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  snake_case						()	->	Optional[Any]:

						'''simple docstring'''



						_snake_case						:					List[str]    =   "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"
						_snake_case						:					Dict    =   Image.open(requests.get(__lowercase ,      stream=__lowercase						).raw						).convert("RGB"						)

						return image
def  snake_case						(__lowercase						)	->	Optional[Any]:

						'''simple docstring'''



						_snake_case						:					Union[str, Any]    =   []
						# 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  snake_case						(__lowercase ,      __lowercase ,      __lowercase						)	->	List[Any]:

						'''simple docstring'''



						_snake_case						:					Optional[int]    =   dct.pop(__lowercase						)
						_snake_case						:					Any    =   val
def  snake_case						(__lowercase ,      __lowercase						)	->	Union[str, Any]:

						'''simple docstring'''



						for i in range(config.vision_config.num_hidden_layers						):
												# read in original q and v biases
												_snake_case						:					Optional[int]    =   state_dict.pop(F"""visual_encoder.blocks.{i}.attn.q_bias"""						)
												_snake_case						:					List[str]    =   state_dict.pop(F"""visual_encoder.blocks.{i}.attn.v_bias"""						)

												# next, set bias in the state dict
												_snake_case						:					List[Any]    =   torch.cat((q_bias, torch.zeros_like(__lowercase ,      requires_grad=__lowercase						), v_bias)						)
												_snake_case						:					Union[str, Any]    =   qkv_bias
def  snake_case						(__lowercase ,      __lowercase						)	->	Tuple:

						'''simple docstring'''



						_snake_case						:					str    =   364 if "coco" in model_name else 224
						_snake_case						:					str    =   BlipaVisionConfig(image_size=__lowercase						).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						:					Any    =   OPTConfig.from_pretrained("facebook/opt-2.7b" ,      eos_token_id=__lowercase						).to_dict()
						elif "opt-6.7b" in model_name:
												_snake_case						:					Any    =   OPTConfig.from_pretrained("facebook/opt-6.7b" ,      eos_token_id=__lowercase						).to_dict()
						elif "t5-xl" in model_name:
												_snake_case						:					Optional[Any]    =   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						:					Optional[int]    =   TaConfig.from_pretrained("google/flan-t5-xxl" ,      dense_act_fn="gelu" ,      bos_token_id=1						).to_dict()

						_snake_case						:					int    =   BlipaConfig(vision_config=__lowercase ,      text_config=__lowercase						)

						return config, image_size
@torch.no_grad()
def  snake_case						(__lowercase ,      __lowercase=None ,      __lowercase=False						)	->	Tuple:

						'''simple docstring'''



						_snake_case						:					Dict    =   (
						    AutoTokenizer.from_pretrained("facebook/opt-2.7b"						)
						    if "opt" in model_name
						    else AutoTokenizer.from_pretrained("google/flan-t5-xl"						)
						)
						_snake_case						:					Tuple    =   tokenizer("\n" ,      add_special_tokens=__lowercase						).input_ids[0]
						_snake_case    ,_snake_case						:					Optional[int]    =   get_blipa_config(__lowercase ,      eos_token_id=__lowercase						)

						_snake_case						:					Optional[Any]    =   BlipaForConditionalGeneration(__lowercase						).eval()

						_snake_case						:					List[Any]    =   {
						    "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						:					Optional[Any]    =   model_name_to_original[model_name]

						# load original model
						print("Loading original model..."						)
						_snake_case						:					str    =   "cuda" if torch.cuda.is_available() else "cpu"
						_snake_case    ,_snake_case    ,_snake_case						:					List[Any]    =   load_model_and_preprocess(
						    name=__lowercase ,      model_type=__lowercase ,      is_eval=__lowercase ,      device=__lowercase						)
						original_model.eval()
						print("Done!"						)

						# update state dict keys
						_snake_case						:					List[Any]    =   original_model.state_dict()
						_snake_case						:					Union[str, Any]    =   create_rename_keys(__lowercase						)
						for src, dest in rename_keys:
												rename_key(__lowercase ,      __lowercase ,      __lowercase						)

