

'''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,
    FlaxCrossAttnUpBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
    FlaxUpBlockaD,
)



@flax.struct.dataclass
class 					lowercase						(  a_ ):
       _lowerCamelCase		:      jnp.ndarray



@flax_register_to_config
class 					lowercase						(  nn.Module,					a_,					a_ ):
       _lowerCamelCase		:      int=   32
       _lowerCamelCase		:      int=   4
       _lowerCamelCase		:      int=   4
       _lowerCamelCase		:      Tuple[str]=   (
           "CrossAttnDownBlock2D",
           "CrossAttnDownBlock2D",
           "CrossAttnDownBlock2D",
           "DownBlock2D",
       )
       _lowerCamelCase		:      Tuple[str]=   ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
       _lowerCamelCase		:      Union[bool, Tuple[bool]]=   False
       _lowerCamelCase		:      Tuple[int]=   (320, 640, 1280, 1280)
       _lowerCamelCase		:      int=   2
       _lowerCamelCase		:      Union[int, Tuple[int]]=   8
       _lowerCamelCase		:      Optional[Union[int, Tuple[int]]]=   None
       _lowerCamelCase		:      int=   1280
       _lowerCamelCase		:      float=   0.0
       _lowerCamelCase		:      bool=   False
       _lowerCamelCase		:      jnp.dtype=   jnp.floataa
       _lowerCamelCase		:      bool=   True
       _lowerCamelCase		:      int=   0
       _lowerCamelCase		:      bool=   False


       def 						_snake_case							(			self    ,			_snake_case)							->				FrozenDict:
        # init input tensors
        UpperCAmelCase_		:      Dict       	=					(1, self.in_channels, self.sample_size, self.sample_size)
        UpperCAmelCase_		:      str       	=					jnp.zeros(_snake_case    ,			dtype=jnp.floataa)
        UpperCAmelCase_		:      int       	=					jnp.ones((1,)    ,			dtype=jnp.intaa)
        UpperCAmelCase_		:      List[Any]       	=					jnp.zeros((1, 1, self.cross_attention_dim)    ,			dtype=jnp.floataa)

        UpperCAmelCase_		:      Dict       	=					jax.random.split(_snake_case)
        UpperCAmelCase_		:      List[str]       	=					{'params': params_rng, 'dropout': dropout_rng}

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


       def 						_snake_case							(			self)							->				Any:
        UpperCAmelCase_		:      Tuple       	=					self.block_out_channels
        UpperCAmelCase_		:      Dict       	=					block_out_channels[0] * 4

        if self.num_attention_heads is not None:
         raise ValueError(
             'At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.')

        # 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.
        UpperCAmelCase_		:      Any       	=					self.num_attention_heads or self.attention_head_dim

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

        # time
        UpperCAmelCase_		:      List[Any]       	=					FlaxTimesteps(
            block_out_channels[0]    ,			flip_sin_to_cos=self.flip_sin_to_cos    ,			freq_shift=self.config.freq_shift)
        UpperCAmelCase_		:      List[str]       	=					FlaxTimestepEmbedding(_snake_case    ,			dtype=self.dtype)

        UpperCAmelCase_		:      Optional[int]       	=					self.only_cross_attention
        if isinstance(_snake_case    ,			_snake_case):
         UpperCAmelCase_		:      List[Any]       	=					(only_cross_attention,) * len(self.down_block_types)

        if isinstance(_snake_case    ,			_snake_case):
         UpperCAmelCase_		:      List[Any]       	=					(num_attention_heads,) * len(self.down_block_types)

