

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 SCREAMING_SNAKE_CASE     (  enum.Enum			):
  """simple docstring"""






  lowerCamelCase   :      List[str]										=0
  lowerCamelCase   :      Union[str, Any]										=1
  lowerCamelCase   :      Optional[int]										=2






@add_end_docstrings(lowerCAmelCase__			)
class SCREAMING_SNAKE_CASE     (  lowerCAmelCase__			):
  """simple docstring"""






  lowerCamelCase   :      int										="\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			:   Dict	,							*lowerCAmelCase			:   Dict	,							**lowerCAmelCase			:   str   )							->     List[str]:






     """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.
        __lowerCAmelCase  :      str         =						None
        if self.model.config.prefix is not None:
           __lowerCAmelCase  :      Optional[int]         =						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.
           __lowerCAmelCase  :      Optional[int]         =						self.XL_PREFIX
        if prefix is not None:
           # Recalculate some generate_kwargs linked to prefix.
           __lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase  :      int         =						self._sanitize_parameters(prefix=a__	,							**self._forward_params   )
           __lowerCAmelCase  :      Union[str, Any]         =						{**self._preprocess_params, **preprocess_params}
           __lowerCAmelCase  :      List[Any]         =						{**self._forward_params, **forward_params}







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]	,							lowerCAmelCase			:   List[str]=None	,							lowerCAmelCase			:   List[str]=None	,							lowerCAmelCase			:   Optional[Any]=None	,							lowerCAmelCase			:   Optional[int]=None	,							lowerCAmelCase			:   Tuple=None	,							lowerCAmelCase			:   Optional[int]=None	,							lowerCAmelCase			:   Optional[int]=None	,							lowerCAmelCase			:   Any=None	,							**lowerCAmelCase			:   int	,							)							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]         =						{}
     if prefix is not None:
        __lowerCAmelCase  :      List[Any]         =						prefix
     if prefix:
        __lowerCAmelCase  :      Union[str, Any]         =						self.tokenizer(
            a__	,							padding=a__	,							add_special_tokens=a__	,							return_tensors=self.framework   )
        __lowerCAmelCase  :      Optional[Any]         =						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\']"""   )
        __lowerCAmelCase  :      Tuple         =						handle_long_generation

     preprocess_params.update(a__   )
     __lowerCAmelCase  :      List[Any]         =						generate_kwargs

     __lowerCAmelCase  :      int         =						{}
     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`"""   )
        __lowerCAmelCase  :      Optional[Any]         =						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`"""   )
        __lowerCAmelCase  :      Union[str, Any]         =						ReturnType.TENSORS
     if return_type is not None:
        __lowerCAmelCase  :      List[str]         =						return_type
     if clean_up_tokenization_spaces is not None:
        __lowerCAmelCase  :      int         =						clean_up_tokenization_spaces

     if stop_sequence is not None:
        __lowerCAmelCase  :      Optional[Any]         =						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."""   )
        __lowerCAmelCase  :      str         =						stop_sequence_ids[0]

     return preprocess_params, forward_params, postprocess_params







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]	,							*lowerCAmelCase			:   Union[str, Any]	,							**lowerCAmelCase			:   str   )							->     Dict:






     """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			:   Optional[int]	,							lowerCAmelCase			:   Tuple	,							**lowerCAmelCase			:   List[str]   )							->     Optional[int]:






     """simple docstring"""







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







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   Dict=""	,							lowerCAmelCase			:   Dict=None	,							**lowerCAmelCase			:   str   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      int         =						self.tokenizer(
         prefix + prompt_text	,							padding=a__	,							add_special_tokens=a__	,							return_tensors=self.framework   )
     __lowerCAmelCase  :      str         =						prompt_text

     if handle_long_generation == "hole":
        __lowerCAmelCase  :      List[Any]         =						inputs["""input_ids"""].shape[-1]
        if "max_new_tokens" in generate_kwargs:
           __lowerCAmelCase  :      Optional[Any]         =						generate_kwargs["""max_new_tokens"""]
        else:
           __lowerCAmelCase  :      str         =						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:
           __lowerCAmelCase  :      Optional[int]         =						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"""   )

