


"""simple docstring"""







import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
def _A						(						lowercase		):







    """simple docstring"""
    a 		=np.inf

    def set_batch_size(lowercase		) -> None:
        nonlocal batch_size
        if isinstance(lowercase       ,    lowercase		):
            a 		=min(lowercase       ,    config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS		)
        elif isinstance(lowercase       ,    lowercase		):
            a 		=min(lowercase       ,    config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS		)
        elif isinstance(lowercase       ,    lowercase		) and feature.dtype == "binary":
            a 		=min(lowercase       ,    config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS		)

    _visit(lowercase       ,    lowercase		)

    return None if batch_size is np.inf else batch_size






class       __A				(       _SCREAMING_SNAKE_CASE			):






 """simple docstring"""




 def __init__(		self ,    __A ,    __A = None ,    __A = None ,    __A = None ,    __A = False ,    __A = False ,    __A = None ,    **__A ,    )					->     Optional[Any]:
     super().__init__(
         __A ,    split=__A ,    features=__A ,    cache_dir=__A ,    keep_in_memory=__A ,    streaming=__A ,    num_proc=__A ,    **__A ,    )
     a 		=path_or_paths if isinstance(__A ,    __A						) else {self.split: path_or_paths}
     a 		=_PACKAGED_DATASETS_MODULES['''parquet'''][1]
     a 		=Parquet(
         cache_dir=__A ,    data_files=__A ,    features=__A ,    hash=__A ,    **__A ,    )





 def       SCREAMING_SNAKE_CASE							(		self						)					->     int:
     # Build iterable dataset
     if self.streaming:
         a 		=self.builder.as_streaming_dataset(split=self.split						)
     # Build regular (map-style) dataset
     else:
         a 		=None
         a 		=None
         a 		=None
         a 		=None

         self.builder.download_and_prepare(
             download_config=__A ,    download_mode=__A ,    verification_mode=__A ,    base_path=__A ,    num_proc=self.num_proc ,    )
         a 		=self.builder.as_dataset(
             split=self.split ,    verification_mode=__A ,    in_memory=self.keep_in_memory						)
     return dataset






class       __A				:






 """simple docstring"""




 def __init__(		self ,    __A ,    __A ,    __A = None ,    **__A ,    )					->     Optional[int]:
     a 		=dataset
     a 		=path_or_buf
     a 		=batch_size or get_writer_batch_size(dataset.features						)
     a 		=parquet_writer_kwargs


 def       SCREAMING_SNAKE_CASE							(		self						)					->     int:
     a 		=self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

     if isinstance(self.path_or_buf ,    (str, bytes, os.PathLike)						):
         with open(self.path_or_buf ,    '''wb+'''						) as buffer:
             a 		=self._write(file_obj=__A ,    batch_size=__A ,    **self.parquet_writer_kwargs						)
     else:
         a 		=self._write(file_obj=self.path_or_buf ,    batch_size=__A ,    **self.parquet_writer_kwargs						)
     return written





 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    **__A						)					->     int:
     a 		=0
     a 		=parquet_writer_kwargs.pop('''path_or_buf''' ,    __A						)
     a 		=self.dataset.features.arrow_schema

     a 		=pq.ParquetWriter(__A ,    schema=__A ,    **__A						)

     for offset in logging.tqdm(
         range(0 ,    len(self.dataset						) ,    __A						) ,    unit='''ba''' ,    disable=not logging.is_progress_bar_enabled() ,    desc='''Creating parquet from Arrow format''' ,    ):
         a 		=query_table(
             table=self.dataset._data ,    key=slice(__A ,    offset + batch_size						) ,    indices=self.dataset._indices if self.dataset._indices is not None else None ,    )
         writer.write_table(__A						)
         written += batch.nbytes
     writer.close()
     return written


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


A_       : List[Any]         =			logging.get_logger(__name__)



class     _a						:


					'''simple docstring'''






					def __init__( self    ,       A__ = None    ,       A__ = None    ,       A__=None    ,       A__=None			):
						if not conversation_id:
							A__							:    List[Any]        =		uuid.uuida()
						if past_user_inputs is None:
							A__							:    Dict        =		[]
						if generated_responses is None:
							A__							:    int        =		[]

