





import logging
import os

from .state import PartialState





class  A__    (							logging.LoggerAdapter	):




   """simple docstring"""



   @staticmethod
   def 							__lowercase		(					lowercase)			->						Any:







          '''simple docstring'''

          a__						:	int =				PartialState()
          return not main_process_only or (main_process_only and state.is_main_process)

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  *lowercase					,  **lowercase)			->						str:







          '''simple docstring'''

          if PartialState._shared_state == {}:
                 raise RuntimeError(
                     'You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.')
          a__						:	Any =				kwargs.pop('main_process_only'					,  lowercase)
          a__						:	Dict =				kwargs.pop('in_order'					,  lowercase)

          if self.isEnabledFor(lowercase):
                 if self._should_log(lowercase):
                        a__ ,							a__						:	Dict =				self.process(lowercase					,  lowercase)
                        self.logger.log(lowercase					,  lowercase					,  *lowercase					,  **lowercase)

                 elif in_order:
                        a__						:	Dict =				PartialState()
                        for i in range(state.num_processes):
                               if i == state.process_index:
                                      a__ ,							a__						:	List[str] =				self.process(lowercase					,  lowercase)
                                      self.logger.log(lowercase					,  lowercase					,  *lowercase					,  **lowercase)
                               state.wait_for_everyone()



def A_	(		A__		,      A__ = None				)  ->		Tuple:
       if log_level is None:
              a__						:	Dict =				os.environ.get('ACCELERATE_LOG_LEVEL'		,      A__				)
       a__						:	str =				logging.getLogger(A__				)
       if log_level is not None:
              logger.setLevel(log_level.upper()				)
              logger.root.setLevel(log_level.upper()				)
       return MultiProcessAdapter(A__		,      {}				)






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        _UpperCamelCase							(      ):
						__SCREAMING_SNAKE_CASE						:     Dict					=		'''https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'''
						__SCREAMING_SNAKE_CASE						:     List[str]					=		Image.open(requests.get(lowercase__    ,       stream=lowercase__      ).raw      ).convert('''RGB'''      )

						return image


def        _UpperCamelCase							(      lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		[]
						# 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        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		dct.pop(lowercase__      )
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		val


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						for i in range(config.vision_config.num_hidden_layers      ):
												# read in original q and v biases
												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		state_dict.pop(F'''visual_encoder.blocks.{i}.attn.q_bias'''      )
												__SCREAMING_SNAKE_CASE						:     int					=		state_dict.pop(F'''visual_encoder.blocks.{i}.attn.v_bias'''      )

												# next, set bias in the state dict
												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		torch.cat((q_bias, torch.zeros_like(lowercase__    ,       requires_grad=lowercase__      ), v_bias)      )
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		qkv_bias


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Any					=		364 if '''coco''' in model_name else 224
						__SCREAMING_SNAKE_CASE						:     List[str]					=		BlipaVisionConfig(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 "opt-2.7b" in model_name:
												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		OPTConfig.from_pretrained('''facebook/opt-2.7b'''    ,       eos_token_id=lowercase__      ).to_dict()
						elif "opt-6.7b" in model_name:
												__SCREAMING_SNAKE_CASE						:     List[Any]					=		OPTConfig.from_pretrained('''facebook/opt-6.7b'''    ,       eos_token_id=lowercase__      ).to_dict()
						elif "t5-xl" in model_name:
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		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						:     Union[str, Any]					=		TaConfig.from_pretrained('''google/flan-t5-xxl'''    ,       dense_act_fn='''gelu'''    ,       bos_token_id=1      ).to_dict()

						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		BlipaConfig(vision_config=lowercase__    ,       text_config=lowercase__      )

						return config, image_size


@torch.no_grad()
def        _UpperCamelCase							(      lowercase__    ,       lowercase__=None    ,       lowercase__=False      ):
						__SCREAMING_SNAKE_CASE						:     Any					=		(
						    AutoTokenizer.from_pretrained('''facebook/opt-2.7b'''      )
						    if '''opt''' in model_name
						    else AutoTokenizer.from_pretrained('''google/flan-t5-xl'''      )
						)
						__SCREAMING_SNAKE_CASE						:     str					=		tokenizer('''\n'''    ,       add_special_tokens=lowercase__      ).input_ids[0]
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		get_blipa_config(lowercase__    ,       eos_token_id=lowercase__      )

						__SCREAMING_SNAKE_CASE						:     int					=		BlipaForConditionalGeneration(lowercase__      ).eval()

						__SCREAMING_SNAKE_CASE						:     int					=		{
						    '''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'''),
						}

