



import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Any						)->	List[str]:
    return EnvironmentCommand()



class      _UpperCAmelCase      (							A__  ):




     @staticmethod
     def 		snake_case_		(					a__):
         A__					=       parser.add_parser('''env''')
         download_parser.set_defaults(func=a__)




     def 		snake_case_		(					self):
         A__					=       huggingface_hub.__version__

         A__					=       '''not installed'''
         A__					=       '''NA'''
         if is_torch_available():
             import torch

             A__					=       torch.__version__
             A__					=       torch.cuda.is_available()

         A__					=       '''not installed'''
         if is_transformers_available():
             import transformers

             A__					=       transformers.__version__

         A__					=       '''not installed'''
         if is_accelerate_available():
             import accelerate

             A__					=       accelerate.__version__

         A__					=       '''not installed'''
         if is_xformers_available():
             import xformers

             A__					=       xformers.__version__

         A__					=       {
             '''`diffusers` version''': version,
             '''Platform''': platform.platform(),
             '''Python version''': platform.python_version(),
             '''PyTorch version (GPU?)''': F"{pt_version} ({pt_cuda_available})",
             '''Huggingface_hub version''': hub_version,
             '''Transformers version''': transformers_version,
             '''Accelerate version''': accelerate_version,
             '''xFormers version''': xformers_version,
             '''Using GPU in script?''': '''<fill in>''',
             '''Using distributed or parallel set-up in script?''': '''<fill in>''',
         }

         print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n''')
         print(self.format_dict(a__))

         return info




     @staticmethod
     def 		snake_case_		(					a__):
         return "\n".join([F"- {prop}: {val}" for prop, val in d.items()]) + "\n"





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

    return image



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Dict						)->	Any:
    A__					=       []
    # 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 		lowerCAmelCase__     (       UpperCamelCase_					:							int			,							UpperCamelCase_					:							Optional[int]			,							UpperCamelCase_					:							int						)->	List[Any]:
    A__					=       dct.pop(UpperCamelCase_						)
    A__					=       val



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

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



def 		lowerCAmelCase__     (       UpperCamelCase_					:							List[Any]			,							UpperCamelCase_					:							List[Any]						)->	int:
    A__					=       3_6_4 if '''coco''' in model_name else 2_2_4
    A__					=       BlipaVisionConfig(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 "opt-2.7b" in model_name:
        A__					=       OPTConfig.from_pretrained('''facebook/opt-2.7b'''			,							eos_token_id=UpperCamelCase_						).to_dict()
    elif "opt-6.7b" in model_name:
        A__					=       OPTConfig.from_pretrained('''facebook/opt-6.7b'''			,							eos_token_id=UpperCamelCase_						).to_dict()
    elif "t5-xl" in model_name:
        A__					=       TaConfig.from_pretrained('''google/flan-t5-xl'''			,							dense_act_fn='''gelu'''			,							bos_token_id=1						).to_dict()
    elif "t5-xxl" in model_name:
        A__					=       TaConfig.from_pretrained('''google/flan-t5-xxl'''			,							dense_act_fn='''gelu'''			,							bos_token_id=1						).to_dict()

    A__					=       BlipaConfig(vision_config=UpperCamelCase_			,							text_config=UpperCamelCase_						)

    return config, image_size



@torch.no_grad()
def 		lowerCAmelCase__     (       UpperCamelCase_					:							Tuple			,							UpperCamelCase_					:							Optional[Any]=None			,							UpperCamelCase_					:							Any=False						)->	Optional[Any]:
    A__					=       (
        AutoTokenizer.from_pretrained('''facebook/opt-2.7b'''						)
        if '''opt''' in model_name
        else AutoTokenizer.from_pretrained('''google/flan-t5-xl'''						)
    )
    A__					=       tokenizer('''\n'''			,							add_special_tokens=UpperCamelCase_						).input_ids[0]
    A__			,						A__					=       get_blipa_config(UpperCamelCase_			,							eos_token_id=UpperCamelCase_						)

    A__					=       BlipaForConditionalGeneration(UpperCamelCase_						).eval()

    A__					=       {
        '''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'''),
    }

    A__			,						A__					=       model_name_to_original[model_name]

    # load original model
    print('''Loading original model...'''						)
    A__					=       '''cuda''' if torch.cuda.is_available() else '''cpu'''
    A__			,						A__			,						A__					=       load_model_and_preprocess(
        name=UpperCamelCase_			,							model_type=UpperCamelCase_			,							is_eval=UpperCamelCase_			,							device=UpperCamelCase_						)
    original_model.eval()
    print('''Done!'''						)

