"""simple docstring"""






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    :       List[str]					     =logging.get_logger(__name__)

__lowerCAmelCase    :       Dict					     ={
    """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  _A						(       lowerCAmelCase	):
      snake_case__			:   Dict							  =	'perceiver'

      def __init__(						self		,       __lowerCAmelCase=256		,       __lowerCAmelCase=1280		,       __lowerCAmelCase=768		,       __lowerCAmelCase=1		,       __lowerCAmelCase=26		,       __lowerCAmelCase=8		,       __lowerCAmelCase=8		,       __lowerCAmelCase=None		,       __lowerCAmelCase=None		,       __lowerCAmelCase="kv"		,       __lowerCAmelCase=1		,       __lowerCAmelCase=1		,       __lowerCAmelCase="gelu"		,       __lowerCAmelCase=0.1		,       __lowerCAmelCase=0.0_2		,       __lowerCAmelCase=1E-12		,       __lowerCAmelCase=True		,       __lowerCAmelCase=262		,       __lowerCAmelCase=2048		,       __lowerCAmelCase=56		,       __lowerCAmelCase=[368, 496]		,       __lowerCAmelCase=16		,       __lowerCAmelCase=1920		,       __lowerCAmelCase=16		,       __lowerCAmelCase=[1, 16, 224, 224]		,       **__lowerCAmelCase		,       ):







            """simple docstring"""



            super().__init__(**__lowerCAmelCase						)

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






class  _A						(       lowerCAmelCase	):

      @property
      def 				A__       (						self						):







            """simple docstring"""



            if self.task == "multiple-choice":
                  lowercase														=	{0: """batch""", 1: """choice""", 2: """sequence"""}
            else:
                  lowercase														=	{0: """batch""", 1: """sequence"""}
            return OrderedDict(
                [
                    ("""inputs""", dynamic_axis),
                    ("""attention_mask""", dynamic_axis),
                ]						)

      @property
      def 				A__       (						self						):







            """simple docstring"""



            return 1E-4







      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase = -1		,       __lowerCAmelCase = -1		,       __lowerCAmelCase = -1		,       __lowerCAmelCase = False		,       __lowerCAmelCase = None		,       __lowerCAmelCase = 3		,       __lowerCAmelCase = 40		,       __lowerCAmelCase = 40		,       ):







            """simple docstring"""



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




"""simple docstring"""






from ..utils import DummyObject, requires_backends





class  _A						(       metaclass=lowerCAmelCase	):
      snake_case__			:   str							  =	['torch', 'transformers', 'onnx']

      def __init__(						self		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(self		,       ["""torch""", """transformers""", """onnx"""]						)

      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """transformers""", """onnx"""]						)







      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """transformers""", """onnx"""]						)






class  _A						(       metaclass=lowerCAmelCase	):
      snake_case__			:   List[Any]							  =	['torch', 'transformers', 'onnx']

      def __init__(						self		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(self		,       ["""torch""", """transformers""", """onnx"""]						)

      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """transformers""", """onnx"""]						)







      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """transformers""", """onnx"""]						)






class  _A						(       metaclass=lowerCAmelCase	):
      snake_case__			:   Optional[Any]							  =	['torch', 'transformers', 'onnx']

      def __init__(						self		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(self		,       ["""torch""", """transformers""", """onnx"""]						)

      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """transformers""", """onnx"""]						)







      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """transformers""", """onnx"""]						)






class  _A						(       metaclass=lowerCAmelCase	):
      snake_case__			:   int							  =	['torch', 'transformers', 'onnx']

      def __init__(						self		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(self		,       ["""torch""", """transformers""", """onnx"""]						)

      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """transformers""", """onnx"""]						)







      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """transformers""", """onnx"""]						)






class  _A						(       metaclass=lowerCAmelCase	):
      snake_case__			:   List[str]							  =	['torch', 'transformers', 'onnx']

      def __init__(						self		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(self		,       ["""torch""", """transformers""", """onnx"""]						)

      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """transformers""", """onnx"""]						)







      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """transformers""", """onnx"""]						)






class  _A						(       metaclass=lowerCAmelCase	):
      snake_case__			:   Optional[Any]							  =	['torch', 'transformers', 'onnx']

      def __init__(						self		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(self		,       ["""torch""", """transformers""", """onnx"""]						)

      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """transformers""", """onnx"""]						)







      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """transformers""", """onnx"""]						)



