





"""simple docstring"""





from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse('3.8'):
  import importlib_metadata
else:
  import importlib.metadata as importlib_metadata


lowercase__				:Optional[Any]				   =       ''


if version.parse(importlib_metadata.version('jiwer')) < version.parse('2.3.0'):




  class 							snake_case							(			tr.AbstractTransform ):

        '''simple docstring'''



        def __init__(							self       :      Union[str, Any]						,				__lowercase       :      str = " " ):


          '''simple docstring'''




          __UpperCAmelCase    :						int       =						sentence_delimiter

        def       A_					(							self       :      Optional[Any]						,				__lowercase       :      str ):


          '''simple docstring'''




          return list(__lowercase )







        def       A_					(							self       :      Tuple						,				__lowercase       :      List[str] ):


          '''simple docstring'''




          __UpperCAmelCase    :						Optional[Any]       =						[]
          for sent_idx, sentence in enumerate(__lowercase ):
            chars.extend(self.process_string(__lowercase ) )
            if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(__lowercase ) - 1:
              chars.append(self.sentence_delimiter )
          return chars


  lowercase__				:Dict				   =       tr.Compose(
      [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
  )
else:
  lowercase__				:Optional[Any]				   =       tr.Compose(
      [
          tr.RemoveMultipleSpaces(),
          tr.Strip(),
          tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
          tr.ReduceToListOfListOfChars(),
      ]
  )


lowercase__				:Union[str, Any]				   =       '\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n'

lowercase__				:Any				   =       '\\nCharacter error rate (CER) is a common metric of the performance of an automatic speech recognition system.\n\nCER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.\n\nCharacter error rate can be computed as:\n\nCER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct characters,\nN is the number of characters in the reference (N=S+D+C).\n\nCER\'s output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the\nperformance of the ASR system with a CER of 0 being a perfect score.\n'

lowercase__				:str				   =       '\nComputes CER score of transcribed segments against references.\nArgs:\n    references: list of references for each speech input.\n    predictions: list of transcribtions to score.\n    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.\nReturns:\n    (float): the character error rate\n\nExamples:\n\n    >>> predictions = ["this is the prediction", "there is an other sample"]\n    >>> references = ["this is the reference", "there is another one"]\n    >>> cer = datasets.load_metric("cer")\n    >>> cer_score = cer.compute(predictions=predictions, references=references)\n    >>> print(cer_score)\n    0.34146341463414637\n'




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,  _KWARGS_DESCRIPTION )
class 							snake_case							(			datasets.Metric ):

      '''simple docstring'''



      def       A_					(							self       :      Union[str, Any] ):


        '''simple docstring'''




        return datasets.MetricInfo(
            description=_DESCRIPTION						,				citation=_CITATION						,				inputs_description=_KWARGS_DESCRIPTION						,				features=datasets.Features(
                {
                    '''predictions''': datasets.Value('''string'''						,				id='''sequence''' ),
                    '''references''': datasets.Value('''string'''						,				id='''sequence''' ),
                } )						,				codebase_urls=['''https://github.com/jitsi/jiwer/''']						,				reference_urls=[
                '''https://en.wikipedia.org/wiki/Word_error_rate''',
                '''https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates''',
            ]						,				)







      def       A_					(							self       :      Optional[Any]						,				__lowercase       :      Tuple						,				__lowercase       :      Optional[int]						,				__lowercase       :      Optional[Any]=False ):


        '''simple docstring'''




        if concatenate_texts:
          return jiwer.compute_measures(
              __lowercase						,				__lowercase						,				truth_transform=__lowercase						,				hypothesis_transform=__lowercase						,				)["wer"]

        __UpperCAmelCase    :						List[str]       =						0
        __UpperCAmelCase    :						int       =						0
        for prediction, reference in zip(__lowercase						,				__lowercase ):
          __UpperCAmelCase    :						Tuple       =						jiwer.compute_measures(
              __lowercase						,				__lowercase						,				truth_transform=__lowercase						,				hypothesis_transform=__lowercase						,				)
          incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
          total += measures["substitutions"] + measures["deletions"] + measures["hits"]

        return incorrect / total






"""simple docstring"""





import argparse
import re

import requests
import torch

# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

from transformers import (
    BertTokenizer,
    BlipConfig,
    BlipForConditionalGeneration,
    BlipForImageTextRetrieval,
    BlipForQuestionAnswering,
)


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->Any:





  """simple docstring"""


