'''simple docstring'''





import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_lowerCAmelCase  :List[str]  				=    16
_lowerCAmelCase  :Optional[Any]  				=    32


def     __lowerCAmelCase						(       a_       ,      a_ = 16		)      ->	Tuple:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Dict    	=       AutoTokenizer.from_pretrained('bert-base-cased'		)
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       load_dataset('glue'       ,      'mrpc'		)

     def tokenize_function(a_		):
          # max_length=None => use the model max length (it's actually the default)
          SCREAMING_SNAKE_CASE					:			int    	=       tokenizer(examples['sentence1']       ,      examples['sentence2']       ,      truncation=a_       ,      max_length=a_		)
          return outputs

     # Apply the method we just defined to all the examples in all the splits of the dataset
     # starting with the main process first:
     with accelerator.main_process_first():
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       datasets.map(
              a_       ,      batched=a_       ,      remove_columns=['idx', 'sentence1', 'sentence2']       ,      )

     # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
     # transformers library
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       tokenized_datasets.rename_column('label'       ,      'labels'		)

     def collate_fn(a_		):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       128 if accelerator.distributed_type == DistributedType.TPU else None
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               SCREAMING_SNAKE_CASE					:			int    	=       16
          elif accelerator.mixed_precision != "no":
               SCREAMING_SNAKE_CASE					:			Dict    	=       8
          else:
               SCREAMING_SNAKE_CASE					:			Any    	=       None

          return tokenizer.pad(
              a_       ,      padding='longest'       ,      max_length=a_       ,      pad_to_multiple_of=a_       ,      return_tensors='pt'       ,      )

     # Instantiate dataloaders.
     SCREAMING_SNAKE_CASE					:			int    	=       DataLoader(
         tokenized_datasets['train']       ,      shuffle=a_       ,      collate_fn=a_       ,      batch_size=a_		)
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       DataLoader(
         tokenized_datasets['validation']       ,      shuffle=a_       ,      collate_fn=a_       ,      batch_size=a_		)

     return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
 from accelerate.test_utils.training import mocked_dataloaders

 _lowerCAmelCase  :Tuple  				=    mocked_dataloaders  # noqa: F811


def     __lowerCAmelCase						(       a_       ,      a_		)      ->	Any:





     '''simple docstring'''





     if os.environ.get('TESTING_MOCKED_DATALOADERS'       ,      a_		) == "1":
          SCREAMING_SNAKE_CASE					:			int    	=       2
     # New Code #
     SCREAMING_SNAKE_CASE					:			Any    	=       int(args.gradient_accumulation_steps		)
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       int(args.local_sgd_steps		)
     # Initialize accelerator
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       Accelerator(
         cpu=args.cpu       ,      mixed_precision=args.mixed_precision       ,      gradient_accumulation_steps=a_		)
     if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
          raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'		)
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       config['lr']
     SCREAMING_SNAKE_CASE					:			Dict    	=       int(config['num_epochs']		)
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       int(config['seed']		)
     SCREAMING_SNAKE_CASE					:			Dict    	=       int(config['batch_size']		)

     SCREAMING_SNAKE_CASE					:			List[str]    	=       evaluate.load('glue'       ,      'mrpc'		)

     set_seed(a_		)
     SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			int    	=       get_dataloaders(a_       ,      a_		)
     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     SCREAMING_SNAKE_CASE					:			str    	=       AutoModelForSequenceClassification.from_pretrained('bert-base-cased'       ,      return_dict=a_		)

     # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
     # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
     # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
     SCREAMING_SNAKE_CASE					:			List[str]    	=       model.to(accelerator.device		)

     # Instantiate optimizer
     SCREAMING_SNAKE_CASE					:			Tuple    	=       AdamW(params=model.parameters()       ,      lr=a_		)

     # Instantiate scheduler
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       get_linear_schedule_with_warmup(
         optimizer=a_       ,      num_warmup_steps=100       ,      num_training_steps=(len(a_		) * num_epochs)       ,      )

     # Prepare everything
     # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
     # prepare method.
     SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			str    	=       accelerator.prepare(
         a_       ,      a_       ,      a_       ,      a_       ,      a_		)

