



'''simple docstring'''






import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin

class 				SCREAMING_SNAKE_CASE(							A__							,		A__			):







	"""simple docstring"""






	@register_to_config
	def __init__(						self			:     int		,			__snake_case			:     int = 128		,			__snake_case			:     int = 256		,			__snake_case			:     float = 20_00.0		,			__snake_case			:     int = 768		,			__snake_case			:     int = 12		,			__snake_case			:     int = 12		,			__snake_case			:     int = 64		,			__snake_case			:     int = 2048		,			__snake_case			:     float = 0.1		,			)			->		Any:
								super().__init__()

								UpperCAmelCase :       List[Any] 							=		nn.Sequential(
								    nn.Linear(__snake_case		,			d_model * 4		,			bias=__snake_case		)		,			nn.SiLU()		,			nn.Linear(d_model * 4		,			d_model * 4		,			bias=__snake_case		)		,			nn.SiLU()		,			)

								UpperCAmelCase :       Union[str, Any] 							=		nn.Embedding(__snake_case		,			__snake_case		)
								UpperCAmelCase :       Any 							=		False

								UpperCAmelCase :       Union[str, Any] 							=		nn.Linear(__snake_case		,			__snake_case		,			bias=__snake_case		)

								UpperCAmelCase :       Tuple 							=		nn.Dropout(p=__snake_case		)

								UpperCAmelCase :       int 							=		nn.ModuleList()
								for lyr_num in range(__snake_case		):
															# FiLM conditional T5 decoder
															UpperCAmelCase :       List[Any] 							=		DecoderLayer(d_model=__snake_case		,			d_kv=__snake_case		,			num_heads=__snake_case		,			d_ff=__snake_case		,			dropout_rate=__snake_case		)
															self.decoders.append(__snake_case		)

								UpperCAmelCase :       Optional[int] 							=		TaLayerNorm(__snake_case		)

								UpperCAmelCase :       Union[str, Any] 							=		nn.Dropout(p=__snake_case		)
								UpperCAmelCase :       Tuple 							=		nn.Linear(__snake_case		,			__snake_case		,			bias=__snake_case		)







	def      A	(						self			:     Any		,			__snake_case			:     List[Any]		,			__snake_case			:     Optional[int]		)			->		Dict:
								UpperCAmelCase :       Dict 							=		torch.mul(query_input.unsqueeze(-1		)		,			key_input.unsqueeze(-2		)		)
								return mask.unsqueeze(-3		)







	def      A	(						self			:     Optional[Any]		,			__snake_case			:     Dict		,			__snake_case			:     Optional[int]		,			__snake_case			:     List[str]		)			->		Any:
								UpperCAmelCase							,		UpperCAmelCase							,		UpperCAmelCase :       Optional[Any] 							=		decoder_input_tokens.shape
								assert decoder_noise_time.shape == (batch,)

								# decoder_noise_time is in [0, 1), so rescale to expected timing range.
								UpperCAmelCase :       Union[str, Any] 							=		get_timestep_embedding(
								    decoder_noise_time * self.config.max_decoder_noise_time		,			embedding_dim=self.config.d_model		,			max_period=self.config.max_decoder_noise_time		,			).to(dtype=self.dtype		)

								UpperCAmelCase :       Optional[Any] 							=		self.conditioning_emb(__snake_case		).unsqueeze(1		)

								assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

								UpperCAmelCase :       Optional[Any] 							=		decoder_input_tokens.shape[1]

								# If we want to use relative positions for audio context, we can just offset
								# this sequence by the length of encodings_and_masks.
								UpperCAmelCase :       int 							=		torch.broadcast_to(
								    torch.arange(__snake_case		,			device=decoder_input_tokens.device		)		,			(batch, seq_length)		,			)

								UpperCAmelCase :       Optional[Any] 							=		self.position_encoding(__snake_case		)

								UpperCAmelCase :       Tuple 							=		self.continuous_inputs_projection(__snake_case		)
								inputs += position_encodings
								UpperCAmelCase :       Optional[Any] 							=		self.dropout(__snake_case		)

								# decoder: No padding present.
								UpperCAmelCase :       List[str] 							=		torch.ones(
								    decoder_input_tokens.shape[:2]		,			device=decoder_input_tokens.device		,			dtype=inputs.dtype		)

