





import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


lowercase__   :				int      =   logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

lowercase__   :				Optional[int]      =   {
    '''base''': AutoModel,
    '''sequence-classification''': AutoModelForSequenceClassification,
    '''question-answering''': AutoModelForQuestionAnswering,
    '''pretraining''': AutoModelForPreTraining,
    '''token-classification''': AutoModelForTokenClassification,
    '''language-modeling''': AutoModelWithLMHead,
    '''summarization''': AutoModelForSeqaSeqLM,
    '''translation''': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
lowercase__   :				int      =   {
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
lowercase__   :				Optional[Any]      =   sorted(arg_to_scheduler.keys())
lowercase__   :				List[str]      =   '''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''


class 			SCREAMING_SNAKE_CASE   (pl.LightningModule							):


     def __init__(						self						,     _UpperCAmelCase						,     _UpperCAmelCase=None						,     _UpperCAmelCase="base"						,     _UpperCAmelCase=None						,     _UpperCAmelCase=None						,     _UpperCAmelCase=None						,     **_UpperCAmelCase						,     ):

          '''simple docstring'''






          super().__init__()
          # TODO: move to self.save_hyperparameters()
          # self.save_hyperparameters()
          # can also expand arguments into trainer signature for easier reading

          self.save_hyperparameters(lowerCAmelCase__)
          __A						:			List[Any]								=	0
          __A						:			str								=	Path(self.hparams.output_dir)
          __A						:			str								=	self.hparams.cache_dir if self.hparams.cache_dir else None
          if config is None:
               __A						:			Any								=	AutoConfig.from_pretrained(
                   self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path						,     **({'num_labels': num_labels} if num_labels is not None else {})						,     cache_dir=lowerCAmelCase__						,     **lowerCAmelCase__						,     )
          else:
               __A						:			PretrainedConfig								=	config

          __A						:			Tuple								=	("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
          for p in extra_model_params:
               if getattr(self.hparams						,     lowerCAmelCase__						,     lowerCAmelCase__):
                    assert hasattr(self.config						,     lowerCAmelCase__), F'model config doesn\'t have a `{p}` attribute'
                    setattr(self.config						,     lowerCAmelCase__						,     getattr(self.hparams						,     lowerCAmelCase__))

          if tokenizer is None:
               __A						:			Union[str, Any]								=	AutoTokenizer.from_pretrained(
                   self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path						,     cache_dir=lowerCAmelCase__						,     )
          else:
               __A						:			PreTrainedTokenizer								=	tokenizer
          __A						:			Any								=	MODEL_MODES[mode]
          if model is None:
               __A						:			int								=	self.model_type.from_pretrained(
                   self.hparams.model_name_or_path						,     from_tf=bool('.ckpt' in self.hparams.model_name_or_path)						,     config=self.config						,     cache_dir=lowerCAmelCase__						,     )
          else:
               __A						:			List[str]								=	model


     def 							SCREAMING_SNAKE_CASE  (						self						,     *_UpperCAmelCase						,     **_UpperCAmelCase):

          '''simple docstring'''






          __A						:			str								=	self.model_type.from_pretrained(*lowerCAmelCase__						,     **lowerCAmelCase__)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Any								=	arg_to_scheduler[self.hparams.lr_scheduler]
          __A						:			Optional[Any]								=	get_schedule_func(
              self.opt						,     num_warmup_steps=self.hparams.warmup_steps						,     num_training_steps=self.total_steps())
          __A						:			List[Any]								=	{"scheduler": scheduler, "interval": "step", "frequency": 1}
          return scheduler


