




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


__a							     =					logging.get_logger(__name__)

__a							     =					{
    '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(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = '''perceiver'''





    def __init__( self  ,_SCREAMING_SNAKE_CASE=256  ,_SCREAMING_SNAKE_CASE=1_280  ,_SCREAMING_SNAKE_CASE=768  ,_SCREAMING_SNAKE_CASE=1  ,_SCREAMING_SNAKE_CASE=26  ,_SCREAMING_SNAKE_CASE=8  ,_SCREAMING_SNAKE_CASE=8  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE="kv"  ,_SCREAMING_SNAKE_CASE=1  ,_SCREAMING_SNAKE_CASE=1  ,_SCREAMING_SNAKE_CASE="gelu"  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=0.02  ,_SCREAMING_SNAKE_CASE=1e-12  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=262  ,_SCREAMING_SNAKE_CASE=2_048  ,_SCREAMING_SNAKE_CASE=56  ,_SCREAMING_SNAKE_CASE=[368, 496]  ,_SCREAMING_SNAKE_CASE=16  ,_SCREAMING_SNAKE_CASE=1_920  ,_SCREAMING_SNAKE_CASE=16  ,_SCREAMING_SNAKE_CASE=[1, 16, 224, 224]  ,**_SCREAMING_SNAKE_CASE  ,)					->						int:
         super().__init__(**_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Optional[int]  =     num_latents
         UpperCAmelCase_     :				Any  =     d_latents
         UpperCAmelCase_     :				List[str]  =     d_model
         UpperCAmelCase_     :				int  =     num_blocks
         UpperCAmelCase_     :				str  =     num_self_attends_per_block
         UpperCAmelCase_     :				Any  =     num_self_attention_heads
         UpperCAmelCase_     :				List[str]  =     num_cross_attention_heads
         UpperCAmelCase_     :				List[Any]  =     qk_channels
         UpperCAmelCase_     :				Union[str, Any]  =     v_channels
         UpperCAmelCase_     :				Optional[Any]  =     cross_attention_shape_for_attention
         UpperCAmelCase_     :				List[str]  =     self_attention_widening_factor
         UpperCAmelCase_     :				Optional[Any]  =     cross_attention_widening_factor
         UpperCAmelCase_     :				str  =     hidden_act
         UpperCAmelCase_     :				Optional[int]  =     attention_probs_dropout_prob
         UpperCAmelCase_     :				int  =     initializer_range
         UpperCAmelCase_     :				Union[str, Any]  =     layer_norm_eps
         UpperCAmelCase_     :				int  =     use_query_residual
         # masked language modeling attributes
         UpperCAmelCase_     :				Union[str, Any]  =     vocab_size
         UpperCAmelCase_     :				Tuple  =     max_position_embeddings
         # image classification attributes
         UpperCAmelCase_     :				Tuple  =     image_size
         # flow attributes
         UpperCAmelCase_     :				int  =     train_size
         # multimodal autoencoding attributes
         UpperCAmelCase_     :				int  =     num_frames
         UpperCAmelCase_     :				Dict  =     audio_samples_per_frame
         UpperCAmelCase_     :				Dict  =     samples_per_patch
         UpperCAmelCase_     :				str  =     output_shape


class     __a(	_a  ):






    """simple docstring"""





    @property
    def 				a__ ( self    )					->						Mapping[str, Mapping[int, str]]:
         if self.task == "multiple-choice":
              UpperCAmelCase_     :				Optional[int]  =     {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
         else:
              UpperCAmelCase_     :				Tuple  =     {0: '''batch''', 1: '''sequence'''}
         return OrderedDict(
             [
                 ('''inputs''', dynamic_axis),
                 ('''attention_mask''', dynamic_axis),
             ]    )





    @property
    def 				a__ ( self    )					->						float:
         return 1e-4





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = -1  ,_SCREAMING_SNAKE_CASE = -1  ,_SCREAMING_SNAKE_CASE = -1  ,_SCREAMING_SNAKE_CASE = False  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = 3  ,_SCREAMING_SNAKE_CASE = 40  ,_SCREAMING_SNAKE_CASE = 40  ,)					->						Mapping[str, Any]:
         # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

         if isinstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ):
              # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
              UpperCAmelCase_     :				Dict  =     compute_effective_axis_dimension(
                  _SCREAMING_SNAKE_CASE  ,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
              UpperCAmelCase_     :				str  =     preprocessor.num_special_tokens_to_add(_SCREAMING_SNAKE_CASE    )
              UpperCAmelCase_     :				Union[str, Any]  =     compute_effective_axis_dimension(
                  _SCREAMING_SNAKE_CASE  ,fixed_dimension=OnnxConfig.default_fixed_sequence  ,num_token_to_add=_SCREAMING_SNAKE_CASE    )
              # Generate dummy inputs according to compute batch and sequence
              UpperCAmelCase_     :				Union[str, Any]  =     [''' '''.join(['''a''']    ) * seq_length] * batch_size
              UpperCAmelCase_     :				Optional[int]  =     dict(preprocessor(_SCREAMING_SNAKE_CASE  ,return_tensors=_SCREAMING_SNAKE_CASE    )    )
              UpperCAmelCase_     :				List[str]  =     inputs.pop('''input_ids'''    )
              return inputs
         elif isinstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ) 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
              UpperCAmelCase_     :				int  =     compute_effective_axis_dimension(_SCREAMING_SNAKE_CASE  ,fixed_dimension=OnnxConfig.default_fixed_batch    )
              UpperCAmelCase_     :				Optional[int]  =     self._generate_dummy_images(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
              UpperCAmelCase_     :				List[str]  =     dict(preprocessor(images=_SCREAMING_SNAKE_CASE  ,return_tensors=_SCREAMING_SNAKE_CASE    )    )
              UpperCAmelCase_     :				str  =     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 __future__ import annotations

from collections.abc import Iterator
from typing import Any




class 			lowerCAmelCase__			:
   def __init__(		self      :	Any							,			snake_case__      :	Any							):


