




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(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = '''perceiver'''





    def __init__( self  ,_SCREAMING_SNAKE_CASE=256  ,_SCREAMING_SNAKE_CASE=1_280  ,_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=2_048  ,_SCREAMING_SNAKE_CASE=56  ,_SCREAMING_SNAKE_CASE=[368, 496]  ,_SCREAMING_SNAKE_CASE=16  ,_SCREAMING_SNAKE_CASE=1_920  ,_SCREAMING_SNAKE_CASE=16  ,_SCREAMING_SNAKE_CASE=[1, 16, 224, 224]  ,**_SCREAMING_SNAKE_CASE  ,)					->						int:
         super().__init__(**_SCREAMING_SNAKE_CASE    )

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


class     __a(	_a  ):






    """simple docstring"""





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





    @property
    def 				a__ ( self    )					->						float:
         return 1e-4





    def 				a__ ( 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  ,)					->						Mapping[str, Any]:
         # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

         if isinstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ):
              # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
              UpperCAmelCase_     :				Dict  =     compute_effective_axis_dimension(
                  _SCREAMING_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_     :				str  =     preprocessor.num_special_tokens_to_add(_SCREAMING_SNAKE_CASE    )
              UpperCAmelCase_     :				Union[str, Any]  =     compute_effective_axis_dimension(
                  _SCREAMING_SNAKE_CASE  ,fixed_dimension=OnnxConfig.default_fixed_sequence  ,num_token_to_add=_SCREAMING_SNAKE_CASE    )
              # Generate dummy inputs according to compute batch and sequence
              UpperCAmelCase_     :				Union[str, Any]  =     [''' '''.join(['''a''']    ) * seq_length] * batch_size
              UpperCAmelCase_     :				Optional[int]  =     dict(preprocessor(_SCREAMING_SNAKE_CASE  ,return_tensors=_SCREAMING_SNAKE_CASE    )    )
              UpperCAmelCase_     :				List[str]  =     inputs.pop('''input_ids'''    )
              return inputs
         elif isinstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_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_     :				int  =     compute_effective_axis_dimension(_SCREAMING_SNAKE_CASE  ,fixed_dimension=OnnxConfig.default_fixed_batch    )
              UpperCAmelCase_     :				Optional[int]  =     self._generate_dummy_images(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
              UpperCAmelCase_     :				List[str]  =     dict(preprocessor(images=_SCREAMING_SNAKE_CASE  ,return_tensors=_SCREAMING_SNAKE_CASE    )    )
              UpperCAmelCase_     :				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.'''    )





import unittest

import numpy as np







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase = None					,		):

     '''simple docstring'''


     UpperCAmelCase_     :				Dict  =     np.shape(_lowercase )
     UpperCAmelCase_     :				Optional[Any]  =     np.shape(_lowercase )
     UpperCAmelCase_     :				Tuple  =     np.shape(_lowercase )

     if shape_a[0] != shape_b[0]:
          UpperCAmelCase_     :				Tuple  =     (
              '''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(_lowercase )

     if shape_b[1] != shape_c[1]:
          UpperCAmelCase_     :				List[Any]  =     (
              '''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(_lowercase )

     UpperCAmelCase_     :				Dict  =     pseudo_inv
     if a_inv is None:
          try:
               UpperCAmelCase_     :				Any  =     np.linalg.inv(_lowercase )
          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  ):






    """simple docstring"""





    def 				a__ ( self    )					->						None:
         UpperCAmelCase_     :				str  =     np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]    )
         UpperCAmelCase_     :				Any  =     np.array([[0, 3], [3, 0], [2, 3]]    )
         UpperCAmelCase_     :				List[str]  =     np.array([[2, 1], [6, 3]]    )

         UpperCAmelCase_     :				Tuple  =     schur_complement(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				List[Any]  =     np.block([[a, b], [b.T, c]]    )

         UpperCAmelCase_     :				List[Any]  =     np.linalg.det(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				List[Any]  =     np.linalg.det(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Any  =     np.linalg.det(_SCREAMING_SNAKE_CASE    )

         self.assertAlmostEqual(_SCREAMING_SNAKE_CASE  ,det_a * det_s    )





    def 				a__ ( self    )					->						None:
         UpperCAmelCase_     :				str  =     np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]    )
         UpperCAmelCase_     :				Optional[int]  =     np.array([[0, 3], [3, 0], [2, 3]]    )
         UpperCAmelCase_     :				Optional[int]  =     np.array([[2, 1], [6, 3]]    )

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





    def 				a__ ( self    )					->						None:
         UpperCAmelCase_     :				Optional[int]  =     np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]    )
         UpperCAmelCase_     :				Optional[Any]  =     np.array([[0, 3], [3, 0], [2, 3]]    )
         UpperCAmelCase_     :				int  =     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()