





import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


logging.set_verbosity_info()
lowercase    : List[Any]    	=  logging.get_logger(__name__)


lowercase    : List[str]    	=  """https://openaipublic.azureedge.net/jukebox/models/"""
lowercase    : Optional[int]    	=  {
    """jukebox-1b-lyrics""": [
        """5b/vqvae.pth.tar""",
        """5b/prior_level_0.pth.tar""",
        """5b/prior_level_1.pth.tar""",
        """1b_lyrics/prior_level_2.pth.tar""",
    ],
    """jukebox-5b-lyrics""": [
        """5b/vqvae.pth.tar""",
        """5b/prior_level_0.pth.tar""",
        """5b/prior_level_1.pth.tar""",
        """5b_lyrics/prior_level_2.pth.tar""",
    ],
}


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
       if key.endswith('''.model.1.bias'''					) and len(key.split('''.'''					)					) > 10:
              _UpperCamelCase              =						key.replace('''.model.1.bias'''    ,       '''.conv1d_1.bias'''					)
       elif key.endswith('''.model.1.weight'''					) and len(key.split('''.'''					)					) > 10:
              _UpperCamelCase              =						key.replace('''.model.1.weight'''    ,       '''.conv1d_1.weight'''					)
       elif key.endswith('''.model.3.bias'''					) and len(key.split('''.'''					)					) > 10:
              _UpperCamelCase              =						key.replace('''.model.3.bias'''    ,       '''.conv1d_2.bias'''					)
       elif key.endswith('''.model.3.weight'''					) and len(key.split('''.'''					)					) > 10:
              _UpperCamelCase              =						key.replace('''.model.3.weight'''    ,       '''.conv1d_2.weight'''					)

       if "conditioner_blocks.0." in key:
              _UpperCamelCase              =						key.replace('''conditioner_blocks.0'''    ,       '''conditioner_blocks'''					)

       if "prime_prior" in key:
              _UpperCamelCase              =						key.replace('''prime_prior'''    ,       '''encoder'''					)

       if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
              _UpperCamelCase              =						key.replace('''.emb.'''    ,       '''.'''					)

       if key.endswith('''k'''					):  # replace vqvae.X.k with vqvae.X.codebook
              return key.replace('''.k'''    ,       '''.codebook'''					)
       if "y_emb." in key:
              return key.replace('''y_emb.'''    ,       '''metadata_embedding.'''					)

       if "x_emb.emb." in key:
              _UpperCamelCase              =						key.replace('''0.x_emb.emb'''    ,       '''embed_tokens'''					)

       if "prime_state_ln" in key:
              return key.replace('''prime_state_ln'''    ,       '''encoder.final_layer_norm'''					)
       if ".ln" in key:
              return key.replace('''.ln'''    ,       '''.layer_norm'''					)
       if "_ln" in key:
              return key.replace('''_ln'''    ,       '''_layer_norm'''					)

       if "prime_state_proj" in key:
              return key.replace('''prime_state_proj'''    ,       '''encoder.proj_in'''					)
       if "prime_x_out" in key:
              return key.replace('''prime_x_out'''    ,       '''encoder.lm_head'''					)
       if "prior.x_out" in key:
              return key.replace('''x_out'''    ,       '''fc_proj_out'''					)
       if "x_emb" in key:
              return key.replace('''x_emb'''    ,       '''embed_tokens'''					)

       return key


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase					):
       _UpperCamelCase              =						{}
       import re

       _UpperCamelCase              =						re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''					)
       _UpperCamelCase              =						re.compile(
           R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''					)
       _UpperCamelCase              =						re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''					)

       _UpperCamelCase              =						re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''					)
       _UpperCamelCase              =						re.compile(
           R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''					)
       _UpperCamelCase              =						re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''					)

