





'''simple docstring'''

import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
  import tensorflow as tf

if is_torch_available():
  import torch


__lowerCAmelCase							:     List[str]		     =logging.get_logger(__name__)






class        UpperCAmelCase     :
 def __init__(    self				:Dict						,	lowercase_				:str = None						,	lowercase_				:uuid.UUID = None						,	lowercase_				:List[str]=None						,	lowercase_				:Optional[int]=None							)->							List[Any]:
     if not conversation_id:
         A__    =      uuid.uuida()
     if past_user_inputs is None:
         A__    =      []
     if generated_responses is None:
         A__    =      []

     A__    =      conversation_id
     A__    =      past_user_inputs
     A__    =      generated_responses
     A__    =      text
 def __eq__(    self				:Union[str, Any]						,	lowercase_				:Tuple							)->							Tuple:
     if not isinstance(lowercase_						,	lowercase_							):
         return False
     if self.uuid == other.uuid:
         return True
     return (
         self.new_user_input == other.new_user_input
         and self.past_user_inputs == other.past_user_inputs
         and self.generated_responses == other.generated_responses
     )
 def 					UpperCAmelCase_							(    self				:Tuple						,	lowercase_				:str						,	lowercase_				:bool = False							)->							Optional[int]:
     if self.new_user_input:
         if overwrite:
             logger.warning(
                 F"User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten "
                 F"with: \"{text}\"."							)
             A__    =      text
         else:
             logger.warning(
                 F"User input added while unprocessed input was existing: \"{self.new_user_input}\" new input "
                 F"ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input"							)
     else:
         A__    =      text
 def 					UpperCAmelCase_							(    self				:List[str]							)->							Optional[Any]:
     if self.new_user_input:
         self.past_user_inputs.append(self.new_user_input							)
     A__    =      None
 def 					UpperCAmelCase_							(    self				:Any						,	lowercase_				:str							)->							List[Any]:
     self.generated_responses.append(lowercase_							)
 def 					UpperCAmelCase_							(    self				:Dict							)->							Dict:
     for user_input, generated_response in zip(self.past_user_inputs						,	self.generated_responses							):
         yield True, user_input
         yield False, generated_response
     if self.new_user_input:
         yield True, self.new_user_input



 def __repr__(    self				:Union[str, Any]							)->							Optional[Any]:
     A__    =      F"Conversation id: {self.uuid} \n"
     for is_user, text in self.iter_texts():
         A__    =      "user" if is_user else "bot"
         output += F"{name} >> {text} \n"
     return output






@add_end_docstrings(
    UpperCamelCase__      ,	r"""
        min_length_for_response (`int`, *optional*, defaults to 32):
            The minimum length (in number of tokens) for a response.
        minimum_tokens (`int`, *optional*, defaults to 10):
            The minimum length of tokens to leave for a response.
    """      ,	)
class        UpperCAmelCase     (	UpperCamelCase__    ):
 def __init__(    self				:Tuple						,	*lowercase_				:Dict						,	**lowercase_				:Optional[Any]							)->							Dict:
     super().__init__(*lowercase_						,	**lowercase_							)
     if self.tokenizer.pad_token_id is None:
         A__    =      self.tokenizer.eos_token
 def 					UpperCAmelCase_							(    self				:Tuple						,	lowercase_				:Optional[int]=None						,	lowercase_				:List[str]=None						,	lowercase_				:List[str]=None						,	**lowercase_				:str							)->							str:
     A__    =      {}
     A__    =      {}
     A__    =      {}

     if min_length_for_response is not None:
         A__    =      min_length_for_response
     if minimum_tokens is not None:
         A__    =      minimum_tokens

     if "max_length" in generate_kwargs:
         A__    =      generate_kwargs["max_length"]
         # self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
     if clean_up_tokenization_spaces is not None:
         A__    =      clean_up_tokenization_spaces

     if generate_kwargs:
         forward_params.update(lowercase_							)
     return preprocess_params, forward_params, postprocess_params
 def __call__(    self				:Optional[int]						,	lowercase_				:Union[Conversation, List[Conversation]]						,	lowercase_				:List[Any]=0						,	**lowercase_				:Union[str, Any]							)->							Dict:
     A__    =      super().__call__(lowercase_						,	num_workers=lowercase_						,	**lowercase_							)
     if isinstance(lowercase_						,	lowercase_							) and len(lowercase_							) == 1:
         return outputs[0]
     return outputs
 def 					UpperCAmelCase_							(    self				:Union[str, Any]						,	lowercase_				:Conversation						,	lowercase_				:Optional[int]=32							)->							Dict[str, Any]:
     if not isinstance(lowercase_						,	lowercase_							):
         raise ValueError("ConversationalPipeline, expects Conversation as inputs"							)
     if conversation.new_user_input is None:
         raise ValueError(
             F"Conversation with UUID {type(conversation.uuid							)} does not contain new user input to process. "
             "Add user inputs with the conversation's `add_user_input` method"							)
     if hasattr(self.tokenizer						,	"_build_conversation_input_ids"							):
         A__    =      self.tokenizer._build_conversation_input_ids(lowercase_							)
     else:
         # If the tokenizer cannot handle conversations, we default to only the old version
         A__    =      self._legacy_parse_and_tokenize(lowercase_							)

     if self.framework == "pt":
         A__    =      torch.LongTensor([input_ids]							)
     elif self.framework == "tf":
         A__    =      tf.constant([input_ids]							)
     return {"input_ids": input_ids, "conversation": conversation}
 def 					UpperCAmelCase_							(    self				:Optional[Any]						,	lowercase_				:Union[str, Any]						,	lowercase_				:Union[str, Any]=10						,	**lowercase_				:Dict							)->							str:
     A__    =      generate_kwargs.get("max_length"						,	self.model.config.max_length							)

