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


SCREAMING_SNAKE_CASE__		:       Dict       						=       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:       List[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    snake_case       (   UpperCamelCase_    ):
  lowercase_								=  'perceiver'




  def __init__(   self  :						List[Any]  ,			a_  :						List[Any]=256  ,			a_  :						List[Any]=1280  ,			a_  :						str=768  ,			a_  :						List[Any]=1  ,			a_  :						Tuple=26  ,			a_  :						Optional[Any]=8  ,			a_  :						str=8  ,			a_  :						int=None  ,			a_  :						Dict=None  ,			a_  :						Dict="kv"  ,			a_  :						List[Any]=1  ,			a_  :						Dict=1  ,			a_  :						Dict="gelu"  ,			a_  :						Dict=0.1  ,			a_  :						Optional[Any]=0.02  ,			a_  :						Dict=1e-1_2  ,			a_  :						Union[str, Any]=True  ,			a_  :						Optional[int]=262  ,			a_  :						str=2048  ,			a_  :						List[str]=56  ,			a_  :						Dict=[368, 496]  ,			a_  :						int=16  ,			a_  :						str=1920  ,			a_  :						List[str]=16  ,			a_  :						List[str]=[1, 16, 224, 224]  ,			**a_  :						Union[str, Any]  ,			)->							Tuple:




       """simple docstring"""
       super().__init__(**a_					)

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




class    snake_case       (   UpperCamelCase_    ):




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




       """simple docstring"""
       if self.task == "multiple-choice":
            SCREAMING_SNAKE_CASE__					:					Optional[int]      =		{0: 'batch', 1: 'choice', 2: 'sequence'}
       else:
            SCREAMING_SNAKE_CASE__					:					List[str]      =		{0: 'batch', 1: 'sequence'}
       return OrderedDict(
           [
               ('inputs', dynamic_axis),
               ('attention_mask', dynamic_axis),
           ]					)




  @property
  def 						__lowercase(   self  :						Tuple					)->							float:




       """simple docstring"""
       return 1e-4




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




       """simple docstring"""
       # 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
            SCREAMING_SNAKE_CASE__					:					List[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
            SCREAMING_SNAKE_CASE__					:					Any      =		preprocessor.num_special_tokens_to_add(a_					)
            SCREAMING_SNAKE_CASE__					:					str      =		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
            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		[' '.join(['a']					) * seq_length] * batch_size
            SCREAMING_SNAKE_CASE__					:					Tuple      =		dict(preprocessor(a_  ,			return_tensors=a_					)					)
            SCREAMING_SNAKE_CASE__					:					Dict      =		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
            SCREAMING_SNAKE_CASE__					:					Dict      =		compute_effective_axis_dimension(a_  ,			fixed_dimension=OnnxConfig.default_fixed_batch					)
            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		self._generate_dummy_images(a_  ,			a_  ,			a_  ,			a_					)
            SCREAMING_SNAKE_CASE__					:					Tuple      =		dict(preprocessor(images=a_  ,			return_tensors=a_					)					)
            SCREAMING_SNAKE_CASE__					:					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.'					)







from __future__ import annotations

import numpy as np



def 							UpperCamelCase_( lowerCamelCase_					)       ->		Optional[int]:
    return np.maximum(0				,					lowerCamelCase_					)


if __name__ == "__main__":
 print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]


