




import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    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""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
__SCREAMING_SNAKE_CASE 						=[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							for attribute in key.split('''.'''     ):
														SCREAMING_SNAKE_CASE_   	=    getattr(_lowerCAmelCase  ,    _lowerCAmelCase     )

							if weight_type is not None:
														SCREAMING_SNAKE_CASE_   	=    getattr(_lowerCAmelCase  ,    _lowerCAmelCase     ).shape
							else:
														SCREAMING_SNAKE_CASE_   	=    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":
														SCREAMING_SNAKE_CASE_   	=    value
							elif weight_type == "weight_g":
														SCREAMING_SNAKE_CASE_   	=    value
							elif weight_type == "weight_v":
														SCREAMING_SNAKE_CASE_   	=    value
							elif weight_type == "bias":
														SCREAMING_SNAKE_CASE_   	=    value
							else:
														SCREAMING_SNAKE_CASE_   	=    value

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

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    []
							SCREAMING_SNAKE_CASE_   	=    fairseq_model.state_dict()

							SCREAMING_SNAKE_CASE_   	=    hf_model.feature_extractor
							SCREAMING_SNAKE_CASE_   	=    hf_model.adapter

							for name, value in fairseq_dict.items():
														SCREAMING_SNAKE_CASE_   	=    False
														if "conv_layers" in name:
																					load_conv_layer(
																					    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    hf_model.config.feat_extract_norm == '''group'''  ,    )
																					SCREAMING_SNAKE_CASE_   	=    True
														elif any(x in name for x in ['''adaptor''', '''w2v_encoder.proj.''', '''w2v_proj_ln.''']     ):
																					load_adapter(_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     )
																					SCREAMING_SNAKE_CASE_   	=    True
														else:
																					for key, mapped_key in MAPPING.items():
																												if key in name or key.split('''w2v_model.'''     )[-1] == name.split('''.'''     )[0]:
																																			SCREAMING_SNAKE_CASE_   	=    True
																																			if "*" in mapped_key:
																																										SCREAMING_SNAKE_CASE_   	=    name.split(_lowerCAmelCase     )[0].split('''.'''     )[-2]
																																										SCREAMING_SNAKE_CASE_   	=    mapped_key.replace('''*'''  ,    _lowerCAmelCase     )
																																			if "weight_g" in name:
																																										SCREAMING_SNAKE_CASE_   	=    '''weight_g'''
																																			elif "weight_v" in name:
																																										SCREAMING_SNAKE_CASE_   	=    '''weight_v'''
																																			elif "bias" in name:
																																										SCREAMING_SNAKE_CASE_   	=    '''bias'''
																																			elif "weight" in name:
																																										SCREAMING_SNAKE_CASE_   	=    '''weight'''
																																			else:
																																										SCREAMING_SNAKE_CASE_   	=    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 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    full_name.split('''conv_layers.'''     )[-1]
							SCREAMING_SNAKE_CASE_   	=    name.split('''.'''     )
							SCREAMING_SNAKE_CASE_   	=    int(items[0]     )
							SCREAMING_SNAKE_CASE_   	=    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."
																					)
																					SCREAMING_SNAKE_CASE_   	=    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."
																					)
																					SCREAMING_SNAKE_CASE_   	=    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."
																					)
																					SCREAMING_SNAKE_CASE_   	=    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."
																					)
																					SCREAMING_SNAKE_CASE_   	=    value
																					logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."     )
							else:
														unused_weights.append(_lowerCAmelCase     )

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    full_name.split('''adaptor.'''     )[-1]
							SCREAMING_SNAKE_CASE_   	=    name.split('''.'''     )

							if items[1].isdigit():
														SCREAMING_SNAKE_CASE_   	=    int(items[1]     )
							else:
														SCREAMING_SNAKE_CASE_   	=    None

