






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

SCREAMING_SNAKE_CASE__				     =			{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}

def        UpperCAmelCase__		(						lowerCamelCase_       :       List[str]   ,  lowerCamelCase_       :       int   ,  lowerCamelCase_       :       Any   ,  lowerCamelCase_       :       Optional[int]   ,  lowerCamelCase_       :       Dict				):
			for attribute in key.split('.'				):
						__a						:  Dict									=     getattr(lowerCamelCase_   ,  lowerCamelCase_				)

			if weight_type is not None:
						__a						:  int									=     getattr(lowerCamelCase_   ,  lowerCamelCase_				).shape
			else:
						__a						:  Optional[Any]									=     hf_pointer.shape

			assert hf_shape == value.shape, (
			    f'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
			    f''' {value.shape} for {full_name}'''
			)

			if weight_type == "weight":
						__a						:  Optional[int]									=     value
			elif weight_type == "weight_g":
						__a						:  str									=     value
			elif weight_type == "weight_v":
						__a						:  Optional[Any]									=     value
			elif weight_type == "bias":
						__a						:  str									=     value
			else:
						__a						:  int									=     value

			logger.info(f'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''				)

def        UpperCAmelCase__		(						lowerCamelCase_       :       Optional[int]   ,  lowerCamelCase_       :       Union[str, Any]   ,  lowerCamelCase_       :       Tuple				):
			__a						:  str									=     []
			__a						:  int									=     fairseq_model.state_dict()

			__a						:  Dict									=     hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

			for name, value in fairseq_dict.items():
						__a						:  Tuple									=     False
						if "conv_layers" in name:
									load_conv_layer(
									    lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  hf_model.config.feat_extract_norm == 'group'   ,  )
									__a						:  List[Any]									=     True
						else:
									for key, mapped_key in MAPPING.items():
												__a						:  List[str]									=     'hubert.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

												if key in name or (key.split('w2v_model.'				)[-1] == name.split('.'				)[0] and not is_finetuned):
															__a						:  List[str]									=     True
															if "*" in mapped_key:
																		__a						:  Optional[Any]									=     name.split(lowerCamelCase_				)[0].split('.'				)[-2]
																		__a						:  Optional[int]									=     mapped_key.replace('*'   ,  lowerCamelCase_				)
															if "weight_g" in name:
																		__a						:  Optional[int]									=     'weight_g'
															elif "weight_v" in name:
																		__a						:  List[str]									=     'weight_v'
															elif "weight" in name:
																		__a						:  str									=     'weight'
															elif "bias" in name:
																		__a						:  Dict									=     'bias'
															else:
																		__a						:  Tuple									=     None
															set_recursively(lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_				)
												continue
						if not is_used:
									unused_weights.append(lowerCamelCase_				)

			logger.warning(f'''Unused weights: {unused_weights}'''				)

def        UpperCAmelCase__		(						lowerCamelCase_       :       List[Any]   ,  lowerCamelCase_       :       int   ,  lowerCamelCase_       :       Dict   ,  lowerCamelCase_       :       Dict   ,  lowerCamelCase_       :       Union[str, Any]				):
			__a						:  Union[str, Any]									=     full_name.split('conv_layers.'				)[-1]
			__a						:  Union[str, Any]									=     name.split('.'				)
			__a						:  List[Any]									=     int(items[0]				)
			__a						:  List[str]									=     int(items[1]				)

			if type_id == 0:
						if "bias" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
									    f'''{full_name} has size {value.shape}, but'''
									    f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
									)
									__a						:  List[str]									=     value
									logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''				)
						elif "weight" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
									    f'''{full_name} has size {value.shape}, but'''
									    f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
									)
									__a						:  Tuple									=     value
									logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''				)
			elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
						if "bias" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
									    f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
									    " found."
									)
									__a						:  str									=     value
									logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
						elif "weight" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
									    f'''{full_name} has size {value.shape}, but'''
									    f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
									)
									__a						:  List[str]									=     value
									logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
			else:
						unused_weights.append(lowerCamelCase_				)

@torch.no_grad()
def        UpperCAmelCase__		(						lowerCamelCase_       :       Optional[Any]   ,  lowerCamelCase_       :       Union[str, Any]   ,  lowerCamelCase_       :       Dict=None   ,  lowerCamelCase_       :       Tuple=None   ,  lowerCamelCase_       :       Union[str, Any]=True				):
			if config_path is not None:
						__a						:  Optional[Any]									=     HubertConfig.from_pretrained(lowerCamelCase_				)
			else:
						__a						:  str									=     HubertConfig()

			if is_finetuned:
						if dict_path:
									__a						:  Dict									=     Dictionary.load(lowerCamelCase_				)

