import os

import jsonlines
import numpy as np
from tqdm import tqdm


a_    					=       2_048
a_    					=       4_096
a_    					=       42
a_    					=       os.environ.pop('PROCESS_TRAIN', 'false')
a_    					=       {'null': 0, 'short': 1, 'long': 2, 'yes': 3, 'no': 4}





def    __lowercase     (						lowerCamelCase					:     Dict	):
 def choose_first(lowerCamelCase					:     List[str]  ,  lowerCamelCase					:     Union[str, Any]=False	):
  assert isinstance(lowerCamelCase  ,  lowerCamelCase	)
  if len(lowerCamelCase	) == 1:
   UpperCamelCase_					:						List[str] =					answer[0]
   return {k: [answer[k]] for k in answer} if is_long_answer else answer
  for a in answer:
   if is_long_answer:
    UpperCamelCase_					:						str =					{k: [a[k]] for k in a}
   if len(a['start_token']	) > 0:
    break
  return a

 UpperCamelCase_					:						Tuple =					{'id': example['id']}
 UpperCamelCase_					:						Tuple =					example['annotations']
 UpperCamelCase_					:						Union[str, Any] =					annotation['yes_no_answer']
 if 0 in yes_no_answer or 1 in yes_no_answer:
  UpperCamelCase_					:						Optional[int] =					['yes'] if 1 in yes_no_answer else ['no']
  UpperCamelCase_					:						Union[str, Any] =					[]
  UpperCamelCase_					:						Dict =					[]
  UpperCamelCase_					:						List[str] =					['<cls>']
 else:
  UpperCamelCase_					:						Union[str, Any] =					['short']
  UpperCamelCase_					:						str =					choose_first(annotation['short_answers']	)
  if len(out['start_token']	) == 0:
   # answer will be long if short is not available
   UpperCamelCase_					:						Tuple =					['long']
   UpperCamelCase_					:						Any =					choose_first(annotation['long_answer']  ,  is_long_answer=lowerCamelCase	)
   UpperCamelCase_					:						str =					[]
  answer.update(lowerCamelCase	)

 # disregard some samples
 if len(answer['start_token']	) > 1 or answer["start_token"] == answer["end_token"]:
  UpperCamelCase_					:						Tuple =					True
 else:
  UpperCamelCase_					:						Optional[int] =					False

 UpperCamelCase_					:						Any =					['start_token', 'end_token', 'start_byte', 'end_byte', 'text']
 if not all(isinstance(answer[k]  ,  lowerCamelCase	) for k in cols	):
  raise ValueError('Issue in ID'  ,  example['id']	)

 return answer





def    __lowercase     (						lowerCamelCase					:     Any  ,  lowerCamelCase					:     str=False	):
 UpperCamelCase_					:						Dict =					_get_single_answer(lowerCamelCase	)
 # bytes are of no use
 del answer["start_byte"]
 del answer["end_byte"]

 # handle yes_no answers explicitly
 if answer["category"][0] in ["yes", "no"]:  # category is list with one element
  UpperCamelCase_					:						Union[str, Any] =					example['document']['tokens']
  UpperCamelCase_					:						Union[str, Any] =					[]
  for i in range(len(doc['token']	)	):
   if not doc["is_html"][i]:
    context.append(doc['token'][i]	)
  return {
      "context": " ".join(lowerCamelCase	),
      "answer": {
          "start_token": -100,  # ignore index in cross-entropy
          "end_token": -100,  # ignore index in cross-entropy
          "category": answer["category"],
          "span": answer["category"],  # extra
      },
  }

 # later, help in removing all no answers
 if answer["start_token"] == [-1]:
  return {
      "context": "None",
      "answer": {
          "start_token": -1,
          "end_token": -1,
          "category": "null",
          "span": "None",  # extra
      },
  }

 # handling normal samples

 UpperCamelCase_					:						str =					['start_token', 'end_token']
 answer.update({k: answer[k][0] if len(answer[k]	) > 0 else answer[k] for k in cols}	)  # e.g. [10] == 10

 UpperCamelCase_					:						int =					example['document']['tokens']
 UpperCamelCase_					:						Any =					answer['start_token']
 UpperCamelCase_					:						Union[str, Any] =					answer['end_token']

 UpperCamelCase_					:						Optional[Any] =					[]
 for i in range(len(doc['token']	)	):
  if not doc["is_html"][i]:
   context.append(doc['token'][i]	)
  else:
   if answer["start_token"] > i:
    start_token -= 1
   if answer["end_token"] > i:
    end_token -= 1
 UpperCamelCase_					:						int =					' '.join(context[start_token:end_token]	)

