

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


lowerCAmelCase							:  Union[str, Any]   	=						logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

lowerCAmelCase							:  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
lowerCAmelCase							:  List[Any]   	=						{
    """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
}
lowerCAmelCase							:  List[Any]   	=						sorted(arg_to_scheduler.keys())
lowerCAmelCase							:  Optional[Any]   	=						"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""



class   __lowercase				( pl.LightningModule	):




			"""simple docstring"""







			def __init__( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     argparse.Namespace   ,						lowerCAmelCase__  :     int=None   ,						lowerCAmelCase__  :     Tuple="base"   ,						lowerCAmelCase__  :     Tuple=None   ,						lowerCAmelCase__  :     Union[str, Any]=None   ,						lowerCAmelCase__  :     int=None   ,						**lowerCAmelCase__  :     Union[str, Any]   ,						):
									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__)
									SCREAMING_SNAKE_CASE_: Any  		=       0
									SCREAMING_SNAKE_CASE_: List[Any]  		=       Path(self.hparams.output_dir)
									SCREAMING_SNAKE_CASE_: str  		=       self.hparams.cache_dir if self.hparams.cache_dir else None
									if config is None:
															SCREAMING_SNAKE_CASE_: int  		=       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:
															SCREAMING_SNAKE_CASE_: PretrainedConfig  		=       config

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       ("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:
															SCREAMING_SNAKE_CASE_: str  		=       AutoTokenizer.from_pretrained(
															    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path   ,						cache_dir=lowerCAmelCase__   ,						)
									else:
															SCREAMING_SNAKE_CASE_: PreTrainedTokenizer  		=       tokenizer
									SCREAMING_SNAKE_CASE_: str  		=       MODEL_MODES[mode]
									if model is None:
															SCREAMING_SNAKE_CASE_: Any  		=       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:
															SCREAMING_SNAKE_CASE_: List[str]  		=       model



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						*lowerCAmelCase__  :     List[Any]   ,						**lowerCAmelCase__  :     Any):
									SCREAMING_SNAKE_CASE_: List[str]  		=       self.model_type.from_pretrained(*lowerCAmelCase__   ,						**lowerCAmelCase__)



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



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: int  		=       self.model
									SCREAMING_SNAKE_CASE_: List[str]  		=       ["bias", "LayerNorm.weight"]
									SCREAMING_SNAKE_CASE_: 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:
															SCREAMING_SNAKE_CASE_: Tuple  		=       Adafactor(
															    lowerCAmelCase__   ,						lr=self.hparams.learning_rate   ,						scale_parameter=lowerCAmelCase__   ,						relative_step=lowerCAmelCase__)

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

									SCREAMING_SNAKE_CASE_: Tuple  		=       self.get_lr_scheduler()

									return [optimizer], [scheduler]



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     str):
									return self.validation_step(lowerCAmelCase__   ,						lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     List[str]):
									return self.validation_end(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: Any  		=       max(1   ,						self.hparams.gpus)  # TODO: consider num_tpu_cores
									SCREAMING_SNAKE_CASE_: List[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  :     Tuple   ,						lowerCAmelCase__  :     Union[str, Any]):
									if stage == "test":
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       len(self.test_dataloader().dataset)
									else:
															SCREAMING_SNAKE_CASE_: Any  		=       self.get_dataloader("train"   ,						self.hparams.train_batch_size   ,						shuffle=lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       len(self.train_dataloader().dataset)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]   ,						lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     bool = False):
									raise NotImplementedError("You must implement this for your task")



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									return self.train_loader



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									return self.get_dataloader("dev"   ,						self.hparams.eval_batch_size   ,						shuffle=lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									return self.get_dataloader("test"   ,						self.hparams.eval_batch_size   ,						shuffle=lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     Any):
									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  :     List[Any]   ,						lowerCAmelCase__  :     Dict[str, Any]):
									SCREAMING_SNAKE_CASE_: List[str]  		=       self.output_dir.joinpath("best_tfmr")
									SCREAMING_SNAKE_CASE_: Tuple  		=       self.step_count
									self.model.save_pretrained(lowerCAmelCase__)
									self.tokenizer.save_pretrained(lowerCAmelCase__)




			@staticmethod
			def        _SCREAMING_SNAKE_CASE			( lowerCAmelCase__  :     Optional[Any]   ,						lowerCAmelCase__  :     Optional[Any]):
									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   __lowercase				( pl.Callback	):




