

import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipaConfig,
    BlipaForConditionalGeneration,
    BlipaProcessor,
    BlipaVisionConfig,
    BlipImageProcessor,
    OPTConfig,
    TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD





def 					__lowerCamelCase	( ):







							'''simple docstring'''






							snake_case_							=    'https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'
							snake_case_							=    Image.open(requests.get(UpperCamelCase__   ,							stream=UpperCamelCase__      ).raw      ).convert('RGB'      )

							return image





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    []
							# fmt: off

							# vision encoder
							rename_keys.append(('visual_encoder.cls_token', 'vision_model.embeddings.class_embedding')      )
							rename_keys.append(('visual_encoder.pos_embed', 'vision_model.embeddings.position_embedding')      )
							rename_keys.append(('visual_encoder.patch_embed.proj.weight', 'vision_model.embeddings.patch_embedding.weight')      )
							rename_keys.append(('visual_encoder.patch_embed.proj.bias', 'vision_model.embeddings.patch_embedding.bias')      )
							rename_keys.append(('ln_vision.weight', 'vision_model.post_layernorm.weight')      )
							rename_keys.append(('ln_vision.bias', 'vision_model.post_layernorm.bias')      )

							for i in range(config.vision_config.num_hidden_layers      ):
														rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.weight''', F'''vision_model.encoder.layers.{i}.layer_norm1.weight''')      )
														rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.bias''', F'''vision_model.encoder.layers.{i}.layer_norm1.bias''')      )
														rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.weight''', F'''vision_model.encoder.layers.{i}.layer_norm2.weight''')      )
														rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.bias''', F'''vision_model.encoder.layers.{i}.layer_norm2.bias''')      )
														rename_keys.append((F'''visual_encoder.blocks.{i}.attn.qkv.weight''', F'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''')      )
														rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.weight''', F'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',)      )
														rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.bias''', F'''vision_model.encoder.layers.{i}.self_attn.projection.bias''')      )
														rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc1.weight''')      )
														rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc1.bias''')      )
														rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc2.weight''')      )
														rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc2.bias''')      )

							# QFormer
							rename_keys.append(('Qformer.bert.embeddings.LayerNorm.weight', 'qformer.layernorm.weight')      )
							rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.layernorm.bias')      )

							# fmt: on
							return rename_keys





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    dct.pop(UpperCamelCase__      )
							snake_case_							=    val





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							for i in range(config.vision_config.num_hidden_layers      ):
														# read in original q and v biases
														snake_case_							=    state_dict.pop(F'''visual_encoder.blocks.{i}.attn.q_bias'''      )
														snake_case_							=    state_dict.pop(F'''visual_encoder.blocks.{i}.attn.v_bias'''      )

														# next, set bias in the state dict
														snake_case_							=    torch.cat((q_bias, torch.zeros_like(UpperCamelCase__   ,							requires_grad=UpperCamelCase__      ), v_bias)      )
														snake_case_							=    qkv_bias





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    364 if 'coco' in model_name else 224
							snake_case_							=    BlipaVisionConfig(image_size=UpperCamelCase__      ).to_dict()

							# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
							# seems like flan-T5 models don't have bos_token_id properly set?
							if "opt-2.7b" in model_name:
														snake_case_							=    OPTConfig.from_pretrained('facebook/opt-2.7b'   ,							eos_token_id=UpperCamelCase__      ).to_dict()
							elif "opt-6.7b" in model_name:
														snake_case_							=    OPTConfig.from_pretrained('facebook/opt-6.7b'   ,							eos_token_id=UpperCamelCase__      ).to_dict()
							elif "t5-xl" in model_name:
														snake_case_							=    TaConfig.from_pretrained('google/flan-t5-xl'   ,							dense_act_fn='gelu'   ,							bos_token_id=1      ).to_dict()
							elif "t5-xxl" in model_name:
														snake_case_							=    TaConfig.from_pretrained('google/flan-t5-xxl'   ,							dense_act_fn='gelu'   ,							bos_token_id=1      ).to_dict()

							snake_case_							=    BlipaConfig(vision_config=UpperCamelCase__   ,							text_config=UpperCamelCase__      )

							return config, image_size





@torch.no_grad()
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__=None   ,							UpperCamelCase__=False      ):







