


"""simple docstring"""




from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, 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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import (
	    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
	    TFTransfoXLForSequenceClassification,
	    TFTransfoXLLMHeadModel,
	    TFTransfoXLModel,
	)
class 					lowerCAmelCase	:




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__						,       )     ->							Union[str, Any]:
								SCREAMING_SNAKE_CASE													=					parent
								SCREAMING_SNAKE_CASE													=					13
								SCREAMING_SNAKE_CASE													=					7
								SCREAMING_SNAKE_CASE													=					30
								SCREAMING_SNAKE_CASE													=					self.seq_length + self.mem_len
								SCREAMING_SNAKE_CASE													=					15
								SCREAMING_SNAKE_CASE													=					True
								SCREAMING_SNAKE_CASE													=					True
								SCREAMING_SNAKE_CASE													=					99
								SCREAMING_SNAKE_CASE													=					[10, 50, 80]
								SCREAMING_SNAKE_CASE													=					32
								SCREAMING_SNAKE_CASE													=					32
								SCREAMING_SNAKE_CASE													=					4
								SCREAMING_SNAKE_CASE													=					8
								SCREAMING_SNAKE_CASE													=					128
								SCREAMING_SNAKE_CASE													=					2
								SCREAMING_SNAKE_CASE													=					2
								SCREAMING_SNAKE_CASE													=					None
								SCREAMING_SNAKE_CASE													=					1
								SCREAMING_SNAKE_CASE													=					0
								SCREAMING_SNAKE_CASE													=					3
								SCREAMING_SNAKE_CASE													=					self.vocab_size - 1
								SCREAMING_SNAKE_CASE													=					0.01







		def 							__A						(	self				)     ->							List[Any]:
								SCREAMING_SNAKE_CASE													=					ids_tensor([self.batch_size, self.seq_length]						,       self.vocab_size				)
								SCREAMING_SNAKE_CASE													=					ids_tensor([self.batch_size, self.seq_length]						,       self.vocab_size				)

								SCREAMING_SNAKE_CASE													=					None
								if self.use_labels:
														SCREAMING_SNAKE_CASE													=					ids_tensor([self.batch_size, self.seq_length]						,       self.vocab_size				)

								SCREAMING_SNAKE_CASE													=					TransfoXLConfig(
								    vocab_size=self.vocab_size						,       mem_len=self.mem_len						,       clamp_len=self.clamp_len						,       cutoffs=self.cutoffs						,       d_model=self.hidden_size						,       d_embed=self.d_embed						,       n_head=self.num_attention_heads						,       d_head=self.d_head						,       d_inner=self.d_inner						,       div_val=self.div_val						,       n_layer=self.num_hidden_layers						,       eos_token_id=self.eos_token_id						,       pad_token_id=self.vocab_size - 1						,       init_range=self.init_range						,       num_labels=self.num_labels						,       )

								return (config, input_ids_a, input_ids_a, lm_labels)







		def 							__A						(	self				)     ->							int:
								random.seed(self.seed				)
								tf.random.set_seed(self.seed				)







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							str:
								SCREAMING_SNAKE_CASE													=					TFTransfoXLModel(_a				)

								SCREAMING_SNAKE_CASE													=					model(_a				).to_tuple()

								SCREAMING_SNAKE_CASE													=					{"input_ids": input_ids_a, "mems": mems_a}

								SCREAMING_SNAKE_CASE													=					model(_a				).to_tuple()

								self.parent.assertEqual(hidden_states_a.shape						,       (self.batch_size, self.seq_length, self.hidden_size)				)
								self.parent.assertEqual(hidden_states_a.shape						,       (self.batch_size, self.seq_length, self.hidden_size)				)
								self.parent.assertListEqual(
								    [mem.shape for mem in mems_a]						,       [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers						,       )
								self.parent.assertListEqual(
								    [mem.shape for mem in mems_a]						,       [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers						,       )







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							str:
								SCREAMING_SNAKE_CASE													=					TFTransfoXLLMHeadModel(_a				)

								SCREAMING_SNAKE_CASE													=					model(_a				).to_tuple()

								SCREAMING_SNAKE_CASE													=					{"input_ids": input_ids_a, "labels": lm_labels}
								SCREAMING_SNAKE_CASE													=					model(_a				).to_tuple()

								SCREAMING_SNAKE_CASE													=					model([input_ids_a, mems_a]				).to_tuple()

								SCREAMING_SNAKE_CASE													=					{"input_ids": input_ids_a, "mems": mems_a, "labels": lm_labels}

