import sys, os

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

from transformers import FlaxGPT2LMHeadModel as Orig_FlaxGPT2LMHeadModel
from vit_gpt2.modeling_flax_gpt2 import FlaxGPT2LMHeadModel

# Main model -  ViTGPT2LM
from vit_gpt2.modeling_flax_vit_gpt2_lm import FlaxViTGPT2LMForConditionalGeneration

# ViT - as encoder
from transformers import ViTFeatureExtractor
from PIL import Image
import requests
import numpy as np
import jax
import jax.numpy as jnp

# GPT2+LM - as decoder
from transformers import GPT2Tokenizer

max_length = 8

# ================================================================================
# Models preparation

vision_model_name = 'google/vit-base-patch16-224-in21k'
text_model_name = 'asi/gpt-fr-cased-small'

project_encoder = False

flax_vit_gpt2_lm = FlaxViTGPT2LMForConditionalGeneration.from_vision_text_pretrained(
    vision_pretrained_model_name_or_path=vision_model_name,
    text_pretrained_model_name_or_path=text_model_name,
    project_encoder=project_encoder
)
model = flax_vit_gpt2_lm

# ================================================================================
# Inputs preparation

feature_extractor = ViTFeatureExtractor.from_pretrained(vision_model_name)
tokenizer = GPT2Tokenizer.from_pretrained(text_model_name)

# encoder data
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
# batch dim is added automatically
encoder_inputs = feature_extractor(images=image, return_tensors="jax")
pixel_values = encoder_inputs.pixel_values

print('=' * 60)
print(f'pixel_values.shape = {pixel_values.shape}')

# decoder data
sentence = 'mon chien est mignon'
# IMPORTANT: For training/evaluation/attention_mask/loss
sentence += ' ' + tokenizer.eos_token
# batch dim is added automatically
# Setup the tokenizer for targets
with tokenizer.as_target_tokenizer():
    labels = tokenizer(sentence, max_length=max_length, padding="max_length", truncation=True, return_tensors="np")


def shift_tokens_right(input_ids: jnp.ndarray, pad_token_id: int, decoder_start_token_id: int) -> jnp.ndarray:
    """
    Shift input ids one token to the right.
    """
    shifted_input_ids = jnp.roll(input_ids, 1, axis=-1)
    shifted_input_ids = jax.ops.index_update(shifted_input_ids, (..., 0), decoder_start_token_id)
    # replace possible -100 values in labels by `pad_token_id`
    shifted_input_ids = jnp.where(shifted_input_ids == -100, pad_token_id, shifted_input_ids)

    return shifted_input_ids

decoder_input_ids = shift_tokens_right(
    jnp.array(labels["input_ids"]),
    model.config.text_config.pad_token_id,
    model.config.decoder_start_token_id
)
decoder_input_ids = np.asarray(decoder_input_ids)
# We need decoder_attention_mask so we can ignore pad tokens from loss
decoder_attention_mask = labels["attention_mask"]

print('=' * 60)
print(f'decoder_inputs = {decoder_input_ids}')
print(f'decoder_input_ids.shape = {decoder_input_ids.shape}')
print(f'decoder_attention_mask = {decoder_attention_mask}')
print(f'decoder_attention_mask.shape = {decoder_attention_mask.shape}')

# ================================================================================
# Check `FlaxGPT2LMHeadModel` has the same results in the new version (when no `encoder_outputs` is provided).

orig_gpt2_lm = Orig_FlaxGPT2LMHeadModel.from_pretrained(text_model_name)
gpt2_lm = FlaxGPT2LMHeadModel.from_pretrained(text_model_name)

# Generation!
num_beams = 1
gen_kwargs = {"max_length": 6, "num_beams": num_beams}

orig_gpt2_generated = orig_gpt2_lm.generate(decoder_input_ids[:, 0:3], **gen_kwargs)
gpt2_generated = gpt2_lm.generate(decoder_input_ids[:, 0:3], **gen_kwargs)

orig_token_ids = np.array(orig_gpt2_generated.sequences)[0]
token_ids = np.array(gpt2_generated.sequences)[0]

orig_caption = tokenizer.decode(orig_token_ids)
caption = tokenizer.decode(token_ids)

print('=' * 60)
print(f'orig. GPT2 generated token ids: {orig_token_ids}')
print(f'GPT2 generated token ids: {token_ids}')

print('=' * 60)
print(f'orig. GPT2 generated caption: {orig_caption}')
print(f'GPT2 generated caption: {caption}')

assert list(orig_token_ids) == list(token_ids)

