


import argparse
import re

import requests
import torch

# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

from transformers import (
    BertTokenizer,
    BlipConfig,
    BlipForConditionalGeneration,
    BlipForImageTextRetrieval,
    BlipForQuestionAnswering,
)


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'''
			__lowercase			       = Image.open(requests.get(_UpperCamelCase							,      stream=_UpperCamelCase	).raw	).convert('''RGB'''	)

			__lowercase			       = transforms.Compose(
			    [
			        transforms.Resize((image_size, image_size)							,      interpolation=InterpolationMode.BICUBIC	),
			        transforms.ToTensor(),
			        transforms.Normalize((0.4814_5466, 0.457_8275, 0.4082_1073)							,      (0.2686_2954, 0.2613_0258, 0.2757_7711)	),
			    ]	)
			__lowercase			       = transform(_UpperCamelCase	).unsqueeze(0	).to(_UpperCamelCase	)
			return image


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			if "visual_encoder" in key:
						__lowercase			       = re.sub('''visual_encoder*'''							,      '''vision_model.encoder'''							,      _UpperCamelCase	)
			if "blocks" in key:
						__lowercase			       = re.sub(r'''blocks'''							,      '''layers'''							,      _UpperCamelCase	)
			if "attn" in key:
						__lowercase			       = re.sub(r'''attn'''							,      '''self_attn'''							,      _UpperCamelCase	)
			if "norm1" in key:
						__lowercase			       = re.sub(r'''norm1'''							,      '''layer_norm1'''							,      _UpperCamelCase	)
			if "norm2" in key:
						__lowercase			       = re.sub(r'''norm2'''							,      '''layer_norm2'''							,      _UpperCamelCase	)
			if "encoder.norm" in key:
						__lowercase			       = re.sub(r'''encoder.norm'''							,      '''post_layernorm'''							,      _UpperCamelCase	)
			if "encoder.patch_embed.proj" in key:
						__lowercase			       = re.sub(r'''encoder.patch_embed.proj'''							,      '''embeddings.patch_embedding'''							,      _UpperCamelCase	)

			if "encoder.pos_embed" in key:
						__lowercase			       = re.sub(r'''encoder.pos_embed'''							,      '''embeddings.position_embedding'''							,      _UpperCamelCase	)
			if "encoder.cls_token" in key:
						__lowercase			       = re.sub(r'''encoder.cls_token'''							,      '''embeddings.class_embedding'''							,      _UpperCamelCase	)

			if "self_attn" in key:
						__lowercase			       = re.sub(r'''self_attn.proj'''							,      '''self_attn.projection'''							,      _UpperCamelCase	)

			return key


@torch.no_grad()
def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase=None	):


			'''simple docstring'''







			if config_path is not None:
						__lowercase			       = BlipConfig.from_pretrained(_UpperCamelCase	)
			else:
						__lowercase			       = BlipConfig(projection_dim=5_12							,      text_config={}							,      vision_config={}	)

			__lowercase			       = BlipForConditionalGeneration(_UpperCamelCase	).eval()

			__lowercase			       = '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'''

			__lowercase			       = blip_decoder(pretrained=_UpperCamelCase							,      image_size=3_84							,      vit='''base'''	)
			__lowercase			       = pt_model.eval()

			__lowercase			       = pt_model.state_dict()
			for key in modified_state_dict.copy():
						__lowercase			       = modified_state_dict.pop(_UpperCamelCase	)
						__lowercase			       = rename_key(_UpperCamelCase	)
						__lowercase			       = value

			hf_model.load_state_dict(_UpperCamelCase	)

			__lowercase			       = 3_84
			__lowercase			       = load_demo_image(image_size=_UpperCamelCase							,      device='''cpu'''	)
			__lowercase			       = BertTokenizer.from_pretrained('''bert-base-uncased'''	)
			__lowercase			       = tokenizer(['''a picture of''']	).input_ids

			__lowercase			       = hf_model.generate(_UpperCamelCase							,      _UpperCamelCase	)

			assert out[0].tolist() == [3_05_22, 10_37, 38_61, 19_97, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

			__lowercase			       = hf_model.generate(_UpperCamelCase	)

			assert out[0].tolist() == [3_05_22, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

			if pytorch_dump_folder_path is not None:
						hf_model.save_pretrained(_UpperCamelCase	)

			# model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
			__lowercase			       = (
			    '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'''
			)

