
'''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


UpperCAmelCase_       :   Optional[Any]   =       logging.get_logger(__name__)

UpperCAmelCase_       :   Tuple   =       {
    '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 ):
    lowerCAmelCase_				    =		'perceiver'



    def __init__(	self      :       Tuple,__A      :       Tuple=2_5_6,__A      :       Dict=1_2_8_0,__A      :       str=7_6_8,__A      :       int=1,__A      :       List[Any]=2_6,__A      :       Optional[int]=8,__A      :       str=8,__A      :       Tuple=None,__A      :       int=None,__A      :       int="kv",__A      :       Tuple=1,__A      :       str=1,__A      :       Union[str, Any]="gelu",__A      :       Optional[Any]=0.1,__A      :       Any=0.02,__A      :       List[Any]=1e-12,__A      :       Any=True,__A      :       Union[str, Any]=2_6_2,__A      :       Tuple=2_0_4_8,__A      :       Any=5_6,__A      :       Optional[int]=[3_6_8, 4_9_6],__A      :       Optional[Any]=1_6,__A      :       Optional[Any]=1_9_2_0,__A      :       Any=1_6,__A      :       Union[str, Any]=[1, 1_6, 2_2_4, 2_2_4],**__A      :       str,):
          super().__init__(**__A     )

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


class  UpperCAmelCase__						(       A ):



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



    @property
    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          return 1e-4



    def   lowerCamelCase_  (	self      :       List[Any],__A      :       Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"],__A      :       int = -1,__A      :       int = -1,__A      :       int = -1,__A      :       bool = False,__A      :       Optional[TensorType] = None,__A      :       int = 3,__A      :       int = 4_0,__A      :       int = 4_0,):
          # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

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

UpperCAmelCase_       :   str   =       (3, 9, -11, 0, 7, 5, 1, -1)
UpperCAmelCase_       :   int   =       (4, 6, 2, 0, 8, 10, 3, -2)

@dataclass
class  UpperCAmelCase__						:
    lowerCAmelCase_				    =		42
    lowerCAmelCase_				    =		42


class  UpperCAmelCase__						:



    def __init__(	self      :       Optional[int],__A      :       Iterable[int]     ):
          _lowerCamelCase				:   Node | None								=   None
          for i in sorted(__A,reverse=__A     ):
                _lowerCamelCase				:   Dict								=   Node(__A,self.head     )



    def __iter__(	self      :       str     ):
          _lowerCamelCase				:   Dict								=   self.head
          while node:
                yield node.data
                _lowerCamelCase				:   Optional[Any]								=   node.next_node



    def __len__(	self      :       str     ):
          return sum(1 for _ in self     )



    def __str__(	self      :       str     ):
          return " -> ".join([str(__A     ) for node in self]     )







def 		A_       (	_lowerCAmelCase   :    SortedLinkedList				,	_lowerCAmelCase   :    SortedLinkedList							):



      """simple docstring"""




      return SortedLinkedList(list(_lowerCAmelCase							) + list(_lowerCAmelCase							)							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 UpperCAmelCase_       :   List[str]   =       SortedLinkedList
 print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))