     # Now we train the model
     for epoch in range(a_		):
          model.train()
          with LocalSGD(
              accelerator=a_       ,      model=a_       ,      local_sgd_steps=a_       ,      enabled=local_sgd_steps is not None		) as local_sgd:
               for step, batch in enumerate(a_		):
                    # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device		)
                    # New code #
                    # We use the new `accumulate` context manager to perform gradient accumulation
                    # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                    with accelerator.accumulate(a_		):
                         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       model(**a_		)
                         SCREAMING_SNAKE_CASE					:			Tuple    	=       output.loss
                         accelerator.backward(a_		)
                         optimizer.step()
                         lr_scheduler.step()
                         optimizer.zero_grad()
                         # LocalSGD-specific line
                         local_sgd.step()

          model.eval()
          for step, batch in enumerate(a_		):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device		)
               with torch.no_grad():
                    SCREAMING_SNAKE_CASE					:			List[Any]    	=       model(**a_		)
               SCREAMING_SNAKE_CASE					:			Dict    	=       outputs.logits.argmax(dim=-1		)
               SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			str    	=       accelerator.gather_for_metrics((predictions, batch['labels'])		)
               metric.add_batch(
                   predictions=a_       ,      references=a_       ,      )

          SCREAMING_SNAKE_CASE					:			List[str]    	=       metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f"""epoch {epoch}:"""       ,      a_		)


def     __lowerCAmelCase						(       )      ->	List[Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[str]    	=       argparse.ArgumentParser(description='Simple example of training script.'		)
     parser.add_argument(
         '--mixed_precision'       ,      type=a_       ,      default=a_       ,      choices=['no', 'fp16', 'bf16', 'fp8']       ,      help='Whether to use mixed precision. Choose'
         'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
         'and an Nvidia Ampere GPU.'       ,      )
     # New Code #
     parser.add_argument(
         '--gradient_accumulation_steps'       ,      type=a_       ,      default=1       ,      help='The number of minibatches to be ran before gradients are accumulated.'       ,      )
     parser.add_argument(
         '--local_sgd_steps'       ,      type=a_       ,      default=8       ,      help='Number of local SGD steps or None to disable local SGD'		)
     parser.add_argument('--cpu'       ,      action='store_true'       ,      help='If passed, will train on the CPU.'		)
     SCREAMING_SNAKE_CASE					:			Dict    	=       parser.parse_args()
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
     training_function(a_       ,      a_		)


if __name__ == "__main__":
 main()




'''simple docstring'''





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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_lowerCAmelCase  :Union[str, Any]  				=    logging.get_logger(__name__)

_lowerCAmelCase  :int  				=    {
    """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json""",
    # See all Marian models at https://huggingface.co/models?filter=marian
}






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   List[str]							=						"marian"
    snake_case__							:   Union[str, Any]							=						["past_key_values"]
    snake_case__							:   str							=						{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






    def __init__(       self    ,							lowercase__=58_101    ,							lowercase__=None    ,							lowercase__=1_024    ,							lowercase__=12    ,							lowercase__=4_096    ,							lowercase__=16    ,							lowercase__=12    ,							lowercase__=4_096    ,							lowercase__=16    ,							lowercase__=0.0    ,							lowercase__=0.0    ,							lowercase__=True    ,							lowercase__=True    ,							lowercase__="gelu"    ,							lowercase__=1_024    ,							lowercase__=0.1    ,							lowercase__=0.0    ,							lowercase__=0.0    ,							lowercase__=0.0_2    ,							lowercase__=58_100    ,							lowercase__=False    ,							lowercase__=58_100    ,							lowercase__=0    ,							lowercase__=0    ,							lowercase__=True    ,							**lowercase__    ,							)							-> int:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       vocab_size
         SCREAMING_SNAKE_CASE					:			Dict    	=       decoder_vocab_size or vocab_size
         SCREAMING_SNAKE_CASE					:			int    	=       max_position_embeddings
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       d_model
         SCREAMING_SNAKE_CASE					:			Dict    	=       encoder_ffn_dim
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       encoder_layers
         SCREAMING_SNAKE_CASE					:			Dict    	=       encoder_attention_heads
         SCREAMING_SNAKE_CASE					:			int    	=       decoder_ffn_dim
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       decoder_layers
         SCREAMING_SNAKE_CASE					:			str    	=       decoder_attention_heads
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       dropout
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       attention_dropout
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       activation_dropout
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       activation_function
         SCREAMING_SNAKE_CASE					:			Tuple    	=       init_std
         SCREAMING_SNAKE_CASE					:			int    	=       encoder_layerdrop
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       decoder_layerdrop
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       use_cache
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       encoder_layers
         SCREAMING_SNAKE_CASE					:			int    	=       scale_embedding  # scale factor will be sqrt(d_model) if True
         SCREAMING_SNAKE_CASE					:			str    	=       share_encoder_decoder_embeddings
         super().__init__(
             pad_token_id=lowercase__    ,							eos_token_id=lowercase__    ,							is_encoder_decoder=lowercase__    ,							decoder_start_token_id=lowercase__    ,							forced_eos_token_id=lowercase__    ,							**lowercase__    ,							)






