

"""simple docstring"""


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


__A		     =    logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

__A		     =    {
    "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
__A		     =    {
    "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
}
__A		     =    sorted(arg_to_scheduler.keys())
__A		     =    "{" + ", ".join(arg_to_scheduler_choices) + "}"





class 					snake_case			(       pl.LightningModule						):

				def __init__(			self							:       Dict  ,						UpperCamelCase__							:       argparse.Namespace  ,						UpperCamelCase__							:       List[str]=None  ,						UpperCamelCase__							:       Optional[Any]="base"  ,						UpperCamelCase__							:       int=None  ,						UpperCamelCase__							:       str=None  ,						UpperCamelCase__							:       Optional[int]=None  ,						**UpperCamelCase__							:       Union[str, Any]  ,						)->     int:



						'''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(UpperCamelCase__)
						__lowerCAmelCase: Any					   =    0
						__lowerCAmelCase: str					   =    Path(self.hparams.output_dir)
						__lowerCAmelCase: List[str]					   =    self.hparams.cache_dir if self.hparams.cache_dir else None
						if config is None:
								__lowerCAmelCase: Optional[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=UpperCamelCase__  ,						**UpperCamelCase__  ,						)
						else:
								__lowerCAmelCase: PretrainedConfig					   =    config

						__lowerCAmelCase: Union[str, Any]					   =    ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
						for p in extra_model_params:
								if getattr(self.hparams  ,						UpperCamelCase__  ,						UpperCamelCase__):
										assert hasattr(self.config  ,						UpperCamelCase__), f"model config doesn't have a `{p}` attribute"
										setattr(self.config  ,						UpperCamelCase__  ,						getattr(self.hparams  ,						UpperCamelCase__))

						if tokenizer is None:
								__lowerCAmelCase: Union[str, Any]					   =    AutoTokenizer.from_pretrained(
								    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path  ,						cache_dir=UpperCamelCase__  ,						)
						else:
								__lowerCAmelCase: PreTrainedTokenizer					   =    tokenizer
						__lowerCAmelCase: str					   =    MODEL_MODES[mode]
						if model is None:
								__lowerCAmelCase: 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=UpperCamelCase__  ,						)
						else:
								__lowerCAmelCase: Dict					   =    model

				def 							lowercase_  (			self							:       int  ,						*UpperCamelCase__							:       Dict  ,						**UpperCamelCase__							:       Union[str, Any])->     Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase: str					   =    self.model_type.from_pretrained(*UpperCamelCase__  ,						**UpperCamelCase__)

				def 							lowercase_  (			self							:       Dict)->     Dict:



						'''simple docstring'''


						__lowerCAmelCase: Union[str, Any]					   =    arg_to_scheduler[self.hparams.lr_scheduler]
						__lowerCAmelCase: Dict					   =    get_schedule_func(
						    self.opt  ,						num_warmup_steps=self.hparams.warmup_steps  ,						num_training_steps=self.total_steps())
						__lowerCAmelCase: Tuple					   =    {"scheduler": scheduler, "interval": "step", "frequency": 1}
						return scheduler

				def 							lowercase_  (			self							:       Optional[int])->     str:



						'''simple docstring'''


						__lowerCAmelCase: Union[str, Any]					   =    self.model
						__lowerCAmelCase: Optional[int]					   =    ["bias", "LayerNorm.weight"]
						__lowerCAmelCase: str					   =    [
						    {
						        "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
						        "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:
								__lowerCAmelCase: Union[str, Any]					   =    Adafactor(
								    UpperCamelCase__  ,						lr=self.hparams.learning_rate  ,						scale_parameter=UpperCamelCase__  ,						relative_step=UpperCamelCase__)

						else:
								__lowerCAmelCase: Any					   =    AdamW(
								    UpperCamelCase__  ,						lr=self.hparams.learning_rate  ,						eps=self.hparams.adam_epsilon)
						__lowerCAmelCase: str					   =    optimizer

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

						return [optimizer], [scheduler]

				def 							lowercase_  (			self							:       List[Any]  ,						UpperCamelCase__							:       List[Any]  ,						UpperCamelCase__							:       List[Any])->     List[str]:



						'''simple docstring'''


						return self.validation_step(UpperCamelCase__  ,						UpperCamelCase__)

				def 							lowercase_  (			self							:       Tuple  ,						UpperCamelCase__							:       Optional[int])->     Optional[Any]:



						'''simple docstring'''


						return self.validation_end(UpperCamelCase__)

				def 							lowercase_  (			self							:       int)->     int:



						'''simple docstring'''


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

				def 							lowercase_  (			self							:       Optional[int]  ,						UpperCamelCase__							:       Union[str, Any])->     str:



						'''simple docstring'''


						if stage == "test":
								__lowerCAmelCase: Optional[Any]					   =    len(self.test_dataloader().dataset)
						else:
								__lowerCAmelCase: Any					   =    self.get_dataloader("train"  ,						self.hparams.train_batch_size  ,						shuffle=UpperCamelCase__)
								__lowerCAmelCase: int					   =    len(self.train_dataloader().dataset)

				def 							lowercase_  (			self							:       int  ,						UpperCamelCase__							:       str  ,						UpperCamelCase__							:       int  ,						UpperCamelCase__							:       bool = False)->     Optional[int]:



						'''simple docstring'''


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

				def 							lowercase_  (			self							:       int)->     Any:



						'''simple docstring'''


						return self.train_loader

				def 							lowercase_  (			self							:       Union[str, Any])->     Tuple:



						'''simple docstring'''


						return self.get_dataloader("dev"  ,						self.hparams.eval_batch_size  ,						shuffle=UpperCamelCase__)

				def 							lowercase_  (			self							:       Optional[int])->     List[Any]:



						'''simple docstring'''


						return self.get_dataloader("test"  ,						self.hparams.eval_batch_size  ,						shuffle=UpperCamelCase__)

				def 							lowercase_  (			self							:       List[str]  ,						UpperCamelCase__							:       Any)->     Union[str, Any]:



						'''simple docstring'''


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

				@pl.utilities.rank_zero_only
				def 							lowercase_  (			self							:       Optional[Any]  ,						UpperCamelCase__							:       Dict[str, Any])->     None:



						'''simple docstring'''


						__lowerCAmelCase: List[str]					   =    self.output_dir.joinpath("best_tfmr")
						__lowerCAmelCase: Dict					   =    self.step_count
						self.model.save_pretrained(UpperCamelCase__)
						self.tokenizer.save_pretrained(UpperCamelCase__)




				@staticmethod
				def 							lowercase_  (			UpperCamelCase__							:       List[str]  ,						UpperCamelCase__							:       Union[str, Any])->     Optional[int]:



						'''simple docstring'''


						parser.add_argument(
						    "--model_name_or_path"  ,						default=UpperCamelCase__  ,						type=UpperCamelCase__  ,						required=UpperCamelCase__  ,						help="Path to pretrained model or model identifier from huggingface.co/models"  ,						)
						parser.add_argument(
						    "--config_name"  ,						default=""  ,						type=UpperCamelCase__  ,						help="Pretrained config name or path if not the same as model_name")
						parser.add_argument(
						    "--tokenizer_name"  ,						default=UpperCamelCase__  ,						type=UpperCamelCase__  ,						help="Pretrained tokenizer name or path if not the same as model_name"  ,						)
						parser.add_argument(
						    "--cache_dir"  ,						default=""  ,						type=UpperCamelCase__  ,						help="Where do you want to store the pre-trained models downloaded from huggingface.co"  ,						)
						parser.add_argument(
						    "--encoder_layerdrop"  ,						type=UpperCamelCase__  ,						help="Encoder layer dropout probability (Optional). Goes into model.config"  ,						)
						parser.add_argument(
						    "--decoder_layerdrop"  ,						type=UpperCamelCase__  ,						help="Decoder layer dropout probability (Optional). Goes into model.config"  ,						)
						parser.add_argument(
						    "--dropout"  ,						type=UpperCamelCase__  ,						help="Dropout probability (Optional). Goes into model.config"  ,						)
						parser.add_argument(
						    "--attention_dropout"  ,						type=UpperCamelCase__  ,						help="Attention dropout probability (Optional). Goes into model.config"  ,						)
						parser.add_argument("--learning_rate"  ,						default=5e-5  ,						type=UpperCamelCase__  ,						help="The initial learning rate for Adam.")
						parser.add_argument(
						    "--lr_scheduler"  ,						default="linear"  ,						choices=UpperCamelCase__  ,						metavar=UpperCamelCase__  ,						type=UpperCamelCase__  ,						help="Learning rate scheduler"  ,						)
						parser.add_argument("--weight_decay"  ,						default=0.0  ,						type=UpperCamelCase__  ,						help="Weight decay if we apply some.")
						parser.add_argument("--adam_epsilon"  ,						default=1e-8  ,						type=UpperCamelCase__  ,						help="Epsilon for Adam optimizer.")
						parser.add_argument("--warmup_steps"  ,						default=0  ,						type=UpperCamelCase__  ,						help="Linear warmup over warmup_steps.")
						parser.add_argument("--num_workers"  ,						default=4  ,						type=UpperCamelCase__  ,						help="kwarg passed to DataLoader")
						parser.add_argument("--num_train_epochs"  ,						dest="max_epochs"  ,						default=3  ,						type=UpperCamelCase__)
						parser.add_argument("--train_batch_size"  ,						default=3_2  ,						type=UpperCamelCase__)
						parser.add_argument("--eval_batch_size"  ,						default=3_2  ,						type=UpperCamelCase__)
						parser.add_argument("--adafactor"  ,						action="store_true")





