"""simple docstring"""







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

     return image



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     List[str]      ):
     lowercase_	:				Optional[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 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Any     ,			__SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     Optional[int]      ):
     lowercase_	:				List[str]													=	dct.pop(__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Optional[Any]													=	val



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

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



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Dict     ,			__SCREAMING_SNAKE_CASE							:     Union[str, Any]      ):
     lowercase_	:				str													=	3_64 if 'coco' in model_name else 2_24
     lowercase_	:				int													=	BlipaVisionConfig(image_size=__SCREAMING_SNAKE_CASE      ).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:
          lowercase_	:				Optional[int]													=	OPTConfig.from_pretrained('facebook/opt-2.7b'     ,			eos_token_id=__SCREAMING_SNAKE_CASE      ).to_dict()
     elif "opt-6.7b" in model_name:
          lowercase_	:				Any													=	OPTConfig.from_pretrained('facebook/opt-6.7b'     ,			eos_token_id=__SCREAMING_SNAKE_CASE      ).to_dict()
     elif "t5-xl" in model_name:
          lowercase_	:				Any													=	TaConfig.from_pretrained('google/flan-t5-xl'     ,			dense_act_fn='gelu'     ,			bos_token_id=1      ).to_dict()
     elif "t5-xxl" in model_name:
          lowercase_	:				List[str]													=	TaConfig.from_pretrained('google/flan-t5-xxl'     ,			dense_act_fn='gelu'     ,			bos_token_id=1      ).to_dict()

     lowercase_	:				Tuple													=	BlipaConfig(vision_config=__SCREAMING_SNAKE_CASE     ,			text_config=__SCREAMING_SNAKE_CASE      )

     return config, image_size



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

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

     lowercase_	:				List[str]													=	{
         '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'),
     }

     lowercase_							,  lowercase_	:				Optional[Any]													=	model_name_to_original[model_name]

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

     # update state dict keys
     lowercase_	:				str													=	original_model.state_dict()
     lowercase_	:				Optional[int]													=	create_rename_keys(__SCREAMING_SNAKE_CASE      )
     for src, dest in rename_keys:
          rename_key(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )

     # some keys can be renamed efficiently
     for key, val in state_dict.copy().items():
          lowercase_	:				Tuple													=	state_dict.pop(__SCREAMING_SNAKE_CASE      )
          if key.startswith('Qformer.bert'      ):
               lowercase_	:				int													=	key.replace('Qformer.bert'     ,			'qformer'      )
          if "attention.self" in key:
               lowercase_	:				Dict													=	key.replace('self'     ,			'attention'      )
          if "opt_proj" in key:
               lowercase_	:				List[Any]													=	key.replace('opt_proj'     ,			'language_projection'      )
          if "t5_proj" in key:
               lowercase_	:				str													=	key.replace('t5_proj'     ,			'language_projection'      )
          if key.startswith('opt'      ):
               lowercase_	:				Optional[Any]													=	key.replace('opt'     ,			'language'      )
          if key.startswith('t5'      ):
               lowercase_	:				Dict													=	key.replace('t5'     ,			'language'      )
          lowercase_	:				int													=	val