							if "adaptor" not in full_name:
														if "proj_ln" in full_name:
																					# has to be layer norm
																					if "bias" in name:
																												assert (
																												    value.shape == adapter.proj_layer_norm.bias.data.shape
																												), F"{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."
																												SCREAMING_SNAKE_CASE_   	=    value
																												logger.info(F"Adapter proj layer norm bias was initialized from {full_name}."     )
																					if "weight" in name:
																												assert (
																												    value.shape == adapter.proj_layer_norm.weight.data.shape
																												), F"{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."
																												SCREAMING_SNAKE_CASE_   	=    value
														else:
																					# has to be projection layer
																					if "bias" in name:
																												assert (
																												    value.shape == adapter.proj.bias.data.shape
																												), F"{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."
																												SCREAMING_SNAKE_CASE_   	=    value
																												logger.info(F"Adapter proj layer bias was initialized from {full_name}."     )
																					if "weight" in name:
																												assert (
																												    value.shape == adapter.proj.weight.data.shape
																												), F"{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."
																												SCREAMING_SNAKE_CASE_   	=    value
																												logger.info(F"Adapter proj layer weight was initialized from {full_name}."     )
							elif isinstance(_lowerCAmelCase  ,    _lowerCAmelCase     ):
														if "bias" in name:
																					assert (
																					    value.shape == adapter.layers[layer_id].conv.bias.data.shape
																					), F"{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."
																					SCREAMING_SNAKE_CASE_   	=    value
																					logger.info(F"Adapter layer {layer_id} bias was initialized from {full_name}."     )
														elif "weight" in name:
																					assert (
																					    value.shape == adapter.layers[layer_id].conv.weight.data.shape
																					), F"{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."
																					SCREAMING_SNAKE_CASE_   	=    value
																					logger.info(F"Adapter layer {layer_id} bias was initialized from {full_name}."     )
							else:
														unused_weights.append(_lowerCAmelCase     )

def 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    emb.weight.shape
							SCREAMING_SNAKE_CASE_   	=    nn.Linear(_lowerCAmelCase  ,    _lowerCAmelCase  ,    bias=_lowerCAmelCase     )
							SCREAMING_SNAKE_CASE_   	=    emb.weight.data
							return lin_layer

@torch.no_grad()
def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    ):
							SCREAMING_SNAKE_CASE_   	=    WavaVecaConfig.from_pretrained(
							    _lowerCAmelCase  ,    add_adapter=_lowerCAmelCase  ,    adapter_stride=_lowerCAmelCase  ,    adapter_kernel_size=_lowerCAmelCase  ,    use_auth_token=_lowerCAmelCase  ,    output_hidden_size=_lowerCAmelCase  ,    )
							SCREAMING_SNAKE_CASE_   	=    MBartConfig.from_pretrained(_lowerCAmelCase     )

							# load model
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    fairseq.checkpoint_utils.load_model_ensemble_and_task(
							    [checkpoint_path]  ,    arg_overrides={
							        '''config_yaml''': config_yaml_path,
							        '''data''': '''/'''.join(dict_path.split('''/'''     )[:-1]     ),
							        '''w2v_path''': checkpoint_path,
							        '''load_pretrained_decoder_from''': None,
							    }  ,    )
							SCREAMING_SNAKE_CASE_   	=    model[0].eval()

							# load feature extractor
							SCREAMING_SNAKE_CASE_   	=    WavaVecaFeatureExtractor.from_pretrained(_lowerCAmelCase  ,    use_auth_token=_lowerCAmelCase     )

							# set weights for wav2vec2 encoder
							SCREAMING_SNAKE_CASE_   	=    WavaVecaModel(_lowerCAmelCase     )

							recursively_load_weights_wavaveca(model.encoder  ,    _lowerCAmelCase     )

							# load decoder weights
							SCREAMING_SNAKE_CASE_   	=    MBartForCausalLM(_lowerCAmelCase     )
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()  ,    strict=_lowerCAmelCase     )
							logger.warning(F"The following keys are missing when loading the decoder weights: {missing_keys}"     )
							logger.warning(F"The following keys are unexpected when loading the decoder weights: {unexpected_keys}"     )

							SCREAMING_SNAKE_CASE_   	=    SpeechEncoderDecoderModel(encoder=_lowerCAmelCase  ,    decoder=_lowerCAmelCase     )
							SCREAMING_SNAKE_CASE_   	=    False

							SCREAMING_SNAKE_CASE_   	=    MBartaaTokenizer(_lowerCAmelCase     )
							tokenizer.save_pretrained(_lowerCAmelCase     )

