
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


lowercase__							:							int									=			logging.get_logger(__name__)

lowercase__							:							Optional[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        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  '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__(**SCREAMING_SNAKE_CASE_			)

													__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        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							@property
							def     A__		(  self			)->   Mapping[str, Mapping[int, str]]:





													'''simple docstring'''






													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     A__		(  self			)->   float:





													'''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     ,	)->   Mapping[str, Any]:





													'''simple docstring'''






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






def 	A_ (							snake_case							:   int = 10 ,      snake_case							:   int = 1000 ,      snake_case							:   bool = True )      ->    int:



						'''simple docstring'''

						assert (
						    isinstance(snake_case ,      snake_case )
						    and isinstance(snake_case ,      snake_case )
						    and isinstance(snake_case ,      snake_case )
						), "Invalid type of value(s) specified to function!"

						if min_val > max_val:
												raise ValueError('''Invalid value for min_val or max_val (min_value < max_value)''' )
						return min_val if option else max_val




def 	A_ (							snake_case							:   int ,      snake_case							:   int )      ->    int:



						'''simple docstring'''

						return int((number_a + number_a) / 2 )




def 	A_ (							snake_case							:   int ,      snake_case							:   int ,      snake_case							:   int )      ->    None:



						'''simple docstring'''

						assert (
						    isinstance(snake_case ,      snake_case ) and isinstance(snake_case ,      snake_case ) and isinstance(snake_case ,      snake_case )
						), 'argument values must be type of "int"'

						if lower > higher:
												raise ValueError('''argument value for lower and higher must be(lower > higher)''' )

						if not lower < to_guess < higher:
												raise ValueError(
												    '''guess value must be within the range of lower and higher value''' )

						def answer(snake_case							:   int ) -> str:
												if number > to_guess:
																		return "high"
												elif number < to_guess:
																		return "low"
												else:
																		return "same"

						print('''started...''' )

						__UpperCamelCase       =			lower
						__UpperCamelCase       =			higher

						__UpperCamelCase       =			[]

						while True:
												__UpperCamelCase       =			get_avg(snake_case ,      snake_case )
												last_numbers.append(snake_case )

												if answer(snake_case ) == "low":
																		__UpperCamelCase       =			number
												elif answer(snake_case ) == "high":
																		__UpperCamelCase       =			number
												else:
																		break

						print(f"guess the number : {last_numbers[-1]}" )
						print(f"details : {last_numbers!s}" )




def 	A_ (							)      ->    None:



						'''simple docstring'''

						__UpperCamelCase       =			int(input('''Enter lower value : ''' ).strip() )
						__UpperCamelCase       =			int(input('''Enter high value : ''' ).strip() )
						__UpperCamelCase       =			int(input('''Enter value to guess : ''' ).strip() )
						guess_the_number(snake_case ,      snake_case ,      snake_case )


if __name__ == "__main__":
				main()




