import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
__UpperCamelCase		:       Optional[int]									=     logging.get_logger(__name__)

__UpperCamelCase		:       Dict									=     {
    '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.grep_linear': 'encoder.layers.*.attention.gru_rel_pos_linear',
    'self_attn.relative_attention_bias': 'encoder.layers.*.attention.rel_attn_embed',
    'self_attn.grep_a': 'encoder.layers.*.attention.gru_rel_pos_const',
    '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': 'ctc_proj',
    'mask_emb': 'masked_spec_embed',
}
__UpperCamelCase		:       List[str]									=     [
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]
def       A	(    _lowercase		,  _lowercase		,  _lowercase		,  _lowercase		,  _lowercase						):
						for attribute in key.split('''.'''						):
												SCREAMING_SNAKE_CASE							:    str							=							getattr(_lowercase		,  _lowercase						)

						if weight_type is not None:
												SCREAMING_SNAKE_CASE							:    List[Any]							=							getattr(_lowercase		,  _lowercase						).shape
						else:
												SCREAMING_SNAKE_CASE							:    str							=							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							:    List[str]							=							value
						elif weight_type == "weight_g":
												SCREAMING_SNAKE_CASE							:    List[Any]							=							value
						elif weight_type == "weight_v":
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							value
						elif weight_type == "bias":
												SCREAMING_SNAKE_CASE							:    List[str]							=							value
						else:
												SCREAMING_SNAKE_CASE							:    Any							=							value

						logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""						)
def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    List[str]							=							[]
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							fairseq_model.state_dict()

						SCREAMING_SNAKE_CASE							:    Dict							=							hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												SCREAMING_SNAKE_CASE							:    str							=							False
												if "conv_layers" in name:
																		load_conv_layer(
																		    _lowercase		,  _lowercase		,  _lowercase		,  _lowercase		,  hf_model.config.feat_extract_norm == '''group'''		,  )
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							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							:    Optional[Any]							=							True
																														if "*" in mapped_key:
																																				SCREAMING_SNAKE_CASE							:    Tuple							=							name.split(_lowercase						)[0].split('''.'''						)[-2]
																																				SCREAMING_SNAKE_CASE							:    Tuple							=							mapped_key.replace('''*'''		,  _lowercase						)
																														if "weight_g" in name:
																																				SCREAMING_SNAKE_CASE							:    Any							=							'''weight_g'''
																														elif "weight_v" in name:
																																				SCREAMING_SNAKE_CASE							:    Optional[int]							=							'''weight_v'''
																														elif "bias" in name and "relative_attention_bias" not in name:
																																				SCREAMING_SNAKE_CASE							:    List[str]							=							'''bias'''
																														elif "weight" in name:
																																				# TODO: don't match quantizer.weight_proj
																																				SCREAMING_SNAKE_CASE							:    Optional[int]							=							'''weight'''
																														else:
																																				SCREAMING_SNAKE_CASE							:    Optional[Any]							=							None

																														set_recursively(_lowercase		,  _lowercase		,  _lowercase		,  _lowercase		,  _lowercase						)
																								continue
												if not is_used:
																		unused_weights.append(_lowercase						)

						logger.warning(f"""Unused weights: {unused_weights}"""						)
def       A	(    _lowercase		,  _lowercase		,  _lowercase		,  _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							full_name.split('''conv_layers.'''						)[-1]
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							name.split('''.'''						)
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							int(items[0]						)
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							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							:    Optional[Any]							=							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							:    str							=							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							:    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."""
																		)
																		SCREAMING_SNAKE_CASE							:    str							=							value
																		logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
						else:
												unused_weights.append(_lowercase						)
@torch.no_grad()
def       A	(    _lowercase		,  _lowercase		,  _lowercase=None						):
						# load the pre-trained checkpoints
						SCREAMING_SNAKE_CASE							:    Any							=							torch.load(_lowercase						)
						SCREAMING_SNAKE_CASE							:    int							=							WavLMConfigOrig(checkpoint['''cfg''']						)
						SCREAMING_SNAKE_CASE							:    Tuple							=							WavLMOrig(_lowercase						)
						model.load_state_dict(checkpoint['''model''']						)
						model.eval()

						if config_path is not None:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							WavLMConfig.from_pretrained(_lowercase						)
						else:
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							WavLMConfig()

						SCREAMING_SNAKE_CASE							:    Dict							=							WavLMModel(_lowercase						)

						recursively_load_weights(_lowercase		,  _lowercase						)

						hf_wavlm.save_pretrained(_lowercase						)


if __name__ == "__main__":
					__UpperCamelCase		:       Optional[int]									=     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('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
					__UpperCamelCase		:       Optional[Any]									=     parser.parse_args()
					convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)






import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
    '''split_dict'''		,  [
        SplitDict(),
        SplitDict({'''train''': SplitInfo(name='''train'''		,  num_bytes=1_337		,  num_examples=42		,  dataset_name='''my_dataset'''						)}						),
        SplitDict({'''train''': SplitInfo(name='''train'''		,  num_bytes=1_337		,  num_examples=42						)}						),
        SplitDict({'''train''': SplitInfo()}						),
    ]		,  )
def       A	(    _lowercase						):
						SCREAMING_SNAKE_CASE							:    Tuple							=							split_dict._to_yaml_list()
						assert len(_lowercase						) == len(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Tuple							=							SplitDict._from_yaml_list(_lowercase						)
						for split_name, split_info in split_dict.items():
												# dataset_name field is deprecated, and is therefore not part of the YAML dump
												SCREAMING_SNAKE_CASE							:    Any							=							None
												# the split name of split_dict takes over the name of the split info object
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							split_name
						assert split_dict == reloaded



@pytest.mark.parametrize(
    '''split_info'''		,  [SplitInfo(), SplitInfo(dataset_name=_lowercase						), SplitInfo(dataset_name='''my_dataset'''						)]						)
def       A	(    _lowercase						):
						# For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
						# field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
						SCREAMING_SNAKE_CASE							:    List[Any]							=							asdict(SplitDict({'''train''': split_info}						)						)
						assert "dataset_name" in split_dict_asdict["train"]
						assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name