							SCREAMING_SNAKE_CASE_   	=    hf_wavavec.config.to_dict()
							SCREAMING_SNAKE_CASE_   	=    tokenizer.pad_token_id
							SCREAMING_SNAKE_CASE_   	=    tokenizer.bos_token_id
							SCREAMING_SNAKE_CASE_   	=    tokenizer.eos_token_id
							SCREAMING_SNAKE_CASE_   	=    '''mbart50'''
							SCREAMING_SNAKE_CASE_   	=    '''wav2vec2'''

							SCREAMING_SNAKE_CASE_   	=    tokenizer.eos_token_id
							SCREAMING_SNAKE_CASE_   	=    2_5_0_0_0_4
							SCREAMING_SNAKE_CASE_   	=    tokenizer.eos_token_id

							SCREAMING_SNAKE_CASE_   	=    SpeechEncoderDecoderConfig.from_dict(_lowerCAmelCase     )

							hf_wavavec.save_pretrained(_lowerCAmelCase     )
							feature_extractor.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_yaml_path""", default=None, type=str, help="""Path to yaml file of fine-tuned model""")
			parser.add_argument(
			    """--encoder_config_path""",
			    default="""facebook/wav2vec2-xls-r-1b""",
			    type=str,
			    help="""Path to hf encoder wav2vec2 checkpoint config""",
			)
			parser.add_argument(
			    """--decoder_config_path""",
			    default="""facebook/mbart-large-50-one-to-many-mmt""",
			    type=str,
			    help="""Path to hf decoder checkpoint config""",
			)
			parser.add_argument("""--add_adapter""", default=True, type=bool, help="""whethere to add model adapter layers""")
			parser.add_argument("""--adapter_stride""", default=2, type=int, help="""stride of adapter layers""")
			parser.add_argument("""--adapter_kernel_size""", default=3, type=int, help="""kernel size of adapter layers""")
			parser.add_argument("""--encoder_output_dim""", default=1_024, type=int, help="""encoder output dim""")
			parser.add_argument("""--start_token_id""", default=250_004, type=int, help="""`decoder_start_token_id` of model config""")

			__SCREAMING_SNAKE_CASE 						=parser.parse_args()
			convert_wavaveca_checkpoint(
			    args.checkpoint_path,
			    args.pytorch_dump_folder_path,
			    args.dict_path,
			    args.config_yaml_path,
			    encoder_config_path=args.encoder_config_path,
			    decoder_config_path=args.decoder_config_path,
			    add_adapter=args.add_adapter,
			    adapter_kernel_size=args.adapter_kernel_size,
			    adapter_stride=args.adapter_stride,
			    decoder_start_token_id=args.start_token_id,
			    encoder_output_dim=args.encoder_output_dim,
			)








import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
			import torch_xla.core.xla_model as xm
			import torch_xla.debug.metrics as met






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				def __init__(							self:   List[Any]		,					*_lowerCamelCase:   Tuple		,					_lowerCamelCase:   Optional[Any]=None		,					_lowerCamelCase:   Dict=None		,					**_lowerCamelCase:   Dict		):
											super().__init__(*_lowerCamelCase		,					**_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    eval_examples
											SCREAMING_SNAKE_CASE_   	=    post_process_function


				def     _A							(							self:   Tuple		,					_lowerCamelCase:   Optional[Dataset] = None		,					_lowerCamelCase:   List[str]=None		,					_lowerCamelCase:   Optional[List[str]] = None		,					_lowerCamelCase:   str = "eval"		,					**_lowerCamelCase:   Union[str, Any]		,					):
											SCREAMING_SNAKE_CASE_   	=    gen_kwargs.copy()
											SCREAMING_SNAKE_CASE_   	=    (
											    gen_kwargs['''max_length'''] if gen_kwargs.get('''max_length'''		) is not None else self.args.generation_max_length
											)
											SCREAMING_SNAKE_CASE_   	=    (
											    gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''		) is not None else self.args.generation_num_beams
											)
											SCREAMING_SNAKE_CASE_   	=    gen_kwargs

											SCREAMING_SNAKE_CASE_   	=    self.eval_dataset if eval_dataset is None else eval_dataset
											SCREAMING_SNAKE_CASE_   	=    self.get_eval_dataloader(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self.eval_examples if eval_examples is None else eval_examples