								SCREAMING_SNAKE_CASE													=					model(_a				).to_tuple()

								self.parent.assertEqual(lm_logits_a.shape						,       (self.batch_size, self.seq_length, self.vocab_size)				)
								self.parent.assertListEqual(
								    [mem.shape for mem in mems_a]						,       [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers						,       )

								self.parent.assertEqual(lm_logits_a.shape						,       (self.batch_size, self.seq_length, self.vocab_size)				)
								self.parent.assertListEqual(
								    [mem.shape for mem in mems_a]						,       [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers						,       )







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							str:
								SCREAMING_SNAKE_CASE													=					TFTransfoXLForSequenceClassification(_a				)
								SCREAMING_SNAKE_CASE													=					model(_a				)
								self.parent.assertEqual(result.logits.shape						,       (self.batch_size, self.num_labels)				)







		def 							__A						(	self				)     ->							Tuple:
								SCREAMING_SNAKE_CASE													=					self.prepare_config_and_inputs()
								(SCREAMING_SNAKE_CASE)													=					config_and_inputs
								SCREAMING_SNAKE_CASE													=					{"input_ids": input_ids_a}
								return config, inputs_dict





@require_tf
class 					lowerCAmelCase	(					lowerCamelCase_	,    lowerCamelCase_	,    unittest.TestCase    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     Dict								=     (
		    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
		)
		SCREAMING_SNAKE_CASE_						:     Tuple								=     () if is_tf_available() else ()
		SCREAMING_SNAKE_CASE_						:     Union[str, Any]								=     (
		    {
		        """feature-extraction""": TFTransfoXLModel,
		        """text-classification""": TFTransfoXLForSequenceClassification,
		        """text-generation""": TFTransfoXLLMHeadModel,
		        """zero-shot""": TFTransfoXLForSequenceClassification,
		    }
		    if is_tf_available()
		    else {}
		)
		# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
		SCREAMING_SNAKE_CASE_						:     str								=     False
		SCREAMING_SNAKE_CASE_						:     Union[str, Any]								=     False
		SCREAMING_SNAKE_CASE_						:     int								=     False
		SCREAMING_SNAKE_CASE_						:     int								=     False







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							Union[str, Any]:
								if pipeline_test_casse_name == "TextGenerationPipelineTests":
														# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
														# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
														# tokenizer.
														return True

								return False







		def 							__A						(	self				)     ->							Optional[int]:
								SCREAMING_SNAKE_CASE													=					TFTransfoXLModelTester(self				)
								SCREAMING_SNAKE_CASE													=					ConfigTester(self						,       config_class=_a						,       d_embed=37				)







		def 							__A						(	self				)     ->							Optional[Any]:
								self.config_tester.run_common_tests()







		def 							__A						(	self				)     ->							Optional[Any]:
								self.model_tester.set_seed()
								SCREAMING_SNAKE_CASE													=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_model(*_a				)







		def 							__A						(	self				)     ->							Union[str, Any]:
								self.model_tester.set_seed()
								SCREAMING_SNAKE_CASE													=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_lm_head(*_a				)







		def 							__A						(	self				)     ->							str:
								SCREAMING_SNAKE_CASE													=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*_a				)







		def 							__A						(	self				)     ->							str:
								SCREAMING_SNAKE_CASE													=					self.model_tester.prepare_config_and_inputs_for_common()
								SCREAMING_SNAKE_CASE													=					[TFTransfoXLForSequenceClassification]

								for model_class in self.all_model_classes:
														SCREAMING_SNAKE_CASE													=					model_class(_a				)
														assert isinstance(model.get_input_embeddings()						,       tf.keras.layers.Layer				)
														if model_class in list_other_models_with_output_ebd:
																				SCREAMING_SNAKE_CASE													=					model.get_output_embeddings()
																				assert isinstance(_a						,       tf.keras.layers.Layer				)
																				SCREAMING_SNAKE_CASE													=					model.get_bias()
																				assert name is None
														else:
																				SCREAMING_SNAKE_CASE													=					model.get_output_embeddings()
																				assert x is None
																				SCREAMING_SNAKE_CASE													=					model.get_bias()
																				assert name is None







		def 							__A						(	self				)     ->							Tuple:
								# TODO JP: Make TransfoXL XLA compliant
								pass







		@slow
		def 							__A						(	self				)     ->							Optional[Any]:
								for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														SCREAMING_SNAKE_CASE													=					TFTransfoXLModel.from_pretrained(_a				)
														self.assertIsNotNone(_a				)







