

import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


A__						=							logging.getLogger(__name__)

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   Optional[Any]       				=  "token-classification"







	def __init__(   self: Tuple  , __UpperCamelCase: Tuple      ):

						'''simple docstring'''





						if type(__UpperCamelCase      ) == dict:
											__magic_name__							=    Namespace(**__UpperCamelCase      )
						__magic_name__							=    import_module('tasks'      )
						try:
											__magic_name__							=    getattr(__UpperCamelCase  , hparams.task_type      )
											__magic_name__							=    token_classification_task_clazz()
						except AttributeError:
											raise ValueError(
											    F'Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '
											    F'Available tasks classes are: {TokenClassificationTask.__subclasses__()}'      )
						__magic_name__							=    self.token_classification_task.get_labels(hparams.labels      )
						__magic_name__							=    CrossEntropyLoss().ignore_index
						super().__init__(__UpperCamelCase  , len(self.labels      )  , self.mode      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]  , **__UpperCamelCase: Optional[Any]      ):

						'''simple docstring'''





						return self.model(**__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: str  , __UpperCamelCase: int  , __UpperCamelCase: str      ):

						'''simple docstring'''





						__magic_name__							=    {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
						if self.config.model_type != "distilbert":
											__magic_name__							=    (
											    batch[2] if self.config.model_type in ['bert', 'xlnet'] else None
											)  # XLM and RoBERTa don"t use token_type_ids

						__magic_name__							=    self(**__UpperCamelCase      )
						__magic_name__							=    outputs[0]
						# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
						return {"loss": loss}







	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





						__magic_name__							=    self.hparams
						for mode in ["train", "dev", "test"]:
											__magic_name__							=    self._feature_file(__UpperCamelCase      )
											if os.path.exists(__UpperCamelCase      ) and not args.overwrite_cache:
																logger.info('Loading features from cached file %s'  , __UpperCamelCase      )
																__magic_name__							=    torch.load(__UpperCamelCase      )
											else:
																logger.info('Creating features from dataset file at %s'  , args.data_dir      )
																__magic_name__							=    self.token_classification_task.read_examples_from_file(args.data_dir  , __UpperCamelCase      )
																__magic_name__							=    self.token_classification_task.convert_examples_to_features(
																    __UpperCamelCase  , self.labels  , args.max_seq_length  , self.tokenizer  , cls_token_at_end=bool(self.config.model_type in ['xlnet']      )  , cls_token=self.tokenizer.cls_token  , cls_token_segment_id=2 if self.config.model_type in ['xlnet'] else 0  , sep_token=self.tokenizer.sep_token  , sep_token_extra=__UpperCamelCase  , pad_on_left=bool(self.config.model_type in ['xlnet']      )  , pad_token=self.tokenizer.pad_token_id  , pad_token_segment_id=self.tokenizer.pad_token_type_id  , pad_token_label_id=self.pad_token_label_id  , )
																logger.info('Saving features into cached file %s'  , __UpperCamelCase      )
																torch.save(__UpperCamelCase  , __UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple  , __UpperCamelCase: int  , __UpperCamelCase: int  , __UpperCamelCase: bool = False      ):

						'''simple docstring'''





						__magic_name__							=    self._feature_file(__UpperCamelCase      )
						logger.info('Loading features from cached file %s'  , __UpperCamelCase      )
						__magic_name__							=    torch.load(__UpperCamelCase      )
						__magic_name__							=    torch.tensor([f.input_ids for f in features]  , dtype=torch.long      )
						__magic_name__							=    torch.tensor([f.attention_mask for f in features]  , dtype=torch.long      )
						if features[0].token_type_ids is not None:
											__magic_name__							=    torch.tensor([f.token_type_ids for f in features]  , dtype=torch.long      )
						else:
											__magic_name__							=    torch.tensor([0 for f in features]  , dtype=torch.long      )
											# HACK(we will not use this anymore soon)
						__magic_name__							=    torch.tensor([f.label_ids for f in features]  , dtype=torch.long      )
						return DataLoader(
						    TensorDataset(__UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase      )  , batch_size=__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]  , __UpperCamelCase: Optional[int]  , __UpperCamelCase: Any      ):

