






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


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

snake_case__      :	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 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  A_						=   """perceiver"""




  def __init__(     self		,		_UpperCAmelCase=256		,		_UpperCAmelCase=1280		,		_UpperCAmelCase=768		,		_UpperCAmelCase=1		,		_UpperCAmelCase=26		,		_UpperCAmelCase=8		,		_UpperCAmelCase=8		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None		,		_UpperCAmelCase="kv"		,		_UpperCAmelCase=1		,		_UpperCAmelCase=1		,		_UpperCAmelCase="gelu"		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=0.0_2		,		_UpperCAmelCase=1e-12		,		_UpperCAmelCase=True		,		_UpperCAmelCase=262		,		_UpperCAmelCase=2048		,		_UpperCAmelCase=56		,		_UpperCAmelCase=[368, 496]		,		_UpperCAmelCase=16		,		_UpperCAmelCase=1920		,		_UpperCAmelCase=16		,		_UpperCAmelCase=[1, 16, 224, 224]		,		**_UpperCAmelCase		,		)			-> Union[str, Any]:
     super().__init__(**_UpperCAmelCase )

     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       (  UpperCAmelCase__    ):






  """simple docstring"""





  @property
  def    _UpperCAmelCase     (     self )			-> Mapping[str, Mapping[int, str]]:
     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    _UpperCAmelCase     (     self )			-> float:
     return 1e-4




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

     if isinstance(_UpperCAmelCase		,		_UpperCAmelCase ):
        # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
        UpperCamelCase_									=     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
        UpperCamelCase_									=     preprocessor.num_special_tokens_to_add(_UpperCAmelCase )
        UpperCamelCase_									=     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
        UpperCamelCase_									=     [' '.join(['a'] ) * seq_length] * batch_size
        UpperCamelCase_									=     dict(preprocessor(_UpperCAmelCase		,		return_tensors=_UpperCAmelCase ) )
        UpperCamelCase_									=     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
        UpperCamelCase_									=     compute_effective_axis_dimension(_UpperCAmelCase		,		fixed_dimension=OnnxConfig.default_fixed_batch )
        UpperCamelCase_									=     self._generate_dummy_images(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )
        UpperCamelCase_									=     dict(preprocessor(images=_UpperCAmelCase		,		return_tensors=_UpperCAmelCase ) )
        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.' )













from typing import Any






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      ):
   _validation(
       __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      )
   # Creates data structures and fill initial step
   UpperCamelCase_									=     {}
   UpperCamelCase_									=     {}
   for state in states_space:
      UpperCamelCase_									=     observations_space[0]
      UpperCamelCase_									=     (
          initial_probabilities[state] * emission_probabilities[state][observation]
      )
      UpperCamelCase_									=     None

   # Fills the data structure with the probabilities of
   # different transitions and pointers to previous states
   for o in range(1    ,      len(__lowercase)):
      UpperCamelCase_									=     observations_space[o]
      UpperCamelCase_									=     observations_space[o - 1]
      for state in states_space:
         # Calculates the argmax for probability function
         UpperCamelCase_									=     ''
         UpperCamelCase_									=     -1
         for k_state in states_space:
            UpperCamelCase_									=     (
                probabilities[(k_state, prior_observation)]
                * transition_probabilities[k_state][state]
                * emission_probabilities[state][observation]
            )
            if probability > max_probability:
               UpperCamelCase_									=     probability
               UpperCamelCase_									=     k_state

            # Update probabilities and pointers dicts
         UpperCamelCase_									=     (
             probabilities[(arg_max, prior_observation)]
             * transition_probabilities[arg_max][state]
             * emission_probabilities[state][observation]
         )

         UpperCamelCase_									=     arg_max

    # The final observation
   UpperCamelCase_									=     observations_space[len(__lowercase) - 1]

   # argmax for given final observation
   UpperCamelCase_									=     ''
   UpperCamelCase_									=     -1
   for k_state in states_space:
      UpperCamelCase_									=     probabilities[(k_state, final_observation)]
      if probability > max_probability:
         UpperCamelCase_									=     probability
         UpperCamelCase_									=     k_state
   UpperCamelCase_									=     arg_max

   # Process pointers backwards
   UpperCamelCase_									=     last_state
   UpperCamelCase_									=     []
   for o in range(len(__lowercase) - 1    ,      -1    ,      -1):
      result.append(__lowercase)
      UpperCamelCase_									=     pointers[previous, observations_space[o]]
   result.reverse()

   return result






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      ):
   _validate_not_empty(
       __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      )
   _validate_lists(__lowercase    ,      __lowercase)
   _validate_dicts(
       __lowercase    ,      __lowercase    ,      __lowercase)






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      ):
   if not all(
       [
           observations_space,
           states_space,
           initial_probabilities,
           transition_probabilities,
           emission_probabilities,
       ]):
      raise ValueError('There\'s an empty parameter')






def     _snake_case    (__lowercase    ,      __lowercase):
   _validate_list(__lowercase    ,      'observations_space')
   _validate_list(__lowercase    ,      'states_space')






def     _snake_case    (__lowercase    ,      __lowercase):
   if not isinstance(_object    ,      __lowercase):
      UpperCamelCase_									=     f"""{var_name} must be a list"""
      raise ValueError(__lowercase)
   else:
      for x in _object:
         if not isinstance(__lowercase    ,      __lowercase):
            UpperCamelCase_									=     f"""{var_name} must be a list of strings"""
            raise ValueError(__lowercase)






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      ):
   _validate_dict(__lowercase    ,      'initial_probabilities'    ,      __lowercase)
   _validate_nested_dict(__lowercase    ,      'transition_probabilities')
   _validate_nested_dict(__lowercase    ,      'emission_probabilities')






def     _snake_case    (__lowercase    ,      __lowercase):
   _validate_dict(_object    ,      __lowercase    ,      __lowercase)
   for x in _object.values():
      _validate_dict(__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase)






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase = False):
   if not isinstance(_object    ,      __lowercase):
      UpperCamelCase_									=     f"""{var_name} must be a dict"""
      raise ValueError(__lowercase)
   if not all(isinstance(__lowercase    ,      __lowercase) for x in _object):
      UpperCamelCase_									=     f"""{var_name} all keys must be strings"""
      raise ValueError(__lowercase)
   if not all(isinstance(__lowercase    ,      __lowercase) for x in _object.values()):
      UpperCamelCase_									=     'nested dictionary ' if nested else ''
      UpperCamelCase_									=     f"""{var_name} {nested_text}all values must be {value_type.__name__}"""
      raise ValueError(__lowercase)


if __name__ == "__main__":
 from doctest import testmod

 testmod()