		@unittest.skip(reason='This model doesn\'t play well with fit() due to not returning a single loss.'				)
		def 							__A						(	self				)     ->							Dict:
								pass





@require_tf
class 					lowerCAmelCase	(					unittest.TestCase    ):




		'''simple docstring'''







		@unittest.skip('Skip test until #12651 is resolved.'				)
		@slow
		def 							__A						(	self				)     ->							Optional[int]:
								SCREAMING_SNAKE_CASE													=					TFTransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103'				)
								# fmt: off
								SCREAMING_SNAKE_CASE													=					tf.convert_to_tensor([[33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0]]						,       dtype=tf.intaa				)  # noqa: E231
								# fmt: on
								#  In 1991 , the remains of Russian Tsar Nicholas II and his family
								#  ( except for Alexei and Maria ) are discovered .
								#  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
								#  remainder of the story . 1883 Western Siberia ,
								#  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
								#  Rasputin has a vision and denounces one of the men as a horse thief . Although his
								#  father initially slaps him for making such an accusation , Rasputin watches as the
								#  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
								#  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
								#  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

								# fmt: off
								SCREAMING_SNAKE_CASE													=					[33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0,33,1,1_857,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,28,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,0]  # noqa: E231
								# fmt: on
								#  In 1991, the remains of Russian Tsar Nicholas II and his family (
								#  except for Alexei and Maria ) are discovered. The voice of young son,
								#  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
								#  1883 Western Siberia, a young Grigori Rasputin is asked by his father
								#  and a group of men to perform magic. Rasputin has a vision and
								#  denounces one of the men as a horse thief. Although his father initially
								#  slaps him for making such an accusation, Rasputin watches as the man
								#  is chased outside and beaten. Twenty years later, Rasputin sees a vision
								#  of the Virgin Mary, prompting him to become a priest.
								#  Rasputin quickly becomes famous, with people, even a bishop, begging for
								#  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
								# Nicholas II and his family were discovered. The voice of <unk> young son,
								# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

								SCREAMING_SNAKE_CASE													=					model.generate(_a						,       max_length=200						,       do_sample=_a				)
								self.assertListEqual(output_ids[0].numpy().tolist()						,       _a				)




import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
snake_case :		Optional[int]							      =  logging.getLogger(__name__)






def   lowerCAmelCase_						(					_snake_case      :  Dict      ,   _snake_case      :  Union[str, Any]   )				->		Tuple:







	'''simple docstring'''


	__magic_name__		:		List[str]	   =     np.argmax(_snake_case      ,   axis=1   )
	return np.sum(outputs == labels   )






def   lowerCAmelCase_						(					_snake_case      :  Optional[Any]   )				->		Dict:







	'''simple docstring'''


	with open(_snake_case      ,   encoding="utf_8"   ) as f:
		__magic_name__		:		List[str]	   =     csv.reader(_snake_case   )
		__magic_name__		:		List[Any]	   =     []
		next(_snake_case   )  # skip the first line
		for line in tqdm(_snake_case   ):
			output.append((" ".join(line[1:5]   ), line[5], line[6], int(line[-1]   ) - 1)   )
	return output






def   lowerCAmelCase_						(					_snake_case      :  str      ,   _snake_case      :  Tuple      ,   _snake_case      :  Union[str, Any]      ,   _snake_case      :  List[Any]      ,   _snake_case      :  Tuple      ,   _snake_case      :  Optional[int]   )				->		int:







	'''simple docstring'''


	__magic_name__		:		Optional[int]	   =     []
	for dataset in encoded_datasets:
		__magic_name__		:		Union[str, Any]	   =     len(_snake_case   )
		__magic_name__		:		Dict	   =     np.zeros((n_batch, 2, input_len)      ,   dtype=np.intaa   )
		__magic_name__		:		List[str]	   =     np.zeros((n_batch, 2)      ,   dtype=np.intaa   )
		__magic_name__		:		Optional[int]	   =     np.full((n_batch, 2, input_len)      ,   fill_value=-100      ,   dtype=np.intaa   )
		__magic_name__		:		int	   =     np.zeros((n_batch,)      ,   dtype=np.intaa   )
		for (
		    i,
		    (story, conta, conta, mc_label),
		) in enumerate(_snake_case   ):
			__magic_name__		:		Dict	   =     [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
			__magic_name__		:		Dict	   =     [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
			__magic_name__		:		str	   =     with_conta
			__magic_name__		:		Tuple	   =     with_conta
			__magic_name__		:		Union[str, Any]	   =     len(_snake_case   ) - 1
			__magic_name__		:		int	   =     len(_snake_case   ) - 1
			__magic_name__		:		Optional[Any]	   =     with_conta
			__magic_name__		:		Optional[Any]	   =     with_conta
			__magic_name__		:		Optional[int]	   =     mc_label
		__magic_name__		:		str	   =     (input_ids, mc_token_ids, lm_labels, mc_labels)
		tensor_datasets.append(tuple(torch.tensor(_snake_case   ) for t in all_inputs   )   )
	return tensor_datasets






