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


SCREAMING_SNAKE_CASE :	Tuple							     =		logging.get_logger(__name__)
class    A_ :

    def __init__(		self :      Any     ,							__SCREAMING_SNAKE_CASE :      Dict = None     ,							__SCREAMING_SNAKE_CASE :      Optional[Any] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=None     ,							__SCREAMING_SNAKE_CASE :      List[Any]=None       ):
     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 :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]       ):
     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 :      Any     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      Optional[Any] = False       ):
     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 :      Tuple       ):
     if self.new_user_input:
      self.past_user_inputs.append(self.new_user_input       )
     __a       =  None

    def 		_UpperCAmelCase   (		self :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]       ):
     self.generated_responses.append(lowercase_       )

    def 		_UpperCAmelCase   (		self :      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 :      int       ):
     __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(
    snake_case__			,       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    A_ (       snake_case__ ):

    def __init__(		self :      str     ,							*__SCREAMING_SNAKE_CASE :      Optional[Any]     ,							**__SCREAMING_SNAKE_CASE :      Optional[int]       ):
     super().__init__(*lowercase_     ,							**lowercase_       )
     if self.tokenizer.pad_token_id is None:
      __a       =  self.tokenizer.eos_token

    def 		_UpperCAmelCase   (		self :      Any     ,							__SCREAMING_SNAKE_CASE :      int=None     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=None     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=None     ,							**__SCREAMING_SNAKE_CASE :      List[Any]       ):
     __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 :      Dict     ,							__SCREAMING_SNAKE_CASE :      List[str]     ,							__SCREAMING_SNAKE_CASE :      List[str]=0     ,							**__SCREAMING_SNAKE_CASE :      Tuple       ):
     __a       =  super().__call__(lowercase_     ,							num_workers=lowercase_     ,							**lowercase_       )
     if isinstance(lowercase_     ,							lowercase_       ) and len(lowercase_       ) == 1:
      return outputs[0]
     return outputs

    def 		_UpperCAmelCase   (		self :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=32       ):
     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 :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Dict=10     ,							**__SCREAMING_SNAKE_CASE :      Optional[int]       ):
     __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     ,							__SCREAMING_SNAKE_CASE :      Any     ,							__SCREAMING_SNAKE_CASE :      Any=True       ):
     __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 :      Any     ,							__SCREAMING_SNAKE_CASE :      int       ):
     __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



from ... import PretrainedConfig


SCREAMING_SNAKE_CASE :	Any							     =		{
    """sijunhe/nezha-cn-base""": """https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json""",
}
class    A_ (       a_ ):
  _SCREAMING_SNAKE_CASE    				=			NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
  _SCREAMING_SNAKE_CASE    				=			"""nezha"""

  def __init__(		self :      Any     ,							__SCREAMING_SNAKE_CASE :      List[str]=2_11_28     ,							__SCREAMING_SNAKE_CASE :      Dict=7_68     ,							__SCREAMING_SNAKE_CASE :      List[Any]=12     ,							__SCREAMING_SNAKE_CASE :      str=12     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=30_72     ,							__SCREAMING_SNAKE_CASE :      Any="gelu"     ,							__SCREAMING_SNAKE_CASE :      int=0.1     ,							__SCREAMING_SNAKE_CASE :      List[str]=0.1     ,							__SCREAMING_SNAKE_CASE :      int=5_12     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=64     ,							__SCREAMING_SNAKE_CASE :      List[Any]=2     ,							__SCREAMING_SNAKE_CASE :      int=0.02     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=1E-12     ,							__SCREAMING_SNAKE_CASE :      int=0.1     ,							__SCREAMING_SNAKE_CASE :      Any=0     ,							__SCREAMING_SNAKE_CASE :      Any=2     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=3     ,							__SCREAMING_SNAKE_CASE :      List[Any]=True     ,							**__SCREAMING_SNAKE_CASE :      List[str]     ,							):
   super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE     ,							bos_token_id=__SCREAMING_SNAKE_CASE     ,							eos_token_id=__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )

   __a       =  vocab_size
   __a       =  hidden_size
   __a       =  num_hidden_layers
   __a       =  num_attention_heads
   __a       =  hidden_act
   __a       =  intermediate_size
   __a       =  hidden_dropout_prob
   __a       =  attention_probs_dropout_prob
   __a       =  max_position_embeddings
   __a       =  max_relative_position
   __a       =  type_vocab_size
   __a       =  initializer_range
   __a       =  layer_norm_eps
   __a       =  classifier_dropout
   __a       =  use_cache