        # down
        UpperCAmelCase_		:      Any       	=					[]
        UpperCAmelCase_		:      str       	=					block_out_channels[0]
        for i, down_block_type in enumerate(self.down_block_types):
         UpperCAmelCase_		:      List[Any]       	=					output_channel
         UpperCAmelCase_		:      Union[str, Any]       	=					block_out_channels[i]
         UpperCAmelCase_		:      Optional[Any]       	=					i == len(_snake_case) - 1

         if down_block_type == "CrossAttnDownBlock2D":
          UpperCAmelCase_		:      Any       	=					FlaxCrossAttnDownBlockaD(
              in_channels=_snake_case    ,			out_channels=_snake_case    ,			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]    ,			use_memory_efficient_attention=self.use_memory_efficient_attention    ,			dtype=self.dtype    ,			)
         else:
          UpperCAmelCase_		:      int       	=					FlaxDownBlockaD(
              in_channels=_snake_case    ,			out_channels=_snake_case    ,			dropout=self.dropout    ,			num_layers=self.layers_per_block    ,			add_downsample=not is_final_block    ,			dtype=self.dtype    ,			)

         down_blocks.append(_snake_case)
        UpperCAmelCase_		:      List[Any]       	=					down_blocks

        # mid
        UpperCAmelCase_		:      Optional[Any]       	=					FlaxUNetMidBlockaDCrossAttn(
            in_channels=block_out_channels[-1]    ,			dropout=self.dropout    ,			num_attention_heads=num_attention_heads[-1]    ,			use_linear_projection=self.use_linear_projection    ,			use_memory_efficient_attention=self.use_memory_efficient_attention    ,			dtype=self.dtype    ,			)

        # up
        UpperCAmelCase_		:      int       	=					[]
        UpperCAmelCase_		:      Optional[Any]       	=					list(reversed(_snake_case))
        UpperCAmelCase_		:      List[str]       	=					list(reversed(_snake_case))
        UpperCAmelCase_		:      Optional[int]       	=					list(reversed(_snake_case))
        UpperCAmelCase_		:      List[str]       	=					reversed_block_out_channels[0]
        for i, up_block_type in enumerate(self.up_block_types):
         UpperCAmelCase_		:      List[Any]       	=					output_channel
         UpperCAmelCase_		:      Optional[int]       	=					reversed_block_out_channels[i]
         UpperCAmelCase_		:      int       	=					reversed_block_out_channels[min(i + 1    ,			len(_snake_case) - 1)]

         UpperCAmelCase_		:      str       	=					i == len(_snake_case) - 1

         if up_block_type == "CrossAttnUpBlock2D":
          UpperCAmelCase_		:      Dict       	=					FlaxCrossAttnUpBlockaD(
              in_channels=_snake_case    ,			out_channels=_snake_case    ,			prev_output_channel=_snake_case    ,			num_layers=self.layers_per_block + 1    ,			num_attention_heads=reversed_num_attention_heads[i]    ,			add_upsample=not is_final_block    ,			dropout=self.dropout    ,			use_linear_projection=self.use_linear_projection    ,			only_cross_attention=only_cross_attention[i]    ,			use_memory_efficient_attention=self.use_memory_efficient_attention    ,			dtype=self.dtype    ,			)
         else:
          UpperCAmelCase_		:      Optional[Any]       	=					FlaxUpBlockaD(
              in_channels=_snake_case    ,			out_channels=_snake_case    ,			prev_output_channel=_snake_case    ,			num_layers=self.layers_per_block + 1    ,			add_upsample=not is_final_block    ,			dropout=self.dropout    ,			dtype=self.dtype    ,			)

         up_blocks.append(_snake_case)
         UpperCAmelCase_		:      List[Any]       	=					output_channel
        UpperCAmelCase_		:      Any       	=					up_blocks

        # out
        UpperCAmelCase_		:      int       	=					nn.GroupNorm(num_groups=32    ,			epsilon=1e-5)
        UpperCAmelCase_		:      Optional[Any]       	=					nn.Conv(
            self.out_channels    ,			kernel_size=(3, 3)    ,			strides=(1, 1)    ,			padding=((1, 1), (1, 1))    ,			dtype=self.dtype    ,			)





       def __call__(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case=None    ,			_snake_case=None    ,			_snake_case = True    ,			_snake_case = False    ,			)							->				Union[FlaxUNetaDConditionOutput, Tuple]:
        # 1. time
        if not isinstance(_snake_case    ,			jnp.ndarray):
         UpperCAmelCase_		:      Optional[Any]       	=					jnp.array([timesteps]    ,			dtype=jnp.intaa)
        elif isinstance(_snake_case    ,			jnp.ndarray) and len(timesteps.shape) == 0:
         UpperCAmelCase_		:      Dict       	=					timesteps.astype(dtype=jnp.floataa)
         UpperCAmelCase_		:      List[str]       	=					jnp.expand_dims(_snake_case    ,			0)

