



'''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_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    _SCREAMING_SNAKE_CASE  (       ):



    """simple docstring"""





    lowerCAmelCase__ :  Tuple							      =					"""https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"""
    lowerCAmelCase__ :  Tuple							      =					Image.open(requests.get(UpperCamelCase							,    stream=UpperCamelCase			).raw			).convert("""RGB"""			)

    return image
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  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.embeddings.layernorm.weight""")			)
    rename_keys.append(("""Qformer.bert.embeddings.LayerNorm.bias""", """qformer.embeddings.layernorm.bias""")			)

    # fmt: on
    return rename_keys
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Tuple							      =					dct.pop(UpperCamelCase			)
    lowerCAmelCase__ :  Optional[Any]							      =					val
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    for i in range(config.vision_config.num_hidden_layers			):
        # read in original q and v biases
        lowerCAmelCase__ :  List[Any]							      =					state_dict.pop(f"""visual_encoder.blocks.{i}.attn.q_bias"""			)
        lowerCAmelCase__ :  str							      =					state_dict.pop(f"""visual_encoder.blocks.{i}.attn.v_bias"""			)

        # next, set bias in the state dict
        lowerCAmelCase__ :  Dict							      =					torch.cat((q_bias, torch.zeros_like(UpperCamelCase							,    requires_grad=UpperCamelCase			), v_bias)			)
        lowerCAmelCase__ :  Optional[int]							      =					qkv_bias
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Optional[int]							      =					364 if """coco""" in model_name else 224
    lowerCAmelCase__ :  str							      =					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:
        lowerCAmelCase__ :  Tuple							      =					TaConfig.from_pretrained("""google/flan-t5-xl"""							,    dense_act_fn="""gelu"""							,    bos_token_id=1			).to_dict()
    elif "t5-xxl" in model_name:
        lowerCAmelCase__ :  Optional[Any]							      =					TaConfig.from_pretrained("""google/flan-t5-xxl"""							,    dense_act_fn="""gelu"""							,    bos_token_id=1			).to_dict()
    elif "vicuna-7b" in model_name:
        lowerCAmelCase__ :  Optional[int]							      =					LlamaConfig.from_pretrained("""decapoda-research/llama-7b-hf"""							,    vocab_size=32001			).to_dict()
    elif "vicuna-13b" in model_name:
        lowerCAmelCase__ :  Optional[Any]							      =					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
    lowerCAmelCase__ :  Dict							      =					InstructBlipQFormerConfig(vocab_size=30523			).to_dict()
    lowerCAmelCase__ :  List[Any]							      =					InstructBlipConfig(vision_config=UpperCamelCase							,    text_config=UpperCamelCase							,    qformer_config=UpperCamelCase			)

    return config, image_size
@torch.no_grad()
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase=None							,    UpperCamelCase=False			):



    """simple docstring"""





    lowerCAmelCase__ :  int							      =					AutoTokenizer.from_pretrained("""bert-base-uncased"""							,    truncation_side="""left"""			)
    qformer_tokenizer.add_special_tokens({"""bos_token""": """[DEC]"""}			)

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

    lowerCAmelCase__      ,   lowerCAmelCase__ :  List[str]							      =					get_blipa_config(UpperCamelCase			)
    lowerCAmelCase__ :  Union[str, Any]							      =					InstructBlipForConditionalGeneration(UpperCamelCase			).eval()

    lowerCAmelCase__ :  Optional[Any]							      =					{
        """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"""),
    }

    lowerCAmelCase__      ,   lowerCAmelCase__ :  List[Any]							      =					model_name_to_original[model_name]

    # load original model
    print("""Loading original model..."""			)
    lowerCAmelCase__ :  str							      =					"""cuda:1""" if torch.cuda.is_available() else """cpu"""
    lowerCAmelCase__ :  Tuple							      =					"""cuda:2""" if torch.cuda.is_available() else """cpu"""
    lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ :  Union[str, Any]							      =					load_model_and_preprocess(
        name=UpperCamelCase							,    model_type=UpperCamelCase							,    is_eval=UpperCamelCase							,    device=UpperCamelCase			)
    original_model.eval()
    print("""Done!"""			)

    # update state dict keys
    lowerCAmelCase__ :  Union[str, Any]							      =					original_model.state_dict()
    lowerCAmelCase__ :  List[Any]							      =					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():
        lowerCAmelCase__ :  List[str]							      =					state_dict.pop(UpperCamelCase			)
        if key.startswith("""Qformer.bert"""			):
            lowerCAmelCase__ :  Optional[int]							      =					key.replace("""Qformer.bert"""							,    """qformer"""			)
        if "attention.self" in key:
            lowerCAmelCase__ :  Tuple							      =					key.replace("""self"""							,    """attention"""			)
        if "llm_proj" in key:
            lowerCAmelCase__ :  int							      =					key.replace("""llm_proj"""							,    """language_projection"""			)
        if "t5_proj" in key:
            lowerCAmelCase__ :  Optional[int]							      =					key.replace("""t5_proj"""							,    """language_projection"""			)
        if key.startswith("""llm_model"""			):
            lowerCAmelCase__ :  Optional[int]							      =					key.replace("""llm_model"""							,    """language_model"""			)
        if key.startswith("""t5"""			):
            lowerCAmelCase__ :  Tuple							      =					key.replace("""t5"""							,    """language"""			)
        lowerCAmelCase__ :  int							      =					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			)

    lowerCAmelCase__ :  List[str]							      =					load_demo_image()
    lowerCAmelCase__ :  Optional[Any]							      =					"""What is unusual about this image?"""