       _UpperCamelCase              =						re.compile(R'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)'''					)
       _UpperCamelCase              =						re.compile(
           R'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''					)
       _UpperCamelCase              =						re.compile(R'''conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)'''					)

       for original_key, value in state_dict.items():
              # rename vqvae.encoder keys
              if re_encoder_block_conv_in.fullmatch(lowerCAmelCase					):
                     _UpperCamelCase              =						re_encoder_block_conv_in.match(lowerCAmelCase					)
                     _UpperCamelCase              =						regex_match.groups()
                     _UpperCamelCase              =						int(groups[2]					) * 2 + int(groups[3]					)
                     _UpperCamelCase              =						f'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'''
                     _UpperCamelCase              =						re_encoder_block_conv_in.sub(lowerCAmelCase    ,       lowerCAmelCase					)

              elif re_encoder_block_resnet.fullmatch(lowerCAmelCase					):
                     _UpperCamelCase              =						re_encoder_block_resnet.match(lowerCAmelCase					)
                     _UpperCamelCase              =						regex_match.groups()
                     _UpperCamelCase              =						int(groups[2]					) * 2 + int(groups[3]					)
                     _UpperCamelCase              =						{'''1''': 1, '''3''': 2}[groups[-2]]
                     _UpperCamelCase              =						f'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.'''
                     _UpperCamelCase              =						f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
                     _UpperCamelCase              =						prefix + resnet_block
                     _UpperCamelCase              =						re_encoder_block_resnet.sub(lowerCAmelCase    ,       lowerCAmelCase					)

              elif re_encoder_block_proj_out.fullmatch(lowerCAmelCase					):
                     _UpperCamelCase              =						re_encoder_block_proj_out.match(lowerCAmelCase					)
                     _UpperCamelCase              =						regex_match.groups()
                     _UpperCamelCase              =						f'''encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'''
                     _UpperCamelCase              =						re_encoder_block_proj_out.sub(lowerCAmelCase    ,       lowerCAmelCase					)

              # rename vqvae.decoder keys
              elif re_decoder_block_conv_out.fullmatch(lowerCAmelCase					):
                     _UpperCamelCase              =						re_decoder_block_conv_out.match(lowerCAmelCase					)
                     _UpperCamelCase              =						regex_match.groups()
                     _UpperCamelCase              =						int(groups[2]					) * 2 + int(groups[3]					) - 2
                     _UpperCamelCase              =						f'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}'''
                     _UpperCamelCase              =						re_decoder_block_conv_out.sub(lowerCAmelCase    ,       lowerCAmelCase					)

              elif re_decoder_block_resnet.fullmatch(lowerCAmelCase					):
                     _UpperCamelCase              =						re_decoder_block_resnet.match(lowerCAmelCase					)
                     _UpperCamelCase              =						regex_match.groups()
                     _UpperCamelCase              =						int(groups[2]					) * 2 + int(groups[3]					) - 2
                     _UpperCamelCase              =						{'''1''': 1, '''3''': 2}[groups[-2]]
                     _UpperCamelCase              =						f'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.'''
                     _UpperCamelCase              =						f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
                     _UpperCamelCase              =						prefix + resnet_block
                     _UpperCamelCase              =						re_decoder_block_resnet.sub(lowerCAmelCase    ,       lowerCAmelCase					)

              elif re_decoder_block_proj_in.fullmatch(lowerCAmelCase					):
                     _UpperCamelCase              =						re_decoder_block_proj_in.match(lowerCAmelCase					)
                     _UpperCamelCase              =						regex_match.groups()
                     _UpperCamelCase              =						f'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
                     _UpperCamelCase              =						re_decoder_block_proj_in.sub(lowerCAmelCase    ,       lowerCAmelCase					)

