




import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

UpperCamelCase								=					16
UpperCamelCase								=					32



def 							_A       (						lowerCAmelCase_					: Accelerator       ,    lowerCAmelCase_					: int = 16      ):
     """simple docstring"""

     lowerCAmelCase__          =			AutoTokenizer.from_pretrained("bert-base-cased"      )
     lowerCAmelCase__          =			load_dataset("glue"       ,    "mrpc"      )

     def tokenize_function(lowerCAmelCase_					: List[Any]      ):
          # max_length=None => use the model max length (it's actually the default)
          lowerCAmelCase__          =			tokenizer(examples["sentence1"]       ,    examples["sentence2"]       ,    truncation=lowerCAmelCase_       ,    max_length=lowerCAmelCase_      )
          return outputs

     # Apply the method we just defined to all the examples in all the splits of the dataset
     # starting with the main process first:
     with accelerator.main_process_first():
          lowerCAmelCase__          =			datasets.map(
              lowerCAmelCase_       ,    batched=lowerCAmelCase_       ,    remove_columns=["idx", "sentence1", "sentence2"]       ,    )

     # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
     # transformers library
     lowerCAmelCase__          =			tokenized_datasets.rename_column("label"       ,    "labels"      )

     def collate_fn(lowerCAmelCase_					: Dict      ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          lowerCAmelCase__          =			128 if accelerator.distributed_type == DistributedType.TPU else None
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               lowerCAmelCase__          =			16
          elif accelerator.mixed_precision != "no":
               lowerCAmelCase__          =			8
          else:
               lowerCAmelCase__          =			None

          return tokenizer.pad(
              lowerCAmelCase_       ,    padding="longest"       ,    max_length=lowerCAmelCase_       ,    pad_to_multiple_of=lowerCAmelCase_       ,    return_tensors="pt"       ,    )

     # Instantiate dataloaders.
     lowerCAmelCase__          =			DataLoader(
         tokenized_datasets["train"]       ,    shuffle=lowerCAmelCase_       ,    collate_fn=lowerCAmelCase_       ,    batch_size=lowerCAmelCase_      )
     lowerCAmelCase__          =			DataLoader(
         tokenized_datasets["validation"]       ,    shuffle=lowerCAmelCase_       ,    collate_fn=lowerCAmelCase_       ,    batch_size=lowerCAmelCase_      )

     return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
     from accelerate.test_utils.training import mocked_dataloaders

     UpperCamelCase								=					mocked_dataloaders  # noqa: F811



def 							_A       (						lowerCAmelCase_					: str       ,    lowerCAmelCase_					: Any      ):
     """simple docstring"""

     if os.environ.get("TESTING_MOCKED_DATALOADERS"       ,    lowerCAmelCase_      ) == "1":
          lowerCAmelCase__          =			2
     # Initialize Accelerator

     # New Code #
     # We pass in "all" to `log_with` to grab all available trackers in the environment
     # Note: If using a custom `Tracker` class, should be passed in here such as:
     # >>> log_with = ["all", MyCustomTrackerClassInstance()]
     if args.with_tracking:
          lowerCAmelCase__          =			Accelerator(
              cpu=args.cpu       ,    mixed_precision=args.mixed_precision       ,    log_with="all"       ,    project_dir=args.project_dir      )
     else:
          lowerCAmelCase__          =			Accelerator(cpu=args.cpu       ,    mixed_precision=args.mixed_precision      )
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     lowerCAmelCase__          =			config["lr"]
     lowerCAmelCase__          =			int(config["num_epochs"]      )
     lowerCAmelCase__          =			int(config["seed"]      )
     lowerCAmelCase__          =			int(config["batch_size"]      )
     set_seed(lowerCAmelCase_      )

     lowerCAmelCase__     ,		lowerCAmelCase__          =			get_dataloaders(lowerCAmelCase_       ,    lowerCAmelCase_      )
     lowerCAmelCase__          =			evaluate.load("glue"       ,    "mrpc"      )

     # If the batch size is too big we use gradient accumulation
     lowerCAmelCase__          =			1
     if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
          lowerCAmelCase__          =			batch_size // MAX_GPU_BATCH_SIZE
          lowerCAmelCase__          =			MAX_GPU_BATCH_SIZE

