





'''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    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							torch.FloatTensor







class    lowerCAmelCase_		(		__magic_name__						,__magic_name__		):




 @register_to_config
 def __init__(	self			,     _lowerCAmelCase = 3			,     _lowerCAmelCase = 3			,     _lowerCAmelCase = ("DownEncoderBlock2D",)			,     _lowerCAmelCase = ("UpDecoderBlock2D",)			,     _lowerCAmelCase = (64,)			,     _lowerCAmelCase = 1			,     _lowerCAmelCase = "silu"			,     _lowerCAmelCase = 3			,     _lowerCAmelCase = 32			,     _lowerCAmelCase = 256			,     _lowerCAmelCase = 32			,     _lowerCAmelCase = None			,     _lowerCAmelCase = 0.18215			,     _lowerCAmelCase = "group"			,     )    ->							int:
     super().__init__()

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

     _lowerCAmelCase				=      vq_embed_dim if vq_embed_dim is not None else latent_channels

     _lowerCAmelCase				=      nn.Convad(_lowerCAmelCase			,     _lowerCAmelCase			,     1		)
     _lowerCAmelCase				=      VectorQuantizer(_lowerCAmelCase			,     _lowerCAmelCase			,     beta=0.25			,     remap=_lowerCAmelCase			,     sane_index_shape=_lowerCAmelCase		)
     _lowerCAmelCase				=      nn.Convad(_lowerCAmelCase			,     _lowerCAmelCase			,     1		)

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




 @apply_forward_hook
 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase = True		)    ->							VQEncoderOutput:
     _lowerCAmelCase				=      self.encoder(_lowerCAmelCase		)
     _lowerCAmelCase				=      self.quant_conv(_lowerCAmelCase		)

     if not return_dict:
         return (h,)

     return VQEncoderOutput(latents=_lowerCAmelCase		)




 @apply_forward_hook
 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase = False			,     _lowerCAmelCase = True		)    ->							Union[DecoderOutput, torch.FloatTensor]:
     # also go through quantization layer
     if not force_not_quantize:
         _lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase				=      self.quantize(_lowerCAmelCase		)
     else:
         _lowerCAmelCase				=      h
     _lowerCAmelCase				=      self.post_quant_conv(_lowerCAmelCase		)
     _lowerCAmelCase				=      self.decoder(_lowerCAmelCase			,     quant if self.config.norm_type == "spatial" else None		)

     if not return_dict:
         return (dec,)

     return DecoderOutput(sample=_lowerCAmelCase		)





 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase = True		)    ->							Union[DecoderOutput, torch.FloatTensor]:
     _lowerCAmelCase				=      sample
     _lowerCAmelCase				=      self.encode(_lowerCAmelCase		).latents
     _lowerCAmelCase				=      self.decode(_lowerCAmelCase		).sample

     if not return_dict:
         return (dec,)

     return DecoderOutput(sample=_lowerCAmelCase		)









'''simple docstring'''


import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	 =							"T5Config"


def 	__a(SCREAMING_SNAKE_CASE_  :					jnp.array		,       SCREAMING_SNAKE_CASE_  :					int		,       SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    _lowerCAmelCase				=      jnp.zeros_like(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]    )
    _lowerCAmelCase				=      shifted_input_ids.at[:, 0].set(SCREAMING_SNAKE_CASE_    )

    _lowerCAmelCase				=      jnp.where(shifted_input_ids == -100		,       SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    )
    return shifted_input_ids

class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							List[str]         =					"mt5"
 __lowerCamelCase						:							Any         =					MTaConfig







class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							List[str]         =					"mt5"
 __lowerCamelCase						:							Dict         =					MTaConfig







class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							Optional[Any]         =					"mt5"
 __lowerCamelCase						:							str         =					MTaConfig


