
'''simple docstring'''







import math
import time

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
					import torch_xla.core.xla_model as xm
					import torch_xla.debug.metrics as met


class 						__UpperCAmelCase      (					_lowerCamelCase						):


							def __init__(       self    ,   *lowerCAmelCase_    ,   lowerCAmelCase_=None    ,   lowerCAmelCase_=None    ,   **lowerCAmelCase_  ):





								"""simple docstring"""






								super().__init__(*lowerCAmelCase_    ,   **lowerCAmelCase_  )
								_snake_case   						=		eval_examples
								_snake_case   						=		post_process_function


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_=None    ,   lowerCAmelCase_=None    ,   lowerCAmelCase_=None    ,   lowerCAmelCase_ = "eval"  ):





								"""simple docstring"""






								_snake_case   						=		self.eval_dataset if eval_dataset is None else eval_dataset
								_snake_case   						=		self.get_eval_dataloader(lowerCAmelCase_  )
								_snake_case   						=		self.eval_examples if eval_examples is None else eval_examples

								# Temporarily disable metric computation, we will do it in the loop here.
								_snake_case   						=		self.compute_metrics
								_snake_case   						=		None
								_snake_case   						=		self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
								_snake_case   						=		time.time()
								try:
									_snake_case   						=		eval_loop(
									    lowerCAmelCase_    ,   description='Evaluation'    ,   prediction_loss_only=True if compute_metrics is None else None    ,   ignore_keys=lowerCAmelCase_    ,   metric_key_prefix=lowerCAmelCase_    ,   )
								finally:
									_snake_case   						=		compute_metrics
								_snake_case   						=		self.args.eval_batch_size * self.args.world_size
								if F'{metric_key_prefix}_jit_compilation_time' in output.metrics:
									start_time += output.metrics[F'{metric_key_prefix}_jit_compilation_time']
								output.metrics.update(
								    speed_metrics(
								        lowerCAmelCase_    ,   lowerCAmelCase_    ,   num_samples=output.num_samples    ,   num_steps=math.ceil(output.num_samples / total_batch_size  )    ,   )  )
								if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
									# Only the main node write the results by default
									_snake_case   						=		self.post_process_function(lowerCAmelCase_    ,   lowerCAmelCase_    ,   output.predictions  )
									_snake_case   						=		self.compute_metrics(lowerCAmelCase_  )

									# Prefix all keys with metric_key_prefix + '_'
									for key in list(metrics.keys()  ):
										if not key.startswith(F'{metric_key_prefix}_'  ):
											_snake_case   						=		metrics.pop(lowerCAmelCase_  )
									metrics.update(output.metrics  )
								else:
									_snake_case   						=		output.metrics

								if self.args.should_log:
									# Only the main node log the results by default
									self.log(lowerCAmelCase_  )

								if self.args.tpu_metrics_debug or self.args.debug:
									# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
									xm.master_print(met.metrics_report()  )

								_snake_case   						=		self.callback_handler.on_evaluate(self.args    ,   self.state    ,   self.control    ,   lowerCAmelCase_  )
								return metrics




							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_=None    ,   lowerCAmelCase_ = "test"  ):





								"""simple docstring"""






								_snake_case   						=		self.get_test_dataloader(lowerCAmelCase_  )

								# Temporarily disable metric computation, we will do it in the loop here.
								_snake_case   						=		self.compute_metrics
								_snake_case   						=		None
								_snake_case   						=		self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
								_snake_case   						=		time.time()
								try:
									_snake_case   						=		eval_loop(
									    lowerCAmelCase_    ,   description='Prediction'    ,   prediction_loss_only=True if compute_metrics is None else None    ,   ignore_keys=lowerCAmelCase_    ,   metric_key_prefix=lowerCAmelCase_    ,   )
								finally:
									_snake_case   						=		compute_metrics
								_snake_case   						=		self.args.eval_batch_size * self.args.world_size
								if F'{metric_key_prefix}_jit_compilation_time' in output.metrics:
									start_time += output.metrics[F'{metric_key_prefix}_jit_compilation_time']
								output.metrics.update(
								    speed_metrics(
								        lowerCAmelCase_    ,   lowerCAmelCase_    ,   num_samples=output.num_samples    ,   num_steps=math.ceil(output.num_samples / total_batch_size  )    ,   )  )

								if self.post_process_function is None or self.compute_metrics is None:
									return output

								_snake_case   						=		self.post_process_function(lowerCAmelCase_    ,   lowerCAmelCase_    ,   output.predictions    ,   'predict'  )
								_snake_case   						=		self.compute_metrics(lowerCAmelCase_  )

								# Prefix all keys with metric_key_prefix + '_'
								for key in list(metrics.keys()  ):
									if not key.startswith(F'{metric_key_prefix}_'  ):
										_snake_case   						=		metrics.pop(lowerCAmelCase_  )
								metrics.update(output.metrics  )
								return PredictionOutput(predictions=predictions.predictions    ,   label_ids=predictions.label_ids    ,   metrics=lowerCAmelCase_  )






