

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

_UpperCAmelCase		:   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""",
}
_UpperCAmelCase		:   Dict										=       [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							for attribute in key.split('.'      ):
														snake_case_							=    getattr(UpperCamelCase__   ,							UpperCamelCase__      )

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

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





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    []
							snake_case_							=    fairseq_model.state_dict()

							snake_case_							=    hf_model.feature_extractor
							snake_case_							=    hf_model.adapter

							for name, value in fairseq_dict.items():
														snake_case_							=    False
														if "conv_layers" in name:
																					load_conv_layer(
																					    UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							hf_model.config.feat_extract_norm == 'group'   ,							)
																					snake_case_							=    True
														elif any(x in name for x in ['adaptor', 'w2v_encoder.proj.', 'w2v_proj_ln.']      ):
																					load_adapter(UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      )
																					snake_case_							=    True
														else:
																					for key, mapped_key in MAPPING.items():
																												if key in name or key.split('w2v_model.'      )[-1] == name.split('.'      )[0]:
																																			snake_case_							=    True
																																			if "*" in mapped_key:
																																										snake_case_							=    name.split(UpperCamelCase__      )[0].split('.'      )[-2]
																																										snake_case_							=    mapped_key.replace('*'   ,							UpperCamelCase__      )
																																			if "weight_g" in name:
																																										snake_case_							=    'weight_g'
																																			elif "weight_v" in name:
																																										snake_case_							=    'weight_v'
																																			elif "bias" in name:
																																										snake_case_							=    'bias'
																																			elif "weight" in name:
																																										snake_case_							=    'weight'
																																			else:
																																										snake_case_							=    None
																																			set_recursively(UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      )
																												continue
														if not is_used:
																					unused_weights.append(UpperCamelCase__      )

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





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    full_name.split('conv_layers.'      )[-1]
							snake_case_							=    name.split('.'      )
							snake_case_							=    int(items[0]      )
							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.'''
																					)
																					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.'''
																					)
																					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."
																					)
																					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.'''
																					)
																					snake_case_							=    value
																					logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''      )
							else:
														unused_weights.append(UpperCamelCase__      )





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    full_name.split('adaptor.'      )[-1]
							snake_case_							=    name.split('.'      )

							if items[1].isdigit():
														snake_case_							=    int(items[1]      )
							else:
														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.'''
																												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.'''
																												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.'''
																												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.'''
																												snake_case_							=    value
																												logger.info(F'''Adapter proj layer weight was initialized from {full_name}.'''      )
							elif isinstance(UpperCamelCase__   ,							UpperCamelCase__      ):
														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.'''
																					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.'''
																					snake_case_							=    value
																					logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.'''      )
							else:
														unused_weights.append(UpperCamelCase__      )





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_				,    snake_case_							=    emb.weight.shape
							snake_case_							=    nn.Linear(UpperCamelCase__   ,							UpperCamelCase__   ,							bias=UpperCamelCase__      )
							snake_case_							=    emb.weight.data
							return lin_layer





@torch.no_grad()
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							):







							'''simple docstring'''






							snake_case_							=    WavaVecaConfig.from_pretrained(
							    UpperCamelCase__   ,							add_adapter=UpperCamelCase__   ,							adapter_stride=UpperCamelCase__   ,							adapter_kernel_size=UpperCamelCase__   ,							use_auth_token=UpperCamelCase__   ,							output_hidden_size=UpperCamelCase__   ,							)
							snake_case_							=    MBartConfig.from_pretrained(UpperCamelCase__      )

							# load model
							snake_case_				,    snake_case_				,    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,
							    }   ,							)
							snake_case_							=    model[0].eval()

							# load feature extractor
							snake_case_							=    WavaVecaFeatureExtractor.from_pretrained(UpperCamelCase__   ,							use_auth_token=UpperCamelCase__      )

							# set weights for wav2vec2 encoder
							snake_case_							=    WavaVecaModel(UpperCamelCase__      )

							recursively_load_weights_wavaveca(model.encoder   ,							UpperCamelCase__      )

							# load decoder weights
							snake_case_							=    MBartForCausalLM(UpperCamelCase__      )
							snake_case_				,    snake_case_							=    hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()   ,							strict=UpperCamelCase__      )
							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}'''      )

							snake_case_							=    SpeechEncoderDecoderModel(encoder=UpperCamelCase__   ,							decoder=UpperCamelCase__      )
							snake_case_							=    False

							snake_case_							=    MBartaaTokenizer(UpperCamelCase__      )
							tokenizer.save_pretrained(UpperCamelCase__      )

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

							snake_case_							=    tokenizer.eos_token_id
							snake_case_							=    250004
							snake_case_							=    tokenizer.eos_token_id

							snake_case_							=    SpeechEncoderDecoderConfig.from_dict(UpperCamelCase__      )

							hf_wavavec.save_pretrained(UpperCamelCase__      )
							feature_extractor.save_pretrained(UpperCamelCase__      )


if __name__ == "__main__":
					_UpperCAmelCase		:   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=1024, type=int, help="""encoder output dim""")
					parser.add_argument("""--start_token_id""", default=25_0004, type=int, help="""`decoder_start_token_id` of model config""")

