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

							return image




def 							lowerCamelCase			(   UpperCAmelCase__ :      Optional[Any]				)    ->   int:
							lowercase_      :    Tuple       		= []
							# 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			(   UpperCAmelCase__ :      List[Any]   ,   UpperCAmelCase__ :      Optional[int]   ,   UpperCAmelCase__ :      List[Any]				)    ->   Dict:
							lowercase_      :    Union[str, Any]       		= dct.pop(__UpperCamelCase				)
							lowercase_      :    Optional[int]       		= val




def 							lowerCamelCase			(   UpperCAmelCase__ :      str   ,   UpperCAmelCase__ :      str				)    ->   List[Any]:






							for i in range(config.vision_config.num_hidden_layers				):
														# read in original q and v biases
														lowercase_      :    Union[str, Any]       		= state_dict.pop(F'''visual_encoder.blocks.{i}.attn.q_bias'''				)
														lowercase_      :    Optional[Any]       		= state_dict.pop(F'''visual_encoder.blocks.{i}.attn.v_bias'''				)

														# next, set bias in the state dict
														lowercase_      :    Union[str, Any]       		= torch.cat((q_bias, torch.zeros_like(__UpperCamelCase   ,   requires_grad=__UpperCamelCase				), v_bias)				)
														lowercase_      :    Optional[Any]       		= qkv_bias




def 							lowerCamelCase			(   UpperCAmelCase__ :      Union[str, Any]				)    ->   List[Any]:
							lowercase_      :    Dict       		= 364 if """coco""" in model_name else 224
							lowercase_      :    str       		= InstructBlipVisionConfig(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 "t5-xl" in model_name:
														lowercase_      :    Optional[int]       		= TaConfig.from_pretrained("""google/flan-t5-xl"""   ,   dense_act_fn="""gelu"""   ,   bos_token_id=1				).to_dict()
							elif "t5-xxl" in model_name:
														lowercase_      :    int       		= TaConfig.from_pretrained("""google/flan-t5-xxl"""   ,   dense_act_fn="""gelu"""   ,   bos_token_id=1				).to_dict()
							elif "vicuna-7b" in model_name:
														lowercase_      :    int       		= LlamaConfig.from_pretrained("""decapoda-research/llama-7b-hf"""   ,   vocab_size=32001				).to_dict()
							elif "vicuna-13b" in model_name:
														lowercase_      :    Union[str, Any]       		= LlamaConfig.from_pretrained("""decapoda-research/llama-13b-hf"""   ,   vocab_size=32001				).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
							lowercase_      :    str       		= InstructBlipQFormerConfig(vocab_size=30523				).to_dict()
							lowercase_      :    List[str]       		= InstructBlipConfig(vision_config=__UpperCamelCase   ,   text_config=__UpperCamelCase   ,   qformer_config=__UpperCamelCase				)

							return config, image_size




@torch.no_grad()
def 							lowerCamelCase			(   UpperCAmelCase__ :      List[str]   ,   UpperCAmelCase__ :      List[Any]=None   ,   UpperCAmelCase__ :      Optional[Any]=False				)    ->   int:
							lowercase_      :    Optional[int]       		= AutoTokenizer.from_pretrained("""bert-base-uncased"""   ,   truncation_side="""left"""				)
							qformer_tokenizer.add_special_tokens({"""bos_token""": """[DEC]"""}				)

							if "t5" in model_name:
														lowercase_      :    Tuple       		= 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>"})
														lowercase_      :    Any       		= LlamaTokenizerFast.from_pretrained(
														    """huggyllama/llama-7b"""   ,   truncation_side="""left"""   ,   bos_token="""</s>"""   ,   unk_token="""</s>"""				)
														tokenizer.add_special_tokens({"""pad_token""": """[PAD]"""}				)

							lowercase_      :    Union[str, Any]       		= get_blipa_config(__UpperCamelCase				)
							lowercase_      :    Optional[Any]       		= InstructBlipForConditionalGeneration(__UpperCamelCase				).eval()

							lowercase_      :    Tuple       		= {
							    """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"""),
							}

							lowercase_      :    int       		= model_name_to_original[model_name]

