


'''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


a_					:    List[Any]          =    logging.get_logger(__name__)

a_					:    Union[str, 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  a     (     snake_case__       ):
 _lowerCAmelCase 					=	"""perceiver"""
 def __init__( self      ,				__magic_name__=2_56      ,				__magic_name__=12_80      ,				__magic_name__=7_68      ,				__magic_name__=1      ,				__magic_name__=26      ,				__magic_name__=8      ,				__magic_name__=8      ,				__magic_name__=None      ,				__magic_name__=None      ,				__magic_name__="kv"      ,				__magic_name__=1      ,				__magic_name__=1      ,				__magic_name__="gelu"      ,				__magic_name__=0.1      ,				__magic_name__=0.0_2      ,				__magic_name__=1e-12      ,				__magic_name__=True      ,				__magic_name__=2_62      ,				__magic_name__=20_48      ,				__magic_name__=56      ,				__magic_name__=[3_68, 4_96]      ,				__magic_name__=16      ,				__magic_name__=19_20      ,				__magic_name__=16      ,				__magic_name__=[1, 16, 2_24, 2_24]      ,				**__magic_name__      ,				)					->							Any:
      super().__init__(**lowercase_						)

      _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  a     (     snake_case__       ):
 @property
 def 							__UpperCAmelCase		( self						)					->							Union[str, Any]:
      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 							__UpperCAmelCase		( self						)					->							str:
      return 1e-4




 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__ = -1      ,				__magic_name__ = -1      ,				__magic_name__ = -1      ,				__magic_name__ = False      ,				__magic_name__ = None      ,				__magic_name__ = 3      ,				__magic_name__ = 40      ,				__magic_name__ = 40      ,				)					->							Union[str, Any]:
      # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

      if isinstance(lowercase_      ,				lowercase_						):
           # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
           _a			      =							compute_effective_axis_dimension(
               lowercase_      ,				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(lowercase_						)
           _a			      =							compute_effective_axis_dimension(
               lowercase_      ,				fixed_dimension=OnnxConfig.default_fixed_sequence      ,				num_token_to_add=lowercase_						)
           # Generate dummy inputs according to compute batch and sequence
           _a			      =							[" ".join(['a']						) * seq_length] * batch_size
           _a			      =							dict(preprocessor(lowercase_      ,				return_tensors=lowercase_						)						)
           _a			      =							inputs.pop('input_ids'						)
           return inputs
      elif isinstance(lowercase_      ,				lowercase_						) 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(lowercase_      ,				fixed_dimension=OnnxConfig.default_fixed_batch						)
           _a			      =							self._generate_dummy_images(lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_						)
           _a			      =							dict(preprocessor(images=lowercase_      ,				return_tensors=lowercase_						)						)
           _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.'						)





'''simple docstring'''






import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict






@pytest.mark.parametrize(
    'split_dict'				,						[
        SplitDict(),
        SplitDict({'train': SplitInfo(name='train'				,						num_bytes=13_37				,						num_examples=42				,						dataset_name='my_dataset'		)}		),
        SplitDict({'train': SplitInfo(name='train'				,						num_bytes=13_37				,						num_examples=42		)}		),
        SplitDict({'train': SplitInfo()}		),
    ]				,						)
def 	_A				(lowerCAmelCase__  :SplitDict		)   ->  Any:




     '''simple docstring'''



     _a			      =							split_dict._to_yaml_list()
     assert len(lowerCAmelCase__		) == len(lowerCAmelCase__		)
     _a			      =							SplitDict._from_yaml_list(lowerCAmelCase__		)
     for split_name, split_info in split_dict.items():
          # dataset_name field is deprecated, and is therefore not part of the YAML dump
          _a			      =							None
          # the split name of split_dict takes over the name of the split info object
          _a			      =							split_name
     assert split_dict == reloaded






@pytest.mark.parametrize(
    'split_info'				,						[SplitInfo(), SplitInfo(dataset_name=lowerCAmelCase__		), SplitInfo(dataset_name='my_dataset'		)]		)
def 	_A				(lowerCAmelCase__  :Optional[Any]		)   ->  List[str]:




     '''simple docstring'''



     _a			      =							asdict(SplitDict({'train': split_info}		)		)
     assert "dataset_name" in split_dict_asdict["train"]
     assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name

