import sys, os

current_path = os.path.dirname(os.path.abspath(__file__))
sys.path.append(current_path)



# Vit - as encoder
from transformers import ViTFeatureExtractor
from PIL import Image
import requests
import numpy as np

url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)

feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224-in21k')
encoder_inputs = feature_extractor(images=image, return_tensors="jax")
pixel_values = encoder_inputs.pixel_values

# GPT2 / GPT2LM - as decoder
from transformers import ViTFeatureExtractor, GPT2Tokenizer

name = 'asi/gpt-fr-cased-small'
tokenizer = GPT2Tokenizer.from_pretrained(name)
decoder_inputs = tokenizer("mon chien est mignon", return_tensors="jax", )
print(decoder_inputs)

# Setup the tokenizer for targets
with tokenizer.as_target_tokenizer():
    labels = tokenizer(
        ['un chien super beau' + ' ' + tokenizer.eos_token, 'un chat' + ' ' + tokenizer.eos_token], max_length=5, padding="max_length", truncation=True, return_tensors="np"
    )
    print(labels)
    exit(0)

inputs = dict(decoder_inputs)
inputs['pixel_values'] = pixel_values
#print(inputs)


# With the LM head in GPT2LM
from vit_gpt2.modeling_flax_vit_gpt2_lm import FlaxViTGPT2LMForConditionalGeneration
flax_vit_gpt2_lm = FlaxViTGPT2LMForConditionalGeneration.from_pretrained('./outputs-small-ds/ckpt_3',)

logits = flax_vit_gpt2_lm(**inputs)[0]
preds = np.argmax(logits, axis=-1)
print('=' * 60)
print('Flax: Vit + modified GPT2LM')
#print(preds)

max_length = 32
num_beams = 16
gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
batch = {'pixel_values': pixel_values}
generation = flax_vit_gpt2_lm.generate(batch['pixel_values'], **gen_kwargs)
print(generation)

token_ids = np.array(generation.sequences)[0]
generation = tokenizer.decode(token_ids)
print(generation)

del flax_vit_gpt2_lm