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     lowerCAmelCase__          =			AutoModelForSequenceClassification.from_pretrained("bert-base-cased"       ,    return_dict=lowerCAmelCase_      )

     # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
     # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
     # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
     lowerCAmelCase__          =			model.to(accelerator.device      )

     # Instantiate optimizer
     lowerCAmelCase__          =			AdamW(params=model.parameters()       ,    lr=lowerCAmelCase_      )

     # Instantiate scheduler
     lowerCAmelCase__          =			get_linear_schedule_with_warmup(
         optimizer=lowerCAmelCase_       ,    num_warmup_steps=100       ,    num_training_steps=(len(lowerCAmelCase_      ) * num_epochs) // gradient_accumulation_steps       ,    )

     # Prepare everything
     # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
     # prepare method.
     lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			accelerator.prepare(
         lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_      )

     # New Code #
     # We need to initialize the trackers we use. Overall configurations can also be stored
     if args.with_tracking:
          lowerCAmelCase__          =			os.path.split(lowerCAmelCase_      )[-1].split("."      )[0]
          accelerator.init_trackers(lowerCAmelCase_       ,    lowerCAmelCase_      )

     # Now we train the model
     for epoch in range(lowerCAmelCase_      ):
          model.train()
          # New Code #
          # For our tracking example, we will log the total loss of each epoch
          if args.with_tracking:
               lowerCAmelCase__          =			0
          for step, batch in enumerate(lowerCAmelCase_      ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device      )
               lowerCAmelCase__          =			model(**lowerCAmelCase_      )
               lowerCAmelCase__          =			outputs.loss
               # New Code #
               if args.with_tracking:
                    total_loss += loss.detach().float()
               lowerCAmelCase__          =			loss / gradient_accumulation_steps
               accelerator.backward(lowerCAmelCase_      )
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

          model.eval()
          for step, batch in enumerate(lowerCAmelCase_      ):
               # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
               batch.to(accelerator.device      )
               with torch.no_grad():
                    lowerCAmelCase__          =			model(**lowerCAmelCase_      )
               lowerCAmelCase__          =			outputs.logits.argmax(dim=-1      )
               lowerCAmelCase__     ,		lowerCAmelCase__          =			accelerator.gather_for_metrics((predictions, batch["labels"])      )
               metric.add_batch(
                   predictions=lowerCAmelCase_       ,    references=lowerCAmelCase_       ,    )

          lowerCAmelCase__          =			metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(F'epoch {epoch}:'       ,    lowerCAmelCase_      )

          # New Code #
          # To actually log, we call `Accelerator.log`
          # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
          if args.with_tracking:
               accelerator.log(
                   {
                       "accuracy": eval_metric["accuracy"],
                       "f1": eval_metric["f1"],
                       "train_loss": total_loss.item() / len(lowerCAmelCase_      ),
                       "epoch": epoch,
                   }       ,    step=lowerCAmelCase_       ,    )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
     if args.with_tracking:
          accelerator.end_training()



def 							_A       (						):
     """simple docstring"""

     lowerCAmelCase__          =			argparse.ArgumentParser(description="Simple example of training script."      )
     parser.add_argument(
         "--mixed_precision"       ,    type=lowerCAmelCase_       ,    default=lowerCAmelCase_       ,    choices=["no", "fp16", "bf16", "fp8"]       ,    help="Whether to use mixed precision. Choose"
         "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
         "and an Nvidia Ampere GPU."       ,    )
     parser.add_argument("--cpu"       ,    action="store_true"       ,    help="If passed, will train on the CPU."      )
     parser.add_argument(
         "--with_tracking"       ,    action="store_true"       ,    help="Whether to load in all available experiment trackers from the environment and use them for logging."       ,    )
     parser.add_argument(
         "--project_dir"       ,    type=lowerCAmelCase_       ,    default="logs"       ,    help="Location on where to store experiment tracking logs` and relevent project information"       ,    )
     lowerCAmelCase__          =			parser.parse_args()
     lowerCAmelCase__          =			{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
     training_function(lowerCAmelCase_       ,    lowerCAmelCase_      )


if __name__ == "__main__":
     main()






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 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				42






