






import enum
import warnings

from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
 import tensorflow as tf


class     _a							(	enum.Enum     ):






  """simple docstring"""





  A_						       = 0
  A_						       = 1
  A_						       = 2




@add_end_docstrings(UpperCAmelCase__     )
class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = """
    In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The
    voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western
    Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision
    and denounces one of the men as a horse thief. Although his father initially slaps him for making such an
    accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
    the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,
    begging for his blessing. <eod> </s> <eos>
    """




  def __init__(  self		,		*_UpperCAmelCase		,		**_UpperCAmelCase			)      ->			List[str]:
     super().__init__(*_UpperCAmelCase		,		**_UpperCAmelCase			)
     self.check_model_type(
         TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == 'tf' else MODEL_FOR_CAUSAL_LM_MAPPING			)
     if "prefix" not in self._preprocess_params:
        # This is very specific. The logic is quite complex and needs to be done
        # as a "default".
        # It also defines both some preprocess_kwargs and generate_kwargs
        # which is why we cannot put them in their respective methods.
        UpperCamelCase_						=  None
        if self.model.config.prefix is not None:
           UpperCamelCase_						=  self.model.config.prefix
        if prefix is None and self.model.__class__.__name__ in [
            "XLNetLMHeadModel",
            "TransfoXLLMHeadModel",
            "TFXLNetLMHeadModel",
            "TFTransfoXLLMHeadModel",
        ]:
           # For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
           UpperCamelCase_						=  self.XL_PREFIX
        if prefix is not None:
           # Recalculate some generate_kwargs linked to prefix.
           UpperCamelCase_		,   UpperCamelCase_		,   UpperCamelCase_						=  self._sanitize_parameters(prefix=_UpperCAmelCase		,		**self._forward_params			)
           UpperCamelCase_						=  {**self._preprocess_params, **preprocess_params}
           UpperCamelCase_						=  {**self._forward_params, **forward_params}




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None		,		**_UpperCAmelCase		,		)      ->			Any:
     UpperCamelCase_						=  {}
     if prefix is not None:
        UpperCamelCase_						=  prefix
     if prefix:
        UpperCamelCase_						=  self.tokenizer(
            _UpperCAmelCase		,		padding=_UpperCAmelCase		,		add_special_tokens=_UpperCAmelCase		,		return_tensors=self.framework			)
        UpperCamelCase_						=  prefix_inputs['input_ids'].shape[-1]

     if handle_long_generation is not None:
        if handle_long_generation not in {"hole"}:
           raise ValueError(
               f"""{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected"""
               ' [None, \'hole\']'			)
        UpperCamelCase_						=  handle_long_generation

     preprocess_params.update(_UpperCAmelCase			)
     UpperCamelCase_						=  generate_kwargs

     UpperCamelCase_						=  {}
     if return_full_text is not None and return_type is None:
        if return_text is not None:
           raise ValueError('`return_text` is mutually exclusive with `return_full_text`'			)
        if return_tensors is not None:
           raise ValueError('`return_full_text` is mutually exclusive with `return_tensors`'			)
        UpperCamelCase_						=  ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
     if return_tensors is not None and return_type is None:
        if return_text is not None:
           raise ValueError('`return_text` is mutually exclusive with `return_tensors`'			)
        UpperCamelCase_						=  ReturnType.TENSORS
     if return_type is not None:
        UpperCamelCase_						=  return_type
     if clean_up_tokenization_spaces is not None:
        UpperCamelCase_						=  clean_up_tokenization_spaces

     if stop_sequence is not None:
        UpperCamelCase_						=  self.tokenizer.encode(_UpperCAmelCase		,		add_special_tokens=_UpperCAmelCase			)
        if len(_UpperCAmelCase			) > 1:
           warnings.warn(
               'Stopping on a multiple token sequence is not yet supported on transformers. The first token of'
               ' the stop sequence will be used as the stop sequence string in the interim.'			)
        UpperCamelCase_						=  stop_sequence_ids[0]

     return preprocess_params, forward_params, postprocess_params




  def  _UpperCAmelCase  (  self		,		*_UpperCAmelCase		,		**_UpperCAmelCase			)      ->			Any:
     # Parse arguments
     if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
        kwargs.update({'add_space_before_punct_symbol': True}			)

     return super()._parse_and_tokenize(*_UpperCAmelCase		,		**_UpperCAmelCase			)




