




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__     :     int		    =       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_       (     A__     ):
	A    							= '''perceiver'''





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




				"""simple docstring"""



				super().__init__(**_lowerCamelCase      )

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




class       a_       (     A__     ):





	@property
	def 						A_(							self      )  ->					str:




				"""simple docstring"""



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





	@property
	def 						A_(							self      )  ->					int:




				"""simple docstring"""



				return 1e-4





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = -1      ,     SCREAMING_SNAKE_CASE = -1      ,     SCREAMING_SNAKE_CASE = -1      ,     SCREAMING_SNAKE_CASE = False      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = 3      ,     SCREAMING_SNAKE_CASE = 40      ,     SCREAMING_SNAKE_CASE = 40      ,     )  ->					Optional[Any]:




				"""simple docstring"""



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






def       A_  (				__lowercase						,	__lowercase						,	__lowercase	):
 if len(__lowercase	) != len(__lowercase	):
  raise ValueError('The length of profit and weight must be same.'	)
 if max_weight <= 0:
  raise ValueError('max_weight must greater than zero.'	)
 if any(p < 0 for p in profit	):
  raise ValueError('Profit can not be negative.'	)
 if any(w < 0 for w in weight	):
  raise ValueError('Weight can not be negative.'	)

 # List created to store profit gained for the 1kg in case of each weight
 # respectively.  Calculate and append profit/weight for each element.
 UpperCamelCase_     :					int   	=[p / w for p, w in zip(__lowercase						,	__lowercase	)]

 # Creating a copy of the list and sorting profit/weight in ascending order
 UpperCamelCase_     :					Optional[int]   	=sorted(__lowercase	)

 # declaring useful variables
 UpperCamelCase_     :					Optional[int]   	=len(__lowercase	)
 UpperCamelCase_     :					Optional[int]   	=0
 UpperCamelCase_     :					List[Any]   	=0
 UpperCamelCase_     :					Optional[int]   	=0

 # loop till the total weight do not reach max limit e.g. 15 kg and till i<length
 while limit <= max_weight and i < length:
  # flag value for encountered greatest element in sorted_profit_by_weight
  UpperCamelCase_     :					List[str]   	=sorted_profit_by_weight[length - i - 1]
  UpperCamelCase_     :					Optional[Any]   	=profit_by_weight.index(__lowercase	)
  UpperCamelCase_     :					str   	=-1

  # check if the weight encountered is less than the total weight
  # encountered before.
  if max_weight - limit >= weight[index]:
   limit += weight[index]
   # Adding profit gained for the given weight 1 ===
   # weight[index]/weight[index]
   gain += 1 * profit[index]
  else:
   # Since the weight encountered is greater than limit, therefore take the
   # required number of remaining kgs and calculate profit for it.
   # weight remaining / weight[index]
   gain += (max_weight - limit) / weight[index] * profit[index]
   break
  i += 1
 return gain


if __name__ == "__main__":
 print(
     'Input profits, weights, and then max_weight (all positive ints) separated by '
     'spaces.'
 )

 __SCREAMING_SNAKE_CASE							=     [int(x) for x in input('Input profits separated by spaces: ').split()]
 __SCREAMING_SNAKE_CASE							=     [int(x) for x in input('Input weights separated by spaces: ').split()]
 __SCREAMING_SNAKE_CASE							=     int(input('Max weight allowed: '))

 # Function Call
 calc_profit(profit, weight, max_weight)