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Any=3       ,     SCREAMING_SNAKE_CASE__					:					int=3       ,     SCREAMING_SNAKE_CASE__					:					List[str]=("DownEncoderBlock2D",)       ,     SCREAMING_SNAKE_CASE__					:					Tuple=(64,)       ,     SCREAMING_SNAKE_CASE__					:					str=2       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=32       ,     SCREAMING_SNAKE_CASE__					:					List[str]="silu"       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=True       ,     )				->	str:
         super().__init__()
         lowerCAmelCase__          =			layers_per_block

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

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

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

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

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

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

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

         lowerCAmelCase__          =			False


    def        a				(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Dict       )				->	int:
         lowerCAmelCase__          =			x
         lowerCAmelCase__          =			self.conv_in(SCREAMING_SNAKE_CASE__       )

         if self.training and self.gradient_checkpointing:

              def create_custom_forward(SCREAMING_SNAKE_CASE__					:					Optional[int]       ):
                   def custom_forward(*SCREAMING_SNAKE_CASE__					:					Dict       ):
                        return module(*SCREAMING_SNAKE_CASE__       )

                   return custom_forward

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

         else:
              # down
              for down_block in self.down_blocks:
                   lowerCAmelCase__          =			down_block(SCREAMING_SNAKE_CASE__       )

              # middle
              lowerCAmelCase__          =			self.mid_block(SCREAMING_SNAKE_CASE__       )

         # post-process
         lowerCAmelCase__          =			self.conv_norm_out(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.conv_act(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.conv_out(SCREAMING_SNAKE_CASE__       )

         return sample






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					str=3       ,     SCREAMING_SNAKE_CASE__					:					Dict=3       ,     SCREAMING_SNAKE_CASE__					:					int=("UpDecoderBlock2D",)       ,     SCREAMING_SNAKE_CASE__					:					Tuple=(64,)       ,     SCREAMING_SNAKE_CASE__					:					Tuple=2       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=32       ,     SCREAMING_SNAKE_CASE__					:					List[str]="silu"       ,     SCREAMING_SNAKE_CASE__					:					List[str]="group"       ,     )				->	Union[str, Any]:
         super().__init__()
         lowerCAmelCase__          =			layers_per_block

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

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

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

         # mid
         lowerCAmelCase__          =			UNetMidBlockaD(
             in_channels=block_out_channels[-1]       ,     resnet_eps=1e-6       ,     resnet_act_fn=SCREAMING_SNAKE_CASE__       ,     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=SCREAMING_SNAKE_CASE__       ,     temb_channels=SCREAMING_SNAKE_CASE__       ,     )

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

              lowerCAmelCase__          =			i == len(SCREAMING_SNAKE_CASE__       ) - 1

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

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

         lowerCAmelCase__          =			False


    def        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Dict=None       )				->	Dict:
         lowerCAmelCase__          =			z
         lowerCAmelCase__          =			self.conv_in(SCREAMING_SNAKE_CASE__       )

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

              def create_custom_forward(SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ):
                   def custom_forward(*SCREAMING_SNAKE_CASE__					:					List[str]       ):
                        return module(*SCREAMING_SNAKE_CASE__       )

                   return custom_forward

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

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

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

              # up
              for up_block in self.up_blocks:
                   lowerCAmelCase__          =			up_block(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )

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

         return sample






class 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

    def __init__(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					Any       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=None       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]="random"       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=False       ,     SCREAMING_SNAKE_CASE__					:					Any=True       )				->	List[Any]:
         super().__init__()
         lowerCAmelCase__          =			n_e
         lowerCAmelCase__          =			vq_embed_dim
         lowerCAmelCase__          =			beta
         lowerCAmelCase__          =			legacy

