


'''simple docstring'''




import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


UpperCAmelCase_      :		Optional[int]          =		"\\n@inproceedings{popovic-2015-chrf,\n    title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",\n    month = sep,\n    year = \"2015\",\n    address = \"Lisbon, Portugal\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W15-3049\",\n    doi = \"10.18653/v1/W15-3049\",\n    pages = \"392--395\",\n}\n@inproceedings{popovic-2017-chrf,\n    title = \"chr{F}++: words helping character n-grams\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Second Conference on Machine Translation\",\n    month = sep,\n    year = \"2017\",\n    address = \"Copenhagen, Denmark\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W17-4770\",\n    doi = \"10.18653/v1/W17-4770\",\n    pages = \"612--618\",\n}\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

UpperCAmelCase_      :		Tuple          =		"\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n"

UpperCAmelCase_      :		str          =		"\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n    predictions (list of str): The predicted sentences.\n    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n    char_order (int): Character n-gram order. Defaults to `6`.\n    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n    eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n    to reference chrF++.py, NLTK and Moses implementations. If `False`,\n    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n    'score' (float): The chrF (chrF++) score,\n    'char_order' (int): The character n-gram order,\n    'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n    'beta' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n    Example 1--a simple example of calculating chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction, references=reference)\n        >>> print(results)\n        {'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}\n\n    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2)\n        >>> print(results)\n        {'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}\n\n    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2,\n        ...                         lowercase=True)\n        >>> print(results)\n        {'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}\n"







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,							_KWARGS_DESCRIPTION  )
class 					a			(				datasets.Metric  ):
 '''simple docstring'''




 def   __UpperCamelCase   (    self      )      ->    str:
    if version.parse(scb.__version__      ) < version.parse('1.4.12'      ):
       raise ImportWarning(
           'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
           'You can install it with `pip install "sacrebleu>=1.4.12"`.'      )
    return datasets.MetricInfo(
        description=_DESCRIPTION		,      citation=_CITATION		,      homepage='https://github.com/mjpost/sacreBLEU#chrf--chrf'		,      inputs_description=_KWARGS_DESCRIPTION		,      features=datasets.Features(
            {
                'predictions': datasets.Value('string'		,      id='sequence'      ),
                'references': datasets.Sequence(datasets.Value('string'		,      id='sequence'      )		,      id='references'      ),
            }      )		,      codebase_urls=['https://github.com/mjpost/sacreBLEU#chrf--chrf']		,      reference_urls=[
            'https://github.com/m-popovic/chrF',
        ]		,      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_ = CHRF.CHAR_ORDER		,      lowerCamelCase_ = CHRF.WORD_ORDER		,      lowerCamelCase_ = CHRF.BETA		,      lowerCamelCase_ = False		,      lowerCamelCase_ = False		,      lowerCamelCase_ = False		,      )      ->    List[str]:
    _a					:  Any						=  len(references[0]      )
    if any(len(lowerCamelCase_      ) != references_per_prediction for refs in references      ):
       raise ValueError('Sacrebleu requires the same number of references for each prediction'      )
    _a					:  Optional[Any]						=  [[refs[i] for refs in references] for i in range(lowerCamelCase_      )]

    _a					:  Union[str, Any]						=  CHRF(lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_      )
    _a					:  Any						=  sb_chrf.corpus_score(lowerCamelCase_		,      lowerCamelCase_      )

    return {
        "score": output.score,
        "char_order": output.char_order,
        "word_order": output.word_order,
        "beta": output.beta,
    }






'''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_  (       ):
   '''simple docstring'''





   _a					:  str						=  'https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'
   _a					:  Any						=  Image.open(requests.get(A		,	stream=A      ).raw      ).convert('RGB'      )

   return image


def    UpperCAmelCase_  (       A      ):
   '''simple docstring'''





   _a					:  Optional[int]						=  []
   # 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_  (       A		,	A		,	A      ):
   '''simple docstring'''





   _a					:  Dict						=  dct.pop(A      )
   _a					:  Tuple						=  val


def    UpperCAmelCase_  (       A		,	A      ):
   '''simple docstring'''





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

      # next, set bias in the state dict
      _a					:  Any						=  torch.cat((q_bias, torch.zeros_like(A		,	requires_grad=A      ), v_bias)      )
      _a					:  Tuple						=  qkv_bias


def    UpperCAmelCase_  (       A      ):
   '''simple docstring'''





