





import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


UpperCAmelCase__						:			Union[str, Any]									=logging.getLogger(__name__)

@dataclass
class       __A     :
				__A      =				field(
				    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}    )
				__A      =				field(
				    default=a    ,					metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}    )
				__A      =				field(
				    default="""NER"""    ,					metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""}    )
				__A      =				field(
				    default=a    ,					metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}    )
				__A      =				field(default=a    ,					metadata={"""help""": """Set this flag to use fast tokenization."""}    )
				# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
				# or just modify its tokenizer_config.json.
				__A      =				field(
				    default=a    ,					metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}    ,					)




@dataclass
class       __A     :
				__A      =				field(
				    metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}    )
				__A      =				field(
				    default=a    ,					metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}    ,					)
				__A      =				field(
				    default=1_28    ,					metadata={
				        """help""": (
				            """The maximum total input sequence length after tokenization. Sequences longer """
				            """than this will be truncated, sequences shorter will be padded."""
				        )
				    }    ,					)
				__A      =				field(
				    default=a    ,					metadata={"""help""": """Overwrite the cached training and evaluation sets"""}    )





def      _lowercase					(						)						->  List[str]:
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.

				lowerCamelCase	 =HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
				if len(sys.argv				) == 2 and sys.argv[1].endswith(""".json"""				):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
				else:
								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =parser.parse_args_into_dataclasses()

				if (
				    os.path.exists(training_args.output_dir				)
				    and os.listdir(training_args.output_dir				)
				    and training_args.do_train
				    and not training_args.overwrite_output_dir
				):
								raise ValueError(
								    F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
								    """ --overwrite_output_dir to overcome."""				)

				lowerCamelCase	 =import_module("""tasks"""				)
				try:
								lowerCamelCase	 =getattr(_UpperCAmelCase			,      model_args.task_type				)
								lowerCamelCase	 =token_classification_task_clazz()
				except AttributeError:
								raise ValueError(
								    F"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
								    F"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""				)

				# Setup logging
				logging.basicConfig(
				    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""			,      datefmt="""%m/%d/%Y %H:%M:%S"""			,      level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN			,      )
				logger.warning(
				    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""			,      training_args.local_rank			,      training_args.device			,      training_args.n_gpu			,      bool(training_args.local_rank != -1				)			,      training_args.fpaa			,      )
				# Set the verbosity to info of the Transformers logger (on main process only):
				if is_main_process(training_args.local_rank				):
								transformers.utils.logging.set_verbosity_info()
								transformers.utils.logging.enable_default_handler()
								transformers.utils.logging.enable_explicit_format()
				logger.info("""Training/evaluation parameters %s"""			,      _UpperCAmelCase				)

				# Set seed
				set_seed(training_args.seed				)

				# Prepare CONLL-2003 task
				lowerCamelCase	 =token_classification_task.get_labels(data_args.labels				)
				lowerCamelCase	 =dict(enumerate(_UpperCAmelCase				)				)
				lowerCamelCase	 =len(_UpperCAmelCase				)

				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				lowerCamelCase	 =AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path			,      num_labels=_UpperCAmelCase			,      idalabel=_UpperCAmelCase			,      labelaid={label: i for i, label in enumerate(_UpperCAmelCase				)}			,      cache_dir=model_args.cache_dir			,      )
				lowerCamelCase	 =AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path			,      cache_dir=model_args.cache_dir			,      use_fast=model_args.use_fast			,      )
				lowerCamelCase	 =AutoModelForTokenClassification.from_pretrained(
				    model_args.model_name_or_path			,      from_tf=bool(""".ckpt""" in model_args.model_name_or_path				)			,      config=_UpperCAmelCase			,      cache_dir=model_args.cache_dir			,      )

				# Get datasets
				lowerCamelCase	 =(
				    TokenClassificationDataset(
				        token_classification_task=_UpperCAmelCase			,      data_dir=data_args.data_dir			,      tokenizer=_UpperCAmelCase			,      labels=_UpperCAmelCase			,      model_type=config.model_type			,      max_seq_length=data_args.max_seq_length			,      overwrite_cache=data_args.overwrite_cache			,      mode=Split.train			,      )
				    if training_args.do_train
				    else None
				)
				lowerCamelCase	 =(
				    TokenClassificationDataset(
				        token_classification_task=_UpperCAmelCase			,      data_dir=data_args.data_dir			,      tokenizer=_UpperCAmelCase			,      labels=_UpperCAmelCase			,      model_type=config.model_type			,      max_seq_length=data_args.max_seq_length			,      overwrite_cache=data_args.overwrite_cache			,      mode=Split.dev			,      )
				    if training_args.do_eval
				    else None
				)

				def align_predictions(_UpperCAmelCase			,      _UpperCAmelCase				) -> Tuple[List[int], List[int]]:
								lowerCamelCase	 =np.argmax(_UpperCAmelCase			,      axis=2				)

								lowerCamelCase ,  lowerCamelCase	 =preds.shape

								lowerCamelCase	 =[[] for _ in range(_UpperCAmelCase				)]
								lowerCamelCase	 =[[] for _ in range(_UpperCAmelCase				)]

