"""simple docstring"""




from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder
@dataclass
class     UpperCamelCase__(			__a ):
						lowerCAmelCase__  :						"DiagonalGaussianDistribution"






class     UpperCamelCase__(			__a		,						__a ):
						lowerCAmelCase__  :						Tuple													=		True





						@register_to_config
						def __init__(       self		,__UpperCAmelCase = 3		,__UpperCAmelCase = 3		,__UpperCAmelCase = ("DownEncoderBlock2D",)		,__UpperCAmelCase = ("UpDecoderBlock2D",)		,__UpperCAmelCase = (64,)		,__UpperCAmelCase = 1		,__UpperCAmelCase = "silu"		,__UpperCAmelCase = 4		,__UpperCAmelCase = 32		,__UpperCAmelCase = 32		,__UpperCAmelCase = 0.1_8_2_1_5		,)					->				List[str]:
													super().__init__()

													# pass init params to Encoder
													A__	     =							Encoder(
													    in_channels=snake_case__		,out_channels=snake_case__		,down_block_types=snake_case__		,block_out_channels=snake_case__		,layers_per_block=snake_case__		,act_fn=snake_case__		,norm_num_groups=snake_case__		,double_z=snake_case__		,)

													# pass init params to Decoder
													A__	     =							Decoder(
													    in_channels=snake_case__		,out_channels=snake_case__		,up_block_types=snake_case__		,block_out_channels=snake_case__		,layers_per_block=snake_case__		,norm_num_groups=snake_case__		,act_fn=snake_case__		,)

													A__	     =							nn.Convad(2 * latent_channels		,2 * latent_channels		,1     )
													A__	     =							nn.Convad(snake_case__		,snake_case__		,1     )

													A__	     =							False
													A__	     =							False

													# only relevant if vae tiling is enabled
													A__	     =							self.config.sample_size
													A__	     =							(
													    self.config.sample_size[0]
													    if isinstance(self.config.sample_size		,(list, tuple)     )
													    else self.config.sample_size
													)
													A__	     =							int(sample_size / (2 ** (len(self.config.block_out_channels     ) - 1))     )
													A__	     =							0.2_5





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase=False     )					->				List[Any]:
													if isinstance(snake_case__		,(Encoder, Decoder)     ):
																				A__	     =							value





						def 				snake_case__	(       self		,__UpperCAmelCase = True     )					->				Union[str, Any]:
													A__	     =							use_tiling





						def 				snake_case__	(       self     )					->				int:
													self.enable_tiling(snake_case__     )





						def 				snake_case__	(       self     )					->				Dict:
													A__	     =							True





						def 				snake_case__	(       self     )					->				Optional[Any]:
													A__	     =							False





						@property
						# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
						def 				snake_case__	(       self     )					->				Optional[Any]:
													A__	     =							{}

													def fn_recursive_add_processors(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     ):
																				if hasattr(snake_case__		,'set_processor'     ):
																											A__	     =							module.processor

																				for sub_name, child in module.named_children():
																											fn_recursive_add_processors(f'''{name}.{sub_name}'''		,snake_case__		,snake_case__     )

																				return processors

													for name, module in self.named_children():
																				fn_recursive_add_processors(snake_case__		,snake_case__		,snake_case__     )

													return processors





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				Tuple:
													A__	     =							len(self.attn_processors.keys()     )

													if isinstance(snake_case__		,snake_case__     ) and len(snake_case__     ) != count:
																				raise ValueError(
																				    f'''A dict of processors was passed, but the number of processors {len(snake_case__     )} does not match the'''
																				    f''' number of attention layers: {count}. Please make sure to pass {count} processor classes.'''     )

													def fn_recursive_attn_processor(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     ):
																				if hasattr(snake_case__		,'set_processor'     ):
																											if not isinstance(snake_case__		,snake_case__     ):
																																		module.set_processor(snake_case__     )
																											else:
																																		module.set_processor(processor.pop(f'''{name}.processor'''     )     )

																				for sub_name, child in module.named_children():
																											fn_recursive_attn_processor(f'''{name}.{sub_name}'''		,snake_case__		,snake_case__     )

													for name, module in self.named_children():
																				fn_recursive_attn_processor(snake_case__		,snake_case__		,snake_case__     )





						def 				snake_case__	(       self     )					->				List[str]:
													self.set_attn_processor(AttnProcessor()     )