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    @property
    # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
    def       _UpperCamelCase    (       self )							-> Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
              SCREAMING_SNAKE_CASE					:			Dict    	=       OrderedDict(
                  [
                      ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                      ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                  ] )

              if self.use_past:
                   SCREAMING_SNAKE_CASE					:			List[Any]    	=       {0: 'batch'}
                   SCREAMING_SNAKE_CASE					:			Any    	=       {0: 'batch', 1: 'past_decoder_sequence + sequence'}
              else:
                   SCREAMING_SNAKE_CASE					:			str    	=       {0: 'batch', 1: 'decoder_sequence'}
                   SCREAMING_SNAKE_CASE					:			int    	=       {0: 'batch', 1: 'decoder_sequence'}

              if self.use_past:
                   self.fill_with_past_key_values_(lowercase__    ,							direction='inputs' )
         elif self.task == "causal-lm":
              # TODO: figure this case out.
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       OrderedDict(
                  [
                      ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                      ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                  ] )
              if self.use_past:
                   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Dict    	=       self.num_layers
                   for i in range(lowercase__ ):
                        SCREAMING_SNAKE_CASE					:			Dict    	=       {0: 'batch', 2: 'past_sequence + sequence'}
                        SCREAMING_SNAKE_CASE					:			Dict    	=       {0: 'batch', 2: 'past_sequence + sequence'}
         else:
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       OrderedDict(
                  [
                      ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                      ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                      ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
                      ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
                  ] )

         return common_inputs






    @property
    # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
    def       _UpperCamelCase    (       self )							-> Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
              SCREAMING_SNAKE_CASE					:			List[str]    	=       super().outputs
         else:
              SCREAMING_SNAKE_CASE					:			Tuple    	=       super(lowercase__    ,							self ).outputs
              if self.use_past:
                   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.num_layers
                   for i in range(lowercase__ ):
                        SCREAMING_SNAKE_CASE					:			int    	=       {0: 'batch', 2: 'past_sequence + sequence'}
                        SCREAMING_SNAKE_CASE					:			List[Any]    	=       {0: 'batch', 2: 'past_sequence + sequence'}
         return common_outputs






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = -1    ,							lowercase__ = -1    ,							lowercase__ = False    ,							lowercase__ = None    ,							)							-> Mapping[str, Any]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self._generate_dummy_inputs_for_encoder_and_decoder(
             lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )

         # Generate decoder inputs
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       seq_length if not self.use_past else 1
         SCREAMING_SNAKE_CASE					:			str    	=       self._generate_dummy_inputs_for_encoder_and_decoder(
             lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       dict(**lowercase__    ,							**lowercase__ )

         if self.use_past:
              if not is_torch_available():
                   raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
              else:
                   import torch
              SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			int    	=       common_inputs['input_ids'].shape
              SCREAMING_SNAKE_CASE					:			Tuple    	=       common_inputs['decoder_input_ids'].shape[1]
              SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			str    	=       self.num_attention_heads
              SCREAMING_SNAKE_CASE					:			int    	=       (
                  batch,
                  num_encoder_attention_heads,
                  encoder_seq_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )
              SCREAMING_SNAKE_CASE					:			str    	=       decoder_seq_length + 3
              SCREAMING_SNAKE_CASE					:			Tuple    	=       (
                  batch,
                  num_decoder_attention_heads,
                  decoder_past_length,
                  self._config.hidden_size // num_decoder_attention_heads,
              )