											# Temporarily disable metric computation, we will do it in the loop here.
											SCREAMING_SNAKE_CASE_   	=    self.compute_metrics
											SCREAMING_SNAKE_CASE_   	=    None
											SCREAMING_SNAKE_CASE_   	=    time.time()
											SCREAMING_SNAKE_CASE_   	=    self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
											try:
																		SCREAMING_SNAKE_CASE_   	=    eval_loop(
																		    _lowerCamelCase		,					description='''Evaluation'''		,					prediction_loss_only=True if compute_metrics is None else None		,					ignore_keys=_lowerCamelCase		,					metric_key_prefix=_lowerCamelCase		,					)
											finally:
																		SCREAMING_SNAKE_CASE_   	=    compute_metrics
											SCREAMING_SNAKE_CASE_   	=    self.args.eval_batch_size * self.args.world_size
											if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
																		start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
											output.metrics.update(
											    speed_metrics(
											        _lowerCamelCase		,					_lowerCamelCase		,					num_samples=output.num_samples		,					num_steps=math.ceil(output.num_samples / total_batch_size		)		,					)		)

											if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
																		# Only the main node write the results by default
																		SCREAMING_SNAKE_CASE_   	=    self.post_process_function(_lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    self.compute_metrics(_lowerCamelCase		)

																		# Prefix all keys with metric_key_prefix + '_'
																		for key in list(metrics.keys()		):
																									if not key.startswith(f"{metric_key_prefix}_"		):
																																SCREAMING_SNAKE_CASE_   	=    metrics.pop(_lowerCamelCase		)

																		metrics.update(output.metrics		)
											else:
																		SCREAMING_SNAKE_CASE_   	=    output.metrics

											if self.args.should_log:
																		# Only the main node log the results by default
																		self.log(_lowerCamelCase		)

											if self.args.tpu_metrics_debug or self.args.debug:
																		# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
																		xm.master_print(met.metrics_report()		)

											SCREAMING_SNAKE_CASE_   	=    self.callback_handler.on_evaluate(self.args		,					self.state		,					self.control		,					_lowerCamelCase		)
											return metrics





				def     _A							(							self:   List[str]		,					_lowerCamelCase:   int		,					_lowerCamelCase:   List[str]		,					_lowerCamelCase:   Optional[int]=None		,					_lowerCamelCase:   str = "test"		,					**_lowerCamelCase:   str		):
											SCREAMING_SNAKE_CASE_   	=    gen_kwargs.copy()

											SCREAMING_SNAKE_CASE_   	=    self.get_test_dataloader(_lowerCamelCase		)

											# Temporarily disable metric computation, we will do it in the loop here.
											SCREAMING_SNAKE_CASE_   	=    self.compute_metrics
											SCREAMING_SNAKE_CASE_   	=    None
											SCREAMING_SNAKE_CASE_   	=    time.time()
											SCREAMING_SNAKE_CASE_   	=    self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
											try:
																		SCREAMING_SNAKE_CASE_   	=    eval_loop(
																		    _lowerCamelCase		,					description='''Prediction'''		,					prediction_loss_only=True if compute_metrics is None else None		,					ignore_keys=_lowerCamelCase		,					metric_key_prefix=_lowerCamelCase		,					)
											finally:
																		SCREAMING_SNAKE_CASE_   	=    compute_metrics

											SCREAMING_SNAKE_CASE_   	=    self.args.eval_batch_size * self.args.world_size
											if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
																		start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
											output.metrics.update(
											    speed_metrics(
											        _lowerCamelCase		,					_lowerCamelCase		,					num_samples=output.num_samples		,					num_steps=math.ceil(output.num_samples / total_batch_size		)		,					)		)
											if self.post_process_function is None or self.compute_metrics is None:
																		return output

											SCREAMING_SNAKE_CASE_   	=    self.post_process_function(_lowerCamelCase		,					_lowerCamelCase		,					_lowerCamelCase		,					'''predict'''		)
											SCREAMING_SNAKE_CASE_   	=    self.compute_metrics(_lowerCamelCase		)

											# Prefix all keys with metric_key_prefix + '_'
											for key in list(metrics.keys()		):
																		if not key.startswith(f"{metric_key_prefix}_"		):
																									SCREAMING_SNAKE_CASE_   	=    metrics.pop(_lowerCamelCase		)
											metrics.update(output.metrics		)
											return PredictionOutput(predictions=predictions.predictions		,					label_ids=predictions.label_ids		,					metrics=_lowerCamelCase		)


