
"""simple docstring"""




import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowerCAmelCase__				=					16
lowerCAmelCase__				=					32



def 							_lowerCamelCase			(			__a,				__a = 16     ):
		SCREAMING_SNAKE_CASE_    			=			AutoTokenizer.from_pretrained('''bert-base-cased'''     )
		SCREAMING_SNAKE_CASE_    			=			load_dataset('''glue''',				'''mrpc'''     )

		def tokenize_function(__a     ):
				# max_length=None => use the model max length (it's actually the default)
				SCREAMING_SNAKE_CASE_    			=			tokenizer(examples['''sentence1'''],				examples['''sentence2'''],				truncation=__a,				max_length=__a     )
				return outputs

		# Apply the method we just defined to all the examples in all the splits of the dataset
		# starting with the main process first:
		with accelerator.main_process_first():
				SCREAMING_SNAKE_CASE_    			=			datasets.map(
				    __a,				batched=__a,				remove_columns=['''idx''', '''sentence1''', '''sentence2'''],				)

		# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
		# transformers library
		SCREAMING_SNAKE_CASE_    			=			tokenized_datasets.rename_column('''label''',				'''labels'''     )

		def collate_fn(__a     ):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				SCREAMING_SNAKE_CASE_    			=			128 if accelerator.distributed_type == DistributedType.TPU else None
				# When using mixed precision we want round multiples of 8/16
				if accelerator.mixed_precision == "fp8":
						SCREAMING_SNAKE_CASE_    			=			16
				elif accelerator.mixed_precision != "no":
						SCREAMING_SNAKE_CASE_    			=			8
				else:
						SCREAMING_SNAKE_CASE_    			=			None

				return tokenizer.pad(
				    __a,				padding='''longest''',				max_length=__a,				pad_to_multiple_of=__a,				return_tensors='''pt''',				)

		# Instantiate dataloaders.
		SCREAMING_SNAKE_CASE_    			=			DataLoader(
		    tokenized_datasets['''train'''],				shuffle=__a,				collate_fn=__a,				batch_size=__a     )
		SCREAMING_SNAKE_CASE_    			=			DataLoader(
		    tokenized_datasets['''validation'''],				shuffle=__a,				collate_fn=__a,				batch_size=__a     )

		return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
						from accelerate.test_utils.training import mocked_dataloaders

						lowerCAmelCase__				=					mocked_dataloaders  # noqa: F811



def 							_lowerCamelCase			(			__a,				__a     ):
		# For testing only
		if os.environ.get('''TESTING_MOCKED_DATALOADERS''',				__a     ) == "1":
				SCREAMING_SNAKE_CASE_    			=			2
		# New Code #
		SCREAMING_SNAKE_CASE_    			=			int(args.gradient_accumulation_steps     )
		# Initialize accelerator
		SCREAMING_SNAKE_CASE_    			=			Accelerator(
		    cpu=args.cpu,				mixed_precision=args.mixed_precision,				gradient_accumulation_steps=__a     )
		if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
				raise NotImplementedError(
				    '''Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'''     )
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		SCREAMING_SNAKE_CASE_    			=			config['''lr''']
		SCREAMING_SNAKE_CASE_    			=			int(config['''num_epochs''']     )
		SCREAMING_SNAKE_CASE_    			=			int(config['''seed''']     )
		SCREAMING_SNAKE_CASE_    			=			int(config['''batch_size''']     )

		SCREAMING_SNAKE_CASE_    			=			evaluate.load('''glue''',				'''mrpc'''     )

		set_seed(__a     )
		SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			get_dataloaders(__a,				__a     )
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		SCREAMING_SNAKE_CASE_    			=			AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''',				return_dict=__a     )

		# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
		# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
		# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
		SCREAMING_SNAKE_CASE_    			=			model.to(accelerator.device     )

		# Instantiate optimizer
		SCREAMING_SNAKE_CASE_    			=			AdamW(params=model.parameters(),				lr=__a     )

		# Instantiate scheduler
		SCREAMING_SNAKE_CASE_    			=			get_linear_schedule_with_warmup(
		    optimizer=__a,				num_warmup_steps=100,				num_training_steps=(len(__a     ) * num_epochs),				)

		# Prepare everything
		# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
		# prepare method.
		SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			accelerator.prepare(
		    __a,				__a,				__a,				__a,				__a     )

		# Now we train the model
		for epoch in range(__a     ):
				model.train()
				for step, batch in enumerate(__a     ):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device     )
						# New code #
						# We use the new `accumulate` context manager to perform gradient accumulation
						# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
						with accelerator.accumulate(__a     ):
								SCREAMING_SNAKE_CASE_    			=			model(**__a     )
								SCREAMING_SNAKE_CASE_    			=			output.loss
								accelerator.backward(__a     )
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

				model.eval()
				for step, batch in enumerate(__a     ):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device     )
						with torch.no_grad():
								SCREAMING_SNAKE_CASE_    			=			model(**__a     )
						SCREAMING_SNAKE_CASE_    			=			outputs.logits.argmax(dim=-1     )
						SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			accelerator.gather_for_metrics((predictions, batch['''labels'''])     )
						metric.add_batch(
						    predictions=__a,				references=__a,				)

