__()
           A_						=      layers_per_block

           A_						=      nn.Convad(
               UpperCamelCase__		,				block_out_channels[-1]		,				kernel_size=3		,				stride=1		,				padding=1		,				)

           A_						=      None
           A_						=      nn.ModuleList([]       )

           A_						=      in_channels if norm_type == """spatial""" else None

           # mid
           A_						=      UNetMidBlockaD(
               in_channels=block_out_channels[-1]		,				resnet_eps=1e-6		,				resnet_act_fn=UpperCamelCase__		,				output_scale_factor=1		,				resnet_time_scale_shift="""default""" if norm_type == """group""" else norm_type		,				attention_head_dim=block_out_channels[-1]		,				resnet_groups=UpperCamelCase__		,				temb_channels=UpperCamelCase__		,				)

           # up
           A_						=      list(reversed(UpperCamelCase__       )       )
           A_						=      reversed_block_out_channels[0]
           for i, up_block_type in enumerate(UpperCamelCase__       ):
                  A_						=      output_channel
                  A_						=      reversed_block_out_channels[i]

                  A_						=      i == len(UpperCamelCase__       ) - 1

                  A_						=      get_up_block(
                      UpperCamelCase__		,				num_layers=self.layers_per_block + 1		,				in_channels=UpperCamelCase__		,				out_channels=UpperCamelCase__		,				prev_output_channel=UpperCamelCase__		,				add_upsample=not is_final_block		,				resnet_eps=1e-6		,				resnet_act_fn=UpperCamelCase__		,				resnet_groups=UpperCamelCase__		,				attention_head_dim=UpperCamelCase__		,				temb_channels=UpperCamelCase__		,				resnet_time_scale_shift=UpperCamelCase__		,				)
                  self.up_blocks.append(UpperCamelCase__       )
                  A_						=      output_channel

           # out
           if norm_type == "spatial":
                  A_						=      SpatialNorm(block_out_channels[0]		,				UpperCamelCase__       )
           else:
                  A_						=      nn.GroupNorm(num_channels=block_out_channels[0]		,				num_groups=UpperCamelCase__		,				eps=1e-6       )
           A_						=      nn.SiLU()
           A_						=      nn.Convad(block_out_channels[0]		,				UpperCamelCase__		,				3		,				padding=1       )

           A_						=      False







    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__=None       )						->      Optional[Any]:





           '''simple docstring'''
           A_						=      z
           A_						=      self.conv_in(UpperCamelCase__       )

           A_						=      next(iter(self.up_blocks.parameters()       )       ).dtype
           if self.training and self.gradient_checkpointing:

                  def create_custom_forward(UpperCamelCase__       ):
                         def custom_forward(*UpperCamelCase__       ):
                                return module(*UpperCamelCase__       )

                         return custom_forward

                  if is_torch_version(""">="""		,				"""1.11.0"""       ):
                         # middle
                         A_						=      torch.utils.checkpoint.checkpoint(
                             create_custom_forward(self.mid_block       )		,				UpperCamelCase__		,				UpperCamelCase__		,				use_reentrant=UpperCamelCase__       )
                         A_						=      sample.to(UpperCamelCase__       )

                         # up
                         for up_block in self.up_blocks:
                                A_						=      torch.utils.checkpoint.checkpoint(
                                    create_custom_forward(UpperCamelCase__       )		,				UpperCamelCase__		,				UpperCamelCase__		,				use_reentrant=UpperCamelCase__       )
                  else:
                         # middle
                         A_						=      torch.utils.checkpoint.checkpoint(
                             create_custom_forward(self.mid_block       )		,				UpperCamelCase__		,				UpperCamelCase__       )
                         A_						=      sample.to(UpperCamelCase__       )

                         # up
                         for up_block in self.up_blocks:
                                A_						=      torch.utils.checkpoint.checkpoint(create_custom_forward(UpperCamelCase__       )		,				UpperCamelCase__		,				UpperCamelCase__       )
           else:
                  # middle
                  A_						=      self.mid_block(UpperCamelCase__		,				UpperCamelCase__       )
                  A_						=      sample.to(UpperCamelCase__       )

                  # up
                  for up_block in self.up_blocks:
                         A_						=      up_block(UpperCamelCase__		,				UpperCamelCase__       )

        # post-process
           if latent_embeds is None:
                  A_						=      self.conv_norm_out(UpperCamelCase__       )
           else:
                  A_						=      self.conv_norm_out(UpperCamelCase__		,				UpperCamelCase__       )
           A_						=      self.conv_act(UpperCamelCase__       )
           A_						=      self.conv_out(UpperCamelCase__       )

           return sample


class 					A__      (      nn.Module			):



    def __init__(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__=None		,				UpperCamelCase__="random"		,				UpperCamelCase__=False		,				UpperCamelCase__=True       )						->      str:





           '''simple docstring'''
           super().__init__()
           A_						=      n_e
           A_						=      vq_embed_dim
           A_						=      beta
           A_						=      legacy

           A_						=      nn.Embedding(self.n_e		,				self.vq_embed_dim       )
           self.embedding.weight.data.uniform_(-1.0 / self.n_e		,				1.0 / self.n_e       )

           A_						=      remap
           if self.remap is not None:
                  self.register_buffer("""used"""		,				torch.tensor(np.load(self.remap       )       )       )
                  A_						=      self.used.shape[0]
                  A_						=      unknown_index  # "random" or "extra" or integer
                  if self.unknown_index == "extra":
                         A_						=      self.re_embed
                         A_						=      self.re_embed + 1
                  print(
                      f'''Remapping {self.n_e} indices to {self.re_embed} indices. '''
                      f'''Using {self.unknown_index} for unknown indices.'''       )
           else:
                  A_						=      n_e

