

"""simple docstring"""




from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
)





@flax.struct.dataclass
class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		jnp.ndarray
			lowerCamelCase__   :		jnp.ndarray





class    lowerCamelCase						(nn.Module			):
			lowerCamelCase__   :		int
			lowerCamelCase__   :		Tuple[int]							=							(1_6, 3_2, 9_6, 2_5_6)
			lowerCamelCase__   :		jnp.dtype							=							jnp.floataa

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      nn.Conv(
						    self.block_out_channels[0]    ,				kernel_size=(3, 3)    ,				padding=((1, 1), (1, 1))    ,				dtype=self.dtype    ,				)

						SCREAMING_SNAKE_CASE__								=      []
						for i in range(len(self.block_out_channels       ) - 1       ):
									SCREAMING_SNAKE_CASE__								=      self.block_out_channels[i]
									SCREAMING_SNAKE_CASE__								=      self.block_out_channels[i + 1]
									SCREAMING_SNAKE_CASE__								=      nn.Conv(
									    __UpperCAmelCase    ,				kernel_size=(3, 3)    ,				padding=((1, 1), (1, 1))    ,				dtype=self.dtype    ,				)
									blocks.append(__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      nn.Conv(
									    __UpperCAmelCase    ,				kernel_size=(3, 3)    ,				strides=(2, 2)    ,				padding=((1, 1), (1, 1))    ,				dtype=self.dtype    ,				)
									blocks.append(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      blocks

						SCREAMING_SNAKE_CASE__								=      nn.Conv(
						    self.conditioning_embedding_channels    ,				kernel_size=(3, 3)    ,				padding=((1, 1), (1, 1))    ,				kernel_init=nn.initializers.zeros_init()    ,				bias_init=nn.initializers.zeros_init()    ,				dtype=self.dtype    ,				)





			def __call__(					self				:						List[Any]    ,				__UpperCAmelCase				:						Optional[Any]       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      self.conv_in(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      nn.silu(__UpperCAmelCase       )

						for block in self.blocks:
									SCREAMING_SNAKE_CASE__								=      block(__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      nn.silu(__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      self.conv_out(__UpperCAmelCase       )

						return embedding





@flax_register_to_config
class    lowerCamelCase						(nn.Module			,A__			,A__			):
			lowerCamelCase__   :		int							=							3_2
			lowerCamelCase__   :		int							=							4
			lowerCamelCase__   :		Tuple[str]							=							(
			    "CrossAttnDownBlock2D",
			    "CrossAttnDownBlock2D",
			    "CrossAttnDownBlock2D",
			    "DownBlock2D",
			)
			lowerCamelCase__   :		Union[bool, Tuple[bool]]							=							False
			lowerCamelCase__   :		Tuple[int]							=							(3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
			lowerCamelCase__   :		int							=							2
			lowerCamelCase__   :		Union[int, Tuple[int]]							=							8
			lowerCamelCase__   :		Optional[Union[int, Tuple[int]]]							=							None
			lowerCamelCase__   :		int							=							1_2_8_0
			lowerCamelCase__   :		float							=							0.0
			lowerCamelCase__   :		bool							=							False
			lowerCamelCase__   :		jnp.dtype							=							jnp.floataa
			lowerCamelCase__   :		bool							=							True
			lowerCamelCase__   :		int							=							0
			lowerCamelCase__   :		str							=							"rgb"
			lowerCamelCase__   :		Tuple[int]							=							(1_6, 3_2, 9_6, 2_5_6)

			def 					SCREAMING_SNAKE_CASE				(					self				:						str    ,				__UpperCAmelCase				:						jax.random.KeyArray       )   ->    FrozenDict:
						# init input tensors
						SCREAMING_SNAKE_CASE__								=      (1, self.in_channels, self.sample_size, self.sample_size)
						SCREAMING_SNAKE_CASE__								=      jnp.zeros(__UpperCAmelCase    ,				dtype=jnp.floataa       )
						SCREAMING_SNAKE_CASE__								=      jnp.ones((1,)    ,				dtype=jnp.intaa       )
						SCREAMING_SNAKE_CASE__								=      jnp.zeros((1, 1, self.cross_attention_dim)    ,				dtype=jnp.floataa       )
						SCREAMING_SNAKE_CASE__								=      (1, 3, self.sample_size * 8, self.sample_size * 8)
						SCREAMING_SNAKE_CASE__								=      jnp.zeros(__UpperCAmelCase    ,				dtype=jnp.floataa       )

