






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


lowerCamelCase__       		=  logging.get_logger(__name__)

lowerCamelCase__       		=  {
    """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_):

       '''simple docstring'''
       lowerCamelCase				:					str								=			"perceiver"



       def __init__(				self      ,    a__=2_56      ,    a__=12_80      ,    a__=7_68      ,    a__=1      ,    a__=26      ,    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_62      ,    a__=20_48      ,    a__=56      ,    a__=[3_68, 4_96]      ,    a__=16      ,    a__=19_20      ,    a__=16      ,    a__=[1, 16, 2_24, 2_24]      ,    **a__      ,    )			->   List[str]:






            '''simple docstring'''





            super().__init__(**a__     )

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




class       snake_case__ (				lowercase_):

       '''simple docstring'''



       @property
       def        __lowercase    (				self     )			->   Mapping[str, Mapping[int, str]]:






            '''simple docstring'''





            if self.task == "multiple-choice":
                 __snake_case      :int 							=    {0: """batch""", 1: """choice""", 2: """sequence"""}
            else:
                 __snake_case      :Any 							=    {0: """batch""", 1: """sequence"""}
            return OrderedDict(
                [
                    ("""inputs""", dynamic_axis),
                    ("""attention_mask""", dynamic_axis),
                ]     )



       @property
       def        __lowercase    (				self     )			->   float:






            '''simple docstring'''





            return 1e-4



       def        __lowercase    (				self      ,    a__      ,    a__ = -1      ,    a__ = -1      ,    a__ = -1      ,    a__ = False      ,    a__ = None      ,    a__ = 3      ,    a__ = 40      ,    a__ = 40      ,    )			->   Mapping[str, Any]:






            '''simple docstring'''





            if isinstance(a__      ,    a__     ):
                 # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
                 __snake_case      :str 							=    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
                 __snake_case      :Union[str, Any] 							=    preprocessor.num_special_tokens_to_add(a__     )
                 __snake_case      :Union[str, Any] 							=    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
                 __snake_case      :List[str] 							=    [""" """.join(["""a"""]     ) * seq_length] * batch_size
                 __snake_case      :int 							=    dict(preprocessor(a__      ,    return_tensors=a__     )     )
                 __snake_case      :Any 							=    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
                 __snake_case      :str 							=    compute_effective_axis_dimension(a__      ,    fixed_dimension=OnnxConfig.default_fixed_batch     )
                 __snake_case      :Union[str, Any] 							=    self._generate_dummy_images(a__      ,    a__      ,    a__      ,    a__     )
                 __snake_case      :Union[str, Any] 							=    dict(preprocessor(images=a__      ,    return_tensors=a__     )     )
                 __snake_case      :List[Any] 							=    inputs.pop("""pixel_values"""     )
                 return inputs
            else:
                 raise ValueError(
                     """Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."""     )










def 	UpperCamelCase	(							snake_case__  : list	):




     '''simple docstring'''






     if not grid or not grid[0]:
          raise TypeError("""The grid does not contain the appropriate information"""	)

     for cell_n in range(1						,len(grid[0]	)	):
          grid[0][cell_n] += grid[0][cell_n - 1]
     __snake_case      :str 							=    grid[0]

     for row_n in range(1						,len(snake_case__	)	):
          __snake_case      :Optional[int] 							=    grid[row_n]
          __snake_case      :Optional[Any] 							=    fill_row(snake_case__						,snake_case__	)
          __snake_case      :Dict 							=    grid[row_n]

     return grid[-1][-1]






def 	UpperCamelCase	(							snake_case__  : list						,snake_case__  : list	):




     '''simple docstring'''






     current_row[0] += row_above[0]
     for cell_n in range(1						,len(snake_case__	)	):
          current_row[cell_n] += min(current_row[cell_n - 1]						,row_above[cell_n]	)

     return current_row


if __name__ == "__main__":
     import doctest

     doctest.testmod()