        UpperCAmelCase_		:      str       	=					self.time_proj(_snake_case)
        UpperCAmelCase_		:      List[str]       	=					self.time_embedding(_snake_case)

        # 2. pre-process
        UpperCAmelCase_		:      Optional[int]       	=					jnp.transpose(_snake_case    ,			(0, 2, 3, 1))
        UpperCAmelCase_		:      List[Any]       	=					self.conv_in(_snake_case)

        # 3. down
        UpperCAmelCase_		:      str       	=					(sample,)
        for down_block in self.down_blocks:
         if isinstance(_snake_case    ,			_snake_case):
          UpperCAmelCase_		:      Dict       	=					down_block(_snake_case    ,			_snake_case    ,			_snake_case    ,			deterministic=not train)
         else:
          UpperCAmelCase_		:      Optional[Any]       	=					down_block(_snake_case    ,			_snake_case    ,			deterministic=not train)
         down_block_res_samples += res_samples

        if down_block_additional_residuals is not None:
         UpperCAmelCase_		:      Optional[int]       	=					()

         for down_block_res_sample, down_block_additional_residual in zip(
             _snake_case    ,			_snake_case):
          down_block_res_sample += down_block_additional_residual
          new_down_block_res_samples += (down_block_res_sample,)

         UpperCAmelCase_		:      Union[str, Any]       	=					new_down_block_res_samples

        # 4. mid
        UpperCAmelCase_		:      Optional[Any]       	=					self.mid_block(_snake_case    ,			_snake_case    ,			_snake_case    ,			deterministic=not train)

        if mid_block_additional_residual is not None:
         sample += mid_block_additional_residual

        # 5. up
        for up_block in self.up_blocks:
         UpperCAmelCase_		:      List[str]       	=					down_block_res_samples[-(self.layers_per_block + 1) :]
         UpperCAmelCase_		:      Dict       	=					down_block_res_samples[: -(self.layers_per_block + 1)]
         if isinstance(_snake_case    ,			_snake_case):
          UpperCAmelCase_		:      str       	=					up_block(
              _snake_case    ,			temb=_snake_case    ,			encoder_hidden_states=_snake_case    ,			res_hidden_states_tuple=_snake_case    ,			deterministic=not train    ,			)
         else:
          UpperCAmelCase_		:      Optional[int]       	=					up_block(_snake_case    ,			temb=_snake_case    ,			res_hidden_states_tuple=_snake_case    ,			deterministic=not train)

        # 6. post-process
        UpperCAmelCase_		:      List[Any]       	=					self.conv_norm_out(_snake_case)
        UpperCAmelCase_		:      int       	=					nn.silu(_snake_case)
        UpperCAmelCase_		:      Any       	=					self.conv_out(_snake_case)
        UpperCAmelCase_		:      Tuple       	=					jnp.transpose(_snake_case    ,			(0, 3, 1, 2))

        if not return_dict:
         return (sample,)

        return FlaxUNetaDConditionOutput(sample=_snake_case)









'''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
)
lowerCAmelCase__										= logging.getLogger(__name__)







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    Union[str, Any]:
	UpperCAmelCase_		:      Any       	=					np.argmax(UpperCamelCase   ,axis=1  )
	return np.sum(outputs == labels  )