								# Translate encoding masks to encoder-decoder masks.
								UpperCAmelCase :       Tuple 							=		[(x, self.encoder_decoder_mask(__snake_case		,			__snake_case		)) for x, y in encodings_and_masks]

								# cross attend style: concat encodings
								UpperCAmelCase :       Optional[Any] 							=		torch.cat([x[0] for x in encodings_and_encdec_masks]		,			dim=1		)
								UpperCAmelCase :       Optional[int] 							=		torch.cat([x[1] for x in encodings_and_encdec_masks]		,			dim=-1		)

								for lyr in self.decoders:
															UpperCAmelCase :       Any 							=		lyr(
															    __snake_case		,			conditioning_emb=__snake_case		,			encoder_hidden_states=__snake_case		,			encoder_attention_mask=__snake_case		,			)[0]

								UpperCAmelCase :       List[Any] 							=		self.decoder_norm(__snake_case		)
								UpperCAmelCase :       Union[str, Any] 							=		self.post_dropout(__snake_case		)

								UpperCAmelCase :       Optional[int] 							=		self.spec_out(__snake_case		)
								return spec_out

class 				SCREAMING_SNAKE_CASE(							nn.Module			):







	"""simple docstring"""






	def __init__(						self			:     Optional[Any]		,			__snake_case			:     Dict		,			__snake_case			:     Optional[int]		,			__snake_case			:     Tuple		,			__snake_case			:     Optional[int]		,			__snake_case			:     Dict		,			__snake_case			:     Union[str, Any]=1E-6		)			->		Optional[Any]:
								super().__init__()
								UpperCAmelCase :       Union[str, Any] 							=		nn.ModuleList()

								# cond self attention: layer 0
								self.layer.append(
								    TaLayerSelfAttentionCond(d_model=__snake_case		,			d_kv=__snake_case		,			num_heads=__snake_case		,			dropout_rate=__snake_case		)		)

								# cross attention: layer 1
								self.layer.append(
								    TaLayerCrossAttention(
								        d_model=__snake_case		,			d_kv=__snake_case		,			num_heads=__snake_case		,			dropout_rate=__snake_case		,			layer_norm_epsilon=__snake_case		,			)		)

								# Film Cond MLP + dropout: last layer
								self.layer.append(
								    TaLayerFFCond(d_model=__snake_case		,			d_ff=__snake_case		,			dropout_rate=__snake_case		,			layer_norm_epsilon=__snake_case		)		)







	def      A	(						self			:     Optional[int]		,			__snake_case			:     List[Any]		,			__snake_case			:     int=None		,			__snake_case			:     List[str]=None		,			__snake_case			:     Optional[int]=None		,			__snake_case			:     Optional[Any]=None		,			__snake_case			:     Any=None		,			)			->		List[str]:
								UpperCAmelCase :       Optional[int] 							=		self.layer[0](
								    __snake_case		,			conditioning_emb=__snake_case		,			attention_mask=__snake_case		,			)

								if encoder_hidden_states is not None:
															UpperCAmelCase :       Any 							=		torch.where(encoder_attention_mask > 0		,			0		,			-1E10		).to(
															    encoder_hidden_states.dtype		)

															UpperCAmelCase :       str 							=		self.layer[1](
															    __snake_case		,			key_value_states=__snake_case		,			attention_mask=__snake_case		,			)

								# Apply Film Conditional Feed Forward layer
								UpperCAmelCase :       Tuple 							=		self.layer[-1](__snake_case		,			__snake_case		)

								return (hidden_states,)

class 				SCREAMING_SNAKE_CASE(							nn.Module			):