								for i in range(_UpperCAmelCase				):
												for j in range(_UpperCAmelCase				):
																if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
																				out_label_list[i].append(label_map[label_ids[i][j]]				)
																				preds_list[i].append(label_map[preds[i][j]]				)

								return preds_list, out_label_list

				def compute_metrics(_UpperCAmelCase				) -> Dict:
								lowerCamelCase ,  lowerCamelCase	 =align_predictions(p.predictions			,      p.label_ids				)
								return {
								    "accuracy_score": accuracy_score(_UpperCAmelCase			,      _UpperCAmelCase				),
								    "precision": precision_score(_UpperCAmelCase			,      _UpperCAmelCase				),
								    "recall": recall_score(_UpperCAmelCase			,      _UpperCAmelCase				),
								    "f1": fa_score(_UpperCAmelCase			,      _UpperCAmelCase				),
								}

				# Data collator
				lowerCamelCase	 =DataCollatorWithPadding(_UpperCAmelCase			,      pad_to_multiple_of=8				) if training_args.fpaa else None

				# Initialize our Trainer
				lowerCamelCase	 =Trainer(
				    model=_UpperCAmelCase			,      args=_UpperCAmelCase			,      train_dataset=_UpperCAmelCase			,      eval_dataset=_UpperCAmelCase			,      compute_metrics=_UpperCAmelCase			,      data_collator=_UpperCAmelCase			,      )

				# Training
				if training_args.do_train:
								trainer.train(
								    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path				) else None				)
								trainer.save_model()
								# For convenience, we also re-save the tokenizer to the same directory,
								# so that you can share your model easily on huggingface.co/models =)
								if trainer.is_world_process_zero():
												tokenizer.save_pretrained(training_args.output_dir				)

    # Evaluation
				lowerCamelCase	 ={}
				if training_args.do_eval:
								logger.info("""*** Evaluate ***"""				)

								lowerCamelCase	 =trainer.evaluate()

								lowerCamelCase	 =os.path.join(training_args.output_dir			,      """eval_results.txt"""				)
								if trainer.is_world_process_zero():
												with open(_UpperCAmelCase			,      """w"""				) as writer:
																logger.info("""***** Eval results *****"""				)
																for key, value in result.items():
																				logger.info("""  %s = %s"""			,      _UpperCAmelCase			,      _UpperCAmelCase				)
																				writer.write("""%s = %s\n""" % (key, value)				)

												results.update(_UpperCAmelCase				)

    # Predict
				if training_args.do_predict:
								lowerCamelCase	 =TokenClassificationDataset(
								    token_classification_task=_UpperCAmelCase			,      data_dir=data_args.data_dir			,      tokenizer=_UpperCAmelCase			,      labels=_UpperCAmelCase			,      model_type=config.model_type			,      max_seq_length=data_args.max_seq_length			,      overwrite_cache=data_args.overwrite_cache			,      mode=Split.test			,      )

								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =trainer.predict(_UpperCAmelCase				)
								lowerCamelCase ,  lowerCamelCase	 =align_predictions(_UpperCAmelCase			,      _UpperCAmelCase				)

								lowerCamelCase	 =os.path.join(training_args.output_dir			,      """test_results.txt"""				)
								if trainer.is_world_process_zero():
												with open(_UpperCAmelCase			,      """w"""				) as writer:
																for key, value in metrics.items():
																				logger.info("""  %s = %s"""			,      _UpperCAmelCase			,      _UpperCAmelCase				)
																				writer.write("""%s = %s\n""" % (key, value)				)

        # Save predictions
								lowerCamelCase	 =os.path.join(training_args.output_dir			,      """test_predictions.txt"""				)
								if trainer.is_world_process_zero():
												with open(_UpperCAmelCase			,      """w"""				) as writer:
																with open(os.path.join(data_args.data_dir			,      """test.txt"""				)			,      """r"""				) as f:
																				token_classification_task.write_predictions_to_file(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)

				return results





def      _lowercase					(						_UpperCAmelCase				)						->  Any:
				# For xla_spawn (TPUs)
				main()


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       __A     (						a    ):
				__A      =				42




class       __A     (						nn.Module    ):


				def __init__(		self   , UpperCAmelCase_=3   , UpperCAmelCase_=3   , UpperCAmelCase_=("DownEncoderBlock2D",)   , UpperCAmelCase_=(64,)   , UpperCAmelCase_=2   , UpperCAmelCase_=32   , UpperCAmelCase_="silu"   , UpperCAmelCase_=True   , ):
								super().__init__()
								lowerCamelCase	 =layers_per_block

								lowerCamelCase	 =torch.nn.Convad(
								    UpperCAmelCase_   , 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(UpperCAmelCase_							):
												lowerCamelCase	 =output_channel
												lowerCamelCase	 =block_out_channels[i]
												lowerCamelCase	 =i == len(UpperCAmelCase_							) - 1

												lowerCamelCase	 =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
								lowerCamelCase	 =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
								lowerCamelCase	 =nn.GroupNorm(num_channels=block_out_channels[-1]   , num_groups=UpperCAmelCase_   , eps=1E-6							)
								lowerCamelCase	 =nn.SiLU()

