


'''simple docstring'''


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_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD






def        __lowerCAmelCase       ():
		_UpperCAmelCase						:				Optional[Any]	    =					"https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"
		_UpperCAmelCase						:				int	    =					Image.open(requests.get(__lowerCAmelCase					,       stream=__lowerCAmelCase      ).raw      ).convert("RGB"      )

		return image






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				str	    =					[]
		# 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.embeddings.layernorm.weight")      )
		rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.embeddings.layernorm.bias")      )

		# fmt: on
		return rename_keys






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				str	    =					dct.pop(__lowerCAmelCase      )
		_UpperCAmelCase						:				List[str]	    =					val






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		for i in range(config.vision_config.num_hidden_layers      ):
				# read in original q and v biases
				_UpperCAmelCase						:				List[Any]	    =					state_dict.pop(F"""visual_encoder.blocks.{i}.attn.q_bias"""      )
				_UpperCAmelCase						:				Tuple	    =					state_dict.pop(F"""visual_encoder.blocks.{i}.attn.v_bias"""      )

				# next, set bias in the state dict
				_UpperCAmelCase						:				Dict	    =					torch.cat((q_bias, torch.zeros_like(__lowerCAmelCase					,       requires_grad=__lowerCAmelCase      ), v_bias)      )
				_UpperCAmelCase						:				int	    =					qkv_bias






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				Optional[int]	    =					364 if "coco" in model_name else 224
		_UpperCAmelCase						:				Optional[Any]	    =					InstructBlipVisionConfig(image_size=__lowerCAmelCase      ).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 "t5-xl" in model_name:
				_UpperCAmelCase						:				Dict	    =					TaConfig.from_pretrained("google/flan-t5-xl"					,       dense_act_fn="gelu"					,       bos_token_id=1      ).to_dict()
		elif "t5-xxl" in model_name:
				_UpperCAmelCase						:				Union[str, Any]	    =					TaConfig.from_pretrained("google/flan-t5-xxl"					,       dense_act_fn="gelu"					,       bos_token_id=1      ).to_dict()
		elif "vicuna-7b" in model_name:
				_UpperCAmelCase						:				Union[str, Any]	    =					LlamaConfig.from_pretrained("decapoda-research/llama-7b-hf"					,       vocab_size=32_001      ).to_dict()
		elif "vicuna-13b" in model_name:
				_UpperCAmelCase						:				List[str]	    =					LlamaConfig.from_pretrained("decapoda-research/llama-13b-hf"					,       vocab_size=32_001      ).to_dict()
		else:
				raise ValueError("Model name not supported"      )

		# the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
		_UpperCAmelCase						:				str	    =					InstructBlipQFormerConfig(vocab_size=30_523      ).to_dict()
		_UpperCAmelCase						:				Optional[int]	    =					InstructBlipConfig(vision_config=__lowerCAmelCase					,       text_config=__lowerCAmelCase					,       qformer_config=__lowerCAmelCase      )

		return config, image_size






@torch.no_grad()
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase=None					,       __lowerCAmelCase=False      ):
		_UpperCAmelCase						:				Dict	    =					AutoTokenizer.from_pretrained("bert-base-uncased"					,       truncation_side="left"      )
		qformer_tokenizer.add_special_tokens({"bos_token": "[DEC]"}      )

		if "t5" in model_name:
				_UpperCAmelCase						:				Dict	    =					TaTokenizerFast.from_pretrained("google/flan-t5-xl"					,       truncation_side="left"      )
		elif "vicuna" in model_name:
				# the following was used in the original implementation:
				# tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
				# tokenizer.add_special_tokens({"pad_token": "[PAD]"})
				# tokenizer.add_special_tokens({"bos_token": "</s>"})
				# tokenizer.add_special_tokens({"eos_token": "</s>"})
				# tokenizer.add_special_tokens({"unk_token": "</s>"})
				_UpperCAmelCase						:				Dict	    =					LlamaTokenizerFast.from_pretrained(
				    "huggyllama/llama-7b"					,       truncation_side="left"					,       bos_token="</s>"					,       unk_token="</s>"      )
				tokenizer.add_special_tokens({"pad_token": "[PAD]"}      )