					_UpperCAmelCase		:   Optional[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

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict





@pytest.mark.parametrize(
    'files'   ,							[
        ['full:README.md', 'dataset_infos.json'],
        ['empty:README.md', 'dataset_infos.json'],
        ['dataset_infos.json'],
        ['full:README.md'],
    ]   ,							)
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    tmp_path_factory.mktemp('dset_infos_dir'      )
							if "full:README.md" in files:
														with open(dataset_infos_dir / 'README.md'   ,							'w'      ) as f:
																					f.write('---\ndataset_info:\n  dataset_size: 42\n---'      )
							if "empty:README.md" in files:
														with open(dataset_infos_dir / 'README.md'   ,							'w'      ) as f:
																					f.write(''      )
    # we want to support dataset_infos.json for backward compatibility
							if "dataset_infos.json" in files:
														with open(dataset_infos_dir / 'dataset_infos.json'   ,							'w'      ) as f:
																					f.write('{"default": {"dataset_size": 42}}'      )
							snake_case_							=    DatasetInfosDict.from_directory(UpperCamelCase__      )
							assert dataset_infos
							assert dataset_infos["default"].dataset_size == 42





@pytest.mark.parametrize(
    'dataset_info'   ,							[
        DatasetInfo(),
        DatasetInfo(
            description='foo'   ,							features=Features({'a': Value('int32'      )}      )   ,							builder_name='builder'   ,							config_name='config'   ,							version='1.0.0'   ,							splits=[{'name': 'train'}]   ,							download_size=42   ,							),
    ]   ,							)
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    str(UpperCamelCase__      )
							dataset_info.write_to_directory(UpperCamelCase__      )
							snake_case_							=    DatasetInfo.from_directory(UpperCamelCase__      )
							assert dataset_info == reloaded
							assert os.path.exists(os.path.join(UpperCamelCase__   ,							'dataset_info.json'      )      )





def 					__lowerCamelCase	( ):







							'''simple docstring'''






							snake_case_							=    DatasetInfo(
							    description='foo'   ,							citation='bar'   ,							homepage='https://foo.bar'   ,							license='CC0'   ,							features=Features({'a': Value('int32'      )}      )   ,							post_processed={}   ,							supervised_keys=()   ,							task_templates=[]   ,							builder_name='builder'   ,							config_name='config'   ,							version='1.0.0'   ,							splits=[{'name': 'train', 'num_examples': 42}]   ,							download_checksums={}   ,							download_size=1337   ,							post_processing_size=442   ,							dataset_size=1234   ,							size_in_bytes=1337 + 442 + 1234   ,							)
							snake_case_							=    dataset_info._to_yaml_dict()
							assert sorted(UpperCamelCase__      ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML      )
							for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
														assert key in dataset_info_yaml_dict
														assert isinstance(dataset_info_yaml_dict[key]   ,							(list, dict, int, str)      )
							snake_case_							=    yaml.safe_dump(UpperCamelCase__      )
							snake_case_							=    yaml.safe_load(UpperCamelCase__      )
							assert dataset_info_yaml_dict == reloaded





def 					__lowerCamelCase	( ):







							'''simple docstring'''






							snake_case_							=    DatasetInfo()
							snake_case_							=    dataset_info._to_yaml_dict()
							assert dataset_info_yaml_dict == {}





@pytest.mark.parametrize(
    'dataset_infos_dict'   ,							[
        DatasetInfosDict(),
        DatasetInfosDict({'default': DatasetInfo()}      ),
        DatasetInfosDict({'my_config_name': DatasetInfo()}      ),
        DatasetInfosDict(
            {
                'default': DatasetInfo(
                    description='foo'   ,							features=Features({'a': Value('int32'      )}      )   ,							builder_name='builder'   ,							config_name='config'   ,							version='1.0.0'   ,							splits=[{'name': 'train'}]   ,							download_size=42   ,							)
            }      ),
        DatasetInfosDict(
            {
                'v1': DatasetInfo(dataset_size=42      ),
                'v2': DatasetInfo(dataset_size=1337      ),
            }      ),
    ]   ,							)
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    str(UpperCamelCase__      )
							dataset_infos_dict.write_to_directory(UpperCamelCase__      )
							snake_case_							=    DatasetInfosDict.from_directory(UpperCamelCase__      )

							# the config_name of the dataset_infos_dict take over the attribute
							for config_name, dataset_info in dataset_infos_dict.items():
														snake_case_							=    config_name
														# the yaml representation doesn't include fields like description or citation
														# so we just test that we can recover what we can from the yaml
														snake_case_							=    DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()      )
							assert dataset_infos_dict == reloaded

							if dataset_infos_dict:
														assert os.path.exists(os.path.join(UpperCamelCase__   ,							'README.md'      )      )