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    List[str]:
	with open(UpperCamelCase   ,encoding='utf_8'  ) as f:
		UpperCAmelCase_		:      int       	=					csv.reader(UpperCamelCase  )
		UpperCAmelCase_		:      Tuple       	=					[]
		next(UpperCamelCase  )  # skip the first line
		for line in tqdm(UpperCamelCase  ):
			output.append((' '.join(line[1:5]  ), line[5], line[6], int(line[-1]  ) - 1)  )
	return output







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )    ->    Optional[int]:
	UpperCAmelCase_		:      str       	=					[]
	for dataset in encoded_datasets:
		UpperCAmelCase_		:      str       	=					len(UpperCamelCase  )
		UpperCAmelCase_		:      str       	=					np.zeros((n_batch, 2, input_len)   ,dtype=np.intaa  )
		UpperCAmelCase_		:      List[Any]       	=					np.zeros((n_batch, 2)   ,dtype=np.intaa  )
		UpperCAmelCase_		:      Any       	=					np.full((n_batch, 2, input_len)   ,fill_value=-1_0_0   ,dtype=np.intaa  )
		UpperCAmelCase_		:      List[Any]       	=					np.zeros((n_batch,)   ,dtype=np.intaa  )
		for (
		    i,
		    (story, conta, conta, mc_label),
		) in enumerate(UpperCamelCase  ):
			UpperCAmelCase_		:      Union[str, Any]       	=					[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
			UpperCAmelCase_		:      List[Any]       	=					[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
			UpperCAmelCase_		:      Any       	=					with_conta
			UpperCAmelCase_		:      Tuple       	=					with_conta
			UpperCAmelCase_		:      Union[str, Any]       	=					len(UpperCamelCase  ) - 1
			UpperCAmelCase_		:      int       	=					len(UpperCamelCase  ) - 1
			UpperCAmelCase_		:      Any       	=					with_conta
			UpperCAmelCase_		:      Any       	=					with_conta
			UpperCAmelCase_		:      Optional[int]       	=					mc_label
		UpperCAmelCase_		:      Tuple       	=					(input_ids, mc_token_ids, lm_labels, mc_labels)
		tensor_datasets.append(tuple(torch.tensor(UpperCamelCase  ) for t in all_inputs  )  )
	return tensor_datasets







def 						SCREAMING_SNAKE_CASE(  )    ->    str:
	UpperCAmelCase_		:      Union[str, Any]       	=					argparse.ArgumentParser()
	parser.add_argument('--model_name'   ,type=UpperCamelCase   ,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=UpperCamelCase   ,type=UpperCamelCase   ,required=UpperCamelCase   ,help='The output directory where the model predictions and checkpoints will be written.'   ,)
	parser.add_argument('--train_dataset'   ,type=UpperCamelCase   ,default=''  )
	parser.add_argument('--eval_dataset'   ,type=UpperCamelCase   ,default=''  )
	parser.add_argument('--seed'   ,type=UpperCamelCase   ,default=4_2  )
	parser.add_argument('--num_train_epochs'   ,type=UpperCamelCase   ,default=3  )
	parser.add_argument('--train_batch_size'   ,type=UpperCamelCase   ,default=8  )
	parser.add_argument('--eval_batch_size'   ,type=UpperCamelCase   ,default=1_6  )
	parser.add_argument('--adam_epsilon'   ,default=1e-8   ,type=UpperCamelCase   ,help='Epsilon for Adam optimizer.'  )
	parser.add_argument('--max_grad_norm'   ,type=UpperCamelCase   ,default=1  )
	parser.add_argument(
	    '--max_steps'   ,default=-1   ,type=UpperCamelCase   ,help=(
	        'If > 0: set total number of training                         steps to perform. Override num_train_epochs.'
	    )   ,)
	parser.add_argument(
	    '--gradient_accumulation_steps'   ,type=UpperCamelCase   ,default=1   ,help='Number of updates steps to accumulate before                        performing a backward/update pass.'   ,)
	parser.add_argument('--learning_rate'   ,type=UpperCamelCase   ,default=6.25e-5  )
	parser.add_argument('--warmup_steps'   ,default=0   ,type=UpperCamelCase   ,help='Linear warmup over warmup_steps.'  )
	parser.add_argument('--lr_schedule'   ,type=UpperCamelCase   ,default='warmup_linear'  )
	parser.add_argument('--weight_decay'   ,type=UpperCamelCase   ,default=0.01  )
	parser.add_argument('--lm_coef'   ,type=UpperCamelCase   ,default=0.9  )
	parser.add_argument('--n_valid'   ,type=UpperCamelCase   ,default=3_7_4  )