						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      jax.random.split(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      {"""params""": params_rng, """dropout""": dropout_rng}

						return self.init(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase       )["params"]

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      self.block_out_channels
						SCREAMING_SNAKE_CASE__								=      block_out_channels[0] * 4

						# If `num_attention_heads` is not defined (which is the case for most models)
						# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
						# The reason for this behavior is to correct for incorrectly named variables that were introduced
						# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
						# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
						# which is why we correct for the naming here.
						SCREAMING_SNAKE_CASE__								=      self.num_attention_heads or self.attention_head_dim

						# input
						SCREAMING_SNAKE_CASE__								=      nn.Conv(
						    block_out_channels[0]    ,				kernel_size=(3, 3)    ,				strides=(1, 1)    ,				padding=((1, 1), (1, 1))    ,				dtype=self.dtype    ,				)

						# time
						SCREAMING_SNAKE_CASE__								=      FlaxTimesteps(
						    block_out_channels[0]    ,				flip_sin_to_cos=self.flip_sin_to_cos    ,				freq_shift=self.config.freq_shift       )
						SCREAMING_SNAKE_CASE__								=      FlaxTimestepEmbedding(__UpperCAmelCase    ,				dtype=self.dtype       )

						SCREAMING_SNAKE_CASE__								=      FlaxControlNetConditioningEmbedding(
						    conditioning_embedding_channels=block_out_channels[0]    ,				block_out_channels=self.conditioning_embedding_out_channels    ,				)

						SCREAMING_SNAKE_CASE__								=      self.only_cross_attention
						if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase       ):
									SCREAMING_SNAKE_CASE__								=      (only_cross_attention,) * len(self.down_block_types       )

						if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase       ):
									SCREAMING_SNAKE_CASE__								=      (num_attention_heads,) * len(self.down_block_types       )

						# down
						SCREAMING_SNAKE_CASE__								=      []
						SCREAMING_SNAKE_CASE__								=      []

						SCREAMING_SNAKE_CASE__								=      block_out_channels[0]

						SCREAMING_SNAKE_CASE__								=      nn.Conv(
						    __UpperCAmelCase    ,				kernel_size=(1, 1)    ,				padding="""VALID"""    ,				kernel_init=nn.initializers.zeros_init()    ,				bias_init=nn.initializers.zeros_init()    ,				dtype=self.dtype    ,				)
						controlnet_down_blocks.append(__UpperCAmelCase       )

						for i, down_block_type in enumerate(self.down_block_types       ):
									SCREAMING_SNAKE_CASE__								=      output_channel
									SCREAMING_SNAKE_CASE__								=      block_out_channels[i]
									SCREAMING_SNAKE_CASE__								=      i == len(__UpperCAmelCase       ) - 1

									if down_block_type == "CrossAttnDownBlock2D":
												SCREAMING_SNAKE_CASE__								=      FlaxCrossAttnDownBlockaD(
												    in_channels=__UpperCAmelCase    ,				out_channels=__UpperCAmelCase    ,				dropout=self.dropout    ,				num_layers=self.layers_per_block    ,				num_attention_heads=num_attention_heads[i]    ,				add_downsample=not is_final_block    ,				use_linear_projection=self.use_linear_projection    ,				only_cross_attention=only_cross_attention[i]    ,				dtype=self.dtype    ,				)
									else:
												SCREAMING_SNAKE_CASE__								=      FlaxDownBlockaD(
												    in_channels=__UpperCAmelCase    ,				out_channels=__UpperCAmelCase    ,				dropout=self.dropout    ,				num_layers=self.layers_per_block    ,				add_downsample=not is_final_block    ,				dtype=self.dtype    ,				)

									down_blocks.append(__UpperCAmelCase       )

									for _ in range(self.layers_per_block       ):
												SCREAMING_SNAKE_CASE__								=      nn.Conv(
												    __UpperCAmelCase    ,				kernel_size=(1, 1)    ,				padding="""VALID"""    ,				kernel_init=nn.initializers.zeros_init()    ,				bias_init=nn.initializers.zeros_init()    ,				dtype=self.dtype    ,				)
												controlnet_down_blocks.append(__UpperCAmelCase       )

									if not is_final_block:
												SCREAMING_SNAKE_CASE__								=      nn.Conv(
												    __UpperCAmelCase    ,				kernel_size=(1, 1)    ,				padding="""VALID"""    ,				kernel_init=nn.initializers.zeros_init()    ,				bias_init=nn.initializers.zeros_init()    ,				dtype=self.dtype    ,				)
												controlnet_down_blocks.append(__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      down_blocks
						SCREAMING_SNAKE_CASE__								=      controlnet_down_blocks

