
"""simple docstring"""




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__   :					Any												=				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_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__   :					int												=				[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case     ,_snake_case     ,_snake_case     ,_snake_case		):
							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
																												SCREAMING_SNAKE_CASE__		: Any     =     """lm_head"""

														SCREAMING_SNAKE_CASE__		: Optional[int]     =     getattr(_snake_case     ,_snake_case		)

							if weight_type is not None:
														SCREAMING_SNAKE_CASE__		: Any     =     getattr(_snake_case     ,_snake_case		).shape
							else:
														SCREAMING_SNAKE_CASE__		: List[Any]     =     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__		: Union[str, Any]     =     value
							elif weight_type == "weight_g":
														SCREAMING_SNAKE_CASE__		: Any     =     value
							elif weight_type == "weight_v":
														SCREAMING_SNAKE_CASE__		: int     =     value
							elif weight_type == "bias":
														SCREAMING_SNAKE_CASE__		: Any     =     value
							else:
														SCREAMING_SNAKE_CASE__		: Dict     =     value

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

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: List[str]     =     []
							SCREAMING_SNAKE_CASE__		: Any     =     fairseq_model.state_dict()

							SCREAMING_SNAKE_CASE__		: int     =     hf_model.unispeech.feature_extractor

							for name, value in fairseq_dict.items():
														SCREAMING_SNAKE_CASE__		: str     =     False
														if "conv_layers" in name:
																					load_conv_layer(
																					    _snake_case     ,_snake_case     ,_snake_case     ,_snake_case     ,hf_model.config.feat_extract_norm == """group"""     ,)
																					SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     True
														else:
																					for key, mapped_key in MAPPING.items():
																												SCREAMING_SNAKE_CASE__		: Optional[int]     =     """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]:
																																			SCREAMING_SNAKE_CASE__		: List[str]     =     True
																																			if "*" in mapped_key:
																																										SCREAMING_SNAKE_CASE__		: List[str]     =     name.split(_snake_case		)[0].split("""."""		)[-2]
																																										SCREAMING_SNAKE_CASE__		: str     =     mapped_key.replace("""*"""     ,_snake_case		)
																																			if "weight_g" in name:
																																										SCREAMING_SNAKE_CASE__		: List[str]     =     """weight_g"""
																																			elif "weight_v" in name:
																																										SCREAMING_SNAKE_CASE__		: Any     =     """weight_v"""
																																			elif "bias" in name:
																																										SCREAMING_SNAKE_CASE__		: str     =     """bias"""
																																			elif "weight" in name:
																																										# TODO: don't match quantizer.weight_proj
																																										SCREAMING_SNAKE_CASE__		: str     =     """weight"""
																																			else:
																																										SCREAMING_SNAKE_CASE__		: Optional[Any]     =     None
																																			set_recursively(_snake_case     ,_snake_case     ,_snake_case     ,_snake_case     ,_snake_case     ,_snake_case		)
																												continue
														if not is_used:
																					unused_weights.append(_snake_case		)

							logger.warning(f'''Unused weights: {unused_weights}'''		)

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case     ,_snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: List[str]     =     full_name.split("""conv_layers."""		)[-1]
							SCREAMING_SNAKE_CASE__		: Any     =     name.split("""."""		)
							SCREAMING_SNAKE_CASE__		: str     =     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__		: str     =     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__		: List[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__		: Optional[Any]     =     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__		: List[str]     =     value
																					logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
							else:
														unused_weights.append(_snake_case		)

@torch.no_grad()
def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case=None     ,_snake_case=None     ,_snake_case=True		):
							if config_path is not None:
														SCREAMING_SNAKE_CASE__		: Dict     =     UniSpeechConfig.from_pretrained(_snake_case		)
							else:
														SCREAMING_SNAKE_CASE__		: Tuple     =     UniSpeechConfig()

							if is_finetuned:
														if dict_path:
																					SCREAMING_SNAKE_CASE__		: Tuple     =     Dictionary.load_from_json(_snake_case		)