  __UpperCAmelCase    :						Tuple       =						'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'''
  __UpperCAmelCase    :						Tuple       =						Image.open(requests.get(UpperCAmelCase_  ,			stream=UpperCAmelCase_  ).raw  ).convert('''RGB'''  )

  __UpperCAmelCase    :						Dict       =						transforms.Compose(
      [
          transforms.Resize((image_size, image_size)  ,			interpolation=InterpolationMode.BICUBIC  ),
          transforms.ToTensor(),
          transforms.Normalize((0.48_145_466, 0.4_578_275, 0.40_821_073)  ,			(0.26_862_954, 0.26_130_258, 0.27_577_711)  ),
      ]  )
  __UpperCAmelCase    :						Optional[Any]       =						transform(UpperCAmelCase_  ).unsqueeze(0  ).to(UpperCAmelCase_  )
  return image


def       lowerCamelCase_       (   UpperCAmelCase_  )						->Optional[Any]:





  """simple docstring"""


  if "visual_encoder" in key:
    __UpperCAmelCase    :						Tuple       =						re.sub('''visual_encoder*'''  ,			'''vision_model.encoder'''  ,			UpperCAmelCase_  )
  if "blocks" in key:
    __UpperCAmelCase    :						List[str]       =						re.sub(R'''blocks'''  ,			'''layers'''  ,			UpperCAmelCase_  )
  if "attn" in key:
    __UpperCAmelCase    :						Union[str, Any]       =						re.sub(R'''attn'''  ,			'''self_attn'''  ,			UpperCAmelCase_  )
  if "norm1" in key:
    __UpperCAmelCase    :						Optional[Any]       =						re.sub(R'''norm1'''  ,			'''layer_norm1'''  ,			UpperCAmelCase_  )
  if "norm2" in key:
    __UpperCAmelCase    :						Optional[int]       =						re.sub(R'''norm2'''  ,			'''layer_norm2'''  ,			UpperCAmelCase_  )
  if "encoder.norm" in key:
    __UpperCAmelCase    :						Union[str, Any]       =						re.sub(R'''encoder.norm'''  ,			'''post_layernorm'''  ,			UpperCAmelCase_  )
  if "encoder.patch_embed.proj" in key:
    __UpperCAmelCase    :						Optional[int]       =						re.sub(R'''encoder.patch_embed.proj'''  ,			'''embeddings.patch_embedding'''  ,			UpperCAmelCase_  )

  if "encoder.pos_embed" in key:
    __UpperCAmelCase    :						List[Any]       =						re.sub(R'''encoder.pos_embed'''  ,			'''embeddings.position_embedding'''  ,			UpperCAmelCase_  )
  if "encoder.cls_token" in key:
    __UpperCAmelCase    :						List[str]       =						re.sub(R'''encoder.cls_token'''  ,			'''embeddings.class_embedding'''  ,			UpperCAmelCase_  )

  if "self_attn" in key:
    __UpperCAmelCase    :						Any       =						re.sub(R'''self_attn.proj'''  ,			'''self_attn.projection'''  ,			UpperCAmelCase_  )

  return key


@torch.no_grad()
def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_=None  )						->Dict:





  """simple docstring"""


  if config_path is not None:
    __UpperCAmelCase    :						Optional[int]       =						BlipConfig.from_pretrained(UpperCAmelCase_  )
  else:
    __UpperCAmelCase    :						Optional[int]       =						BlipConfig(projection_dim=5_12  ,			text_config={}  ,			vision_config={}  )

  __UpperCAmelCase    :						Optional[Any]       =						BlipForConditionalGeneration(UpperCAmelCase_  ).eval()

  __UpperCAmelCase    :						List[str]       =						'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'''

  __UpperCAmelCase    :						List[Any]       =						blip_decoder(pretrained=UpperCAmelCase_  ,			image_size=3_84  ,			vit='''base'''  )
  __UpperCAmelCase    :						str       =						pt_model.eval()

  __UpperCAmelCase    :						List[Any]       =						pt_model.state_dict()
  for key in modified_state_dict.copy():
    __UpperCAmelCase    :						List[str]       =						modified_state_dict.pop(UpperCAmelCase_  )
    __UpperCAmelCase    :						Optional[int]       =						rename_key(UpperCAmelCase_  )
    __UpperCAmelCase    :						List[Any]       =						value

  hf_model.load_state_dict(UpperCAmelCase_  )

