
"""simple docstring"""


import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(nlp.__version__) != version.parse('''0.8.3'''):
		raise Exception('''requires gluonnlp == 0.8.3''')

if version.parse(mx.__version__) != version.parse('''1.5.0'''):
		raise Exception('''requires mxnet == 1.5.0''')

logging.set_verbosity_info()
_lowercase							   =				logging.get_logger(__name__)

_lowercase							   =				'''The Nymphenburg Palace is a beautiful palace in Munich!'''






def       _snake_case				(		snake_case__   :     str	,      snake_case__   :     str     ):
	A						   =   {
	    'attention_cell': 'multi_head',
	    'num_layers': 4,
	    'units': 1024,
	    'hidden_size': 768,
	    'max_length': 512,
	    'num_heads': 8,
	    'scaled': True,
	    'dropout': 0.1,
	    'use_residual': True,
	    'embed_size': 1024,
	    'embed_dropout': 0.1,
	    'word_embed': None,
	    'layer_norm_eps': 1e-5,
	    'token_type_vocab_size': 2,
	}

	A						   =   bort_4_8_768_1024_hparams

	# Let's construct the original Bort model here
	# Taken from official BERT implementation, see:
	# https://github.com/alexa/bort/blob/master/bort/bort.py
	A						   =   BERTEncoder(
	    attention_cell=predefined_args['attention_cell']	,      num_layers=predefined_args['num_layers']	,      units=predefined_args['units']	,      hidden_size=predefined_args['hidden_size']	,      max_length=predefined_args['max_length']	,      num_heads=predefined_args['num_heads']	,      scaled=predefined_args['scaled']	,      dropout=predefined_args['dropout']	,      output_attention=snake_case__	,      output_all_encodings=snake_case__	,      use_residual=predefined_args['use_residual']	,      activation=predefined_args.get('activation'	,      'gelu'     )	,      layer_norm_eps=predefined_args.get('layer_norm_eps'	,      snake_case__     )	,      )

	# Vocab information needs to be fetched first
	# It's the same as RoBERTa, so RobertaTokenizer can be used later
	A						   =   'openwebtext_ccnews_stories_books_cased'

	# Specify download folder to Gluonnlp's vocab
	A						   =   os.path.join(get_home_dir()	,      'models'     )
	A						   =   _load_vocab(snake_case__	,      snake_case__	,      snake_case__	,      cls=snake_case__     )

	A						   =   nlp.model.BERTModel(
	    snake_case__	,      len(snake_case__     )	,      units=predefined_args['units']	,      embed_size=predefined_args['embed_size']	,      embed_dropout=predefined_args['embed_dropout']	,      word_embed=predefined_args['word_embed']	,      use_pooler=snake_case__	,      use_token_type_embed=snake_case__	,      token_type_vocab_size=predefined_args['token_type_vocab_size']	,      use_classifier=snake_case__	,      use_decoder=snake_case__	,      )

	original_bort.load_parameters(snake_case__	,      cast_dtype=snake_case__	,      ignore_extra=snake_case__     )
	A						   =   original_bort._collect_params_with_prefix()

	# Build our config 🤗
	A						   =   {
	    'architectures': ['BertForMaskedLM'],
	    'attention_probs_dropout_prob': predefined_args['dropout'],
	    'hidden_act': 'gelu',
	    'hidden_dropout_prob': predefined_args['dropout'],
	    'hidden_size': predefined_args['embed_size'],
	    'initializer_range': 0.02,
	    'intermediate_size': predefined_args['hidden_size'],
	    'layer_norm_eps': predefined_args['layer_norm_eps'],
	    'max_position_embeddings': predefined_args['max_length'],
	    'model_type': 'bort',
	    'num_attention_heads': predefined_args['num_heads'],
	    'num_hidden_layers': predefined_args['num_layers'],
	    'pad_token_id': 1,  # 2 = BERT, 1 = RoBERTa
	    'type_vocab_size': 1,  # 2 = BERT, 1 = RoBERTa
	    'vocab_size': len(snake_case__     ),
	}

	A						   =   BertConfig.from_dict(snake_case__     )
	A						   =   BertForMaskedLM(snake_case__     )
	hf_bort_model.eval()

	# Parameter mapping table (Gluonnlp to Transformers)
	# * denotes layer index
	#
	# | Gluon Parameter                                                | Transformers Parameter
	# | -------------------------------------------------------------- | ----------------------
	# | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
	# | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
	# | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
	# | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
	# | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
	# | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
	# | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
	# | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
	# | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
	# | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
	# | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
	# | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
	# | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
	# | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
	# | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
	# | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
	# | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
	# | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
	# | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
	# | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

