
"""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		       =							logging.get_logger(__name__)

_a		       =							{
    "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'''
  SCREAMING_SNAKE_CASE__			:						Union[str, Any]       =		"""perceiver"""







  def __init__(		self				,   lowercase_=256				,   lowercase_=1280				,   lowercase_=768				,   lowercase_=1				,   lowercase_=26				,   lowercase_=8				,   lowercase_=8				,   lowercase_=None				,   lowercase_=None				,   lowercase_="kv"				,   lowercase_=1				,   lowercase_=1				,   lowercase_="gelu"				,   lowercase_=0.1				,   lowercase_=0.02				,   lowercase_=1E-1_2				,   lowercase_=True				,   lowercase_=262				,   lowercase_=2048				,   lowercase_=56				,   lowercase_=[368, 496]				,   lowercase_=16				,   lowercase_=1920				,   lowercase_=16				,   lowercase_=[1, 16, 224, 224]				,   **lowercase_				,   ):

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

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




class A_	(_UpperCAmelCase      ):




  '''simple docstring'''







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     if self.task == "multiple-choice":
        UpperCAmelCase_					:					Any			     =    {0: """batch""", 1: """choice""", 2: """sequence"""}
     else:
        UpperCAmelCase_					:					Dict			     =    {0: """batch""", 1: """sequence"""}
     return OrderedDict(
         [
             ("inputs", dynamic_axis),
             ("attention_mask", dynamic_axis),
         ]  )







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     return 1E-4







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = -1				,   lowercase_ = -1				,   lowercase_ = -1				,   lowercase_ = False				,   lowercase_ = None				,   lowercase_ = 3				,   lowercase_ = 40				,   lowercase_ = 40				,   ):

     """simple docstring"""
     # 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
        UpperCAmelCase_					:					Optional[int]			     =    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
        UpperCAmelCase_					:					Union[str, Any]			     =    preprocessor.num_special_tokens_to_add(lowercase_  )
        UpperCAmelCase_					:					List[Any]			     =    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
        UpperCAmelCase_					:					Optional[int]			     =    [""" """.join(["a"]  ) * seq_length] * batch_size
        UpperCAmelCase_					:					Tuple			     =    dict(preprocessor(lowercase_				,   return_tensors=lowercase_  )  )
        UpperCAmelCase_					:					Dict			     =    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
        UpperCAmelCase_					:					Any			     =    compute_effective_axis_dimension(lowercase_				,   fixed_dimension=OnnxConfig.default_fixed_batch  )
        UpperCAmelCase_					:					Optional[int]			     =    self._generate_dummy_images(lowercase_				,   lowercase_				,   lowercase_				,   lowercase_  )
        UpperCAmelCase_					:					Tuple			     =    dict(preprocessor(images=lowercase_				,   return_tensors=lowercase_  )  )
        UpperCAmelCase_					:					Optional[Any]			     =    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 operator as op




def 							lowerCamelCase	(      UpperCAmelCase__       :					Optional[Any] )			->      int:
							lowercase_					:			Optional[Any]										=       []
							lowercase_					:			str										=       lambda UpperCAmelCase__      ,      UpperCAmelCase__ : int(x / y )  # noqa: E731 integer division operation
							lowercase_					:			Optional[Any]										=       {
							    """^""": op.pow,
							    """*""": op.mul,
							    """/""": div,
							    """+""": op.add,
							    """-""": op.sub,
							}  # operators & their respective operation

							# print table header
							print("""Symbol""".center(8 )      ,      """Action""".center(12 )      ,      """Stack"""      ,      sep=""" | """ )
							print("""-""" * (30 + len(UpperCAmelCase__ )) )

							for x in post_fix:
														if x.isdigit():  # if x in digit
																					stack.append(UpperCAmelCase__ )  # append x to stack
																					# output in tabular format
																					print(x.rjust(8 )      ,      ("""push(""" + x + """)""").ljust(12 )      ,      """,""".join(UpperCAmelCase__ )      ,      sep=""" | """ )
														else:
																					lowercase_					:			str										=       stack.pop()  # pop stack
																					# output in tabular format
																					print("""""".rjust(8 )      ,      ("""pop(""" + b + """)""").ljust(12 )      ,      """,""".join(UpperCAmelCase__ )      ,      sep=""" | """ )

																					lowercase_					:			Optional[int]										=       stack.pop()  # pop stack
																					# output in tabular format
																					print("""""".rjust(8 )      ,      ("""pop(""" + a + """)""").ljust(12 )      ,      """,""".join(UpperCAmelCase__ )      ,      sep=""" | """ )

																					stack.append(
																					    str(opr[x](int(UpperCAmelCase__ )      ,      int(UpperCAmelCase__ ) ) ) )  # evaluate the 2 values popped from stack & push result to stack
																					# output in tabular format
																					print(
																					    x.rjust(8 )      ,      ("""push(""" + a + x + b + """)""").ljust(12 )      ,      """,""".join(UpperCAmelCase__ )      ,      sep=""" | """      ,      )

							return int(stack[0] )


if __name__ == "__main__":
						_lowercase						: Tuple						=						input("\n\nEnter a Postfix Equation (space separated) = ").split(" ")
						print("\n\tResult = ", solve(Postfix))