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			List[str]								=	self.model
          __A						:			Dict								=	["bias", "LayerNorm.weight"]
          __A						:			List[str]								=	[
              {
                  "params": [
                      p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)
                  ],  # check this named paramters
                  "weight_decay": self.hparams.weight_decay,
              },
              {
                  "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
                  "weight_decay": 0.0,
              },
          ]
          if self.hparams.adafactor:
               __A						:			Dict								=	Adafactor(
                   lowerCAmelCase__						,     lr=self.hparams.learning_rate						,     scale_parameter=lowerCAmelCase__						,     relative_step=lowerCAmelCase__)

          else:
               __A						:			Optional[int]								=	AdamW(
                   lowerCAmelCase__						,     lr=self.hparams.learning_rate						,     eps=self.hparams.adam_epsilon)
          __A						:			Optional[Any]								=	optimizer

          __A						:			Union[str, Any]								=	self.get_lr_scheduler()

          return [optimizer], [scheduler]


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          return self.validation_step(lowerCAmelCase__						,     lowerCAmelCase__)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          return self.validation_end(lowerCAmelCase__)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Union[str, Any]								=	max(1						,     self.hparams.gpus)  # TODO: consider num_tpu_cores
          __A						:			Union[str, Any]								=	self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
          return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          if stage == "test":
               __A						:			int								=	len(self.test_dataloader().dataset)
          else:
               __A						:			Optional[Any]								=	self.get_dataloader('train'						,     self.hparams.train_batch_size						,     shuffle=lowerCAmelCase__)
               __A						:			Union[str, Any]								=	len(self.train_dataloader().dataset)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase = False):

          '''simple docstring'''






          raise NotImplementedError('You must implement this for your task')


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return self.train_loader


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return self.get_dataloader('dev'						,     self.hparams.eval_batch_size						,     shuffle=lowerCAmelCase__)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return self.get_dataloader('test'						,     self.hparams.eval_batch_size						,     shuffle=lowerCAmelCase__)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          return os.path.join(
              self.hparams.data_dir						,     'cached_{}_{}_{}'.format(
                  lowerCAmelCase__						,     list(filter(lowerCAmelCase__						,     self.hparams.model_name_or_path.split('/'))).pop()						,     str(self.hparams.max_seq_length)						,     )						,     )


     @pl.utilities.rank_zero_only
     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Tuple								=	self.output_dir.joinpath('best_tfmr')
          __A						:			str								=	self.step_count
          self.model.save_pretrained(lowerCAmelCase__)
          self.tokenizer.save_pretrained(lowerCAmelCase__)


     @staticmethod
     def 							SCREAMING_SNAKE_CASE  (						_UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          parser.add_argument(
              '--model_name_or_path'						,     default=lowerCAmelCase__						,     type=lowerCAmelCase__						,     required=lowerCAmelCase__						,     help='Path to pretrained model or model identifier from huggingface.co/models'						,     )
          parser.add_argument(
              '--config_name'						,     default=''						,     type=lowerCAmelCase__						,     help='Pretrained config name or path if not the same as model_name')
          parser.add_argument(
              '--tokenizer_name'						,     default=lowerCAmelCase__						,     type=lowerCAmelCase__						,     help='Pretrained tokenizer name or path if not the same as model_name'						,     )
          parser.add_argument(
              '--cache_dir'						,     default=str(Path(lowerCAmelCase__).parent / 'test_run' / 'cache')						,     type=lowerCAmelCase__						,     help='Where do you want to store the pre-trained models downloaded from huggingface.co'						,     )
          parser.add_argument(
              '--encoder_layerdrop'						,     type=lowerCAmelCase__						,     help='Encoder layer dropout probability (Optional). Goes into model.config'						,     )
          parser.add_argument(
              '--decoder_layerdrop'						,     type=lowerCAmelCase__						,     help='Decoder layer dropout probability (Optional). Goes into model.config'						,     )
          parser.add_argument(
              '--dropout'						,     type=lowerCAmelCase__						,     help='Dropout probability (Optional). Goes into model.config'						,     )
          parser.add_argument(
              '--attention_dropout'						,     type=lowerCAmelCase__						,     help='Attention dropout probability (Optional). Goes into model.config'						,     )
          parser.add_argument('--learning_rate'						,     default=5e-5						,     type=lowerCAmelCase__						,     help='The initial learning rate for Adam.')
          parser.add_argument(
              '--lr_scheduler'						,     default='linear'						,     choices=lowerCAmelCase__						,     metavar=lowerCAmelCase__						,     type=lowerCAmelCase__						,     help='Learning rate scheduler'						,     )
          parser.add_argument('--weight_decay'						,     default=0.0						,     type=lowerCAmelCase__						,     help='Weight decay if we apply some.')
          parser.add_argument('--adam_epsilon'						,     default=1e-8						,     type=lowerCAmelCase__						,     help='Epsilon for Adam optimizer.')
          parser.add_argument('--warmup_steps'						,     default=0						,     type=lowerCAmelCase__						,     help='Linear warmup over warmup_steps.')
          parser.add_argument('--num_workers'						,     default=4						,     type=lowerCAmelCase__						,     help='kwarg passed to DataLoader')
          parser.add_argument('--num_train_epochs'						,     dest='max_epochs'						,     default=3						,     type=lowerCAmelCase__)
          parser.add_argument('--train_batch_size'						,     default=32						,     type=lowerCAmelCase__)
          parser.add_argument('--eval_batch_size'						,     default=32						,     type=lowerCAmelCase__)
          parser.add_argument('--adafactor'						,     action='store_true')




