

"""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


__UpperCamelCase  :      Optional[Any]							      =		logging.get_logger(__name__)

__UpperCamelCase  :      str							      =		{
    '''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					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       "perceiver"







					def __init__(       self			:				Dict   ,lowercase_			:				int=2_5_6   ,lowercase_			:				Optional[Any]=1_2_8_0   ,lowercase_			:				int=7_6_8   ,lowercase_			:				Dict=1   ,lowercase_			:				List[str]=2_6   ,lowercase_			:				Dict=8   ,lowercase_			:				Any=8   ,lowercase_			:				Optional[int]=None   ,lowercase_			:				List[str]=None   ,lowercase_			:				List[str]="kv"   ,lowercase_			:				int=1   ,lowercase_			:				Union[str, Any]=1   ,lowercase_			:				Optional[Any]="gelu"   ,lowercase_			:				Any=0.1   ,lowercase_			:				Optional[int]=0.02   ,lowercase_			:				Union[str, Any]=1E-12   ,lowercase_			:				Optional[int]=True   ,lowercase_			:				Tuple=2_6_2   ,lowercase_			:				List[str]=2_0_4_8   ,lowercase_			:				str=5_6   ,lowercase_			:				Any=[3_6_8, 4_9_6]   ,lowercase_			:				Union[str, Any]=1_6   ,lowercase_			:				Optional[int]=1_9_2_0   ,lowercase_			:				str=1_6   ,lowercase_			:				Optional[Any]=[1, 1_6, 2_2_4, 2_2_4]   ,**lowercase_			:				Any   ,):
						super().__init__(**lowercase_		)

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







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



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







					@property
					def        __lowerCAmelCase (       self			:				List[str]		):
						return 1E-4







					def        __lowerCAmelCase (       self			:				Tuple   ,lowercase_			:				Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]   ,lowercase_			:				int = -1   ,lowercase_			:				int = -1   ,lowercase_			:				int = -1   ,lowercase_			:				bool = False   ,lowercase_			:				Optional[TensorType] = None   ,lowercase_			:				int = 3   ,lowercase_			:				int = 4_0   ,lowercase_			:				int = 4_0   ,):
						# 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
							lowerCAmelCase__	:						Any  				=	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
							lowerCAmelCase__	:						Tuple  				=	preprocessor.num_special_tokens_to_add(lowercase_		)
							lowerCAmelCase__	:						Optional[int]  				=	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
							lowerCAmelCase__	:						Optional[Any]  				=	[''' '''.join(['''a''']		) * seq_length] * batch_size
							lowerCAmelCase__	:						Optional[Any]  				=	dict(preprocessor(lowercase_   ,return_tensors=lowercase_		)		)
							lowerCAmelCase__	:						List[str]  				=	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
							lowerCAmelCase__	:						Dict  				=	compute_effective_axis_dimension(lowercase_   ,fixed_dimension=OnnxConfig.default_fixed_batch		)
							lowerCAmelCase__	:						List[str]  				=	self._generate_dummy_images(lowercase_   ,lowercase_   ,lowercase_   ,lowercase_		)
							lowerCAmelCase__	:						Tuple  				=	dict(preprocessor(images=lowercase_   ,return_tensors=lowercase_		)		)
							lowerCAmelCase__	:						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 torch

from transformers import AutoModel







class 				SCREAMING_SNAKE_CASE					(		torch.nn.Module       ):






					"""simple docstring"""



					def __init__(       self			:				Tuple   ,lowercase_			:				Dict="sayef/fsner-bert-base-uncased"		):
						super(lowercase_   ,self		).__init__()

						lowerCAmelCase__	:						int  				=	AutoModel.from_pretrained(lowercase_   ,return_dict=lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	torch.nn.CosineSimilarity(3   ,1E-08		)
						lowerCAmelCase__	:						List[str]  				=	torch.nn.Softmax(dim=1		)







					def        __lowerCAmelCase (       self			:				str   ,**lowercase_			:				int		):
						return self.bert(**lowercase_		).last_hidden_state







					def        __lowerCAmelCase (       self			:				List[Any]   ,lowercase_			:				Optional[int]		):
						return token_embeddings.sum(2   ,keepdim=lowercase_		)







					def        __lowerCAmelCase (       self			:				Dict   ,lowercase_			:				int   ,lowercase_			:				str   ,lowercase_			:				Tuple=1		):
						return self.softmax(T * self.cos(lowercase_   ,lowercase_		)		)







					def        __lowerCAmelCase (       self			:				Optional[Any]   ,lowercase_			:				str   ,lowercase_			:				Union[str, Any]		):
						lowerCAmelCase__	:						List[Any]  				=	W_supports['''sizes'''].tolist()
						lowerCAmelCase__	:						Dict  				=	W_supports['''start_token_id'''].item()
						lowerCAmelCase__	:						Union[str, Any]  				=	W_supports['''end_token_id'''].item()

						del W_supports["sizes"]
						del W_supports["start_token_id"]
						del W_supports["end_token_id"]

						lowerCAmelCase__	:						Optional[Any]  				=	self.BERT(**lowercase_		)
						lowerCAmelCase__	:						int  				=	self.BERT(**lowercase_		)

						lowerCAmelCase__	:						List[str]  				=	None
						lowerCAmelCase__	:						Union[str, Any]  				=	None

						lowerCAmelCase__	:						int  				=	W_supports['''input_ids'''] == start_token_id
						lowerCAmelCase__	:						Optional[Any]  				=	W_supports['''input_ids'''] == end_token_id

						for i, size in enumerate(lowercase_		):
							if i == 0:
								lowerCAmelCase__	:						str  				=	0
							else:
								lowerCAmelCase__	:						List[Any]  				=	support_sizes[i - 1]

							lowerCAmelCase__	:						Optional[Any]  				=	S[s : s + size][start_token_masks[s : s + size]]
							lowerCAmelCase__	:						List[Any]  				=	S[s : s + size][end_token_masks[s : s + size]]

							lowerCAmelCase__	:						Union[str, Any]  				=	torch.matmul(q[i]   ,s_start.T		).sum(1		).softmax(0		)
							lowerCAmelCase__	:						Any  				=	torch.matmul(q[i]   ,s_end.T		).sum(1		).softmax(0		)

							if p_starts is not None:
								lowerCAmelCase__	:						List[Any]  				=	torch.vstack((p_starts, p_start)		)
								lowerCAmelCase__	:						List[Any]  				=	torch.vstack((p_ends, p_end)		)
							else:
								lowerCAmelCase__	:						Union[str, Any]  				=	p_start
								lowerCAmelCase__	:						str  				=	p_end

						return p_starts, p_ends




