
'''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							:   List[Any]					=     logging.get_logger(__name__)

__lowercase							:   Optional[int]					=     {
    '''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 	__lowercase				(  _lowercase    ):
   lowerCamelCase			:		str	      =			"perceiver"

   def __init__(self			,   A=2_5_6			,   A=1_2_8_0			,   A=7_6_8			,   A=1			,   A=2_6			,   A=8			,   A=8			,   A=None			,   A=None			,   A="kv"			,   A=1			,   A=1			,   A="gelu"			,   A=0.1			,   A=0.02			,   A=1E-12			,   A=True			,   A=2_6_2			,   A=2_0_4_8			,   A=5_6			,   A=[3_6_8, 4_9_6]			,   A=1_6			,   A=1_9_2_0			,   A=1_6			,   A=[1, 1_6, 2_2_4, 2_2_4]			,   **A			,   ):
        super().__init__(**A      )

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

class 	__lowercase				(  _lowercase    ):

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

   @property
   def        UpperCAmelCase__ (self      ):
        return 1E-4






   def        UpperCAmelCase__ (self			,   A			,   A = -1			,   A = -1			,   A = -1			,   A = False			,   A = None			,   A = 3			,   A = 4_0			,   A = 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_					:      Optional[Any]     =		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_					:      List[str]     =		preprocessor.num_special_tokens_to_add(A      )
             lowerCamelCase_					:      Dict     =		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_					:      Dict     =		[''' '''.join(['''a''']      ) * seq_length] * batch_size
             lowerCamelCase_					:      Dict     =		dict(preprocessor(A			,   return_tensors=A      )      )
             lowerCamelCase_					:      Optional[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_					:      Tuple     =		compute_effective_axis_dimension(A			,   fixed_dimension=OnnxConfig.default_fixed_batch      )
             lowerCamelCase_					:      Tuple     =		self._generate_dummy_images(A			,   A			,   A			,   A      )
             lowerCamelCase_					:      List[Any]     =		dict(preprocessor(images=A			,   return_tensors=A      )      )
             lowerCamelCase_					:      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 decimal import Decimal

from numpy import array




def lowercase_	(   _lowercase				)     ->							list[list[float]]:
					'''simple docstring'''

					lowerCamelCase_					:      Tuple     =		Decimal

					# Check if the provided matrix has 2 rows and 2 columns
					# since this implementation only works for 2x2 matrices
					if len(_lowercase				) == 2 and len(matrix[0]				) == 2 and len(matrix[1]				) == 2:
										# Calculate the determinant of the matrix
										lowerCamelCase_					:      Union[str, Any]     =		float(
										    d(matrix[0][0]				) * d(matrix[1][1]				) - d(matrix[1][0]				) * d(matrix[0][1]				)				)
										if determinant == 0:
															raise ValueError('''This matrix has no inverse.'''				)

										# Creates a copy of the matrix with swapped positions of the elements
										lowerCamelCase_					:      Optional[Any]     =		[[0.0, 0.0], [0.0, 0.0]]
										lowerCamelCase_,      lowerCamelCase_					:      int     =		matrix[1][1], matrix[0][0]
										lowerCamelCase_,      lowerCamelCase_					:      Dict     =		-matrix[1][0], -matrix[0][1]

										# Calculate the inverse of the matrix
										return [
										    [(float(d(_lowercase				)				) / determinant) or 0.0 for n in row] for row in swapped_matrix
										]
					elif (
					    len(_lowercase				) == 3
					    and len(matrix[0]				) == 3
					    and len(matrix[1]				) == 3
					    and len(matrix[2]				) == 3
					):
										# Calculate the determinant of the matrix using Sarrus rule
										lowerCamelCase_					:      Tuple     =		float(
										    (
										        (d(matrix[0][0]				) * d(matrix[1][1]				) * d(matrix[2][2]				))
										        + (d(matrix[0][1]				) * d(matrix[1][2]				) * d(matrix[2][0]				))
										        + (d(matrix[0][2]				) * d(matrix[1][0]				) * d(matrix[2][1]				))
										    )
										    - (
										        (d(matrix[0][2]				) * d(matrix[1][1]				) * d(matrix[2][0]				))
										        + (d(matrix[0][1]				) * d(matrix[1][0]				) * d(matrix[2][2]				))
										        + (d(matrix[0][0]				) * d(matrix[1][2]				) * d(matrix[2][1]				))
										    )				)
										if determinant == 0:
															raise ValueError('''This matrix has no inverse.'''				)

										# Creating cofactor matrix
										lowerCamelCase_					:      Any     =		[
										    [d(0.0				), d(0.0				), d(0.0				)],
										    [d(0.0				), d(0.0				), d(0.0				)],
										    [d(0.0				), d(0.0				), d(0.0				)],
										]
										lowerCamelCase_					:      Union[str, Any]     =		(d(matrix[1][1]				) * d(matrix[2][2]				)) - (
										    d(matrix[1][2]				) * d(matrix[2][1]				)
										)
										lowerCamelCase_					:      Any     =		-(
										    (d(matrix[1][0]				) * d(matrix[2][2]				)) - (d(matrix[1][2]				) * d(matrix[2][0]				))
										)
										lowerCamelCase_					:      Tuple     =		(d(matrix[1][0]				) * d(matrix[2][1]				)) - (
										    d(matrix[1][1]				) * d(matrix[2][0]				)
										)
										lowerCamelCase_					:      Optional[Any]     =		-(
										    (d(matrix[0][1]				) * d(matrix[2][2]				)) - (d(matrix[0][2]				) * d(matrix[2][1]				))
										)
										lowerCamelCase_					:      Tuple     =		(d(matrix[0][0]				) * d(matrix[2][2]				)) - (
										    d(matrix[0][2]				) * d(matrix[2][0]				)
										)
										lowerCamelCase_					:      List[Any]     =		-(
										    (d(matrix[0][0]				) * d(matrix[2][1]				)) - (d(matrix[0][1]				) * d(matrix[2][0]				))
										)
										lowerCamelCase_					:      Any     =		(d(matrix[0][1]				) * d(matrix[1][2]				)) - (
										    d(matrix[0][2]				) * d(matrix[1][1]				)
										)
										lowerCamelCase_					:      Tuple     =		-(
										    (d(matrix[0][0]				) * d(matrix[1][2]				)) - (d(matrix[0][2]				) * d(matrix[1][0]				))
										)
										lowerCamelCase_					:      str     =		(d(matrix[0][0]				) * d(matrix[1][1]				)) - (
										    d(matrix[0][1]				) * d(matrix[1][0]				)
										)

										# Transpose the cofactor matrix (Adjoint matrix)
										lowerCamelCase_					:      str     =		array(_lowercase				)
										for i in range(3				):
															for j in range(3				):
																				lowerCamelCase_					:      int     =		cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
										lowerCamelCase_					:      int     =		array(_lowercase				)
										for i in range(3				):
															for j in range(3				):
																				inverse_matrix[i][j] /= d(_lowercase				)

        # Calculate the inverse of the matrix
										return [[float(d(_lowercase				)				) or 0.0 for n in row] for row in inverse_matrix]
					raise ValueError('''Please provide a matrix of size 2x2 or 3x3.'''				)


