






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__UpperCamelCase					:		Dict	  =						logging.get_logger(__name__)

__UpperCamelCase					:		List[str]	  =						{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.linear_k""": """encoder.layers.*.self_attn.linear_k""",
    """self_attn.linear_v""": """encoder.layers.*.self_attn.linear_v""",
    """self_attn.linear_q""": """encoder.layers.*.self_attn.linear_q""",
    """self_attn.pos_bias_u""": """encoder.layers.*.self_attn.pos_bias_u""",
    """self_attn.pos_bias_v""": """encoder.layers.*.self_attn.pos_bias_v""",
    """self_attn.linear_out""": """encoder.layers.*.self_attn.linear_out""",
    """self_attn.linear_pos""": """encoder.layers.*.self_attn.linear_pos""",
    """self_attn.rotary_emb""": """encoder.embed_positions""",
    """self_attn_layer_norm""": """encoder.layers.*.self_attn_layer_norm""",
    """conv_module.pointwise_conv1""": """encoder.layers.*.conv_module.pointwise_conv1""",
    """conv_module.pointwise_conv2""": """encoder.layers.*.conv_module.pointwise_conv2""",
    """conv_module.depthwise_conv""": """encoder.layers.*.conv_module.depthwise_conv""",
    """conv_module.batch_norm""": """encoder.layers.*.conv_module.batch_norm""",
    """conv_module.layer_norm""": """encoder.layers.*.conv_module.layer_norm""",
    """ffn1.w_1""": """encoder.layers.*.ffn1.intermediate_dense""",
    """ffn1.w_2""": """encoder.layers.*.ffn1.output_dense""",
    """ffn1.layer_norm""": """encoder.layers.*.ffn1_layer_norm""",
    """ffn2.w_1""": """encoder.layers.*.ffn2.intermediate_dense""",
    """ffn2.w_2""": """encoder.layers.*.ffn2.output_dense""",
    """ffn2.layer_norm""": """encoder.layers.*.ffn2_layer_norm""",
    """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					:		Optional[Any]	  =						[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]
def      a_ (      _A      ,   _A      ,   _A      ,   _A      ,   _A  )			->			Optional[int]:







				"""simple docstring"""
				for attribute in key.split('.'  ):
								snake_case__									=     getattr(_A      ,   _A  )

				if weight_type is not None:
								snake_case__									=     getattr(_A      ,   _A  ).shape
				else:
								snake_case__									=     hf_pointer.shape

				if hf_shape != value.shape:
								raise ValueError(
								    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
				elif weight_type == "running_mean":
								snake_case__									=     value
				elif weight_type == "running_var":
								snake_case__									=     value
				elif weight_type == "num_batches_tracked":
								snake_case__									=     value
				elif weight_type == "inv_freq":
								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      a_ (      _A      ,   _A      ,   _A  )			->			Any:







				"""simple docstring"""
				snake_case__									=     []
				snake_case__									=     fairseq_model.state_dict()

				snake_case__									=     hf_model.wavaveca_conformer.feature_extractor

				for name, value in fairseq_dict.items():
								snake_case__									=     False
								if "conv_layers" in name:
												load_conv_layer(
												    _A      ,   _A      ,   _A      ,   _A      ,   hf_model.config.feat_extract_norm == 'group'      ,   )
												snake_case__									=     True
								else:
												for key, mapped_key in MAPPING.items():
																snake_case__									=     'wav2vec2_conformer.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
																if key in name or key.split('w2v_model.'  )[-1] == name.split('.'  )[0]:
																				snake_case__									=     True
																				if "*" in mapped_key:
																								snake_case__									=     name.split(_A  )[0].split('.'  )[-2]
																								snake_case__									=     mapped_key.replace('*'      ,   _A  )
																				if "pos_bias_u" in name:
																								snake_case__									=     None
																				elif "pos_bias_v" in name:
																								snake_case__									=     None
																				elif "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:
																								# TODO: don't match quantizer.weight_proj
																								snake_case__									=     'weight'
																				elif "running_mean" in name:
																								snake_case__									=     'running_mean'
																				elif "inv_freq" in name:
																								snake_case__									=     'inv_freq'
																				elif "running_var" in name:
																								snake_case__									=     'running_var'
																				elif "num_batches_tracked" in name:
																								snake_case__									=     'num_batches_tracked'
																				else:
																								snake_case__									=     None
																				set_recursively(_A      ,   _A      ,   _A      ,   _A      ,   _A  )
																continue
								if not is_used:
												unused_weights.append(_A  )

				logger.warning(f'''Unused weights: {unused_weights}'''  )
def      a_ (      _A      ,   _A      ,   _A      ,   _A      ,   _A  )			->			Optional[Any]:







				"""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:
												if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																raise ValueError(
																    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:
												if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																raise ValueError(
																    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:
												if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																raise ValueError(
																    f'''{full_name} has size {value.shape}, but'''
																    f''' {feature_extractor.conv_layers[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:
												if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																raise ValueError(
																    f'''{full_name} has size {value.shape}, but'''
																    f''' {feature_extractor.conv_layers[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(_A  )
@torch.no_grad()
def      a_ (      _A      ,   _A      ,   _A=None      ,   _A=None      ,   _A=True  )			->			Tuple:







				"""simple docstring"""
				if config_path is not None:
								snake_case__									=     WavaVecaConformerConfig.from_pretrained(_A      ,   hidden_act='swish'  )
				else:
								snake_case__									=     WavaVecaConformerConfig()

				if "rope" in checkpoint_path:
								snake_case__									=     'rotary'

				if is_finetuned:
								if dict_path:
												snake_case__									=     Dictionary.load(_A  )

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												snake_case__									=     target_dict.pad_index
												snake_case__									=     target_dict.bos_index
												snake_case__									=     target_dict.eos_index
												snake_case__									=     len(target_dict.symbols  )
												snake_case__									=     os.path.join(_A      ,   'vocab.json'  )
												if not os.path.isdir(_A  ):
																logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(_A  )  )
																return
												os.makedirs(_A      ,   exist_ok=_A  )
												snake_case__									=     target_dict.indices

												# fairseq has the <pad> and <s> switched
												snake_case__									=     0
												snake_case__									=     1
												with open(_A      ,   'w'      ,   encoding='utf-8'  ) as vocab_handle:
																json.dump(_A      ,   _A  )
												snake_case__									=     WavaVecaCTCTokenizer(
												    _A      ,   unk_token=target_dict.unk_word      ,   pad_token=target_dict.pad_word      ,   bos_token=target_dict.bos_word      ,   eos_token=target_dict.eos_word      ,   word_delimiter_token='|'      ,   do_lower_case=_A      ,   )
												snake_case__									=     True if config.feat_extract_norm == 'layer' else False
												snake_case__									=     WavaVecaFeatureExtractor(
												    feature_size=1      ,   sampling_rate=16000      ,   padding_value=0      ,   do_normalize=_A      ,   return_attention_mask=_A      ,   )
												snake_case__									=     WavaVecaProcessor(feature_extractor=_A      ,   tokenizer=_A  )
												processor.save_pretrained(_A  )

								snake_case__									=     WavaVecaConformerForCTC(_A  )
				else:
								snake_case__									=     WavaVecaConformerForPreTraining(_A  )

				if is_finetuned:
								snake_case__							,						snake_case__							,						snake_case__									=     fairseq.checkpoint_utils.load_model_ensemble_and_task(
								    [checkpoint_path]      ,   arg_overrides={'data': '/'.join(dict_path.split('/'  )[:-1]  )}  )
				else:
								snake_case__									=     argparse.Namespace(task='audio_pretraining'  )
								snake_case__									=     fairseq.tasks.setup_task(_A  )

								snake_case__							,						snake_case__							,						snake_case__									=     fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      ,   task=_A  )

				snake_case__									=     model[0].eval()

				recursively_load_weights(_A      ,   _A      ,   not is_finetuned  )

				hf_wavavec.save_pretrained(_A  )


if __name__ == "__main__":
							__UpperCamelCase					:		Union[str, 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("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
							parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
							parser.add_argument(
							    """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
							)
							__UpperCamelCase					:		int	  =						parser.parse_args()
							convert_wavaveca_conformer_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
							)












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,
)


__UpperCamelCase					:		int	  =						{"""configuration_xglm""": ["""XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XGLMConfig"""]}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Dict	  =						["""XGLMTokenizer"""]

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Optional[int]	  =						["""XGLMTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		List[str]	  =						[
							    """XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """XGLMForCausalLM""",
							    """XGLMModel""",
							    """XGLMPreTrainedModel""",
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Any	  =						[
							    """FlaxXGLMForCausalLM""",
							    """FlaxXGLMModel""",
							    """FlaxXGLMPreTrainedModel""",
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		str	  =						[
							    """TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFXGLMForCausalLM""",
							    """TFXGLMModel""",
							    """TFXGLMPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm import XGLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm_fast import XGLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xglm import (
														    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXGLMForCausalLM,
														    TFXGLMModel,
														    TFXGLMPreTrainedModel,
														)


else:
							import sys

							__UpperCamelCase					:		Union[str, Any]	  =						_LazyModule(__name__, globals()["""__file__"""], _import_structure)




