'''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  UpperCamelCase__					(					)							->	Any:
  __UpperCAmelCase:					Optional[int]		= """https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"""
  __UpperCAmelCase:					Optional[int]		= Image.open(requests.get(_lowercase   ,    stream=_lowercase     ).raw     ).convert("""RGB"""     )

  return image






def  UpperCamelCase__					(					_lowercase    :				Dict     )							->	Optional[Any]:
  __UpperCAmelCase:					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  UpperCamelCase__					(					_lowercase    :				Any   ,    _lowercase    :				Any   ,    _lowercase    :				Dict     )							->	Union[str, Any]:
  __UpperCAmelCase:					List[str]		= dct.pop(_lowercase     )
  __UpperCAmelCase:					Dict		= val






def  UpperCamelCase__					(					_lowercase    :				List[str]   ,    _lowercase    :				Dict     )							->	Optional[int]:
  for i in range(config.vision_config.num_hidden_layers     ):
    # read in original q and v biases
    __UpperCAmelCase:					Optional[Any]		= state_dict.pop(F'''visual_encoder.blocks.{i}.attn.q_bias'''     )
    __UpperCAmelCase:					Any		= state_dict.pop(F'''visual_encoder.blocks.{i}.attn.v_bias'''     )

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






def  UpperCamelCase__					(					_lowercase    :				str     )							->	List[Any]:
  __UpperCAmelCase:					List[Any]		= 3_6_4 if """coco""" in model_name else 2_2_4
  __UpperCAmelCase:					List[str]		= InstructBlipVisionConfig(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 "t5-xl" in model_name:
    __UpperCAmelCase:					Union[str, Any]		= TaConfig.from_pretrained("""google/flan-t5-xl"""   ,    dense_act_fn="""gelu"""   ,    bos_token_id=1     ).to_dict()
  elif "t5-xxl" in model_name:
    __UpperCAmelCase:					str		= TaConfig.from_pretrained("""google/flan-t5-xxl"""   ,    dense_act_fn="""gelu"""   ,    bos_token_id=1     ).to_dict()
  elif "vicuna-7b" in model_name:
    __UpperCAmelCase:					Optional[int]		= LlamaConfig.from_pretrained("""decapoda-research/llama-7b-hf"""   ,    vocab_size=3_2_0_0_1     ).to_dict()
  elif "vicuna-13b" in model_name:
    __UpperCAmelCase:					Optional[Any]		= LlamaConfig.from_pretrained("""decapoda-research/llama-13b-hf"""   ,    vocab_size=3_2_0_0_1     ).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
  __UpperCAmelCase:					List[str]		= InstructBlipQFormerConfig(vocab_size=3_0_5_2_3     ).to_dict()
  __UpperCAmelCase:					Dict		= InstructBlipConfig(vision_config=_lowercase   ,    text_config=_lowercase   ,    qformer_config=_lowercase     )

  return config, image_size






@torch.no_grad()
def  UpperCamelCase__					(					_lowercase    :				str   ,    _lowercase    :				Union[str, Any]=None   ,    _lowercase    :				Optional[int]=False     )							->	str:
  __UpperCAmelCase:					Any		= AutoTokenizer.from_pretrained("""bert-base-uncased"""   ,    truncation_side="""left"""     )
  qformer_tokenizer.add_special_tokens({"""bos_token""": """[DEC]"""}     )

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

  __UpperCAmelCase, __UpperCAmelCase:					Optional[Any]		= get_blipa_config(_lowercase     )
  __UpperCAmelCase:					Optional[int]		= InstructBlipForConditionalGeneration(_lowercase     ).eval()

  __UpperCAmelCase:					int		= {
      """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"""),
  }

  __UpperCAmelCase, __UpperCAmelCase:					Any		= model_name_to_original[model_name]

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

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

  # read in qv biases
  read_in_q_v_bias(_lowercase   ,    _lowercase     )

  # note: weights get loaded in torch.float32 by default
  hf_model.load_state_dict(_lowercase   ,    strict=_lowercase     )