class 					snake_case			(       pl.Callback						):

				def 							lowercase_  (			self							:       List[Any]  ,						UpperCamelCase__							:       str  ,						UpperCamelCase__							:       Optional[int])->     Dict:



						'''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 					snake_case			(       pl.Callback						):

				def 							lowercase_  (			self							:       List[str]  ,						UpperCamelCase__							:       Dict  ,						UpperCamelCase__							:       Optional[Any])->     int:



						'''simple docstring'''


						__lowerCAmelCase: List[Any]					   =    trainer.lr_schedulers[0]["scheduler"]
						__lowerCAmelCase: int					   =    {f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr())}
						pl_module.logger.log_metrics(UpperCamelCase__)

				def 							lowercase_  (			self							:       Dict  ,						UpperCamelCase__							:       pl.Trainer  ,						UpperCamelCase__							:       pl.LightningModule)->     Optional[Any]:



						'''simple docstring'''


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




				def 							lowercase_  (			self							:       Dict  ,						UpperCamelCase__							:       pl.Trainer  ,						UpperCamelCase__							:       pl.LightningModule)->     str:



						'''simple docstring'''


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






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				):
		#  To allow all pl args uncomment the following line
		#  parser = pl.Trainer.add_argparse_args(parser)
		parser.add_argument(
		    "--output_dir"      ,				default=__SCREAMING_SNAKE_CASE      ,				type=__SCREAMING_SNAKE_CASE      ,				required=__SCREAMING_SNAKE_CASE      ,				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=__SCREAMING_SNAKE_CASE      ,				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=__SCREAMING_SNAKE_CASE				)
		parser.add_argument("--max_grad_norm"      ,				dest="gradient_clip_val"      ,				default=1.0      ,				type=__SCREAMING_SNAKE_CASE      ,				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=__SCREAMING_SNAKE_CASE      ,				default=1      ,				help="Number of updates steps to accumulate before performing a backward/update pass."      ,				)
		parser.add_argument("--seed"      ,				type=__SCREAMING_SNAKE_CASE      ,				default=4_2      ,				help="random seed for initialization"				)
		parser.add_argument(
		    "--data_dir"      ,				default=__SCREAMING_SNAKE_CASE      ,				type=__SCREAMING_SNAKE_CASE      ,				required=__SCREAMING_SNAKE_CASE      ,				help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."      ,				)






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

		# init model
		__lowerCAmelCase: Any					   =    Path(model.hparams.output_dir				)
		odir.mkdir(exist_ok=__SCREAMING_SNAKE_CASE				)

		# add custom checkpoints
		if checkpoint_callback is None:
				__lowerCAmelCase: Union[str, Any]					   =    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(__SCREAMING_SNAKE_CASE				)
		if logging_callback is None:
				__lowerCAmelCase: List[str]					   =    LoggingCallback()

		__lowerCAmelCase: Optional[Any]					   =    {}

		# TODO: remove with PyTorch 1.6 since pl uses native amp
		if args.fpaa:
				__lowerCAmelCase: Union[str, Any]					   =    1_6
				# train_params["amp_level"] = args.fp16_opt_level

		if args.gpus > 1:
				__lowerCAmelCase: int					   =    "auto"  # "ddp"
				__lowerCAmelCase: Optional[int]					   =    "ddp"

		__lowerCAmelCase: str					   =    args.accumulate_grad_batches
		__lowerCAmelCase: Optional[Any]					   =    None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
		__lowerCAmelCase: Union[str, Any]					   =    "auto"