    # create processor
    lowerCAmelCase__ :  Dict							      =					BlipImageProcessor(
        size={"""height""": image_size, """width""": image_size}							,    image_mean=UpperCamelCase							,    image_std=UpperCamelCase			)
    lowerCAmelCase__ :  List[str]							      =					InstructBlipProcessor(
        image_processor=UpperCamelCase							,    tokenizer=UpperCamelCase							,    qformer_tokenizer=UpperCamelCase							,    )
    lowerCAmelCase__ :  str							      =					processor(images=UpperCamelCase							,    text=UpperCamelCase							,    return_tensors="""pt"""			).to(UpperCamelCase			)

    # make sure processor creates exact same pixel values
    lowerCAmelCase__ :  Tuple							      =					vis_processors["""eval"""](UpperCamelCase			).unsqueeze(0			).to(UpperCamelCase			)
    lowerCAmelCase__ :  Optional[int]							      =					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:
            lowerCAmelCase__ :  Union[str, Any]							      =					original_model({"""image""": original_pixel_values, """text_input""": [prompt]}			).logits
            lowerCAmelCase__ :  Union[str, Any]							      =					hf_model(**UpperCamelCase			).logits
        else:
            lowerCAmelCase__ :  List[Any]							      =					original_model(
                {"""image""": original_pixel_values, """text_input""": [prompt], """text_output""": ["""\n"""]}			).logits
            lowerCAmelCase__ :  Optional[Any]							      =					tokenizer("""\n"""							,    return_tensors="""pt"""			).input_ids.to(UpperCamelCase			)
            lowerCAmelCase__ :  Any							      =					label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id							,    -100			)
            lowerCAmelCase__ :  Any							      =					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
    lowerCAmelCase__ :  Any							      =					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..."""			)
    lowerCAmelCase__ :  Any							      =					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..."""			)
    lowerCAmelCase__ :  Optional[int]							      =					hf_model.generate(
        **UpperCamelCase							,    do_sample=UpperCamelCase							,    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?
        lowerCAmelCase__ :  Any							      =					2
    print("""Original generation:"""							,    UpperCamelCase			)
    lowerCAmelCase__ :  str							      =					processor.batch_decode(UpperCamelCase							,    skip_special_tokens=UpperCamelCase			)
    lowerCAmelCase__ :  str							      =					[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      						=	argparse.ArgumentParser()
 _lowerCAmelCase      						=	[
     '''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      						=	parser.parse_args()

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







'''simple docstring'''





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
 import torch

 from transformers import XLMRobertaModel






@require_sentencepiece
@require_tokenizers
@require_torch
class        lowerCAmelCase_(				unittest.TestCase     ):
  '''simple docstring'''




  @slow
  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      lowerCAmelCase__ :  Tuple							      =					XLMRobertaModel.from_pretrained("""xlm-roberta-base"""      )
      lowerCAmelCase__ :  Optional[int]							      =					torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]      )
      # The dog is cute and lives in the garden house

      lowerCAmelCase__ :  str							      =					torch.Size((1, 12, 768)      )  # batch_size, sequence_length, embedding_vector_dim
      lowerCAmelCase__ :  Dict							      =					torch.tensor(
          [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]]      )
      #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
      #  xlmr.eval()
      #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
      with torch.no_grad():
          lowerCAmelCase__ :  Optional[int]							      =					model(__UpperCAmelCase      )["""last_hidden_state"""].detach()
      self.assertEqual(output.shape  ,__UpperCAmelCase      )
      # compare the actual values for a slice of last dim
      self.assertTrue(torch.allclose(output[:, :, -1]  ,__UpperCAmelCase  ,atol=1E-3      )      )






  @slow
  def 							UpperCAmelCase_	(    self      )    ->		int:
      lowerCAmelCase__ :  Union[str, Any]							      =					XLMRobertaModel.from_pretrained("""xlm-roberta-large"""      )
      lowerCAmelCase__ :  Optional[Any]							      =					torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]      )
      # The dog is cute and lives in the garden house

      lowerCAmelCase__ :  Dict							      =					torch.Size((1, 12, 1024)      )  # batch_size, sequence_length, embedding_vector_dim
      lowerCAmelCase__ :  Union[str, Any]							      =					torch.tensor(
          [[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]]      )
      #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
      #  xlmr.eval()
      #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
      with torch.no_grad():
          lowerCAmelCase__ :  Union[str, Any]							      =					model(__UpperCAmelCase      )["""last_hidden_state"""].detach()
      self.assertEqual(output.shape  ,__UpperCAmelCase      )
      # compare the actual values for a slice of last dim
      self.assertTrue(torch.allclose(output[:, :, -1]  ,__UpperCAmelCase  ,atol=1E-3      )      )