			"""simple docstring"""







			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     Tuple   ,						lowerCAmelCase__  :     str):
									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   __lowercase				( pl.Callback	):




			"""simple docstring"""







			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     int):
									# print(pl_module.model.rag)
									for name, param in pl_module.model.rag.named_parameters():
															if param.grad is None:
																					print(lowerCAmelCase__)







class   __lowercase				( pl.Callback	):




			"""simple docstring"""







			def        _SCREAMING_SNAKE_CASE			( self  :     int   ,						lowerCAmelCase__  :     Dict   ,						lowerCAmelCase__  :     Optional[Any]):
									SCREAMING_SNAKE_CASE_: List[str]  		=       trainer.lr_schedulers[0]["scheduler"]
									SCREAMING_SNAKE_CASE_: str  		=       {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  :     Optional[int]   ,						lowerCAmelCase__  :     pl.Trainer   ,						lowerCAmelCase__  :     pl.LightningModule):
									rank_zero_info("***** Validation results *****")
									SCREAMING_SNAKE_CASE_: Tuple  		=       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  :     int   ,						lowerCAmelCase__  :     pl.Trainer   ,						lowerCAmelCase__  :     pl.LightningModule):
									rank_zero_info("***** Test results *****")
									SCREAMING_SNAKE_CASE_: Dict  		=       trainer.callback_metrics
									# Log and save results to file
									SCREAMING_SNAKE_CASE_: 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 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						#  To allow all pl args uncomment the following line
						#  parser = pl.Trainer.add_argparse_args(parser)
						parser.add_argument(
						    "--output_dir"				,	default=str(Path(_UpperCAmelCase    ).parent / "test_run" / "model_checkpoints"    )				,	type=_UpperCAmelCase				,	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=_UpperCAmelCase				,	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=_UpperCAmelCase    )
						parser.add_argument("--max_grad_norm"				,	dest="gradient_clip_val"				,	default=1.0				,	type=_UpperCAmelCase				,	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=_UpperCAmelCase				,	default=1				,	help="Number of updates steps to accumulate before performing a backward/update pass."				,	)
						parser.add_argument("--seed"				,	type=_UpperCAmelCase				,	default=42				,	help="random seed for initialization"    )
						parser.add_argument(
						    "--data_dir"				,	default=str(Path(_UpperCAmelCase    ).parent / "test_run" / "dummy-train-data"    )				,	type=_UpperCAmelCase				,	help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."				,	)







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase=None				,	_UpperCAmelCase=True				,	_UpperCAmelCase=[]				,	_UpperCAmelCase=None				,	_UpperCAmelCase=None				,	**_UpperCAmelCase				,	):
						pl.seed_everything(args.seed    )

						# init model
						SCREAMING_SNAKE_CASE_: List[str]  		=       Path(model.hparams.output_dir    )
						odir.mkdir(exist_ok=_UpperCAmelCase    )

						# add custom checkpoints
						if checkpoint_callback is None:
												SCREAMING_SNAKE_CASE_: 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(_UpperCAmelCase    )
						if logging_callback is None:
												SCREAMING_SNAKE_CASE_: List[str]  		=       LoggingCallback()

						SCREAMING_SNAKE_CASE_: int  		=       {}

						if args.fpaa:
												SCREAMING_SNAKE_CASE_: List[Any]  		=       16

						if args.gpus > 1:
												SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       "auto"
												SCREAMING_SNAKE_CASE_: int  		=       "ddp"

						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       args.accumulate_grad_batches
						SCREAMING_SNAKE_CASE_: Any  		=       None
						SCREAMING_SNAKE_CASE_: int  		=       "auto"

						SCREAMING_SNAKE_CASE_: List[str]  		=       pl.Trainer.from_argparse_args(
						    _UpperCAmelCase				,	weights_summary=_UpperCAmelCase				,	callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]				,	logger=_UpperCAmelCase				,	val_check_interval=1				,	num_sanity_val_steps=2				,	**_UpperCAmelCase				,	)

						if args.do_train:
												trainer.fit(_UpperCAmelCase    )