						'''simple docstring'''





						"""Compute validation""" ""
						__magic_name__							=    {'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}
						if self.config.model_type != "distilbert":
											__magic_name__							=    (
											    batch[2] if self.config.model_type in ['bert', 'xlnet'] else None
											)  # XLM and RoBERTa don"t use token_type_ids
						__magic_name__							=    self(**__UpperCamelCase      )
						__magic_name__,	__magic_name__							=    outputs[:2]
						__magic_name__							=    logits.detach().cpu().numpy()
						__magic_name__							=    inputs['labels'].detach().cpu().numpy()
						return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}







	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]  , __UpperCamelCase: Optional[int]      ):

						'''simple docstring'''





						__magic_name__							=    torch.stack([x['val_loss'] for x in outputs]      ).mean()
						__magic_name__							=    np.concatenate([x['pred'] for x in outputs]  , axis=0      )
						__magic_name__							=    np.argmax(__UpperCamelCase  , axis=2      )
						__magic_name__							=    np.concatenate([x['target'] for x in outputs]  , axis=0      )

						__magic_name__							=    dict(enumerate(self.labels      )      )
						__magic_name__							=    [[] for _ in range(out_label_ids.shape[0]      )]
						__magic_name__							=    [[] for _ in range(out_label_ids.shape[0]      )]

						for i in range(out_label_ids.shape[0]      ):
											for j in range(out_label_ids.shape[1]      ):
																if out_label_ids[i, j] != self.pad_token_label_id:
																					out_label_list[i].append(label_map[out_label_ids[i][j]]      )
																					preds_list[i].append(label_map[preds[i][j]]      )

						__magic_name__							=    {
						    'val_loss': val_loss_mean,
						    'accuracy_score': accuracy_score(__UpperCamelCase  , __UpperCamelCase      ),
						    'precision': precision_score(__UpperCamelCase  , __UpperCamelCase      ),
						    'recall': recall_score(__UpperCamelCase  , __UpperCamelCase      ),
						    'f1': fa_score(__UpperCamelCase  , __UpperCamelCase      ),
						}

						__magic_name__							=    dict(results.items()      )
						__magic_name__							=    results
						return ret, preds_list, out_label_list







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]  , __UpperCamelCase: Dict      ):

						'''simple docstring'''





						__magic_name__,	__magic_name__,	__magic_name__							=    self._eval_end(__UpperCamelCase      )
						__magic_name__							=    ret['log']
						return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]  , __UpperCamelCase: List[str]      ):

						'''simple docstring'''





						__magic_name__,	__magic_name__,	__magic_name__							=    self._eval_end(__UpperCamelCase      )

						# Converting to the dict required by pl
						# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
						# pytorch_lightning/trainer/logging.py#L139
						__magic_name__							=    ret['log']
						# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
						return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







	@staticmethod
	def 	_SCREAMING_SNAKE_CASE							(   __UpperCamelCase: List[Any]  , __UpperCamelCase: str      ):

						'''simple docstring'''





						BaseTransformer.add_model_specific_args(__UpperCamelCase  , __UpperCamelCase      )
						parser.add_argument(
						    '--task_type'  , default='NER'  , type=__UpperCamelCase  , help='Task type to fine tune in training (e.g. NER, POS, etc)'      )
						parser.add_argument(
						    '--max_seq_length'  , default=1_28  , type=__UpperCamelCase  , help=(
						        'The maximum total input sequence length after tokenization. Sequences longer '
						        'than this will be truncated, sequences shorter will be padded.'
						    )  , )

						parser.add_argument(
						    '--labels'  , default=''  , type=__UpperCamelCase  , help='Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'  , )
						parser.add_argument(
						    '--gpus'  , default=0  , type=__UpperCamelCase  , help='The number of GPUs allocated for this, it is by default 0 meaning none'  , )

