






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


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

__UpperCamelCase       :		Union[str, 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': 'ctc_proj',
    'mask_emb': 'masked_spec_embed',
}
__UpperCamelCase       :		List[str]							=    [
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]
def   a_			(   _A   , _A   , _A   , _A   , _A   , _A	)      ->      Union[str, Any]:







				"""simple docstring"""
				for attribute in key.split('.'	):
								if is_finetuned:
												if attribute in ["quantizer", "project_q", "project_hid"]:
																# those layers are only relevant for pretraining and should be dropped
																return

												if attribute == "ctc_proj":
																# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
																snake_case__						=  'lm_head'

								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   a_			(   _A   , _A   , _A	)      ->      Optional[int]:







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

				snake_case__						=  hf_model.unispeech.feature_extractor

				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
								else:
												for key, mapped_key in MAPPING.items():
																snake_case__						=  'unispeech.' + 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(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:
																								# TODO: don't match quantizer.weight_proj
																								snake_case__						=  'weight'
																				else:
																								snake_case__						=  None
																				set_recursively(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_	)
																continue
								if not is_used:
												unused_weights.append(UpperCAmelCase_	)

				logger.warning(f'''Unused weights: {unused_weights}'''	)
def   a_			(   _A   , _A   , _A   , _A   , _A	)      ->      List[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:
												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_	)
@torch.no_grad()
def   a_			(   _A   , _A   , _A=None   , _A=None   , _A=True	)      ->      Union[str, Any]:







				"""simple docstring"""
				if config_path is not None:
								snake_case__						=  UniSpeechConfig.from_pretrained(UpperCAmelCase_	)
				else:
								snake_case__						=  UniSpeechConfig()

				if is_finetuned:
								if dict_path:
												snake_case__						=  Dictionary.load_from_json(UpperCAmelCase_	)

												# 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(UpperCAmelCase_   , 'vocab.json'	)
												if not os.path.isdir(UpperCAmelCase_	):
																logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(UpperCAmelCase_	)	)
																return
												os.makedirs(UpperCAmelCase_   , exist_ok=UpperCAmelCase_	)
												snake_case__						=  target_dict.indices

												# fairseq has the <pad> and <s> switched
												snake_case__						=  42
												snake_case__						=  43
												with open(UpperCAmelCase_   , 'w'   , encoding='utf-8'	) as vocab_handle:
																json.dump(UpperCAmelCase_   , UpperCAmelCase_	)
												snake_case__						=  WavaVecaPhonemeCTCTokenizer(
												    UpperCAmelCase_   , 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=UpperCAmelCase_   , )
												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=UpperCAmelCase_   , return_attention_mask=UpperCAmelCase_   , )
												snake_case__						=  WavaVecaProcessor(feature_extractor=UpperCAmelCase_   , tokenizer=UpperCAmelCase_	)
												processor.save_pretrained(UpperCAmelCase_	)

								snake_case__						=  UniSpeechForCTC(UpperCAmelCase_	)
				else:
								snake_case__						=  UniSpeechForPreTraining(UpperCAmelCase_	)

				if is_finetuned:
								snake_case__						=  fairseq.checkpoint_utils.load_model_ensemble_and_task(
								    [checkpoint_path]   , arg_overrides={'data': '/'.join(dict_path.split('/'	)[:-1]	), 'w2v_path': checkpoint_path}	)
				else:
								snake_case__						=  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	)

				snake_case__						=  model[0].eval()

				recursively_load_weights(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_	)

				hf_unispeech.save_pretrained(UpperCAmelCase_	)


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       :		Optional[int]							=    parser.parse_args()
							convert_unispeech_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
							)









'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


snake_case_					:      Union[str, Any]    			=    {
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_					:      str    			=    [
					    'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'AdaptiveEmbedding',
					    'TransfoXLForSequenceClassification',
					    'TransfoXLLMHeadModel',
					    'TransfoXLModel',
					    'TransfoXLPreTrainedModel',
					    'load_tf_weights_in_transfo_xl',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_					:      Any    			=    [
					    'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFAdaptiveEmbedding',
					    'TFTransfoXLForSequenceClassification',
					    'TFTransfoXLLMHeadModel',
					    'TFTransfoXLMainLayer',
					    'TFTransfoXLModel',
					    'TFTransfoXLPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
					from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_transfo_xl import (
										    TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    AdaptiveEmbedding,
										    TransfoXLForSequenceClassification,
										    TransfoXLLMHeadModel,
										    TransfoXLModel,
										    TransfoXLPreTrainedModel,
										    load_tf_weights_in_transfo_xl,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_transfo_xl import (
										    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFAdaptiveEmbedding,
										    TFTransfoXLForSequenceClassification,
										    TFTransfoXLLMHeadModel,
										    TFTransfoXLMainLayer,
										    TFTransfoXLModel,
										    TFTransfoXLPreTrainedModel,
										)

else:
					import sys

					snake_case_					:      Dict    			=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