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

   return config, image_size


@torch.no_grad()
def    UpperCAmelCase_  (       A		,	A=None		,	A=False      ):
   '''simple docstring'''





   _a					:  Tuple						=  AutoTokenizer.from_pretrained('bert-base-uncased'		,	truncation_side='left'      )
   qformer_tokenizer.add_special_tokens({'bos_token': '[DEC]'}      )

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

   _a		,						_a					:  Tuple						=  get_blipa_config(A      )
   _a					:  Optional[Any]						=  InstructBlipForConditionalGeneration(A      ).eval()

   _a					:  Union[str, 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'),
   }

   _a		,						_a					:  Dict						=  model_name_to_original[model_name]

   # load original model
   print('Loading original model...'      )
   _a					:  int						=  'cuda:1' if torch.cuda.is_available() else 'cpu'
   _a					:  Any						=  'cuda:2' if torch.cuda.is_available() else 'cpu'
   _a		,						_a		,						_a					:  Optional[int]						=  load_model_and_preprocess(
       name=A		,	model_type=A		,	is_eval=A		,	device=A      )
   original_model.eval()
   print('Done!'      )

   # update state dict keys
   _a					:  Tuple						=  original_model.state_dict()
   _a					:  List[Any]						=  create_rename_keys(A      )
   for src, dest in rename_keys:
      rename_key(A		,	A		,	A      )

   # some keys can be renamed efficiently
   for key, val in state_dict.copy().items():
      _a					:  Union[str, Any]						=  state_dict.pop(A      )
      if key.startswith('Qformer.bert'      ):
         _a					:  Union[str, Any]						=  key.replace('Qformer.bert'		,	'qformer'      )
      if "attention.self" in key:
         _a					:  List[Any]						=  key.replace('self'		,	'attention'      )
      if "llm_proj" in key:
         _a					:  int						=  key.replace('llm_proj'		,	'language_projection'      )
      if "t5_proj" in key:
         _a					:  str						=  key.replace('t5_proj'		,	'language_projection'      )
      if key.startswith('llm_model'      ):
         _a					:  int						=  key.replace('llm_model'		,	'language_model'      )
      if key.startswith('t5'      ):
         _a					:  Tuple						=  key.replace('t5'		,	'language'      )
      _a					:  Optional[Any]						=  val

   # read in qv biases
   read_in_q_v_bias(A		,	A      )

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

   _a					:  Dict						=  load_demo_image()
   _a					:  Any						=  'What is unusual about this image?'

   # create processor
   _a					:  Any						=  BlipImageProcessor(
       size={'height': image_size, 'width': image_size}		,	image_mean=A		,	image_std=A      )
   _a					:  Dict						=  InstructBlipProcessor(
       image_processor=A		,	tokenizer=A		,	qformer_tokenizer=A		,	)
   _a					:  str						=  processor(images=A		,	text=A		,	return_tensors='pt'      ).to(A      )

   # make sure processor creates exact same pixel values
   _a					:  str						=  vis_processors['eval'](A      ).unsqueeze(0      ).to(A      )
   _a					:  Optional[int]						=  inputs.pixel_values
   assert torch.allclose(original_pixel_values.to(pixel_values.device      )		,	A      )

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

   print('Generating with original model...'      )
   _a					:  Optional[int]						=  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...'      )
   _a					:  Dict						=  hf_model.generate(
       **A		,	do_sample=A		,	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?
      _a					:  Tuple						=  2
   print('Original generation:'		,	A      )
   _a					:  Dict						=  processor.batch_decode(A		,	skip_special_tokens=A      )
   _a					:  Tuple						=  [text.strip() for text in output_text]
   print('HF generation:'		,	A      )

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

   if push_to_hub:
      processor.push_to_hub(f'''Salesforce/{model_name}'''      )
      hf_model.push_to_hub(f'''Salesforce/{model_name}'''      )


if __name__ == "__main__":
 UpperCAmelCase_      :		Optional[int]          =		argparse.ArgumentParser()
 UpperCAmelCase_      :		List[str]          =		[
     "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",
 )

 UpperCAmelCase_      :		Optional[int]          =		parser.parse_args()

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