						# mid
						SCREAMING_SNAKE_CASE__								=      block_out_channels[-1]
						SCREAMING_SNAKE_CASE__								=      FlaxUNetMidBlockaDCrossAttn(
						    in_channels=__UpperCAmelCase    ,				dropout=self.dropout    ,				num_attention_heads=num_attention_heads[-1]    ,				use_linear_projection=self.use_linear_projection    ,				dtype=self.dtype    ,				)

						SCREAMING_SNAKE_CASE__								=      nn.Conv(
						    __UpperCAmelCase    ,				kernel_size=(1, 1)    ,				padding="""VALID"""    ,				kernel_init=nn.initializers.zeros_init()    ,				bias_init=nn.initializers.zeros_init()    ,				dtype=self.dtype    ,				)





			def __call__(					self				:						List[Any]    ,				__UpperCAmelCase				:						Optional[Any]    ,				__UpperCAmelCase				:						Optional[int]    ,				__UpperCAmelCase				:						Union[str, Any]    ,				__UpperCAmelCase				:						Optional[int]    ,				__UpperCAmelCase				:						float = 1.0    ,				__UpperCAmelCase				:						bool = True    ,				__UpperCAmelCase				:						bool = False    ,				)   ->    Union[FlaxControlNetOutput, Tuple]:
						SCREAMING_SNAKE_CASE__								=      self.controlnet_conditioning_channel_order
						if channel_order == "bgr":
									SCREAMING_SNAKE_CASE__								=      jnp.flip(__UpperCAmelCase    ,				axis=1       )

						# 1. time
						if not isinstance(__UpperCAmelCase    ,				jnp.ndarray       ):
									SCREAMING_SNAKE_CASE__								=      jnp.array([timesteps]    ,				dtype=jnp.intaa       )
						elif isinstance(__UpperCAmelCase    ,				jnp.ndarray       ) and len(timesteps.shape       ) == 0:
									SCREAMING_SNAKE_CASE__								=      timesteps.astype(dtype=jnp.floataa       )
									SCREAMING_SNAKE_CASE__								=      jnp.expand_dims(__UpperCAmelCase    ,				0       )

						SCREAMING_SNAKE_CASE__								=      self.time_proj(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.time_embedding(__UpperCAmelCase       )

						# 2. pre-process
						SCREAMING_SNAKE_CASE__								=      jnp.transpose(__UpperCAmelCase    ,				(0, 2, 3, 1)       )
						SCREAMING_SNAKE_CASE__								=      self.conv_in(__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      jnp.transpose(__UpperCAmelCase    ,				(0, 2, 3, 1)       )
						SCREAMING_SNAKE_CASE__								=      self.controlnet_cond_embedding(__UpperCAmelCase       )
						sample += controlnet_cond

						# 3. down
						SCREAMING_SNAKE_CASE__								=      (sample,)
						for down_block in self.down_blocks:
									if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase       ):
												SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      down_block(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				deterministic=not train       )
									else:
												SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      down_block(__UpperCAmelCase    ,				__UpperCAmelCase    ,				deterministic=not train       )
									down_block_res_samples += res_samples

						# 4. mid
						SCREAMING_SNAKE_CASE__								=      self.mid_block(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				deterministic=not train       )

						# 5. contronet blocks
						SCREAMING_SNAKE_CASE__								=      ()
						for down_block_res_sample, controlnet_block in zip(__UpperCAmelCase    ,				self.controlnet_down_blocks       ):
									SCREAMING_SNAKE_CASE__								=      controlnet_block(__UpperCAmelCase       )
									controlnet_down_block_res_samples += (down_block_res_sample,)

						SCREAMING_SNAKE_CASE__								=      controlnet_down_block_res_samples

						SCREAMING_SNAKE_CASE__								=      self.controlnet_mid_block(__UpperCAmelCase       )

