'''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


########################################################################
# This is a fully working simple example to use Accelerate
# and perform 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
#
########################################################################


lowercase__  :      Optional[int]        =					16
lowercase__  :      Optional[int]        =					32



def  __lowerCamelCase       (      _UpperCamelCase  :  Accelerator							,    _UpperCamelCase  :  int = 16					):


      '''simple docstring'''


      UpperCAmelCase_            =  AutoTokenizer.from_pretrained('''bert-base-cased'''					)
      UpperCAmelCase_            =  load_dataset('''glue'''							,    '''mrpc'''					)

      def tokenize_function(_UpperCamelCase  :  Tuple					):
            # max_length=None => use the model max length (it's actually the default)
            UpperCAmelCase_            =  tokenizer(examples['''sentence1''']							,    examples['''sentence2''']							,    truncation=_UpperCamelCase							,    max_length=_UpperCamelCase					)
            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():
            UpperCAmelCase_            =  datasets.map(
                _UpperCamelCase							,    batched=_UpperCamelCase							,    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
      UpperCAmelCase_            =  tokenized_datasets.rename_column('''label'''							,    '''labels'''					)

      def collate_fn(_UpperCamelCase  :  List[str]					):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            UpperCAmelCase_            =  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":
                  UpperCAmelCase_            =  16
            elif accelerator.mixed_precision != "no":
                  UpperCAmelCase_            =  8
            else:
                  UpperCAmelCase_            =  None

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

      # Instantiate dataloaders.
      UpperCAmelCase_            =  DataLoader(
          tokenized_datasets['''train''']							,    shuffle=_UpperCamelCase							,    collate_fn=_UpperCamelCase							,    batch_size=_UpperCamelCase					)
      UpperCAmelCase_            =  DataLoader(
          tokenized_datasets['''validation''']							,    shuffle=_UpperCamelCase							,    collate_fn=_UpperCamelCase							,    batch_size=_UpperCamelCase					)

      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

  lowercase__  :      int        =					mocked_dataloaders  # noqa: F811



def  __lowerCamelCase       (      _UpperCamelCase  :  Union[str, Any]							,    _UpperCamelCase  :  str					):


      '''simple docstring'''


      if os.environ.get('''TESTING_MOCKED_DATALOADERS'''							,    _UpperCamelCase					) == "1":
            UpperCAmelCase_            =  2
      # New Code #
      UpperCAmelCase_            =  int(args.gradient_accumulation_steps					)
      # Initialize accelerator
      UpperCAmelCase_            =  Accelerator(
          cpu=args.cpu							,    mixed_precision=args.mixed_precision							,    gradient_accumulation_steps=_UpperCamelCase					)
      if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
            raise NotImplementedError(
                '''Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'''					)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      UpperCAmelCase_            =  config['''lr''']
      UpperCAmelCase_            =  int(config['''num_epochs''']					)
      UpperCAmelCase_            =  int(config['''seed''']					)
      UpperCAmelCase_            =  int(config['''batch_size''']					)

      UpperCAmelCase_            =  evaluate.load('''glue'''							,    '''mrpc'''					)

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

      # 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).
      UpperCAmelCase_            =  model.to(accelerator.device					)

      # Instantiate optimizer
      UpperCAmelCase_            =  AdamW(params=model.parameters()							,    lr=_UpperCamelCase					)

      # Instantiate scheduler
      UpperCAmelCase_            =  get_linear_schedule_with_warmup(
          optimizer=_UpperCamelCase							,    num_warmup_steps=100							,    num_training_steps=(len(_UpperCamelCase					) * 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.
      UpperCAmelCase_      ,		UpperCAmelCase_      ,		UpperCAmelCase_      ,		UpperCAmelCase_      ,		UpperCAmelCase_            =  accelerator.prepare(
          _UpperCamelCase							,    _UpperCamelCase							,    _UpperCamelCase							,    _UpperCamelCase							,    _UpperCamelCase					)

      # Now we train the model
      for epoch in range(_UpperCamelCase					):
            model.train()
            for step, batch in enumerate(_UpperCamelCase					):
                  # 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(_UpperCamelCase					):
                        UpperCAmelCase_            =  model(**_UpperCamelCase					)
                        UpperCAmelCase_            =  output.loss
                        accelerator.backward(_UpperCamelCase					)
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