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

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

  # make sure processor creates exact same pixel values
  __UpperCAmelCase:					str		= vis_processors["""eval"""](_lowercase     ).unsqueeze(0     ).to(_lowercase     )
  __UpperCAmelCase:					List[Any]		= inputs.pixel_values
  assert torch.allclose(original_pixel_values.to(pixel_values.device     )   ,    _lowercase     )

  original_model.to(_lowercase     )
  hf_model.to(_lowercase     )
  with torch.no_grad():
    if "vicuna" in model_name:
      __UpperCAmelCase:					List[str]		= original_model({"""image""": original_pixel_values, """text_input""": [prompt]}     ).logits
      __UpperCAmelCase:					List[Any]		= hf_model(**_lowercase     ).logits
    else:
      __UpperCAmelCase:					Union[str, Any]		= original_model(
          {"""image""": original_pixel_values, """text_input""": [prompt], """text_output""": ["""\n"""]}     ).logits
      __UpperCAmelCase:					Dict		= tokenizer("""\n"""   ,    return_tensors="""pt"""     ).input_ids.to(_lowercase     )
      __UpperCAmelCase:					List[str]		= label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id   ,    -1_0_0     )
      __UpperCAmelCase:					str		= hf_model(**_lowercase   ,    labels=_lowercase     ).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
  __UpperCAmelCase:					Union[str, Any]		= 1E-4 if """vicuna""" in model_name else 1E-5
  assert torch.allclose(original_logits.to(logits.device     )   ,    _lowercase   ,    atol=_lowercase     )
  print("""Looks ok!"""     )

  print("""Generating with original model..."""     )
  __UpperCAmelCase:					str		= 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..."""     )
  __UpperCAmelCase:					Tuple		= hf_model.generate(
      **_lowercase   ,    do_sample=_lowercase   ,    num_beams=5   ,    max_length=2_5_6   ,    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?
    __UpperCAmelCase:					str		= 2
  print("""Original generation:"""   ,    _lowercase     )
  __UpperCAmelCase:					List[str]		= processor.batch_decode(_lowercase   ,    skip_special_tokens=_lowercase     )
  __UpperCAmelCase:					Dict		= [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'''Salesforce/{model_name}'''     )
    hf_model.push_to_hub(F'''Salesforce/{model_name}'''     )


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

    SCREAMING_SNAKE_CASE_	   =			parser.parse_args()

    convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_	   =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_	   =			{
    'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}




class    a (       __lowerCAmelCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					"""timesformer"""







  def __init__(							self   ,    snake_case_=224   ,    snake_case_=16   ,    snake_case_=3   ,    snake_case_=8   ,    snake_case_=768   ,    snake_case_=12   ,    snake_case_=12   ,    snake_case_=3072   ,    snake_case_="gelu"   ,    snake_case_=0.0   ,    snake_case_=0.0   ,    snake_case_=0.0_2   ,    snake_case_=1e-6   ,    snake_case_=True   ,    snake_case_="divided_space_time"   ,    snake_case_=0   ,    **snake_case_   ,    ):

    '''simple docstring'''







    super().__init__(**snake_case_					)

    __UpperCAmelCase:					Tuple		= image_size
    __UpperCAmelCase:					List[Any]		= patch_size
    __UpperCAmelCase:					Optional[Any]		= num_channels
    __UpperCAmelCase:					int		= num_frames

    __UpperCAmelCase:					List[str]		= hidden_size
    __UpperCAmelCase:					List[str]		= num_hidden_layers
    __UpperCAmelCase:					Dict		= num_attention_heads
    __UpperCAmelCase:					List[str]		= intermediate_size
    __UpperCAmelCase:					Union[str, Any]		= hidden_act
    __UpperCAmelCase:					Dict		= hidden_dropout_prob
    __UpperCAmelCase:					Optional[Any]		= attention_probs_dropout_prob
    __UpperCAmelCase:					Union[str, Any]		= initializer_range
    __UpperCAmelCase:					List[Any]		= layer_norm_eps
    __UpperCAmelCase:					int		= qkv_bias

    __UpperCAmelCase:					str		= attention_type
    __UpperCAmelCase:					Tuple		= drop_path_rate