def   lowerCAmelCase_						(					)				->		List[Any]:







	'''simple docstring'''


	__magic_name__		:		Any	   =     argparse.ArgumentParser()
	parser.add_argument("--model_name"      ,   type=_snake_case      ,   default="openai-gpt"      ,   help="pretrained model name"   )
	parser.add_argument("--do_train"      ,   action="store_true"      ,   help="Whether to run training."   )
	parser.add_argument("--do_eval"      ,   action="store_true"      ,   help="Whether to run eval on the dev set."   )
	parser.add_argument(
	    "--output_dir"      ,   default=_snake_case      ,   type=_snake_case      ,   required=_snake_case      ,   help="The output directory where the model predictions and checkpoints will be written."      ,   )
	parser.add_argument("--train_dataset"      ,   type=_snake_case      ,   default=""   )
	parser.add_argument("--eval_dataset"      ,   type=_snake_case      ,   default=""   )
	parser.add_argument("--seed"      ,   type=_snake_case      ,   default=42   )
	parser.add_argument("--num_train_epochs"      ,   type=_snake_case      ,   default=3   )
	parser.add_argument("--train_batch_size"      ,   type=_snake_case      ,   default=8   )
	parser.add_argument("--eval_batch_size"      ,   type=_snake_case      ,   default=16   )
	parser.add_argument("--adam_epsilon"      ,   default=1E-8      ,   type=_snake_case      ,   help="Epsilon for Adam optimizer."   )
	parser.add_argument("--max_grad_norm"      ,   type=_snake_case      ,   default=1   )
	parser.add_argument(
	    "--max_steps"      ,   default=-1      ,   type=_snake_case      ,   help=(
	        "If > 0: set total number of training                         steps to perform. Override num_train_epochs."
	    )      ,   )
	parser.add_argument(
	    "--gradient_accumulation_steps"      ,   type=_snake_case      ,   default=1      ,   help="Number of updates steps to accumulate before                        performing a backward/update pass."      ,   )
	parser.add_argument("--learning_rate"      ,   type=_snake_case      ,   default=6.25E-5   )
	parser.add_argument("--warmup_steps"      ,   default=0      ,   type=_snake_case      ,   help="Linear warmup over warmup_steps."   )
	parser.add_argument("--lr_schedule"      ,   type=_snake_case      ,   default="warmup_linear"   )
	parser.add_argument("--weight_decay"      ,   type=_snake_case      ,   default=0.01   )
	parser.add_argument("--lm_coef"      ,   type=_snake_case      ,   default=0.9   )
	parser.add_argument("--n_valid"      ,   type=_snake_case      ,   default=374   )

	parser.add_argument("--server_ip"      ,   type=_snake_case      ,   default=""      ,   help="Can be used for distant debugging."   )
	parser.add_argument("--server_port"      ,   type=_snake_case      ,   default=""      ,   help="Can be used for distant debugging."   )
	__magic_name__		:		List[Any]	   =     parser.parse_args()
	print(_snake_case   )

	if args.server_ip and args.server_port:
		# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
		import ptvsd

		print("Waiting for debugger attach"   )
		ptvsd.enable_attach(address=(args.server_ip, args.server_port)      ,   redirect_output=_snake_case   )
		ptvsd.wait_for_attach()

	random.seed(args.seed   )
	np.random.seed(args.seed   )
	torch.manual_seed(args.seed   )
	torch.cuda.manual_seed_all(args.seed   )

	__magic_name__		:		Dict	   =     torch.device("cuda" if torch.cuda.is_available() else "cpu"   )
	__magic_name__		:		Optional[int]	   =     torch.cuda.device_count()
	logger.info("device: {}, n_gpu {}".format(_snake_case      ,   _snake_case   )   )

	if not args.do_train and not args.do_eval:
		raise ValueError("At least one of `do_train` or `do_eval` must be True."   )

	if not os.path.exists(args.output_dir   ):
		os.makedirs(args.output_dir   )

