"""simple docstring"""


from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
  from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
class   UpperCAmelCase_	(				lowercase__   ):
    snake_case_											=      c_float
    @property
    def 		_lowerCamelCase      (	self			:       int		)						->    int:
       return sizeof(self.value		)


def   __UpperCAmelCase     (	_snake_case		:       int,				_snake_case		:       int,				_snake_case		:       int = 0       ):
   # < 0 is possible if using a dynamic axis
   if dimension <= 0:
      _lowercase							     =       fixed_dimension

   dimension -= num_token_to_add
   return dimension


def   __UpperCAmelCase     (	_snake_case		:       int,				_snake_case		:       ParameterFormat       ):
   return num_parameters * dtype.size


def   __UpperCAmelCase     (	_snake_case		:       str       ):
   from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

   try:
      return AutoProcessor.from_pretrained(_snake_case       )
   except (ValueError, OSError, KeyError):
      _lowercase     ,  _lowercase							     =       None, None
      try:
         _lowercase							     =       AutoTokenizer.from_pretrained(_snake_case       )
      except (OSError, KeyError):
         pass
      try:
         _lowercase							     =       AutoFeatureExtractor.from_pretrained(_snake_case       )
      except (OSError, KeyError):
         pass

      if tokenizer is not None and feature_extractor is not None:
         raise ValueError(
             f"""Couldn't auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor."""       )
      elif tokenizer is None and feature_extractor is None:
         return None
      elif tokenizer is not None:
         return tokenizer
      else:
         return feature_extractor
"""simple docstring"""


__UpperCamelCase       :    List[Any]         =		"\n# Diffusers installation\n! pip install diffusers transformers datasets accelerate\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/diffusers.git\n"

__UpperCamelCase       :    Any         =		[{"type": "code", "content": INSTALL_CONTENT}]