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

																					# fairseq has the <pad> and <s> switched
																					SCREAMING_SNAKE_CASE__		: List[Any]     =     42
																					SCREAMING_SNAKE_CASE__		: Any     =     43
																					with open(_snake_case     ,"""w"""     ,encoding="""utf-8"""		) as vocab_handle:
																												json.dump(_snake_case     ,_snake_case		)
																					SCREAMING_SNAKE_CASE__		: List[Any]     =     WavaVecaPhonemeCTCTokenizer(
																					    _snake_case     ,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=_snake_case     ,)
																					SCREAMING_SNAKE_CASE__		: Tuple     =     True if config.feat_extract_norm == """layer""" else False
																					SCREAMING_SNAKE_CASE__		: Tuple     =     WavaVecaFeatureExtractor(
																					    feature_size=1     ,sampling_rate=16_000     ,padding_value=0     ,do_normalize=_snake_case     ,return_attention_mask=_snake_case     ,)
																					SCREAMING_SNAKE_CASE__		: Optional[Any]     =     WavaVecaProcessor(feature_extractor=_snake_case     ,tokenizer=_snake_case		)
																					processor.save_pretrained(_snake_case		)

														SCREAMING_SNAKE_CASE__		: Tuple     =     UniSpeechForCTC(_snake_case		)
							else:
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     UniSpeechForPreTraining(_snake_case		)

							if is_finetuned:
														SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: Dict     =     fairseq.checkpoint_utils.load_model_ensemble_and_task(
														    [checkpoint_path]     ,arg_overrides={"""data""": """/""".join(dict_path.split("""/"""		)[:-1]		), """w2v_path""": checkpoint_path}		)
							else:
														SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: Optional[int]     =     fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		)

							SCREAMING_SNAKE_CASE__		: str     =     model[0].eval()

							recursively_load_weights(_snake_case     ,_snake_case     ,_snake_case		)

							hf_unispeech.save_pretrained(_snake_case		)


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__   :					Tuple												=				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"""




import math
import os
import sys

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: List[str]     =     """"""
							try:
														with open(_snake_case     ,"""rb"""		) as binary_file:
																					SCREAMING_SNAKE_CASE__		: str     =     binary_file.read()
														for dat in data:
																					SCREAMING_SNAKE_CASE__		: int     =     f'''{dat:08b}'''
																					result += curr_byte
														return result
							except OSError:
														print("""File not accessible"""		)
														sys.exit()

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case     ,_snake_case		):
							lexicon.pop(_snake_case		)
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     last_match_id

							if math.loga(_snake_case		).is_integer():
														for curr_key in lexicon:
																					SCREAMING_SNAKE_CASE__		: Optional[int]     =     """0""" + lexicon[curr_key]

							SCREAMING_SNAKE_CASE__		: Tuple     =     bin(_snake_case		)[2:]

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: List[str]     =     {"""0""": """0""", """1""": """1"""}
							SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: str     =     """""", """"""
							SCREAMING_SNAKE_CASE__		: int     =     len(_snake_case		)

							for i in range(len(_snake_case		)		):
														curr_string += data_bits[i]
														if curr_string not in lexicon:
																					continue

														SCREAMING_SNAKE_CASE__		: Dict     =     lexicon[curr_string]
														result += last_match_id
														add_key_to_lexicon(_snake_case     ,_snake_case     ,_snake_case     ,_snake_case		)
														index += 1
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     """"""

							while curr_string != "" and curr_string not in lexicon:
														curr_string += "0"

							if curr_string != "":
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     lexicon[curr_string]
														result += last_match_id

							return result

def lowercase_						(     _snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: str     =     os.path.getsize(_snake_case		)
							SCREAMING_SNAKE_CASE__		: Any     =     bin(_snake_case		)[2:]
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     len(_snake_case		)

							return "0" * (length_length - 1) + file_length_binary + compressed

def lowercase_						(     _snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: Tuple     =     8
							try:
														with open(_snake_case     ,"""wb"""		) as opened_file:
																					SCREAMING_SNAKE_CASE__		: Optional[int]     =     [
																					    to_write[i : i + byte_length]
																					    for i in range(0     ,len(_snake_case		)     ,_snake_case		)
																					]

																					if len(result_byte_array[-1]		) % byte_length == 0:
																												result_byte_array.append("""10000000"""		)
																					else:
																												result_byte_array[-1] += "1" + "0" * (
																												    byte_length - len(result_byte_array[-1]		) - 1
																												)

																					for elem in result_byte_array:
																												opened_file.write(int(_snake_case     ,2		).to_bytes(1     ,byteorder="""big"""		)		)
							except OSError:
														print("""File not accessible"""		)
														sys.exit()

def lowercase_						(     _snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     read_file_binary(_snake_case		)
							SCREAMING_SNAKE_CASE__		: List[str]     =     compress_data(_snake_case		)
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     add_file_length(_snake_case     ,_snake_case		)
							write_file_binary(_snake_case     ,_snake_case		)


if __name__ == "__main__":
					compress(sys.argv[1], sys.argv[2])