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

							# update state dict keys
							lowercase_      :    Any       		= original_model.state_dict()
							lowercase_      :    List[str]       		= 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():
														lowercase_      :    int       		= state_dict.pop(__UpperCamelCase				)
														if key.startswith("""Qformer.bert"""				):
																					lowercase_      :    Any       		= key.replace("""Qformer.bert"""   ,   """qformer"""				)
														if "attention.self" in key:
																					lowercase_      :    Tuple       		= key.replace("""self"""   ,   """attention"""				)
														if "llm_proj" in key:
																					lowercase_      :    Dict       		= key.replace("""llm_proj"""   ,   """language_projection"""				)
														if "t5_proj" in key:
																					lowercase_      :    Dict       		= key.replace("""t5_proj"""   ,   """language_projection"""				)
														if key.startswith("""llm_model"""				):
																					lowercase_      :    Union[str, Any]       		= key.replace("""llm_model"""   ,   """language_model"""				)
														if key.startswith("""t5"""				):
																					lowercase_      :    List[str]       		= key.replace("""t5"""   ,   """language"""				)
														lowercase_      :    Optional[Any]       		= val

							# read in qv biases
							read_in_q_v_bias(__UpperCamelCase   ,   __UpperCamelCase				)

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

							lowercase_      :    Dict       		= load_demo_image()
							lowercase_      :    Optional[Any]       		= """What is unusual about this image?"""

							# create processor
							lowercase_      :    int       		= BlipImageProcessor(
							    size={"""height""": image_size, """width""": image_size}   ,   image_mean=__UpperCamelCase   ,   image_std=__UpperCamelCase				)
							lowercase_      :    List[str]       		= InstructBlipProcessor(
							    image_processor=__UpperCamelCase   ,   tokenizer=__UpperCamelCase   ,   qformer_tokenizer=__UpperCamelCase   ,   )
							lowercase_      :    int       		= processor(images=__UpperCamelCase   ,   text=__UpperCamelCase   ,   return_tensors="""pt"""				).to(__UpperCamelCase				)

							# make sure processor creates exact same pixel values
							lowercase_      :    Tuple       		= vis_processors["""eval"""](__UpperCamelCase				).unsqueeze(0				).to(__UpperCamelCase				)
							lowercase_      :    Dict       		= inputs.pixel_values
							assert torch.allclose(original_pixel_values.to(pixel_values.device				)   ,   __UpperCamelCase				)

							original_model.to(__UpperCamelCase				)
							hf_model.to(__UpperCamelCase				)
							with torch.no_grad():
														if "vicuna" in model_name:
																					lowercase_      :    Any       		= original_model({"""image""": original_pixel_values, """text_input""": [prompt]}				).logits
																					lowercase_      :    Dict       		= hf_model(**__UpperCamelCase				).logits
														else:
																					lowercase_      :    Union[str, Any]       		= original_model(
																					    {"""image""": original_pixel_values, """text_input""": [prompt], """text_output""": ["""\n"""]}				).logits
																					lowercase_      :    List[Any]       		= tokenizer("""\n"""   ,   return_tensors="""pt"""				).input_ids.to(__UpperCamelCase				)
																					lowercase_      :    int       		= label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id   ,   -100				)
																					lowercase_      :    int       		= hf_model(**__UpperCamelCase   ,   labels=__UpperCamelCase				).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
							lowercase_      :    str       		= 1e-4 if """vicuna""" in model_name else 1e-5
							assert torch.allclose(original_logits.to(logits.device				)   ,   __UpperCamelCase   ,   atol=__UpperCamelCase				)
							print("""Looks ok!"""				)