  def __call__(  self		,		_UpperCAmelCase		,		**_UpperCAmelCase			)      ->			Any:
     return super().__call__(_UpperCAmelCase		,		**_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase=""		,		_UpperCAmelCase=None		,		**_UpperCAmelCase			)      ->			Tuple:
     UpperCamelCase_						=  self.tokenizer(
         prefix + prompt_text		,		padding=_UpperCAmelCase		,		add_special_tokens=_UpperCAmelCase		,		return_tensors=self.framework			)
     UpperCamelCase_						=  prompt_text

     if handle_long_generation == "hole":
        UpperCamelCase_						=  inputs['input_ids'].shape[-1]
        if "max_new_tokens" in generate_kwargs:
           UpperCamelCase_						=  generate_kwargs['max_new_tokens']
        else:
           UpperCamelCase_						=  generate_kwargs.get('max_length'		,		self.model.config.max_length			) - cur_len
           if new_tokens < 0:
              raise ValueError('We cannot infer how many new tokens are expected'			)
        if cur_len + new_tokens > self.tokenizer.model_max_length:
           UpperCamelCase_						=  self.tokenizer.model_max_length - new_tokens
           if keep_length <= 0:
              raise ValueError(
                  'We cannot use `hole` to handle this generation the number of desired tokens exceeds the'
                  ' models max length'			)

           UpperCamelCase_						=  inputs['input_ids'][:, -keep_length:]
           if "attention_mask" in inputs:
              UpperCamelCase_						=  inputs['attention_mask'][:, -keep_length:]

     return inputs




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		**_UpperCAmelCase			)      ->			Dict:
     UpperCamelCase_						=  model_inputs['input_ids']
     UpperCamelCase_						=  model_inputs.get('attention_mask'		,		_UpperCAmelCase			)
     # Allow empty prompts
     if input_ids.shape[1] == 0:
        UpperCamelCase_						=  None
        UpperCamelCase_						=  None
        UpperCamelCase_						=  1
     else:
        UpperCamelCase_						=  input_ids.shape[0]
     UpperCamelCase_						=  model_inputs.pop('prompt_text'			)

     # If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
     # generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
     UpperCamelCase_						=  generate_kwargs.pop('prefix_length'		,		0			)
     if prefix_length > 0:
        UpperCamelCase_						=  'max_new_tokens' in generate_kwargs or (
            'generation_config' in generate_kwargs
            and generate_kwargs['generation_config'].max_new_tokens is not None
        )
        if not has_max_new_tokens:
           UpperCamelCase_						=  generate_kwargs.get('max_length'			) or self.model.config.max_length
           generate_kwargs["max_length"] += prefix_length
        UpperCamelCase_						=  'min_new_tokens' in generate_kwargs or (
            'generation_config' in generate_kwargs
            and generate_kwargs['generation_config'].min_new_tokens is not None
        )
        if not has_min_new_tokens and "min_length" in generate_kwargs:
           generate_kwargs["min_length"] += prefix_length

        # BS x SL
     UpperCamelCase_						=  self.model.generate(input_ids=_UpperCAmelCase		,		attention_mask=_UpperCAmelCase		,		**_UpperCAmelCase			)
     UpperCamelCase_						=  generated_sequence.shape[0]
     if self.framework == "pt":
        UpperCamelCase_						=  generated_sequence.reshape(_UpperCAmelCase		,		out_b // in_b		,		*generated_sequence.shape[1:]			)
     elif self.framework == "tf":
        UpperCamelCase_						=  tf.reshape(_UpperCAmelCase		,		(in_b, out_b // in_b, *generated_sequence.shape[1:])			)
     return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase=ReturnType.FULL_TEXT		,		_UpperCAmelCase=True			)      ->			Optional[int]:
     UpperCamelCase_						=  model_outputs['generated_sequence'][0]
     UpperCamelCase_						=  model_outputs['input_ids']
     UpperCamelCase_						=  model_outputs['prompt_text']
     UpperCamelCase_						=  generated_sequence.numpy().tolist()
     UpperCamelCase_						=  []
     for sequence in generated_sequence:
        if return_type == ReturnType.TENSORS:
           UpperCamelCase_						=  {'generated_token_ids': sequence}
        elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
           # Decode text
           UpperCamelCase_						=  self.tokenizer.decode(
               _UpperCAmelCase		,		skip_special_tokens=_UpperCAmelCase		,		clean_up_tokenization_spaces=_UpperCAmelCase		,		)

           # Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
           if input_ids is None:
              UpperCamelCase_						=  0
           else:
              UpperCamelCase_						=  len(
                  self.tokenizer.decode(
                      input_ids[0]		,		skip_special_tokens=_UpperCAmelCase		,		clean_up_tokenization_spaces=_UpperCAmelCase		,		)			)

           if return_type == ReturnType.FULL_TEXT:
              UpperCamelCase_						=  prompt_text + text[prompt_length:]
           else:
              UpperCamelCase_						=  text[prompt_length:]

