

from argparse import ArgumentParser

from . import BaseTransformersCLICommand


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[str]       ):





    """simple docstring"""


    return DownloadCommand(args.model ,   args.cache_dir ,   args.force ,   args.trust_remote_code       )





class       __snake_case  (    UpperCamelCase__       ):

  @staticmethod
  def 						lowerCAmelCase__     (					_A):
      SCREAMING_SNAKE_CASE_									=						parser.add_parser('download')
      download_parser.add_argument(
          '--cache-dir'						,		type=_A						,		default=_A						,		help='Path to location to store the models')
      download_parser.add_argument(
          '--force'						,		action='store_true'						,		help='Force the model to be download even if already in cache-dir')
      download_parser.add_argument(
          '--trust-remote-code'						,		action='store_true'						,		help='Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine'						,		)
      download_parser.add_argument('model'						,		type=_A						,		help='Name of the model to download')
      download_parser.set_defaults(func=_A)

  def __init__(					self						,		_A						,		_A						,		_A						,		_A):
      SCREAMING_SNAKE_CASE_									=						model
      SCREAMING_SNAKE_CASE_									=						cache
      SCREAMING_SNAKE_CASE_									=						force
      SCREAMING_SNAKE_CASE_									=						trust_remote_code


  def 						lowerCAmelCase__     (					self):
      from ..models.auto import AutoModel, AutoTokenizer

      AutoModel.from_pretrained(
          self._model						,		cache_dir=self._cache						,		force_download=self._force						,		trust_remote_code=self._trust_remote_code)
      AutoTokenizer.from_pretrained(
          self._model						,		cache_dir=self._cache						,		force_download=self._force						,		trust_remote_code=self._trust_remote_code)








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       __snake_case  (    lowerCAmelCase__       ):
  __lowerCAmelCase     :	torch.FloatTensor






class       __snake_case  (    lowerCAmelCase__     , lowerCAmelCase__       ):

  @register_to_config
  def __init__(					self						,		_A = 3						,		_A = 3						,		_A = ("DownEncoderBlock2D",)						,		_A = ("UpDecoderBlock2D",)						,		_A = (64,)						,		_A = 1						,		_A = "silu"						,		_A = 3						,		_A = 32						,		_A = 256						,		_A = 32						,		_A = None						,		_A = 0.1_8_2_1_5						,		_A = "group"						,		):
      super().__init__()

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

      SCREAMING_SNAKE_CASE_									=						vq_embed_dim if vq_embed_dim is not None else latent_channels

      SCREAMING_SNAKE_CASE_									=						nn.Convad(_A						,		_A						,		1)
      SCREAMING_SNAKE_CASE_									=						VectorQuantizer(_A						,		_A						,		beta=0.2_5						,		remap=_A						,		sane_index_shape=_A)
      SCREAMING_SNAKE_CASE_									=						nn.Convad(_A						,		_A						,		1)

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

  @apply_forward_hook
  def 						lowerCAmelCase__     (					self						,		_A						,		_A = True):
      SCREAMING_SNAKE_CASE_									=						self.encoder(_A)
      SCREAMING_SNAKE_CASE_									=						self.quant_conv(_A)

      if not return_dict:
          return (h,)

      return VQEncoderOutput(latents=_A)

  @apply_forward_hook
  def 						lowerCAmelCase__     (					self						,		_A						,		_A = False						,		_A = True):
      # also go through quantization layer
      if not force_not_quantize:
          SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						self.quantize(_A)
      else:
          SCREAMING_SNAKE_CASE_									=						h
      SCREAMING_SNAKE_CASE_									=						self.post_quant_conv(_A)
      SCREAMING_SNAKE_CASE_									=						self.decoder(_A						,		quant if self.config.norm_type == 'spatial' else None)

      if not return_dict:
          return (dec,)

      return DecoderOutput(sample=_A)


  def 						lowerCAmelCase__     (					self						,		_A						,		_A = True):
      SCREAMING_SNAKE_CASE_									=						sample
      SCREAMING_SNAKE_CASE_									=						self.encode(_A).latents
      SCREAMING_SNAKE_CASE_									=						self.decode(_A).sample

      if not return_dict:
          return (dec,)

      return DecoderOutput(sample=_A)


