


import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


_lowercase							:   int						=						50_0000

_lowercase						,				_lowercase							:   List[Any]						=						os.path.split(__file__)
_lowercase							:   Any						=						os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))





@get_duration
def 	_lowerCAmelCase					( UpperCamelCase__:  datasets.Dataset				,       **UpperCamelCase__:  Union[str, Any]    )			-> Optional[int]:





	"""simple docstring"""







	A             =					dataset.map(**UpperCamelCase__    )





@get_duration
def 	_lowerCAmelCase					( UpperCamelCase__:  datasets.Dataset				,       **UpperCamelCase__:  List[str]    )			-> str:





	"""simple docstring"""







	A             =					dataset.filter(**UpperCamelCase__    )





def 	_lowerCAmelCase					( )			-> Optional[Any]:





	"""simple docstring"""







	A             =					{"""num examples""": SPEED_TEST_N_EXAMPLES}
	with tempfile.TemporaryDirectory() as tmp_dir:
		A             =					datasets.Features({"""text""": datasets.Value("""string"""    ), """numbers""": datasets.Value("""float32"""    )}    )
		A             =					generate_example_dataset(
		    os.path.join(UpperCamelCase__				,       """dataset.arrow"""    )				,       UpperCamelCase__				,       num_examples=UpperCamelCase__    )

		A             =					transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""				,       use_fast=UpperCamelCase__    )

		def tokenize(UpperCamelCase__:  Union[str, Any]    ):
			return tokenizer(examples["""text"""]    )

		A             =					map(UpperCamelCase__    )

		A             =					map(UpperCamelCase__				,       batched=UpperCamelCase__    )

		A             =					map(UpperCamelCase__				,       function=lambda UpperCamelCase__    : None				,       batched=UpperCamelCase__    )

		with dataset.formatted_as(type="""numpy"""    ):
			A             =					map(UpperCamelCase__				,       function=lambda UpperCamelCase__    : None				,       batched=UpperCamelCase__    )

		with dataset.formatted_as(type="""pandas"""    ):
			A             =					map(UpperCamelCase__				,       function=lambda UpperCamelCase__    : None				,       batched=UpperCamelCase__    )

		with dataset.formatted_as(type="""torch"""				,       columns="""numbers"""    ):
			A             =					map(UpperCamelCase__				,       function=lambda UpperCamelCase__    : None				,       batched=UpperCamelCase__    )

		with dataset.formatted_as(type="""tensorflow"""				,       columns="""numbers"""    ):
			A             =					map(UpperCamelCase__				,       function=lambda UpperCamelCase__    : None				,       batched=UpperCamelCase__    )

		A             =					map(UpperCamelCase__				,       function=UpperCamelCase__				,       batched=UpperCamelCase__    )

		A             =					filter(UpperCamelCase__    )

		# Activate later when tokenizer support batched inputs
		# with dataset.formatted_as(type='numpy'):
		#     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

	with open(UpperCamelCase__				,       """wb"""    ) as f:
		f.write(json.dumps(UpperCamelCase__    ).encode("""utf-8"""    )    )


if __name__ == "__main__":  # useful to run the profiler
				benchmark_map_filter()







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					( )			-> Tuple:





	"""simple docstring"""







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

	return image





def 	_lowerCAmelCase					( UpperCamelCase__:  Union[str, Any]    )			-> Dict:





	"""simple docstring"""







	A             =					[]
	# 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__:  Tuple				,       UpperCamelCase__:  List[Any]				,       UpperCamelCase__:  Union[str, Any]    )			-> Optional[int]:





	"""simple docstring"""







	A             =					dct.pop(UpperCamelCase__    )
	A             =					val





def 	_lowerCAmelCase					( UpperCamelCase__:  List[Any]				,       UpperCamelCase__:  Union[str, Any]    )			-> Tuple:





	"""simple docstring"""







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

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





def 	_lowerCAmelCase					( UpperCamelCase__:  str				,       UpperCamelCase__:  Tuple    )			-> Dict:





	"""simple docstring"""







