





'''simple docstring'''
import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class      snake_case     (    __lowerCamelCase			,       unittest.TestCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Tuple					=GPTSanJapaneseTokenizer
 SCREAMING_SNAKE_CASE_	:					Optional[Any]					=False
 SCREAMING_SNAKE_CASE_	:					Optional[Any]					={"do_clean_text": False, "add_prefix_space": False}






 def        _lowerCamelCase					(       self       :							str	):
        super().setUp()

        # fmt: off
        __UpperCamelCase       =  ['こん', 'こんに', 'にちは', 'ばんは', '世界,㔺界', '、', '。', '<BR>', '<SP>', '<TAB>', '<URL>', '<EMAIL>', '<TEL>', '<DATE>', '<PRICE>', '<BLOCK>', '<KIGOU>', '<U2000U2BFF>', '<|emoji1|>', '<unk>', '<|bagoftoken|>', '<|endoftext|>']
        # fmt: on
        __UpperCamelCase       =  {'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}}  # 😀
        __UpperCamelCase       =  {'unk_token': '<unk>'}

        __UpperCamelCase       =  os.path.join(self.tmpdirname							,	VOCAB_FILES_NAMES['vocab_file']	)
        __UpperCamelCase       =  os.path.join(self.tmpdirname							,	VOCAB_FILES_NAMES['emoji_file']	)
        with open(self.vocab_file							,	'w'							,	encoding='utf-8'	) as vocab_writer:
               vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]	)	)
        with open(self.emoji_file							,	'w'	) as emoji_writer:
               emoji_writer.write(json.dumps(__A	)	)






 def        _lowerCamelCase					(       self       :							Union[str, Any]							,	**__A       :							List[Any]	):
        kwargs.update(self.special_tokens_map	)
        return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname							,	**__A	)






 def        _lowerCamelCase					(       self       :							Any							,	__A       :							Dict	):
        __UpperCamelCase       =  'こんにちは、世界。 \nこんばんは、㔺界。😀'
        __UpperCamelCase       =  'こんにちは、世界。 \nこんばんは、世界。😀'
        return input_text, output_text






 def        _lowerCamelCase					(       self       :							Union[str, Any]							,	__A       :							Any	):
        __UpperCamelCase       ,						__UpperCamelCase       =  self.get_input_output_texts(__A	)
        __UpperCamelCase       =  tokenizer.encode(__A							,	add_special_tokens=__A	)
        __UpperCamelCase       =  tokenizer.decode(__A							,	clean_up_tokenization_spaces=__A	)
        return text, ids






 def        _lowerCamelCase					(       self       :							int	):
        pass  # TODO add if relevant






 def        _lowerCamelCase					(       self       :							Union[str, Any]	):
        pass  # TODO add if relevant






 def        _lowerCamelCase					(       self       :							Optional[int]	):
        pass  # TODO add if relevant






 def        _lowerCamelCase					(       self       :							Optional[int]	):
        __UpperCamelCase       =  self.get_tokenizer()

        # Testing tokenization
        __UpperCamelCase       =  'こんにちは、世界。　こんばんは、㔺界。'
        __UpperCamelCase       =  ['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
        __UpperCamelCase       =  tokenizer.tokenize(__A	)
        self.assertListEqual(__A							,	__A	)

        # Testing conversion to ids without special tokens
        __UpperCamelCase       =  [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
        __UpperCamelCase       =  tokenizer.convert_tokens_to_ids(__A	)
        self.assertListEqual(__A							,	__A	)

        # Testing conversion to ids with special tokens
        __UpperCamelCase       =  tokens + [tokenizer.unk_token]
        __UpperCamelCase       =  [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 1_9]
        __UpperCamelCase       =  tokenizer.convert_tokens_to_ids(__A	)
        self.assertListEqual(__A							,	__A	)






 def        _lowerCamelCase					(       self       :							Dict	):
        __UpperCamelCase       =  self.get_tokenizer()

        # Testing tokenization
        __UpperCamelCase       =  'こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。'
        __UpperCamelCase       =  'こんにちは、、、、世界。こんばんは、、、、世界。'
        __UpperCamelCase       =  tokenizer.encode(__A	)
        __UpperCamelCase       =  tokenizer.decode(__A	)
        self.assertEqual(__A							,	__A	)






 @slow
 def        _lowerCamelCase					(       self       :							Any	):
        __UpperCamelCase       =  self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'	)

        # Testing tokenization
        __UpperCamelCase       =  'こんにちは、世界。'
        __UpperCamelCase       =  'こんばんは、㔺界。😀'
        __UpperCamelCase       =  'こんにちは、世界。こんばんは、世界。😀'
        __UpperCamelCase       =  tokenizer.encode(prefix_text + input_text	)
        __UpperCamelCase       =  tokenizer.encode(''							,	prefix_text=prefix_text + input_text	)
        __UpperCamelCase       =  tokenizer.encode(__A							,	prefix_text=__A	)
        __UpperCamelCase       =  tokenizer.decode(__A	)
        __UpperCamelCase       =  tokenizer.decode(__A	)
        __UpperCamelCase       =  tokenizer.decode(__A	)
        self.assertEqual(__A							,	__A	)
        self.assertEqual(__A							,	__A	)
        self.assertEqual(__A							,	__A	)