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							data
        UpperCAmelCase__						:    Node | None							=							None




class 			lowerCAmelCase__			:
   def __init__(		self      :	Dict							):


        '''simple docstring'''
        UpperCAmelCase__						:    Tuple							=							None
        UpperCAmelCase__						:    Optional[int]							=							None
   def __iter__(		self      :	Tuple							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							self.head
        while self.head:
             yield node.data
             UpperCAmelCase__						:    Optional[Any]							=							node.next
             if node == self.head:
                  break
   def __len__(		self      :	Tuple							):


        '''simple docstring'''
        return sum(1 for _ in self							)
   def __repr__(		self      :	str							):


        '''simple docstring'''
        return "->".join(str(snake_case__							) for item in iter(self							)							)
   def       __a							(		self      :	Optional[Any]							,			snake_case__      :	Any							):


        '''simple docstring'''
        self.insert_nth(len(self							)							,			snake_case__							)
   def       __a							(		self      :	Optional[Any]							,			snake_case__      :	Any							):


        '''simple docstring'''
        self.insert_nth(0							,			snake_case__							)
   def       __a							(		self      :	Any							,			snake_case__      :	int							,			snake_case__      :	Any							):


        '''simple docstring'''
        if index < 0 or index > len(self							):
             raise IndexError("list index out of range."							)
        UpperCAmelCase__						:    Any							=							Node(snake_case__							)
        if self.head is None:
             UpperCAmelCase__						:    Union[str, Any]							=							new_node  # first node points itself
             UpperCAmelCase__						:    List[str]							=							new_node
        elif index == 0:  # insert at head
             UpperCAmelCase__						:    Optional[Any]							=							self.head
             UpperCAmelCase__						:    List[Any]							=							new_node
        else:
             UpperCAmelCase__						:    Union[str, Any]							=							self.head
             for _ in range(index - 1							):
                  UpperCAmelCase__						:    Dict							=							temp.next
             UpperCAmelCase__						:    Tuple							=							temp.next
             UpperCAmelCase__						:    str							=							new_node
             if index == len(self							) - 1:  # insert at tail
                  UpperCAmelCase__						:    Union[str, Any]							=							new_node
   def       __a							(		self      :	List[Any]							):


        '''simple docstring'''
        return self.delete_nth(0							)
   def       __a							(		self      :	Optional[Any]							):


        '''simple docstring'''
        return self.delete_nth(len(self							) - 1							)
   def       __a							(		self      :	Tuple							,			snake_case__      :	int = 0							):


        '''simple docstring'''
        if not 0 <= index < len(self							):
             raise IndexError("list index out of range."							)
        UpperCAmelCase__						:    Union[str, Any]							=							self.head
        if self.head == self.tail:  # just one node
             UpperCAmelCase__						:    str							=							None
        elif index == 0:  # delete head node
             UpperCAmelCase__						:    List[Any]							=							self.tail.next.next
             UpperCAmelCase__						:    Dict							=							self.head.next
        else:
             UpperCAmelCase__						:    List[Any]							=							self.head
             for _ in range(index - 1							):
                  UpperCAmelCase__						:    Tuple							=							temp.next
             UpperCAmelCase__						:    Dict							=							temp.next
             UpperCAmelCase__						:    Dict							=							temp.next.next
             if index == len(self							) - 1:  # delete at tail
                  UpperCAmelCase__						:    int							=							temp
        return delete_node.data




   def       __a							(		self      :	Any							):


        '''simple docstring'''
        return len(self							) == 0




def 	SCREAMING_SNAKE_CASE__       (  )->		None:






     '''simple docstring'''



     UpperCAmelCase__						:    str							=							CircularLinkedList()
     assert len(snake_case				) == 0
     assert circular_linked_list.is_empty() is True
     assert str(snake_case				) == ""

     try:
          circular_linked_list.delete_front()
          raise AssertionError  # This should not happen
     except IndexError:
          assert True  # This should happen

     try:
          circular_linked_list.delete_tail()
          raise AssertionError  # This should not happen
     except IndexError:
          assert True  # This should happen

     try:
          circular_linked_list.delete_nth(-1				)
          raise AssertionError
     except IndexError:
          assert True

     try:
          circular_linked_list.delete_nth(0				)
          raise AssertionError
     except IndexError:
          assert True

     assert circular_linked_list.is_empty() is True
     for i in range(5				):
          assert len(snake_case				) == i
          circular_linked_list.insert_nth(snake_case			,					i + 1				)
     assert str(snake_case				) == "->".join(str(snake_case				) for i in range(1			,					6				)				)

     circular_linked_list.insert_tail(6				)
     assert str(snake_case				) == "->".join(str(snake_case				) for i in range(1			,					7				)				)
     circular_linked_list.insert_head(0				)
     assert str(snake_case				) == "->".join(str(snake_case				) for i in range(0			,					7				)				)

     assert circular_linked_list.delete_front() == 0
     assert circular_linked_list.delete_tail() == 6
     assert str(snake_case				) == "->".join(str(snake_case				) for i in range(1			,					6				)				)
     assert circular_linked_list.delete_nth(2				) == 3

     circular_linked_list.insert_nth(2			,					3				)
     assert str(snake_case				) == "->".join(str(snake_case				) for i in range(1			,					6				)				)

     assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
 import doctest

 doctest.testmod()


