






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     :							Any    =logging.getLogger(__name__)







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase      )			->     Dict:
     UpperCamelCase__      :       List[Any]	    =  np.argmax(__lowerCAmelCase ,   axis=1      )
     return np.sum(outputs == labels      )







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







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     Dict:
     UpperCamelCase__      :       Optional[int]	    =  []
     for dataset in encoded_datasets:
          UpperCamelCase__      :       List[Any]	    =  len(__lowerCAmelCase      )
          UpperCamelCase__      :       List[str]	    =  np.zeros((n_batch, 2, input_len) ,   dtype=np.intaa      )
          UpperCamelCase__      :       Tuple	    =  np.zeros((n_batch, 2) ,   dtype=np.intaa      )
          UpperCamelCase__      :       str	    =  np.full((n_batch, 2, input_len) ,   fill_value=-100 ,   dtype=np.intaa      )
          UpperCamelCase__      :       Union[str, Any]	    =  np.zeros((n_batch,) ,   dtype=np.intaa      )
          for (
              i,
              (story, conta, conta, mc_label),
          ) in enumerate(__lowerCAmelCase      ):
               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__      :       str	    =  with_conta
               UpperCamelCase__      :       Union[str, Any]	    =  with_conta
               UpperCamelCase__      :       str	    =  len(__lowerCAmelCase      ) - 1
               UpperCamelCase__      :       List[Any]	    =  len(__lowerCAmelCase      ) - 1
               UpperCamelCase__      :       Dict	    =  with_conta
               UpperCamelCase__      :       Tuple	    =  with_conta
               UpperCamelCase__      :       Dict	    =  mc_label
          UpperCamelCase__      :       List[str]	    =  (input_ids, mc_token_ids, lm_labels, mc_labels)
          tensor_datasets.append(tuple(torch.tensor(__lowerCAmelCase      ) for t in all_inputs      )      )
     return tensor_datasets







def 		SCREAMING_SNAKE_CASE							( )			->     Tuple:
     UpperCamelCase__      :       int	    =  argparse.ArgumentParser()
     parser.add_argument("--model_name" ,   type=__lowerCAmelCase ,   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=__lowerCAmelCase ,   type=__lowerCAmelCase ,   required=__lowerCAmelCase ,   help="The output directory where the model predictions and checkpoints will be written." ,   )
     parser.add_argument("--train_dataset" ,   type=__lowerCAmelCase ,   default=""      )
     parser.add_argument("--eval_dataset" ,   type=__lowerCAmelCase ,   default=""      )
     parser.add_argument("--seed" ,   type=__lowerCAmelCase ,   default=42      )
     parser.add_argument("--num_train_epochs" ,   type=__lowerCAmelCase ,   default=3      )
     parser.add_argument("--train_batch_size" ,   type=__lowerCAmelCase ,   default=8      )
     parser.add_argument("--eval_batch_size" ,   type=__lowerCAmelCase ,   default=16      )
     parser.add_argument("--adam_epsilon" ,   default=1E-8 ,   type=__lowerCAmelCase ,   help="Epsilon for Adam optimizer."      )
     parser.add_argument("--max_grad_norm" ,   type=__lowerCAmelCase ,   default=1      )
     parser.add_argument(
         "--max_steps" ,   default=-1 ,   type=__lowerCAmelCase ,   help=(
             "If > 0: set total number of training                         steps to perform. Override num_train_epochs."
         ) ,   )
     parser.add_argument(
         "--gradient_accumulation_steps" ,   type=__lowerCAmelCase ,   default=1 ,   help="Number of updates steps to accumulate before                        performing a backward/update pass." ,   )
     parser.add_argument("--learning_rate" ,   type=__lowerCAmelCase ,   default=6.2_5E-5      )
     parser.add_argument("--warmup_steps" ,   default=0 ,   type=__lowerCAmelCase ,   help="Linear warmup over warmup_steps."      )
     parser.add_argument("--lr_schedule" ,   type=__lowerCAmelCase ,   default="warmup_linear"      )
     parser.add_argument("--weight_decay" ,   type=__lowerCAmelCase ,   default=0.0_1      )
     parser.add_argument("--lm_coef" ,   type=__lowerCAmelCase ,   default=0.9      )
     parser.add_argument("--n_valid" ,   type=__lowerCAmelCase ,   default=374      )

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

     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=__lowerCAmelCase      )
          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__      :       Any	    =  torch.device("cuda" if torch.cuda.is_available() else "cpu"      )
     UpperCamelCase__      :       Tuple	    =  torch.cuda.device_count()
     logger.info("device: {}, n_gpu {}".format(__lowerCAmelCase ,   __lowerCAmelCase      )      )