class 			SCREAMING_SNAKE_CASE   (pl.Callback							):


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          if (
              trainer.is_global_zero and trainer.global_rank == 0
          ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
               pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.




class 			SCREAMING_SNAKE_CASE   (pl.Callback							):


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          for name, param in pl_module.model.rag.named_parameters():
               if param.grad is None:
                    print(lowerCAmelCase__)




class 			SCREAMING_SNAKE_CASE   (pl.Callback							):


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Any								=	trainer.lr_schedulers[0]["scheduler"]
          __A						:			Union[str, Any]								=	{F'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr())}
          pl_module.logger.log_metrics(lowerCAmelCase__)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          rank_zero_info('***** Validation results *****')
          __A						:			Dict								=	trainer.callback_metrics
          # Log results
          for key in sorted(lowerCAmelCase__):
               if key not in ["log", "progress_bar"]:
                    rank_zero_info('{} = {}\n'.format(lowerCAmelCase__						,     str(metrics[key])))


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          rank_zero_info('***** Test results *****')
          __A						:			Any								=	trainer.callback_metrics
          # Log and save results to file
          __A						:			Union[str, Any]								=	os.path.join(pl_module.hparams.output_dir						,     'test_results.txt')
          with open(lowerCAmelCase__						,     'w') as writer:
               for key in sorted(lowerCAmelCase__):
                    if key not in ["log", "progress_bar"]:
                         rank_zero_info('{} = {}\n'.format(lowerCAmelCase__						,     str(metrics[key])))
                         writer.write('{} = {}\n'.format(lowerCAmelCase__						,     str(metrics[key])))






def 	_lowerCAmelCase      (  __snake_case  :			Tuple			,							__snake_case  :			Tuple      )					->					None:
     parser.add_argument(
         '--output_dir'			,							default=str(Path(_lowercase      ).parent / 'test_run' / 'model_checkpoints'      )			,							type=_lowercase			,							help='The output directory where the model predictions and checkpoints will be written.'			,							)
     parser.add_argument(
         '--fp16'			,							action='store_true'			,							help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'			,							)