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

            UpperCAmelCase_            =  metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F"""epoch {epoch}:"""							,    _UpperCamelCase					)



def  __lowerCamelCase       (      ):


      '''simple docstring'''


      UpperCAmelCase_            =  argparse.ArgumentParser(description='''Simple example of training script.'''					)
      parser.add_argument(
          '''--mixed_precision'''							,    type=_UpperCamelCase							,    default=_UpperCamelCase							,    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=_UpperCamelCase							,    default=1							,    help='''The number of minibatches to be ran before gradients are accumulated.'''							,    )
      parser.add_argument('''--cpu'''							,    action='''store_true'''							,    help='''If passed, will train on the CPU.'''					)
      UpperCAmelCase_            =  parser.parse_args()
      UpperCAmelCase_            =  {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
      training_function(_UpperCamelCase							,    _UpperCamelCase					)


if __name__ == "__main__":
  main()






'''simple docstring'''
from __future__ import annotations

import os
import tempfile
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow

from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import (
          AutoTokenizer,
          TFAutoModelForCausalLM,
          TFAutoModelForSeqaSeqLM,
          TFAutoModelForSpeechSeqaSeq,
          TFAutoModelForVisionaSeq,
          TFBartForConditionalGeneration,
          TFLogitsProcessorList,
          TFMinLengthLogitsProcessor,
          tf_top_k_top_p_filtering,
      )

if is_tensorflow_text_available():
      import tensorflow_text as text







@require_tf
class lowerCamelCase							(	unittest.TestCase					):




  '''simple docstring'''



  def  lowerCAmelCase__ (      self			:       Dict  )				->List[str]:
        UpperCAmelCase_            =  tf.convert_to_tensor(
            [
                [
                    8.222_0991,  # 3rd highest value; idx. 0
                    -0.562_0044,
                    5.2322_9752,
                    4.038_6393,
                    -6.879_8378,
                    -0.5478_5802,
                    -3.201_2153,
                    2.9277_7176,
                    1.8817_1953,
                    7.3534_1276,  # 5th highest value; idx. 9
                    8.4320_7833,  # 2nd highest value; idx. 10
                    -9.8571_1836,
                    -5.9620_9236,
                    -1.1303_9161,
                    -7.111_5294,
                    -0.836_9633,
                    -5.318_6408,
                    7.0642_7407,
                    0.8136_9344,
                    -0.8202_3817,
                    -5.917_9796,
                    0.5881_3443,
                    -6.9977_8438,
                    4.7155_1189,
                    -0.1877_1637,
                    7.4402_0759,  # 4th highest value; idx. 25
                    9.3845_0987,  # 1st highest value; idx. 26
                    2.1266_2941,
                    -9.3256_2038,
                    2.3565_2522,
                ],  # cummulative prob of 5 highest values <= 0.6
                [
                    0.5842_5518,
                    4.5313_9238,
                    -5.5751_0464,
                    -6.2803_0699,
                    -7.1952_9503,
                    -4.0212_2551,
                    1.3933_7037,
                    -6.0670_7057,
                    1.5948_0517,
                    -9.64_3119,
                    0.0390_7799,
                    0.6723_1762,
                    -8.8820_6726,
                    6.2711_5922,  # 4th highest value; idx. 13
                    2.2852_0723,
                    4.8276_7506,
                    4.3042_1368,
                    8.827_5313,  # 2nd highest value; idx. 17
                    5.4402_9958,  # 5th highest value; idx. 18
                    -4.473_5794,
                    7.3857_9536,  # 3rd highest value; idx. 20
                    -2.9105_1663,
                    2.6194_6077,
                    -2.567_4762,
                    -9.4895_9302,
                    -4.0292_2645,
                    -1.3541_6918,
                    9.6770_2323,  # 1st highest value; idx. 27
                    -5.8947_8553,
                    1.8537_0467,
                ],  # cummulative prob of 5 highest values <= 0.6
            ]  ,							dtype=tf.floataa  ,							)

        UpperCAmelCase_            =  tf.convert_to_tensor(
            [[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]]  ,							dtype=tf.intaa  ,							)  # expected non filtered idx as noted above

        UpperCAmelCase_            =  tf.convert_to_tensor(
            [8.22_2099, 7.353_4126, 8.43_2078, 7.440_2075, 9.3_8451, 6.27_1159, 8.82_7531, 5.440_2995, 7.385_7956, 9.67_7023]  ,							dtype=tf.floataa  ,							)  # expected non filtered values as noted above