           A_						=      sane_index_shape



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      Optional[Any]:





           '''simple docstring'''
           A_						=      inds.shape
           assert len(UpperCamelCase__       ) > 1
           A_						=      inds.reshape(ishape[0]		,				-1       )
           A_						=      self.used.to(UpperCamelCase__       )
           A_						=      (inds[:, :, None] == used[None, None, ...]).long()
           A_						=      match.argmax(-1       )
           A_						=      match.sum(2       ) < 1
           if self.unknown_index == "random":
                  A_						=      torch.randint(0		,				self.re_embed		,				size=new[unknown].shape       ).to(device=new.device       )
           else:
                  A_						=      self.unknown_index
           return new.reshape(UpperCamelCase__       )



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      Dict:





           '''simple docstring'''
           A_						=      inds.shape
           assert len(UpperCamelCase__       ) > 1
           A_						=      inds.reshape(ishape[0]		,				-1       )
           A_						=      self.used.to(UpperCamelCase__       )
           if self.re_embed > self.used.shape[0]:  # extra token
                  A_						=      0  # simply set to zero
           A_						=      torch.gather(used[None, :][inds.shape[0] * [0], :]		,				1		,				UpperCamelCase__       )
           return back.reshape(UpperCamelCase__       )



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      str:





           '''simple docstring'''
           # reshape z -> (batch, height, width, channel) and flatten
           A_						=      z.permute(0		,				2		,				3		,				1       ).contiguous()
           A_						=      z.view(-1		,				self.vq_embed_dim       )

           # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
           A_						=      torch.argmin(torch.cdist(UpperCamelCase__		,				self.embedding.weight       )		,				dim=1       )

           A_						=      self.embedding(UpperCamelCase__       ).view(z.shape       )
           A_						=      None
           A_						=      None

           # compute loss for embedding
           if not self.legacy:
                  A_						=      self.beta * torch.mean((z_q.detach() - z) ** 2       ) + torch.mean((z_q - z.detach()) ** 2       )
           else:
                  A_						=      torch.mean((z_q.detach() - z) ** 2       ) + self.beta * torch.mean((z_q - z.detach()) ** 2       )

           # preserve gradients
           A_						=      z + (z_q - z).detach()

           # reshape back to match original input shape
           A_						=      z_q.permute(0		,				3		,				1		,				2       ).contiguous()

           if self.remap is not None:
                  A_						=      min_encoding_indices.reshape(z.shape[0]		,				-1       )  # add batch axis
                  A_						=      self.remap_to_used(UpperCamelCase__       )
                  A_						=      min_encoding_indices.reshape(-1		,				1       )  # flatten

           if self.sane_index_shape:
                  A_						=      min_encoding_indices.reshape(z_q.shape[0]		,				z_q.shape[2]		,				z_q.shape[3]       )

           return z_q, loss, (perplexity, min_encodings, min_encoding_indices)







    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__       )						->      Optional[int]:





           '''simple docstring'''
           # shape specifying (batch, height, width, channel)
           if self.remap is not None:
                  A_						=      indices.reshape(shape[0]		,				-1       )  # add batch axis
                  A_						=      self.unmap_to_all(UpperCamelCase__       )
                  A_						=      indices.reshape(-1       )  # flatten again

           # get quantized latent vectors
           A_						=      self.embedding(UpperCamelCase__       )

           if shape is not None:
                  A_						=      z_q.view(UpperCamelCase__       )
                  # reshape back to match original input shape
                  A_						=      z_q.permute(0		,				3		,				1		,				2       ).contiguous()

           return z_q


class 					A__      (      _snake_case			):



    def __init__(					self		,				UpperCamelCase__		,				UpperCamelCase__=False       )						->      Dict:





           '''simple docstring'''
           A_						=      parameters
           A_					,			A_						=      torch.chunk(UpperCamelCase__		,				2		,				dim=1       )
           A_						=      torch.clamp(self.logvar		,				-30.0		,				20.0       )
           A_						=      deterministic
           A_						=      torch.exp(0.5 * self.logvar       )
           A_						=      torch.exp(self.logvar       )
           if self.deterministic:
                  A_						=      A_						=      torch.zeros_like(
                      self.mean		,				device=self.parameters.device		,				dtype=self.parameters.dtype       )



    def 					snake_case_			(					self		,				UpperCamelCase__ = None       )						->      torch.FloatTensor:





           '''simple docstring'''
           # make sure sample is on the same device as the parameters and has same dtype
           A_						=      randn_tensor(
               self.mean.shape		,				generator=UpperCamelCase__		,				device=self.parameters.device		,				dtype=self.parameters.dtype       )
           A_						=      self.mean + self.std * sample
           return x



    def 					snake_case_			(					self		,				UpperCamelCase__=None       )						->      int:





           '''simple docstring'''
           if self.deterministic:
                  return torch.Tensor([0.0]       )
           else:
                  if other is None:
                         return 0.5 * torch.sum(torch.pow(self.mean		,				2       ) + self.var - 1.0 - self.logvar		,				dim=[1, 2, 3]       )
                  else:
                         return 0.5 * torch.sum(
                             torch.pow(self.mean - other.mean		,				2       ) / other.var
                             + self.var / other.var
                             - 1.0
                             - self.logvar
                             + other.logvar		,				dim=[1, 2, 3]		,				)



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__=[1, 2, 3]       )						->      Optional[Any]:





           '''simple docstring'''
           if self.deterministic:
                  return torch.Tensor([0.0]       )
           A_						=      np.log(2.0 * np.pi       )
           return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean		,				2       ) / self.var		,				dim=UpperCamelCase__       )







    def 					snake_case_			(					self       )						->      List[Any]:





           '''simple docstring'''
           return self.mean
