




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:     List[str]									= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE:     Optional[Any]									= {
    '''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:     int									= [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )->	Optional[Any]:




		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 )->	Optional[Any]:
		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 )->	Dict:
		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 )->	Tuple:
		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 )->	Union[str, Any]:
		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    ,						)->	List[str]:
		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_					      =							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_					      =							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_					      =							250004
		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:     Dict									= 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_0_2_4, type=int, help='''encoder output dim''')
						parser.add_argument('''--start_token_id''', default=2_5_0_0_0_4, type=int, help='''`decoder_start_token_id` of model config''')

						SCREAMING_SNAKE_CASE:     int									= 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 os

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

import datasets


SCREAMING_SNAKE_CASE__     					=			datasets.logging.get_logger(__name__)


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

SCREAMING_SNAKE_CASE__     					=			"\\nBLEURT 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)\nand 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\nit for your specific application (the latter is expected to perform better).\n\nSee the project's README at https://github.com/google-research/bleurt#readme for more information.\n"

SCREAMING_SNAKE_CASE__     					=			"\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    'scores': List of scores.\nExamples:\n\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> bleurt = datasets.load_metric(\"bleurt\")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results[\"scores\"]])\n    [1.03, 1.04]\n"

SCREAMING_SNAKE_CASE__     					=			{
    "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							):






		def _snake_case		(						self				:     List[str]):
				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 _snake_case		(						self				:     Any   ,  UpperCAmelCase				:     Any):
				# check that config name specifies a valid BLEURT model
				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').")
						SCREAMING_SNAKE_CASE_				:str				     =						"bleurt-base-128"

				if self.config_name.lower() in CHECKPOINT_URLS:
						SCREAMING_SNAKE_CASE_				:Dict				     =						self.config_name.lower()

				elif self.config_name.upper() in CHECKPOINT_URLS:
						SCREAMING_SNAKE_CASE_				:Dict				     =						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
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name])
				SCREAMING_SNAKE_CASE_				:Dict				     =						score.BleurtScorer(os.path.join(UpperCAmelCase   ,  UpperCAmelCase))







		def _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     List[str]   ,  UpperCAmelCase				:     Union[str, Any]):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self.scorer.score(references=UpperCAmelCase   ,  candidates=UpperCAmelCase)
				return {"scores": scores}






