
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     lowercase		( a__	,  a__		):




							@register_to_config
							def __init__(    self       ,A__ = 1_2_8       ,A__ = 2_5_6       ,A__ = 2_0_0_0.0       ,A__ = 7_6_8       ,A__ = 1_2       ,A__ = 1_2       ,A__ = 6_4       ,A__ = 2_0_4_8       ,A__ = 0.1       ,):
												super().__init__()

												lowercase						     =						nn.Sequential(
												    nn.Linear(_lowerCamelCase       ,d_model * 4       ,bias=_lowerCamelCase)       ,nn.SiLU()       ,nn.Linear(d_model * 4       ,d_model * 4       ,bias=_lowerCamelCase)       ,nn.SiLU()       ,)

												lowercase						     =						nn.Embedding(_lowerCamelCase       ,_lowerCamelCase)
												lowercase						     =						False

												lowercase						     =						nn.Linear(_lowerCamelCase       ,_lowerCamelCase       ,bias=_lowerCamelCase)

												lowercase						     =						nn.Dropout(p=_lowerCamelCase)

												lowercase						     =						nn.ModuleList()
												for lyr_num in range(_lowerCamelCase):
																	# FiLM conditional T5 decoder
																	lowercase						     =						DecoderLayer(d_model=_lowerCamelCase       ,d_kv=_lowerCamelCase       ,num_heads=_lowerCamelCase       ,d_ff=_lowerCamelCase       ,dropout_rate=_lowerCamelCase)
																	self.decoders.append(_lowerCamelCase)

												lowercase						     =						TaLayerNorm(_lowerCamelCase)

												lowercase						     =						nn.Dropout(p=_lowerCamelCase)
												lowercase						     =						nn.Linear(_lowerCamelCase       ,_lowerCamelCase       ,bias=_lowerCamelCase)




							def 			A__				(    self       ,A__       ,A__):
												lowercase						     =						torch.mul(query_input.unsqueeze(-1)       ,key_input.unsqueeze(-2))
												return mask.unsqueeze(-3)





							def 			A__				(    self       ,A__       ,A__       ,A__):
												lowercase						     =						decoder_input_tokens.shape
												assert decoder_noise_time.shape == (batch,)

												# decoder_noise_time is in [0, 1), so rescale to expected timing range.
												lowercase						     =						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)

												lowercase						     =						self.conditioning_emb(_lowerCamelCase).unsqueeze(1)

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

												lowercase						     =						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.
												lowercase						     =						torch.broadcast_to(
												    torch.arange(_lowerCamelCase       ,device=decoder_input_tokens.device)       ,(batch, seq_length)       ,)

												lowercase						     =						self.position_encoding(_lowerCamelCase)

												lowercase						     =						self.continuous_inputs_projection(_lowerCamelCase)
												inputs += position_encodings
												lowercase						     =						self.dropout(_lowerCamelCase)

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

												# Translate encoding masks to encoder-decoder masks.
												lowercase						     =						[(x, self.encoder_decoder_mask(_lowerCamelCase       ,_lowerCamelCase)) for x, y in encodings_and_masks]

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

												for lyr in self.decoders:
																	lowercase						     =						lyr(
																	    _lowerCamelCase       ,conditioning_emb=_lowerCamelCase       ,encoder_hidden_states=_lowerCamelCase       ,encoder_attention_mask=_lowerCamelCase       ,)[0]

												lowercase						     =						self.decoder_norm(_lowerCamelCase)
												lowercase						     =						self.post_dropout(_lowerCamelCase)

												lowercase						     =						self.spec_out(_lowerCamelCase)
												return spec_out







class     lowercase		( nn.Module		):




							def __init__(    self       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__=1E-6):
												super().__init__()
												lowercase						     =						nn.ModuleList()

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

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

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





							def 			A__				(    self       ,A__       ,A__=None       ,A__=None       ,A__=None       ,A__=None       ,A__=None       ,):
												lowercase						     =						self.layer[0](
												    _lowerCamelCase       ,conditioning_emb=_lowerCamelCase       ,attention_mask=_lowerCamelCase       ,)