		_UpperCAmelCase    ,			_UpperCAmelCase						:				Tuple	    =					get_blipa_config(__lowerCAmelCase      )
		_UpperCAmelCase						:				List[Any]	    =					InstructBlipForConditionalGeneration(__lowerCAmelCase      ).eval()

		_UpperCAmelCase						:				List[str]	    =					{
		    "instructblip-vicuna-7b": ("blip2_vicuna_instruct", "vicuna7b"),
		    "instructblip-vicuna-13b": ("blip2_vicuna_instruct", "vicuna13b"),
		    "instructblip-flan-t5-xl": ("blip2_t5_instruct", "flant5xl"),
		    "instructblip-flan-t5-xxl": ("blip2_t5_instruct", "flant5xxl"),
		}

		_UpperCAmelCase    ,			_UpperCAmelCase						:				List[Any]	    =					model_name_to_original[model_name]

		# load original model
		print("Loading original model..."      )
		_UpperCAmelCase						:				Tuple	    =					"cuda:1" if torch.cuda.is_available() else "cpu"
		_UpperCAmelCase						:				List[Any]	    =					"cuda:2" if torch.cuda.is_available() else "cpu"
		_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase						:				Dict	    =					load_model_and_preprocess(
		    name=__lowerCAmelCase					,       model_type=__lowerCAmelCase					,       is_eval=__lowerCAmelCase					,       device=__lowerCAmelCase      )
		original_model.eval()
		print("Done!"      )

		# update state dict keys
		_UpperCAmelCase						:				List[str]	    =					original_model.state_dict()
		_UpperCAmelCase						:				int	    =					create_rename_keys(__lowerCAmelCase      )
		for src, dest in rename_keys:
				rename_key(__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      )

		# some keys can be renamed efficiently
		for key, val in state_dict.copy().items():
				_UpperCAmelCase						:				Union[str, Any]	    =					state_dict.pop(__lowerCAmelCase      )
				if key.startswith("Qformer.bert"      ):
						_UpperCAmelCase						:				List[Any]	    =					key.replace("Qformer.bert"					,       "qformer"      )
				if "attention.self" in key:
						_UpperCAmelCase						:				Any	    =					key.replace("self"					,       "attention"      )
				if "llm_proj" in key:
						_UpperCAmelCase						:				str	    =					key.replace("llm_proj"					,       "language_projection"      )
				if "t5_proj" in key:
						_UpperCAmelCase						:				Dict	    =					key.replace("t5_proj"					,       "language_projection"      )
				if key.startswith("llm_model"      ):
						_UpperCAmelCase						:				int	    =					key.replace("llm_model"					,       "language_model"      )
				if key.startswith("t5"      ):
						_UpperCAmelCase						:				List[Any]	    =					key.replace("t5"					,       "language"      )
				_UpperCAmelCase						:				List[str]	    =					val

		# read in qv biases
		read_in_q_v_bias(__lowerCAmelCase					,       __lowerCAmelCase      )

		# note: weights get loaded in torch.float32 by default
		hf_model.load_state_dict(__lowerCAmelCase					,       strict=__lowerCAmelCase      )

		_UpperCAmelCase						:				Tuple	    =					load_demo_image()
		_UpperCAmelCase						:				str	    =					"What is unusual about this image?"

		# create processor
		_UpperCAmelCase						:				Optional[int]	    =					BlipImageProcessor(
		    size={"height": image_size, "width": image_size}					,       image_mean=__lowerCAmelCase					,       image_std=__lowerCAmelCase      )
		_UpperCAmelCase						:				Any	    =					InstructBlipProcessor(
		    image_processor=__lowerCAmelCase					,       tokenizer=__lowerCAmelCase					,       qformer_tokenizer=__lowerCAmelCase					,       )
		_UpperCAmelCase						:				Any	    =					processor(images=__lowerCAmelCase					,       text=__lowerCAmelCase					,       return_tensors="pt"      ).to(__lowerCAmelCase      )