	# Load tokenizer and model
	# This loading functions also add new tokens and embeddings called `special tokens`
	# These new embeddings will be fine-tuned on the RocStories dataset
	__magic_name__		:		List[Any]	   =     ["_start_", "_delimiter_", "_classify_"]
	__magic_name__		:		Optional[int]	   =     OpenAIGPTTokenizer.from_pretrained(args.model_name   )
	tokenizer.add_tokens(_snake_case   )
	__magic_name__		:		Optional[Any]	   =     tokenizer.convert_tokens_to_ids(_snake_case   )
	__magic_name__		:		List[str]	   =     OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name   )
	model.resize_token_embeddings(len(_snake_case   )   )
	model.to(_snake_case   )

	# Load and encode the datasets
	def tokenize_and_encode(_snake_case      :  str   ):
		if isinstance(_snake_case      ,   _snake_case   ):
			return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(_snake_case   )   )
		elif isinstance(_snake_case      ,   _snake_case   ):
			return obj
		return [tokenize_and_encode(_snake_case   ) for o in obj]

	logger.info("Encoding dataset..."   )
	__magic_name__		:		Optional[int]	   =     load_rocstories_dataset(args.train_dataset   )
	__magic_name__		:		str	   =     load_rocstories_dataset(args.eval_dataset   )
	__magic_name__		:		int	   =     (train_dataset, eval_dataset)
	__magic_name__		:		List[str]	   =     tokenize_and_encode(_snake_case   )

	# Compute the max input length for the Transformer
	__magic_name__		:		Optional[Any]	   =     model.config.n_positions // 2 - 2
	__magic_name__		:		Optional[int]	   =     max(
	    len(story[:max_length]   ) + max(len(conta[:max_length]   )      ,   len(conta[:max_length]   )   ) + 3
	    for dataset in encoded_datasets
	    for story, conta, conta, _ in dataset   )
	__magic_name__		:		List[str]	   =     min(_snake_case      ,   model.config.n_positions   )  # Max size of input for the pre-trained model

	# Prepare inputs tensors and dataloaders
	__magic_name__		:		List[Any]	   =     pre_process_datasets(_snake_case      ,   _snake_case      ,   _snake_case      ,   *_snake_case   )
	__magic_name__   ,    __magic_name__		:		Optional[int]	   =     tensor_datasets[0], tensor_datasets[1]

	__magic_name__		:		Tuple	   =     TensorDataset(*_snake_case   )
	__magic_name__		:		Union[str, Any]	   =     RandomSampler(_snake_case   )
	__magic_name__		:		Dict	   =     DataLoader(_snake_case      ,   sampler=_snake_case      ,   batch_size=args.train_batch_size   )

	__magic_name__		:		Any	   =     TensorDataset(*_snake_case   )
	__magic_name__		:		Optional[Any]	   =     SequentialSampler(_snake_case   )
	__magic_name__		:		int	   =     DataLoader(_snake_case      ,   sampler=_snake_case      ,   batch_size=args.eval_batch_size   )

	# Prepare optimizer
	if args.do_train:
		if args.max_steps > 0:
			__magic_name__		:		Tuple	   =     args.max_steps
			__magic_name__		:		List[str]	   =     args.max_steps // (len(_snake_case   ) // args.gradient_accumulation_steps) + 1
		else:
			__magic_name__		:		List[str]	   =     len(_snake_case   ) // args.gradient_accumulation_steps * args.num_train_epochs

		__magic_name__		:		str	   =     list(model.named_parameters()   )
		__magic_name__		:		Dict	   =     ["bias", "LayerNorm.bias", "LayerNorm.weight"]
		__magic_name__		:		str	   =     [
		    {
		        "params": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay   )],
		        "weight_decay": args.weight_decay,
		    },
		    {"params": [p for n, p in param_optimizer if any(nd in n for nd in no_decay   )], "weight_decay": 0.0},
		]
		__magic_name__		:		str	   =     AdamW(_snake_case      ,   lr=args.learning_rate      ,   eps=args.adam_epsilon   )
		__magic_name__		:		List[str]	   =     get_linear_schedule_with_warmup(
		    _snake_case      ,   num_warmup_steps=args.warmup_steps      ,   num_training_steps=_snake_case   )