							print("""Generating with original model..."""				)
							lowercase_      :    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..."""				)
							lowercase_      :    str       		= hf_model.generate(
							    **__UpperCamelCase   ,   do_sample=__UpperCamelCase   ,   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?
														lowercase_      :    Any       		= 2
							print("""Original generation:"""   ,   __UpperCamelCase				)
							lowercase_      :    List[Any]       		= processor.batch_decode(__UpperCamelCase   ,   skip_special_tokens=__UpperCamelCase				)
							lowercase_      :    str       		= [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'''Salesforce/{model_name}'''				)
														hf_model.push_to_hub(F'''Salesforce/{model_name}'''				)


if __name__ == "__main__":
						_lowercase		:				List[str]	       =		argparse.ArgumentParser()
						_lowercase		:				Optional[Any]	       =		[
						    '''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",
						)

						_lowercase		:				str	       =		parser.parse_args()

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






'''simple docstring'''




import unittest

import numpy as np




def 							lowerCamelCase			(   UpperCAmelCase__ :      np.ndarray   ,   UpperCAmelCase__ :      np.ndarray   ,   UpperCAmelCase__ :      np.ndarray   ,   UpperCAmelCase__ :      np.ndarray | None = None   ,   )    ->   np.ndarray:
							lowercase_      :    List[Any]       		= np.shape(UpperCAmelCase__				)
							lowercase_      :    Dict       		= np.shape(UpperCAmelCase__				)
							lowercase_      :    int       		= np.shape(UpperCAmelCase__				)

							if shape_a[0] != shape_b[0]:
														lowercase_      :    Optional[int]       		= (
														    """Expected the same number of rows for A and B. """
														    F'''Instead found A of size {shape_a} and B of size {shape_b}'''
														)
														raise ValueError(UpperCAmelCase__				)

							if shape_b[1] != shape_c[1]:
														lowercase_      :    Optional[Any]       		= (
														    """Expected the same number of columns for B and C. """
														    F'''Instead found B of size {shape_b} and C of size {shape_c}'''
														)
														raise ValueError(UpperCAmelCase__				)

							lowercase_      :    Any       		= pseudo_inv
							if a_inv is None:
														try:
																					lowercase_      :    List[str]       		= np.linalg.inv(UpperCAmelCase__				)
														except np.linalg.LinAlgError:
																					raise ValueError(
																					    """Input matrix A is not invertible. Cannot compute Schur complement."""				)

							return mat_c - mat_b.T @ a_inv @ mat_b





class 					__magic_name__		(					unittest.TestCase):







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
										lowercase_      :    Tuple       		= np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]					)
										lowercase_      :    int       		= np.array([[0, 3], [3, 0], [2, 3]]					)
										lowercase_      :    Dict       		= np.array([[2, 1], [6, 3]]					)

										lowercase_      :    Union[str, Any]       		= schur_complement(lowercase_       ,    lowercase_       ,    lowercase_					)

										lowercase_      :    List[Any]       		= np.block([[a, b], [b.T, c]]					)

										lowercase_      :    Optional[int]       		= np.linalg.det(lowercase_					)
										lowercase_      :    int       		= np.linalg.det(lowercase_					)
										lowercase_      :    int       		= np.linalg.det(lowercase_					)

										self.assertAlmostEqual(lowercase_       ,    det_a * det_s					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										lowercase_      :    int       		= np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]					)
										lowercase_      :    Optional[Any]       		= np.array([[0, 3], [3, 0], [2, 3]]					)
										lowercase_      :    Union[str, Any]       		= np.array([[2, 1], [6, 3]]					)

										with self.assertRaises(lowercase_					):
																	schur_complement(lowercase_       ,    lowercase_       ,    lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										lowercase_      :    List[Any]       		= np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]					)
										lowercase_      :    List[Any]       		= np.array([[0, 3], [3, 0], [2, 3]]					)
										lowercase_      :    str       		= np.array([[2, 1, 3], [6, 3, 5]]					)

										with self.assertRaises(lowercase_					):
																	schur_complement(lowercase_       ,    lowercase_       ,    lowercase_					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						unittest.main()





