

import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[Any]       ):





    """simple docstring"""


    return EnvironmentCommand()


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[int]       ):





    """simple docstring"""


    return EnvironmentCommand(args.accelerate_config_file       )





class       __snake_case  (    lowerCAmelCase__       ):

  @staticmethod
  def 						lowerCAmelCase__     (					_A):
      SCREAMING_SNAKE_CASE_									=						parser.add_parser('env')
      download_parser.set_defaults(func=_A)
      download_parser.add_argument(
          '--accelerate-config_file'						,		default=_A						,		help='The accelerate config file to use for the default values in the launching script.'						,		)
      download_parser.set_defaults(func=_A)

  def __init__(					self						,		_A						,		*_A):
      SCREAMING_SNAKE_CASE_									=						accelerate_config_file

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						'not installed'
      if is_safetensors_available():
          import safetensors

          SCREAMING_SNAKE_CASE_									=						safetensors.__version__
      elif importlib.util.find_spec('safetensors') is not None:
          import safetensors

          SCREAMING_SNAKE_CASE_									=						f"""{safetensors.__version__} but is ignored because of PyTorch version too old."""

      SCREAMING_SNAKE_CASE_									=						'not installed'
      SCREAMING_SNAKE_CASE_									=						SCREAMING_SNAKE_CASE_									=						'not found'
      if is_accelerate_available():
          import accelerate
          from accelerate.commands.config import default_config_file, load_config_from_file

          SCREAMING_SNAKE_CASE_									=						accelerate.__version__
          # Get the default from the config file.
          if self._accelerate_config_file is not None or os.path.isfile(_A):
              SCREAMING_SNAKE_CASE_									=						load_config_from_file(self._accelerate_config_file).to_dict()

          SCREAMING_SNAKE_CASE_									=						(
              '\n'.join([f"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()])
              if isinstance(_A						,		_A)
              else f"""\t{accelerate_config}"""
          )

      SCREAMING_SNAKE_CASE_									=						'not installed'
      SCREAMING_SNAKE_CASE_									=						'NA'
      if is_torch_available():
          import torch

          SCREAMING_SNAKE_CASE_									=						torch.__version__
          SCREAMING_SNAKE_CASE_									=						torch.cuda.is_available()

      SCREAMING_SNAKE_CASE_									=						'not installed'
      SCREAMING_SNAKE_CASE_									=						'NA'
      if is_tf_available():
          import tensorflow as tf

          SCREAMING_SNAKE_CASE_									=						tf.__version__
          try:
              # deprecated in v2.1
              SCREAMING_SNAKE_CASE_									=						tf.test.is_gpu_available()
          except AttributeError:
              # returns list of devices, convert to bool
              SCREAMING_SNAKE_CASE_									=						bool(tf.config.list_physical_devices('GPU'))

      SCREAMING_SNAKE_CASE_									=						'not installed'
      SCREAMING_SNAKE_CASE_									=						'not installed'
      SCREAMING_SNAKE_CASE_									=						'not installed'
      SCREAMING_SNAKE_CASE_									=						'NA'
      if is_flax_available():
          import flax
          import jax
          import jaxlib

          SCREAMING_SNAKE_CASE_									=						flax.__version__
          SCREAMING_SNAKE_CASE_									=						jax.__version__
          SCREAMING_SNAKE_CASE_									=						jaxlib.__version__
          SCREAMING_SNAKE_CASE_									=						jax.lib.xla_bridge.get_backend().platform

      SCREAMING_SNAKE_CASE_									=						{
          '`transformers` version': version,
          'Platform': platform.platform(),
          'Python version': platform.python_version(),
          'Huggingface_hub version': huggingface_hub.__version__,
          'Safetensors version': f"""{safetensors_version}""",
          'Accelerate version': f"""{accelerate_version}""",
          'Accelerate config': f"""{accelerate_config_str}""",
          'PyTorch version (GPU?)': f"""{pt_version} ({pt_cuda_available})""",
          'Tensorflow version (GPU?)': f"""{tf_version} ({tf_cuda_available})""",
          'Flax version (CPU?/GPU?/TPU?)': f"""{flax_version} ({jax_backend})""",
          'Jax version': f"""{jax_version}""",
          'JaxLib version': f"""{jaxlib_version}""",
          'Using GPU in script?': '<fill in>',
          'Using distributed or parallel set-up in script?': '<fill in>',
      }

      print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n')
      print(self.format_dict(_A))

      return info


  @staticmethod
  def 						lowerCAmelCase__     (					_A):
      return "\n".join([f"""- {prop}: {val}""" for prop, val in d.items()]) + "\n"





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)