 # checking above code
 if assertion:
  UpperCamelCase_					:						Dict =					doc['is_html'][answer['start_token'] : answer['end_token']]
  UpperCamelCase_					:						Any =					doc['token'][answer['start_token'] : answer['end_token']]
  UpperCamelCase_					:						Union[str, Any] =					' '.join([old[i] for i in range(len(lowerCamelCase	)	) if not is_html[i]]	)
  if new != old:
   print('ID:'  ,  example['id']	)
   print('New:'  ,  lowerCamelCase  ,  end='\n'	)
   print('Old:'  ,  lowerCamelCase  ,  end='\n\n'	)

 return {
     "context": " ".join(lowerCamelCase	),
     "answer": {
         "start_token": start_token,
         "end_token": end_token - 1,  # this makes it inclusive
         "category": answer["category"],  # either long or short
         "span": new,  # extra
     },
 }





def    __lowercase     (						lowerCamelCase					:     Dict  ,  lowerCamelCase					:     List[str]  ,  lowerCamelCase					:     Optional[int]=2048  ,  lowerCamelCase					:     int=4096  ,  lowerCamelCase					:     List[str]=True	):
 # overlap will be of doc_stride - q_len

 UpperCamelCase_					:						Optional[Any] =					get_context_and_ans(lowerCamelCase  ,  assertion=lowerCamelCase	)
 UpperCamelCase_					:						Union[str, Any] =					out['answer']

 # later, removing these samples
 if answer["start_token"] == -1:
  return {
      "example_id": example["id"],
      "input_ids": [[-1]],
      "labels": {
          "start_token": [-1],
          "end_token": [-1],
          "category": ["null"],
      },
  }

 UpperCamelCase_					:						str =					tokenizer(example['question']['text']  ,  out['context']	).input_ids
 UpperCamelCase_					:						str =					input_ids.index(tokenizer.sep_token_id	) + 1

 # return yes/no
 if answer["category"][0] in ["yes", "no"]:  # category is list with one element
  UpperCamelCase_					:						List[Any] =					[]
  UpperCamelCase_					:						Dict =					[]
  UpperCamelCase_					:						Any =					input_ids[:q_len]
  UpperCamelCase_					:						Any =					range(lowerCamelCase  ,  len(lowerCamelCase	)  ,  max_length - doc_stride	)
  for i in doc_start_indices:
   UpperCamelCase_					:						Tuple =					i + max_length - q_len
   UpperCamelCase_					:						Optional[int] =					input_ids[i:end_index]
   inputs.append(q_indices + slice	)
   category.append(answer['category'][0]	)
   if slice[-1] == tokenizer.sep_token_id:
    break

  return {
      "example_id": example["id"],
      "input_ids": inputs,
      "labels": {
          "start_token": [-100] * len(lowerCamelCase	),
          "end_token": [-100] * len(lowerCamelCase	),
          "category": category,
      },
  }

 UpperCamelCase_					:						Optional[Any] =					out['context'].split()
 UpperCamelCase_					:						Optional[int] =					splitted_context[answer['end_token']]
 UpperCamelCase_					:						List[str] =					len(
     tokenizer(
         ' '.join(splitted_context[: answer['start_token']]	)  ,  add_special_tokens=lowerCamelCase  ,  ).input_ids	)
 UpperCamelCase_					:						List[Any] =					len(
     tokenizer(' '.join(splitted_context[: answer['end_token']]	)  ,  add_special_tokens=lowerCamelCase	).input_ids	)

 answer["start_token"] += q_len
 answer["end_token"] += q_len

 # fixing end token
 UpperCamelCase_					:						List[Any] =					len(tokenizer(lowerCamelCase  ,  add_special_tokens=lowerCamelCase	).input_ids	)
 if num_sub_tokens > 1:
  answer["end_token"] += num_sub_tokens - 1

