






"""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


_lowerCAmelCase	:	int				=      logging.get_logger(__name__)

_lowerCAmelCase	:	str				=      {
    '''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 			lowerCAmelCase__			(    lowerCamelCase_   ):
   SCREAMING_SNAKE_CASE_    ="perceiver"
   def __init__(		self      :	List[str]							,			snake_case__      :	Tuple=2_5_6							,			snake_case__      :	Optional[int]=1_2_8_0							,			snake_case__      :	Optional[Any]=7_6_8							,			snake_case__      :	List[Any]=1							,			snake_case__      :	Dict=2_6							,			snake_case__      :	Any=8							,			snake_case__      :	Dict=8							,			snake_case__      :	List[Any]=None							,			snake_case__      :	str=None							,			snake_case__      :	Optional[int]="kv"							,			snake_case__      :	List[Any]=1							,			snake_case__      :	str=1							,			snake_case__      :	List[Any]="gelu"							,			snake_case__      :	int=0.1							,			snake_case__      :	List[str]=0.02							,			snake_case__      :	Dict=1e-12							,			snake_case__      :	Union[str, Any]=True							,			snake_case__      :	List[str]=2_6_2							,			snake_case__      :	Optional[Any]=2_0_4_8							,			snake_case__      :	Optional[Any]=5_6							,			snake_case__      :	List[Any]=[3_6_8, 4_9_6]							,			snake_case__      :	Dict=1_6							,			snake_case__      :	Union[str, Any]=1_9_2_0							,			snake_case__      :	Any=1_6							,			snake_case__      :	Any=[1, 1_6, 2_2_4, 2_2_4]							,			**snake_case__      :	Any							,			):


        '''simple docstring'''
        super().__init__(**snake_case__							)

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




class 			lowerCAmelCase__			(    lowerCamelCase_   ):
   @property
   def       __a							(		self      :	Dict							):


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


        '''simple docstring'''
        return 1e-4




   def       __a							(		self      :	Optional[int]							,			snake_case__      :	int							,			snake_case__      :	Optional[int] = -1							,			snake_case__      :	List[str] = -1							,			snake_case__      :	Optional[int] = -1							,			snake_case__      :	Any = False							,			snake_case__      :	Optional[Any] = None							,			snake_case__      :	int = 3							,			snake_case__      :	List[Any] = 4_0							,			snake_case__      :	List[Any] = 4_0							,			):


        '''simple docstring'''
        # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

        if isinstance(snake_case__							,			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(
                 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__						:    Tuple							=							preprocessor.num_special_tokens_to_add(snake_case__							)
             UpperCAmelCase__						:    int							=							compute_effective_axis_dimension(
                 snake_case__							,			fixed_dimension=OnnxConfig.default_fixed_sequence							,			num_token_to_add=snake_case__							)
             # Generate dummy inputs according to compute batch and sequence
             UpperCAmelCase__						:    List[Any]							=							[' '.join(["a"]							) * seq_length] * batch_size
             UpperCAmelCase__						:    Dict							=							dict(preprocessor(snake_case__							,			return_tensors=snake_case__							)							)
             UpperCAmelCase__						:    Optional[int]							=							inputs.pop("input_ids"							)
             return inputs
        elif isinstance(snake_case__							,			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__						:    Dict							=							compute_effective_axis_dimension(snake_case__							,			fixed_dimension=OnnxConfig.default_fixed_batch							)
             UpperCAmelCase__						:    Tuple							=							self._generate_dummy_images(snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							)
             UpperCAmelCase__						:    Optional[Any]							=							dict(preprocessor(images=snake_case__							,			return_tensors=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 warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


UpperCamelCase__ :   str						=							logging.get_logger(__name__)







class 				lowerCAmelCase_       (					lowerCamelCase_							):






				def __init__(			self      ,*snake_case__      ,**snake_case__ ):
							warnings.warn(
							    'The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
							    ' Please use PoolFormerImageProcessor instead.'      ,snake_case__      ,)
							super().__init__(*snake_case__      ,**snake_case__ )



