





from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


a_			=					logging.get_logger(__name__)

a_			=					{
    """deepmind/language-perceiver""": """https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json""",
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}



class  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  """perceiver"""




 def __init__(						self	,				__UpperCAmelCase=256	,				__UpperCAmelCase=1280	,				__UpperCAmelCase=768	,				__UpperCAmelCase=1	,				__UpperCAmelCase=26	,				__UpperCAmelCase=8	,				__UpperCAmelCase=8	,				__UpperCAmelCase=None	,				__UpperCAmelCase=None	,				__UpperCAmelCase="kv"	,				__UpperCAmelCase=1	,				__UpperCAmelCase=1	,				__UpperCAmelCase="gelu"	,				__UpperCAmelCase=0.1	,				__UpperCAmelCase=0.02	,				__UpperCAmelCase=1E-1_2	,				__UpperCAmelCase=True	,				__UpperCAmelCase=262	,				__UpperCAmelCase=2048	,				__UpperCAmelCase=56	,				__UpperCAmelCase=[368, 496]	,				__UpperCAmelCase=16	,				__UpperCAmelCase=1920	,				__UpperCAmelCase=16	,				__UpperCAmelCase=[1, 16, 224, 224]	,				**__UpperCAmelCase	,				):



    '''simple docstring'''
    super().__init__(**__a				)

    __lowerCamelCase													=    num_latents
    __lowerCamelCase													=    d_latents
    __lowerCamelCase													=    d_model
    __lowerCamelCase													=    num_blocks
    __lowerCamelCase													=    num_self_attends_per_block
    __lowerCamelCase													=    num_self_attention_heads
    __lowerCamelCase													=    num_cross_attention_heads
    __lowerCamelCase													=    qk_channels
    __lowerCamelCase													=    v_channels
    __lowerCamelCase													=    cross_attention_shape_for_attention
    __lowerCamelCase													=    self_attention_widening_factor
    __lowerCamelCase													=    cross_attention_widening_factor
    __lowerCamelCase													=    hidden_act
    __lowerCamelCase													=    attention_probs_dropout_prob
    __lowerCamelCase													=    initializer_range
    __lowerCamelCase													=    layer_norm_eps
    __lowerCamelCase													=    use_query_residual
    # masked language modeling attributes
    __lowerCamelCase													=    vocab_size
    __lowerCamelCase													=    max_position_embeddings
    # image classification attributes
    __lowerCamelCase													=    image_size
    # flow attributes
    __lowerCamelCase													=    train_size
    # multimodal autoencoding attributes
    __lowerCamelCase													=    num_frames
    __lowerCamelCase													=    audio_samples_per_frame
    __lowerCamelCase													=    samples_per_patch
    __lowerCamelCase													=    output_shape



class  __lowerCAmelCase     (	lowerCAmelCase__   ):




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    if self.task == "multiple-choice":
       __lowerCamelCase													=    {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
    else:
       __lowerCamelCase													=    {0: '''batch''', 1: '''sequence'''}
    return OrderedDict(
        [
            ('''inputs''', dynamic_axis),
            ('''attention_mask''', dynamic_axis),
        ]				)




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return 1E-4




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase = -1	,				__UpperCAmelCase = -1	,				__UpperCAmelCase = -1	,				__UpperCAmelCase = False	,				__UpperCAmelCase = None	,				__UpperCAmelCase = 3	,				__UpperCAmelCase = 40	,				__UpperCAmelCase = 40	,				):



    '''simple docstring'''
    # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

    if isinstance(__a	,				__a				):
       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       __lowerCamelCase													=    compute_effective_axis_dimension(
           __a	,				fixed_dimension=OnnxConfig.default_fixed_batch	,				num_token_to_add=0				)
       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       __lowerCamelCase													=    preprocessor.num_special_tokens_to_add(__a				)
       __lowerCamelCase													=    compute_effective_axis_dimension(
           __a	,				fixed_dimension=OnnxConfig.default_fixed_sequence	,				num_token_to_add=__a				)
       # Generate dummy inputs according to compute batch and sequence
       __lowerCamelCase													=    [''' '''.join(['''a''']				) * seq_length] * batch_size
       __lowerCamelCase													=    dict(preprocessor(__a	,				return_tensors=__a				)				)
       __lowerCamelCase													=    inputs.pop('''input_ids'''				)
       return inputs
    elif isinstance(__a	,				__a				) and preprocessor.model_input_names[0] == "pixel_values":
       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       __lowerCamelCase													=    compute_effective_axis_dimension(__a	,				fixed_dimension=OnnxConfig.default_fixed_batch				)
       __lowerCamelCase													=    self._generate_dummy_images(__a	,				__a	,				__a	,				__a				)
       __lowerCamelCase													=    dict(preprocessor(images=__a	,				return_tensors=__a				)				)
       __lowerCamelCase													=    inputs.pop('''pixel_values'''				)
       return inputs
    else:
       raise ValueError(
           '''Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.'''				)











