



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()
SCREAMING_SNAKE_CASE__   			=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   			=   {
    '''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''',
}
SCREAMING_SNAKE_CASE__   			=   [
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   Dict:
			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
												A__	  =     'lm_head'

						A__	  =     getattr(__UpperCamelCase			,						__UpperCamelCase       )

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

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


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   List[Any]:
			A__	  =     []
			A__	  =     fairseq_model.state_dict()

			A__	  =     hf_model.unispeech.feature_extractor

			for name, value in fairseq_dict.items():
						A__	  =     False
						if "conv_layers" in name:
									load_conv_layer(
									    __UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						hf_model.config.feat_extract_norm == 'group'			,						)
									A__	  =     True
						else:
									for key, mapped_key in MAPPING.items():
												A__	  =     '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]:
															A__	  =     True
															if "*" in mapped_key:
																		A__	  =     name.split(__UpperCamelCase       )[0].split('.'       )[-2]
																		A__	  =     mapped_key.replace('*'			,						__UpperCamelCase       )
															if "weight_g" in name:
																		A__	  =     'weight_g'
															elif "weight_v" in name:
																		A__	  =     'weight_v'
															elif "bias" in name:
																		A__	  =     'bias'
															elif "weight" in name:
																		# TODO: don't match quantizer.weight_proj
																		A__	  =     'weight'
															else:
																		A__	  =     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    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   Tuple:
			A__	  =     full_name.split('conv_layers.'       )[-1]
			A__	  =     name.split('.'       )
			A__	  =     int(items[0]       )
			A__	  =     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.'''
									)
									A__	  =     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.'''
									)
									A__	  =     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."
									)
									A__	  =     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.'''
									)
									A__	  =     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    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase=None			,						__UpperCamelCase=None			,						__UpperCamelCase=True       )    ->   Optional[Any]:
			if config_path is not None:
						A__	  =     UniSpeechConfig.from_pretrained(__UpperCamelCase       )
			else:
						A__	  =     UniSpeechConfig()

			if is_finetuned:
						if dict_path:
									A__	  =     Dictionary.load_from_json(__UpperCamelCase       )

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									A__	  =     target_dict.pad_index
									A__	  =     target_dict.bos_index
									A__	  =     target_dict.eos_index
									A__	  =     len(target_dict.symbols       )
									A__	  =     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       )
									A__	  =     target_dict.indices

									# fairseq has the <pad> and <s> switched
									A__	  =     42
									A__	  =     43
									with open(__UpperCamelCase			,						'w'			,						encoding='utf-8'       ) as vocab_handle:
												json.dump(__UpperCamelCase			,						__UpperCamelCase       )
									A__	  =     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			,						)
									A__	  =     True if config.feat_extract_norm == 'layer' else False
									A__	  =     WavaVecaFeatureExtractor(
									    feature_size=1			,						sampling_rate=16_000			,						padding_value=0			,						do_normalize=__UpperCamelCase			,						return_attention_mask=__UpperCamelCase			,						)
									A__	  =     WavaVecaProcessor(feature_extractor=__UpperCamelCase			,						tokenizer=__UpperCamelCase       )
									processor.save_pretrained(__UpperCamelCase       )

						A__	  =     UniSpeechForCTC(__UpperCamelCase       )
			else:
						A__	  =     UniSpeechForPreTraining(__UpperCamelCase       )

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

			A__	  =     model[0].eval()

			recursively_load_weights(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )

			hf_unispeech.save_pretrained(__UpperCamelCase       )


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__   			=   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'''
							)
							SCREAMING_SNAKE_CASE__   			=   parser.parse_args()
							convert_unispeech_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
							)









SCREAMING_SNAKE_CASE__   			=   '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

SCREAMING_SNAKE_CASE__   			=   [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
SCREAMING_SNAKE_CASE__   			=   {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}




