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

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


__lowerCAmelCase			:     Any           =   logging.get_logger(__name__)

__lowerCAmelCase			:     Any           =   {
    'deepmind/language-perceiver': 'https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json',
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}




class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  a__           =		"""perceiver"""
  def __init__( self			: List[str]			,							UpperCamelCase__			: Any=256			,							UpperCamelCase__			: Union[str, Any]=1280			,							UpperCamelCase__			: Optional[int]=768			,							UpperCamelCase__			: Optional[Any]=1			,							UpperCamelCase__			: Tuple=26			,							UpperCamelCase__			: Optional[Any]=8			,							UpperCamelCase__			: Dict=8			,							UpperCamelCase__			: int=None			,							UpperCamelCase__			: str=None			,							UpperCamelCase__			: List[str]="kv"			,							UpperCamelCase__			: List[Any]=1			,							UpperCamelCase__			: List[str]=1			,							UpperCamelCase__			: List[Any]="gelu"			,							UpperCamelCase__			: List[str]=0.1			,							UpperCamelCase__			: List[str]=0.02			,							UpperCamelCase__			: Optional[int]=1E-12			,							UpperCamelCase__			: Dict=True			,							UpperCamelCase__			: Dict=262			,							UpperCamelCase__			: List[Any]=2048			,							UpperCamelCase__			: Tuple=56			,							UpperCamelCase__			: Optional[int]=[368, 496]			,							UpperCamelCase__			: str=16			,							UpperCamelCase__			: Union[str, Any]=1920			,							UpperCamelCase__			: Tuple=16			,							UpperCamelCase__			: List[Any]=[1, 16, 224, 224]			,							**UpperCamelCase__			: str			,							)			->	List[Any]:







    """simple docstring"""






    super().__init__(**UpperCamelCase__    )

    __magic_name__				     =       num_latents
    __magic_name__				     =       d_latents
    __magic_name__				     =       d_model
    __magic_name__				     =       num_blocks
    __magic_name__				     =       num_self_attends_per_block
    __magic_name__				     =       num_self_attention_heads
    __magic_name__				     =       num_cross_attention_heads
    __magic_name__				     =       qk_channels
    __magic_name__				     =       v_channels
    __magic_name__				     =       cross_attention_shape_for_attention
    __magic_name__				     =       self_attention_widening_factor
    __magic_name__				     =       cross_attention_widening_factor
    __magic_name__				     =       hidden_act
    __magic_name__				     =       attention_probs_dropout_prob
    __magic_name__				     =       initializer_range
    __magic_name__				     =       layer_norm_eps
    __magic_name__				     =       use_query_residual
    # masked language modeling attributes
    __magic_name__				     =       vocab_size
    __magic_name__				     =       max_position_embeddings
    # image classification attributes
    __magic_name__				     =       image_size
    # flow attributes
    __magic_name__				     =       train_size
    # multimodal autoencoding attributes
    __magic_name__				     =       num_frames
    __magic_name__				     =       audio_samples_per_frame
    __magic_name__				     =       samples_per_patch
    __magic_name__				     =       output_shape







class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  @property
  def 							_lowercase							( self			: Union[str, Any]    )			->	Mapping[str, Mapping[int, str]]:







    """simple docstring"""






    if self.task == "multiple-choice":
      __magic_name__				     =       {0: """batch""", 1: """choice""", 2: """sequence"""}
    else:
      __magic_name__				     =       {0: """batch""", 1: """sequence"""}
    return OrderedDict(
        [
            ("""inputs""", dynamic_axis),
            ("""attention_mask""", dynamic_axis),
        ]    )
  @property
  def 							_lowercase							( self			: str    )			->	float:







    """simple docstring"""






    return 1E-4





  def 							_lowercase							( self			: int			,							UpperCamelCase__			: Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]			,							UpperCamelCase__			: int = -1			,							UpperCamelCase__			: int = -1			,							UpperCamelCase__			: int = -1			,							UpperCamelCase__			: bool = False			,							UpperCamelCase__			: Optional[TensorType] = None			,							UpperCamelCase__			: int = 3			,							UpperCamelCase__			: int = 40			,							UpperCamelCase__			: int = 40			,							)			->	Mapping[str, Any]:







    """simple docstring"""






    if isinstance(UpperCamelCase__			,							UpperCamelCase__    ):
      # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
      __magic_name__				     =       compute_effective_axis_dimension(
          UpperCamelCase__			,							fixed_dimension=OnnxConfig.default_fixed_batch			,							num_token_to_add=0    )
      # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
      __magic_name__				     =       preprocessor.num_special_tokens_to_add(UpperCamelCase__    )
      __magic_name__				     =       compute_effective_axis_dimension(
          UpperCamelCase__			,							fixed_dimension=OnnxConfig.default_fixed_sequence			,							num_token_to_add=UpperCamelCase__    )
      # Generate dummy inputs according to compute batch and sequence
      __magic_name__				     =       [""" """.join(["""a"""]    ) * seq_length] * batch_size
      __magic_name__				     =       dict(preprocessor(UpperCamelCase__			,							return_tensors=UpperCamelCase__    )    )
      __magic_name__				     =       inputs.pop("""input_ids"""    )
      return inputs
    elif isinstance(UpperCamelCase__			,							UpperCamelCase__    ) and preprocessor.model_input_names[0] == "pixel_values":
      # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
      __magic_name__				     =       compute_effective_axis_dimension(UpperCamelCase__			,							fixed_dimension=OnnxConfig.default_fixed_batch    )
      __magic_name__				     =       self._generate_dummy_images(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__    )
      __magic_name__				     =       dict(preprocessor(images=UpperCamelCase__			,							return_tensors=UpperCamelCase__    )    )
      __magic_name__				     =       inputs.pop("""pixel_values"""    )
      return inputs
    else:
      raise ValueError(
          """Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."""    )



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							:Any							 =				{
					    '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							:int							 =				{
					    'linear': PIL.Image.LINEAR,
					    'bilinear': PIL.Image.BILINEAR,
					    'bicubic': PIL.Image.BICUBIC,
					    'lanczos': PIL.Image.LANCZOS,
					    'nearest': PIL.Image.NEAREST,
					}


def 						UpperCAmelCase			(	a_						)		->	Optional[Any]:







				"""simple docstring"""



				__A   			= (images / 2 + 0.5).clamp(0		,   1						)
				__A   			= images.cpu().permute(0		,   2		,   3		,   1						).float().numpy()
				__A   			= numpy_to_pil(a_						)
				return images


def 						UpperCAmelCase			(	a_						)		->	int:







				"""simple docstring"""



				if images.ndim == 3:
								__A   			= images[None, ...]
				__A   			= (images * 2_5_5).round().astype("uint8"						)
				if images.shape[-1] == 1:
								# special case for grayscale (single channel) images
								__A   			= [Image.fromarray(image.squeeze()		,   mode="L"						) for image in images]
				else:
								__A   			= [Image.fromarray(a_						) for image in images]

				return pil_images
