

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


_UpperCAmelCase		:   Dict										=       logging.get_logger(__name__)







class  lowercase				:




				def __init__(					self      ,   snake_case = None      ,   snake_case = None      ,   snake_case=None      ,   snake_case=None       ):
											if not conversation_id:
																		snake_case_							=    uuid.uuida()
											if past_user_inputs is None:
																		snake_case_							=    []
											if generated_responses is None:
																		snake_case_							=    []

											snake_case_							=    conversation_id
											snake_case_							=    past_user_inputs
											snake_case_							=    generated_responses
											snake_case_							=    text




				def __eq__(					self      ,   snake_case       ):
											if not isinstance(snake_case      ,   snake_case       ):
																		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      ,   snake_case      ,   snake_case = 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}".'''       )
																									snake_case_							=    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:
																		snake_case_							=    text




				def     a							(					self       ):
											if self.new_user_input:
																		self.past_user_inputs.append(self.new_user_input       )
											snake_case_							=    None




				def     a							(					self      ,   snake_case       ):
											self.generated_responses.append(snake_case       )




				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       ):
											snake_case_							=    F'''Conversation id: {self.uuid} \n'''
											for is_user, text in self.iter_texts():
																		snake_case_							=    'user' if is_user else 'bot'
																		output += F'''{name} >> {text} \n'''
											return output







@add_end_docstrings(
    lowercase_	,  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  lowercase				(       lowercase_			):




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




				def     a							(					self      ,   snake_case=None      ,   snake_case=None      ,   snake_case=None      ,   **snake_case       ):
											snake_case_							=    {}
											snake_case_							=    {}
											snake_case_							=    {}

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

											if "max_length" in generate_kwargs:
																		snake_case_							=    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:
																		snake_case_							=    clean_up_tokenization_spaces

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




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




				def     a							(					self      ,   snake_case      ,   snake_case=32       ):
											if not isinstance(snake_case      ,   snake_case       ):
																		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'       ):
																		snake_case_							=    self.tokenizer._build_conversation_input_ids(snake_case       )
											else:
																		# If the tokenizer cannot handle conversations, we default to only the old version
																		snake_case_							=    self._legacy_parse_and_tokenize(snake_case       )

											if self.framework == "pt":
																		snake_case_							=    torch.LongTensor([input_ids]       )
											elif self.framework == "tf":
																		snake_case_							=    tf.constant([input_ids]       )
											return {"input_ids": input_ids, "conversation": conversation}




				def     a							(					self      ,   snake_case      ,   snake_case=10      ,   **snake_case       ):
											snake_case_							=    generate_kwargs.get('max_length'      ,   self.model.config.max_length       )

											snake_case_							=    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})'''       )
																		snake_case_							=    max_length - minimum_tokens
																		snake_case_							=    model_inputs['input_ids'][:, -trim:]
																		if "attention_mask" in model_inputs:
																									snake_case_							=    model_inputs['attention_mask'][:, -trim:]
											snake_case_							=    model_inputs.pop('conversation'       )
											snake_case_							=    max_length
											snake_case_							=    self.model.generate(**snake_case      ,   **snake_case       )
											if self.model.config.is_encoder_decoder:
																		snake_case_							=    1
											else:
																		snake_case_							=    n
											return {"output_ids": output_ids[:, start_position:], "conversation": conversation}




				def     a							(					self      ,   snake_case      ,   snake_case=True       ):
											snake_case_							=    model_outputs['output_ids']
											snake_case_							=    self.tokenizer.decode(
											    output_ids[0]      ,   skip_special_tokens=snake_case      ,   clean_up_tokenization_spaces=snake_case      ,   )
											snake_case_							=    model_outputs['conversation']
											conversation.mark_processed()
											conversation.append_response(snake_case       )
											return conversation




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

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







import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    {}
							snake_case_							=    tokenizer(example['content']   ,							truncation=UpperCamelCase__      )['input_ids']
							snake_case_							=    len(example['content']      ) / len(output['input_ids']      )
							return output


_UpperCAmelCase		:   Dict										=       HfArgumentParser(PretokenizationArguments)
_UpperCAmelCase		:   List[Any]										=       parser.parse_args()
if args.num_workers is None:
					_UpperCAmelCase		:   Union[str, Any]										=       multiprocessing.cpu_count()
_UpperCAmelCase		:   int										=       AutoTokenizer.from_pretrained(args.tokenizer_dir)

_UpperCAmelCase		:   Optional[int]										=       time.time()
_UpperCAmelCase		:   List[str]										=       load_dataset(args.dataset_name, split="""train""")
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

_UpperCAmelCase		:   Tuple										=       time.time()
_UpperCAmelCase		:   Union[str, Any]										=       ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        """repo_name""",
        """path""",
        """copies""",
        """size""",
        """content""",
        """license""",
        """hash""",
        """line_mean""",
        """line_max""",
        """alpha_frac""",
        """autogenerated""",
    ],
)
print(F'''Dataset tokenized in {time.time()-t_start:.2f}s''')

_UpperCAmelCase		:   Dict										=       time.time()
ds.push_to_hub(args.tokenized_data_repo)


print(F'''Data pushed to the hub in {time.time()-t_start:.2f}s''')