	parser.add_argument('--server_ip'   ,type=UpperCamelCase   ,default=''   ,help='Can be used for distant debugging.'  )
	parser.add_argument('--server_port'   ,type=UpperCamelCase   ,default=''   ,help='Can be used for distant debugging.'  )
	UpperCAmelCase_		:      Optional[Any]       	=					parser.parse_args()
	print(UpperCamelCase  )

	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=UpperCamelCase  )
		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  )

	UpperCAmelCase_		:      Union[str, Any]       	=					torch.device('cuda' if torch.cuda.is_available() else 'cpu'  )
	UpperCAmelCase_		:      Dict       	=					torch.cuda.device_count()
	logger.info('device: {}, n_gpu {}'.format(UpperCamelCase   ,UpperCamelCase  )  )

	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
	UpperCAmelCase_		:      Optional[int]       	=					['_start_', '_delimiter_', '_classify_']
	UpperCAmelCase_		:      Union[str, Any]       	=					OpenAIGPTTokenizer.from_pretrained(args.model_name  )
	tokenizer.add_tokens(UpperCamelCase  )
	UpperCAmelCase_		:      Optional[Any]       	=					tokenizer.convert_tokens_to_ids(UpperCamelCase  )
	UpperCAmelCase_		:      Union[str, Any]       	=					OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name  )
	model.resize_token_embeddings(len(UpperCamelCase  )  )
	model.to(UpperCamelCase  )

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

	logger.info('Encoding dataset...'  )
	UpperCAmelCase_		:      List[str]       	=					load_rocstories_dataset(args.train_dataset  )
	UpperCAmelCase_		:      Optional[Any]       	=					load_rocstories_dataset(args.eval_dataset  )
	UpperCAmelCase_		:      Dict       	=					(train_dataset, eval_dataset)
	UpperCAmelCase_		:      Union[str, Any]       	=					tokenize_and_encode(UpperCamelCase  )

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

	# Prepare inputs tensors and dataloaders
	UpperCAmelCase_		:      Optional[int]       	=					pre_process_datasets(UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,*UpperCamelCase  )
	UpperCAmelCase_	,    UpperCAmelCase_		:      Dict       	=					tensor_datasets[0], tensor_datasets[1]

	UpperCAmelCase_		:      Optional[int]       	=					TensorDataset(*UpperCamelCase  )
	UpperCAmelCase_		:      Optional[Any]       	=					RandomSampler(UpperCamelCase  )
	UpperCAmelCase_		:      int       	=					DataLoader(UpperCamelCase   ,sampler=UpperCamelCase   ,batch_size=args.train_batch_size  )

	UpperCAmelCase_		:      int       	=					TensorDataset(*UpperCamelCase  )
	UpperCAmelCase_		:      Optional[int]       	=					SequentialSampler(UpperCamelCase  )
	UpperCAmelCase_		:      Tuple       	=					DataLoader(UpperCamelCase   ,sampler=UpperCamelCase   ,batch_size=args.eval_batch_size  )

