<commit_before>import datetime
import json
import os
import pickle

import numpy as np

from dialogue import qa_pipeline, data_helpers
from dialogue.errors import ParameterMissingError
from daphne_context.models import UserInformation, DialogueHistory, DialogueContext
from dialogue.nn_models import nn_models


def classify_command_role(command, daphne_version):
    cleaned_command = data_helpers.clean_str(command)

    # Get model
    loaded_model = nn_models[daphne_version]["general"]

    # Map data into vocabulary
    model_folder_path = os.path.join(os.getcwd(), "dialogue", "models", daphne_version, "general")
    vocab_path = os.path.join(model_folder_path, "tokenizer.pickle")
    with open(vocab_path, 'rb') as handle:
        tokenizer = pickle.load(handle)

    x = tokenizer.texts_to_sequences([cleaned_command])
    expected_input_length = loaded_model.layers[0].input_shape[0][1]
    x = np.array([x[0] + [0] * (expected_input_length - len(x[0]))])
    print("\nEvaluating...\n")

    # Evaluation
    # ==================================================
    # evaluate loaded model on test data
    result_logits = loaded_model.predict(x)
    prediction = data_helpers.get_label_using_logits(result_logits, top_number=1)
    return prediction[0]


def command_type_predictions(processed_command, daphne_version, module_name):
    cleaned_question = data_helpers.clean_str(processed_command)

    # Get model
    loaded_model = nn_models[daphne_version][module_name]

    # Map data into vocabulary
    model_folder_path = os.path.join(os.getcwd(), "dialogue", "models", daphne_version, module_name)
    vocab_path = os.path.join(model_folder_path, "tokenizer.pickle")
    with open(vocab_path, 'rb') as handle:
        tokenizer = pickle.load(handle)

    x = tokenizer.texts_to_sequences([cleaned_question])
    expected_input_length = loaded_model.layers[0].input_shape[0][1]
    x = np.array([x[0] + [0] * (expected_input_length - len(x[0]))])
    print("\nEvaluating...\n")

    # Evaluation
    # ==================================================
    # evaluate loaded model on test data
    result_logits = loaded_model.predict(x)

    return result_logits


def get_top_types(logits, daphne_version, module_name, top_number):
    numerical_labels = data_helpers.get_label_using_logits(logits, top_number=top_number)[0]

    named_labels = []
    type_info_folder = os.path.join(os.getcwd(), daphne_version, "dialogue", "command_types", module_name)
    for filename in sorted(os.listdir(type_info_folder)):
        specific_label = int(filename.split('.', 1)[0])
        named_labels.append(specific_label)
    command_types = []
    for label in numerical_labels:
        command_types.append(named_labels[label])
    return command_types


def error_answers(objective, missing_param):
    return {<commit_msg>Fix for .DS_Store filename error in macOS<commit_after>import datetime
import json
import os
import pickle
import glob

import numpy as np

from dialogue import qa_pipeline, data_helpers
from dialogue.errors import ParameterMissingError
from daphne_context.models import UserInformation, DialogueHistory, DialogueContext
from dialogue.nn_models import nn_models


def classify_command_role(command, daphne_version):
    cleaned_command = data_helpers.clean_str(command)

    # Get model
    loaded_model = nn_models[daphne_version]["general"]

    # Map data into vocabulary
    model_folder_path = os.path.join(os.getcwd(), "dialogue", "models", daphne_version, "general")
    vocab_path = os.path.join(model_folder_path, "tokenizer.pickle")
    with open(vocab_path, 'rb') as handle:
        tokenizer = pickle.load(handle)

    x = tokenizer.texts_to_sequences([cleaned_command])
    expected_input_length = loaded_model.layers[0].input_shape[0][1]
    x = np.array([x[0] + [0] * (expected_input_length - len(x[0]))])
    print("\nEvaluating...\n")

    # Evaluation
    # ==================================================
    # evaluate loaded model on test data
    result_logits = loaded_model.predict(x)
    prediction = data_helpers.get_label_using_logits(result_logits, top_number=1)
    return prediction[0]


def command_type_predictions(processed_command, daphne_version, module_name):
    cleaned_question = data_helpers.clean_str(processed_command)

    # Get model
    loaded_model = nn_models[daphne_version][module_name]

    # Map data into vocabulary
    model_folder_path = os.path.join(os.getcwd(), "dialogue", "models", daphne_version, module_name)
    vocab_path = os.path.join(model_folder_path, "tokenizer.pickle")
    with open(vocab_path, 'rb') as handle:
        tokenizer = pickle.load(handle)

    x = tokenizer.texts_to_sequences([cleaned_question])
    expected_input_length = loaded_model.layers[0].input_shape[0][1]
    x = np.array([x[0] + [0] * (expected_input_length - len(x[0]))])
    print("\nEvaluating...\n")

    # Evaluation
    # ==================================================
    # evaluate loaded model on test data
    result_logits = loaded_model.predict(x)

    return result_logits


def get_top_types(logits, daphne_version, module_name, top_number):
    numerical_labels = data_helpers.get_label_using_logits(logits, top_number=top_number)[0]

    named_labels = []
    type_info_folder = os.path.join(os.getcwd(), daphne_version, "dialogue", "command_types", module_name)
    for filename in sorted(glob.glob(os.path.join(type_info_folder, "*.json"))):
        specific_label = int(os.path.basename(filename).split('.', 1)[0])
        named_labels.append(specific_label)
    command_types = []
    for label in numerical_labels:
        command_types.append(named_labels[label])
    return command_types


def error_answers(objective, missing_param):
    return {
{'fraction_non_alphanumeric': 0.13782329456691547, 'fraction_numerical': 0.005901753167852804, 'mean_word_length': 4.796780684104628, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 3, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '6413429', 'n_tokens_mistral': 1865, 'n_tokens_neox': 1822, 'n_words': 349}