						A__							:    uuid.UUID        =		conversation_id
						A__							:    List[str]        =		past_user_inputs
						A__							:    List[str]        =		generated_responses
						A__							:    Optional[str]        =		text






					def __eq__( self    ,       A__			):
						if not isinstance(A__    ,       A__			):
							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   __A			( self    ,       A__    ,       A__ = 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__							:    str        =		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__							:    Tuple        =		text






					def   __A			( self			):
						if self.new_user_input:
							self.past_user_inputs.append(self.new_user_input			)
						A__							:    Tuple        =		None






					def   __A			( self    ,       A__			):
						self.generated_responses.append(A__			)






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



@add_end_docstrings(
    __magic_name__		,   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						(__magic_name__				):


					'''simple docstring'''






					def __init__( self    ,       *A__    ,       **A__			):
						super().__init__(*A__    ,       **A__			)
						if self.tokenizer.pad_token_id is None:
							A__							:    Tuple        =		self.tokenizer.eos_token






					def   __A			( self    ,       A__=None    ,       A__=None    ,       A__=None    ,       **A__			):
						A__							:    Tuple        =		{}
						A__							:    List[str]        =		{}
						A__							:    Union[str, Any]        =		{}

						if min_length_for_response is not None:
							A__							:    str        =		min_length_for_response
						if minimum_tokens is not None:
							A__							:    List[str]        =		minimum_tokens

						if "max_length" in generate_kwargs:
							A__							:    List[Any]        =		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__							:    Optional[int]        =		clean_up_tokenization_spaces

						if generate_kwargs:
							forward_params.update(A__			)
						return preprocess_params, forward_params, postprocess_params






					def __call__( self    ,       A__    ,       A__=0    ,       **A__			):
						A__							:    Optional[Any]        =		super().__call__(A__    ,       num_workers=A__    ,       **A__			)
						if isinstance(A__    ,       A__			) and len(A__			) == 1:
							return outputs[0]
						return outputs






					def   __A			( self    ,       A__    ,       A__=32			):
						if not isinstance(A__    ,       A__			):
							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__							:    List[str]        =		self.tokenizer._build_conversation_input_ids(A__			)
						else:
							# If the tokenizer cannot handle conversations, we default to only the old version
							A__							:    Tuple        =		self._legacy_parse_and_tokenize(A__			)

						if self.framework == "pt":
							A__							:    List[str]        =		torch.LongTensor([input_ids]			)
						elif self.framework == "tf":
							A__							:    Optional[Any]        =		tf.constant([input_ids]			)
						return {"input_ids": input_ids, "conversation": conversation}






					def   __A			( self    ,       A__    ,       A__=10    ,       **A__			):
						A__							:    List[Any]        =		generate_kwargs.get("""max_length"""    ,       self.model.config.max_length			)

						A__							:    Optional[int]        =		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__							:    Dict        =		max_length - minimum_tokens
							A__							:    Optional[int]        =		model_inputs["""input_ids"""][:, -trim:]
							if "attention_mask" in model_inputs:
								A__							:    str        =		model_inputs["""attention_mask"""][:, -trim:]
						A__							:    List[str]        =		model_inputs.pop("""conversation"""			)
						A__							:    Dict        =		max_length
						A__							:    str        =		self.model.generate(**A__    ,       **A__			)
						if self.model.config.is_encoder_decoder:
							A__							:    Union[str, Any]        =		1
						else:
							A__							:    Optional[Any]        =		n
						return {"output_ids": output_ids[:, start_position:], "conversation": conversation}






					def   __A			( self    ,       A__    ,       A__=True			):
						A__							:    Dict        =		model_outputs["""output_ids"""]
						A__							:    str        =		self.tokenizer.decode(
						    output_ids[0]    ,       skip_special_tokens=A__    ,       clean_up_tokenization_spaces=A__    ,       )
						A__							:    Optional[int]        =		model_outputs["""conversation"""]
						conversation.mark_processed()
						conversation.append_response(A__			)
						return conversation






					def   __A			( self    ,       A__			):
						A__							:    str        =		self.tokenizer.eos_token_id
						A__							:    Tuple        =		[]
						for is_user, text in conversation.iter_texts():
							if eos_token_id is not None:
								input_ids.extend(self.tokenizer.encode(A__    ,       add_special_tokens=A__			) + [eos_token_id]			)
							else:
								input_ids.extend(self.tokenizer.encode(A__    ,       add_special_tokens=A__			)			)

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