     parser.add_argument(
         '--fp16_opt_level'			,							type=_lowercase			,							default='O2'			,							help=(
             'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
             'See details at https://nvidia.github.io/apex/amp.html'
         )			,							)
     parser.add_argument('--n_tpu_cores'			,							dest='tpu_cores'			,							type=_lowercase      )
     parser.add_argument('--max_grad_norm'			,							dest='gradient_clip_val'			,							default=1.0			,							type=_lowercase			,							help='Max gradient norm'      )
     parser.add_argument('--do_train'			,							action='store_true'			,							help='Whether to run training.'      )
     parser.add_argument('--do_predict'			,							action='store_true'			,							help='Whether to run predictions on the test set.'      )
     parser.add_argument(
         '--gradient_accumulation_steps'			,							dest='accumulate_grad_batches'			,							type=_lowercase			,							default=1			,							help='Number of updates steps to accumulate before performing a backward/update pass.'			,							)
     parser.add_argument('--seed'			,							type=_lowercase			,							default=42			,							help='random seed for initialization'      )
     parser.add_argument(
         '--data_dir'			,							default=str(Path(_lowercase      ).parent / 'test_run' / 'dummy-train-data'      )			,							type=_lowercase			,							help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'			,							)







def 	_lowerCAmelCase      (  __snake_case  :			BaseTransformer			,							__snake_case  :			argparse.Namespace			,							__snake_case  :			Tuple=None			,							__snake_case  :			int=True			,							__snake_case  :			List[str]=[]			,							__snake_case  :			Union[str, Any]=None			,							__snake_case  :			Any=None			,							**__snake_case  :			Any			,							)					->					Tuple:
     pl.seed_everything(args.seed      )

     # init model
     __A						:			int								=	Path(model.hparams.output_dir      )
     odir.mkdir(exist_ok=_lowercase      )

     # add custom checkpoints
     if checkpoint_callback is None:
          __A						:			Optional[int]								=	pl.callbacks.ModelCheckpoint(
              filepath=args.output_dir			,							prefix='checkpoint'			,							monitor='val_loss'			,							mode='min'			,							save_top_k=1      )
     if early_stopping_callback:
          extra_callbacks.append(_lowercase      )
     if logging_callback is None:
          __A						:			str								=	LoggingCallback()

     __A						:			Optional[Any]								=	{}

     if args.fpaa:
          __A						:			Dict								=	16

     if args.gpus > 1:
          __A						:			Dict								=	"auto"
          __A						:			Tuple								=	"ddp"

     __A						:			Optional[int]								=	args.accumulate_grad_batches
     __A						:			Tuple								=	None
     __A						:			str								=	"auto"

     __A						:			Union[str, Any]								=	pl.Trainer.from_argparse_args(
         _lowercase			,							weights_summary=_lowercase			,							callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]			,							logger=_lowercase			,							val_check_interval=1			,							num_sanity_val_steps=2			,							**_lowercase			,							)

     if args.do_train:
          trainer.fit(_lowercase      )

     else:
          print('RAG modeling tests with new set functions successfuly executed!'      )
     return trainer






'''simple docstring'''




import random

import torch
from huggingface_hub import HfApi

from diffusers import UNetaDModel


lowercase__   :				Optional[int]      =   HfApi()

