



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 			_snake_case     (   lowercase__):
				UpperCamelCase__			:   Any       						="""perceiver"""




				def __init__(  self   :  Any,   __lowercase   :  Optional[Any]=256,   __lowercase   :  List[str]=1280,   __lowercase   :  Dict=768,   __lowercase   :  int=1,   __lowercase   :  Dict=26,   __lowercase   :  Any=8,   __lowercase   :  List[Any]=8,   __lowercase   :  Dict=None,   __lowercase   :  List[Any]=None,   __lowercase   :  str="kv",   __lowercase   :  str=1,   __lowercase   :  Optional[Any]=1,   __lowercase   :  str="gelu",   __lowercase   :  List[str]=0.1,   __lowercase   :  int=0.02,   __lowercase   :  Union[str, Any]=1e-1_2,   __lowercase   :  Optional[Any]=True,   __lowercase   :  Optional[Any]=262,   __lowercase   :  str=2048,   __lowercase   :  Optional[Any]=56,   __lowercase   :  str=[368, 496],   __lowercase   :  str=16,   __lowercase   :  int=1920,   __lowercase   :  Dict=16,   __lowercase   :  List[Any]=[1, 16, 224, 224],   **__lowercase   :  str,   ):
							super().__init__(**__lowercase  )

							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 			_snake_case     (   lowercase__):




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




				def 							A__ (  self   :  Tuple,   __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,   ):
							# 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
										lowercase__   					=			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
										lowercase__   					=			preprocessor.num_special_tokens_to_add(__lowercase  )
										lowercase__   					=			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
										lowercase__   					=			[" ".join(["a"]  ) * seq_length] * batch_size
										lowercase__   					=			dict(preprocessor(__lowercase,   return_tensors=__lowercase  )  )
										lowercase__   					=			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
										lowercase__   					=			compute_effective_axis_dimension(__lowercase,   fixed_dimension=OnnxConfig.default_fixed_batch  )
										lowercase__   					=			self._generate_dummy_images(__lowercase,   __lowercase,   __lowercase,   __lowercase  )
										lowercase__   					=			dict(preprocessor(images=__lowercase,   return_tensors=__lowercase  )  )
										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."  )









from graphs.minimum_spanning_tree_kruskal import kruskal



def __lowerCAmelCase	(       ):
			lowercase__   					=			9
			lowercase__   					=			[
			    [0, 1, 4],
			    [0, 7, 8],
			    [1, 2, 8],
			    [7, 8, 7],
			    [7, 6, 1],
			    [2, 8, 2],
			    [8, 6, 6],
			    [2, 3, 7],
			    [2, 5, 4],
			    [6, 5, 2],
			    [3, 5, 14],
			    [3, 4, 9],
			    [5, 4, 10],
			    [1, 7, 11],
			]

			lowercase__   					=			kruskal(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

			lowercase__   					=			[
			    [7, 6, 1],
			    [2, 8, 2],
			    [6, 5, 2],
			    [0, 1, 4],
			    [2, 5, 4],
			    [2, 3, 7],
			    [0, 7, 8],
			    [3, 4, 9],
			]

			assert sorted(SCREAMING_SNAKE_CASE_					) == sorted(SCREAMING_SNAKE_CASE_					)