	"""simple docstring"""






	def __init__(						self			:     str		,			__snake_case			:     Tuple		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     Tuple		,			__snake_case			:     Union[str, Any]		)			->		Optional[int]:
								super().__init__()
								UpperCAmelCase :       Optional[int] 							=		TaLayerNorm(__snake_case		)
								UpperCAmelCase :       Union[str, Any] 							=		TaFiLMLayer(in_features=d_model * 4		,			out_features=__snake_case		)
								UpperCAmelCase :       Union[str, Any] 							=		Attention(query_dim=__snake_case		,			heads=__snake_case		,			dim_head=__snake_case		,			out_bias=__snake_case		,			scale_qk=__snake_case		)
								UpperCAmelCase :       Union[str, Any] 							=		nn.Dropout(__snake_case		)







	def      A	(						self			:     Optional[Any]		,			__snake_case			:     Optional[Any]		,			__snake_case			:     Optional[int]=None		,			__snake_case			:     Union[str, Any]=None		,			)			->		Optional[Any]:
								# pre_self_attention_layer_norm
								UpperCAmelCase :       Union[str, Any] 							=		self.layer_norm(__snake_case		)

								if conditioning_emb is not None:
															UpperCAmelCase :       Optional[int] 							=		self.FiLMLayer(__snake_case		,			__snake_case		)

								# Self-attention block
								UpperCAmelCase :       Tuple 							=		self.attention(__snake_case		)

								UpperCAmelCase :       Optional[int] 							=		hidden_states + self.dropout(__snake_case		)

								return hidden_states

class 				SCREAMING_SNAKE_CASE(							nn.Module			):







	"""simple docstring"""






	def __init__(						self			:     Union[str, Any]		,			__snake_case			:     List[Any]		,			__snake_case			:     Tuple		,			__snake_case			:     int		,			__snake_case			:     List[Any]		,			__snake_case			:     Any		)			->		Union[str, Any]:
								super().__init__()
								UpperCAmelCase :       Optional[int] 							=		Attention(query_dim=__snake_case		,			heads=__snake_case		,			dim_head=__snake_case		,			out_bias=__snake_case		,			scale_qk=__snake_case		)
								UpperCAmelCase :       List[str] 							=		TaLayerNorm(__snake_case		,			eps=__snake_case		)
								UpperCAmelCase :       Optional[Any] 							=		nn.Dropout(__snake_case		)







	def      A	(						self			:     Any		,			__snake_case			:     int		,			__snake_case			:     int=None		,			__snake_case			:     int=None		,			)			->		List[Any]:
								UpperCAmelCase :       Tuple 							=		self.layer_norm(__snake_case		)
								UpperCAmelCase :       Any 							=		self.attention(
								    __snake_case		,			encoder_hidden_states=__snake_case		,			attention_mask=attention_mask.squeeze(1		)		,			)
								UpperCAmelCase :       Optional[Any] 							=		hidden_states + self.dropout(__snake_case		)
								return layer_output

class 				SCREAMING_SNAKE_CASE(							nn.Module			):







	"""simple docstring"""






	def __init__(						self			:     int		,			__snake_case			:     Tuple		,			__snake_case			:     str		,			__snake_case			:     Tuple		,			__snake_case			:     int		)			->		int:
								super().__init__()
								UpperCAmelCase :       List[str] 							=		TaDenseGatedActDense(d_model=__snake_case		,			d_ff=__snake_case		,			dropout_rate=__snake_case		)
								UpperCAmelCase :       int 							=		TaFiLMLayer(in_features=d_model * 4		,			out_features=__snake_case		)
								UpperCAmelCase :       Any 							=		TaLayerNorm(__snake_case		,			eps=__snake_case		)
								UpperCAmelCase :       Dict 							=		nn.Dropout(__snake_case		)







	def      A	(						self			:     List[Any]		,			__snake_case			:     List[Any]		,			__snake_case			:     Dict=None		)			->		str:
								UpperCAmelCase :       List[Any] 							=		self.layer_norm(__snake_case		)
								if conditioning_emb is not None:
															UpperCAmelCase :       Tuple 							=		self.film(__snake_case		,			__snake_case		)

								UpperCAmelCase :       Union[str, Any] 							=		self.DenseReluDense(__snake_case		)
								UpperCAmelCase :       Tuple 							=		hidden_states + self.dropout(__snake_case		)
								return hidden_states

class 				SCREAMING_SNAKE_CASE(							nn.Module			):