           __lowerCAmelCase  :      Optional[Any]         =						inputs["""input_ids"""][:, -keep_length:]
           if "attention_mask" in inputs:
              __lowerCAmelCase  :      str         =						inputs["""attention_mask"""][:, -keep_length:]

     return inputs







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							lowerCAmelCase			:   str	,							**lowerCAmelCase			:   Tuple   )							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]         =						model_inputs["""input_ids"""]
     __lowerCAmelCase  :      List[Any]         =						model_inputs.get("""attention_mask"""	,							a__   )
     # Allow empty prompts
     if input_ids.shape[1] == 0:
        __lowerCAmelCase  :      Optional[Any]         =						None
        __lowerCAmelCase  :      Union[str, Any]         =						None
        __lowerCAmelCase  :      Any         =						1
     else:
        __lowerCAmelCase  :      List[str]         =						input_ids.shape[0]
     __lowerCAmelCase  :      Tuple         =						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.
     __lowerCAmelCase  :      Optional[int]         =						generate_kwargs.pop("""prefix_length"""	,							0   )
     if prefix_length > 0:
        __lowerCAmelCase  :      int         =						"""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:
           __lowerCAmelCase  :      Optional[Any]         =						generate_kwargs.get("""max_length"""   ) or self.model.config.max_length
           generate_kwargs["max_length"] += prefix_length
        __lowerCAmelCase  :      Dict         =						"""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
     __lowerCAmelCase  :      Dict         =						self.model.generate(input_ids=a__	,							attention_mask=a__	,							**a__   )
     __lowerCAmelCase  :      Union[str, Any]         =						generated_sequence.shape[0]
     if self.framework == "pt":
        __lowerCAmelCase  :      Dict         =						generated_sequence.reshape(a__	,							out_b // in_b	,							*generated_sequence.shape[1:]   )
     elif self.framework == "tf":
        __lowerCAmelCase  :      Union[str, Any]         =						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 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   Any=ReturnType.FULL_TEXT	,							lowerCAmelCase			:   Any=True   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      Dict         =						model_outputs["""generated_sequence"""][0]
     __lowerCAmelCase  :      List[Any]         =						model_outputs["""input_ids"""]
     __lowerCAmelCase  :      List[Any]         =						model_outputs["""prompt_text"""]
     __lowerCAmelCase  :      List[Any]         =						generated_sequence.numpy().tolist()
     __lowerCAmelCase  :      Optional[int]         =						[]
     for sequence in generated_sequence:
        if return_type == ReturnType.TENSORS:
           __lowerCAmelCase  :      List[Any]         =						{"""generated_token_ids""": sequence}
        elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
           # Decode text
           __lowerCAmelCase  :      str         =						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:
              __lowerCAmelCase  :      Any         =						0
           else:
              __lowerCAmelCase  :      List[Any]         =						len(
                  self.tokenizer.decode(
                      input_ids[0]	,							skip_special_tokens=a__	,							clean_up_tokenization_spaces=a__	,							)   )

           if return_type == ReturnType.FULL_TEXT:
              __lowerCAmelCase  :      List[str]         =						prompt_text + text[prompt_length:]
           else:
              __lowerCAmelCase  :      List[str]         =						text[prompt_length:]

           __lowerCAmelCase  :      Optional[int]         =						{"""generated_text""": all_text}
        records.append(a__   )

     return records





import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


__UpperCAmelCase								=			logging.get_logger(__name__)





def      snake_case_				()      ->     str:
   # Get the sagemaker specific mp parameters from smp_options variable.
   __lowerCAmelCase  :      List[Any]    =						os.getenv("""SM_HP_MP_PARAMETERS""" ,       """{}"""					)
   try:
      # Parse it and check the field "partitions" is included, it is required for model parallel.
      __lowerCAmelCase  :      Any    =						json.loads(__A					)
      if "partitions" not in smp_options:
         return False
   except json.JSONDecodeError:
      return False