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									__a						:  List[Any]									=     target_dict.pad_index
									__a						:  List[Any]									=     target_dict.bos_index
									__a						:  Tuple									=     target_dict.eos_index
									__a						:  List[str]									=     len(target_dict.symbols				)
									__a						:  Union[str, Any]									=     os.path.join(lowerCamelCase_   ,  'vocab.json'				)
									if not os.path.isdir(lowerCamelCase_				):
												logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(lowerCamelCase_				)				)
												return
									os.makedirs(lowerCamelCase_   ,  exist_ok=lowerCamelCase_				)
									with open(lowerCamelCase_   ,  'w'   ,  encoding='utf-8'				) as vocab_handle:
												json.dump(target_dict.indices   ,  lowerCamelCase_				)
									__a						:  Optional[int]									=     WavaVecaCTCTokenizer(
									    lowerCamelCase_   ,  unk_token=target_dict.unk_word   ,  pad_token=target_dict.pad_word   ,  bos_token=target_dict.bos_word   ,  eos_token=target_dict.eos_word   ,  word_delimiter_token='|'   ,  do_lower_case=lowerCamelCase_   ,  )
									__a						:  Dict									=     True if config.feat_extract_norm == 'layer' else False
									__a						:  Tuple									=     WavaVecaFeatureExtractor(
									    feature_size=1   ,  sampling_rate=1_6_0_0_0   ,  padding_value=0   ,  do_normalize=lowerCamelCase_   ,  return_attention_mask=lowerCamelCase_   ,  )
									__a						:  List[Any]									=     WavaVecaProcessor(feature_extractor=lowerCamelCase_   ,  tokenizer=lowerCamelCase_				)
									processor.save_pretrained(lowerCamelCase_				)

						__a						:  Union[str, Any]									=     HubertForCTC(lowerCamelCase_				)
			else:
						__a						:  int									=     HubertModel(lowerCamelCase_				)

			if is_finetuned:
						__a						,					__a						,					__a						:  Any									=     fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]   ,  arg_overrides={'data': '/'.join(dict_path.split('/'				)[:-1]				)}				)
			else:
						__a						,					__a						,					__a						:  Tuple									=     fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				)

			__a						:  Optional[Any]									=     model[0].eval()

			recursively_load_weights(lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_				)

			hf_wavavec.save_pretrained(lowerCamelCase_				)


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__				     =			argparse.ArgumentParser()
							parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
							parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
							parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
							parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
							parser.add_argument(
							    '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
							)
							SCREAMING_SNAKE_CASE__				     =			parser.parse_args()
							convert_hubert_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
							)









'''simple docstring'''







import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


lowerCAmelCase     :  Optional[int]											=    datasets.logging.get_logger(__name__)


lowerCAmelCase     :  List[str]											=    """\
@inproceedings{bleurt,
  title={BLEURT: Learning Robust Metrics for Text Generation},
  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},
  booktitle={ACL},
  year={2020},
  url={https://arxiv.org/abs/2004.04696}
}
"""

lowerCAmelCase     :  List[Any]											=    """\
BLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)
and then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune
it for your specific application (the latter is expected to perform better).

See the project's README at https://github.com/google-research/bleurt#readme for more information.
"""

lowerCAmelCase     :  str											=    """
BLEURT score.

Args:
    `predictions` (list of str): prediction/candidate sentences
    `references` (list of str): reference sentences
    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.

Returns:
    'scores': List of scores.
Examples:

    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> bleurt = datasets.load_metric(\"bleurt\")
    >>> results = bleurt.compute(predictions=predictions, references=references)
    >>> print([round(v, 2) for v in results[\"scores\"]])
    [1.03, 1.04]
"""

lowerCAmelCase     :  Optional[Any]											=    {
    """bleurt-tiny-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip""",
    """bleurt-tiny-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip""",
    """bleurt-base-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip""",
    """bleurt-base-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip""",
    """bleurt-large-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip""",
    """bleurt-large-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip""",
    """BLEURT-20-D3""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip""",
    """BLEURT-20-D6""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip""",
    """BLEURT-20-D12""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip""",
    """BLEURT-20""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip""",
}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,       _KWARGS_DESCRIPTION						)
class 						UpperCamelCase__       (			datasets.Metric						):



     """simple docstring"""






     def a						(			self ):

            '''simple docstring'''

            return datasets.MetricInfo(
                description=_DESCRIPTION   ,						citation=_CITATION   ,						homepage='https://github.com/google-research/bleurt'   ,						inputs_description=_KWARGS_DESCRIPTION   ,						features=datasets.Features(
                    {
                        'predictions': datasets.Value('string'   ,						id='sequence' ),
                        'references': datasets.Value('string'   ,						id='sequence' ),
                    } )   ,						codebase_urls=['https://github.com/google-research/bleurt']   ,						reference_urls=['https://github.com/google-research/bleurt', 'https://arxiv.org/abs/2004.04696']   ,						)







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            if self.config_name == "default":
                   logger.warning(
                       'Using default BLEURT-Base checkpoint for sequence maximum length 128. '
                       'You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').' )
                   _lowerCAmelCase     :				Tuple       =							'bleurt-base-128'

            if self.config_name.lower() in CHECKPOINT_URLS:
                   _lowerCAmelCase     :				int       =							self.config_name.lower()

            elif self.config_name.upper() in CHECKPOINT_URLS:
                   _lowerCAmelCase     :				str       =							self.config_name.upper()

            else:
                   raise KeyError(
                       F'{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}' )

            # download the model checkpoint specified by self.config_name and set up the scorer
            _lowerCAmelCase     :				Optional[Any]       =							dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name] )
            _lowerCAmelCase     :				str       =							score.BleurtScorer(os.path.join(snake_case__   ,						snake_case__ ) )







     def a						(			self   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							self.scorer.score(references=snake_case__   ,						candidates=snake_case__ )
            return {"scores": scores}