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_			=					logging.getLogger(__name__)





def 		a__			(		_UpperCamelCase			: str ,_UpperCamelCase			: List[Any]							):
   __lowerCamelCase													=    np.argmax(_UpperCamelCase ,axis=1							)
   return np.sum(outputs == labels							)





def 		a__			(		_UpperCamelCase			: Optional[int]							):
   with open(_UpperCamelCase ,encoding='''utf_8'''							) as f:
      __lowerCamelCase													=    csv.reader(_UpperCamelCase							)
      __lowerCamelCase													=    []
      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 		a__			(		_UpperCamelCase			: Any ,_UpperCamelCase			: Dict ,_UpperCamelCase			: str ,_UpperCamelCase			: Union[str, Any] ,_UpperCamelCase			: Any ,_UpperCamelCase			: Dict							):
   __lowerCamelCase													=    []
   for dataset in encoded_datasets:
      __lowerCamelCase													=    len(_UpperCamelCase							)
      __lowerCamelCase													=    np.zeros((n_batch, 2, input_len) ,dtype=np.intaa							)
      __lowerCamelCase													=    np.zeros((n_batch, 2) ,dtype=np.intaa							)
      __lowerCamelCase													=    np.full((n_batch, 2, input_len) ,fill_value=-1_00 ,dtype=np.intaa							)
      __lowerCamelCase													=    np.zeros((n_batch,) ,dtype=np.intaa							)
      for (
          i,
          (story, conta, conta, mc_label),
      ) in enumerate(_UpperCamelCase							):
         __lowerCamelCase													=    [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
         __lowerCamelCase													=    [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
         __lowerCamelCase													=    with_conta
         __lowerCamelCase													=    with_conta
         __lowerCamelCase													=    len(_UpperCamelCase							) - 1
         __lowerCamelCase													=    len(_UpperCamelCase							) - 1
         __lowerCamelCase													=    with_conta
         __lowerCamelCase													=    with_conta
         __lowerCamelCase													=    mc_label
      __lowerCamelCase													=    (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 		a__			(		):
   __lowerCamelCase													=    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=42							)
   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=16							)
   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_74							)

   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.'''							)
   __lowerCamelCase													=    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							)

   __lowerCamelCase													=    torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''							)
   __lowerCamelCase													=    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
   __lowerCamelCase													=    ['''_start_''', '''_delimiter_''', '''_classify_''']
   __lowerCamelCase													=    OpenAIGPTTokenizer.from_pretrained(args.model_name							)
   tokenizer.add_tokens(_UpperCamelCase							)
   __lowerCamelCase													=    tokenizer.convert_tokens_to_ids(_UpperCamelCase							)
   __lowerCamelCase													=    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			: Dict							):
      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...'''							)
   __lowerCamelCase													=    load_rocstories_dataset(args.train_dataset							)
   __lowerCamelCase													=    load_rocstories_dataset(args.eval_dataset							)
   __lowerCamelCase													=    (train_dataset, eval_dataset)
   __lowerCamelCase													=    tokenize_and_encode(_UpperCamelCase							)

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

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

   __lowerCamelCase													=    TensorDataset(*_UpperCamelCase							)
   __lowerCamelCase													=    RandomSampler(_UpperCamelCase							)
   __lowerCamelCase													=    DataLoader(_UpperCamelCase ,sampler=_UpperCamelCase ,batch_size=args.train_batch_size							)

   __lowerCamelCase													=    TensorDataset(*_UpperCamelCase							)
   __lowerCamelCase													=    SequentialSampler(_UpperCamelCase							)
   __lowerCamelCase													=    DataLoader(_UpperCamelCase ,sampler=_UpperCamelCase ,batch_size=args.eval_batch_size							)

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

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

   if args.do_train:
      __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    0, 0, None
      model.train()
      for _ in trange(int(args.num_train_epochs							) ,desc='''Epoch'''							):
         __lowerCamelCase													=    0
         __lowerCamelCase													=    0
         __lowerCamelCase													=    tqdm(_UpperCamelCase ,desc='''Training'''							)
         for step, batch in enumerate(_UpperCamelCase							):
            __lowerCamelCase													=    tuple(t.to(_UpperCamelCase							) for t in batch							)
            __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    batch
            __lowerCamelCase													=    model(_UpperCamelCase ,mc_token_ids=_UpperCamelCase ,lm_labels=_UpperCamelCase ,mc_labels=_UpperCamelCase							)
            __lowerCamelCase													=    args.lm_coef * losses[0] + losses[1]
            loss.backward()
            optimizer.step()
            scheduler.step()
            optimizer.zero_grad()
            tr_loss += loss.item()
            __lowerCamelCase													=    (
                loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
            )
            nb_tr_steps += 1
            __lowerCamelCase													=    '''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
      __lowerCamelCase													=    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`
      __lowerCamelCase													=    os.path.join(args.output_dir ,_UpperCamelCase							)
      __lowerCamelCase													=    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
      __lowerCamelCase													=    OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir							)
      __lowerCamelCase													=    OpenAIGPTTokenizer.from_pretrained(args.output_dir							)
      model.to(_UpperCamelCase							)

   if args.do_eval:
      model.eval()
      __lowerCamelCase							,__lowerCamelCase													=    0, 0
      __lowerCamelCase							,__lowerCamelCase													=    0, 0
      for batch in tqdm(_UpperCamelCase ,desc='''Evaluating'''							):
         __lowerCamelCase													=    tuple(t.to(_UpperCamelCase							) for t in batch							)
         __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    batch
         with torch.no_grad():
            __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    model(
                _UpperCamelCase ,mc_token_ids=_UpperCamelCase ,lm_labels=_UpperCamelCase ,mc_labels=_UpperCamelCase							)

         __lowerCamelCase													=    mc_logits.detach().cpu().numpy()
         __lowerCamelCase													=    mc_labels.to('''cpu'''							).numpy()
         __lowerCamelCase													=    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

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

      __lowerCamelCase													=    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()