     # read in qv biases
     read_in_q_v_bias(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )

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

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

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

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

     original_model.to(__SCREAMING_SNAKE_CASE      )
     hf_model.to(__SCREAMING_SNAKE_CASE      )
     with torch.no_grad():
          if "opt" in model_name:
               lowercase_	:				Optional[int]													=	original_model({'image': original_pixel_values, 'text_input': ['']}      ).logits
               lowercase_	:				Union[str, Any]													=	hf_model(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      ).logits
          else:
               lowercase_	:				Optional[int]													=	original_model(
                   {'image': original_pixel_values, 'text_input': ['\n'], 'text_output': ['\n']}      ).logits
               lowercase_	:				Tuple													=	input_ids.masked_fill(input_ids == tokenizer.pad_token_id     ,			-1_00      )
               lowercase_	:				Any													=	hf_model(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			labels=__SCREAMING_SNAKE_CASE      ).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":
          lowercase_	:				int													=	torch.tensor(
              [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]]     ,			device=__SCREAMING_SNAKE_CASE      )
          assert torch.allclose(logits[0, :3, :3]     ,			__SCREAMING_SNAKE_CASE     ,			atol=1E-4      )
     elif model_name == "blip2-flan-t5-xl-coco":
          lowercase_	:				Tuple													=	torch.tensor(
              [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]]     ,			device=__SCREAMING_SNAKE_CASE      )
     else:
          # cast to same type
          lowercase_	:				Optional[Any]													=	logits.dtype
          assert torch.allclose(original_logits.to(__SCREAMING_SNAKE_CASE      )     ,			__SCREAMING_SNAKE_CASE     ,			atol=1E-2      )
     print('Looks ok!'      )

     print('Generating a caption...'      )
     lowercase_	:				List[str]													=	''
     lowercase_	:				str													=	tokenizer(__SCREAMING_SNAKE_CASE     ,			return_tensors='pt'      ).input_ids.to(__SCREAMING_SNAKE_CASE      )

     lowercase_	:				Tuple													=	original_model.generate({'image': original_pixel_values}      )
     lowercase_	:				str													=	hf_model.generate(
         __SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			do_sample=__SCREAMING_SNAKE_CASE     ,			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:'     ,			__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Any													=	input_ids.shape[1]
     lowercase_	:				Optional[Any]													=	processor.batch_decode(outputs[:, prompt_length:]     ,			skip_special_tokens=__SCREAMING_SNAKE_CASE      )
     lowercase_	:				List[str]													=	[text.strip() for text in output_text]
     print('HF generation:'     ,			__SCREAMING_SNAKE_CASE      )

     if pytorch_dump_folder_path is not None:
          processor.save_pretrained(__SCREAMING_SNAKE_CASE      )
          hf_model.save_pretrained(__SCREAMING_SNAKE_CASE      )

     if push_to_hub:
          processor.push_to_hub(F'''nielsr/{model_name}'''      )
          hf_model.push_to_hub(F'''nielsr/{model_name}'''      )


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

       __SCREAMING_SNAKE_CASE    	=parser.parse_args()

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


"""simple docstring"""







from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml







class        UpperCamelCase		(			yaml.SafeLoader  ):
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				str													=	[self.constructed_objects[key_node] for key_node, _ in node.value]
        lowercase_	:				str													=	[tuple(__UpperCamelCase			) if isinstance(__UpperCamelCase     ,__UpperCamelCase			) else key for key in keys]
        lowercase_	:				List[Any]													=	Counter(__UpperCamelCase			)
        lowercase_	:				str													=	[key for key in counter if counter[key] > 1]
        if duplicate_keys:
             raise TypeError(f'''Got duplicate yaml keys: {duplicate_keys}'''			)







   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase=False			)	->      List[Any]:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	super().construct_mapping(__UpperCamelCase     ,deep=__UpperCamelCase			)
        self._check_no_duplicates_on_constructed_node(__UpperCamelCase			)
        return mapping






def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     str      ):
     lowercase_	:				Tuple													=	list(readme_content.splitlines()      )
     if full_content and full_content[0] == "---" and "---" in full_content[1:]:
          lowercase_	:				Dict													=	full_content[1:].index('---'      ) + 1
          lowercase_	:				Optional[int]													=	'\n'.join(full_content[1:sep_idx]      )
          return yamlblock, "\n".join(full_content[sep_idx + 1 :]      )

     return None, "\n".join(__SCREAMING_SNAKE_CASE      )







class        UpperCamelCase		(			lowercase_  ):
   # class attributes
   lowercase							=  {'train_eval_index'}  # train-eval-index in the YAML metadata
   @classmethod
   def 			_UpperCAmelCase   (			cls     ,__UpperCamelCase			)	->      "DatasetMetadata":


