




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	:		int							       =   logging.get_logger(__name__)

_lowerCamelCase	:		Optional[Any]							       =   {
    """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 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      '''perceiver'''


    def __init__(			self      :	Any					, UpperCAmelCase__      :	Optional[Any]=256					, UpperCAmelCase__      :	Any=1_280					, UpperCAmelCase__      :	int=768					, UpperCAmelCase__      :	Optional[Any]=1					, UpperCAmelCase__      :	List[Any]=26					, UpperCAmelCase__      :	Union[str, Any]=8					, UpperCAmelCase__      :	int=8					, UpperCAmelCase__      :	int=None					, UpperCAmelCase__      :	Tuple=None					, UpperCAmelCase__      :	str="kv"					, UpperCAmelCase__      :	Tuple=1					, UpperCAmelCase__      :	Optional[Any]=1					, UpperCAmelCase__      :	List[Any]="gelu"					, UpperCAmelCase__      :	List[str]=0.1					, UpperCAmelCase__      :	List[Any]=0.02					, UpperCAmelCase__      :	str=1e-12					, UpperCAmelCase__      :	str=True					, UpperCAmelCase__      :	Any=262					, UpperCAmelCase__      :	str=2_048					, UpperCAmelCase__      :	Any=56					, UpperCAmelCase__      :	Dict=[368, 496]					, UpperCAmelCase__      :	Union[str, Any]=16					, UpperCAmelCase__      :	List[Any]=1_920					, UpperCAmelCase__      :	List[Any]=16					, UpperCAmelCase__      :	Optional[Any]=[1, 16, 224, 224]					, **UpperCAmelCase__      :	Dict					, )						->List[str]:





           '''simple docstring'''

           super().__init__(**UpperCAmelCase__)

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


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any])						->Mapping[str, Mapping[int, str]]:





           '''simple docstring'''

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


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->float:





           '''simple docstring'''

           return 1e-4





    def        SCREAMING_SNAKE_CASE							(			self      :	int					, UpperCAmelCase__      :	Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]					, UpperCAmelCase__      :	int = -1					, UpperCAmelCase__      :	int = -1					, UpperCAmelCase__      :	int = -1					, UpperCAmelCase__      :	bool = False					, UpperCAmelCase__      :	Optional[TensorType] = None					, UpperCAmelCase__      :	int = 3					, UpperCAmelCase__      :	int = 40					, UpperCAmelCase__      :	int = 40					, )						->Mapping[str, Any]:





           '''simple docstring'''

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








_lowerCamelCase	:		Optional[int]							       =   65521







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       int:



       """simple docstring"""

       A__       =    1
       A__       =    0
       for plain_chr in plain_text:
              A__       =    (a + ord(lowercase_						)) % MOD_ADLER
              A__       =    (b + a) % MOD_ADLER
       return (b << 16) | a