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









import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
						import torch



@is_pipeline_test
class   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			_UpperCAmelCase   :							List[Any]										=     MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
			_UpperCAmelCase   :							str										=     TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



			def        _SCREAMING_SNAKE_CASE			( self  :     Any   ,						lowerCAmelCase__  :     List[str]   ,						lowerCAmelCase__  :     List[str]   ,						lowerCAmelCase__  :     Dict):
									SCREAMING_SNAKE_CASE_: Any  		=       TextaTextGenerationPipeline(model=lowerCAmelCase__   ,						tokenizer=lowerCAmelCase__)
									return generator, ["Something to write", "Something else"]



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     Dict   ,						lowerCAmelCase__  :     Any):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       generator("Something there")
									self.assertEqual(lowerCAmelCase__   ,						[{"generated_text": ANY(lowerCAmelCase__)}])
									# These are encoder decoder, they don't just append to incoming string
									self.assertFalse(outputs[0]["generated_text"].startswith("Something there"))

									SCREAMING_SNAKE_CASE_: List[Any]  		=       generator(["This is great !", "Something else"]   ,						num_return_sequences=2   ,						do_sample=lowerCAmelCase__)
									self.assertEqual(
									    lowerCAmelCase__   ,						[
									        [{"generated_text": ANY(lowerCAmelCase__)}, {"generated_text": ANY(lowerCAmelCase__)}],
									        [{"generated_text": ANY(lowerCAmelCase__)}, {"generated_text": ANY(lowerCAmelCase__)}],
									    ]   ,						)

									SCREAMING_SNAKE_CASE_: Dict  		=       generator(
									    ["This is great !", "Something else"]   ,						num_return_sequences=2   ,						batch_size=2   ,						do_sample=lowerCAmelCase__)
									self.assertEqual(
									    lowerCAmelCase__   ,						[
									        [{"generated_text": ANY(lowerCAmelCase__)}, {"generated_text": ANY(lowerCAmelCase__)}],
									        [{"generated_text": ANY(lowerCAmelCase__)}, {"generated_text": ANY(lowerCAmelCase__)}],
									    ]   ,						)

									with self.assertRaises(lowerCAmelCase__):
															generator(4)



			@require_torch
			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       pipeline("text2text-generation"   ,						model="patrickvonplaten/t5-tiny-random"   ,						framework="pt")
									# do_sample=False necessary for reproducibility
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       generator("Something there"   ,						do_sample=lowerCAmelCase__)
									self.assertEqual(lowerCAmelCase__   ,						[{"generated_text": ""}])

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       3
									SCREAMING_SNAKE_CASE_: Any  		=       generator(
									    "Something there"   ,						num_return_sequences=lowerCAmelCase__   ,						num_beams=lowerCAmelCase__   ,						)
									SCREAMING_SNAKE_CASE_: Any  		=       [
									    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide Beide"},
									    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide"},
									    {"generated_text": ""},
									]
									self.assertEqual(lowerCAmelCase__   ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: int  		=       generator("This is a test"   ,						do_sample=lowerCAmelCase__   ,						num_return_sequences=2   ,						return_tensors=lowerCAmelCase__)
									self.assertEqual(
									    lowerCAmelCase__   ,						[
									        {"generated_token_ids": ANY(torch.Tensor)},
									        {"generated_token_ids": ANY(torch.Tensor)},
									    ]   ,						)
									SCREAMING_SNAKE_CASE_: str  		=       generator.model.config.eos_token_id
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       "<pad>"
									SCREAMING_SNAKE_CASE_: Tuple  		=       generator(
									    ["This is a test", "This is a second test"]   ,						do_sample=lowerCAmelCase__   ,						num_return_sequences=2   ,						batch_size=2   ,						return_tensors=lowerCAmelCase__   ,						)
									self.assertEqual(
									    lowerCAmelCase__   ,						[
									        [
									            {"generated_token_ids": ANY(torch.Tensor)},
									            {"generated_token_ids": ANY(torch.Tensor)},
									        ],
									        [
									            {"generated_token_ids": ANY(torch.Tensor)},
									            {"generated_token_ids": ANY(torch.Tensor)},
									        ],
									    ]   ,						)




			@require_tf
			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       pipeline("text2text-generation"   ,						model="patrickvonplaten/t5-tiny-random"   ,						framework="tf")
									# do_sample=False necessary for reproducibility
									SCREAMING_SNAKE_CASE_: List[Any]  		=       generator("Something there"   ,						do_sample=lowerCAmelCase__)
									self.assertEqual(lowerCAmelCase__   ,						[{"generated_text": ""}])