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

																	lowercase						     =						self.layer[1](
																	    _lowerCamelCase       ,key_value_states=_lowerCamelCase       ,attention_mask=_lowerCamelCase       ,)

												# Apply Film Conditional Feed Forward layer
												lowercase						     =						self.layer[-1](_lowerCamelCase       ,_lowerCamelCase)

												return (hidden_states,)







class     lowercase		( nn.Module		):




							def __init__(    self       ,A__       ,A__       ,A__       ,A__):
												super().__init__()
												lowercase						     =						TaLayerNorm(_lowerCamelCase)
												lowercase						     =						TaFiLMLayer(in_features=d_model * 4       ,out_features=_lowerCamelCase)
												lowercase						     =						Attention(query_dim=_lowerCamelCase       ,heads=_lowerCamelCase       ,dim_head=_lowerCamelCase       ,out_bias=_lowerCamelCase       ,scale_qk=_lowerCamelCase)
												lowercase						     =						nn.Dropout(_lowerCamelCase)





							def 			A__				(    self       ,A__       ,A__=None       ,A__=None       ,):
												# pre_self_attention_layer_norm
												lowercase						     =						self.layer_norm(_lowerCamelCase)

												if conditioning_emb is not None:
																	lowercase						     =						self.FiLMLayer(_lowerCamelCase       ,_lowerCamelCase)

												# Self-attention block
												lowercase						     =						self.attention(_lowerCamelCase)

												lowercase						     =						hidden_states + self.dropout(_lowerCamelCase)

												return hidden_states







class     lowercase		( nn.Module		):




							def __init__(    self       ,A__       ,A__       ,A__       ,A__       ,A__):
												super().__init__()
												lowercase						     =						Attention(query_dim=_lowerCamelCase       ,heads=_lowerCamelCase       ,dim_head=_lowerCamelCase       ,out_bias=_lowerCamelCase       ,scale_qk=_lowerCamelCase)
												lowercase						     =						TaLayerNorm(_lowerCamelCase       ,eps=_lowerCamelCase)
												lowercase						     =						nn.Dropout(_lowerCamelCase)





							def 			A__				(    self       ,A__       ,A__=None       ,A__=None       ,):
												lowercase						     =						self.layer_norm(_lowerCamelCase)
												lowercase						     =						self.attention(
												    _lowerCamelCase       ,encoder_hidden_states=_lowerCamelCase       ,attention_mask=attention_mask.squeeze(1)       ,)
												lowercase						     =						hidden_states + self.dropout(_lowerCamelCase)
												return layer_output







class     lowercase		( nn.Module		):




							def __init__(    self       ,A__       ,A__       ,A__       ,A__):
												super().__init__()
												lowercase						     =						TaDenseGatedActDense(d_model=_lowerCamelCase       ,d_ff=_lowerCamelCase       ,dropout_rate=_lowerCamelCase)
												lowercase						     =						TaFiLMLayer(in_features=d_model * 4       ,out_features=_lowerCamelCase)
												lowercase						     =						TaLayerNorm(_lowerCamelCase       ,eps=_lowerCamelCase)
												lowercase						     =						nn.Dropout(_lowerCamelCase)





							def 			A__				(    self       ,A__       ,A__=None):
												lowercase						     =						self.layer_norm(_lowerCamelCase)
												if conditioning_emb is not None:
																	lowercase						     =						self.film(_lowerCamelCase       ,_lowerCamelCase)

												lowercase						     =						self.DenseReluDense(_lowerCamelCase)
												lowercase						     =						hidden_states + self.dropout(_lowerCamelCase)
												return hidden_states







class     lowercase		( nn.Module		):




							def __init__(    self       ,A__       ,A__       ,A__):
												super().__init__()
												lowercase						     =						nn.Linear(_lowerCamelCase       ,_lowerCamelCase       ,bias=_lowerCamelCase)
												lowercase						     =						nn.Linear(_lowerCamelCase       ,_lowerCamelCase       ,bias=_lowerCamelCase)
												lowercase						     =						nn.Linear(_lowerCamelCase       ,_lowerCamelCase       ,bias=_lowerCamelCase)
												lowercase						     =						nn.Dropout(_lowerCamelCase)
												lowercase						     =						NewGELUActivation()