    # update state dict keys
    A__					=       original_model.state_dict()
    A__					=       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():
        A__					=       state_dict.pop(UpperCamelCase_						)
        if key.startswith('''Qformer.bert'''						):
            A__					=       key.replace('''Qformer.bert'''			,							'''qformer'''						)
        if "attention.self" in key:
            A__					=       key.replace('''self'''			,							'''attention'''						)
        if "opt_proj" in key:
            A__					=       key.replace('''opt_proj'''			,							'''language_projection'''						)
        if "t5_proj" in key:
            A__					=       key.replace('''t5_proj'''			,							'''language_projection'''						)
        if key.startswith('''opt'''						):
            A__					=       key.replace('''opt'''			,							'''language'''						)
        if key.startswith('''t5'''						):
            A__					=       key.replace('''t5'''			,							'''language'''						)
        A__					=       val

    # read in qv biases
    read_in_q_v_bias(UpperCamelCase_			,							UpperCamelCase_						)

    A__			,						A__					=       hf_model.load_state_dict(UpperCamelCase_			,							strict=UpperCamelCase_						)
    assert len(UpperCamelCase_						) == 0
    assert unexpected_keys == ["qformer.embeddings.position_ids"]

    A__					=       load_demo_image()
    A__					=       vis_processors['''eval'''](UpperCamelCase_						).unsqueeze(0						).to(UpperCamelCase_						)
    A__					=       tokenizer(['''\n''']			,							return_tensors='''pt'''						).input_ids.to(UpperCamelCase_						)

    # create processor
    A__					=       BlipImageProcessor(
        size={'''height''': image_size, '''width''': image_size}			,							image_mean=UpperCamelCase_			,							image_std=UpperCamelCase_						)
    A__					=       BlipaProcessor(image_processor=UpperCamelCase_			,							tokenizer=UpperCamelCase_						)
    A__					=       processor(images=UpperCamelCase_			,							return_tensors='''pt'''						).pixel_values.to(UpperCamelCase_						)

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

    original_model.to(UpperCamelCase_						)
    hf_model.to(UpperCamelCase_						)
    with torch.no_grad():
        if "opt" in model_name:
            A__					=       original_model({'''image''': original_pixel_values, '''text_input''': ['''''']}						).logits
            A__					=       hf_model(UpperCamelCase_			,							UpperCamelCase_						).logits
        else:
            A__					=       original_model(
                {'''image''': original_pixel_values, '''text_input''': ['''\n'''], '''text_output''': ['''\n''']}						).logits
            A__					=       input_ids.masked_fill(input_ids == tokenizer.pad_token_id			,							-1_0_0						)
            A__					=       hf_model(UpperCamelCase_			,							UpperCamelCase_			,							labels=UpperCamelCase_						).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":
        A__					=       torch.tensor(
            [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]]			,							device=UpperCamelCase_						)
        assert torch.allclose(logits[0, :3, :3]			,							UpperCamelCase_			,							atol=1E-4						)
    elif model_name == "blip2-flan-t5-xl-coco":
        A__					=       torch.tensor(
            [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]]			,							device=UpperCamelCase_						)
    else:
        # cast to same type
        A__					=       logits.dtype
        assert torch.allclose(original_logits.to(UpperCamelCase_						)			,							UpperCamelCase_			,							atol=1E-2						)
    print('''Looks ok!'''						)

    print('''Generating a caption...'''						)
    A__					=       ''''''
    A__					=       tokenizer(UpperCamelCase_			,							return_tensors='''pt'''						).input_ids.to(UpperCamelCase_						)

    A__					=       original_model.generate({'''image''': original_pixel_values}						)
    A__					=       hf_model.generate(
        UpperCamelCase_			,							UpperCamelCase_			,							do_sample=UpperCamelCase_			,							num_beams=5			,							max_length=3_0			,							min_length=1			,							top_p=0.9			,							repetition_penalty=1.0			,							length_penalty=1.0			,							temperature=1			,							)
    print('''Original generation:'''			,							UpperCamelCase_						)
    A__					=       input_ids.shape[1]
    A__					=       processor.batch_decode(outputs[:, prompt_length:]			,							skip_special_tokens=UpperCamelCase_						)
    A__					=       [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"nielsr/{model_name}"						)
        hf_model.push_to_hub(f"nielsr/{model_name}"						)


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

   _lowercase    	=		parser.parse_args()

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