     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__      :       Tuple	    =  OpenAIGPTTokenizer.from_pretrained(args.model_name      )
     tokenizer.add_tokens(__lowerCAmelCase      )
     UpperCamelCase__      :       str	    =  tokenizer.convert_tokens_to_ids(__lowerCAmelCase      )
     UpperCamelCase__      :       List[str]	    =  OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name      )
     model.resize_token_embeddings(len(__lowerCAmelCase      )      )
     model.to(__lowerCAmelCase      )

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

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

     # Compute the max input length for the Transformer
     UpperCamelCase__      :       Optional[Any]	    =  model.config.n_positions // 2 - 2
     UpperCamelCase__      :       Optional[Any]	    =  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__      :       Union[str, Any]	    =  min(__lowerCAmelCase ,   model.config.n_positions      )  # Max size of input for the pre-trained model

     # Prepare inputs tensors and dataloaders
     UpperCamelCase__      :       Dict	    =  pre_process_datasets(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   *__lowerCAmelCase      )
     UpperCamelCase__    ,					UpperCamelCase__      :       Tuple	    =  tensor_datasets[0], tensor_datasets[1]

     UpperCamelCase__      :       Any	    =  TensorDataset(*__lowerCAmelCase      )
     UpperCamelCase__      :       List[Any]	    =  RandomSampler(__lowerCAmelCase      )
     UpperCamelCase__      :       Dict	    =  DataLoader(__lowerCAmelCase ,   sampler=__lowerCAmelCase ,   batch_size=args.train_batch_size      )

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

     # Prepare optimizer
     if args.do_train:
          if args.max_steps > 0:
               UpperCamelCase__      :       str	    =  args.max_steps
               UpperCamelCase__      :       int	    =  args.max_steps // (len(__lowerCAmelCase      ) // args.gradient_accumulation_steps) + 1
          else:
               UpperCamelCase__      :       Optional[Any]	    =  len(__lowerCAmelCase      ) // args.gradient_accumulation_steps * args.num_train_epochs

          UpperCamelCase__      :       Optional[int]	    =  list(model.named_parameters()      )
          UpperCamelCase__      :       List[str]	    =  ["bias", "LayerNorm.bias", "LayerNorm.weight"]
          UpperCamelCase__      :       Tuple	    =  [
              {
                  "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__      :       Union[str, Any]	    =  AdamW(__lowerCAmelCase ,   lr=args.learning_rate ,   eps=args.adam_epsilon      )
          UpperCamelCase__      :       Any	    =  get_linear_schedule_with_warmup(
              __lowerCAmelCase ,   num_warmup_steps=args.warmup_steps ,   num_training_steps=__lowerCAmelCase      )

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

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

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

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

          # Load a trained model and vocabulary that you have fine-tuned
          UpperCamelCase__      :       Tuple	    =  OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir      )
          UpperCamelCase__      :       Optional[Any]	    =  OpenAIGPTTokenizer.from_pretrained(args.output_dir      )
          model.to(__lowerCAmelCase      )

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

               UpperCamelCase__      :       int	    =  mc_logits.detach().cpu().numpy()
               UpperCamelCase__      :       Optional[int]	    =  mc_labels.to("cpu"      ).numpy()
               UpperCamelCase__      :       Optional[int]	    =  accuracy(__lowerCAmelCase ,   __lowerCAmelCase      )

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

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

          UpperCamelCase__      :       Any	    =  eval_loss / nb_eval_steps
          UpperCamelCase__      :       Any	    =  eval_accuracy / nb_eval_examples
          UpperCamelCase__      :       List[str]	    =  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__      :       Dict	    =  os.path.join(args.output_dir ,   "eval_results.txt"      )
          with open(__lowerCAmelCase ,   "w"      ) as writer:
               logger.info("***** Eval results *****"      )
               for key in sorted(result.keys()      ):
                    logger.info("  %s = %s" ,   __lowerCAmelCase ,   str(result[key]      )      )
                    writer.write("%s = %s\n" % (key, str(result[key]      ))      )


if __name__ == "__main__":
 main()







import unittest

from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
 import jax

 from transformers.models.big_bird.modeling_flax_big_bird import (
     FlaxBigBirdForCausalLM,
     FlaxBigBirdForMaskedLM,
     FlaxBigBirdForMultipleChoice,
     FlaxBigBirdForPreTraining,
     FlaxBigBirdForQuestionAnswering,
     FlaxBigBirdForSequenceClassification,
     FlaxBigBirdForTokenClassification,
     FlaxBigBirdModel,
 )





