




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				(		SCREAMING_SNAKE_CASE__     ):
			__a					    =       """perceiver"""






			def __init__(    self						,		_SCREAMING_SNAKE_CASE=256						,		_SCREAMING_SNAKE_CASE=1280						,		_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=2048						,		_SCREAMING_SNAKE_CASE=56						,		_SCREAMING_SNAKE_CASE=[368, 496]						,		_SCREAMING_SNAKE_CASE=16						,		_SCREAMING_SNAKE_CASE=1920						,		_SCREAMING_SNAKE_CASE=16						,		_SCREAMING_SNAKE_CASE=[1, 16, 224, 224]						,		**_SCREAMING_SNAKE_CASE						,		):
									super().__init__(**snake_case__			)

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




class       _A				(		SCREAMING_SNAKE_CASE__     ):






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






			@property
			def 				UpperCAmelCase (    self			):
									return 1e-4






			def 				UpperCAmelCase (    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						,		):
									# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

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





import unittest

import numpy as np




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case = None			,     )						->				np.ndarray:
						_UpperCAmelCase      =			np.shape(snake_case       )
						_UpperCAmelCase      =			np.shape(snake_case       )
						_UpperCAmelCase      =			np.shape(snake_case       )

						if shape_a[0] != shape_b[0]:
												_UpperCAmelCase      =			(
												    """Expected the same number of rows for A and B. """
												    f"Instead found A of size {shape_a} and B of size {shape_b}"
												)
												raise ValueError(snake_case       )

						if shape_b[1] != shape_c[1]:
												_UpperCAmelCase      =			(
												    """Expected the same number of columns for B and C. """
												    f"Instead found B of size {shape_b} and C of size {shape_c}"
												)
												raise ValueError(snake_case       )

						_UpperCAmelCase      =			pseudo_inv
						if a_inv is None:
												try:
																		_UpperCAmelCase      =			np.linalg.inv(snake_case       )
												except np.linalg.LinAlgError:
																		raise ValueError(
																		    """Input matrix A is not invertible. Cannot compute Schur complement."""       )

						return mat_c - mat_b.T @ a_inv @ mat_b




class       _A				(		unittest.TestCase     ):






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
								_UpperCAmelCase      =			np.array([[0, 3], [3, 0], [2, 3]]			)
								_UpperCAmelCase      =			np.array([[2, 1], [6, 3]]			)

								_UpperCAmelCase      =			schur_complement(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			np.block([[a, b], [b.T, c]]			)

								_UpperCAmelCase      =			np.linalg.det(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			np.linalg.det(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			np.linalg.det(_SCREAMING_SNAKE_CASE			)

								self.assertAlmostEqual(_SCREAMING_SNAKE_CASE						,		det_a * det_s			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
								_UpperCAmelCase      =			np.array([[0, 3], [3, 0], [2, 3]]			)
								_UpperCAmelCase      =			np.array([[2, 1], [6, 3]]			)

								with self.assertRaises(_SCREAMING_SNAKE_CASE			):
														schur_complement(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
								_UpperCAmelCase      =			np.array([[0, 3], [3, 0], [2, 3]]			)
								_UpperCAmelCase      =			np.array([[2, 1, 3], [6, 3, 5]]			)

								with self.assertRaises(_SCREAMING_SNAKE_CASE			):
														schur_complement(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				unittest.main()