	# Helper function to convert MXNET Arrays to PyTorch
	def to_torch(snake_case__   :     Any     ) -> nn.Parameter:
		return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()     )     )

	# Check param shapes and map new HF param back
	def check_and_map_params(snake_case__   :     Tuple	,      snake_case__   :     Union[str, Any]     ):
		A						   =   hf_param.shape

		A						   =   to_torch(params[gluon_param]     )
		A						   =   gluon_param.shape

		assert (
		    shape_hf == shape_gluon
		), F'The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers'

		return gluon_param

	A						   =   check_and_map_params(
	    hf_bort_model.bert.embeddings.word_embeddings.weight	,      'word_embed.0.weight'     )
	A						   =   check_and_map_params(
	    hf_bort_model.bert.embeddings.position_embeddings.weight	,      'encoder.position_weight'     )
	A						   =   check_and_map_params(
	    hf_bort_model.bert.embeddings.LayerNorm.bias	,      'encoder.layer_norm.beta'     )
	A						   =   check_and_map_params(
	    hf_bort_model.bert.embeddings.LayerNorm.weight	,      'encoder.layer_norm.gamma'     )

	# Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
	A						   =   torch.zeros_like(
	    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data     )

	for i in range(hf_bort_config.num_hidden_layers     ):
		A						   =   hf_bort_model.bert.encoder.layer[i]

		# self attention
		A						   =   layer.attention.self

		A						   =   check_and_map_params(
		    self_attn.key.bias.data	,      F'encoder.transformer_cells.{i}.attention_cell.proj_key.bias'     )

		A						   =   check_and_map_params(
		    self_attn.key.weight.data	,      F'encoder.transformer_cells.{i}.attention_cell.proj_key.weight'     )
		A						   =   check_and_map_params(
		    self_attn.query.bias.data	,      F'encoder.transformer_cells.{i}.attention_cell.proj_query.bias'     )
		A						   =   check_and_map_params(
		    self_attn.query.weight.data	,      F'encoder.transformer_cells.{i}.attention_cell.proj_query.weight'     )
		A						   =   check_and_map_params(
		    self_attn.value.bias.data	,      F'encoder.transformer_cells.{i}.attention_cell.proj_value.bias'     )
		A						   =   check_and_map_params(
		    self_attn.value.weight.data	,      F'encoder.transformer_cells.{i}.attention_cell.proj_value.weight'     )

		# self attention output
		A						   =   layer.attention.output

		A						   =   check_and_map_params(
		    self_output.dense.bias	,      F'encoder.transformer_cells.{i}.proj.bias'     )
		A						   =   check_and_map_params(
		    self_output.dense.weight	,      F'encoder.transformer_cells.{i}.proj.weight'     )
		A						   =   check_and_map_params(
		    self_output.LayerNorm.bias	,      F'encoder.transformer_cells.{i}.layer_norm.beta'     )
		A						   =   check_and_map_params(
		    self_output.LayerNorm.weight	,      F'encoder.transformer_cells.{i}.layer_norm.gamma'     )

		# intermediate
		A						   =   layer.intermediate

		A						   =   check_and_map_params(
		    intermediate.dense.bias	,      F'encoder.transformer_cells.{i}.ffn.ffn_1.bias'     )
		A						   =   check_and_map_params(
		    intermediate.dense.weight	,      F'encoder.transformer_cells.{i}.ffn.ffn_1.weight'     )

		# output
		A						   =   layer.output

		A						   =   check_and_map_params(
		    bert_output.dense.bias	,      F'encoder.transformer_cells.{i}.ffn.ffn_2.bias'     )
		A						   =   check_and_map_params(
		    bert_output.dense.weight	,      F'encoder.transformer_cells.{i}.ffn.ffn_2.weight'     )
		A						   =   check_and_map_params(
		    bert_output.LayerNorm.bias	,      F'encoder.transformer_cells.{i}.ffn.layer_norm.beta'     )
		A						   =   check_and_map_params(
		    bert_output.LayerNorm.weight	,      F'encoder.transformer_cells.{i}.ffn.layer_norm.gamma'     )

	# Save space and energy 🎄
	hf_bort_model.half()

	# Compare output of both models
	A						   =   RobertaTokenizer.from_pretrained('roberta-base'     )

	A						   =   tokenizer.encode_plus(snake_case__     )['input_ids']

	# Get gluon output
	A						   =   mx.nd.array([input_ids]     )
	A						   =   original_bort(inputs=snake_case__	,      token_types=[]     )

	# Get Transformer output (save and reload model again)
	hf_bort_model.save_pretrained(snake_case__     )
	A						   =   BertModel.from_pretrained(snake_case__     )
	hf_bort_model.eval()

