

'''simple docstring'''

from __future__ import annotations

from typing import Generic, TypeVar

lowercase__         =      TypeVar("T")




class 	A_			(    Generic[T]							):






     '''simple docstring'''



     def __init__(		self			:					Any					,      lowercase_			:					T	)						->     List[str]:
      UpperCAmelCase	:     List[Any]						 =			data
      UpperCAmelCase	:     str						 =			self
      UpperCAmelCase	:     str						 =			0




class 	A_			(    Generic[T]							):






     '''simple docstring'''



     def __init__(		self			:					int	)						->     Tuple:
      UpperCAmelCase	:     dict[T, DisjointSetTreeNode[T]]						 =			{}


     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					T	)						->     Union[str, Any]:
      UpperCAmelCase	:     List[Any]						 =			DisjointSetTreeNode(UpperCAmelCase_	)


     def        UpperCAmelCase_	(		self			:					List[Any]					,      lowercase_			:					T	)						->     int:
      UpperCAmelCase	:     List[str]						 =			self.map[data]
      if elem_ref != elem_ref.parent:
       UpperCAmelCase	:     Optional[int]						 =			self.find_set(elem_ref.parent.data	)
      return elem_ref.parent


     def        UpperCAmelCase_	(		self			:					Tuple					,      lowercase_			:					DisjointSetTreeNode[T]					,      lowercase_			:					DisjointSetTreeNode[T]	)						->     List[Any]:
      # helper function for union operation
      if nodea.rank > nodea.rank:
       UpperCAmelCase	:     Optional[Any]						 =			nodea
      else:
       UpperCAmelCase	:     str						 =			nodea
       if nodea.rank == nodea.rank:
        nodea.rank += 1




     def        UpperCAmelCase_	(		self			:					List[str]					,      lowercase_			:					T					,      lowercase_			:					T	)						->     str:
      self.link(self.find_set(UpperCAmelCase_	)					,      self.find_set(UpperCAmelCase_	)	)




class 	A_			(    Generic[T]							):






     '''simple docstring'''



     def __init__(		self			:					Tuple	)						->     List[Any]:
      UpperCAmelCase	:     dict[T, dict[T, int]]						 =			{}


     def        UpperCAmelCase_	(		self			:					List[Any]					,      lowercase_			:					T	)						->     Dict:
      # add a node ONLY if its not present in the graph
      if node not in self.connections:
       UpperCAmelCase	:     Optional[int]						 =			{}


     def        UpperCAmelCase_	(		self			:					Any					,      lowercase_			:					T					,      lowercase_			:					T					,      lowercase_			:					int	)						->     Any:
      self.add_node(UpperCAmelCase_	)
      self.add_node(UpperCAmelCase_	)
      UpperCAmelCase	:     Union[str, Any]						 =			weight
      UpperCAmelCase	:     Any						 =			weight




     def        UpperCAmelCase_	(		self			:					List[str]	)						->     str:
      UpperCAmelCase	:     Dict						 =			[]
      UpperCAmelCase	:     Union[str, Any]						 =			set()
      for start in self.connections:
       for end in self.connections[start]:
        if (start, end) not in seen:
         seen.add((end, start)	)
         edges.append((start, end, self.connections[start][end])	)
      edges.sort(key=lambda lowercase_	: x[2]	)

      # creating the disjoint set
      UpperCAmelCase	:     List[str]						 =			DisjointSetTree[T]()
      for node in self.connections:
       disjoint_set.make_set(UpperCAmelCase_	)

      # MST generation
      UpperCAmelCase	:     Optional[Any]						 =			0
      UpperCAmelCase	:     List[Any]						 =			0
      UpperCAmelCase	:     Dict						 =			GraphUndirectedWeighted[T]()
      while num_edges < len(self.connections	) - 1:
       UpperCAmelCase	:     List[str]						 =			edges[index]
       index += 1
       UpperCAmelCase	:     List[str]						 =			disjoint_set.find_set(UpperCAmelCase_	)
       UpperCAmelCase	:     Optional[int]						 =			disjoint_set.find_set(UpperCAmelCase_	)
       if parent_u != parent_v:
        num_edges += 1
        graph.add_edge(UpperCAmelCase_					,      UpperCAmelCase_					,      UpperCAmelCase_	)
        disjoint_set.union(UpperCAmelCase_					,      UpperCAmelCase_	)
      return graph







'''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


lowercase__         =      logging.get_logger(__name__)

lowercase__         =      {
    "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_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     Optional[int]  		=  """perceiver"""


     def __init__(		self			:					str					,      lowercase_			:					List[str]=256					,      lowercase_			:					List[str]=1_280					,      lowercase_			:					str=768					,      lowercase_			:					Tuple=1					,      lowercase_			:					str=26					,      lowercase_			:					List[Any]=8					,      lowercase_			:					int=8					,      lowercase_			:					List[str]=None					,      lowercase_			:					Dict=None					,      lowercase_			:					int="kv"					,      lowercase_			:					Union[str, Any]=1					,      lowercase_			:					List[str]=1					,      lowercase_			:					Any="gelu"					,      lowercase_			:					Optional[Any]=0.1					,      lowercase_			:					str=0.02					,      lowercase_			:					Optional[Any]=1E-12					,      lowercase_			:					Any=True					,      lowercase_			:					Union[str, Any]=262					,      lowercase_			:					Union[str, Any]=2_048					,      lowercase_			:					Optional[int]=56					,      lowercase_			:					int=[368, 496]					,      lowercase_			:					str=16					,      lowercase_			:					Optional[int]=1_920					,      lowercase_			:					Tuple=16					,      lowercase_			:					int=[1, 16, 224, 224]					,      **lowercase_			:					Union[str, Any]					,      )						->     List[str]:
      super().__init__(**lowercase_	)

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




class 	A_			(    _snake_case							):






     '''simple docstring'''



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


     @property
     def        UpperCAmelCase_	(		self			:					str	)						->     float:
      return 1E-4




     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]					,      lowercase_			:					int = -1					,      lowercase_			:					int = -1					,      lowercase_			:					int = -1					,      lowercase_			:					bool = False					,      lowercase_			:					Optional[TensorType] = None					,      lowercase_			:					int = 3					,      lowercase_			:					int = 40					,      lowercase_			:					int = 40					,      )						->     Mapping[str, Any]:
      # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

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