        UpperCAmelCase_            =  tf_top_k_top_p_filtering(UpperCAmelCase__  ,							top_k=10  ,							top_p=0.6  ,							min_tokens_to_keep=4  )

        UpperCAmelCase_            =  output[output != -float('''inf'''  )]
        UpperCAmelCase_            =  tf.cast(
            tf.where(tf.not_equal(UpperCAmelCase__  ,							tf.constant(-float('''inf'''  )  ,							dtype=tf.floataa  )  )  )  ,							dtype=tf.intaa  ,							)

        tf.debugging.assert_near(UpperCAmelCase__  ,							UpperCAmelCase__  ,							rtol=1e-12  )
        tf.debugging.assert_equal(UpperCAmelCase__  ,							UpperCAmelCase__  )







@require_tf
class lowerCamelCase							(	unittest.TestCase     ,		lowerCamelCase					):




  '''simple docstring'''



  if is_tf_available():
    lowerCAmelCase__										=	{
        '''AutoModelForCausalLM''': TFAutoModelForCausalLM,
        '''AutoModelForSpeechSeq2Seq''': TFAutoModelForSpeechSeqaSeq,
        '''AutoModelForSeq2SeqLM''': TFAutoModelForSeqaSeqLM,
        '''AutoModelForVision2Seq''': TFAutoModelForVisionaSeq,
        '''LogitsProcessorList''': TFLogitsProcessorList,
        '''MinLengthLogitsProcessor''': TFMinLengthLogitsProcessor,
        '''create_tensor_fn''': tf.convert_to_tensor,
        '''floats_tensor''': floats_tensor,
        '''return_tensors''': '''tf''',
    }






  @slow
  def  lowerCAmelCase__ (      self			:       Dict  )				->Optional[Any]:
        # TF-only test: tf.saved_model export
        UpperCAmelCase_            =  TFAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''  )
        UpperCAmelCase_            =  2
        UpperCAmelCase_            =  2







        class lowerCamelCase							(	tf.Module					):




          '''simple docstring'''



          def __init__(      self			:       List[str]  ,							UpperCAmelCase__			:       List[str]  )				->Dict:
                super(UpperCAmelCase__  ,							self  ).__init__()
                UpperCAmelCase_            =  model






          @tf.function(
              input_signature=(
                  tf.TensorSpec((None, input_length)  ,							tf.intaa  ,							name='''input_ids'''  ),
                  tf.TensorSpec((None, input_length)  ,							tf.intaa  ,							name='''attention_mask'''  ),
              )  ,							jit_compile=UpperCAmelCase__  ,							)
          def  lowerCAmelCase__ (      self			:       Union[str, Any]  ,							UpperCAmelCase__			:       Union[str, Any]  ,							UpperCAmelCase__			:       List[str]  )				->int:
                UpperCAmelCase_            =  self.model.generate(
                    input_ids=UpperCAmelCase__  ,							attention_mask=UpperCAmelCase__  ,							max_new_tokens=UpperCAmelCase__  ,							return_dict_in_generate=UpperCAmelCase__  ,							)
                return {"sequences": outputs["sequences"]}






        UpperCAmelCase_            =  [[2, 0], [102, 103]]
        UpperCAmelCase_            =  [[1, 0], [1, 1]]
        UpperCAmelCase_            =  DummyModel(model=UpperCAmelCase__  )
        with tempfile.TemporaryDirectory() as tmp_dir:
              tf.saved_model.save(UpperCAmelCase__  ,							UpperCAmelCase__  ,							signatures={'''serving_default''': dummy_model.serving}  )
              UpperCAmelCase_            =  tf.saved_model.load(UpperCAmelCase__  ).signatures['''serving_default''']
              for batch_size in range(1  ,							len(UpperCAmelCase__  ) + 1  ):
                    UpperCAmelCase_            =  {
                        '''input_ids''': tf.constant(dummy_input_ids[:batch_size]  ),
                        '''attention_mask''': tf.constant(dummy_attention_masks[:batch_size]  ),
                    }
                    UpperCAmelCase_            =  serving_func(**UpperCAmelCase__  )['''sequences''']
                    UpperCAmelCase_            =  test_model.generate(**UpperCAmelCase__  ,							max_new_tokens=UpperCAmelCase__  )
                    tf.debugging.assert_equal(UpperCAmelCase__  ,							UpperCAmelCase__  )






