# -*- coding: utf-8 -*-
"""using_dataset_hugginface.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1soGxkZu4antYbYG23GioJ6zoSt_GhSNT
"""

"""**Hugginface loggin for push on Hub**"""
###
#
#  Used bibliografy:
#    https://huggingface.co/learn/nlp-course/chapter5/5
#
###

import os
import time
import math
from huggingface_hub import login
from datasets import load_dataset, concatenate_datasets
from functools import reduce
from pathlib import Path
import pandas as pd
import numpy as np


# Load model directly
from transformers import AutoTokenizer

HF_TOKEN = ''
DATASET_TO_LOAD = 'spanish_health_output.json'
DATASET_TO_UPDATE = 'somosnlp/spanish_medica_llm'
BAD_CHAIN = [
   'es como usted puede verificarlo',
   'Un sitio oficial del Gobierno de Estados Unidos',
   'lo en sitios web oficiales y seguros.',
   'forma segura a un sitio web .gov. Comparta informaci', 
   'Gobierno de Estados Unidos.',
   'pertenece a una organizaci',
   '(\r\n              \n ) o ',
   'Un sitio\r\n'
]
#Loggin to Huggin Face
login(token = HF_TOKEN)



royalListOfCode = {}
issues_path = 'dataset'
tokenizer = AutoTokenizer.from_pretrained("DeepESP/gpt2-spanish-medium")
DATASET_SOURCE_ID = '2'
#Read current path
path = Path(__file__).parent.absolute()

dataset_CODING = pd.read_json(str(path) + os.sep + DATASET_TO_LOAD,  encoding="utf8")

    # raw_text: Texto asociado al documento, pregunta, caso clínico u otro tipo de información.

    # topic: (puede ser healthcare_treatment, healthcare_diagnosis, tema, respuesta a pregunta, o estar vacío p.ej en el texto abierto)

    # speciality: (especialidad médica a la que se relaciona el raw_text p.ej: cardiología, cirugía, otros)

    # raw_text_type: (puede ser caso clínico, open_text, question)

    # topic_type: (puede ser medical_topic, medical_diagnostic,answer,natural_medicine_topic, other, o vacio)

    # source: Identificador de la fuente asociada al documento que aparece en el README y descripción del dataset.

    # country: Identificador del país de procedencia de la fuente (p.ej.; ch, es) usando el estándar ISO 3166-1 alfa-2 (Códigos de país de dos letras.).
cantemistDstDict = {
  'raw_text': '',
  'topic': '',
  'speciallity': '',
  'raw_text_type': 'open_text',
  'topic_type': 'other',
  'source': DATASET_SOURCE_ID,
  'country': 'es',
  'document_id': ''
}

def getExtraTexInformation(item, data_top_columname):
     optionalTag = ["Healthtopics Name", "titles", "subtitles", "paragraphs"]
     text = ""

     for key in data_top_columname:
          if key not in optionalTag:
              if not np.isnan(item[key]) and len(item[key]) > 1:
                text += str(item[key]) + '\n'          
              
     return text
               
totalOfTokens = 0
corpusToLoad = []
countCopySeveralDocument = 0
counteOriginalDocument = 0
data_top_columname = dataset_CODING.head()

def verifyRepetelyChain(paragraph):
    return '' if len([ x for x in BAD_CHAIN if paragraph.find(x) != -1]) > 0 else paragraph
    

for index, item in dataset_CODING.iterrows():      

        if len(item['paragraphs']) > 1:
           text = reduce(lambda a, b: verifyRepetelyChain(a) + "\n "+ verifyRepetelyChain(b), item['paragraphs'], "")
        else:
           text = getExtraTexInformation(item, data_top_columname)
        #Find topic or diagnosti clasification about the text
       
        counteOriginalDocument += 1  
        newCorpusRow = cantemistDstDict.copy()

          #print('Current text has ', currentSizeOfTokens)
          #print('Total of tokens is ', totalOfTokens)

        listOfTokens = []
        try:
          listOfTokens = tokenizer.tokenize(text)
        except Exception:
           raise Exception('Error')
             
        currentSizeOfTokens = len(listOfTokens)
        totalOfTokens += currentSizeOfTokens

        newCorpusRow['topic'] =  item['Healthtopics Name']  if item['Healthtopics Name'] else reduce(lambda a, b: a + "\n "+ b, item['titles'], "")
        newCorpusRow['raw_text'] = text
        idFile = counteOriginalDocument
        newCorpusRow['document_id'] = str(idFile)
        corpusToLoad.append(newCorpusRow)
        
      
df = pd.DataFrame.from_records(corpusToLoad)

if os.path.exists(f"{str(path)}/{issues_path}/spanish_medical_llms.jsonl"):
  os.remove(f"{str(path)}/{issues_path}/spanish_medical_llms.jsonl")

df.to_json(f"{str(path)}/{issues_path}/spanish_medical_llms.jsonl", orient="records", lines=True)
print(
        f"Downloaded all the issues for {DATASET_TO_LOAD}! Dataset stored at {issues_path}/spanish_medical_llms.jsonl"
)

print(' On dataset there are as document ', counteOriginalDocument)
print(' On dataset there are as copy document ', countCopySeveralDocument)
print(' On dataset there are as size of Tokens ', totalOfTokens)
file = Path(f"{str(path)}/{issues_path}/spanish_medical_llms.jsonl")  # or Path('./doc.txt')
size = file.stat().st_size
print ('File size on Kilobytes (kB)', size >> 10)  # 5242880 kilobytes (kB)
print ('File size on Megabytes  (MB)', size >> 20 ) # 5120 megabytes (MB)
print ('File size on Gigabytes (GB)', size >> 30 ) # 5 gigabytes (GB)

#Once the issues are downloaded we can load them locally using our 
local_spanish_dataset = load_dataset("json", data_files=f"{str(path)}/{issues_path}/spanish_medical_llms.jsonl", split="train")

##Update local dataset with cloud dataset
try:  
  spanish_dataset = load_dataset(DATASET_TO_UPDATE, split="train")
  new_spanish_dataset = concatenate_datasets([spanish_dataset, local_spanish_dataset])
except Exception:
  print ('<== Exception ==> ')
  raise Exception
  #new_spanish_dataset = local_spanish_dataset

new_spanish_dataset.push_to_hub(DATASET_TO_UPDATE)

print(new_spanish_dataset)

# Augmenting the dataset

#Importan if exist element on DATASET_TO_UPDATE we must to update element 
# in list, and review if the are repeted elements

#spanish_dataset.push_to_hub(DATASET_TO_UPDATE)