              SCREAMING_SNAKE_CASE					:			List[str]    	=       torch.cat(
                  [common_inputs['decoder_attention_mask'], torch.ones(lowercase__    ,							lowercase__ )]    ,							dim=1 )

              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       []
              # If the number of encoder and decoder layers are present in the model configuration, both are considered
              SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Dict    	=       self.num_layers
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       min(lowercase__    ,							lowercase__ )
              SCREAMING_SNAKE_CASE					:			Tuple    	=       max(lowercase__    ,							lowercase__ ) - min_num_layers
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

              for _ in range(lowercase__ ):
                   common_inputs["past_key_values"].append(
                       (
                           torch.zeros(lowercase__ ),
                           torch.zeros(lowercase__ ),
                           torch.zeros(lowercase__ ),
                           torch.zeros(lowercase__ ),
                       ) )
              # TODO: test this.
              SCREAMING_SNAKE_CASE					:			str    	=       encoder_shape if remaining_side_name == 'encoder' else decoder_shape
              for _ in range(lowercase__    ,							lowercase__ ):
                   common_inputs["past_key_values"].append((torch.zeros(lowercase__ ), torch.zeros(lowercase__ )) )
         return common_inputs






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = -1    ,							lowercase__ = -1    ,							lowercase__ = False    ,							lowercase__ = None    ,							)							-> Mapping[str, Any]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self._generate_dummy_inputs_for_encoder_and_decoder(
             lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )

         if self.use_past:
              if not is_torch_available():
                   raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
              else:
                   import torch
              SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Dict    	=       common_inputs['input_ids'].shape
              # Not using the same length for past_key_values
              SCREAMING_SNAKE_CASE					:			Dict    	=       seqlen + 2
              SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			str    	=       self.num_layers
              SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.num_attention_heads
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       (
                  batch,
                  num_encoder_attention_heads,
                  past_key_values_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )

              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       common_inputs['attention_mask'].dtype
              SCREAMING_SNAKE_CASE					:			List[str]    	=       torch.cat(
                  [common_inputs['attention_mask'], torch.ones(lowercase__    ,							lowercase__    ,							dtype=lowercase__ )]    ,							dim=1 )
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       [
                  (torch.zeros(lowercase__ ), torch.zeros(lowercase__ )) for _ in range(lowercase__ )
              ]
         return common_inputs






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = -1    ,							lowercase__ = -1    ,							lowercase__ = False    ,							lowercase__ = None    ,							)							-> Mapping[str, Any]:
         # Copied from OnnxConfig.generate_dummy_inputs
         # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         SCREAMING_SNAKE_CASE					:			List[str]    	=       compute_effective_axis_dimension(
             lowercase__    ,							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
         SCREAMING_SNAKE_CASE					:			List[str]    	=       tokenizer.num_special_tokens_to_add(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Dict    	=       compute_effective_axis_dimension(
             lowercase__    ,							fixed_dimension=OnnxConfig.default_fixed_sequence    ,							num_token_to_add=lowercase__ )

         # Generate dummy inputs according to compute batch and sequence
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       [' '.join([tokenizer.unk_token] ) * seq_length] * batch_size
         SCREAMING_SNAKE_CASE					:			Tuple    	=       dict(tokenizer(lowercase__    ,							return_tensors=lowercase__ ) )
         return common_inputs






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = -1    ,							lowercase__ = -1    ,							lowercase__ = False    ,							lowercase__ = None    ,							)							-> Mapping[str, Any]:
         if self.task in ["default", "seq2seq-lm"]:
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                  lowercase__    ,							batch_size=lowercase__    ,							seq_length=lowercase__    ,							is_pair=lowercase__    ,							framework=lowercase__ )

         else:
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self._generate_dummy_inputs_for_causal_lm(
                  lowercase__    ,							batch_size=lowercase__    ,							seq_length=lowercase__    ,							is_pair=lowercase__    ,							framework=lowercase__ )

         return common_inputs






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> int:
         if self.task in ["default", "seq2seq-lm"]:
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       super()._flatten_past_key_values_(lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )
         else:
              SCREAMING_SNAKE_CASE					:			Any    	=       super(lowercase__    ,							self )._flatten_past_key_values_(
                  lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )






    @property
    def       _UpperCamelCase    (       self )							-> float:
         return 1E-4