              # rename prior cond.model to upsampler.upsample_block and resnet
              elif re_prior_cond_conv_out.fullmatch(lowerCAmelCase					):
                     _UpperCamelCase              =						re_prior_cond_conv_out.match(lowerCAmelCase					)
                     _UpperCamelCase              =						regex_match.groups()
                     _UpperCamelCase              =						int(groups[1]					) * 2 + int(groups[2]					) - 2
                     _UpperCamelCase              =						f'''conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'''
                     _UpperCamelCase              =						re_prior_cond_conv_out.sub(lowerCAmelCase    ,       lowerCAmelCase					)

              elif re_prior_cond_resnet.fullmatch(lowerCAmelCase					):
                     _UpperCamelCase              =						re_prior_cond_resnet.match(lowerCAmelCase					)
                     _UpperCamelCase              =						regex_match.groups()
                     _UpperCamelCase              =						int(groups[1]					) * 2 + int(groups[2]					) - 2
                     _UpperCamelCase              =						{'''1''': 1, '''3''': 2}[groups[-2]]
                     _UpperCamelCase              =						f'''conditioner_blocks.upsampler.upsample_block.{block_index}.'''
                     _UpperCamelCase              =						f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
                     _UpperCamelCase              =						prefix + resnet_block
                     _UpperCamelCase              =						re_prior_cond_resnet.sub(lowerCAmelCase    ,       lowerCAmelCase					)

              elif re_prior_cond_proj_in.fullmatch(lowerCAmelCase					):
                     _UpperCamelCase              =						re_prior_cond_proj_in.match(lowerCAmelCase					)
                     _UpperCamelCase              =						regex_match.groups()
                     _UpperCamelCase              =						f'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
                     _UpperCamelCase              =						re_prior_cond_proj_in.sub(lowerCAmelCase    ,       lowerCAmelCase					)

              # keep original key
              else:
                     _UpperCamelCase              =						original_key

              _UpperCamelCase              =						replace_key(lowerCAmelCase					)

              if f'''{key_prefix}.{key}''' not in model_state_dict or key is None:
                     print(f'''failed converting {original_key} to {key}, does not match'''					)

              # handle missmatched shape
              elif value.shape != model_state_dict[f'''{key_prefix}.{key}'''].shape:
                     _UpperCamelCase              =						model_state_dict[f'''{key_prefix}.{key}''']
                     print(f'''{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match'''					)
                     _UpperCamelCase              =						original_key

              _UpperCamelCase              =						original_key
              _UpperCamelCase              =						value

       return new_dict


@torch.no_grad()
def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase=None    ,       lowerCAmelCase=None					):
       for file in MODEL_MAPPING[model_name]:
              if not os.path.isfile(f'''{pytorch_dump_folder_path}/{file.split("/"					)[-1]}'''					):
                     _UpperCamelCase              =						requests.get(f'''{PREFIX}{file}'''    ,       allow_redirects=lowerCAmelCase					)
                     os.makedirs(f'''{pytorch_dump_folder_path}/'''    ,       exist_ok=lowerCAmelCase					)
                     open(f'''{pytorch_dump_folder_path}/{file.split("/"					)[-1]}'''    ,       '''wb'''					).write(r.content					)

       _UpperCamelCase              =						MODEL_MAPPING[model_name.split('''/'''					)[-1]]

       _UpperCamelCase              =						JukeboxConfig.from_pretrained(lowerCAmelCase					)
       _UpperCamelCase              =						JukeboxModel(lowerCAmelCase					)

       _UpperCamelCase              =						[]
       _UpperCamelCase              =						{}
       for i, dict_name in enumerate(lowerCAmelCase					):
              _UpperCamelCase              =						torch.load(f'''{pytorch_dump_folder_path}/{dict_name.split("/"					)[-1]}'''					)['''model''']

              _UpperCamelCase              =						{}
              for k in old_dic.keys():
                     if k.endswith('''.b'''					):
                            _UpperCamelCase              =						old_dic[k]
                     elif k.endswith('''.w'''					):
                            _UpperCamelCase              =						old_dic[k]
                     elif "level_2" not in dict_name and "cond.model." in k:
                            _UpperCamelCase              =						old_dic[k]
                     else:
                            _UpperCamelCase              =						old_dic[k]

              _UpperCamelCase              =						'''vqvae''' if i == 0 else f'''priors.{3 - i}'''
              _UpperCamelCase              =						fix_jukebox_keys(lowerCAmelCase    ,       model.state_dict()    ,       lowerCAmelCase    ,       lowerCAmelCase					)
              weight_dict.append(lowerCAmelCase					)