			__lowercase			       = blip_vqa(pretrained=_UpperCamelCase							,      image_size=_UpperCamelCase							,      vit='''base'''	)
			vqa_model.eval()

			__lowercase			       = vqa_model.state_dict()
			for key in modified_state_dict.copy():
						__lowercase			       = modified_state_dict.pop(_UpperCamelCase	)
						__lowercase			       = rename_key(_UpperCamelCase	)
						__lowercase			       = value

			__lowercase			       = BlipForQuestionAnswering(_UpperCamelCase	)

			hf_vqa_model.load_state_dict(_UpperCamelCase	)

			__lowercase			       = ['''How many dogs are in this image?''']
			__lowercase			       = tokenizer(_UpperCamelCase							,      return_tensors='''pt'''	).input_ids

			__lowercase			       = hf_vqa_model.generate(_UpperCamelCase							,      _UpperCamelCase	)
			print(tokenizer.decode(answer[0]	)	)

			assert tokenizer.decode(answer[0]	) == "[UNK] 1 [SEP]"
			if pytorch_dump_folder_path is not None:
						hf_vqa_model.save_pretrained(pytorch_dump_folder_path + '''_vqa'''	)

			__lowercase			       = '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'''

			__lowercase			       = blip_itm(pretrained=_UpperCamelCase							,      image_size=_UpperCamelCase							,      vit='''base'''	)
			itm_model.eval()

			__lowercase			       = itm_model.state_dict()
			for key in modified_state_dict.copy():
						__lowercase			       = modified_state_dict.pop(_UpperCamelCase	)
						__lowercase			       = rename_key(_UpperCamelCase	)
						__lowercase			       = value

			__lowercase			       = BlipForImageTextRetrieval(_UpperCamelCase	)

			__lowercase			       = ['''A picture of a woman with a dog sitting in a beach''']
			__lowercase			       = tokenizer(
			    _UpperCamelCase							,      return_tensors='''pt'''							,      padding='''max_length'''							,      truncation=_UpperCamelCase							,      max_length=35							,      ).input_ids

			hf_itm_model.load_state_dict(_UpperCamelCase	)
			hf_itm_model.eval()

			__lowercase			       = hf_itm_model(_UpperCamelCase							,      _UpperCamelCase							,      use_itm_head=_UpperCamelCase	)
			__lowercase			       = hf_itm_model(_UpperCamelCase							,      _UpperCamelCase							,      use_itm_head=_UpperCamelCase	)

			assert out[0].item() == 0.2110_6874_9427_7954
			assert torch.nn.functional.softmax(out_itm[0]							,      dim=1	)[:, 1].item() == 0.4_5698_8453_8650_5127

			if pytorch_dump_folder_path is not None:
						hf_itm_model.save_pretrained(pytorch_dump_folder_path + '''_itm'''	)


if __name__ == "__main__":
	a			:			str												=					argparse.ArgumentParser()
	parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
	parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
	a			:			Any												=					parser.parse_args()

	convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)






# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

a			:			Union[str, Any]												=					'''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
a			:			Tuple												=					[
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
a			:			List[Any]												=					dict(zip(vocab, range(len(vocab))))
a			:			Optional[int]												=					['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
	a			:			Union[str, Any]												=					Path(tmpdirname)
	a			:			Dict												=					build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
	a			:			Optional[int]												=					build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
	a			:			Tuple												=					build_dir / VOCAB_FILES_NAMES['''merges_file''']
	with open(src_vocab_file, '''w''') as fp:
		fp.write(json.dumps(vocab_tokens))
	with open(tgt_vocab_file, '''w''') as fp:
		fp.write(json.dumps(vocab_tokens))
	with open(merges_file, '''w''') as fp:
		fp.write('''\n'''.join(merges))

	a			:			Optional[int]												=					FSMTTokenizer(
	    langs=['''en''', '''ru'''],
	    src_vocab_size=len(vocab),
	    tgt_vocab_size=len(vocab),
	    src_vocab_file=src_vocab_file,
	    tgt_vocab_file=tgt_vocab_file,
	    merges_file=merges_file,
	)

a			:			List[str]												=					FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1000,
    tgt_vocab_size=1000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

a			:			Union[str, Any]												=					FSMTForConditionalGeneration(config)
print(f'''num of params {tiny_model.num_parameters()}''')

# Test
a			:			Dict												=					tokenizer(['''Making tiny model'''], return_tensors='''pt''')
a			:			Union[str, Any]												=					tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-ru