        '''simple docstring'''


        with open(__UpperCamelCase     ,encoding='utf-8'			) as readme_file:
             lowercase_							,  lowercase_	:				Optional[int]													=	_split_yaml_from_readme(readme_file.read()			)
        if yaml_string is not None:
             return cls.from_yaml_string(__UpperCamelCase			)
        else:
             return cls()
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      List[Any]:


        '''simple docstring'''


        if path.exists():
             with open(__UpperCamelCase     ,encoding='utf-8'			) as readme_file:
                  lowercase_	:				Dict													=	readme_file.read()
        else:
             lowercase_	:				int													=	None
        lowercase_	:				Any													=	self._to_readme(__UpperCamelCase			)
        with open(__UpperCamelCase     ,'w'     ,encoding='utf-8'			) as readme_file:
             readme_file.write(__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase = None			)	->      str:


        '''simple docstring'''


        if readme_content is not None:
             lowercase_							,  lowercase_	:				Optional[Any]													=	_split_yaml_from_readme(__UpperCamelCase			)
             lowercase_	:				Optional[Any]													=	'---\n' + self.to_yaml_string() + '---\n' + content
        else:
             lowercase_	:				Tuple													=	'---\n' + self.to_yaml_string() + '---\n'
        return full_content
   @classmethod
   def 			_UpperCAmelCase   (			cls     ,__UpperCamelCase			)	->      "DatasetMetadata":


        '''simple docstring'''


        lowercase_	:				List[str]													=	yaml.load(__UpperCamelCase     ,Loader=_NoDuplicateSafeLoader			) or {}

        # Convert the YAML keys to DatasetMetadata fields
        lowercase_	:				Dict													=	{
            (key.replace('-'     ,'_'			) if key.replace('-'     ,'_'			) in cls._FIELDS_WITH_DASHES else key): value
            for key, value in metadata_dict.items()
        }
        return cls(**__UpperCamelCase			)







   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        return yaml.safe_dump(
            {
                (key.replace('_'     ,'-'			) if key in self._FIELDS_WITH_DASHES else key): value
                for key, value in self.items()
            }     ,sort_keys=__UpperCamelCase     ,allow_unicode=__UpperCamelCase     ,encoding='utf-8'     ,).decode('utf-8'			)






__SCREAMING_SNAKE_CASE    	={
    "image-classification": [],
    "translation": [],
    "image-segmentation": [],
    "fill-mask": [],
    "automatic-speech-recognition": [],
    "token-classification": [],
    "sentence-similarity": [],
    "audio-classification": [],
    "question-answering": [],
    "summarization": [],
    "zero-shot-classification": [],
    "table-to-text": [],
    "feature-extraction": [],
    "other": [],
    "multiple-choice": [],
    "text-classification": [],
    "text-to-image": [],
    "text2text-generation": [],
    "zero-shot-image-classification": [],
    "tabular-classification": [],
    "tabular-regression": [],
    "image-to-image": [],
    "tabular-to-text": [],
    "unconditional-image-generation": [],
    "text-retrieval": [],
    "text-to-speech": [],
    "object-detection": [],
    "audio-to-audio": [],
    "text-generation": [],
    "conversational": [],
    "table-question-answering": [],
    "visual-question-answering": [],
    "image-to-text": [],
    "reinforcement-learning": [],
    "voice-activity-detection": [],
    "time-series-forecasting": [],
    "document-question-answering": [],
}


if __name__ == "__main__":
       from argparse import ArgumentParser

       __SCREAMING_SNAKE_CASE    	=ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
       ap.add_argument("readme_filepath")
       __SCREAMING_SNAKE_CASE    	=ap.parse_args()

       __SCREAMING_SNAKE_CASE    	=Path(args.readme_filepath)
       __SCREAMING_SNAKE_CASE    	=DatasetMetadata.from_readme(readme_filepath)
       print(dataset_metadata)
       dataset_metadata.to_readme(readme_filepath)