	if args.do_train:
		__magic_name__   ,    __magic_name__   ,    __magic_name__		:		Union[str, Any]	   =     0, 0, None
		model.train()
		for _ in trange(int(args.num_train_epochs   )      ,   desc="Epoch"   ):
			__magic_name__		:		List[str]	   =     0
			__magic_name__		:		Tuple	   =     0
			__magic_name__		:		Dict	   =     tqdm(_snake_case      ,   desc="Training"   )
			for step, batch in enumerate(_snake_case   ):
				__magic_name__		:		Optional[Any]	   =     tuple(t.to(_snake_case   ) for t in batch   )
				__magic_name__   ,    __magic_name__   ,    __magic_name__   ,    __magic_name__		:		Dict	   =     batch
				__magic_name__		:		Optional[Any]	   =     model(_snake_case      ,   mc_token_ids=_snake_case      ,   lm_labels=_snake_case      ,   mc_labels=_snake_case   )
				__magic_name__		:		Optional[Any]	   =     args.lm_coef * losses[0] + losses[1]
				loss.backward()
				optimizer.step()
				scheduler.step()
				optimizer.zero_grad()
				tr_loss += loss.item()
				__magic_name__		:		List[str]	   =     (
				    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
				)
				nb_tr_steps += 1
				__magic_name__		:		int	   =     "Training loss: {:.2e} lr: {:.2e}".format(_snake_case      ,   scheduler.get_lr()[0]   )

    # Save a trained model
	if args.do_train:
		# Save a trained model, configuration and tokenizer
		__magic_name__		:		Dict	   =     model.module if hasattr(_snake_case      ,   "module"   ) else model  # Only save the model itself

		# If we save using the predefined names, we can load using `from_pretrained`
		__magic_name__		:		List[Any]	   =     os.path.join(args.output_dir      ,   _snake_case   )
		__magic_name__		:		Dict	   =     os.path.join(args.output_dir      ,   _snake_case   )

		torch.save(model_to_save.state_dict()      ,   _snake_case   )
		model_to_save.config.to_json_file(_snake_case   )
		tokenizer.save_vocabulary(args.output_dir   )

		# Load a trained model and vocabulary that you have fine-tuned
		__magic_name__		:		Dict	   =     OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir   )
		__magic_name__		:		Optional[int]	   =     OpenAIGPTTokenizer.from_pretrained(args.output_dir   )
		model.to(_snake_case   )

	if args.do_eval:
		model.eval()
		__magic_name__   ,    __magic_name__		:		Any	   =     0, 0
		__magic_name__   ,    __magic_name__		:		Union[str, Any]	   =     0, 0
		for batch in tqdm(_snake_case      ,   desc="Evaluating"   ):
			__magic_name__		:		int	   =     tuple(t.to(_snake_case   ) for t in batch   )
			__magic_name__   ,    __magic_name__   ,    __magic_name__   ,    __magic_name__		:		Union[str, Any]	   =     batch
			with torch.no_grad():
				__magic_name__   ,    __magic_name__   ,    __magic_name__   ,    __magic_name__		:		Dict	   =     model(
				    _snake_case      ,   mc_token_ids=_snake_case      ,   lm_labels=_snake_case      ,   mc_labels=_snake_case   )

			__magic_name__		:		Tuple	   =     mc_logits.detach().cpu().numpy()
			__magic_name__		:		Any	   =     mc_labels.to("cpu"   ).numpy()
			__magic_name__		:		str	   =     accuracy(_snake_case      ,   _snake_case   )

			eval_loss += mc_loss.mean().item()
			eval_accuracy += tmp_eval_accuracy

			nb_eval_examples += input_ids.size(0   )
			nb_eval_steps += 1

		__magic_name__		:		Tuple	   =     eval_loss / nb_eval_steps
		__magic_name__		:		List[Any]	   =     eval_accuracy / nb_eval_examples
		__magic_name__		:		int	   =     tr_loss / nb_tr_steps if args.do_train else None
		__magic_name__		:		Any	   =     {"eval_loss": eval_loss, "eval_accuracy": eval_accuracy, "train_loss": train_loss}

		__magic_name__		:		int	   =     os.path.join(args.output_dir      ,   "eval_results.txt"   )
		with open(_snake_case      ,   "w"   ) as writer:
			logger.info("***** Eval results *****"   )
			for key in sorted(result.keys()   ):
				logger.info("  %s = %s"      ,   _snake_case      ,   str(result[key]   )   )
				writer.write("%s = %s\n" % (key, str(result[key]   ))   )


if __name__ == "__main__":
				main()