  __UpperCAmelCase    :						str       =						3_84
  __UpperCAmelCase    :						Tuple       =						load_demo_image(image_size=UpperCAmelCase_  ,			device='''cpu'''  )
  __UpperCAmelCase    :						Optional[Any]       =						BertTokenizer.from_pretrained('''bert-base-uncased'''  )
  __UpperCAmelCase    :						Optional[int]       =						tokenizer(['''a picture of''']  ).input_ids

  __UpperCAmelCase    :						Union[str, Any]       =						hf_model.generate(UpperCAmelCase_  ,			UpperCAmelCase_  )

  assert out[0].tolist() == [3_05_22, 10_37, 38_61, 19_97, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

  __UpperCAmelCase    :						Optional[int]       =						hf_model.generate(UpperCAmelCase_  )

  assert out[0].tolist() == [3_05_22, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

  if pytorch_dump_folder_path is not None:
    hf_model.save_pretrained(UpperCAmelCase_  )

  # model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
  __UpperCAmelCase    :						Optional[Any]       =						(
      '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'''
  )

  __UpperCAmelCase    :						List[str]       =						blip_vqa(pretrained=UpperCAmelCase_  ,			image_size=UpperCAmelCase_  ,			vit='''base'''  )
  vqa_model.eval()

  __UpperCAmelCase    :						Dict       =						vqa_model.state_dict()
  for key in modified_state_dict.copy():
    __UpperCAmelCase    :						List[Any]       =						modified_state_dict.pop(UpperCAmelCase_  )
    __UpperCAmelCase    :						int       =						rename_key(UpperCAmelCase_  )
    __UpperCAmelCase    :						Union[str, Any]       =						value

  __UpperCAmelCase    :						List[str]       =						BlipForQuestionAnswering(UpperCAmelCase_  )

  hf_vqa_model.load_state_dict(UpperCAmelCase_  )

  __UpperCAmelCase    :						str       =						['''How many dogs are in this image?''']
  __UpperCAmelCase    :						Dict       =						tokenizer(UpperCAmelCase_  ,			return_tensors='''pt'''  ).input_ids

  __UpperCAmelCase    :						Any       =						hf_vqa_model.generate(UpperCAmelCase_  ,			UpperCAmelCase_  )
  print(tokenizer.decode(answer[0]  )  )

  assert tokenizer.decode(answer[0]  ) == "[UNK] 1 [SEP]"
  if pytorch_dump_folder_path is not None:
    hf_vqa_model.save_pretrained(pytorch_dump_folder_path + '''_vqa'''  )

  __UpperCAmelCase    :						List[Any]       =						'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'''

  __UpperCAmelCase    :						List[str]       =						blip_itm(pretrained=UpperCAmelCase_  ,			image_size=UpperCAmelCase_  ,			vit='''base'''  )
  itm_model.eval()

  __UpperCAmelCase    :						Any       =						itm_model.state_dict()
  for key in modified_state_dict.copy():
    __UpperCAmelCase    :						List[str]       =						modified_state_dict.pop(UpperCAmelCase_  )
    __UpperCAmelCase    :						Dict       =						rename_key(UpperCAmelCase_  )
    __UpperCAmelCase    :						int       =						value

  __UpperCAmelCase    :						Optional[int]       =						BlipForImageTextRetrieval(UpperCAmelCase_  )

  __UpperCAmelCase    :						List[str]       =						['''A picture of a woman with a dog sitting in a beach''']
  __UpperCAmelCase    :						Optional[int]       =						tokenizer(
      UpperCAmelCase_  ,			return_tensors='''pt'''  ,			padding='''max_length'''  ,			truncation=UpperCAmelCase_  ,			max_length=35  ,			).input_ids

  hf_itm_model.load_state_dict(UpperCAmelCase_  )
  hf_itm_model.eval()

  __UpperCAmelCase    :						List[Any]       =						hf_itm_model(UpperCAmelCase_  ,			UpperCAmelCase_  ,			use_itm_head=UpperCAmelCase_  )
  __UpperCAmelCase    :						int       =						hf_itm_model(UpperCAmelCase_  ,			UpperCAmelCase_  ,			use_itm_head=UpperCAmelCase_  )

  assert out[0].item() == 0.2_110_687_494_277_954
  assert torch.nn.functional.softmax(out_itm[0]  ,			dim=1  )[:, 1].item() == 0.45_698_845_386_505_127

  if pytorch_dump_folder_path is not None:
    hf_itm_model.save_pretrained(pytorch_dump_folder_path + '''_itm'''  )


if __name__ == "__main__":
  lowercase__				:str				   =       argparse.ArgumentParser()
  parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
  parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
  lowercase__				:int				   =       parser.parse_args()

  convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)