	"""simple docstring"""






	def __init__(						self			:     Optional[Any]		,			__snake_case			:     int		,			__snake_case			:     Any		,			__snake_case			:     Union[str, Any]		)			->		str:
								super().__init__()
								UpperCAmelCase :       Tuple 							=		nn.Linear(__snake_case		,			__snake_case		,			bias=__snake_case		)
								UpperCAmelCase :       Optional[int] 							=		nn.Linear(__snake_case		,			__snake_case		,			bias=__snake_case		)
								UpperCAmelCase :       Tuple 							=		nn.Linear(__snake_case		,			__snake_case		,			bias=__snake_case		)
								UpperCAmelCase :       int 							=		nn.Dropout(__snake_case		)
								UpperCAmelCase :       Dict 							=		NewGELUActivation()







	def      A	(						self			:     Optional[int]		,			__snake_case			:     List[Any]		)			->		Tuple:
								UpperCAmelCase :       List[str] 							=		self.act(self.wi_a(__snake_case		)		)
								UpperCAmelCase :       str 							=		self.wi_a(__snake_case		)
								UpperCAmelCase :       Any 							=		hidden_gelu * hidden_linear
								UpperCAmelCase :       Any 							=		self.dropout(__snake_case		)

								UpperCAmelCase :       Union[str, Any] 							=		self.wo(__snake_case		)
								return hidden_states

class 				SCREAMING_SNAKE_CASE(							nn.Module			):







	"""simple docstring"""






	def __init__(						self			:     List[str]		,			__snake_case			:     str		,			__snake_case			:     List[str]=1E-6		)			->		str:
								super().__init__()
								UpperCAmelCase :       int 							=		nn.Parameter(torch.ones(__snake_case		)		)
								UpperCAmelCase :       Union[str, Any] 							=		eps







	def      A	(						self			:     List[Any]		,			__snake_case			:     Dict		)			->		List[str]:
								# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
								# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
								# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
								# half-precision inputs is done in fp32

								UpperCAmelCase :       Tuple 							=		hidden_states.to(torch.floataa		).pow(2		).mean(-1		,			keepdim=__snake_case		)
								UpperCAmelCase :       Optional[Any] 							=		hidden_states * torch.rsqrt(variance + self.variance_epsilon		)

								# convert into half-precision if necessary
								if self.weight.dtype in [torch.floataa, torch.bfloataa]:
															UpperCAmelCase :       Optional[int] 							=		hidden_states.to(self.weight.dtype		)

								return self.weight * hidden_states

class 				SCREAMING_SNAKE_CASE(							nn.Module			):







	"""simple docstring"""






	def      A	(						self			:     Tuple		,			__snake_case			:     torch.Tensor		)			->		torch.Tensor:
								return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi		) * (input + 0.04_47_15 * torch.pow(__snake_case		,			3.0		))		))

class 				SCREAMING_SNAKE_CASE(							nn.Module			):







	"""simple docstring"""






	def __init__(						self			:     Optional[int]		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     Union[str, Any]		)			->		List[str]:
								super().__init__()
								UpperCAmelCase :       List[str] 							=		nn.Linear(__snake_case		,			out_features * 2		,			bias=__snake_case		)







	def      A	(						self			:     Dict		,			__snake_case			:     List[str]		,			__snake_case			:     str		)			->		int:
								UpperCAmelCase :       Dict 							=		self.scale_bias(__snake_case		)
								UpperCAmelCase							,		UpperCAmelCase :       Union[str, Any] 							=		torch.chunk(__snake_case		,			2		,			-1		)
								UpperCAmelCase :       Optional[int] 							=		x * (1 + scale) + shift
								return x










'''simple docstring'''






import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
						import torch

						from transformers import (
						    MraForMaskedLM,
						    MraForMultipleChoice,
						    MraForQuestionAnswering,
						    MraForSequenceClassification,
						    MraForTokenClassification,
						    MraModel,
						)
						from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST

class 				SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	def __init__(						self			:     Optional[int]		,			__snake_case			:     str		,			__snake_case			:     Union[str, Any]=2		,			__snake_case			:     Optional[int]=8		,			__snake_case			:     Any=True		,			__snake_case			:     Union[str, Any]=True		,			__snake_case			:     Dict=True		,			__snake_case			:     int=True		,			__snake_case			:     List[Any]=99		,			__snake_case			:     str=16		,			__snake_case			:     Tuple=5		,			__snake_case			:     Tuple=2		,			__snake_case			:     str=36		,			__snake_case			:     Dict="gelu"		,			__snake_case			:     str=0.0		,			__snake_case			:     Optional[int]=0.0		,			__snake_case			:     Optional[int]=512		,			__snake_case			:     Optional[Any]=16		,			__snake_case			:     int=2		,			__snake_case			:     int=0.02		,			__snake_case			:     str=3		,			__snake_case			:     Dict=4		,			__snake_case			:     str=None		,			)			->		Optional[int]:
								UpperCAmelCase :       Optional[int] 							=		parent
								UpperCAmelCase :       Tuple 							=		batch_size
								UpperCAmelCase :       List[str] 							=		seq_length
								UpperCAmelCase :       List[Any] 							=		is_training
								UpperCAmelCase :       int 							=		use_input_mask
								UpperCAmelCase :       Any 							=		use_token_type_ids
								UpperCAmelCase :       str 							=		use_labels
								UpperCAmelCase :       Union[str, Any] 							=		vocab_size
								UpperCAmelCase :       List[str] 							=		hidden_size
								UpperCAmelCase :       Optional[Any] 							=		num_hidden_layers
								UpperCAmelCase :       Union[str, Any] 							=		num_attention_heads
								UpperCAmelCase :       Optional[Any] 							=		intermediate_size
								UpperCAmelCase :       Union[str, Any] 							=		hidden_act
								UpperCAmelCase :       int 							=		hidden_dropout_prob
								UpperCAmelCase :       Optional[int] 							=		attention_probs_dropout_prob
								UpperCAmelCase :       Union[str, Any] 							=		max_position_embeddings
								UpperCAmelCase :       str 							=		type_vocab_size
								UpperCAmelCase :       List[str] 							=		type_sequence_label_size
								UpperCAmelCase :       Tuple 							=		initializer_range
								UpperCAmelCase :       Optional[Any] 							=		num_labels
								UpperCAmelCase :       Optional[int] 							=		num_choices
								UpperCAmelCase :       Any 							=		scope







	def      A	(						self			:     Tuple		)			->		Union[str, Any]:
								UpperCAmelCase :       Optional[int] 							=		ids_tensor([self.batch_size, self.seq_length]		,			self.vocab_size		)

								UpperCAmelCase :       Optional[int] 							=		None
								if self.use_input_mask:
															UpperCAmelCase :       Dict 							=		random_attention_mask([self.batch_size, self.seq_length]		)

								UpperCAmelCase :       Dict 							=		None
								if self.use_token_type_ids:
															UpperCAmelCase :       Union[str, Any] 							=		ids_tensor([self.batch_size, self.seq_length]		,			self.type_vocab_size		)

								UpperCAmelCase :       str 							=		None
								UpperCAmelCase :       Tuple 							=		None
								UpperCAmelCase :       int 							=		None
								if self.use_labels:
															UpperCAmelCase :       str 							=		ids_tensor([self.batch_size]		,			self.type_sequence_label_size		)
															UpperCAmelCase :       Dict 							=		ids_tensor([self.batch_size, self.seq_length]		,			self.num_labels		)
															UpperCAmelCase :       str 							=		ids_tensor([self.batch_size]		,			self.num_choices		)

								UpperCAmelCase :       List[str] 							=		self.get_config()

								return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







	def      A	(						self			:     int		)			->		Tuple:
								return MraConfig(
								    vocab_size=self.vocab_size		,			hidden_size=self.hidden_size		,			num_hidden_layers=self.num_hidden_layers		,			num_attention_heads=self.num_attention_heads		,			intermediate_size=self.intermediate_size		,			hidden_act=self.hidden_act		,			hidden_dropout_prob=self.hidden_dropout_prob		,			attention_probs_dropout_prob=self.attention_probs_dropout_prob		,			max_position_embeddings=self.max_position_embeddings		,			type_vocab_size=self.type_vocab_size		,			is_decoder=__snake_case		,			initializer_range=self.initializer_range		,			)







	def      A	(						self			:     Optional[Any]		)			->		Any:
								UpperCAmelCase :       Optional[Any] 							=		self.get_config()
								UpperCAmelCase :       int 							=		300
								return config







	def      A	(						self			:     Optional[Any]		)			->		Any:
								(
								    (
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		
								) :       Tuple 							=		self.prepare_config_and_inputs()

