



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      __lowercase       (			)    ->			int:
							__SCREAMING_SNAKE_CASE									= '''https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'''
							__SCREAMING_SNAKE_CASE									= Image.open(requests.get(__UpperCAmelCase							,       stream=__UpperCAmelCase       ).raw       ).convert('RGB'       )

							return image






def      __lowercase       (			a__       )    ->			Union[str, Any]:
							__SCREAMING_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      __lowercase       (			a__							,       a__							,       a__       )    ->			List[str]:
							__SCREAMING_SNAKE_CASE									= dct.pop(__UpperCAmelCase       )
							__SCREAMING_SNAKE_CASE									= val






def      __lowercase       (			a__							,       a__       )    ->			Union[str, Any]:
							for i in range(config.vision_config.num_hidden_layers       ):
														# read in original q and v biases
														__SCREAMING_SNAKE_CASE									= state_dict.pop(f"""visual_encoder.blocks.{i}.attn.q_bias"""       )
														__SCREAMING_SNAKE_CASE									= state_dict.pop(f"""visual_encoder.blocks.{i}.attn.v_bias"""       )

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






def      __lowercase       (			a__       )    ->			str:
							__SCREAMING_SNAKE_CASE									= 3_64 if '''coco''' in model_name else 2_24
							__SCREAMING_SNAKE_CASE									= 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:
														__SCREAMING_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:
														__SCREAMING_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:
														__SCREAMING_SNAKE_CASE									= LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf'							,       vocab_size=3_20_01       ).to_dict()
							elif "vicuna-13b" in model_name:
														__SCREAMING_SNAKE_CASE									= LlamaConfig.from_pretrained('decapoda-research/llama-13b-hf'							,       vocab_size=3_20_01       ).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
							__SCREAMING_SNAKE_CASE									= InstructBlipQFormerConfig(vocab_size=3_05_23       ).to_dict()
							__SCREAMING_SNAKE_CASE									= InstructBlipConfig(vision_config=__UpperCAmelCase							,       text_config=__UpperCAmelCase							,       qformer_config=__UpperCAmelCase       )

							return config, image_size






@torch.no_grad()
def      __lowercase       (			a__							,       a__=None							,       a__=False       )    ->			Tuple:
							__SCREAMING_SNAKE_CASE									= AutoTokenizer.from_pretrained('bert-base-uncased'							,       truncation_side='left'       )
							qformer_tokenizer.add_special_tokens({'bos_token': '[DEC]'}       )

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

							__SCREAMING_SNAKE_CASE									= get_blipa_config(__UpperCAmelCase       )
							__SCREAMING_SNAKE_CASE									= InstructBlipForConditionalGeneration(__UpperCAmelCase       ).eval()

							__SCREAMING_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'''),
							}

							__SCREAMING_SNAKE_CASE									= model_name_to_original[model_name]

							# load original model
							print('Loading original model...'       )
							__SCREAMING_SNAKE_CASE									= '''cuda:1''' if torch.cuda.is_available() else '''cpu'''
							__SCREAMING_SNAKE_CASE									= '''cuda:2''' if torch.cuda.is_available() else '''cpu'''
							__SCREAMING_SNAKE_CASE									= load_model_and_preprocess(
							    name=__UpperCAmelCase							,       model_type=__UpperCAmelCase							,       is_eval=__UpperCAmelCase							,       device=__UpperCAmelCase       )
							original_model.eval()
							print('Done!'       )

							# update state dict keys
							__SCREAMING_SNAKE_CASE									= original_model.state_dict()
							__SCREAMING_SNAKE_CASE									= 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():
														__SCREAMING_SNAKE_CASE									= state_dict.pop(__UpperCAmelCase       )
														if key.startswith('Qformer.bert'       ):
																					__SCREAMING_SNAKE_CASE									= key.replace('Qformer.bert'							,       'qformer'       )
														if "attention.self" in key:
																					__SCREAMING_SNAKE_CASE									= key.replace('self'							,       'attention'       )
														if "llm_proj" in key:
																					__SCREAMING_SNAKE_CASE									= key.replace('llm_proj'							,       'language_projection'       )
														if "t5_proj" in key:
																					__SCREAMING_SNAKE_CASE									= key.replace('t5_proj'							,       'language_projection'       )
														if key.startswith('llm_model'       ):
																					__SCREAMING_SNAKE_CASE									= key.replace('llm_model'							,       'language_model'       )
														if key.startswith('t5'       ):
																					__SCREAMING_SNAKE_CASE									= key.replace('t5'							,       'language'       )
														__SCREAMING_SNAKE_CASE									= 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       )

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

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

							# make sure processor creates exact same pixel values
							__SCREAMING_SNAKE_CASE									= vis_processors['''eval'''](__UpperCAmelCase       ).unsqueeze(0       ).to(__UpperCAmelCase       )
							__SCREAMING_SNAKE_CASE									= 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:
																					__SCREAMING_SNAKE_CASE									= original_model({'image': original_pixel_values, 'text_input': [prompt]}       ).logits
																					__SCREAMING_SNAKE_CASE									= hf_model(**__UpperCAmelCase       ).logits
														else:
																					__SCREAMING_SNAKE_CASE									= original_model(
																					    {'image': original_pixel_values, 'text_input': [prompt], 'text_output': ['\n']}       ).logits
																					__SCREAMING_SNAKE_CASE									= tokenizer('\n'							,       return_tensors='pt'       ).input_ids.to(__UpperCAmelCase       )
																					__SCREAMING_SNAKE_CASE									= label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id							,       -1_00       )
																					__SCREAMING_SNAKE_CASE									= 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
							__SCREAMING_SNAKE_CASE									= 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...'       )
							__SCREAMING_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...'       )
							__SCREAMING_SNAKE_CASE									= hf_model.generate(
							    **__UpperCAmelCase							,       do_sample=__UpperCAmelCase							,       num_beams=5							,       max_length=2_56							,       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?
														__SCREAMING_SNAKE_CASE									= 2
							print('Original generation:'							,       __UpperCAmelCase       )
							__SCREAMING_SNAKE_CASE									= processor.batch_decode(__UpperCAmelCase							,       skip_special_tokens=__UpperCAmelCase       )
							__SCREAMING_SNAKE_CASE									= [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__":
			lowerCAmelCase__		:					Optional[int]							=argparse.ArgumentParser()
			lowerCAmelCase__		:					List[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''',
			)

			lowerCAmelCase__		:					Any							=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_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__		:					Optional[int]							={'''configuration_fnet''': ['''FNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FNetConfig''']}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Optional[int]							=['''FNetTokenizer''']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Optional[Any]							=['''FNetTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Tuple							=[
			    '''FNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''FNetForMaskedLM''',
			    '''FNetForMultipleChoice''',
			    '''FNetForNextSentencePrediction''',
			    '''FNetForPreTraining''',
			    '''FNetForQuestionAnswering''',
			    '''FNetForSequenceClassification''',
			    '''FNetForTokenClassification''',
			    '''FNetLayer''',
			    '''FNetModel''',
			    '''FNetPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_fnet import FNetTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_fnet_fast import FNetTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_fnet import (
						    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FNetForMaskedLM,
						    FNetForMultipleChoice,
						    FNetForNextSentencePrediction,
						    FNetForPreTraining,
						    FNetForQuestionAnswering,
						    FNetForSequenceClassification,
						    FNetForTokenClassification,
						    FNetLayer,
						    FNetModel,
						    FNetPreTrainedModel,
						)


else:
			import sys

			lowerCAmelCase__		:					Any							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






