






'''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    lowercase__			(				)->			Union[str, Any]:
      UpperCamelCase      = """https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"""
      UpperCamelCase      = Image.open(requests.get(__UpperCamelCase		,						stream=__UpperCamelCase   ).raw   ).convert("""RGB"""   )

      return image







def    lowercase__			(				__UpperCamelCase   )->			Dict:
      UpperCamelCase      = []
      # 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    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			Union[str, Any]:
      UpperCamelCase      = dct.pop(__UpperCamelCase   )
      UpperCamelCase      = val







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase   )->			Union[str, Any]:
      for i in range(config.vision_config.num_hidden_layers   ):
            # read in original q and v biases
            UpperCamelCase      = state_dict.pop(F"visual_encoder.blocks.{i}.attn.q_bias"   )
            UpperCamelCase      = state_dict.pop(F"visual_encoder.blocks.{i}.attn.v_bias"   )

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







def    lowercase__			(				__UpperCamelCase   )->			Tuple:
      UpperCamelCase      = 364 if """coco""" in model_name else 224
      UpperCamelCase      = 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:
            UpperCamelCase      = TaConfig.from_pretrained("""google/flan-t5-xl"""		,						dense_act_fn="""gelu"""		,						bos_token_id=1   ).to_dict()
      elif "t5-xxl" in model_name:
            UpperCamelCase      = TaConfig.from_pretrained("""google/flan-t5-xxl"""		,						dense_act_fn="""gelu"""		,						bos_token_id=1   ).to_dict()
      elif "vicuna-7b" in model_name:
            UpperCamelCase      = LlamaConfig.from_pretrained("""decapoda-research/llama-7b-hf"""		,						vocab_size=32001   ).to_dict()
      elif "vicuna-13b" in model_name:
            UpperCamelCase      = 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
      UpperCamelCase      = InstructBlipQFormerConfig(vocab_size=30523   ).to_dict()
      UpperCamelCase      = InstructBlipConfig(vision_config=__UpperCamelCase		,						text_config=__UpperCamelCase		,						qformer_config=__UpperCamelCase   )

      return config, image_size







@torch.no_grad()
def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase=None		,						__UpperCamelCase=False   )->			str:
      UpperCamelCase      = AutoTokenizer.from_pretrained("""bert-base-uncased"""		,						truncation_side="""left"""   )
      qformer_tokenizer.add_special_tokens({"""bos_token""": """[DEC]"""}   )

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

      UpperCamelCase  ,UpperCamelCase      = get_blipa_config(__UpperCamelCase   )
      UpperCamelCase      = InstructBlipForConditionalGeneration(__UpperCamelCase   ).eval()

      UpperCamelCase      = {
          """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      = model_name_to_original[model_name]

      # load original model
      print("""Loading original model..."""   )
      UpperCamelCase      = """cuda:1""" if torch.cuda.is_available() else """cpu"""
      UpperCamelCase      = """cuda:2""" if torch.cuda.is_available() else """cpu"""
      UpperCamelCase  ,UpperCamelCase  ,UpperCamelCase      = load_model_and_preprocess(
          name=__UpperCamelCase		,						model_type=__UpperCamelCase		,						is_eval=__UpperCamelCase		,						device=__UpperCamelCase   )
      original_model.eval()
      print("""Done!"""   )

      # update state dict keys
      UpperCamelCase      = original_model.state_dict()
      UpperCamelCase      = 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():
            UpperCamelCase      = state_dict.pop(__UpperCamelCase   )
            if key.startswith("""Qformer.bert"""   ):
                  UpperCamelCase      = key.replace("""Qformer.bert"""		,						"""qformer"""   )
            if "attention.self" in key:
                  UpperCamelCase      = key.replace("""self"""		,						"""attention"""   )
            if "llm_proj" in key:
                  UpperCamelCase      = key.replace("""llm_proj"""		,						"""language_projection"""   )
            if "t5_proj" in key:
                  UpperCamelCase      = key.replace("""t5_proj"""		,						"""language_projection"""   )
            if key.startswith("""llm_model"""   ):
                  UpperCamelCase      = key.replace("""llm_model"""		,						"""language_model"""   )
            if key.startswith("""t5"""   ):
                  UpperCamelCase      = key.replace("""t5"""		,						"""language"""   )
            UpperCamelCase      = 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   )

      UpperCamelCase      = load_demo_image()
      UpperCamelCase      = """What is unusual about this image?"""

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

      # make sure processor creates exact same pixel values
      UpperCamelCase      = vis_processors["""eval"""](__UpperCamelCase   ).unsqueeze(0   ).to(__UpperCamelCase   )
      UpperCamelCase      = 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:
                  UpperCamelCase      = original_model({"""image""": original_pixel_values, """text_input""": [prompt]}   ).logits
                  UpperCamelCase      = hf_model(**__UpperCamelCase   ).logits
            else:
                  UpperCamelCase      = original_model(
                      {"""image""": original_pixel_values, """text_input""": [prompt], """text_output""": ["""\n"""]}   ).logits
                  UpperCamelCase      = tokenizer("""\n"""		,						return_tensors="""pt"""   ).input_ids.to(__UpperCamelCase   )
                  UpperCamelCase      = label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id		,						-100   )
                  UpperCamelCase      = 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
      UpperCamelCase      = 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..."""   )
      UpperCamelCase      = 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      = 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?
            UpperCamelCase      = 2
      print("""Original generation:"""		,						__UpperCamelCase   )
      UpperCamelCase      = processor.batch_decode(__UpperCamelCase		,						skip_special_tokens=__UpperCamelCase   )
      UpperCamelCase      = [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__":
     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 typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
     import PIL