							'''simple docstring'''






							snake_case_							=    (
							    AutoTokenizer.from_pretrained('facebook/opt-2.7b'      )
							    if 'opt' in model_name
							    else AutoTokenizer.from_pretrained('google/flan-t5-xl'      )
							)
							snake_case_							=    tokenizer('\n'   ,							add_special_tokens=UpperCamelCase__      ).input_ids[0]
							snake_case_				,    snake_case_							=    get_blipa_config(UpperCamelCase__   ,							eos_token_id=UpperCamelCase__      )

							snake_case_							=    BlipaForConditionalGeneration(UpperCamelCase__      ).eval()

							snake_case_							=    {
							    'blip2-opt-2.7b': ('blip2_opt', 'pretrain_opt2.7b'),
							    'blip2-opt-6.7b': ('blip2_opt', 'pretrain_opt6.7b'),
							    'blip2-opt-2.7b-coco': ('blip2_opt', 'caption_coco_opt2.7b'),
							    'blip2-opt-6.7b-coco': ('blip2_opt', 'caption_coco_opt6.7b'),
							    'blip2-flan-t5-xl': ('blip2_t5', 'pretrain_flant5xl'),
							    'blip2-flan-t5-xl-coco': ('blip2_t5', 'caption_coco_flant5xl'),
							    'blip2-flan-t5-xxl': ('blip2_t5', 'pretrain_flant5xxl'),
							}

							snake_case_				,    snake_case_							=    model_name_to_original[model_name]

							# load original model
							print('Loading original model...'      )
							snake_case_							=    'cuda' if torch.cuda.is_available() else 'cpu'
							snake_case_				,    snake_case_				,    snake_case_							=    load_model_and_preprocess(
							    name=UpperCamelCase__   ,							model_type=UpperCamelCase__   ,							is_eval=UpperCamelCase__   ,							device=UpperCamelCase__      )
							original_model.eval()
							print('Done!'      )

							# update state dict keys
							snake_case_							=    original_model.state_dict()
							snake_case_							=    create_rename_keys(UpperCamelCase__      )
							for src, dest in rename_keys:
														rename_key(UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      )

							# some keys can be renamed efficiently
							for key, val in state_dict.copy().items():
														snake_case_							=    state_dict.pop(UpperCamelCase__      )
														if key.startswith('Qformer.bert'      ):
																					snake_case_							=    key.replace('Qformer.bert'   ,							'qformer'      )
														if "attention.self" in key:
																					snake_case_							=    key.replace('self'   ,							'attention'      )
														if "opt_proj" in key:
																					snake_case_							=    key.replace('opt_proj'   ,							'language_projection'      )
														if "t5_proj" in key:
																					snake_case_							=    key.replace('t5_proj'   ,							'language_projection'      )
														if key.startswith('opt'      ):
																					snake_case_							=    key.replace('opt'   ,							'language'      )
														if key.startswith('t5'      ):
																					snake_case_							=    key.replace('t5'   ,							'language'      )
														snake_case_							=    val

							# read in qv biases
							read_in_q_v_bias(UpperCamelCase__   ,							UpperCamelCase__      )

							snake_case_				,    snake_case_							=    hf_model.load_state_dict(UpperCamelCase__   ,							strict=UpperCamelCase__      )
							assert len(UpperCamelCase__      ) == 0
							assert unexpected_keys == ["qformer.embeddings.position_ids"]

							snake_case_							=    load_demo_image()
							snake_case_							=    vis_processors['eval'](UpperCamelCase__      ).unsqueeze(0      ).to(UpperCamelCase__      )
							snake_case_							=    tokenizer(['\n']   ,							return_tensors='pt'      ).input_ids.to(UpperCamelCase__      )

							# create processor
							snake_case_							=    BlipImageProcessor(
							    size={'height': image_size, 'width': image_size}   ,							image_mean=UpperCamelCase__   ,							image_std=UpperCamelCase__      )
							snake_case_							=    BlipaProcessor(image_processor=UpperCamelCase__   ,							tokenizer=UpperCamelCase__      )
							snake_case_							=    processor(images=UpperCamelCase__   ,							return_tensors='pt'      ).pixel_values.to(UpperCamelCase__      )

