





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


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

UpperCAmelCase						:   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__   ):


				"""simple docstring"""


				UpperCamelCase    :  Optional[int]					    =	'''perceiver'''



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

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





class 						_A(					snake_case__   ):


				"""simple docstring"""


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



				@property
				def 					UpperCAmelCase_		(			self     ):
									return 1e-4





				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A = -1     ,					_A = -1     ,					_A = -1     ,					_A = False     ,					_A = None     ,					_A = 3     ,					_A = 40     ,					_A = 40     ,					):
									# copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

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











def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      list[int]:
					__A	:						Optional[int]       =				int(a  )

					# Initialize Result
					__A	:						Optional[int]       =				[]

					# Traverse through all denomination
					for denomination in reversed(a  ):
										# Find denominations
										while int(a  ) >= int(a  ):
															total_value -= int(a  )
															answer.append(a  )  # Append the "answers" array

					return answer


# Driver Code
if __name__ == "__main__":
				UpperCAmelCase						:   List[str]			  =     []
				UpperCAmelCase						:   Optional[int]			  =     '''0'''

				if (
				    input('''Do you want to enter your denominations ? (yY/n): ''').strip().lower()
				    == "y"
				):
								UpperCAmelCase						:   List[Any]			  =     int(input('''Enter the number of denominations you want to add: ''').strip())

								for i in range(0, n):
												denominations.append(int(input(F"""Denomination {i}: """).strip()))
								UpperCAmelCase						:   int			  =     input('''Enter the change you want to make in Indian Currency: ''').strip()
				else:
								# All denominations of Indian Currency if user does not enter
								UpperCAmelCase						:   Optional[int]			  =     [1, 2, 5, 10, 20, 50, 1_00, 5_00, 20_00]
								UpperCAmelCase						:   Tuple			  =     input('''Enter the change you want to make: ''').strip()

				if int(value) == 0 or int(value) < 0:
								print('''The total value cannot be zero or negative.''')

				else:
								print(F"""Following is minimal change for {value}: """)
								UpperCAmelCase						:   Optional[int]			  =     find_minimum_change(denominations, value)
								# Print result
								for i in range(len(answer)):
												print(answer[i], end=''' ''')