 UpperCamelCase_					:						int =					input_ids[answer['start_token'] : answer['end_token'] + 1]  # right & left are inclusive
 UpperCamelCase_					:						Tuple =					answer['start_token']
 UpperCamelCase_					:						str =					answer['end_token']

 if assertion:
  UpperCamelCase_					:						Union[str, Any] =					tokenizer.decode(lowerCamelCase	)
  if answer["span"] != new:
   print('ISSUE IN TOKENIZATION'	)
   print('OLD:'  ,  answer['span']	)
   print('NEW:'  ,  lowerCamelCase  ,  end='\n\n'	)

 if len(lowerCamelCase	) <= max_length:
  return {
      "example_id": example["id"],
      "input_ids": [input_ids],
      "labels": {
          "start_token": [answer["start_token"]],
          "end_token": [answer["end_token"]],
          "category": answer["category"],
      },
  }

 UpperCamelCase_					:						Tuple =					input_ids[:q_len]
 UpperCamelCase_					:						Union[str, Any] =					range(lowerCamelCase  ,  len(lowerCamelCase	)  ,  max_length - doc_stride	)

 UpperCamelCase_					:						int =					[]
 UpperCamelCase_					:						str =					[]
 UpperCamelCase_					:						Optional[int] =					[]
 UpperCamelCase_					:						List[str] =					[]  # null, yes, no, long, short
 for i in doc_start_indices:
  UpperCamelCase_					:						Tuple =					i + max_length - q_len
  UpperCamelCase_					:						int =					input_ids[i:end_index]
  inputs.append(q_indices + slice	)
  assert len(inputs[-1]	) <= max_length, "Issue in truncating length"

  if start_token >= i and end_token <= end_index - 1:
   UpperCamelCase_					:						str =					start_token - i + q_len
   UpperCamelCase_					:						int =					end_token - i + q_len
   answers_category.append(answer['category'][0]	)  # ["short"] -> "short"
  else:
   UpperCamelCase_					:						Any =					-100
   UpperCamelCase_					:						Optional[Any] =					-100
   answers_category.append('null'	)
  UpperCamelCase_					:						Tuple =					inputs[-1][start_token : end_token + 1]

  answers_start_token.append(lowerCamelCase	)
  answers_end_token.append(lowerCamelCase	)
  if assertion:
   if new != old and new != [tokenizer.cls_token_id]:
    print('ISSUE in strided for ID:'  ,  example['id']	)
    print('New:'  ,  tokenizer.decode(lowerCamelCase	)	)
    print('Old:'  ,  tokenizer.decode(lowerCamelCase	)  ,  end='\n\n'	)
  if slice[-1] == tokenizer.sep_token_id:
   break

 return {
     "example_id": example["id"],
     "input_ids": inputs,
     "labels": {
         "start_token": answers_start_token,
         "end_token": answers_end_token,
         "category": answers_category,
     },
 }





def    __lowercase     (						lowerCamelCase					:     Optional[int]  ,  lowerCamelCase					:     Tuple  ,  lowerCamelCase					:     Optional[Any]=2048  ,  lowerCamelCase					:     Optional[int]=4096  ,  lowerCamelCase					:     Union[str, Any]=False	):
 UpperCamelCase_					:						Dict =					get_strided_contexts_and_ans(
     lowerCamelCase  ,  lowerCamelCase  ,  doc_stride=lowerCamelCase  ,  max_length=lowerCamelCase  ,  assertion=lowerCamelCase  ,  )

 return example





def    __lowercase     (						lowerCamelCase					:     List[str]  ,  lowerCamelCase					:     Any	):
 with jsonlines.open(lowerCamelCase  ,  'a'	) as writer:
  for example in tqdm(lowerCamelCase  ,  total=len(lowerCamelCase	)  ,  desc='Saving samples ... '	):
   UpperCamelCase_					:						Optional[Any] =					example['labels']
   for ids, start, end, cat in zip(
       example['input_ids']  ,  labels['start_token']  ,  labels['end_token']  ,  labels['category']  ,  ):
    if start == -1 and end == -1:
     continue  # leave waste samples with no answer
    if cat == "null" and np.random.rand() < 0.6:
     continue  # removing 50 % samples
    writer.write(
        {
            'input_ids': ids,
            'start_token': start,
            'end_token': end,
            'category': CATEGORY_MAPPING[cat],
        }	)


if __name__ == "__main__":
   from datasets import load_dataset

   from transformers import BigBirdTokenizer

   a_    					=       load_dataset('natural_questions')
   a_    					=       BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base')

   a_    					=       data['train' if PROCESS_TRAIN == 'true' else 'validation']

   a_    					=       {
       'tokenizer': tokenizer,
       'doc_stride': DOC_STRIDE,
       'max_length': MAX_LENGTH,
       'assertion': False,
   }
   a_    					=       data.map(prepare_inputs, fn_kwargs=fn_kwargs)
   a_    					=       data.remove_columns(['annotations', 'document', 'id', 'question'])
   print(data)