						# some keys can be renamed efficiently
						for key, val in state_dict.copy().items():
												_snake_case						:					Any    =   state_dict.pop(__lowercase						)
												if key.startswith("Qformer.bert"						):
																		_snake_case						:					Any    =   key.replace("Qformer.bert" ,      "qformer"						)
												if "attention.self" in key:
																		_snake_case						:					int    =   key.replace("self" ,      "attention"						)
												if "opt_proj" in key:
																		_snake_case						:					List[Any]    =   key.replace("opt_proj" ,      "language_projection"						)
												if "t5_proj" in key:
																		_snake_case						:					Optional[Any]    =   key.replace("t5_proj" ,      "language_projection"						)
												if key.startswith("opt"						):
																		_snake_case						:					str    =   key.replace("opt" ,      "language"						)
												if key.startswith("t5"						):
																		_snake_case						:					Tuple    =   key.replace("t5" ,      "language"						)
												_snake_case						:					Tuple    =   val

						# read in qv biases
						read_in_q_v_bias(__lowercase ,      __lowercase						)

						_snake_case    ,_snake_case						:					Any    =   hf_model.load_state_dict(__lowercase ,      strict=__lowercase						)
						assert len(__lowercase						) == 0
						assert unexpected_keys == ["qformer.embeddings.position_ids"]

						_snake_case						:					Dict    =   load_demo_image()
						_snake_case						:					Any    =   vis_processors["eval"](__lowercase						).unsqueeze(0						).to(__lowercase						)
						_snake_case						:					Union[str, Any]    =   tokenizer(["\n"] ,      return_tensors="pt"						).input_ids.to(__lowercase						)

						# create processor
						_snake_case						:					Optional[int]    =   BlipImageProcessor(
						    size={"height": image_size, "width": image_size} ,      image_mean=__lowercase ,      image_std=__lowercase						)
						_snake_case						:					str    =   BlipaProcessor(image_processor=__lowercase ,      tokenizer=__lowercase						)
						_snake_case						:					str    =   processor(images=__lowercase ,      return_tensors="pt"						).pixel_values.to(__lowercase						)

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

						original_model.to(__lowercase						)
						hf_model.to(__lowercase						)
						with torch.no_grad():
												if "opt" in model_name:
																		_snake_case						:					Tuple    =   original_model({"image": original_pixel_values, "text_input": [""]}						).logits
																		_snake_case						:					Optional[Any]    =   hf_model(__lowercase ,      __lowercase						).logits
												else:
																		_snake_case						:					Dict    =   original_model(
																		    {"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]}						).logits
																		_snake_case						:					Tuple    =   input_ids.masked_fill(input_ids == tokenizer.pad_token_id ,      -100						)
																		_snake_case						:					List[str]    =   hf_model(__lowercase ,      __lowercase ,      labels=__lowercase						).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						:					List[str]    =   torch.tensor(
												    [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]] ,      device=__lowercase						)
												assert torch.allclose(logits[0, :3, :3] ,      __lowercase ,      atol=1e-4						)
						elif model_name == "blip2-flan-t5-xl-coco":
												_snake_case						:					Optional[Any]    =   torch.tensor(
												    [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]] ,      device=__lowercase						)
						else:
												# cast to same type
												_snake_case						:					List[Any]    =   logits.dtype
												assert torch.allclose(original_logits.to(__lowercase						) ,      __lowercase ,      atol=1e-2						)
						print("Looks ok!"						)

						print("Generating a caption..."						)
						_snake_case						:					Optional[int]    =   ""
						_snake_case						:					Tuple    =   tokenizer(__lowercase ,      return_tensors="pt"						).input_ids.to(__lowercase						)

						_snake_case						:					List[Any]    =   original_model.generate({"image": original_pixel_values}						)
						_snake_case						:					Tuple    =   hf_model.generate(
						    __lowercase ,      __lowercase ,      do_sample=__lowercase ,      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:" ,      __lowercase						)
						_snake_case						:					Any    =   input_ids.shape[1]
						_snake_case						:					Optional[int]    =   processor.batch_decode(outputs[:, prompt_length:] ,      skip_special_tokens=__lowercase						)
						_snake_case						:					Tuple    =   [text.strip() for text in output_text]
						print("HF generation:" ,      __lowercase						)

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

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


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:   Tuple         =			argparse.ArgumentParser()
				__SCREAMING_SNAKE_CASE						:   Any         =			[
				    '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',
				)