class       __a  (   unittest.TestCase	):







 def __init__(		self :       int							,       SCREAMING_SNAKE_CASE :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       Dict=2							,       SCREAMING_SNAKE_CASE :       List[Any]=56							,       SCREAMING_SNAKE_CASE :       List[str]=True							,       SCREAMING_SNAKE_CASE :       str=True							,       SCREAMING_SNAKE_CASE :       Any=True							,       SCREAMING_SNAKE_CASE :       int=True							,       SCREAMING_SNAKE_CASE :       List[str]=99							,       SCREAMING_SNAKE_CASE :       str=32							,       SCREAMING_SNAKE_CASE :       Optional[int]=2							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=2							,       SCREAMING_SNAKE_CASE :       Dict=7							,       SCREAMING_SNAKE_CASE :       List[Any]="gelu_new"							,       SCREAMING_SNAKE_CASE :       List[Any]=0.1							,       SCREAMING_SNAKE_CASE :       int=0.1							,       SCREAMING_SNAKE_CASE :       Any=5_12							,       SCREAMING_SNAKE_CASE :       Dict=16							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=2							,       SCREAMING_SNAKE_CASE :       Any=0.0_2							,       SCREAMING_SNAKE_CASE :       Optional[Any]=4							,       SCREAMING_SNAKE_CASE :       int="block_sparse"							,       SCREAMING_SNAKE_CASE :       int=True							,       SCREAMING_SNAKE_CASE :       Optional[Any]=False							,       SCREAMING_SNAKE_CASE :       Tuple=2							,       SCREAMING_SNAKE_CASE :       Dict=3							,       ):


      '''simple docstring'''






      UpperCamelCase__      :       List[str]	    =  parent
      UpperCamelCase__      :       Union[str, Any]	    =  batch_size
      UpperCamelCase__      :       Union[str, Any]	    =  seq_length
      UpperCamelCase__      :       Dict	    =  is_training
      UpperCamelCase__      :       Optional[int]	    =  use_attention_mask
      UpperCamelCase__      :       List[str]	    =  use_token_type_ids
      UpperCamelCase__      :       Dict	    =  use_labels
      UpperCamelCase__      :       Optional[int]	    =  vocab_size
      UpperCamelCase__      :       List[Any]	    =  hidden_size
      UpperCamelCase__      :       List[str]	    =  num_hidden_layers
      UpperCamelCase__      :       List[str]	    =  num_attention_heads
      UpperCamelCase__      :       int	    =  intermediate_size
      UpperCamelCase__      :       str	    =  hidden_act
      UpperCamelCase__      :       Tuple	    =  hidden_dropout_prob
      UpperCamelCase__      :       Any	    =  attention_probs_dropout_prob
      UpperCamelCase__      :       str	    =  max_position_embeddings
      UpperCamelCase__      :       Tuple	    =  type_vocab_size
      UpperCamelCase__      :       Dict	    =  type_sequence_label_size
      UpperCamelCase__      :       Optional[Any]	    =  initializer_range
      UpperCamelCase__      :       Any	    =  num_choices

      UpperCamelCase__      :       Dict	    =  rescale_embeddings
      UpperCamelCase__      :       Union[str, Any]	    =  attention_type
      UpperCamelCase__      :       int	    =  use_bias
      UpperCamelCase__      :       List[Any]	    =  block_size
      UpperCamelCase__      :       Union[str, Any]	    =  num_random_blocks







 def  __lowercase (		self :       List[str]						):


      '''simple docstring'''






      UpperCamelCase__      :       Dict	    =  ids_tensor([self.batch_size, self.seq_length]							,       self.vocab_size						)

      UpperCamelCase__      :       Any	    =  None
      if self.use_attention_mask:
           UpperCamelCase__      :       str	    =  random_attention_mask([self.batch_size, self.seq_length]						)

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

      UpperCamelCase__      :       List[Any]	    =  BigBirdConfig(
          vocab_size=self.vocab_size							,       hidden_size=self.hidden_size							,       num_hidden_layers=self.num_hidden_layers							,       num_attention_heads=self.num_attention_heads							,       intermediate_size=self.intermediate_size							,       hidden_act=self.hidden_act							,       hidden_dropout_prob=self.hidden_dropout_prob							,       attention_probs_dropout_prob=self.attention_probs_dropout_prob							,       max_position_embeddings=self.max_position_embeddings							,       type_vocab_size=self.type_vocab_size							,       is_decoder=SCREAMING_SNAKE_CASE							,       initializer_range=self.initializer_range							,       attention_type=self.attention_type							,       block_size=self.block_size							,       num_random_blocks=self.num_random_blocks							,       use_bias=self.use_bias							,       rescale_embeddings=self.rescale_embeddings							,       )

      return config, input_ids, token_type_ids, attention_mask







 def  __lowercase (		self :       Any						):