		__lowerCAmelCase: Any					   =    pl.Trainer.from_argparse_args(
		    __SCREAMING_SNAKE_CASE      ,				weights_summary=__SCREAMING_SNAKE_CASE      ,				callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]      ,				logger=__SCREAMING_SNAKE_CASE      ,				**__SCREAMING_SNAKE_CASE      ,				)

		if args.do_train:
				trainer.fit(__SCREAMING_SNAKE_CASE				)

		return trainer





"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import OpenAIGPTConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers.models.openai.modeling_tf_openai import (
			    TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
			    TFOpenAIGPTDoubleHeadsModel,
			    TFOpenAIGPTForSequenceClassification,
			    TFOpenAIGPTLMHeadModel,
			    TFOpenAIGPTModel,
			)





class 					snake_case			:

				def __init__(			self							:       str  ,						UpperCamelCase__							:       Optional[int]  ,						)->     Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase: str					   =    parent
						__lowerCAmelCase: Union[str, Any]					   =    1_3
						__lowerCAmelCase: Union[str, Any]					   =    7
						__lowerCAmelCase: List[Any]					   =    True
						__lowerCAmelCase: Any					   =    True
						__lowerCAmelCase: List[Any]					   =    True
						__lowerCAmelCase: str					   =    True
						__lowerCAmelCase: List[Any]					   =    True
						__lowerCAmelCase: Tuple					   =    9_9
						__lowerCAmelCase: Dict					   =    3_2
						__lowerCAmelCase: Optional[Any]					   =    2
						__lowerCAmelCase: Union[str, Any]					   =    4
						__lowerCAmelCase: Union[str, Any]					   =    3_7
						__lowerCAmelCase: List[str]					   =    "gelu"
						__lowerCAmelCase: List[str]					   =    0.1
						__lowerCAmelCase: List[str]					   =    0.1
						__lowerCAmelCase: Union[str, Any]					   =    5_1_2
						__lowerCAmelCase: int					   =    1_6
						__lowerCAmelCase: int					   =    2
						__lowerCAmelCase: str					   =    0.02
						__lowerCAmelCase: Union[str, Any]					   =    3
						__lowerCAmelCase: str					   =    4
						__lowerCAmelCase: List[Any]					   =    None
						__lowerCAmelCase: int					   =    self.vocab_size - 1

				def 							lowercase_  (			self							:       Union[str, Any])->     Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase: Any					   =    ids_tensor([self.batch_size, self.seq_length]  ,						self.vocab_size)

						__lowerCAmelCase: str					   =    None
						if self.use_input_mask:
								__lowerCAmelCase: List[Any]					   =    random_attention_mask([self.batch_size, self.seq_length])

						__lowerCAmelCase: int					   =    None
						if self.use_token_type_ids:
								__lowerCAmelCase: Any					   =    ids_tensor([self.batch_size, self.seq_length]  ,						self.type_vocab_size)

						__lowerCAmelCase: Dict					   =    None
						if self.use_mc_token_ids:
								__lowerCAmelCase: Union[str, Any]					   =    ids_tensor([self.batch_size, self.num_choices]  ,						self.seq_length)

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

						__lowerCAmelCase: int					   =    OpenAIGPTConfig(
						    vocab_size=self.vocab_size  ,						n_embd=self.hidden_size  ,						n_layer=self.num_hidden_layers  ,						n_head=self.num_attention_heads  ,						n_positions=self.max_position_embeddings  ,						pad_token_id=self.pad_token_id  ,						)

						__lowerCAmelCase: Union[str, Any]					   =    ids_tensor([self.num_hidden_layers, self.num_attention_heads]  ,						2)

						return (
						    config,
						    input_ids,
						    input_mask,
						    head_mask,
						    token_type_ids,
						    mc_token_ids,
						    sequence_labels,
						    token_labels,
						    choice_labels,
						)

				def 							lowercase_  (			self							:       Optional[int]  ,						UpperCamelCase__							:       int  ,						UpperCamelCase__							:       Any  ,						UpperCamelCase__							:       List[Any]  ,						UpperCamelCase__							:       int  ,						UpperCamelCase__							:       Dict  ,						*UpperCamelCase__							:       Dict)->     Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase: List[str]					   =    TFOpenAIGPTModel(config=UpperCamelCase__)
						__lowerCAmelCase: Tuple					   =    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
						__lowerCAmelCase: List[Any]					   =    model(UpperCamelCase__)