								UpperCAmelCase :       Dict 							=		True
								UpperCAmelCase :       Tuple 							=		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
								UpperCAmelCase :       str 							=		ids_tensor([self.batch_size, self.seq_length]		,			vocab_size=2		)

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    input_mask,
								    sequence_labels,
								    token_labels,
								    choice_labels,
								    encoder_hidden_states,
								    encoder_attention_mask,
								)







	def      A	(						self			:     Union[str, Any]		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     int		,			__snake_case			:     Optional[int]		,			__snake_case			:     int		,			__snake_case			:     Dict		,			__snake_case			:     Tuple		,			__snake_case			:     Optional[Any]		)			->		List[str]:
								UpperCAmelCase :       int 							=		MraModel(config=__snake_case		)
								model.to(__snake_case		)
								model.eval()
								UpperCAmelCase :       Tuple 							=		model(__snake_case		,			attention_mask=__snake_case		,			token_type_ids=__snake_case		)
								UpperCAmelCase :       Optional[int] 							=		model(__snake_case		,			token_type_ids=__snake_case		)
								UpperCAmelCase :       Dict 							=		model(__snake_case		)
								self.parent.assertEqual(result.last_hidden_state.shape		,			(self.batch_size, self.seq_length, self.hidden_size)		)







	def      A	(						self			:     Tuple		,			__snake_case			:     Optional[Any]		,			__snake_case			:     List[str]		,			__snake_case			:     List[str]		,			__snake_case			:     int		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     Optional[Any]		,			__snake_case			:     Any		,			__snake_case			:     List[Any]		,			__snake_case			:     Optional[Any]		,			)			->		Tuple:
								UpperCAmelCase :       str 							=		True
								UpperCAmelCase :       Tuple 							=		MraModel(__snake_case		)
								model.to(__snake_case		)
								model.eval()
								UpperCAmelCase :       Optional[int] 							=		model(
								    __snake_case		,			attention_mask=__snake_case		,			token_type_ids=__snake_case		,			encoder_hidden_states=__snake_case		,			encoder_attention_mask=__snake_case		,			)
								UpperCAmelCase :       Optional[Any] 							=		model(
								    __snake_case		,			attention_mask=__snake_case		,			token_type_ids=__snake_case		,			encoder_hidden_states=__snake_case		,			)
								UpperCAmelCase :       str 							=		model(__snake_case		,			attention_mask=__snake_case		,			token_type_ids=__snake_case		)
								self.parent.assertEqual(result.last_hidden_state.shape		,			(self.batch_size, self.seq_length, self.hidden_size)		)







	def      A	(						self			:     Tuple		,			__snake_case			:     str		,			__snake_case			:     Optional[Any]		,			__snake_case			:     List[str]		,			__snake_case			:     Tuple		,			__snake_case			:     Optional[Any]		,			__snake_case			:     List[str]		,			__snake_case			:     int		)			->		Any:
								UpperCAmelCase :       Dict 							=		MraForMaskedLM(config=__snake_case		)
								model.to(__snake_case		)
								model.eval()
								UpperCAmelCase :       Optional[int] 							=		model(__snake_case		,			attention_mask=__snake_case		,			token_type_ids=__snake_case		,			labels=__snake_case		)
								self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.seq_length, self.vocab_size)		)







	def      A	(						self			:     Tuple		,			__snake_case			:     Tuple		,			__snake_case			:     Dict		,			__snake_case			:     Dict		,			__snake_case			:     Any		,			__snake_case			:     int		,			__snake_case			:     Optional[Any]		,			__snake_case			:     Tuple		)			->		Optional[int]:
								UpperCAmelCase :       List[str] 							=		MraForQuestionAnswering(config=__snake_case		)
								model.to(__snake_case		)
								model.eval()
								UpperCAmelCase :       List[Any] 							=		model(
								    __snake_case		,			attention_mask=__snake_case		,			token_type_ids=__snake_case		,			start_positions=__snake_case		,			end_positions=__snake_case		,			)
								self.parent.assertEqual(result.start_logits.shape		,			(self.batch_size, self.seq_length)		)
								self.parent.assertEqual(result.end_logits.shape		,			(self.batch_size, self.seq_length)		)