						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Any					=		model_name_to_original[model_name]

						# load original model
						print('''Loading original model...'''      )
						__SCREAMING_SNAKE_CASE						:     List[str]					=		'''cuda''' if torch.cuda.is_available() else '''cpu'''
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Dict					=		load_model_and_preprocess(
						    name=lowercase__    ,       model_type=lowercase__    ,       is_eval=lowercase__    ,       device=lowercase__      )
						original_model.eval()
						print('''Done!'''      )

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

						# read in qv biases
						read_in_q_v_bias(lowercase__    ,       lowercase__      )

						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     List[str]					=		hf_model.load_state_dict(lowercase__    ,       strict=lowercase__      )
						assert len(lowercase__      ) == 0
						assert unexpected_keys == ["qformer.embeddings.position_ids"]

						__SCREAMING_SNAKE_CASE						:     List[str]					=		load_demo_image()
						__SCREAMING_SNAKE_CASE						:     Any					=		vis_processors['''eval'''](lowercase__      ).unsqueeze(0      ).to(lowercase__      )
						__SCREAMING_SNAKE_CASE						:     str					=		tokenizer(['''\n''']    ,       return_tensors='''pt'''      ).input_ids.to(lowercase__      )

						# create processor
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		BlipImageProcessor(
						    size={'''height''': image_size, '''width''': image_size}    ,       image_mean=lowercase__    ,       image_std=lowercase__      )
						__SCREAMING_SNAKE_CASE						:     int					=		BlipaProcessor(image_processor=lowercase__    ,       tokenizer=lowercase__      )
						__SCREAMING_SNAKE_CASE						:     Any					=		processor(images=lowercase__    ,       return_tensors='''pt'''      ).pixel_values.to(lowercase__      )

						# make sure processor creates exact same pixel values
						assert torch.allclose(lowercase__    ,       lowercase__      )

						original_model.to(lowercase__      )
						hf_model.to(lowercase__      )
						with torch.no_grad():
												if "opt" in model_name:
																		__SCREAMING_SNAKE_CASE						:     Dict					=		original_model({'''image''': original_pixel_values, '''text_input''': ['''''']}      ).logits
																		__SCREAMING_SNAKE_CASE						:     Dict					=		hf_model(lowercase__    ,       lowercase__      ).logits
												else:
																		__SCREAMING_SNAKE_CASE						:     int					=		original_model(
																		    {'''image''': original_pixel_values, '''text_input''': ['''\n'''], '''text_output''': ['''\n''']}      ).logits
																		__SCREAMING_SNAKE_CASE						:     List[Any]					=		input_ids.masked_fill(input_ids == tokenizer.pad_token_id    ,       -100      )
																		__SCREAMING_SNAKE_CASE						:     Optional[int]					=		hf_model(lowercase__    ,       lowercase__    ,       labels=lowercase__      ).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":
												__SCREAMING_SNAKE_CASE						:     Dict					=		torch.tensor(
												    [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]]    ,       device=lowercase__      )
												assert torch.allclose(logits[0, :3, :3]    ,       lowercase__    ,       atol=1e-4      )
						elif model_name == "blip2-flan-t5-xl-coco":
												__SCREAMING_SNAKE_CASE						:     Any					=		torch.tensor(
												    [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]]    ,       device=lowercase__      )
						else:
												# cast to same type
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		logits.dtype
												assert torch.allclose(original_logits.to(lowercase__      )    ,       lowercase__    ,       atol=1e-2      )
						print('''Looks ok!'''      )

						print('''Generating a caption...'''      )
						__SCREAMING_SNAKE_CASE						:     Any					=		''''''
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		tokenizer(lowercase__    ,       return_tensors='''pt'''      ).input_ids.to(lowercase__      )

						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		original_model.generate({'''image''': original_pixel_values}      )
						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		hf_model.generate(
						    lowercase__    ,       lowercase__    ,       do_sample=lowercase__    ,       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:'''    ,       lowercase__      )
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		input_ids.shape[1]
						__SCREAMING_SNAKE_CASE						:     Any					=		processor.batch_decode(outputs[:, prompt_length:]    ,       skip_special_tokens=lowercase__      )
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		[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'''nielsr/{model_name}'''      )
												hf_model.push_to_hub(F'''nielsr/{model_name}'''      )


if __name__ == "__main__":
						__lowerCAmelCase	: List[str]  				=argparse.ArgumentParser()
						__lowerCAmelCase	: Tuple  				=[
						    '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',
						)

						__lowerCAmelCase	: List[Any]  				=parser.parse_args()

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