           UpperCamelCase_						=  {'generated_text': all_text}
        records.append(_UpperCAmelCase			)

     return records













from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
 import datasets


class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  @slow
  @require_torch
  def  _UpperCAmelCase  (  self			)      ->			List[Any]:
     UpperCamelCase_						=  EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'		,		'prajjwal1/bert-tiny'			)
     UpperCamelCase_						=  BertTokenizer.from_pretrained('bert-base-uncased'			)

     UpperCamelCase_						=  bertabert.config.encoder.vocab_size
     UpperCamelCase_						=  tokenizer.sep_token_id
     UpperCamelCase_						=  tokenizer.cls_token_id
     UpperCamelCase_						=  128

     UpperCamelCase_						=  datasets.load_dataset('cnn_dailymail'		,		'3.0.0'		,		split='train[:1%]'			)
     UpperCamelCase_						=  datasets.load_dataset('cnn_dailymail'		,		'3.0.0'		,		split='validation[:1%]'			)

     UpperCamelCase_						=  train_dataset.select(range(32			)			)
     UpperCamelCase_						=  val_dataset.select(range(16			)			)

     UpperCamelCase_						=  4

     def _map_to_encoder_decoder_inputs(_UpperCAmelCase			):
        # Tokenizer will automatically set [BOS] <text> [EOS]
        UpperCamelCase_						=  tokenizer(batch['article']		,		padding='max_length'		,		truncation=_UpperCAmelCase		,		max_length=512			)
        UpperCamelCase_						=  tokenizer(batch['highlights']		,		padding='max_length'		,		truncation=_UpperCAmelCase		,		max_length=128			)
        UpperCamelCase_						=  inputs.input_ids
        UpperCamelCase_						=  inputs.attention_mask

        UpperCamelCase_						=  outputs.input_ids
        UpperCamelCase_						=  outputs.input_ids.copy()
        UpperCamelCase_						=  [
            [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
        ]
        UpperCamelCase_						=  outputs.attention_mask

        assert all(len(_UpperCAmelCase			) == 512 for x in inputs.input_ids			)
        assert all(len(_UpperCAmelCase			) == 128 for x in outputs.input_ids			)

        return batch

     def _compute_metrics(_UpperCAmelCase			):
        UpperCamelCase_						=  pred.label_ids
        UpperCamelCase_						=  pred.predictions

        # all unnecessary tokens are removed
        UpperCamelCase_						=  tokenizer.batch_decode(_UpperCAmelCase		,		skip_special_tokens=_UpperCAmelCase			)
        UpperCamelCase_						=  tokenizer.batch_decode(_UpperCAmelCase		,		skip_special_tokens=_UpperCAmelCase			)

        UpperCamelCase_						=  sum([int(pred_str[i] == label_str[i]			) for i in range(len(_UpperCAmelCase			)			)]			) / len(_UpperCAmelCase			)

        return {"accuracy": accuracy}

     # map train dataset
     UpperCamelCase_						=  train_dataset.map(
         _map_to_encoder_decoder_inputs		,		batched=_UpperCAmelCase		,		batch_size=_UpperCAmelCase		,		remove_columns=['article', 'highlights']		,		)
     train_dataset.set_format(
         type='torch'		,		columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']		,		)

     # same for validation dataset
     UpperCamelCase_						=  val_dataset.map(
         _map_to_encoder_decoder_inputs		,		batched=_UpperCAmelCase		,		batch_size=_UpperCAmelCase		,		remove_columns=['article', 'highlights']		,		)
     val_dataset.set_format(
         type='torch'		,		columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']		,		)

     UpperCamelCase_						=  self.get_auto_remove_tmp_dir()

     UpperCamelCase_						=  SeqaSeqTrainingArguments(
         output_dir=_UpperCAmelCase		,		per_device_train_batch_size=_UpperCAmelCase		,		per_device_eval_batch_size=_UpperCAmelCase		,		predict_with_generate=_UpperCAmelCase		,		evaluation_strategy='steps'		,		do_train=_UpperCAmelCase		,		do_eval=_UpperCAmelCase		,		warmup_steps=0		,		eval_steps=2		,		logging_steps=2		,		)

     # instantiate trainer
     UpperCamelCase_						=  SeqaSeqTrainer(
         model=_UpperCAmelCase		,		args=_UpperCAmelCase		,		compute_metrics=_compute_metrics		,		train_dataset=_UpperCAmelCase		,		eval_dataset=_UpperCAmelCase		,		tokenizer=_UpperCAmelCase		,		)

     # start training
     trainer.train()