						parser.add_argument(
						    '--overwrite_cache'  , action='store_true'  , help='Overwrite the cached training and evaluation sets'      )

						return parser


if __name__ == "__main__":
						A__						=							argparse.ArgumentParser()
						add_generic_args(parser, os.getcwd())
						A__						=							NERTransformer.add_model_specific_args(parser, os.getcwd())
						A__						=							parser.parse_args()
						A__						=							NERTransformer(args)
						A__						=							generic_train(model, args)

						if args.do_predict:
												# See https://github.com/huggingface/transformers/issues/3159
												# pl use this default format to create a checkpoint:
												# https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
												# /pytorch_lightning/callbacks/model_checkpoint.py#L322
												A__						=							sorted(glob.glob(os.path.join(args.output_dir, "checkpoint-epoch=*.ckpt"), recursive=True))
												A__						=							model.load_from_checkpoint(checkpoints[-1])
												trainer.test(model)







import unittest

from diffusers.models.unet_ad_blocks import *  # noqa F403
from diffusers.utils import torch_device

from .test_unet_blocks_common import UNetBlockTesterMixin

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   str       				=  DownBlockaD  # noqa F405
	_lowercase  :   Union[str, Any]       				=  "down"







	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]      ):

						'''simple docstring'''





						__magic_name__							=    [-0.0232, -0.9869, 0.8054, -0.0637, -0.1688, -1.4264, 0.4470, -1.3394, 0.0904]
						super().test_output(__UpperCamelCase      )




class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   List[str]       				=  ResnetDownsampleBlockaD  # noqa F405
	_lowercase  :   Union[str, Any]       				=  "down"







	def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						__magic_name__							=    [0.0710, 0.2410, -0.7320, -1.0757, -1.1343, 0.3540, -0.0133, -0.2576, 0.0948]
						super().test_output(__UpperCamelCase      )




class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   Dict       				=  AttnDownBlockaD  # noqa F405
	_lowercase  :   List[Any]       				=  "down"







	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''simple docstring'''





						__magic_name__							=    [0.0636, 0.8964, -0.6234, -1.0131, 0.0844, 0.4935, 0.3437, 0.0911, -0.2957]
						super().test_output(__UpperCamelCase      )




class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   int       				=  CrossAttnDownBlockaD  # noqa F405
	_lowercase  :   Any       				=  "down"







	def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						__magic_name__,	__magic_name__							=    super().prepare_init_args_and_inputs_for_common()
						__magic_name__							=    32
						return init_dict, inputs_dict







	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





						__magic_name__							=    [0.2238, -0.7396, -0.2255, -0.3829, 0.1925, 1.1665, 0.0603, -0.7295, 0.1983]
						super().test_output(__UpperCamelCase      )




class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   Union[str, Any]       				=  SimpleCrossAttnDownBlockaD  # noqa F405
	_lowercase  :   List[str]       				=  "down"







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''simple docstring'''





						return super().get_dummy_input(include_encoder_hidden_states=__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						__magic_name__,	__magic_name__							=    super().prepare_init_args_and_inputs_for_common()
						__magic_name__							=    32
						return init_dict, inputs_dict







	@unittest.skipIf(torch_device == 'mps'  , 'MPS result is not consistent'      )
	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						__magic_name__							=    [0.7921, -0.0992, -0.1962, -0.7695, -0.4242, 0.7804, 0.4737, 0.2765, 0.3338]
						super().test_output(__UpperCamelCase      )




class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   str       				=  SkipDownBlockaD  # noqa F405
	_lowercase  :   Union[str, Any]       				=  "down"







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						return super().get_dummy_input(include_skip_sample=__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]      ):

						'''simple docstring'''





						__magic_name__							=    [-0.0845, -0.2087, -0.2465, 0.0971, 0.1900, -0.0484, 0.2664, 0.4179, 0.5069]
						super().test_output(__UpperCamelCase      )




class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   Tuple       				=  AttnSkipDownBlockaD  # noqa F405
	_lowercase  :   str       				=  "down"