							def 			A__				(    self       ,A__):
												lowercase						     =						self.act(self.wi_a(_lowerCamelCase))
												lowercase						     =						self.wi_a(_lowerCamelCase)
												lowercase						     =						hidden_gelu * hidden_linear
												lowercase						     =						self.dropout(_lowerCamelCase)

												lowercase						     =						self.wo(_lowerCamelCase)
												return hidden_states







class     lowercase		( nn.Module		):




							def __init__(    self       ,A__       ,A__=1E-6):
												super().__init__()
												lowercase						     =						nn.Parameter(torch.ones(_lowerCamelCase))
												lowercase						     =						eps





							def 			A__				(    self       ,A__):
												# 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

												lowercase						     =						hidden_states.to(torch.floataa).pow(2).mean(-1       ,keepdim=_lowerCamelCase)
												lowercase						     =						hidden_states * torch.rsqrt(variance + self.variance_epsilon)

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

												return self.weight * hidden_states







class     lowercase		( nn.Module		):




							def 			A__				(    self       ,A__):
												return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.044715 * torch.pow(_lowerCamelCase       ,3.0))))







class     lowercase		( nn.Module		):




							def __init__(    self       ,A__       ,A__):
												super().__init__()
												lowercase						     =						nn.Linear(_lowerCamelCase       ,out_features * 2       ,bias=_lowerCamelCase)





							def 			A__				(    self       ,A__       ,A__):
												lowercase						     =						self.scale_bias(_lowerCamelCase)
												lowercase						     =						torch.chunk(_lowerCamelCase       ,2       ,-1)
												lowercase						     =						x * (1 + scale) + shift
												return x






import numpy as np

import datasets


lowercase__		:Dict			  =   "\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Prof. P. C. Mahalanobis in 1936\nand has been used in various statistical applications ever since\n[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]\n"

lowercase__		:List[Any]			  =   "\\n@article{de2000mahalanobis,\n  title={The mahalanobis distance},\n  author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},\n  journal={Chemometrics and intelligent laboratory systems},\n  volume={50},\n  number={1},\n  pages={1--18},\n  year={2000},\n  publisher={Elsevier}\n}\n"

lowercase__		:Dict			  =   "\nArgs:\n    X: List of datapoints to be compared with the `reference_distribution`.\n    reference_distribution: List of datapoints from the reference distribution we want to compare to.\nReturns:\n    mahalanobis: The Mahalonobis distance for each datapoint in `X`.\nExamples:\n\n    >>> mahalanobis_metric = datasets.load_metric(\"mahalanobis\")\n    >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])\n    >>> print(results)\n    {'mahalanobis': array([0.5])}\n"







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,  _KWARGS_DESCRIPTION		)
class     lowercase		( datasets.Metric		):




							def 			A__				(    self):
												return datasets.MetricInfo(
												    description=_DESCRIPTION       ,citation=_CITATION       ,inputs_description=_KWARGS_DESCRIPTION       ,features=datasets.Features(
												        {
												            '''X''': datasets.Sequence(datasets.Value('''float'''       ,id='''sequence''')       ,id='''X'''),
												        })       ,)





							def 			A__				(    self       ,A__       ,A__):
												# convert to numpy arrays
												lowercase						     =						np.array(A__)
												lowercase						     =						np.array(A__)

												# Assert that arrays are 2D
												if len(X.shape) != 2:
																	raise ValueError('''Expected `X` to be a 2D vector''')
												if len(reference_distribution.shape) != 2:
																	raise ValueError('''Expected `reference_distribution` to be a 2D vector''')
												if reference_distribution.shape[0] < 2:
																	raise ValueError(
																	    '''Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension''')

												# Get mahalanobis distance for each prediction
												lowercase						     =						X - np.mean(A__)
												lowercase						     =						np.cov(reference_distribution.T)
												try:
																	lowercase						     =						np.linalg.inv(A__)
												except np.linalg.LinAlgError:
																	lowercase						     =						np.linalg.pinv(A__)
												lowercase						     =						np.dot(A__       ,A__)
												lowercase						     =						np.dot(A__       ,X_minus_mu.T).diagonal()

												return {"mahalanobis": mahal_dist}