		# make sure processor creates exact same pixel values
		_UpperCAmelCase						:				List[str]	    =					vis_processors["eval"](__lowerCAmelCase      ).unsqueeze(0      ).to(__lowerCAmelCase      )
		_UpperCAmelCase						:				Optional[Any]	    =					inputs.pixel_values
		assert torch.allclose(original_pixel_values.to(pixel_values.device      )					,       __lowerCAmelCase      )

		original_model.to(__lowerCAmelCase      )
		hf_model.to(__lowerCAmelCase      )
		with torch.no_grad():
				if "vicuna" in model_name:
						_UpperCAmelCase						:				Any	    =					original_model({"image": original_pixel_values, "text_input": [prompt]}      ).logits
						_UpperCAmelCase						:				Any	    =					hf_model(**__lowerCAmelCase      ).logits
				else:
						_UpperCAmelCase						:				List[str]	    =					original_model(
						    {"image": original_pixel_values, "text_input": [prompt], "text_output": ["\n"]}      ).logits
						_UpperCAmelCase						:				Dict	    =					tokenizer("\n"					,       return_tensors="pt"      ).input_ids.to(__lowerCAmelCase      )
						_UpperCAmelCase						:				List[Any]	    =					label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id					,       -100      )
						_UpperCAmelCase						:				Optional[Any]	    =					hf_model(**__lowerCAmelCase					,       labels=__lowerCAmelCase      ).logits

		print("First values of original logits:"					,       original_logits[0, :3, :3]      )
		print("First values of HF logits:"					,       logits[0, :3, :3]      )

		# assert values
		assert original_logits.shape == logits.shape
		_UpperCAmelCase						:				int	    =					1e-4 if "vicuna" in model_name else 1e-5
		assert torch.allclose(original_logits.to(logits.device      )					,       __lowerCAmelCase					,       atol=__lowerCAmelCase      )
		print("Looks ok!"      )

		print("Generating with original model..."      )
		_UpperCAmelCase						:				Optional[int]	    =					original_model.generate({"image": original_pixel_values, "prompt": prompt}					,       num_beams=5      )

		# important: we need to cast the weights of the HF model to the appropriate type
		print("Generating with HF model..."      )
		_UpperCAmelCase						:				Union[str, Any]	    =					hf_model.generate(
		    **__lowerCAmelCase					,       do_sample=__lowerCAmelCase					,       num_beams=5					,       max_length=256					,       min_length=1					,       top_p=0.9					,       repetition_penalty=1.5					,       length_penalty=1.0					,       temperature=1					,       )
		if "vicuna" in model_name:
				# convert output id 0 to 2 (eos_token_id)
				# TODO add this in the generate method?
				_UpperCAmelCase						:				str	    =					2
		print("Original generation:"					,       __lowerCAmelCase      )
		_UpperCAmelCase						:				Dict	    =					processor.batch_decode(__lowerCAmelCase					,       skip_special_tokens=__lowerCAmelCase      )
		_UpperCAmelCase						:				List[Any]	    =					[text.strip() for text in output_text]
		print("HF generation:"					,       __lowerCAmelCase      )

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

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


if __name__ == "__main__":
						lowerCamelCase__					=   argparse.ArgumentParser()
						lowerCamelCase__					=   [
						    'instructblip-vicuna-7b',
						    'instructblip-vicuna-13b',
						    'instructblip-flan-t5-xl',
						    'instructblip-flan-t5-xxl',
						]
						parser.add_argument(
						    '--model_name',
						    default='instructblip-flan-t5-xl',
						    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',
						)

						lowerCamelCase__					=   parser.parse_args()

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







'''simple docstring'''


import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property






@unittest.skipUnless(os.path.exists(UpperCAmelCase__		)			,				"Tatoeba directory does not exist."		)
class 		lowerCAmelCase__	(	unittest.TestCase		):




			@cached_property
			def 							lowerCAmelCase__			(						self     :   Optional[Any]       )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					tempfile.mkdtemp()
					return TatoebaConverter(save_dir=lowerCamelCase__       )




			@slow
			def 							lowerCAmelCase__			(						self     :   Optional[int]       )      ->List[str]:



					'''simple docstring'''







					self.resolver.convert_models(["heb-eng"]       )






			@slow
			def 							lowerCAmelCase__			(						self     :   List[Any]       )      ->Any:



					'''simple docstring'''







					_UpperCAmelCase    ,			_UpperCAmelCase						:				Tuple	    =					self.resolver.write_model_card("opus-mt-he-en"		,     dry_run=lowerCamelCase__       )
					assert mmeta["long_pair"] == "heb-eng"