      '''simple docstring'''






      UpperCamelCase__      :       int	    =  self.prepare_config_and_inputs()
      UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__      :       Tuple	    =  config_and_inputs
      UpperCamelCase__      :       int	    =  {
          "input_ids": input_ids,
          "token_type_ids": token_type_ids,
          "attention_mask": attention_mask,
      }
      return config, inputs_dict







@require_flax
class       __a  (   A__	,       unittest.TestCase	):
 _lowerCAmelCase	:	Optional[Any]				=			(
     (
         FlaxBigBirdForCausalLM,
         FlaxBigBirdModel,
         FlaxBigBirdForPreTraining,
         FlaxBigBirdForMaskedLM,
         FlaxBigBirdForMultipleChoice,
         FlaxBigBirdForQuestionAnswering,
         FlaxBigBirdForSequenceClassification,
         FlaxBigBirdForTokenClassification,
     )
     if is_flax_available()
     else ()
 )

 _lowerCAmelCase	:	Dict				=			False
 _lowerCAmelCase	:	Optional[int]				=			False







 def  __lowercase (		self :       Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       Any	    =  FlaxBigBirdModelTester(self						)







 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def  __lowercase (		self :       List[Any]						):


      '''simple docstring'''






      super().test_from_pretrained_save_pretrained()







 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def  __lowercase (		self :       Optional[Any]						):


      '''simple docstring'''






      super().test_from_pretrained_with_no_automatic_init()







 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def  __lowercase (		self :       List[Any]						):


      '''simple docstring'''






      super().test_no_automatic_init()







 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def  __lowercase (		self :       Union[str, Any]						):


      '''simple docstring'''






      super().test_hidden_states_output()







 @slow
 def  __lowercase (		self :       str						):


      '''simple docstring'''






      for model_class_name in self.all_model_classes:
           UpperCamelCase__      :       Any	    =  model_class_name.from_pretrained("google/bigbird-roberta-base"						)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       Optional[int]						):


      '''simple docstring'''






      if self.test_attn_probs:
           super().test_attention_outputs()







 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def  __lowercase (		self :       Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__    ,					UpperCamelCase__      :       Tuple	    =  self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__						):
                UpperCamelCase__      :       Dict	    =  self._prepare_for_class(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)
                UpperCamelCase__      :       Union[str, Any]	    =  model_class(SCREAMING_SNAKE_CASE						)

                @jax.jit
                def model_jitted(SCREAMING_SNAKE_CASE :       str							,       SCREAMING_SNAKE_CASE :       List[str]=None							,       **SCREAMING_SNAKE_CASE :       List[Any]						):
                     return model(input_ids=SCREAMING_SNAKE_CASE							,       attention_mask=SCREAMING_SNAKE_CASE							,       **SCREAMING_SNAKE_CASE						)

                with self.subTest("JIT Enabled"						):
                     UpperCamelCase__      :       Tuple	    =  model_jitted(**SCREAMING_SNAKE_CASE						).to_tuple()

                with self.subTest("JIT Disabled"						):
                     with jax.disable_jit():
                          UpperCamelCase__      :       List[Any]	    =  model_jitted(**SCREAMING_SNAKE_CASE						).to_tuple()

                self.assertEqual(len(SCREAMING_SNAKE_CASE						)							,       len(SCREAMING_SNAKE_CASE						)						)
                for jitted_output, output in zip(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						):
                     self.assertEqual(jitted_output.shape							,       output.shape						)







 def  __lowercase (		self :       int							,       SCREAMING_SNAKE_CASE :       int							,       SCREAMING_SNAKE_CASE :       int							,       SCREAMING_SNAKE_CASE :       Union[str, Any]							,       SCREAMING_SNAKE_CASE :       Any=1e-5							,       SCREAMING_SNAKE_CASE :       Tuple="outputs"							,       SCREAMING_SNAKE_CASE :       Optional[Any]=None						):


      '''simple docstring'''






      if name.startswith("outputs.attentions"						):
           return
      else:
           super().check_pt_flax_outputs(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)