							# make sure processor creates exact same pixel values
							assert torch.allclose(UpperCamelCase__   ,							UpperCamelCase__      )

							original_model.to(UpperCamelCase__      )
							hf_model.to(UpperCamelCase__      )
							with torch.no_grad():
														if "opt" in model_name:
																					snake_case_							=    original_model({'image': original_pixel_values, 'text_input': ['']}      ).logits
																					snake_case_							=    hf_model(UpperCamelCase__   ,							UpperCamelCase__      ).logits
														else:
																					snake_case_							=    original_model(
																					    {'image': original_pixel_values, 'text_input': ['\n'], 'text_output': ['\n']}      ).logits
																					snake_case_							=    input_ids.masked_fill(input_ids == tokenizer.pad_token_id   ,							-100      )
																					snake_case_							=    hf_model(UpperCamelCase__   ,							UpperCamelCase__   ,							labels=UpperCamelCase__      ).logits

							assert original_logits.shape == logits.shape
							print('First values of original logits:'   ,							original_logits[0, :3, :3]      )
							print('First values of HF logits:'   ,							logits[0, :3, :3]      )

							# assert values
							if model_name == "blip2-flan-t5-xl":
														snake_case_							=    torch.tensor(
														    [[-41.58_50, -4.44_40, -8.99_22], [-47.43_22, -5.91_43, -1.73_40]]   ,							device=UpperCamelCase__      )
														assert torch.allclose(logits[0, :3, :3]   ,							UpperCamelCase__   ,							atol=1E-4      )
							elif model_name == "blip2-flan-t5-xl-coco":
														snake_case_							=    torch.tensor(
														    [[-57.01_09, -9.89_67, -12.62_80], [-68.65_78, -12.71_91, -10.50_65]]   ,							device=UpperCamelCase__      )
							else:
														# cast to same type
														snake_case_							=    logits.dtype
														assert torch.allclose(original_logits.to(UpperCamelCase__      )   ,							UpperCamelCase__   ,							atol=1E-2      )
							print('Looks ok!'      )

							print('Generating a caption...'      )
							snake_case_							=    ''
							snake_case_							=    tokenizer(UpperCamelCase__   ,							return_tensors='pt'      ).input_ids.to(UpperCamelCase__      )

							snake_case_							=    original_model.generate({'image': original_pixel_values}      )
							snake_case_							=    hf_model.generate(
							    UpperCamelCase__   ,							UpperCamelCase__   ,							do_sample=UpperCamelCase__   ,							num_beams=5   ,							max_length=30   ,							min_length=1   ,							top_p=0.9   ,							repetition_penalty=1.0   ,							length_penalty=1.0   ,							temperature=1   ,							)
							print('Original generation:'   ,							UpperCamelCase__      )
							snake_case_							=    input_ids.shape[1]
							snake_case_							=    processor.batch_decode(outputs[:, prompt_length:]   ,							skip_special_tokens=UpperCamelCase__      )
							snake_case_							=    [text.strip() for text in output_text]
							print('HF generation:'   ,							UpperCamelCase__      )

							if pytorch_dump_folder_path is not None:
														processor.save_pretrained(UpperCamelCase__      )
														hf_model.save_pretrained(UpperCamelCase__      )

							if push_to_hub:
														processor.push_to_hub(F'''nielsr/{model_name}'''      )
														hf_model.push_to_hub(F'''nielsr/{model_name}'''      )


if __name__ == "__main__":
					_UpperCAmelCase		:   Optional[Any]										=       argparse.ArgumentParser()
					_UpperCAmelCase		:   List[str]										=       [
					    """blip2-opt-2.7b""",
					    """blip2-opt-6.7b""",
					    """blip2-opt-2.7b-coco""",
					    """blip2-opt-6.7b-coco""",
					    """blip2-flan-t5-xl""",
					    """blip2-flan-t5-xl-coco""",
					    """blip2-flan-t5-xxl""",
					]
					parser.add_argument(
					    """--model_name""",
					    default="""blip2-opt-2.7b""",
					    choices=choices,
					    type=str,
					    help="""Path to hf config.json of model to convert""",
					)
					parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
					parser.add_argument(
					    """--push_to_hub""",
					    action="""store_true""",
					    help="""Whether to push the model and processor to the hub after converting""",
					)

