

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 a():





	'''simple docstring'''
	snake_case_   		=			'https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'
	snake_case_   		=			Image.open(requests.get(lowercase__      ,			stream=lowercase__						).raw						).convert('RGB'						)

	return image





def a(lowercase__						):





	'''simple docstring'''
	snake_case_   		=			[]
	# 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 a(lowercase__      ,			lowercase__      ,			lowercase__						):





	'''simple docstring'''
	snake_case_   		=			dct.pop(lowercase__						)
	snake_case_   		=			val





def a(lowercase__      ,			lowercase__						):





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

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





def a(lowercase__						):





	'''simple docstring'''
	snake_case_   		=			364 if 'coco' in model_name else 224
	snake_case_   		=			InstructBlipVisionConfig(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 "t5-xl" in model_name:
		snake_case_   		=			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_   		=			TaConfig.from_pretrained('google/flan-t5-xxl'      ,			dense_act_fn='gelu'      ,			bos_token_id=1						).to_dict()
	elif "vicuna-7b" in model_name:
		snake_case_   		=			LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf'      ,			vocab_size=32001						).to_dict()
	elif "vicuna-13b" in model_name:
		snake_case_   		=			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
	snake_case_   		=			InstructBlipQFormerConfig(vocab_size=30523						).to_dict()
	snake_case_   		=			InstructBlipConfig(vision_config=lowercase__      ,			text_config=lowercase__      ,			qformer_config=lowercase__						)

	return config, image_size





@torch.no_grad()
def a(lowercase__      ,			lowercase__=None      ,			lowercase__=False						):





	'''simple docstring'''
	snake_case_   		=			AutoTokenizer.from_pretrained('bert-base-uncased'      ,			truncation_side='left'						)
	qformer_tokenizer.add_special_tokens({'bos_token': '[DEC]'}						)

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

	snake_case_		,							snake_case_   		=			get_blipa_config(lowercase__						)
	snake_case_   		=			InstructBlipForConditionalGeneration(lowercase__						).eval()

	snake_case_   		=			{
	    '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'),
	}

	snake_case_		,							snake_case_   		=			model_name_to_original[model_name]

	# load original model
	print('Loading original model...'						)
	snake_case_   		=			'cuda:1' if torch.cuda.is_available() else 'cpu'
	snake_case_   		=			'cuda:2' if torch.cuda.is_available() else 'cpu'
	snake_case_		,							snake_case_		,							snake_case_   		=			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_   		=			original_model.state_dict()
	snake_case_   		=			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_   		=			state_dict.pop(lowercase__						)
		if key.startswith('Qformer.bert'						):
			snake_case_   		=			key.replace('Qformer.bert'      ,			'qformer'						)
		if "attention.self" in key:
			snake_case_   		=			key.replace('self'      ,			'attention'						)
		if "llm_proj" in key:
			snake_case_   		=			key.replace('llm_proj'      ,			'language_projection'						)
		if "t5_proj" in key:
			snake_case_   		=			key.replace('t5_proj'      ,			'language_projection'						)
		if key.startswith('llm_model'						):
			snake_case_   		=			key.replace('llm_model'      ,			'language_model'						)
		if key.startswith('t5'						):
			snake_case_   		=			key.replace('t5'      ,			'language'						)
		snake_case_   		=			val

	# read in qv biases
	read_in_q_v_bias(lowercase__      ,			lowercase__						)

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

	snake_case_   		=			load_demo_image()
	snake_case_   		=			'What is unusual about this image?'

	# create processor
	snake_case_   		=			BlipImageProcessor(
	    size={'height': image_size, 'width': image_size}      ,			image_mean=lowercase__      ,			image_std=lowercase__						)
	snake_case_   		=			InstructBlipProcessor(
	    image_processor=lowercase__      ,			tokenizer=lowercase__      ,			qformer_tokenizer=lowercase__      ,			)
	snake_case_   		=			processor(images=lowercase__      ,			text=lowercase__      ,			return_tensors='pt'						).to(lowercase__						)

	# make sure processor creates exact same pixel values
	snake_case_   		=			vis_processors['eval'](lowercase__						).unsqueeze(0						).to(lowercase__						)
	snake_case_   		=			inputs.pixel_values
	assert torch.allclose(original_pixel_values.to(pixel_values.device						)      ,			lowercase__						)

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

	print('Generating with original model...'						)
	snake_case_   		=			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...'						)
	snake_case_   		=			hf_model.generate(
	    **lowercase__      ,			do_sample=lowercase__      ,			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?
		snake_case_   		=			2
	print('Original generation:'      ,			lowercase__						)
	snake_case_   		=			processor.batch_decode(lowercase__      ,			skip_special_tokens=lowercase__						)
	snake_case_   		=			[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"""Salesforce/{model_name}"""						)
		hf_model.push_to_hub(f"""Salesforce/{model_name}"""						)


if __name__ == "__main__":
		A									=  argparse.ArgumentParser()
		A									=  [
		    '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',
		)

		A									=  parser.parse_args()

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



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


A									=  {
    'configuration_speech_to_text': ['SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Speech2TextConfig'],
    'processing_speech_to_text': ['Speech2TextProcessor'],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A									=  ['Speech2TextTokenizer']

try:
		if not is_speech_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A									=  ['Speech2TextFeatureExtractor']

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A									=  [
		    'TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFSpeech2TextForConditionalGeneration',
		    'TFSpeech2TextModel',
		    'TFSpeech2TextPreTrainedModel',
		]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A									=  [
		    'SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'Speech2TextForConditionalGeneration',
		    'Speech2TextModel',
		    'Speech2TextPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
		from .processing_speech_to_text import SpeechaTextProcessor

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_speech_to_text import SpeechaTextTokenizer

		try:
				if not is_speech_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_speech_to_text import (
				    TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFSpeechaTextForConditionalGeneration,
				    TFSpeechaTextModel,
				    TFSpeechaTextPreTrainedModel,
				)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_speech_to_text import (
				    SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SpeechaTextForConditionalGeneration,
				    SpeechaTextModel,
				    SpeechaTextPreTrainedModel,
				)

else:
		import sys

		A									=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