						@apply_forward_hook
						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase = True     )					->				str:
													if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
																				return self.tiled_encode(snake_case__		,return_dict=snake_case__     )

													if self.use_slicing and x.shape[0] > 1:
																				A__	     =							[self.encoder(snake_case__     ) for x_slice in x.split(1     )]
																				A__	     =							torch.cat(snake_case__     )
													else:
																				A__	     =							self.encoder(snake_case__     )

													A__	     =							self.quant_conv(snake_case__     )
													A__	     =							DiagonalGaussianDistribution(snake_case__     )

													if not return_dict:
																				return (posterior,)

													return AutoencoderKLOutput(latent_dist=snake_case__     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase = True     )					->				int:
													if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
																				return self.tiled_decode(snake_case__		,return_dict=snake_case__     )

													A__	     =							self.post_quant_conv(snake_case__     )
													A__	     =							self.decoder(snake_case__     )

													if not return_dict:
																				return (dec,)

													return DecoderOutput(sample=snake_case__     )





						@apply_forward_hook
						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase = True     )					->				Any:
													if self.use_slicing and z.shape[0] > 1:
																				A__	     =							[self._decode(snake_case__     ).sample for z_slice in z.split(1     )]
																				A__	     =							torch.cat(snake_case__     )
													else:
																				A__	     =							self._decode(snake_case__     ).sample

													if not return_dict:
																				return (decoded,)

													return DecoderOutput(sample=snake_case__     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )					->				List[Any]:
													A__	     =							min(a.shape[2]		,b.shape[2]		,snake_case__     )
													for y in range(snake_case__     ):
																				A__	     =							a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
													return b





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )					->				List[Any]:
													A__	     =							min(a.shape[3]		,b.shape[3]		,snake_case__     )
													for x in range(snake_case__     ):
																				A__	     =							a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
													return b





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase = True     )					->				Union[str, Any]:
													A__	     =							int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)     )
													A__	     =							int(self.tile_latent_min_size * self.tile_overlap_factor     )
													A__	     =							self.tile_latent_min_size - blend_extent

													# Split the image into 512x512 tiles and encode them separately.
													A__	     =							[]
													for i in range(0		,x.shape[2]		,snake_case__     ):
																				A__	     =							[]
																				for j in range(0		,x.shape[3]		,snake_case__     ):
																											A__	     =							x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
																											A__	     =							self.encoder(snake_case__     )
																											A__	     =							self.quant_conv(snake_case__     )
																											row.append(snake_case__     )
																				rows.append(snake_case__     )
													A__	     =							[]
													for i, row in enumerate(snake_case__     ):
																				A__	     =							[]
																				for j, tile in enumerate(snake_case__     ):
																											# blend the above tile and the left tile
																											# to the current tile and add the current tile to the result row
																											if i > 0:
																																		A__	     =							self.blend_v(rows[i - 1][j]		,snake_case__		,snake_case__     )
																											if j > 0:
																																		A__	     =							self.blend_h(row[j - 1]		,snake_case__		,snake_case__     )
																											result_row.append(tile[:, :, :row_limit, :row_limit]     )
																				result_rows.append(torch.cat(snake_case__		,dim=3     )     )

