from checklist_plus.text_generation.llm import LLMTextGenerator

tg = LLMTextGenerator(openai_api_key="your-api-key", model_name="gpt-4o-mini")

# Smart paraphrasing with style control
paraphrases = tg.paraphrase(
    "The weather is nice today",
    n_paraphrases=3,
    style="formal",
    length_preference="longer",
)
# → ["Today's meteorological conditions are quite favorable",
#    "The atmospheric conditions are particularly pleasant today", ...]

# Intelligent negation
negated = tg.negate_sentence("I love this movie", n_variations=2)
# → ["I hate this movie", "I don't love this movie"]

# Detect and mask entities
result = tg.detect_and_mask_entities(
    "I bought an iPhone for $999 yesterday", entity_type="brand names"
)
# → {
#     "original_text": "I bought an iPhone for $999 yesterday",
#     "masked_text": "I bought a [MASK] for $999 yesterday",
#     "contains_entities": True,
#     "entities": ["iPhone"]
# }

from checklist_plus.perturb import Perturb
import spacy

nlp = spacy.load("en_core_web_sm")
data = ["The meeting is at 10:30 on Sept 14, tickets cost $45"]
parsed_data = list(nlp.pipe(data))

# Target specific entity types for number changes
ret = Perturb.perturb(
    parsed_data,
    Perturb.change_number,
    entity_types=["DATE", "MONEY"],  # Only change dates and money
    skip_abbreviations=False,  # Include numbers like '2' and '4'
    n=3,
)
# → Changes "14" to "16", "$45" to "$54", but preserves "10:30"

# Initialize editor with LLM capabilities
editor = Editor()

# Traditional template generation (original feature)
templates = editor.template(
    "{first_name} is {a:profession} from {country}.",
    profession=["lawyer", "doctor", "accountant"],
)

# NEW: LLM-enhanced features through text generator
editor.tg = tg  # Attach LLM text generator

# Entity detection through editor
entities = editor.tg.detect_entities("Apple released the new MacBook", "brand names")
# → {"text": "Apple released the new MacBook", "contains_entities": True, "entities": ["Apple", "MacBook"]}

from checklist_plus.perturb import LLMPerturb

perturb = LLMPerturb(openai_api_key="your-key")

# Advanced negation with context
negated = perturb.add_negation_llm(
    ["I love programming", "This is excellent"], n_variations=2, context="casual"
)

from checklist_plus.editor import Editor

# Initialize editor with LLM capabilities
llm_editor = Editor(
    use_llm=True, model_name="gpt-4o-mini", openai_api_key="your-api-key"
)

# Smart template filling with context
templates = llm_editor.template(
    "The {mask} is very {adj}.",
    adj=["beautiful", "interesting", "amazing"],
    context="travel destinations",
    n_completions=3,
)

# LLM-powered paraphrasing
paraphrases = llm_editor.paraphrase_llm(
    "The weather is beautiful today",
    n_paraphrases=3,
    style="formal",
    length_preference="longer",
)

# Context-aware word suggestions
suggestions = llm_editor.suggest("This is a {mask} movie.", context="science fiction")

# Smart synonyms and antonyms
synonyms = llm_editor.synonyms("The food is hot.", "hot")
antonyms = llm_editor.antonyms("The weather is cold.", "cold")

from checklist_plus.editor import Editor

editor = Editor()
ret = editor.template(
    "{first_name} is {a:profession} from {country}.",
    profession=["lawyer", "doctor", "accountant"],
)
# → ['Mary is a doctor from Afghanistan.', 'Jordan is an accountant from Indonesia.', ...]

from checklist_plus.perturb import Perturb
import spacy

nlp = spacy.load("en_core_web_sm")
data = ["John is a doctor", "Mary is a nurse"]
parsed_data = list(nlp.pipe(data))

# Rule-based perturbations (original)
ret = Perturb.perturb(parsed_data, Perturb.change_names, n=2)

# LLM-enhanced negation
ret_llm = perturb.add_negation_llm(["The service was good", "I liked the food"])
print(ret_llm)

from checklist_plus.test_types import MFT, INV, DIR
from checklist_plus.expect import Expect

# Minimum Functionality Tests
test1 = MFT(
    editor.template("This is {a:adj} {mask}.", adj=["good", "great"]).data,
    labels=1,
    name="Positive sentiment",
)

# Invariance Tests
test2 = INV(**Perturb.perturb(data, Perturb.add_typos))

# Directional Expectation Tests
test3 = DIR(
    **Perturb.perturb(data, add_negative_phrase),
    expect=Expect.monotonic(label=1, increasing=False)
)

# Run tests
test1.run(wrapped_model)
test1.summary()