# ================================================================================

# model data
model_inputs = {
    'pixel_values': pixel_values,
    'attention_mask': None,
    'decoder_input_ids': decoder_input_ids,
    'decoder_attention_mask': decoder_attention_mask,
    'decoder_position_ids': None,
}

# ================================================================================
# Check `model.__call__()`

# Model call
model_outputs = model(**model_inputs)
logits = model_outputs[0]
preds = np.argmax(logits, axis=-1)

print('=' * 60)
print('Flax: Vit-GPT2-LM')
print('predicted token ids:')
print(preds)

# encoder_last_hidden_state = model_outputs['encoder_last_hidden_state']
# print(encoder_last_hidden_state)
# encoder_kwargs = {}
# encoder_outputs = flax_vit_gpt2_lm.encode(pixel_values, return_dict=True, **encoder_kwargs)
# print(encoder_outputs['last_hidden_state'])

# ================================================================================
# Check generation

# Generation!
num_beams = 1
gen_kwargs = {"max_length": max_length, "num_beams": num_beams}

batch = {'pixel_values': pixel_values}
generated = model.generate(batch['pixel_values'], **gen_kwargs)
token_ids = np.array(generated.sequences)[0]

print('=' * 60)
print(f'generated token ids: {token_ids}')

caption = tokenizer.decode(token_ids)

print('=' * 60)
print(f'generated caption: {caption}')

# ================================================================================
# Check save & load

# save
os.makedirs('./model/', exist_ok=True)
model.save_pretrained(save_directory='./model/')

# load
_model = FlaxViTGPT2LMForConditionalGeneration.from_pretrained('./model/')

# check if the result is the same as before
_generated = _model.generate(batch['pixel_values'], **gen_kwargs)
_token_ids = np.array(_generated.sequences)[0]

print('=' * 60)
print(f'new generated token ids: {_token_ids}')
print(f'token_ids == new_token_ids: {token_ids == _token_ids}')

# ================================================================================
# Check PyTorch version's output - it should be the same as above

import torch
from transformers import ViTModel, GPT2Config, GPT2LMHeadModel

vision_model_pt = ViTModel.from_pretrained(vision_model_name)
config = GPT2Config.from_pretrained(text_model_name)
# config.is_encoder_decoder = True
config.add_cross_attention = True
text_model_pt = GPT2LMHeadModel.from_pretrained(text_model_name, config=config)

encoder_pt_inputs = feature_extractor(images=image, return_tensors="pt")
encoder_pt_outputs = vision_model_pt(**encoder_pt_inputs)
encoder_hidden_states = encoder_pt_outputs.last_hidden_state

# model data
text_model_pt_inputs = {
    'input_ids': torch.tensor(decoder_input_ids, dtype=torch.int32),
    'attention_mask': torch.tensor(decoder_attention_mask, dtype=torch.int32),
    'position_ids': None,
    'encoder_hidden_states': encoder_hidden_states
}

# Model call
text_model_pt_outputs = text_model_pt(**text_model_pt_inputs)
logits = text_model_pt_outputs[0]
preds = np.argmax(logits.detach().numpy(), axis=-1)

print('=' * 60)
print('PyTroch: Vit --> GPT2-LM')
print('predicted token ids:')
print(preds)

#generated = text_model_pt.generate(encoder_outputs=vision_model_pt_outputs, **gen_kwargs)
#token_ids = np.array(generated.sequences)[0]

#print('=' * 60)
#print(f'Pytorch\'s GPT2 LM generated token ids: {token_ids}')

#caption = tokenizer.decode(token_ids)

#print('=' * 60)
#print(f'Pytorch\'s GPT2 LM generated caption: {caption}')