'''simple docstring'''





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'''







 __lowerCamelCase    :       Optional[int]          =       0
 __lowerCamelCase    :       Any          =       1
 __lowerCamelCase    :       List[Any]          =       2






@add_end_docstrings(A						)
class __A			(  A						):



 '''simple docstring'''







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



 def __init__(self ,   *A ,   **A       )    ->     Tuple:


      """simple docstring"""



      super().__init__(*A ,   **A       )
      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.
           _a      =  None
           if self.model.config.prefix is not None:
                _a      =  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.
                _a      =  self.XL_PREFIX
           if prefix is not None:
                # Recalculate some generate_kwargs linked to prefix.
                _a  ,    _a  ,    _a      =  self._sanitize_parameters(prefix=A ,   **self._forward_params       )
                _a      =  {**self._preprocess_params, **preprocess_params}
                _a      =  {**self._forward_params, **forward_params}



 def 					a__  (self ,   A=None ,   A=None ,   A=None ,   A=None ,   A=None ,   A=None ,   A=None ,   A=None ,   **A ,   )    ->     str:


      """simple docstring"""



      _a      =  {}
      if prefix is not None:
           _a      =  prefix
      if prefix:
           _a      =  self.tokenizer(
               A ,   padding=A ,   add_special_tokens=A ,   return_tensors=self.framework       )
           _a      =  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\']'''       )
           _a      =  handle_long_generation

      preprocess_params.update(A       )
      _a      =  generate_kwargs

      _a      =  {}
      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`'''       )
           _a      =  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`'''       )
           _a      =  ReturnType.TENSORS
      if return_type is not None:
           _a      =  return_type
      if clean_up_tokenization_spaces is not None:
           _a      =  clean_up_tokenization_spaces

      if stop_sequence is not None:
           _a      =  self.tokenizer.encode(A ,   add_special_tokens=A       )
           if len(A       ) > 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.'''       )
           _a      =  stop_sequence_ids[0]

      return preprocess_params, forward_params, postprocess_params



 def 					a__  (self ,   *A ,   **A       )    ->     List[Any]:


      """simple docstring"""



      if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
           kwargs.update({'''add_space_before_punct_symbol''': True}       )

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



 def __call__(self ,   A ,   **A       )    ->     int:


      """simple docstring"""



      return super().__call__(A ,   **A       )



 def 					a__  (self ,   A ,   A="" ,   A=None ,   **A       )    ->     Any:


      """simple docstring"""



      _a      =  self.tokenizer(
          prefix + prompt_text ,   padding=A ,   add_special_tokens=A ,   return_tensors=self.framework       )
      _a      =  prompt_text

      if handle_long_generation == "hole":
           _a      =  inputs['''input_ids'''].shape[-1]
           if "max_new_tokens" in generate_kwargs:
                _a      =  generate_kwargs['''max_new_tokens''']
           else:
                _a      =  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:
                _a      =  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'''       )

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

      return inputs



 def 					a__  (self ,   A ,   **A       )    ->     Any:


      """simple docstring"""



      _a      =  model_inputs['''input_ids''']
      _a      =  model_inputs.get('''attention_mask''' ,   A       )
      # Allow empty prompts
      if input_ids.shape[1] == 0:
           _a      =  None
           _a      =  None
           _a      =  1
      else:
           _a      =  input_ids.shape[0]
      _a      =  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.
      _a      =  generate_kwargs.pop('''prefix_length''' ,   0       )
      if prefix_length > 0:
           _a      =  '''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:
                _a      =  generate_kwargs.get('''max_length'''       ) or self.model.config.max_length
                generate_kwargs["max_length"] += prefix_length
           _a      =  '''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
      _a      =  self.model.generate(input_ids=A ,   attention_mask=A ,   **A       )
      _a      =  generated_sequence.shape[0]
      if self.framework == "pt":
           _a      =  generated_sequence.reshape(A ,   out_b // in_b ,   *generated_sequence.shape[1:]       )
      elif self.framework == "tf":
           _a      =  tf.reshape(A ,   (in_b, out_b // in_b, *generated_sequence.shape[1:])       )
      return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}



 def 					a__  (self ,   A ,   A=ReturnType.FULL_TEXT ,   A=True       )    ->     str:


      """simple docstring"""



      _a      =  model_outputs['''generated_sequence'''][0]
      _a      =  model_outputs['''input_ids''']
      _a      =  model_outputs['''prompt_text''']
      _a      =  generated_sequence.numpy().tolist()
      _a      =  []
      for sequence in generated_sequence:
           if return_type == ReturnType.TENSORS:
                _a      =  {'''generated_token_ids''': sequence}
           elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
                # Decode text
                _a      =  self.tokenizer.decode(
                    A ,   skip_special_tokens=A ,   clean_up_tokenization_spaces=A ,   )

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

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

                _a      =  {'''generated_text''': all_text}
           records.append(A       )

      return records