   # Get the sagemaker specific framework parameters from mpi_options variable.
   __lowerCAmelCase  :      Any    =						os.getenv("""SM_FRAMEWORK_PARAMS""" ,       """{}"""					)
   try:
      # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
      __lowerCAmelCase  :      Union[str, Any]    =						json.loads(__A					)
      if not mpi_options.get("""sagemaker_mpi_enabled""" ,       __A					):
         return False
   except json.JSONDecodeError:
      return False
   # Lastly, check if the `smdistributed` module is present.
   return importlib.util.find_spec("""smdistributed"""					) is not None


if is_sagemaker_model_parallel_available():
   import smdistributed.modelparallel.torch as smp

   smp.init()



@dataclass
class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  lowerCamelCase   :      str										=field(
      default=""       ,							metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"}       ,							)







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     Optional[int]:






     """simple docstring"""







     super().__post_init__()
     warnings.warn(
         """`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use """
         """`TrainingArguments` instead."""	,							lowerCAmelCase	,							)







  @cached_property
  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     "torch.device":






     """simple docstring"""







     logger.info("""PyTorch: setting up devices"""   )
     if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
        logger.warning(
            """torch.distributed process group is initialized, but local_rank == -1. """
            """In order to use Torch DDP, launch your script with `python -m torch.distributed.launch"""   )
     if self.no_cuda:
        __lowerCAmelCase  :      Optional[Any]    =						torch.device("""cpu"""   )
        __lowerCAmelCase  :      Union[str, Any]    =						0
     elif is_sagemaker_model_parallel_available():
        __lowerCAmelCase  :      int    =						smp.local_rank()
        __lowerCAmelCase  :      str    =						torch.device("""cuda"""	,							lowerCAmelCase   )
        __lowerCAmelCase  :      Tuple    =						1
     elif is_sagemaker_dp_enabled():
        import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

        torch.distributed.init_process_group(backend="""smddp"""	,							timeout=self.ddp_timeout_delta   )
        __lowerCAmelCase  :      List[str]    =						int(os.getenv("""SMDATAPARALLEL_LOCAL_RANK"""   )   )
        __lowerCAmelCase  :      Tuple    =						torch.device("""cuda"""	,							self.local_rank   )
        __lowerCAmelCase  :      Optional[int]    =						1
     elif self.local_rank == -1:
        # if n_gpu is > 1 we'll use nn.DataParallel.
        # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
        # Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
        # trigger an error that a device index is missing. Index 0 takes into account the
        # GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
        # will use the first GPU in that env, i.e. GPU#1
        __lowerCAmelCase  :      List[Any]    =						torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""   )
        # Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
        # the default value.
        __lowerCAmelCase  :      Optional[int]    =						torch.cuda.device_count()
     else:
        # Here, we'll use torch.distributed.
        # Initializes the distributed backend which will take care of synchronizing nodes/GPUs
        if not torch.distributed.is_initialized():
           torch.distributed.init_process_group(backend="""nccl"""	,							timeout=self.ddp_timeout_delta   )
        __lowerCAmelCase  :      List[str]    =						torch.device("""cuda"""	,							self.local_rank   )
        __lowerCAmelCase  :      int    =						1

     if device.type == "cuda":
        torch.cuda.set_device(lowerCAmelCase   )

     return device







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Any:






     """simple docstring"""







     if is_sagemaker_model_parallel_available():
        return smp.dp_size()

     return super().world_size







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Tuple:






     """simple docstring"""







     return not is_sagemaker_model_parallel_available()







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Tuple:






     """simple docstring"""







     return False


