"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_a      :		Optional[int]           =   logging.get_logger(__name__)

_a      :		Tuple           =   {
    'sail/poolformer_s12': 'https://huggingface.co/sail/poolformer_s12/resolve/main/config.json',
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Union[str, Any]							=		"poolformer"






     def __init__(		self				,     a__=3				,     a__=16				,     a__=16				,     a__=3				,     a__=4.0				,     a__=[2, 2, 6, 2]				,     a__=[64, 128, 320, 512]				,     a__=[7, 3, 3, 3]				,     a__=[4, 2, 2, 2]				,     a__=[2, 1, 1, 1]				,     a__=4				,     a__=0.0				,     a__="gelu"				,     a__=True				,     a__=1e-5				,     a__=0.0_2				,     **a__				,     ):
       _lowerCAmelCase						:       List[Any]  			=     num_channels
       _lowerCAmelCase						:       str  			=     patch_size
       _lowerCAmelCase						:       Dict  			=     stride
       _lowerCAmelCase						:       Optional[int]  			=     padding
       _lowerCAmelCase						:       Optional[int]  			=     pool_size
       _lowerCAmelCase						:       Dict  			=     hidden_sizes
       _lowerCAmelCase						:       Optional[int]  			=     mlp_ratio
       _lowerCAmelCase						:       Optional[int]  			=     depths
       _lowerCAmelCase						:       Dict  			=     patch_sizes
       _lowerCAmelCase						:       Tuple  			=     strides
       _lowerCAmelCase						:       Any  			=     num_encoder_blocks
       _lowerCAmelCase						:       Any  			=     drop_path_rate
       _lowerCAmelCase						:       Tuple  			=     hidden_act
       _lowerCAmelCase						:       Optional[Any]  			=     use_layer_scale
       _lowerCAmelCase						:       List[str]  			=     layer_scale_init_value
       _lowerCAmelCase						:       List[Any]  			=     initializer_range
       super().__init__(**a__  )







class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Optional[Any]							=		version.parse("1.11"		)






     @property
     def     __A  (		self  ):
       return OrderedDict(
           [
               ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
           ]  )






     @property
     def     __A  (		self  ):
       return 2e-3











'''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


__SCREAMING_SNAKE_CASE     :Dict       =				logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     :int       =				{
    '''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_	(  lowerCAmelCase_			):
 _lowerCamelCase  : int           =							"""perceiver"""




 def __init__(					self       :    Any	,			snake_case_       :    List[Any]=2_5_6	,			snake_case_       :    str=1_2_8_0	,			snake_case_       :    Optional[int]=7_6_8	,			snake_case_       :    int=1	,			snake_case_       :    List[Any]=2_6	,			snake_case_       :    Dict=8	,			snake_case_       :    List[Any]=8	,			snake_case_       :    Tuple=None	,			snake_case_       :    Tuple=None	,			snake_case_       :    Any="kv"	,			snake_case_       :    Any=1	,			snake_case_       :    List[str]=1	,			snake_case_       :    Optional[int]="gelu"	,			snake_case_       :    List[Any]=0.1	,			snake_case_       :    Dict=0.0_2	,			snake_case_       :    int=1e-12	,			snake_case_       :    List[str]=True	,			snake_case_       :    str=2_6_2	,			snake_case_       :    Optional[Any]=2_0_4_8	,			snake_case_       :    Union[str, Any]=5_6	,			snake_case_       :    Dict=[3_6_8, 4_9_6]	,			snake_case_       :    Tuple=1_6	,			snake_case_       :    Union[str, Any]=1_9_2_0	,			snake_case_       :    List[Any]=1_6	,			snake_case_       :    Tuple=[1, 1_6, 2_2_4, 2_2_4]	,			**snake_case_       :    List[Any]	,			):
      super().__init__(**snake_case_    )

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







class   A_	(  lowerCAmelCase_			):




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




 @property
 def      lowercase	(					self       :    Optional[Any]    ):
      return 1e-4




 def      lowercase	(					self       :    List[str]	,			snake_case_       :    Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]	,			snake_case_       :    int = -1	,			snake_case_       :    int = -1	,			snake_case_       :    int = -1	,			snake_case_       :    bool = False	,			snake_case_       :    Optional[TensorType] = None	,			snake_case_       :    int = 3	,			snake_case_       :    int = 4_0	,			snake_case_       :    int = 4_0	,			):
      # 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     							=   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     							=   preprocessor.num_special_tokens_to_add(snake_case_    )
           _UpperCAmelCase     							=   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     							=   [" ".join(["a"]    ) * seq_length] * batch_size
           _UpperCAmelCase     							=   dict(preprocessor(snake_case_	,			return_tensors=snake_case_    )    )
           _UpperCAmelCase     							=   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     							=   compute_effective_axis_dimension(snake_case_	,			fixed_dimension=OnnxConfig.default_fixed_batch    )
           _UpperCAmelCase     							=   self._generate_dummy_images(snake_case_	,			snake_case_	,			snake_case_	,			snake_case_    )
           _UpperCAmelCase     							=   dict(preprocessor(images=snake_case_	,			return_tensors=snake_case_    )    )
           _UpperCAmelCase     							=   inputs.pop("pixel_values"    )
           return inputs
      else:
           raise ValueError(
               "Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."    )