						# 6. scaling
						SCREAMING_SNAKE_CASE__								=      [sample * conditioning_scale for sample in down_block_res_samples]
						mid_block_res_sample *= conditioning_scale

						if not return_dict:
									return (down_block_res_samples, mid_block_res_sample)

						return FlaxControlNetOutput(
						    down_block_res_samples=__UpperCAmelCase    ,				mid_block_res_sample=__UpperCAmelCase       )





"""simple docstring"""




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
)
A_							:			Any											= logging.getLogger(__name__)

def 	A       (     snake_case__		,				snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      np.argmax(snake_case__		,				axis=1    )
			return np.sum(outputs == labels    )

def 	A       (     snake_case__    ):




			'''simple docstring'''


			with open(snake_case__		,				encoding="""utf_8"""    ) as f:
						SCREAMING_SNAKE_CASE__								=      csv.reader(snake_case__    )
						SCREAMING_SNAKE_CASE__								=      []
						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 	A       (     snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      []
			for dataset in encoded_datasets:
						SCREAMING_SNAKE_CASE__								=      len(snake_case__    )
						SCREAMING_SNAKE_CASE__								=      np.zeros((n_batch, 2, input_len)		,				dtype=np.intaa    )
						SCREAMING_SNAKE_CASE__								=      np.zeros((n_batch, 2)		,				dtype=np.intaa    )
						SCREAMING_SNAKE_CASE__								=      np.full((n_batch, 2, input_len)		,				fill_value=-1_00		,				dtype=np.intaa    )
						SCREAMING_SNAKE_CASE__								=      np.zeros((n_batch,)		,				dtype=np.intaa    )
						for (
						    i,
						    (story, conta, conta, mc_label),
						) in enumerate(snake_case__    ):
									SCREAMING_SNAKE_CASE__								=      [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
									SCREAMING_SNAKE_CASE__								=      [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
									SCREAMING_SNAKE_CASE__								=      with_conta
									SCREAMING_SNAKE_CASE__								=      with_conta
									SCREAMING_SNAKE_CASE__								=      len(snake_case__    ) - 1
									SCREAMING_SNAKE_CASE__								=      len(snake_case__    ) - 1
									SCREAMING_SNAKE_CASE__								=      with_conta
									SCREAMING_SNAKE_CASE__								=      with_conta
									SCREAMING_SNAKE_CASE__								=      mc_label
						SCREAMING_SNAKE_CASE__								=      (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 	A       (     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      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=3_74    )

			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."""    )
			SCREAMING_SNAKE_CASE__								=      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    )

			SCREAMING_SNAKE_CASE__								=      torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""    )
			SCREAMING_SNAKE_CASE__								=      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
			SCREAMING_SNAKE_CASE__								=      ["""_start_""", """_delimiter_""", """_classify_"""]
			SCREAMING_SNAKE_CASE__								=      OpenAIGPTTokenizer.from_pretrained(args.model_name    )
			tokenizer.add_tokens(snake_case__    )
			SCREAMING_SNAKE_CASE__								=      tokenizer.convert_tokens_to_ids(snake_case__    )
			SCREAMING_SNAKE_CASE__								=      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__    ):
						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..."""    )
			SCREAMING_SNAKE_CASE__								=      load_rocstories_dataset(args.train_dataset    )
			SCREAMING_SNAKE_CASE__								=      load_rocstories_dataset(args.eval_dataset    )
			SCREAMING_SNAKE_CASE__								=      (train_dataset, eval_dataset)
			SCREAMING_SNAKE_CASE__								=      tokenize_and_encode(snake_case__    )

			# Compute the max input length for the Transformer
			SCREAMING_SNAKE_CASE__								=      model.config.n_positions // 2 - 2
			SCREAMING_SNAKE_CASE__								=      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    )
			SCREAMING_SNAKE_CASE__								=      min(snake_case__		,				model.config.n_positions    )  # Max size of input for the pre-trained model

			# Prepare inputs tensors and dataloaders
			SCREAMING_SNAKE_CASE__								=      pre_process_datasets(snake_case__		,				snake_case__		,				snake_case__		,				*snake_case__    )
			SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      tensor_datasets[0], tensor_datasets[1]

			SCREAMING_SNAKE_CASE__								=      TensorDataset(*snake_case__    )
			SCREAMING_SNAKE_CASE__								=      RandomSampler(snake_case__    )
			SCREAMING_SNAKE_CASE__								=      DataLoader(snake_case__		,				sampler=snake_case__		,				batch_size=args.train_batch_size    )