	def      A	(						self			:     str		,			__snake_case			:     Optional[int]		,			__snake_case			:     List[str]		,			__snake_case			:     str		,			__snake_case			:     int		,			__snake_case			:     Optional[Any]		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     List[Any]		)			->		int:
								UpperCAmelCase :       int 							=		self.num_labels
								UpperCAmelCase :       Union[str, Any] 							=		MraForSequenceClassification(__snake_case		)
								model.to(__snake_case		)
								model.eval()
								UpperCAmelCase :       List[str] 							=		model(__snake_case		,			attention_mask=__snake_case		,			token_type_ids=__snake_case		,			labels=__snake_case		)
								self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.num_labels)		)







	def      A	(						self			:     str		,			__snake_case			:     Dict		,			__snake_case			:     Optional[Any]		,			__snake_case			:     Dict		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     Union[str, Any]		,			__snake_case			:     Dict		)			->		int:
								UpperCAmelCase :       Tuple 							=		self.num_labels
								UpperCAmelCase :       List[str] 							=		MraForTokenClassification(config=__snake_case		)
								model.to(__snake_case		)
								model.eval()
								UpperCAmelCase :       str 							=		model(__snake_case		,			attention_mask=__snake_case		,			token_type_ids=__snake_case		,			labels=__snake_case		)
								self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.seq_length, self.num_labels)		)







	def      A	(						self			:     str		,			__snake_case			:     int		,			__snake_case			:     Any		,			__snake_case			:     Tuple		,			__snake_case			:     Optional[int]		,			__snake_case			:     List[str]		,			__snake_case			:     str		,			__snake_case			:     Union[str, Any]		)			->		Optional[Any]:
								UpperCAmelCase :       Tuple 							=		self.num_choices
								UpperCAmelCase :       int 							=		MraForMultipleChoice(config=__snake_case		)
								model.to(__snake_case		)
								model.eval()
								UpperCAmelCase :       str 							=		input_ids.unsqueeze(1		).expand(-1		,			self.num_choices		,			-1		).contiguous()
								UpperCAmelCase :       List[Any] 							=		token_type_ids.unsqueeze(1		).expand(-1		,			self.num_choices		,			-1		).contiguous()
								UpperCAmelCase :       List[str] 							=		input_mask.unsqueeze(1		).expand(-1		,			self.num_choices		,			-1		).contiguous()
								UpperCAmelCase :       List[str] 							=		model(
								    __snake_case		,			attention_mask=__snake_case		,			token_type_ids=__snake_case		,			labels=__snake_case		,			)
								self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.num_choices)		)







	def      A	(						self			:     str		)			->		Dict:
								UpperCAmelCase :       Any 							=		self.prepare_config_and_inputs()
								(
								    (
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		(
								    UpperCAmelCase
								)							,		
								) :       List[str] 							=		config_and_inputs
								UpperCAmelCase :       Any 							=		{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
								return config, inputs_dict

@require_torch
class 				SCREAMING_SNAKE_CASE(							A__							,		unittest.TestCase			):







	"""simple docstring"""






	lowerCamelCase__			 =       (
	    (
	        MraModel,
	        MraForMaskedLM,
	        MraForMultipleChoice,
	        MraForQuestionAnswering,
	        MraForSequenceClassification,
	        MraForTokenClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	lowerCamelCase__			 =       False
	lowerCamelCase__			 =       False
	lowerCamelCase__			 =       False
	lowerCamelCase__			 =       False

	lowerCamelCase__			 =       ()







	def      A	(						self			:     int		)			->		Union[str, Any]:
								UpperCAmelCase :       List[str] 							=		MraModelTester(self		)
								UpperCAmelCase :       Optional[int] 							=		ConfigTester(self		,			config_class=__snake_case		,			hidden_size=37		)







	def      A	(						self			:     Optional[Any]		)			->		str:
								self.config_tester.run_common_tests()







	def      A	(						self			:     Tuple		)			->		Optional[Any]:
								UpperCAmelCase :       Optional[int] 							=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__snake_case		)