 @slow
 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        __UpperCamelCase       =  self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'	)

        # Testing tokenization
        __UpperCamelCase       =  'こんにちは、世界。'
        __UpperCamelCase       =  'こんばんは、㔺界。😀'

        __UpperCamelCase       =  len(tokenizer.encode(__A	)	) - 2
        __UpperCamelCase       =  len(tokenizer.encode(__A	)	) - 2

        __UpperCamelCase       =  [1] + [0] * (len_prefix + len_text + 1)
        __UpperCamelCase       =  [1] * (len_prefix + len_text + 1) + [0]
        __UpperCamelCase       =  [1] + [1] * (len_prefix) + [0] * (len_text + 1)

        __UpperCamelCase       =  tokenizer(prefix_text + input_text	).token_type_ids
        __UpperCamelCase       =  tokenizer(''							,	prefix_text=prefix_text + input_text	).token_type_ids
        __UpperCamelCase       =  tokenizer(__A							,	prefix_text=__A	).token_type_ids
        self.assertListEqual(__A							,	__A	)
        self.assertListEqual(__A							,	__A	)
        self.assertListEqual(__A							,	__A	)






 @slow
 def        _lowerCamelCase					(       self       :							Any	):
        __UpperCamelCase       =  self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'	)

        __UpperCamelCase       =  tokenizer.encode('あンいワ'	)
        __UpperCamelCase       =  tokenizer.encode(''							,	prefix_text='あンいワ'	)
        __UpperCamelCase       =  tokenizer.encode('いワ'							,	prefix_text='あン'	)

        self.assertEqual(tokenizer.decode(__A	)							,	tokenizer.decode(__A	)	)
        self.assertEqual(tokenizer.decode(__A	)							,	tokenizer.decode(__A	)	)
        self.assertNotEqual(__A							,	__A	)
        self.assertNotEqual(__A							,	__A	)
        self.assertEqual(x_token_a[1]							,	x_token_a[-1]	)  # SEG token
        self.assertEqual(x_token_a[1]							,	x_token_a[3]	)  # SEG token






 @slow
 def        _lowerCamelCase					(       self       :							List[str]	):
        __UpperCamelCase       =  self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'	)

        __UpperCamelCase       =  [['武田信玄', 'は、'], ['織田信長', 'の配下の、']]
        __UpperCamelCase       =  tokenizer(__A							,	padding=__A	)
        __UpperCamelCase       =  tokenizer.batch_encode_plus(__A							,	padding=__A	)

        # fmt: off
        __UpperCamelCase       =  [[3_5_9_9_3, 8_6_4_0, 2_5_9_4_8, 3_5_9_9_8, 3_0_6_4_7, 3_5_6_7_5, 3_5_9_9_9, 3_5_9_9_9], [3_5_9_9_3, 1_0_3_8_2, 9_8_6_8, 3_5_9_9_8, 3_0_6_4_6, 9_4_5_9, 3_0_6_4_6, 3_5_6_7_5]]
        __UpperCamelCase       =  [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
        __UpperCamelCase       =  [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
        # fmt: on
        self.assertListEqual(x_token.input_ids							,	__A	)
        self.assertListEqual(x_token.token_type_ids							,	__A	)
        self.assertListEqual(x_token.attention_mask							,	__A	)
        self.assertListEqual(x_token_a.input_ids							,	__A	)
        self.assertListEqual(x_token_a.token_type_ids							,	__A	)
        self.assertListEqual(x_token_a.attention_mask							,	__A	)






 def        _lowerCamelCase					(       self       :							str	):
        # Intentionally convert some words to accommodate character fluctuations unique to Japanese
        pass






 def        _lowerCamelCase					(       self       :							str	):
        # tokenizer has no padding token
        pass

"""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 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  torch.FloatTensor






class 			UpperCamelCase__(	nn.Module       ):





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





						def __init__(				self						,__UpperCAmelCase=3						,__UpperCAmelCase=3						,__UpperCAmelCase=("UpDecoderBlock2D",)						,__UpperCAmelCase=(64,)						,__UpperCAmelCase=2						,__UpperCAmelCase=32						,__UpperCAmelCase="silu"						,__UpperCAmelCase="group"						,)    -> Any:
													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   )    -> Dict:
													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 			UpperCamelCase__(	nn.Module       ):





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase=None						,__UpperCAmelCase="random"						,__UpperCAmelCase=False						,__UpperCAmelCase=True   )    -> Tuple:
													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   )    -> List[str]:
													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   )    -> Optional[Any]:
													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   )    -> Dict:
													# 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   )    -> str:
													# 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 			UpperCamelCase__(	__A       ):





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase=False   )    -> Any:
													A__		   =							parameters
													A__   ,     A__		   =							torch.chunk(__UpperCAmelCase						,2						,dim=1   )
													A__		   =							torch.clamp(self.logvar						,-3_0.0						,2_0.0   )
													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:
													# 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:
													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]   )    -> List[Any]:
													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   )    -> Optional[Any]:
													return self.mean