# soft dependency
if is_pytesseract_available():
     import pytesseract

SCREAMING_SNAKE_CASE__     =       logging.get_logger(__name__)







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			int:
      return [
          int(1000 * (box[0] / width)   ),
          int(1000 * (box[1] / height)   ),
          int(1000 * (box[2] / width)   ),
          int(1000 * (box[3] / height)   ),
      ]







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase = None   )->			Dict:
      UpperCamelCase      = tesseract_config if tesseract_config is not None else """"""

      # apply OCR
      UpperCamelCase      = to_pil_image(__UpperCamelCase   )
      UpperCamelCase  ,UpperCamelCase      = pil_image.size
      UpperCamelCase      = pytesseract.image_to_data(__UpperCamelCase		,						lang=__UpperCamelCase		,						output_type="""dict"""		,						config=__UpperCamelCase   )
      UpperCamelCase  ,UpperCamelCase  ,UpperCamelCase  ,UpperCamelCase  ,UpperCamelCase      = data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

      # filter empty words and corresponding coordinates
      UpperCamelCase      = [idx for idx, word in enumerate(__UpperCamelCase   ) if not word.strip()]
      UpperCamelCase      = [word for idx, word in enumerate(__UpperCamelCase   ) if idx not in irrelevant_indices]
      UpperCamelCase      = [coord for idx, coord in enumerate(__UpperCamelCase   ) if idx not in irrelevant_indices]
      UpperCamelCase      = [coord for idx, coord in enumerate(__UpperCamelCase   ) if idx not in irrelevant_indices]
      UpperCamelCase      = [coord for idx, coord in enumerate(__UpperCamelCase   ) if idx not in irrelevant_indices]
      UpperCamelCase      = [coord for idx, coord in enumerate(__UpperCamelCase   ) if idx not in irrelevant_indices]

      # turn coordinates into (left, top, left+width, top+height) format
      UpperCamelCase      = []
      for x, y, w, h in zip(__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   ):
            UpperCamelCase      = [x, y, x + w, y + h]
            actual_boxes.append(__UpperCamelCase   )

      # finally, normalize the bounding boxes
      UpperCamelCase      = []
      for box in actual_boxes:
            normalized_boxes.append(normalize_box(__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )   )

      assert len(__UpperCamelCase   ) == len(__UpperCamelCase   ), "Not as many words as there are bounding boxes"

      return words, normalized_boxes






class     a_						(      lowerCamelCase    ):
  lowercase							=				["""pixel_values"""]

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE = True      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR      ,		_SCREAMING_SNAKE_CASE = True      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = ""      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				None:



        """simple docstring"""






        super().__init__(**_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = size if size is not None else {"""height""": 224, """width""": 224}
        UpperCamelCase      = get_size_dict(_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = do_resize
        UpperCamelCase      = size
        UpperCamelCase      = resample
        UpperCamelCase      = apply_ocr
        UpperCamelCase      = ocr_lang
        UpperCamelCase      = tesseract_config

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR      ,		_SCREAMING_SNAKE_CASE = None      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				np.ndarray:



        """simple docstring"""






        UpperCamelCase      = get_size_dict(_SCREAMING_SNAKE_CASE				)
        if "height" not in size or "width" not in size:
              raise ValueError(F"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"				)
        UpperCamelCase      = (size["""height"""], size["""width"""])
        return resize(_SCREAMING_SNAKE_CASE      ,		size=_SCREAMING_SNAKE_CASE      ,		resample=_SCREAMING_SNAKE_CASE      ,		data_format=_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)


  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				PIL.Image.Image:



        """simple docstring"""






        UpperCamelCase      = do_resize if do_resize is not None else self.do_resize
        UpperCamelCase      = size if size is not None else self.size
        UpperCamelCase      = get_size_dict(_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = resample if resample is not None else self.resample
        UpperCamelCase      = apply_ocr if apply_ocr is not None else self.apply_ocr
        UpperCamelCase      = ocr_lang if ocr_lang is not None else self.ocr_lang
        UpperCamelCase      = tesseract_config if tesseract_config is not None else self.tesseract_config

        UpperCamelCase      = make_list_of_images(_SCREAMING_SNAKE_CASE				)

        if not valid_images(_SCREAMING_SNAKE_CASE				):
              raise ValueError(
                  """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                  """torch.Tensor, tf.Tensor or jax.ndarray."""				)

        if do_resize and size is None:
              raise ValueError("""Size must be specified if do_resize is True."""				)

        # All transformations expect numpy arrays.
        UpperCamelCase      = [to_numpy_array(_SCREAMING_SNAKE_CASE				) for image in images]

        if apply_ocr:
              requires_backends(self      ,		"""pytesseract"""				)
              UpperCamelCase      = []
              UpperCamelCase      = []
              for image in images:
                    UpperCamelCase  ,UpperCamelCase      = apply_tesseract(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
                    words_batch.append(_SCREAMING_SNAKE_CASE				)
                    boxes_batch.append(_SCREAMING_SNAKE_CASE				)

        if do_resize:
              UpperCamelCase      = [self.resize(image=_SCREAMING_SNAKE_CASE      ,		size=_SCREAMING_SNAKE_CASE      ,		resample=_SCREAMING_SNAKE_CASE				) for image in images]

        # flip color channels from RGB to BGR (as Detectron2 requires this)
        UpperCamelCase      = [flip_channel_order(_SCREAMING_SNAKE_CASE				) for image in images]
        UpperCamelCase      = [to_channel_dimension_format(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				) for image in images]

        UpperCamelCase      = BatchFeature(data={"""pixel_values""": images}      ,		tensor_type=_SCREAMING_SNAKE_CASE				)

        if apply_ocr:
              UpperCamelCase      = words_batch
              UpperCamelCase      = boxes_batch
        return data