	def      A	(						self			:     List[Any]		)			->		Optional[Any]:
								UpperCAmelCase :       Tuple 							=		self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
															UpperCAmelCase :       List[Any] 							=		type
															self.model_tester.create_and_check_model(*__snake_case		)







	def      A	(						self			:     Tuple		)			->		Dict:
								UpperCAmelCase :       List[Any] 							=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*__snake_case		)







	def      A	(						self			:     Tuple		)			->		List[str]:
								UpperCAmelCase :       Tuple 							=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_multiple_choice(*__snake_case		)







	def      A	(						self			:     int		)			->		Dict:
								UpperCAmelCase :       Optional[int] 							=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*__snake_case		)







	def      A	(						self			:     Dict		)			->		Optional[int]:
								UpperCAmelCase :       Any 							=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*__snake_case		)







	def      A	(						self			:     Any		)			->		Optional[int]:
								UpperCAmelCase :       List[str] 							=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*__snake_case		)







	@slow
	def      A	(						self			:     Dict		)			->		Any:
								for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															UpperCAmelCase :       str 							=		MraModel.from_pretrained(__snake_case		)
															self.assertIsNotNone(__snake_case		)







	@unittest.skip(reason='''MRA does not output attentions'''		)
	def      A	(						self			:     str		)			->		Optional[Any]:
								return

@require_torch
class 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






	@slow
	def      A	(						self			:     Tuple		)			->		List[Any]:
								UpperCAmelCase :       int 							=		MraModel.from_pretrained('''uw-madison/mra-base-512-4'''		)
								UpperCAmelCase :       Optional[Any] 							=		torch.arange(256		).unsqueeze(0		)

								with torch.no_grad():
															UpperCAmelCase :       List[Any] 							=		model(__snake_case		)[0]

								UpperCAmelCase :       Optional[Any] 							=		torch.Size((1, 256, 768)		)
								self.assertEqual(output.shape		,			__snake_case		)

								UpperCAmelCase :       Any 							=		torch.tensor(
								    [[[-0.01_40, 0.08_30, -0.03_81], [0.15_46, 0.14_02, 0.02_20], [0.11_62, 0.08_51, 0.01_65]]]		)

								self.assertTrue(torch.allclose(output[:, :3, :3]		,			__snake_case		,			atol=1E-4		)		)







	@slow
	def      A	(						self			:     Optional[Any]		)			->		Any:
								UpperCAmelCase :       Optional[int] 							=		MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4'''		)
								UpperCAmelCase :       Dict 							=		torch.arange(256		).unsqueeze(0		)

								with torch.no_grad():
															UpperCAmelCase :       List[Any] 							=		model(__snake_case		)[0]

								UpperCAmelCase :       int 							=		50265

								UpperCAmelCase :       int 							=		torch.Size((1, 256, vocab_size)		)
								self.assertEqual(output.shape		,			__snake_case		)

								UpperCAmelCase :       Union[str, Any] 							=		torch.tensor(
								    [[[9.25_95, -3.60_38, 11.88_19], [9.38_69, -3.26_93, 11.09_56], [11.85_24, -3.49_38, 13.12_10]]]		)

								self.assertTrue(torch.allclose(output[:, :3, :3]		,			__snake_case		,			atol=1E-4		)		)







	@slow
	def      A	(						self			:     str		)			->		List[Any]:
								UpperCAmelCase :       List[Any] 							=		MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3'''		)
								UpperCAmelCase :       List[Any] 							=		torch.arange(4096		).unsqueeze(0		)

								with torch.no_grad():
															UpperCAmelCase :       Tuple 							=		model(__snake_case		)[0]

								UpperCAmelCase :       Optional[int] 							=		50265

								UpperCAmelCase :       Tuple 							=		torch.Size((1, 4096, vocab_size)		)
								self.assertEqual(output.shape		,			__snake_case		)

								UpperCAmelCase :       Optional[int] 							=		torch.tensor(
								    [[[5.47_89, -2.35_64, 7.50_64], [7.90_67, -1.33_69, 9.96_68], [9.07_12, -1.81_06, 7.03_80]]]		)

								self.assertTrue(torch.allclose(output[:, :3, :3]		,			__snake_case		,			atol=1E-4		)		)