						__lowerCAmelCase: List[Any]					   =    [input_ids, input_mask]
						__lowerCAmelCase: Tuple					   =    model(UpperCamelCase__)

						__lowerCAmelCase: List[Any]					   =    model(UpperCamelCase__)

						self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, self.seq_length, self.hidden_size))

				def 							lowercase_  (			self							:       Dict  ,						UpperCamelCase__							:       List[str]  ,						UpperCamelCase__							:       Union[str, Any]  ,						UpperCamelCase__							:       List[Any]  ,						UpperCamelCase__							:       Optional[int]  ,						UpperCamelCase__							:       int  ,						*UpperCamelCase__							:       Optional[Any])->     Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase: Any					   =    TFOpenAIGPTLMHeadModel(config=UpperCamelCase__)
						__lowerCAmelCase: int					   =    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
						__lowerCAmelCase: Any					   =    model(UpperCamelCase__)
						self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.seq_length, self.vocab_size))

				def 							lowercase_  (			self							:       Tuple  ,						UpperCamelCase__							:       str  ,						UpperCamelCase__							:       Union[str, Any]  ,						UpperCamelCase__							:       Tuple  ,						UpperCamelCase__							:       Optional[int]  ,						UpperCamelCase__							:       List[str]  ,						UpperCamelCase__							:       Optional[Any]  ,						*UpperCamelCase__							:       Optional[Any])->     Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase: Union[str, Any]					   =    TFOpenAIGPTDoubleHeadsModel(config=UpperCamelCase__)

						__lowerCAmelCase: List[str]					   =    tf.tile(tf.expand_dims(UpperCamelCase__  ,						1)  ,						(1, self.num_choices, 1))
						__lowerCAmelCase: Union[str, Any]					   =    tf.tile(tf.expand_dims(UpperCamelCase__  ,						1)  ,						(1, self.num_choices, 1))
						__lowerCAmelCase: Optional[Any]					   =    tf.tile(tf.expand_dims(UpperCamelCase__  ,						1)  ,						(1, self.num_choices, 1))

						__lowerCAmelCase: str					   =    {
						    "input_ids": multiple_choice_inputs_ids,
						    "mc_token_ids": mc_token_ids,
						    "attention_mask": multiple_choice_input_mask,
						    "token_type_ids": multiple_choice_token_type_ids,
						}
						__lowerCAmelCase: Optional[int]					   =    model(UpperCamelCase__)
						self.parent.assertEqual(
						    result.logits.shape  ,						(self.batch_size, self.num_choices, self.seq_length, self.vocab_size))
						self.parent.assertEqual(result.mc_logits.shape  ,						(self.batch_size, self.num_choices))

				def 							lowercase_  (			self							:       List[str]  ,						UpperCamelCase__							:       List[Any]  ,						UpperCamelCase__							:       Optional[Any]  ,						UpperCamelCase__							:       Any  ,						UpperCamelCase__							:       str  ,						UpperCamelCase__							:       Optional[Any]  ,						*UpperCamelCase__							:       List[str])->     Any:



						'''simple docstring'''


						__lowerCAmelCase: str					   =    self.num_labels
						__lowerCAmelCase: Dict					   =    ids_tensor([self.batch_size]  ,						self.type_sequence_label_size)
						__lowerCAmelCase: List[str]					   =    {
						    "input_ids": input_ids,
						    "attention_mask": input_mask,
						    "token_type_ids": token_type_ids,
						    "labels": sequence_labels,
						}
						__lowerCAmelCase: str					   =    TFOpenAIGPTForSequenceClassification(UpperCamelCase__)
						__lowerCAmelCase: Optional[int]					   =    model(UpperCamelCase__)
						self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_labels))




				def 							lowercase_  (			self							:       Any)->     List[str]:



						'''simple docstring'''


						__lowerCAmelCase: List[str]					   =    self.prepare_config_and_inputs()

						(
						    (
						    __lowerCAmelCase
						)   ,				(
						    __lowerCAmelCase
						)   ,				(
						    __lowerCAmelCase
						)   ,				(
						    __lowerCAmelCase
						)   ,				(
						    __lowerCAmelCase
						)   ,				(
						    __lowerCAmelCase
						)   ,				(
						    __lowerCAmelCase
						)   ,				(
						    __lowerCAmelCase
						)   ,				(
						    __lowerCAmelCase
						)   ,				
						): List[Any]					   =    config_and_inputs