   np.random.seed(SEED)
   a_    					=       'nq-training.jsonl' if PROCESS_TRAIN == 'true' else 'nq-validation.jsonl'
   save_to_disk(data, file_name=cache_file_name)




import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
a_    					=       logging.get_logger(__name__)





def    __lowercase     (						lowerCamelCase					:     Dict  ,  lowerCamelCase					:     Union[str, Any]  ,  lowerCamelCase					:     Any	):
 UpperCamelCase_					:						List[Any] =					UniSpeechSatForSequenceClassification.from_pretrained(lowerCamelCase  ,  config=lowerCamelCase	)
 UpperCamelCase_					:						Any =					downstream_dict['projector.weight']
 UpperCamelCase_					:						List[str] =					downstream_dict['projector.bias']
 UpperCamelCase_					:						Tuple =					downstream_dict['model.post_net.linear.weight']
 UpperCamelCase_					:						int =					downstream_dict['model.post_net.linear.bias']
 return model





def    __lowercase     (						lowerCamelCase					:     List[Any]  ,  lowerCamelCase					:     Tuple  ,  lowerCamelCase					:     Union[str, Any]	):
 UpperCamelCase_					:						Any =					UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCamelCase  ,  config=lowerCamelCase	)
 UpperCamelCase_					:						Tuple =					downstream_dict['model.linear.weight']
 UpperCamelCase_					:						Any =					downstream_dict['model.linear.bias']
 return model





def    __lowercase     (						lowerCamelCase					:     Optional[int]  ,  lowerCamelCase					:     Optional[int]  ,  lowerCamelCase					:     List[Any]	):
 UpperCamelCase_					:						str =					UniSpeechSatForXVector.from_pretrained(lowerCamelCase  ,  config=lowerCamelCase	)
 UpperCamelCase_					:						Optional[Any] =					downstream_dict['connector.weight']
 UpperCamelCase_					:						int =					downstream_dict['connector.bias']
 for i, kernel_size in enumerate(hf_config.tdnn_kernel	):
  UpperCamelCase_					:						Union[str, Any] =					downstream_dict[
      F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
  ]
  UpperCamelCase_					:						Tuple =					downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

 UpperCamelCase_					:						int =					downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
 UpperCamelCase_					:						Optional[int] =					downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
 UpperCamelCase_					:						Dict =					downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
 UpperCamelCase_					:						Optional[int] =					downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
 UpperCamelCase_					:						List[Any] =					downstream_dict['objective.W']
 return model





@torch.no_grad()
def    __lowercase     (						lowerCamelCase					:     Optional[Any]  ,  lowerCamelCase					:     int  ,  lowerCamelCase					:     Any  ,  lowerCamelCase					:     int	):
 UpperCamelCase_					:						int =					torch.load(lowerCamelCase  ,  map_location='cpu'	)

 UpperCamelCase_					:						Any =					checkpoint['Downstream']

 UpperCamelCase_					:						Dict =					UniSpeechSatConfig.from_pretrained(lowerCamelCase	)
 UpperCamelCase_					:						str =					WavaVecaFeatureExtractor.from_pretrained(
     lowerCamelCase  ,  return_attention_mask=lowerCamelCase  ,  do_normalize=lowerCamelCase	)

 UpperCamelCase_					:						int =					hf_config.architectures[0]
 if arch.endswith('ForSequenceClassification'	):
  UpperCamelCase_					:						Tuple =					convert_classification(lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase	)
 elif arch.endswith('ForAudioFrameClassification'	):
  UpperCamelCase_					:						List[str] =					convert_diarization(lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase	)
 elif arch.endswith('ForXVector'	):
  UpperCamelCase_					:						Union[str, Any] =					convert_xvector(lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase	)
 else:
  raise NotImplementedError(F"S3PRL weights conversion is not supported for {arch}"	)

 if hf_config.use_weighted_layer_sum:
  UpperCamelCase_					:						Tuple =					checkpoint['Featurizer']['weights']

 hf_feature_extractor.save_pretrained(lowerCamelCase	)
 hf_model.save_pretrained(lowerCamelCase	)


if __name__ == "__main__":
   a_    					=       argparse.ArgumentParser()
   parser.add_argument(
       '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
   )
   parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
   parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
   parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
   a_    					=       parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



