





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
		from ... import FeatureExtractionMixin, TensorType

SCREAMING_SNAKE_CASE__       :			Any			      =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       :			Any			      =						{
    """openai/imagegpt-small""": """""",
    """openai/imagegpt-medium""": """""",
    """openai/imagegpt-large""": """""",
}







class   __lowerCAmelCase			(					_UpperCamelCase  ):
			_UpperCamelCase :						Tuple     		=				"""imagegpt"""
			_UpperCamelCase :						str     		=				["""past_key_values"""]
			_UpperCamelCase :						Union[str, Any]     		=				{
			    """hidden_size""": """n_embd""",
			    """max_position_embeddings""": """n_positions""",
			    """num_attention_heads""": """n_head""",
			    """num_hidden_layers""": """n_layer""",
			}





			def __init__( self					,			snake_case=512 + 1					,			snake_case=32 * 32					,			snake_case=512					,			snake_case=24					,			snake_case=8					,			snake_case=None					,			snake_case="quick_gelu"					,			snake_case=0.1					,			snake_case=0.1					,			snake_case=0.1					,			snake_case=1E-5					,			snake_case=0.02					,			snake_case=True					,			snake_case=True					,			snake_case=False					,			snake_case=False					,			snake_case=False					,			**snake_case					,			)				->  List[Any]:
						"""simple docstring"""





						a__	:						int      		=  vocab_size
						a__	:						Union[str, Any]      		=  n_positions
						a__	:						List[str]      		=  n_embd
						a__	:						List[str]      		=  n_layer
						a__	:						List[Any]      		=  n_head
						a__	:						List[str]      		=  n_inner
						a__	:						int      		=  activation_function
						a__	:						Dict      		=  resid_pdrop
						a__	:						List[Any]      		=  embd_pdrop
						a__	:						List[Any]      		=  attn_pdrop
						a__	:						int      		=  layer_norm_epsilon
						a__	:						Optional[int]      		=  initializer_range
						a__	:						List[str]      		=  scale_attn_weights
						a__	:						str      		=  use_cache
						a__	:						str      		=  scale_attn_by_inverse_layer_idx
						a__	:						int      		=  reorder_and_upcast_attn
						a__	:						List[Any]      		=  tie_word_embeddings

						super().__init__(tie_word_embeddings=snake_case					,			**snake_case      )







class   __lowerCAmelCase			(					_UpperCamelCase  ):





			@property
			def 	_snake_case ( self      )				->  Mapping[str, Mapping[int, str]]:
						"""simple docstring"""





						return OrderedDict(
						    [
						        ("input_ids", {0: "batch", 1: "sequence"}),
						    ]      )





			def 	_snake_case ( self					,			snake_case					,			snake_case = 1					,			snake_case = -1					,			snake_case = False					,			snake_case = None					,			snake_case = 3					,			snake_case = 32					,			snake_case = 32					,			)				->  Mapping[str, Any]:
						"""simple docstring"""





						a__	:						Tuple      		=  self._generate_dummy_images(snake_case					,			snake_case					,			snake_case					,			snake_case      )
						a__	:						Any      		=  dict(preprocessor(images=snake_case					,			return_tensors=snake_case      )      )

						return inputs








import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
  SCREAMING_SNAKE_CASE__       :			int			      =						{
      """linear""": PIL.Image.Resampling.BILINEAR,
      """bilinear""": PIL.Image.Resampling.BILINEAR,
      """bicubic""": PIL.Image.Resampling.BICUBIC,
      """lanczos""": PIL.Image.Resampling.LANCZOS,
      """nearest""": PIL.Image.Resampling.NEAREST,
  }
else:
  SCREAMING_SNAKE_CASE__       :			Dict			      =						{
      """linear""": PIL.Image.LINEAR,
      """bilinear""": PIL.Image.BILINEAR,
      """bicubic""": PIL.Image.BICUBIC,
      """lanczos""": PIL.Image.LANCZOS,
      """nearest""": PIL.Image.NEAREST,
  }






def       _A  (      lowerCamelCase   ):
   a__	:						Optional[int]      		=  (images / 2 + 0.5).clamp(0							, 1   )
   a__	:						List[Any]      		=  images.cpu().permute(0							, 2							, 3							, 1   ).float().numpy()
   a__	:						int      		=  numpy_to_pil(lowerCamelCase   )
   return images






def       _A  (      lowerCamelCase   ):
   if images.ndim == 3:
      a__	:						Tuple      		=  images[None, ...]
   a__	:						Dict      		=  (images * 255).round().astype("uint8"   )
   if images.shape[-1] == 1:
      # special case for grayscale (single channel) images
      a__	:						str      		=  [Image.fromarray(image.squeeze()							, mode="L"   ) for image in images]
   else:
      a__	:						List[Any]      		=  [Image.fromarray(lowerCamelCase   ) for image in images]

   return pil_images