													A__	     =							torch.cat(snake_case__		,dim=2     )
													A__	     =							DiagonalGaussianDistribution(snake_case__     )

													if not return_dict:
																				return (posterior,)

													return AutoencoderKLOutput(latent_dist=snake_case__     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase = True     )					->				Any:
													A__	     =							int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)     )
													A__	     =							int(self.tile_sample_min_size * self.tile_overlap_factor     )
													A__	     =							self.tile_sample_min_size - blend_extent

													# Split z into overlapping 64x64 tiles and decode them separately.
													# The tiles have an overlap to avoid seams between tiles.
													A__	     =							[]
													for i in range(0		,z.shape[2]		,snake_case__     ):
																				A__	     =							[]
																				for j in range(0		,z.shape[3]		,snake_case__     ):
																											A__	     =							z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
																											A__	     =							self.post_quant_conv(snake_case__     )
																											A__	     =							self.decoder(snake_case__     )
																											row.append(snake_case__     )
																				rows.append(snake_case__     )
													A__	     =							[]
													for i, row in enumerate(snake_case__     ):
																				A__	     =							[]
																				for j, tile in enumerate(snake_case__     ):
																											# blend the above tile and the left tile
																											# to the current tile and add the current tile to the result row
																											if i > 0:
																																		A__	     =							self.blend_v(rows[i - 1][j]		,snake_case__		,snake_case__     )
																											if j > 0:
																																		A__	     =							self.blend_h(row[j - 1]		,snake_case__		,snake_case__     )
																											result_row.append(tile[:, :, :row_limit, :row_limit]     )
																				result_rows.append(torch.cat(snake_case__		,dim=3     )     )

													A__	     =							torch.cat(snake_case__		,dim=2     )
													if not return_dict:
																				return (dec,)

													return DecoderOutput(sample=snake_case__     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase = False		,__UpperCAmelCase = True		,__UpperCAmelCase = None		,)					->				int:
													A__	     =							sample
													A__	     =							self.encode(snake_case__     ).latent_dist
													if sample_posterior:
																				A__	     =							posterior.sample(generator=snake_case__     )
													else:
																				A__	     =							posterior.mode()
													A__	     =							self.decode(snake_case__     ).sample

													if not return_dict:
																				return (dec,)

													return DecoderOutput(sample=snake_case__     )






"""simple docstring"""




import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class     UpperCamelCase__(			__A		,						unittest.TestCase ):
						lowerCAmelCase__  :						Optional[int]													=		CpmAntTokenizer
						lowerCAmelCase__  :						List[str]													=		False





						def 				snake_case__	(       self     )					->				Dict:
													super().setUp()

													A__	     =							[
													    '<d>',
													    '</d>',
													    '<s>',
													    '</s>',
													    '</_>',
													    '<unk>',
													    '<pad>',
													    '</n>',
													    '我',
													    '是',
													    'C',
													    'P',
													    'M',
													    'A',
													    'n',
													    't',
													]
													A__	     =							os.path.join(self.tmpdirname		,VOCAB_FILES_NAMES['vocab_file']     )
													with open(self.vocab_file		,'w'		,encoding='utf-8'     ) as vocab_writer:
																				vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]     )     )





						@tooslow
						def 				snake_case__	(       self     )					->				List[Any]:
													A__	     =							CpmAntTokenizer.from_pretrained('openbmb/cpm-ant-10b'     )
													A__	     =							'今天天气真好！'
													A__	     =							['今天', '天气', '真', '好', '！']
													A__	     =							tokenizer.tokenize(__UpperCAmelCase     )
													self.assertListEqual(__UpperCAmelCase		,__UpperCAmelCase     )
													A__	     =							'今天天气真好！'
													A__	     =							[tokenizer.bos_token] + tokens

													A__	     =							[6, 98_02, 1_49_62, 20_82, 8_31, 2_44]
													self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase     )		,__UpperCAmelCase     )

													A__	     =							tokenizer.decode(__UpperCAmelCase     )
													self.assertEqual(__UpperCAmelCase		,__UpperCAmelCase     )