       _UpperCamelCase              =						weight_dict.pop(0					)
       model.vqvae.load_state_dict(lowerCAmelCase					)
       for i in range(len(lowerCAmelCase					)					):
              model.priors[i].load_state_dict(weight_dict[2 - i]					)

       Path(lowerCAmelCase					).mkdir(exist_ok=lowerCAmelCase					)
       with open(f'''{pytorch_dump_folder_path}/mapping.json'''    ,       '''w'''					) as txtfile:
              json.dump(lowerCAmelCase    ,       lowerCAmelCase					)

       print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''					)
       model.save_pretrained(lowerCAmelCase					)

       return weight_dict


if __name__ == "__main__":
       lowercase    : Tuple    	=  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--model_name""",
           default="""jukebox-5b-lyrics""",
           type=str,
           help="""Name of the model you'd like to convert.""",
       )
       parser.add_argument(
           """--pytorch_dump_folder_path""",
           default="""jukebox-5b-lyrics-converted""",
           type=str,
           help="""Path to the output PyTorch model directory.""",
       )
       lowercase    : Any    	=  parser.parse_args()
       convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)













import datasets

from .evaluate import evaluate


lowercase    : str    	=  """\
@article{hendrycks2021cuad,
      title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
      author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
      journal={arXiv preprint arXiv:2103.06268},
      year={2021}
}
"""

lowercase    : Tuple    	=  """
This metric wrap the official scoring script for version 1 of the Contract
Understanding Atticus Dataset (CUAD).
Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510
commercial legal contracts that have been manually labeled to identify 41 categories of important
clauses that lawyers look for when reviewing contracts in connection with corporate transactions.
"""

lowercase    : List[str]    	=  """
Computes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).
Args:
    predictions: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair as given in the references (see below)
        - 'prediction_text': list of possible texts for the answer, as a list of strings
        depending on a threshold on the confidence probability of each prediction.
    references: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair (see above),
        - 'answers': a Dict in the CUAD dataset format
            {
                'text': list of possible texts for the answer, as a list of strings
                'answer_start': list of start positions for the answer, as a list of ints
            }
            Note that answer_start values are not taken into account to compute the metric.
Returns:
    'exact_match': Exact match (the normalized answer exactly match the gold answer)
    'f1': The F-score of predicted tokens versus the gold answer
    'aupr': Area Under the Precision-Recall curve
    'prec_at_80_recall': Precision at 80% recall
    'prec_at_90_recall': Precision at 90% recall
Examples:
    >>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
    >>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
    >>> cuad_metric = datasets.load_metric(\"cuad\")
    >>> results = cuad_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}
"""

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

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return datasets.MetricInfo(
                description=_DESCRIPTION,       citation=_CITATION,       inputs_description=_KWARGS_DESCRIPTION,       features=datasets.Features(
                    {
                        '''predictions''': {
                            '''id''': datasets.Value('''string'''      ),
                            '''prediction_text''': datasets.features.Sequence(datasets.Value('''string'''      )      ),
                        },
                        '''references''': {
                            '''id''': datasets.Value('''string'''      ),
                            '''answers''': datasets.features.Sequence(
                                {
                                    '''text''': datasets.Value('''string'''      ),
                                    '''answer_start''': datasets.Value('''int32'''      ),
                                }      ),
                        },
                    }      ),       codebase_urls=['''https://www.atticusprojectai.org/cuad'''],       reference_urls=['''https://www.atticusprojectai.org/cuad'''],       )



     def 	_UpperCamelCase     (      self,       A,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						{prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
            _UpperCamelCase              =						[
                {
                    '''paragraphs''': [
                        {
                            '''qas''': [
                                {
                                    '''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
                                    '''id''': ref['''id'''],
                                }
                                for ref in references
                            ]
                        }
                    ]
                }
            ]
            _UpperCamelCase              =						evaluate(dataset=A,       predictions=A      )
            return score