	A						   =   tokenizer.encode_plus(snake_case__	,      return_tensors='pt'     )
	A						   =   hf_bort_model(**snake_case__     )[0]

	A						   =   output_gluon[0].asnumpy()
	A						   =   output_hf[0].detach().numpy()

	A						   =   np.max(np.abs(hf_layer - gluon_layer     )     ).item()
	A						   =   np.allclose(snake_case__	,      snake_case__	,      atol=1e-3     )

	if success:
		print('✔️ Both model do output the same tensors'     )
	else:
		print('❌ Both model do **NOT** output the same tensors'     )
		print('Absolute difference is:'	,      snake_case__     )


if __name__ == "__main__":
		_lowercase							   =				argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--bort_checkpoint_path''', default=None, type=str, required=True, help='''Path the official Bort params file.'''
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		_lowercase							   =				parser.parse_args()
		convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)

"""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 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    torch.FloatTensor







class 				lowerCAmelCase_				(      nn.Module    ):



						'''simple docstring'''


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

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

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

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

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

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

							A						   =   False




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      Optional[int]      )     ->   Union[str, Any]:
							A						   =   x
							A						   =   self.conv_in(A_      )

							if self.training and self.gradient_checkpointing:

								def create_custom_forward(A_     :      Dict      ):
									def custom_forward(*A_     :      Tuple      ):
										return module(*A_      )

									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(A_      )  ,A_  ,use_reentrant=A_      )
									# middle
									A						   =   torch.utils.checkpoint.checkpoint(
									    create_custom_forward(self.mid_block      )  ,A_  ,use_reentrant=A_      )
								else:
									for down_block in self.down_blocks:
										A						   =   torch.utils.checkpoint.checkpoint(create_custom_forward(A_      )  ,A_      )
									# middle
									A						   =   torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block      )  ,A_      )

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

								# middle
								A						   =   self.mid_block(A_      )

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

							return sample







class 				lowerCAmelCase_				(      nn.Module    ):



						'''simple docstring'''


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

							A						   =   nn.Convad(
							    A_  ,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=A_  ,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=A_  ,temb_channels=A_  ,)

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

								A						   =   i == len(A_      ) - 1

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

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

							A						   =   False




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      str  ,A_     :      Union[str, Any]=None      )     ->   Any:
							A						   =   z
							A						   =   self.conv_in(A_      )

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

								def create_custom_forward(A_     :      List[Any]      ):
									def custom_forward(*A_     :      Tuple      ):
										return module(*A_      )

									return custom_forward

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

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

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

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

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

							return sample







class 				lowerCAmelCase_				(      nn.Module    ):



						'''simple docstring'''


						def __init__( self     :      Optional[int]  ,A_     :      Optional[int]  ,A_     :      Any  ,A_     :      str  ,A_     :      Dict=None  ,A_     :      List[Any]="random"  ,A_     :      Optional[int]=False  ,A_     :      str=True      )     ->   List[str]:
							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 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      Optional[int]      )     ->   Any:
							A						   =   inds.shape
							assert len(A_      ) > 1
							A						   =   inds.reshape(ishape[0]  ,-1      )
							A						   =   self.used.to(A_      )
							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(A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      Optional[Any]      )     ->   List[Any]:
							A						   =   inds.shape
							assert len(A_      ) > 1
							A						   =   inds.reshape(ishape[0]  ,-1      )
							A						   =   self.used.to(A_      )
							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  ,A_      )
							return back.reshape(A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      List[Any]      )     ->   str:
							# 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(A_  ,self.embedding.weight      )  ,dim=1      )

							A						   =   self.embedding(A_      ).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(A_      )
								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 						_SCREAMING_SNAKE_CASE      ( self     :      Dict  ,A_     :      Dict  ,A_     :      str      )     ->   Union[str, Any]:
							# 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(A_      )
								A						   =   indices.reshape(-1      )  # flatten again

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

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

							return z_q







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						def __init__( self     :      str  ,A_     :      Tuple  ,A_     :      Dict=False      )     ->   List[str]:
							A						   =   parameters
							A   ,      A						   =   torch.chunk(A_  ,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 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,A_     :      Optional[torch.Generator] = None      )     ->   torch.FloatTensor:
							# make sure sample is on the same device as the parameters and has same dtype
							A						   =   randn_tensor(
							    self.mean.shape  ,generator=A_  ,device=self.parameters.device  ,dtype=self.parameters.dtype      )
							A						   =   self.mean + self.std * sample
							return x


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,A_     :      Tuple=None      )     ->   int:
							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 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      List[str]  ,A_     :      Union[str, Any]=[1, 2, 3]      )     ->   List[str]:
							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=A_      )




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Optional[int]:
							return self.mean