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						return super().get_dummy_input(include_skip_sample=__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						__magic_name__							=    [0.5539, 0.1609, 0.4924, 0.0537, -0.1995, 0.4050, 0.0979, -0.2721, -0.0642]
						super().test_output(__UpperCamelCase      )




class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   Optional[int]       				=  DownEncoderBlockaD  # noqa F405
	_lowercase  :   List[str]       				=  "down"







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]      ):

						'''simple docstring'''





						return super().get_dummy_input(include_temb=__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						__magic_name__							=    {
						    'in_channels': 32,
						    'out_channels': 32,
						}
						__magic_name__							=    self.dummy_input
						return init_dict, inputs_dict







	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]      ):

						'''simple docstring'''





						__magic_name__							=    [1.1102, 0.5302, 0.4872, -0.0023, -0.8042, 0.0483, -0.3489, -0.5632, 0.7626]
						super().test_output(__UpperCamelCase      )




class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   List[Any]       				=  AttnDownEncoderBlockaD  # noqa F405
	_lowercase  :   Optional[Any]       				=  "down"







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						return super().get_dummy_input(include_temb=__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    {
						    'in_channels': 32,
						    'out_channels': 32,
						}
						__magic_name__							=    self.dummy_input
						return init_dict, inputs_dict







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						__magic_name__							=    [0.8966, -0.1486, 0.8568, 0.8141, -0.9046, -0.1342, -0.0972, -0.7417, 0.1538]
						super().test_output(__UpperCamelCase      )




class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   List[Any]       				=  UNetMidBlockaD  # noqa F405
	_lowercase  :   Any       				=  "mid"







	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]      ):

						'''simple docstring'''





						__magic_name__							=    {
						    'in_channels': 32,
						    'temb_channels': 1_28,
						}
						__magic_name__							=    self.dummy_input
						return init_dict, inputs_dict







	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





						__magic_name__							=    [-0.1062, 1.7248, 0.3494, 1.4569, -0.0910, -1.2421, -0.9984, 0.6736, 1.0028]
						super().test_output(__UpperCamelCase      )




class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   str       				=  UNetMidBlockaDCrossAttn  # noqa F405
	_lowercase  :   int       				=  "mid"







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						__magic_name__,	__magic_name__							=    super().prepare_init_args_and_inputs_for_common()
						__magic_name__							=    32
						return init_dict, inputs_dict







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						__magic_name__							=    [0.0187, 2.4220, 0.4484, 1.1203, -0.6121, -1.5122, -0.8270, 0.7851, 1.8335]
						super().test_output(__UpperCamelCase      )




class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   Tuple       				=  UNetMidBlockaDSimpleCrossAttn  # noqa F405
	_lowercase  :   str       				=  "mid"







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''simple docstring'''





						return super().get_dummy_input(include_encoder_hidden_states=__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						__magic_name__,	__magic_name__							=    super().prepare_init_args_and_inputs_for_common()
						__magic_name__							=    32
						return init_dict, inputs_dict







	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''simple docstring'''





						__magic_name__							=    [0.7143, 1.9974, 0.5448, 1.3977, 0.1282, -1.1237, -1.4238, 0.5530, 0.8880]
						super().test_output(__UpperCamelCase      )




class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   List[Any]       				=  UpBlockaD  # noqa F405
	_lowercase  :   List[Any]       				=  "up"







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						return super().get_dummy_input(include_res_hidden_states_tuple=__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''simple docstring'''





						__magic_name__							=    [-0.2041, -0.4165, -0.3022, 0.0041, -0.6628, -0.7053, 0.1928, -0.0325, 0.0523]
						super().test_output(__UpperCamelCase      )




class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   List[Any]       				=  ResnetUpsampleBlockaD  # noqa F405
	_lowercase  :   Dict       				=  "up"







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						return super().get_dummy_input(include_res_hidden_states_tuple=__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						__magic_name__							=    [0.2287, 0.3549, -0.1346, 0.4797, -0.1715, -0.9649, 0.7305, -0.5864, -0.6244]
						super().test_output(__UpperCamelCase      )




class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   Any       				=  CrossAttnUpBlockaD  # noqa F405
	_lowercase  :   Union[str, Any]       				=  "up"