				__SCREAMING_SNAKE_CASE						:   Tuple         =			parser.parse_args()

				convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list



@require_torchaudio
@require_sentencepiece
class        lowercase_   (					unittest.TestCase		):



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					str    =   "laion/clap-htsat-unfused"
													_snake_case						:					Dict    =   tempfile.mkdtemp()



							def 	UpperCamelCase	(						self				,    **lowercase_    ):
													return RobertaTokenizer.from_pretrained(self.checkpoint				,    **lowercase_    )



							def 	UpperCamelCase	(						self				,    **lowercase_    ):
													return ClapFeatureExtractor.from_pretrained(self.checkpoint				,    **lowercase_    )



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



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[int]    =   self.get_tokenizer()
													_snake_case						:					List[Any]    =   self.get_feature_extractor()

													_snake_case						:					Optional[Any]    =   ClapProcessor(tokenizer=lowercase_				,    feature_extractor=lowercase_    )

													processor.save_pretrained(self.tmpdirname    )
													_snake_case						:					Tuple    =   ClapProcessor.from_pretrained(self.tmpdirname    )

													self.assertEqual(processor.tokenizer.get_vocab()				,    tokenizer.get_vocab()    )
													self.assertIsInstance(processor.tokenizer				,    lowercase_    )

													self.assertEqual(processor.feature_extractor.to_json_string()				,    feature_extractor.to_json_string()    )
													self.assertIsInstance(processor.feature_extractor				,    lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Any    =   ClapProcessor(tokenizer=self.get_tokenizer()				,    feature_extractor=self.get_feature_extractor()    )
													processor.save_pretrained(self.tmpdirname    )

													_snake_case						:					Any    =   self.get_tokenizer(bos_token="(BOS)"				,    eos_token="(EOS)"    )
													_snake_case						:					List[Any]    =   self.get_feature_extractor(do_normalize=lowercase_				,    padding_value=1.0    )

													_snake_case						:					List[Any]    =   ClapProcessor.from_pretrained(
													    self.tmpdirname				,    bos_token="(BOS)"				,    eos_token="(EOS)"				,    do_normalize=lowercase_				,    padding_value=1.0    )

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

													self.assertEqual(processor.feature_extractor.to_json_string()				,    feature_extractor_add_kwargs.to_json_string()    )
													self.assertIsInstance(processor.feature_extractor				,    lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Tuple    =   self.get_feature_extractor()
													_snake_case						:					Union[str, Any]    =   self.get_tokenizer()

													_snake_case						:					Optional[Any]    =   ClapProcessor(tokenizer=lowercase_				,    feature_extractor=lowercase_    )

													_snake_case						:					List[str]    =   floats_list((3, 1_000)    )

													_snake_case						:					Union[str, Any]    =   feature_extractor(lowercase_				,    return_tensors="np"    )
													_snake_case						:					Any    =   processor(audios=lowercase_				,    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 	UpperCamelCase	(						self    ):
													_snake_case						:					str    =   self.get_feature_extractor()
													_snake_case						:					Optional[Any]    =   self.get_tokenizer()

													_snake_case						:					Dict    =   ClapProcessor(tokenizer=lowercase_				,    feature_extractor=lowercase_    )

													_snake_case						:					Any    =   "This is a test string"

													_snake_case						:					Optional[Any]    =   processor(text=lowercase_    )

													_snake_case						:					Optional[Any]    =   tokenizer(lowercase_    )

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



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Dict    =   self.get_feature_extractor()
													_snake_case						:					Dict    =   self.get_tokenizer()

													_snake_case						:					List[Any]    =   ClapProcessor(tokenizer=lowercase_				,    feature_extractor=lowercase_    )

													_snake_case						:					Dict    =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

													_snake_case						:					List[Any]    =   processor.batch_decode(lowercase_    )
													_snake_case						:					Optional[int]    =   tokenizer.batch_decode(lowercase_    )

													self.assertListEqual(lowercase_				,    lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[str]    =   self.get_feature_extractor()
													_snake_case						:					str    =   self.get_tokenizer()

													_snake_case						:					Optional[int]    =   ClapProcessor(tokenizer=lowercase_				,    feature_extractor=lowercase_    )

													self.assertListEqual(
													    processor.model_input_names[2:]				,    feature_extractor.model_input_names				,    msg="`processor` and `feature_extractor` model input names do not match"				,    )