


"""simple docstring"""



from dataclasses import dataclass
from typing import 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 .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer
@dataclass
class 					__magic_name__		(			__UpperCAmelCase     ):
							__A			:      torch.FloatTensor







class 					__magic_name__		(			__UpperCAmelCase			,						__UpperCAmelCase     ):






							@register_to_config
							def __init__(   self  :    Any  ,     snake_case__  :    int = 3  ,     snake_case__  :    int = 3  ,     snake_case__  :    Tuple[str] = ("DownEncoderBlock2D",)  ,     snake_case__  :    Tuple[str] = ("UpDecoderBlock2D",)  ,     snake_case__  :    Tuple[int] = (6_4,)  ,     snake_case__  :    int = 1  ,     snake_case__  :    str = "silu"  ,     snake_case__  :    int = 3  ,     snake_case__  :    int = 3_2  ,     snake_case__  :    int = 2_5_6  ,     snake_case__  :    int = 3_2  ,     snake_case__  :    Optional[int] = None  ,     snake_case__  :    float = 0.1_82_15  ,     snake_case__  :    str = "group"  ,     ):

														'''simple docstring'''






														super().__init__()

														# pass init params to Encoder
														lowercase		:Dict       							=				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__  ,     )

														lowercase		:int       							=				vq_embed_dim if vq_embed_dim is not None else latent_channels

														lowercase		:List[str]       							=				nn.Convad(snake_case__  ,     snake_case__  ,     1				)
														lowercase		:List[Any]       							=				VectorQuantizer(snake_case__  ,     snake_case__  ,     beta=0.25  ,     remap=snake_case__  ,     sane_index_shape=snake_case__				)
														lowercase		:int       							=				nn.Convad(snake_case__  ,     snake_case__  ,     1				)

														# pass init params to Decoder
														lowercase		:Any       							=				Decoder(
														    in_channels=snake_case__  ,     out_channels=snake_case__  ,     up_block_types=snake_case__  ,     block_out_channels=snake_case__  ,     layers_per_block=snake_case__  ,     act_fn=snake_case__  ,     norm_num_groups=snake_case__  ,     norm_type=snake_case__  ,     )






							@apply_forward_hook
							def  __snake_case						(   self  :    List[str]  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    bool = True				):

														'''simple docstring'''






														lowercase		:List[str]       							=				self.encoder(snake_case__				)
														lowercase		:Any       							=				self.quant_conv(snake_case__				)

														if not return_dict:
																					return (h,)

														return VQEncoderOutput(latents=snake_case__				)






							@apply_forward_hook
							def  __snake_case						(   self  :    int  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    bool = False  ,     snake_case__  :    bool = True				):

														'''simple docstring'''






														if not force_not_quantize:
																					lowercase		:str       							=				self.quantize(snake_case__				)
														else:
																					lowercase		:Any       							=				h
														lowercase		:int       							=				self.post_quant_conv(snake_case__				)
														lowercase		:List[str]       							=				self.decoder(snake_case__  ,     quant if self.config.norm_type == '''spatial''' else None				)

														if not return_dict:
																					return (dec,)

														return DecoderOutput(sample=snake_case__				)







							def  __snake_case						(   self  :    List[Any]  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    bool = True				):

														'''simple docstring'''






														lowercase		:Any       							=				sample
														lowercase		:int       							=				self.encode(snake_case__				).latents
														lowercase		:Optional[int]       							=				self.decode(snake_case__				).sample

														if not return_dict:
																					return (dec,)

														return DecoderOutput(sample=snake_case__				)





"""simple docstring"""



# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..utils import cached_file


# docstyle-ignore
UpperCAmelCase      		=			'''
Human: <<task>>

Assistant: '''


UpperCAmelCase      		=			'''huggingface-tools/default-prompts'''
UpperCAmelCase      		=			{'''chat''': '''chat_prompt_template.txt''', '''run''': '''run_prompt_template.txt'''}






def       lowerCamelCase					(a_						:int				,				a_						:str				,				a_						:Dict="run")     ->							Optional[Any]:
       if prompt_or_repo_id is None:
              lowercase		:Tuple       							=				DEFAULT_PROMPTS_REPO

       # prompt is considered a repo ID when it does not contain any kind of space
       if re.search('''\\s'''				,				a_) is not None:
              return prompt_or_repo_id

       lowercase		:List[str]       							=				cached_file(
           a_				,				PROMPT_FILES[mode]				,				repo_type='''dataset'''				,				user_agent={'''agent''': agent_name})
       with open(a_				,				'''r'''				,				encoding='''utf-8''') as f:
              return f.read()