					_UpperCAmelCase		:   Union[str, Any]										=       parser.parse_args()

					convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor







@require_vision
class  lowercase				(       unittest.TestCase			):




				def     a							(					self       ):
											snake_case_							=    tempfile.mkdtemp()

											snake_case_							=    [
											    '[UNK]',
											    '[CLS]',
											    '[SEP]',
											    '[PAD]',
											    '[MASK]',
											    '的',
											    '价',
											    '格',
											    '是',
											    '15',
											    '便',
											    'alex',
											    '##andra',
											    '，',
											    '。',
											    '-',
											    't',
											    'shirt',
											]
											snake_case_							=    os.path.join(self.tmpdirname      ,   VOCAB_FILES_NAMES['vocab_file']       )
											with open(self.vocab_file      ,   'w'      ,   encoding='utf-8'       ) as vocab_writer:
																		vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]       )       )

											snake_case_							=    {
											    'do_resize': True,
											    'size': {'height': 224, 'width': 224},
											    'do_center_crop': True,
											    'crop_size': {'height': 18, 'width': 18},
											    'do_normalize': True,
											    'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
											    'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
											    'do_convert_rgb': True,
											}
											snake_case_							=    os.path.join(self.tmpdirname      ,   snake_case       )
											with open(self.image_processor_file      ,   'w'      ,   encoding='utf-8'       ) as fp:
																		json.dump(snake_case      ,   snake_case       )




				def     a							(					self      ,   **snake_case       ):
											return BertTokenizer.from_pretrained(self.tmpdirname      ,   **snake_case       )




				def     a							(					self      ,   **snake_case       ):
											return BertTokenizerFast.from_pretrained(self.tmpdirname      ,   **snake_case       )




				def     a							(					self      ,   **snake_case       ):
											return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname      ,   **snake_case       )




				def     a							(					self       ):
											shutil.rmtree(self.tmpdirname       )




				def     a							(					self       ):
											snake_case_							=    [np.random.randint(255      ,   size=(3, 30, 400)      ,   dtype=np.uinta       )]

											snake_case_							=    [Image.fromarray(np.moveaxis(snake_case      ,   0      ,   -1       )       ) for x in image_inputs]

											return image_inputs




				def     a							(					self       ):
											snake_case_							=    self.get_tokenizer()
											snake_case_							=    self.get_rust_tokenizer()
											snake_case_							=    self.get_image_processor()

											snake_case_							=    ChineseCLIPProcessor(tokenizer=snake_case      ,   image_processor=snake_case       )
											processor_slow.save_pretrained(self.tmpdirname       )
											snake_case_							=    ChineseCLIPProcessor.from_pretrained(self.tmpdirname      ,   use_fast=snake_case       )

											snake_case_							=    ChineseCLIPProcessor(tokenizer=snake_case      ,   image_processor=snake_case       )
											processor_fast.save_pretrained(self.tmpdirname       )
											snake_case_							=    ChineseCLIPProcessor.from_pretrained(self.tmpdirname       )

											self.assertEqual(processor_slow.tokenizer.get_vocab()      ,   tokenizer_slow.get_vocab()       )
											self.assertEqual(processor_fast.tokenizer.get_vocab()      ,   tokenizer_fast.get_vocab()       )
											self.assertEqual(tokenizer_slow.get_vocab()      ,   tokenizer_fast.get_vocab()       )
											self.assertIsInstance(processor_slow.tokenizer      ,   snake_case       )
											self.assertIsInstance(processor_fast.tokenizer      ,   snake_case       )