			SCREAMING_SNAKE_CASE__								=      TensorDataset(*snake_case__    )
			SCREAMING_SNAKE_CASE__								=      SequentialSampler(snake_case__    )
			SCREAMING_SNAKE_CASE__								=      DataLoader(snake_case__		,				sampler=snake_case__		,				batch_size=args.eval_batch_size    )

			# Prepare optimizer
			if args.do_train:
						if args.max_steps > 0:
									SCREAMING_SNAKE_CASE__								=      args.max_steps
									SCREAMING_SNAKE_CASE__								=      args.max_steps // (len(snake_case__    ) // args.gradient_accumulation_steps) + 1
						else:
									SCREAMING_SNAKE_CASE__								=      len(snake_case__    ) // args.gradient_accumulation_steps * args.num_train_epochs

						SCREAMING_SNAKE_CASE__								=      list(model.named_parameters()    )
						SCREAMING_SNAKE_CASE__								=      ["""bias""", """LayerNorm.bias""", """LayerNorm.weight"""]
						SCREAMING_SNAKE_CASE__								=      [
						    {
						        """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},
						]
						SCREAMING_SNAKE_CASE__								=      AdamW(snake_case__		,				lr=args.learning_rate		,				eps=args.adam_epsilon    )
						SCREAMING_SNAKE_CASE__								=      get_linear_schedule_with_warmup(
						    snake_case__		,				num_warmup_steps=args.warmup_steps		,				num_training_steps=snake_case__    )

			if args.do_train:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      0, 0, None
						model.train()
						for _ in trange(int(args.num_train_epochs    )		,				desc="""Epoch"""    ):
									SCREAMING_SNAKE_CASE__								=      0
									SCREAMING_SNAKE_CASE__								=      0
									SCREAMING_SNAKE_CASE__								=      tqdm(snake_case__		,				desc="""Training"""    )
									for step, batch in enumerate(snake_case__    ):
												SCREAMING_SNAKE_CASE__								=      tuple(t.to(snake_case__    ) for t in batch    )
												SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      batch
												SCREAMING_SNAKE_CASE__								=      model(snake_case__		,				mc_token_ids=snake_case__		,				lm_labels=snake_case__		,				mc_labels=snake_case__    )
												SCREAMING_SNAKE_CASE__								=      args.lm_coef * losses[0] + losses[1]
												loss.backward()
												optimizer.step()
												scheduler.step()
												optimizer.zero_grad()
												tr_loss += loss.item()
												SCREAMING_SNAKE_CASE__								=      (
												    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
												)
												nb_tr_steps += 1
												SCREAMING_SNAKE_CASE__								=      """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
						SCREAMING_SNAKE_CASE__								=      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`
						SCREAMING_SNAKE_CASE__								=      os.path.join(args.output_dir		,				snake_case__    )
						SCREAMING_SNAKE_CASE__								=      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
						SCREAMING_SNAKE_CASE__								=      OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir    )
						SCREAMING_SNAKE_CASE__								=      OpenAIGPTTokenizer.from_pretrained(args.output_dir    )
						model.to(snake_case__    )

			if args.do_eval:
						model.eval()
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      0, 0
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      0, 0
						for batch in tqdm(snake_case__		,				desc="""Evaluating"""    ):
									SCREAMING_SNAKE_CASE__								=      tuple(t.to(snake_case__    ) for t in batch    )
									SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      batch
									with torch.no_grad():
												SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      model(
												    snake_case__		,				mc_token_ids=snake_case__		,				lm_labels=snake_case__		,				mc_labels=snake_case__    )

									SCREAMING_SNAKE_CASE__								=      mc_logits.detach().cpu().numpy()
									SCREAMING_SNAKE_CASE__								=      mc_labels.to("""cpu"""    ).numpy()
									SCREAMING_SNAKE_CASE__								=      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

						SCREAMING_SNAKE_CASE__								=      eval_loss / nb_eval_steps
						SCREAMING_SNAKE_CASE__								=      eval_accuracy / nb_eval_examples
						SCREAMING_SNAKE_CASE__								=      tr_loss / nb_tr_steps if args.do_train else None
						SCREAMING_SNAKE_CASE__								=      {"""eval_loss""": eval_loss, """eval_accuracy""": eval_accuracy, """train_loss""": train_loss}

						SCREAMING_SNAKE_CASE__								=      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()