lowercase__   :				Dict      =   {}
# fmt: off
lowercase__   :				List[str]      =   torch.tensor([
    -0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,
    1.2342, -2.2485, 0.4636, 0.8076, -0.7991, 0.3969, 0.8498, 0.9189,
    -1.8887, -3.3522, 0.7639, 0.2040, 0.6271, -2.7148, -1.6316, 3.0839,
    0.3186, 0.2721, -0.9759, -1.2461, 2.6257, 1.3557
])
lowercase__   :				Tuple      =   torch.tensor([
    -2.3639, -2.5344, 0.0054, -0.6674, 1.5990, 1.0158, 0.3124, -2.1436,
    1.8795, -2.5429, -0.1566, -0.3973, 1.2490, 2.6447, 1.2283, -0.5208,
    -2.8154, -3.5119, 2.3838, 1.2033, 1.7201, -2.1256, -1.4576, 2.7948,
    2.4204, -0.9752, -1.2546, 0.8027, 3.2758, 3.1365
])
lowercase__   :				Optional[Any]      =   torch.tensor([
    -0.6531, -0.6891, -0.3172, -0.5375, -0.9140, -0.5367, -0.1175, -0.7869,
    -0.3808, -0.4513, -0.2098, -0.0083, 0.3183, 0.5140, 0.2247, -0.1304,
    -0.1302, -0.2802, -0.2084, -0.2025, -0.4967, -0.4873, -0.0861, 0.6925,
    0.0250, 0.1290, -0.1543, 0.6316, 1.0460, 1.4943
])
lowercase__   :				List[Any]      =   torch.tensor([
    0.0911, 0.1107, 0.0182, 0.0435, -0.0805, -0.0608, 0.0381, 0.2172,
    -0.0280, 0.1327, -0.0299, -0.0255, -0.0050, -0.1170, -0.1046, 0.0309,
    0.1367, 0.1728, -0.0533, -0.0748, -0.0534, 0.1624, 0.0384, -0.1805,
    -0.0707, 0.0642, 0.0220, -0.0134, -0.1333, -0.1505
])
lowercase__   :				Dict      =   torch.tensor([
    0.1321, 0.1337, 0.0440, 0.0622, -0.0591, -0.0370, 0.0503, 0.2133,
    -0.0177, 0.1415, -0.0116, -0.0112, 0.0044, -0.0980, -0.0789, 0.0395,
    0.1502, 0.1785, -0.0488, -0.0514, -0.0404, 0.1539, 0.0454, -0.1559,
    -0.0665, 0.0659, 0.0383, -0.0005, -0.1266, -0.1386
])
lowercase__   :				Optional[int]      =   torch.tensor([
    0.1154, 0.1218, 0.0307, 0.0526, -0.0711, -0.0541, 0.0366, 0.2078,
    -0.0267, 0.1317, -0.0226, -0.0193, -0.0014, -0.1055, -0.0902, 0.0330,
    0.1391, 0.1709, -0.0562, -0.0693, -0.0560, 0.1482, 0.0381, -0.1683,
    -0.0681, 0.0661, 0.0331, -0.0046, -0.1268, -0.1431
])
lowercase__   :				List[Any]      =   torch.tensor([
    0.1192, 0.1240, 0.0414, 0.0606, -0.0557, -0.0412, 0.0430, 0.2042,
    -0.0200, 0.1385, -0.0115, -0.0132, 0.0017, -0.0965, -0.0802, 0.0398,
    0.1433, 0.1747, -0.0458, -0.0533, -0.0407, 0.1545, 0.0419, -0.1574,
    -0.0645, 0.0626, 0.0341, -0.0010, -0.1199, -0.1390
])
lowercase__   :				List[str]      =   torch.tensor([
    0.1075, 0.1074, 0.0205, 0.0431, -0.0774, -0.0607, 0.0298, 0.2042,
    -0.0320, 0.1267, -0.0281, -0.0250, -0.0064, -0.1091, -0.0946, 0.0290,
    0.1328, 0.1650, -0.0580, -0.0738, -0.0586, 0.1440, 0.0337, -0.1746,
    -0.0712, 0.0605, 0.0250, -0.0099, -0.1316, -0.1473
])
lowercase__   :				Dict      =   torch.tensor([
    -1.4572, -2.0481, -0.0414, -0.6005, 1.4136, 0.5848, 0.4028, -2.7330,
    1.2212, -2.1228, 0.2155, 0.4039, 0.7662, 2.0535, 0.7477, -0.3243,
    -2.1758, -2.7648, 1.6947, 0.7026, 1.2338, -1.6078, -0.8682, 2.2810,
    1.8574, -0.5718, -0.5586, -0.0186, 2.3415, 2.1251])
lowercase__   :				Optional[int]      =   torch.tensor([