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

         lowerCAmelCase__          =			remap
         if self.remap is not None:
              self.register_buffer("used"       ,     torch.tensor(np.load(self.remap       )       )       )
              lowerCAmelCase__          =			self.used.shape[0]
              lowerCAmelCase__          =			unknown_index  # "random" or "extra" or integer
              if self.unknown_index == "extra":
                   lowerCAmelCase__          =			self.re_embed
                   lowerCAmelCase__          =			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:
              lowerCAmelCase__          =			n_e

         lowerCAmelCase__          =			sane_index_shape


    def        a				(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       )				->	Optional[int]:
         lowerCAmelCase__          =			inds.shape
         assert len(SCREAMING_SNAKE_CASE__       ) > 1
         lowerCAmelCase__          =			inds.reshape(ishape[0]       ,     -1       )
         lowerCAmelCase__          =			self.used.to(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			(inds[:, :, None] == used[None, None, ...]).long()
         lowerCAmelCase__          =			match.argmax(-1       )
         lowerCAmelCase__          =			match.sum(2       ) < 1
         if self.unknown_index == "random":
              lowerCAmelCase__          =			torch.randint(0       ,     self.re_embed       ,     size=new[unknown].shape       ).to(device=new.device       )
         else:
              lowerCAmelCase__          =			self.unknown_index
         return new.reshape(SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Dict       )				->	Optional[int]:
         lowerCAmelCase__          =			inds.shape
         assert len(SCREAMING_SNAKE_CASE__       ) > 1
         lowerCAmelCase__          =			inds.reshape(ishape[0]       ,     -1       )
         lowerCAmelCase__          =			self.used.to(SCREAMING_SNAKE_CASE__       )
         if self.re_embed > self.used.shape[0]:  # extra token
              lowerCAmelCase__          =			0  # simply set to zero
         lowerCAmelCase__          =			torch.gather(used[None, :][inds.shape[0] * [0], :]       ,     1       ,     SCREAMING_SNAKE_CASE__       )
         return back.reshape(SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Tuple       )				->	Tuple:
         # reshape z -> (batch, height, width, channel) and flatten
         lowerCAmelCase__          =			z.permute(0       ,     2       ,     3       ,     1       ).contiguous()
         lowerCAmelCase__          =			z.view(-1       ,     self.vq_embed_dim       )

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

         lowerCAmelCase__          =			self.embedding(SCREAMING_SNAKE_CASE__       ).view(z.shape       )
         lowerCAmelCase__          =			None
         lowerCAmelCase__          =			None

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

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

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

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

         if self.sane_index_shape:
              lowerCAmelCase__          =			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        a				(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					str       )				->	Any:
         # shape specifying (batch, height, width, channel)
         if self.remap is not None:
              lowerCAmelCase__          =			indices.reshape(shape[0]       ,     -1       )  # add batch axis
              lowerCAmelCase__          =			self.unmap_to_all(SCREAMING_SNAKE_CASE__       )
              lowerCAmelCase__          =			indices.reshape(-1       )  # flatten again

         # get quantized latent vectors
         lowerCAmelCase__          =			self.embedding(SCREAMING_SNAKE_CASE__       )

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

         return z_q






class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    def __init__(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Any=False       )				->	Optional[int]:
         lowerCAmelCase__          =			parameters
         lowerCAmelCase__     ,		lowerCAmelCase__          =			torch.chunk(SCREAMING_SNAKE_CASE__       ,     2       ,     dim=1       )
         lowerCAmelCase__          =			torch.clamp(self.logvar       ,     -30.0       ,     20.0       )
         lowerCAmelCase__          =			deterministic
         lowerCAmelCase__          =			torch.exp(0.5 * self.logvar       )
         lowerCAmelCase__          =			torch.exp(self.logvar       )
         if self.deterministic:
              lowerCAmelCase__          =			lowerCAmelCase__          =			torch.zeros_like(
                  self.mean       ,     device=self.parameters.device       ,     dtype=self.parameters.dtype       )


    def        a				(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					Optional[torch.Generator] = None       )				->	torch.FloatTensor:
         # make sure sample is on the same device as the parameters and has same dtype
         lowerCAmelCase__          =			randn_tensor(
             self.mean.shape       ,     generator=SCREAMING_SNAKE_CASE__       ,     device=self.parameters.device       ,     dtype=self.parameters.dtype       )
         lowerCAmelCase__          =			self.mean + self.std * sample
         return x


    def        a				(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=None       )				->	Union[str, Any]:
         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        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					List[str]=[1, 2, 3]       )				->	Tuple:
         if self.deterministic:
              return torch.Tensor([0.0]       )
         lowerCAmelCase__          =			np.log(2.0 * np.pi       )
         return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean       ,     2       ) / self.var       ,     dim=SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Union[str, Any]       )				->	Dict:
         return self.mean