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

								lowerCamelCase	 =False


				def _snake_case       (		self   , UpperCAmelCase_							):
								lowerCamelCase	 =x
								lowerCamelCase	 =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:
																				lowerCamelCase	 =torch.utils.checkpoint.checkpoint(
																				    create_custom_forward(UpperCAmelCase_							)   , UpperCAmelCase_   , use_reentrant=UpperCAmelCase_							)
																# middle
																lowerCamelCase	 =torch.utils.checkpoint.checkpoint(
																    create_custom_forward(self.mid_block							)   , UpperCAmelCase_   , use_reentrant=UpperCAmelCase_							)
												else:
																for down_block in self.down_blocks:
																				lowerCamelCase	 =torch.utils.checkpoint.checkpoint(create_custom_forward(UpperCAmelCase_							)   , UpperCAmelCase_							)
																# middle
																lowerCamelCase	 =torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block							)   , UpperCAmelCase_							)

								else:
												# down
												for down_block in self.down_blocks:
																lowerCamelCase	 =down_block(UpperCAmelCase_							)

												# middle
												lowerCamelCase	 =self.mid_block(UpperCAmelCase_							)

								# post-process
								lowerCamelCase	 =self.conv_norm_out(UpperCAmelCase_							)
								lowerCamelCase	 =self.conv_act(UpperCAmelCase_							)
								lowerCamelCase	 =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"   , ):
								super().__init__()
								lowerCamelCase	 =layers_per_block

								lowerCamelCase	 =nn.Convad(
								    UpperCAmelCase_   , 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=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
								lowerCamelCase	 =list(reversed(UpperCAmelCase_							)							)
								lowerCamelCase	 =reversed_block_out_channels[0]
								for i, up_block_type in enumerate(UpperCAmelCase_							):
												lowerCamelCase	 =output_channel
												lowerCamelCase	 =reversed_block_out_channels[i]

												lowerCamelCase	 =i == len(UpperCAmelCase_							) - 1

												lowerCamelCase	 =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_							)
												lowerCamelCase	 =output_channel

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

								lowerCamelCase	 =False


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_=None							):
								lowerCamelCase	 =z
								lowerCamelCase	 =self.conv_in(UpperCAmelCase_							)

								lowerCamelCase	 =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
																lowerCamelCase	 =torch.utils.checkpoint.checkpoint(
																    create_custom_forward(self.mid_block							)   , UpperCAmelCase_   , UpperCAmelCase_   , use_reentrant=UpperCAmelCase_							)
																lowerCamelCase	 =sample.to(UpperCAmelCase_							)

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

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

												# up
												for up_block in self.up_blocks:
																lowerCamelCase	 =up_block(UpperCAmelCase_   , UpperCAmelCase_							)

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

								return sample




class       __A     (						nn.Module    ):


				def __init__(		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_=None   , UpperCAmelCase_="random"   , UpperCAmelCase_=False   , UpperCAmelCase_=True							):
								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 _snake_case       (		self   , UpperCAmelCase_							):
								lowerCamelCase	 =inds.shape
								assert len(UpperCAmelCase_							) > 1
								lowerCamelCase	 =inds.reshape(ishape[0]   , -1							)
								lowerCamelCase	 =self.used.to(UpperCAmelCase_							)
								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(UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_							):
								lowerCamelCase	 =inds.shape
								assert len(UpperCAmelCase_							) > 1
								lowerCamelCase	 =inds.reshape(ishape[0]   , -1							)
								lowerCamelCase	 =self.used.to(UpperCAmelCase_							)
								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   , UpperCAmelCase_							)
								return back.reshape(UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_							):
								# 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(UpperCAmelCase_   , self.embedding.weight							)   , dim=1							)

								lowerCamelCase	 =self.embedding(UpperCAmelCase_							).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(UpperCAmelCase_							)
												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 _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_							):
								# 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(UpperCAmelCase_							)
												lowerCamelCase	 =indices.reshape(-1							)  # flatten again

								# get quantized latent vectors
								lowerCamelCase	 =self.embedding(UpperCAmelCase_							)

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

								return z_q




class       __A     (						a    ):


				def __init__(		self   , UpperCAmelCase_   , UpperCAmelCase_=False							):
								lowerCamelCase	 =parameters
								lowerCamelCase ,  lowerCamelCase	 =torch.chunk(UpperCAmelCase_   , 2   , dim=1							)
								lowerCamelCase	 =torch.clamp(self.logvar   , -3_0.0   , 2_0.0							)
								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 _snake_case       (		self   , UpperCAmelCase_ = None							):
								# make sure sample is on the same device as the parameters and has same dtype
								lowerCamelCase	 =randn_tensor(
								    self.mean.shape   , generator=UpperCAmelCase_   , device=self.parameters.device   , dtype=self.parameters.dtype							)
								lowerCamelCase	 =self.mean + self.std * sample
								return x


				def _snake_case       (		self   , UpperCAmelCase_=None							):
								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]							):
								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=UpperCAmelCase_							)


				def _snake_case       (		self							):
								return self.mean