  @slow
  def  lowerCAmelCase__ (      self			:       Tuple  )				->Optional[int]:
        # TF-only test: tf.saved_model export
        UpperCAmelCase_            =  TFAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''  )
        UpperCAmelCase_            =  1
        UpperCAmelCase_            =  2







        class lowerCamelCase							(	tf.Module					):




          '''simple docstring'''



          def __init__(      self			:       int  ,							UpperCAmelCase__			:       int  )				->List[str]:
                super(UpperCAmelCase__  ,							self  ).__init__()
                UpperCAmelCase_            =  model






          @tf.function(
              input_signature=(
                  tf.TensorSpec((batch_size, None)  ,							tf.intaa  ,							name='''input_ids'''  ),
                  tf.TensorSpec((batch_size, None)  ,							tf.intaa  ,							name='''attention_mask'''  ),
              )  ,							jit_compile=UpperCAmelCase__  ,							)
          def  lowerCAmelCase__ (      self			:       Dict  ,							UpperCAmelCase__			:       List[str]  ,							UpperCAmelCase__			:       Optional[Any]  )				->int:
                UpperCAmelCase_            =  self.model.generate(
                    input_ids=UpperCAmelCase__  ,							attention_mask=UpperCAmelCase__  ,							max_new_tokens=UpperCAmelCase__  ,							return_dict_in_generate=UpperCAmelCase__  ,							)
                return {"sequences": outputs["sequences"]}






        UpperCAmelCase_            =  [[2], [102, 103]]
        UpperCAmelCase_            =  [[1], [1, 1]]
        UpperCAmelCase_            =  DummyModel(model=UpperCAmelCase__  )
        with tempfile.TemporaryDirectory() as tmp_dir:
              tf.saved_model.save(UpperCAmelCase__  ,							UpperCAmelCase__  ,							signatures={'''serving_default''': dummy_model.serving}  )
              UpperCAmelCase_            =  tf.saved_model.load(UpperCAmelCase__  ).signatures['''serving_default''']
              for input_row in range(len(UpperCAmelCase__  )  ):
                    UpperCAmelCase_            =  {
                        '''input_ids''': tf.constant([dummy_input_ids[input_row]]  ),
                        '''attention_mask''': tf.constant([dummy_attention_masks[input_row]]  ),
                    }
                    UpperCAmelCase_            =  serving_func(**UpperCAmelCase__  )['''sequences''']
                    UpperCAmelCase_            =  test_model.generate(**UpperCAmelCase__  ,							max_new_tokens=UpperCAmelCase__  )
                    tf.debugging.assert_equal(UpperCAmelCase__  ,							UpperCAmelCase__  )






  @slow
  @require_tensorflow_text
  def  lowerCAmelCase__ (      self			:       Optional[Any]  )				->int:
        # TF-only test: tf.saved_model export
        with tempfile.TemporaryDirectory() as tmp_dir:
              # file needed to load the TF tokenizer
              hf_hub_download(repo_id='''google/flan-t5-small'''  ,							filename='''spiece.model'''  ,							local_dir=UpperCAmelCase__  )







              class lowerCamelCase							(	tf.keras.layers.Layer					):




                '''simple docstring'''



                def __init__(      self			:       List[str]  )				->Any:
                      super().__init__()
                      UpperCAmelCase_            =  text.SentencepieceTokenizer(
                          model=tf.io.gfile.GFile(os.path.join(UpperCAmelCase__  ,							'''spiece.model'''  )  ,							'''rb'''  ).read()  )
                      UpperCAmelCase_            =  TFAutoModelForSeqaSeqLM.from_pretrained('''hf-internal-testing/tiny-random-t5'''  )






                def  lowerCAmelCase__ (      self			:       Any  ,							UpperCAmelCase__			:       Union[str, Any]  ,							*UpperCAmelCase__			:       Union[str, Any]  ,							**UpperCAmelCase__			:       str  )				->List[str]:
                      UpperCAmelCase_            =  self.tokenizer.tokenize(UpperCAmelCase__  )
                      UpperCAmelCase_      ,		UpperCAmelCase_            =  text.pad_model_inputs(
                          UpperCAmelCase__  ,							max_seq_length=64  ,							pad_value=self.model.config.pad_token_id  )
                      UpperCAmelCase_            =  self.model.generate(input_ids=UpperCAmelCase__  ,							attention_mask=UpperCAmelCase__  )
                      return self.tokenizer.detokenize(UpperCAmelCase__  )