    -1.3690, -1.9720, -0.4090, -0.6966, 1.4660, 0.9938, -0.1385, -2.7324,
    0.7736, -1.8917, 0.2923, 0.4293, 0.1693, 1.4112, 1.1887, -0.3181,
    -2.2160, -2.6381, 1.3170, 0.8163, 0.9240, -1.6544, -0.6099, 2.5259,
    1.6430, -0.9090, -0.9392, -0.0126, 2.4268, 2.3266
])
lowercase__   :				List[str]      =   torch.tensor([
    -1.3525, -1.9628, -0.3956, -0.6860, 1.4664, 1.0014, -0.1259, -2.7212,
    0.7772, -1.8811, 0.2996, 0.4388, 0.1704, 1.4029, 1.1701, -0.3027,
    -2.2053, -2.6287, 1.3350, 0.8131, 0.9274, -1.6292, -0.6098, 2.5131,
    1.6505, -0.8958, -0.9298, -0.0151, 2.4257, 2.3355
])
lowercase__   :				Optional[int]      =   torch.tensor([
    -2.0585, -2.7897, -0.2850, -0.8940, 1.9052, 0.5702, 0.6345, -3.8959,
    1.5932, -3.2319, 0.1974, 0.0287, 1.7566, 2.6543, 0.8387, -0.5351,
    -3.2736, -4.3375, 2.9029, 1.6390, 1.4640, -2.1701, -1.9013, 2.9341,
    3.4981, -0.6255, -1.1644, -0.1591, 3.7097, 3.2066
])
lowercase__   :				int      =   torch.tensor([
    -2.3139, -2.5594, -0.0197, -0.6785, 1.7001, 1.1606, 0.3075, -2.1740,
    1.8071, -2.5630, -0.0926, -0.3811, 1.2116, 2.6246, 1.2731, -0.5398,
    -2.8153, -3.6140, 2.3893, 1.3262, 1.6258, -2.1856, -1.3267, 2.8395,
    2.3779, -1.0623, -1.2468, 0.8959, 3.3367, 3.2243
])
lowercase__   :				int      =   torch.tensor([
    -2.0628, -2.7667, -0.2089, -0.8263, 2.0539, 0.5992, 0.6495, -3.8336,
    1.6025, -3.2817, 0.1721, -0.0633, 1.7516, 2.7039, 0.8100, -0.5908,
    -3.2113, -4.4343, 2.9257, 1.3632, 1.5562, -2.1489, -1.9894, 3.0560,
    3.3396, -0.7328, -1.0417, 0.0383, 3.7093, 3.2343
])
lowercase__   :				List[Any]      =   torch.tensor([
    -1.4574, -2.0569, -0.0473, -0.6117, 1.4018, 0.5769, 0.4129, -2.7344,
    1.2241, -2.1397, 0.2000, 0.3937, 0.7616, 2.0453, 0.7324, -0.3391,
    -2.1746, -2.7744, 1.6963, 0.6921, 1.2187, -1.6172, -0.8877, 2.2439,
    1.8471, -0.5839, -0.5605, -0.0464, 2.3250, 2.1219
])
# fmt: on

lowercase__   :				str      =   api.list_models(filter='''diffusers''')
for mod in models:
  if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
    lowercase__   :				int      =   '''/home/patrick/google_checkpoints/''' + mod.modelId.split('''/''')[-1]

    print(f"""Started running {mod.modelId}!!!""")

    if mod.modelId.startswith('''CompVis'''):
      lowercase__   :				Optional[Any]      =   UNetaDModel.from_pretrained(local_checkpoint, subfolder='''unet''')
    else:
      lowercase__   :				Tuple      =   UNetaDModel.from_pretrained(local_checkpoint)

    torch.manual_seed(0)
    random.seed(0)

    lowercase__   :				List[str]      =   torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
    lowercase__   :				int      =   torch.tensor([10] * noise.shape[0])
    with torch.no_grad():
      lowercase__   :				Tuple      =   model(noise, time_step).sample

    assert torch.allclose(
        logits[0, 0, 0, :30], results['''_'''.join('''_'''.join(mod.modelId.split('''/''')).split('''-'''))], atol=1e-3
    )
    print(f"""{mod.modelId} has passed successfully!!!""")