						__lowerCAmelCase: List[Any]					   =    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
						return config, inputs_dict





@require_tf
class 					snake_case			(       __snake_case,							__snake_case,							unittest.TestCase						):
				SCREAMING_SNAKE_CASE_     :   Dict        =						(
				    (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification)
				    if is_tf_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE_     :   List[str]        =						(
				    (TFOpenAIGPTLMHeadModel,) if is_tf_available() else ()
				)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
				SCREAMING_SNAKE_CASE_     :   int        =						(
				    {
				        """feature-extraction""": TFOpenAIGPTModel,
				        """text-classification""": TFOpenAIGPTForSequenceClassification,
				        """text-generation""": TFOpenAIGPTLMHeadModel,
				        """zero-shot""": TFOpenAIGPTForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				SCREAMING_SNAKE_CASE_     :   List[Any]        =						False
				SCREAMING_SNAKE_CASE_     :   Tuple        =						False

				def 							lowercase_  (			self							:       List[str]  ,						UpperCamelCase__							:       int  ,						UpperCamelCase__							:       Union[str, Any]  ,						UpperCamelCase__							:       Optional[int]  ,						UpperCamelCase__							:       Union[str, Any]  ,						UpperCamelCase__							:       Optional[Any])->     Union[str, Any]:



						'''simple docstring'''


						if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
								# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
								# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
								# tiny config could not be created.
								return True

						return False

				def 							lowercase_  (			self							:       str)->     Tuple:



						'''simple docstring'''


						__lowerCAmelCase: Optional[int]					   =    TFOpenAIGPTModelTester(self)
						__lowerCAmelCase: List[Any]					   =    ConfigTester(self  ,						config_class=UpperCamelCase__  ,						n_embd=3_7)

				def 							lowercase_  (			self							:       List[Any])->     List[Any]:



						'''simple docstring'''


						self.config_tester.run_common_tests()

				def 							lowercase_  (			self							:       Tuple)->     Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase: Optional[Any]					   =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_openai_gpt_model(*UpperCamelCase__)

				def 							lowercase_  (			self							:       Union[str, Any])->     Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase: Optional[Any]					   =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_openai_gpt_lm_head(*UpperCamelCase__)

				def 							lowercase_  (			self							:       Optional[Any])->     Dict:



						'''simple docstring'''


						__lowerCAmelCase: List[Any]					   =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_openai_gpt_double_head(*UpperCamelCase__)

				def 							lowercase_  (			self							:       List[str])->     Tuple:



						'''simple docstring'''


						__lowerCAmelCase: Optional[int]					   =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*UpperCamelCase__)




				@slow
				def 							lowercase_  (			self							:       str)->     Dict:



						'''simple docstring'''


						for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								__lowerCAmelCase: str					   =    TFOpenAIGPTModel.from_pretrained(UpperCamelCase__)
								self.assertIsNotNone(UpperCamelCase__)





@require_tf
class 					snake_case			(       unittest.TestCase						):

				@slow
				def 							lowercase_  (			self							:       Union[str, Any])->     List[str]:



						'''simple docstring'''


						__lowerCAmelCase: Optional[Any]					   =    TFOpenAIGPTLMHeadModel.from_pretrained("openai-gpt")
						__lowerCAmelCase: List[str]					   =    tf.convert_to_tensor([[4_8_1, 4_7_3_5, 5_4_4]]  ,						dtype=tf.intaa)  # the president is
						__lowerCAmelCase: Union[str, Any]					   =    [
						    4_8_1,
						    4_7_3_5,
						    5_4_4,
						    2_4_6,
						    9_6_3,
						    8_7_0,
						    7_6_2,
						    2_3_9,
						    2_4_4,
						    4_0_4_7_7,
						    2_4_4,
						    2_4_9,
						    7_1_9,
						    8_8_1,
						    4_8_7,
						    5_4_4,
						    2_4_0,
						    2_4_4,
						    6_0_3,
						    4_8_1,
						]  # the president is a very good man. " \n " i\'m sure he is, " said the

						__lowerCAmelCase: str					   =    model.generate(UpperCamelCase__  ,						do_sample=UpperCamelCase__)
						self.assertListEqual(output_ids[0].numpy().tolist()  ,						UpperCamelCase__)