              UpperCAmelCase_            =  CompleteSentenceTransformer()
              UpperCAmelCase_            =  tf.keras.layers.Input(shape=(1,)  ,							dtype=tf.string  ,							name='''inputs'''  )
              UpperCAmelCase_            =  complete_model(UpperCAmelCase__  )
              UpperCAmelCase_            =  tf.keras.Model(UpperCAmelCase__  ,							UpperCAmelCase__  )
              keras_model.save(UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       Optional[int]  )				->Tuple:
        # Has PT equivalent: this test relies on random sampling
        UpperCAmelCase_            =  {
            '''do_sample''': True,
            '''num_beams''': 1,
            '''top_p''': 0.7,
            '''top_k''': 10,
            '''temperature''': 0.7,
        }
        UpperCAmelCase_            =  14

        UpperCAmelCase_            =  AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''  )
        UpperCAmelCase_            =  '''Hello, my dog is cute and'''
        UpperCAmelCase_            =  tokenizer(UpperCAmelCase__  ,							return_tensors='''tf'''  )
        UpperCAmelCase_            =  TFAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''  )

        UpperCAmelCase_            =  638
        # forces the generation to happen on CPU, to avoid GPU-related quirks
        with tf.device(''':/CPU:0'''  ):
              tf.random.set_seed(0  )
              UpperCAmelCase_            =  model.generate(**UpperCAmelCase__  ,							eos_token_id=UpperCAmelCase__  ,							**UpperCAmelCase__  )
        self.assertTrue(expectation == len(generated_tokens[0]  )  )

        UpperCAmelCase_            =  [638, 198]
        with tf.device(''':/CPU:0'''  ):
              tf.random.set_seed(0  )
              UpperCAmelCase_            =  model.generate(**UpperCAmelCase__  ,							eos_token_id=UpperCAmelCase__  ,							**UpperCAmelCase__  )
        self.assertTrue(expectation == len(generated_tokens[0]  )  )






  def  lowerCAmelCase__ (      self			:       Optional[int]  )				->List[str]:
        # Has PT equivalent: ample use of framework-specific code
        UpperCAmelCase_            =  AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bart'''  )
        UpperCAmelCase_            =  '''Hugging Face is a technology company based in New York and Paris.'''
        UpperCAmelCase_            =  bart_tokenizer(UpperCAmelCase__  ,							return_tensors='''tf'''  ).input_ids
        UpperCAmelCase_            =  TFBartForConditionalGeneration.from_pretrained('''hf-internal-testing/tiny-random-bart'''  )
        UpperCAmelCase_            =  bart_model.generate(UpperCAmelCase__  ).numpy()







        class lowerCamelCase							(	lowerCamelCase					):




          '''simple docstring'''



          def  lowerCAmelCase__ (      self			:       int  ,							UpperCAmelCase__			:       Dict  ,							UpperCAmelCase__			:       List[Any]=None  ,							**UpperCAmelCase__			:       int  )				->List[str]:
                return super().call(UpperCAmelCase__  ,							**UpperCAmelCase__  )






        UpperCAmelCase_            =  FakeBart.from_pretrained('''hf-internal-testing/tiny-random-bart'''  )
        UpperCAmelCase_            =  bart_model.generate(UpperCAmelCase__  ,							foo='''bar'''  ).numpy()
        self.assertTrue(np.array_equal(UpperCAmelCase__  ,							UpperCAmelCase__  )  )







        class lowerCamelCase							(	bart_model.model.encoder.__class__					):




          '''simple docstring'''



          def  lowerCAmelCase__ (      self			:       str  ,							UpperCAmelCase__			:       List[str]  ,							**UpperCAmelCase__			:       Dict  )				->Any:
                return super().call(UpperCAmelCase__  ,							**UpperCAmelCase__  )






        UpperCAmelCase_            =  FakeEncoder(bart_model.config  ,							bart_model.model.shared  )
        UpperCAmelCase_            =  fake_encoder

        # Normal generation still works (the output will be different because the encoder weights are different)
        UpperCAmelCase_            =  bart_model.generate(UpperCAmelCase__  ).numpy()
        with self.assertRaises(UpperCAmelCase__  ):
              # FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
              bart_model.generate(UpperCAmelCase__  ,							foo='''bar'''  )