				SCREAMING_SNAKE_CASE_    			=			metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(F'epoch {epoch}:',				__a     )



def 							_lowerCamelCase			(			):
		SCREAMING_SNAKE_CASE_    			=			argparse.ArgumentParser(description='''Simple example of training script.'''     )
		parser.add_argument(
		    '''--mixed_precision''',				type=__a,				default=__a,				choices=['''no''', '''fp16''', '''bf16''', '''fp8'''],				help='''Whether to use mixed precision. Choose'''
		    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
		    '''and an Nvidia Ampere GPU.''',				)
		# New Code #
		parser.add_argument(
		    '''--gradient_accumulation_steps''',				type=__a,				default=1,				help='''The number of minibatches to be ran before gradients are accumulated.''',				)
		parser.add_argument('''--cpu''',				action='''store_true''',				help='''If passed, will train on the CPU.'''     )
		SCREAMING_SNAKE_CASE_    			=			parser.parse_args()
		SCREAMING_SNAKE_CASE_    			=			{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
		training_function(__a,				__a     )


if __name__ == "__main__":
						main()

"""simple docstring"""




import argparse
import re

import requests
import torch

# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

from transformers import (
    BertTokenizer,
    BlipConfig,
    BlipForConditionalGeneration,
    BlipForImageTextRetrieval,
    BlipForQuestionAnswering,
)



def 							_lowerCamelCase			(			__a,				__a     ):
		SCREAMING_SNAKE_CASE_    			=			'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'''
		SCREAMING_SNAKE_CASE_    			=			Image.open(requests.get(__a,				stream=__a     ).raw     ).convert('''RGB'''     )

		SCREAMING_SNAKE_CASE_    			=			transforms.Compose(
		    [
		        transforms.Resize((image_size, image_size),				interpolation=InterpolationMode.BICUBIC     ),
		        transforms.ToTensor(),
		        transforms.Normalize((0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3),				(0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1)     ),
		    ]     )
		SCREAMING_SNAKE_CASE_    			=			transform(__a     ).unsqueeze(0     ).to(__a     )
		return image



def 							_lowerCamelCase			(			__a     ):
		if "visual_encoder" in key:
				SCREAMING_SNAKE_CASE_    			=			re.sub('''visual_encoder*''',				'''vision_model.encoder''',				__a     )
		if "blocks" in key:
				SCREAMING_SNAKE_CASE_    			=			re.sub(r'''blocks''',				'''layers''',				__a     )
		if "attn" in key:
				SCREAMING_SNAKE_CASE_    			=			re.sub(r'''attn''',				'''self_attn''',				__a     )
		if "norm1" in key:
				SCREAMING_SNAKE_CASE_    			=			re.sub(r'''norm1''',				'''layer_norm1''',				__a     )
		if "norm2" in key:
				SCREAMING_SNAKE_CASE_    			=			re.sub(r'''norm2''',				'''layer_norm2''',				__a     )
		if "encoder.norm" in key:
				SCREAMING_SNAKE_CASE_    			=			re.sub(r'''encoder.norm''',				'''post_layernorm''',				__a     )
		if "encoder.patch_embed.proj" in key:
				SCREAMING_SNAKE_CASE_    			=			re.sub(r'''encoder.patch_embed.proj''',				'''embeddings.patch_embedding''',				__a     )

		if "encoder.pos_embed" in key:
				SCREAMING_SNAKE_CASE_    			=			re.sub(r'''encoder.pos_embed''',				'''embeddings.position_embedding''',				__a     )
		if "encoder.cls_token" in key:
				SCREAMING_SNAKE_CASE_    			=			re.sub(r'''encoder.cls_token''',				'''embeddings.class_embedding''',				__a     )

		if "self_attn" in key:
				SCREAMING_SNAKE_CASE_    			=			re.sub(r'''self_attn.proj''',				'''self_attn.projection''',				__a     )

		return key



@torch.no_grad()
def 							_lowerCamelCase			(			__a,				__a=None     ):
		if config_path is not None:
				SCREAMING_SNAKE_CASE_    			=			BlipConfig.from_pretrained(__a     )
		else:
				SCREAMING_SNAKE_CASE_    			=			BlipConfig(projection_dim=512,				text_config={},				vision_config={}     )

		SCREAMING_SNAKE_CASE_    			=			BlipForConditionalGeneration(__a     ).eval()

		SCREAMING_SNAKE_CASE_    			=			'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'''