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

		UpperCAmelCase_		:      Any       	=					list(model.named_parameters()  )
		UpperCAmelCase_		:      Any       	=					['bias', 'LayerNorm.bias', 'LayerNorm.weight']
		UpperCAmelCase_		:      Dict       	=					[
		    {
		        '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},
		]
		UpperCAmelCase_		:      Any       	=					AdamW(UpperCamelCase   ,lr=args.learning_rate   ,eps=args.adam_epsilon  )
		UpperCAmelCase_		:      List[str]       	=					get_linear_schedule_with_warmup(
		    UpperCamelCase   ,num_warmup_steps=args.warmup_steps   ,num_training_steps=UpperCamelCase  )

	if args.do_train:
		UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_		:      List[Any]       	=					0, 0, None
		model.train()
		for _ in trange(int(args.num_train_epochs  )   ,desc='Epoch'  ):
			UpperCAmelCase_		:      Union[str, Any]       	=					0
			UpperCAmelCase_		:      Any       	=					0
			UpperCAmelCase_		:      List[Any]       	=					tqdm(UpperCamelCase   ,desc='Training'  )
			for step, batch in enumerate(UpperCamelCase  ):
				UpperCAmelCase_		:      Optional[Any]       	=					tuple(t.to(UpperCamelCase  ) for t in batch  )
				UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_		:      Tuple       	=					batch
				UpperCAmelCase_		:      Dict       	=					model(UpperCamelCase   ,mc_token_ids=UpperCamelCase   ,lm_labels=UpperCamelCase   ,mc_labels=UpperCamelCase  )
				UpperCAmelCase_		:      int       	=					args.lm_coef * losses[0] + losses[1]
				loss.backward()
				optimizer.step()
				scheduler.step()
				optimizer.zero_grad()
				tr_loss += loss.item()
				UpperCAmelCase_		:      Dict       	=					(
				    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
				)
				nb_tr_steps += 1
				UpperCAmelCase_		:      List[Any]       	=					'Training loss: {:.2e} lr: {:.2e}'.format(UpperCamelCase   ,scheduler.get_lr()[0]  )

    # Save a trained model
	if args.do_train:
		# Save a trained model, configuration and tokenizer
		UpperCAmelCase_		:      int       	=					model.module if hasattr(UpperCamelCase   ,'module'  ) else model  # Only save the model itself

		# If we save using the predefined names, we can load using `from_pretrained`
		UpperCAmelCase_		:      int       	=					os.path.join(args.output_dir   ,UpperCamelCase  )
		UpperCAmelCase_		:      Any       	=					os.path.join(args.output_dir   ,UpperCamelCase  )

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

		# Load a trained model and vocabulary that you have fine-tuned
		UpperCAmelCase_		:      int       	=					OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir  )
		UpperCAmelCase_		:      Dict       	=					OpenAIGPTTokenizer.from_pretrained(args.output_dir  )
		model.to(UpperCamelCase  )

	if args.do_eval:
		model.eval()
		UpperCAmelCase_	,    UpperCAmelCase_		:      Union[str, Any]       	=					0, 0
		UpperCAmelCase_	,    UpperCAmelCase_		:      Any       	=					0, 0
		for batch in tqdm(UpperCamelCase   ,desc='Evaluating'  ):
			UpperCAmelCase_		:      Union[str, Any]       	=					tuple(t.to(UpperCamelCase  ) for t in batch  )
			UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_		:      Any       	=					batch
			with torch.no_grad():
				UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_		:      Union[str, Any]       	=					model(
				    UpperCamelCase   ,mc_token_ids=UpperCamelCase   ,lm_labels=UpperCamelCase   ,mc_labels=UpperCamelCase  )

			UpperCAmelCase_		:      Optional[Any]       	=					mc_logits.detach().cpu().numpy()
			UpperCAmelCase_		:      int       	=					mc_labels.to('cpu'  ).numpy()
			UpperCAmelCase_		:      Optional[int]       	=					accuracy(UpperCamelCase   ,UpperCamelCase  )

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

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

		UpperCAmelCase_		:      str       	=					eval_loss / nb_eval_steps
		UpperCAmelCase_		:      str       	=					eval_accuracy / nb_eval_examples
		UpperCAmelCase_		:      List[Any]       	=					tr_loss / nb_tr_steps if args.do_train else None
		UpperCAmelCase_		:      Dict       	=					{'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'train_loss': train_loss}

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


if __name__ == "__main__":
						main()