	A             =					3_64 if """coco""" in model_name else 2_24
	A             =					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:
		A             =					OPTConfig.from_pretrained("""facebook/opt-2.7b"""				,       eos_token_id=UpperCamelCase__    ).to_dict()
	elif "opt-6.7b" in model_name:
		A             =					OPTConfig.from_pretrained("""facebook/opt-6.7b"""				,       eos_token_id=UpperCamelCase__    ).to_dict()
	elif "t5-xl" in model_name:
		A             =					TaConfig.from_pretrained("""google/flan-t5-xl"""				,       dense_act_fn="""gelu"""				,       bos_token_id=1    ).to_dict()
	elif "t5-xxl" in model_name:
		A             =					TaConfig.from_pretrained("""google/flan-t5-xxl"""				,       dense_act_fn="""gelu"""				,       bos_token_id=1    ).to_dict()

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

	return config, image_size





@torch.no_grad()
def 	_lowerCAmelCase					( UpperCamelCase__:  str				,       UpperCamelCase__:  Optional[Any]=None				,       UpperCamelCase__:  Tuple=False    )			-> List[str]:





	"""simple docstring"""







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

	A             =					BlipaForConditionalGeneration(UpperCamelCase__    ).eval()

	A             =					{
	    """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"""),
	}

	A       ,      A             =					model_name_to_original[model_name]

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

	# update state dict keys
	A             =					original_model.state_dict()
	A             =					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():
		A             =					state_dict.pop(UpperCamelCase__    )
		if key.startswith("""Qformer.bert"""    ):
			A             =					key.replace("""Qformer.bert"""				,       """qformer"""    )
		if "attention.self" in key:
			A             =					key.replace("""self"""				,       """attention"""    )
		if "opt_proj" in key:
			A             =					key.replace("""opt_proj"""				,       """language_projection"""    )
		if "t5_proj" in key:
			A             =					key.replace("""t5_proj"""				,       """language_projection"""    )
		if key.startswith("""opt"""    ):
			A             =					key.replace("""opt"""				,       """language"""    )
		if key.startswith("""t5"""    ):
			A             =					key.replace("""t5"""				,       """language"""    )
		A             =					val

	# read in qv biases
	read_in_q_v_bias(UpperCamelCase__				,       UpperCamelCase__    )

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

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

	# create processor
	A             =					BlipImageProcessor(
	    size={"""height""": image_size, """width""": image_size}				,       image_mean=UpperCamelCase__				,       image_std=UpperCamelCase__    )
	A             =					BlipaProcessor(image_processor=UpperCamelCase__				,       tokenizer=UpperCamelCase__    )
	A             =					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:
			A             =					original_model({"""image""": original_pixel_values, """text_input""": [""""""]}    ).logits
			A             =					hf_model(UpperCamelCase__				,       UpperCamelCase__    ).logits
		else:
			A             =					original_model(
			    {"""image""": original_pixel_values, """text_input""": ["""\n"""], """text_output""": ["""\n"""]}    ).logits
			A             =					input_ids.masked_fill(input_ids == tokenizer.pad_token_id				,       -1_00    )
			A             =					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":
		A             =					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":
		A             =					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
		A             =					logits.dtype
		assert torch.allclose(original_logits.to(UpperCamelCase__    )				,       UpperCamelCase__				,       atol=1e-2    )
	print("""Looks ok!"""    )

	print("""Generating a caption..."""    )
	A             =					""""""
	A             =					tokenizer(UpperCamelCase__				,       return_tensors="""pt"""    ).input_ids.to(UpperCamelCase__    )

	A             =					original_model.generate({"""image""": original_pixel_values}    )
	A             =					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__    )
	A             =					input_ids.shape[1]
	A             =					processor.batch_decode(outputs[:, prompt_length:]				,       skip_special_tokens=UpperCamelCase__    )
	A             =					[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__":
				_lowercase							:   List[Any]						=						argparse.ArgumentParser()
				_lowercase							:   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",
				)

				_lowercase							:   Tuple						=						parser.parse_args()

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



