


"""simple docstring"""







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()
a__    :							Union[str, Any]            =					logging.get_logger(__name__)

a__    :							int            =					{
    "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",
}
a__    :							List[Any]            =					[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




		'''simple docstring'''


		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        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




		'''simple docstring'''


		__SCREAMING_SNAKE_CASE						       =    []
		__SCREAMING_SNAKE_CASE						       =    fairseq_model.state_dict()

		__SCREAMING_SNAKE_CASE						       =    hf_model.feature_extractor

		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
				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 and "relative_attention_bias" not in name:
												__SCREAMING_SNAKE_CASE						       =    "bias"
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												__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        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




		'''simple docstring'''


		__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_       )




@torch.no_grad()
def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=None       ):




		'''simple docstring'''


		__SCREAMING_SNAKE_CASE						       =    torch.load(lowerCAmelCase_       )
		__SCREAMING_SNAKE_CASE						       =    WavLMConfigOrig(checkpoint["cfg"]       )
		__SCREAMING_SNAKE_CASE						       =    WavLMOrig(lowerCAmelCase_       )
		model.load_state_dict(checkpoint["model"]       )
		model.eval()

		if config_path is not None:
				__SCREAMING_SNAKE_CASE						       =    WavLMConfig.from_pretrained(lowerCAmelCase_       )
		else:
				__SCREAMING_SNAKE_CASE						       =    WavLMConfig()

		__SCREAMING_SNAKE_CASE						       =    WavLMModel(lowerCAmelCase_       )

		recursively_load_weights(lowerCAmelCase_  ,						lowerCAmelCase_       )

		hf_wavlm.save_pretrained(lowerCAmelCase_       )


if __name__ == "__main__":
		a__    :							Any            =					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''')
		a__    :							List[Any]            =					parser.parse_args()
		convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)




"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
		from ..ta.tokenization_ta import TaTokenizer
else:
		from ...utils.dummy_sentencepiece_objects import TaTokenizer

a__    :							Dict            =					TaTokenizer

if is_tokenizers_available():
		from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
		from ...utils.dummy_tokenizers_objects import TaTokenizerFast

a__    :							Any            =					TaTokenizerFast

a__    :							Tuple            =					{'''configuration_mt5''': ['''MT5Config''', '''MT5OnnxConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__    :							int            =					[
		    '''MT5EncoderModel''',
		    '''MT5ForConditionalGeneration''',
		    '''MT5ForQuestionAnswering''',
		    '''MT5Model''',
		    '''MT5PreTrainedModel''',
		    '''MT5Stack''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__    :							str            =					['''TFMT5EncoderModel''', '''TFMT5ForConditionalGeneration''', '''TFMT5Model''']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__    :							Union[str, Any]            =					['''FlaxMT5EncoderModel''', '''FlaxMT5ForConditionalGeneration''', '''FlaxMT5Model''']


if TYPE_CHECKING:
		from .configuration_mta import MTaConfig, MTaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mta import (
				    MTaEncoderModel,
				    MTaForConditionalGeneration,
				    MTaForQuestionAnswering,
				    MTaModel,
				    MTaPreTrainedModel,
				    MTaStack,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
		import sys

		a__    :							str            =					_LazyModule(
		    __name__,
		    globals()['''__file__'''],
		    _import_structure,
		    extra_objects={'''MT5Tokenizer''': MTaTokenizer, '''MT5TokenizerFast''': MTaTokenizerFast},
		    module_spec=__spec__,
		)