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						return super().get_dummy_input(include_res_hidden_states_tuple=__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





						__magic_name__,	__magic_name__							=    super().prepare_init_args_and_inputs_for_common()
						__magic_name__							=    32
						return init_dict, inputs_dict







	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''simple docstring'''





						__magic_name__							=    [-0.1403, -0.3515, -0.0420, -0.1425, 0.3167, 0.5094, -0.2181, 0.5931, 0.5582]
						super().test_output(__UpperCamelCase      )




class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   str       				=  SimpleCrossAttnUpBlockaD  # noqa F405
	_lowercase  :   Tuple       				=  "up"







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]      ):

						'''simple docstring'''





						return super().get_dummy_input(include_res_hidden_states_tuple=__UpperCamelCase  , include_encoder_hidden_states=__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]      ):

						'''simple docstring'''





						__magic_name__,	__magic_name__							=    super().prepare_init_args_and_inputs_for_common()
						__magic_name__							=    32
						return init_dict, inputs_dict







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						__magic_name__							=    [0.2645, 0.1480, 0.0909, 0.8044, -0.9758, -0.9083, 0.0994, -1.1453, -0.7402]
						super().test_output(__UpperCamelCase      )




class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   Optional[Any]       				=  AttnUpBlockaD  # noqa F405
	_lowercase  :   Optional[int]       				=  "up"







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





						return super().get_dummy_input(include_res_hidden_states_tuple=__UpperCamelCase      )







	@unittest.skipIf(torch_device == 'mps'  , 'MPS result is not consistent'      )
	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]      ):

						'''simple docstring'''





						__magic_name__							=    [0.0979, 0.1326, 0.0021, 0.0659, 0.2249, 0.0059, 0.1132, 0.5952, 0.1033]
						super().test_output(__UpperCamelCase      )




class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   Union[str, Any]       				=  SkipUpBlockaD  # noqa F405
	_lowercase  :   int       				=  "up"







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]      ):

						'''simple docstring'''





						return super().get_dummy_input(include_res_hidden_states_tuple=__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]      ):

						'''simple docstring'''





						__magic_name__							=    [-0.0893, -0.1234, -0.1506, -0.0332, 0.0123, -0.0211, 0.0566, 0.0143, 0.0362]
						super().test_output(__UpperCamelCase      )




class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   Union[str, Any]       				=  AttnSkipUpBlockaD  # noqa F405
	_lowercase  :   Optional[Any]       				=  "up"







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						return super().get_dummy_input(include_res_hidden_states_tuple=__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						__magic_name__							=    [0.0361, 0.0617, 0.2787, -0.0350, 0.0342, 0.3421, -0.0843, 0.0913, 0.3015]
						super().test_output(__UpperCamelCase      )




class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   List[str]       				=  UpDecoderBlockaD  # noqa F405
	_lowercase  :   List[str]       				=  "up"







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						return super().get_dummy_input(include_temb=__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						__magic_name__							=    {'in_channels': 32, 'out_channels': 32}

						__magic_name__							=    self.dummy_input
						return init_dict, inputs_dict







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    [0.4404, 0.1998, -0.9886, -0.3320, -0.3128, -0.7034, -0.6955, -0.2338, -0.3137]
						super().test_output(__UpperCamelCase      )




class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   Optional[Any]       				=  AttnUpDecoderBlockaD  # noqa F405
	_lowercase  :   Any       				=  "up"







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						return super().get_dummy_input(include_temb=__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





						__magic_name__							=    {'in_channels': 32, 'out_channels': 32}

						__magic_name__							=    self.dummy_input
						return init_dict, inputs_dict







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						__magic_name__							=    [0.6738, 0.4491, 0.1055, 1.0710, 0.7316, 0.3339, 0.3352, 0.1023, 0.3568]
						super().test_output(__UpperCamelCase      )