											self.assertEqual(processor_slow.image_processor.to_json_string()      ,   image_processor.to_json_string()       )
											self.assertEqual(processor_fast.image_processor.to_json_string()      ,   image_processor.to_json_string()       )
											self.assertIsInstance(processor_slow.image_processor      ,   snake_case       )
											self.assertIsInstance(processor_fast.image_processor      ,   snake_case       )




				def     a							(					self       ):
											snake_case_							=    ChineseCLIPProcessor(tokenizer=self.get_tokenizer()      ,   image_processor=self.get_image_processor()       )
											processor.save_pretrained(self.tmpdirname       )

											snake_case_							=    self.get_tokenizer(cls_token='(CLS)'      ,   sep_token='(SEP)'       )
											snake_case_							=    self.get_image_processor(do_normalize=snake_case       )

											snake_case_							=    ChineseCLIPProcessor.from_pretrained(
											    self.tmpdirname      ,   cls_token='(CLS)'      ,   sep_token='(SEP)'      ,   do_normalize=snake_case       )

											self.assertEqual(processor.tokenizer.get_vocab()      ,   tokenizer_add_kwargs.get_vocab()       )
											self.assertIsInstance(processor.tokenizer      ,   snake_case       )

											self.assertEqual(processor.image_processor.to_json_string()      ,   image_processor_add_kwargs.to_json_string()       )
											self.assertIsInstance(processor.image_processor      ,   snake_case       )




				def     a							(					self       ):
											snake_case_							=    self.get_image_processor()
											snake_case_							=    self.get_tokenizer()

											snake_case_							=    ChineseCLIPProcessor(tokenizer=snake_case      ,   image_processor=snake_case       )

											snake_case_							=    self.prepare_image_inputs()

											snake_case_							=    image_processor(snake_case      ,   return_tensors='np'       )
											snake_case_							=    processor(images=snake_case      ,   return_tensors='np'       )

											for key in input_feat_extract.keys():
																		self.assertAlmostEqual(input_feat_extract[key].sum()      ,   input_processor[key].sum()      ,   delta=1e-2       )




				def     a							(					self       ):
											snake_case_							=    self.get_image_processor()
											snake_case_							=    self.get_tokenizer()

											snake_case_							=    ChineseCLIPProcessor(tokenizer=snake_case      ,   image_processor=snake_case       )

											snake_case_							=    'Alexandra，T-shirt的价格是15便士。'

											snake_case_							=    processor(text=snake_case       )

											snake_case_							=    tokenizer(snake_case       )

											for key in encoded_tok.keys():
																		self.assertListEqual(encoded_tok[key]      ,   encoded_processor[key]       )




				def     a							(					self       ):
											snake_case_							=    self.get_image_processor()
											snake_case_							=    self.get_tokenizer()

											snake_case_							=    ChineseCLIPProcessor(tokenizer=snake_case      ,   image_processor=snake_case       )

											snake_case_							=    'Alexandra，T-shirt的价格是15便士。'
											snake_case_							=    self.prepare_image_inputs()

											snake_case_							=    processor(text=snake_case      ,   images=snake_case       )

											self.assertListEqual(list(inputs.keys()       )      ,   ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']       )

											# test if it raises when no input is passed
											with pytest.raises(snake_case       ):
																		processor()




				def     a							(					self       ):
											snake_case_							=    self.get_image_processor()
											snake_case_							=    self.get_tokenizer()

											snake_case_							=    ChineseCLIPProcessor(tokenizer=snake_case      ,   image_processor=snake_case       )

											snake_case_							=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											snake_case_							=    processor.batch_decode(snake_case       )
											snake_case_							=    tokenizer.batch_decode(snake_case       )

											self.assertListEqual(snake_case      ,   snake_case       )




				def     a							(					self       ):
											snake_case_							=    self.get_image_processor()
											snake_case_							=    self.get_tokenizer()

											snake_case_							=    ChineseCLIPProcessor(tokenizer=snake_case      ,   image_processor=snake_case       )

											snake_case_							=    'Alexandra，T-shirt的价格是15便士。'
											snake_case_							=    self.prepare_image_inputs()

											snake_case_							=    processor(text=snake_case      ,   images=snake_case       )

											self.assertListEqual(list(inputs.keys()       )      ,   processor.model_input_names       )