		SCREAMING_SNAKE_CASE_    			=			blip_decoder(pretrained=__a,				image_size=384,				vit='''base'''     )
		SCREAMING_SNAKE_CASE_    			=			pt_model.eval()

		SCREAMING_SNAKE_CASE_    			=			pt_model.state_dict()
		for key in modified_state_dict.copy():
				SCREAMING_SNAKE_CASE_    			=			modified_state_dict.pop(__a     )
				SCREAMING_SNAKE_CASE_    			=			rename_key(__a     )
				SCREAMING_SNAKE_CASE_    			=			value

		hf_model.load_state_dict(__a     )

		SCREAMING_SNAKE_CASE_    			=			384
		SCREAMING_SNAKE_CASE_    			=			load_demo_image(image_size=__a,				device='''cpu'''     )
		SCREAMING_SNAKE_CASE_    			=			BertTokenizer.from_pretrained('''bert-base-uncased'''     )
		SCREAMING_SNAKE_CASE_    			=			tokenizer(['''a picture of''']     ).input_ids

		SCREAMING_SNAKE_CASE_    			=			hf_model.generate(__a,				__a     )

		assert out[0].tolist() == [30_522, 1_037, 3_861, 1_997, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]

		SCREAMING_SNAKE_CASE_    			=			hf_model.generate(__a     )

		assert out[0].tolist() == [30_522, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]

		if pytorch_dump_folder_path is not None:
				hf_model.save_pretrained(__a     )

		# model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
		SCREAMING_SNAKE_CASE_    			=			(
		    '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'''
		)

		SCREAMING_SNAKE_CASE_    			=			blip_vqa(pretrained=__a,				image_size=__a,				vit='''base'''     )
		vqa_model.eval()

		SCREAMING_SNAKE_CASE_    			=			vqa_model.state_dict()
		for key in modified_state_dict.copy():
				SCREAMING_SNAKE_CASE_    			=			modified_state_dict.pop(__a     )
				SCREAMING_SNAKE_CASE_    			=			rename_key(__a     )
				SCREAMING_SNAKE_CASE_    			=			value

		SCREAMING_SNAKE_CASE_    			=			BlipForQuestionAnswering(__a     )

		hf_vqa_model.load_state_dict(__a     )

		SCREAMING_SNAKE_CASE_    			=			['''How many dogs are in this image?''']
		SCREAMING_SNAKE_CASE_    			=			tokenizer(__a,				return_tensors='''pt'''     ).input_ids

		SCREAMING_SNAKE_CASE_    			=			hf_vqa_model.generate(__a,				__a     )
		print(tokenizer.decode(answer[0]     )     )

		assert tokenizer.decode(answer[0]     ) == "[UNK] 1 [SEP]"
		if pytorch_dump_folder_path is not None:
				hf_vqa_model.save_pretrained(pytorch_dump_folder_path + '''_vqa'''     )

		SCREAMING_SNAKE_CASE_    			=			'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'''

		SCREAMING_SNAKE_CASE_    			=			blip_itm(pretrained=__a,				image_size=__a,				vit='''base'''     )
		itm_model.eval()

		SCREAMING_SNAKE_CASE_    			=			itm_model.state_dict()
		for key in modified_state_dict.copy():
				SCREAMING_SNAKE_CASE_    			=			modified_state_dict.pop(__a     )
				SCREAMING_SNAKE_CASE_    			=			rename_key(__a     )
				SCREAMING_SNAKE_CASE_    			=			value

		SCREAMING_SNAKE_CASE_    			=			BlipForImageTextRetrieval(__a     )

		SCREAMING_SNAKE_CASE_    			=			['''A picture of a woman with a dog sitting in a beach''']
		SCREAMING_SNAKE_CASE_    			=			tokenizer(
		    __a,				return_tensors='''pt''',				padding='''max_length''',				truncation=__a,				max_length=35,				).input_ids

		hf_itm_model.load_state_dict(__a     )
		hf_itm_model.eval()

		SCREAMING_SNAKE_CASE_    			=			hf_itm_model(__a,				__a,				use_itm_head=__a     )
		SCREAMING_SNAKE_CASE_    			=			hf_itm_model(__a,				__a,				use_itm_head=__a     )

		assert out[0].item() == 0.2_1_1_0_6_8_7_4_9_4_2_7_7_9_5_4
		assert torch.nn.functional.softmax(out_itm[0],				dim=1     )[:, 1].item() == 0.4_5_6_9_8_8_4_5_3_8_6_5_0_5_1_2_7

		if pytorch_dump_folder_path is not None:
				hf_itm_model.save_pretrained(pytorch_dump_folder_path + '''_itm'''     )


if __name__ == "__main__":
						lowerCAmelCase__				=					argparse.ArgumentParser()
						parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
						parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
						lowerCAmelCase__				=					parser.parse_args()

						convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)