     A__    =      model_inputs["input_ids"].shape[1]
     if max_length - minimum_tokens < n:
         logger.warning(F"Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})"							)
         A__    =      max_length - minimum_tokens
         A__    =      model_inputs["input_ids"][:, -trim:]
         if "attention_mask" in model_inputs:
             A__    =      model_inputs["attention_mask"][:, -trim:]
     A__    =      model_inputs.pop("conversation"							)
     A__    =      max_length
     A__    =      self.model.generate(**lowercase_						,	**lowercase_							)
     if self.model.config.is_encoder_decoder:
         A__    =      1
     else:
         A__    =      n
     return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
 def 					UpperCAmelCase_							(    self				:Dict						,	lowercase_				:Any						,	lowercase_				:str=True							)->							Tuple:
     A__    =      model_outputs["output_ids"]
     A__    =      self.tokenizer.decode(
         output_ids[0]						,	skip_special_tokens=lowercase_						,	clean_up_tokenization_spaces=lowercase_						,	)
     A__    =      model_outputs["conversation"]
     conversation.mark_processed()
     conversation.append_response(lowercase_							)
     return conversation



 def 					UpperCAmelCase_							(    self				:Optional[Any]						,	lowercase_				:Conversation							)->							Dict:
     A__    =      self.tokenizer.eos_token_id
     A__    =      []
     for is_user, text in conversation.iter_texts():
         if eos_token_id is not None:
             input_ids.extend(self.tokenizer.encode(lowercase_						,	add_special_tokens=lowercase_							) + [eos_token_id]							)
         else:
             input_ids.extend(self.tokenizer.encode(lowercase_						,	add_special_tokens=lowercase_							)							)

     if len(lowercase_							) > self.tokenizer.model_max_length:
         A__    =      input_ids[-self.tokenizer.model_max_length :]
     return input_ids










'''simple docstring'''

import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase							:     List[str]		     =logging.get_logger(__name__)





def 						UpperCamelCase    (							_lowerCamelCase : str	):
    A__    =      torch.load(_lowerCamelCase						,	map_location="cpu"	)
    if "model" in sd.keys():
        A__    =      torch.load(_lowerCamelCase						,	map_location="cpu"	)["model"]

    # pop unnecessary weights
    A__    =      [
        "decoder.version",
        "decoder.output_projection.weight",
    ]
    for key in keys_to_delete:
        if key in sd:
            sd.pop(_lowerCamelCase	)

    A__    =      {
        "decoder.project_in_dim.weight": "decoder.project_in.weight",
        "decoder.project_out_dim.weight": "decoder.project_out.weight",
        "decoder.layer_norm.weight": "decoder.final_layer_norm.weight",
        "decoder.layer_norm.bias": "decoder.final_layer_norm.bias",
    }
    for old_key, new_key in keys_to_rename.items():
        if old_key in sd:
            A__    =      sd.pop(_lowerCamelCase	)

    A__    =      list(sd.keys()	)
    for key in keys:
        if ".qkv_proj." in key:
            A__    =      sd[key]
            # We split QKV in separate Q,K,V

            A__    =      key.replace(".qkv_proj."						,	".q_proj."	)
            A__    =      key.replace(".qkv_proj."						,	".k_proj."	)
            A__    =      key.replace(".qkv_proj."						,	".v_proj."	)

            A__    =      value.shape[0]
            assert depth % 3 == 0
            # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
            # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
            A__,     A__,     A__    =      torch.split(_lowerCamelCase						,	depth // 3						,	dim=0	)

            A__    =      q
            A__    =      k
            A__    =      v
            del sd[key]

    return sd





@torch.no_grad()
def 						UpperCamelCase    (							_lowerCamelCase : List[Any]						,	_lowerCamelCase : Any						,	_lowerCamelCase : Dict=None	):
    A__    =      load_checkpoint(_lowerCamelCase	)

    if config is not None:
        A__    =      OPTConfig.from_pretrained(_lowerCamelCase	)
    else:
        A__    =      OPTConfig()

    A__    =      OPTModel(_lowerCamelCase	).half().eval()
    model.load_state_dict(_lowerCamelCase	)

    # Check results
    Path(_lowerCamelCase	).mkdir(exist_ok=_lowerCamelCase	)
    model.save_pretrained(_lowerCamelCase	)


if __name__ == "__main__":
  __lowerCAmelCase							:     List[str]		     =argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--fairseq_path",
      type=str,
      help=(
          "path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"
          " https://huggingface.co/models?other=opt_metasq"
      ),
  )
  parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
  parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.")
  __lowerCAmelCase							:     List[Any]		     =parser.parse_args()
  convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)



