



'''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()
a     :    int      						=					logging.get_logger(__name__)

a     :    Optional[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_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     :    Optional[int]      						=					[
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase	)   ->    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
																												UpperCAmelCase  :	Any							  =	"""lm_head"""

														UpperCAmelCase  :	Dict							  =	getattr(_lowercase			,       _lowercase	)

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

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



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    Dict:
							UpperCAmelCase  :	str							  =	[]
							UpperCAmelCase  :	Optional[Any]							  =	fairseq_model.state_dict()

							UpperCAmelCase  :	Any							  =	hf_model.unispeech.feature_extractor

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

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



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase	)   ->    Optional[Any]:
							UpperCAmelCase  :	Union[str, Any]							  =	full_name.split("""conv_layers."""	)[-1]
							UpperCAmelCase  :	List[Any]							  =	name.split("""."""	)
							UpperCAmelCase  :	Optional[int]							  =	int(items[0]	)
							UpperCAmelCase  :	Tuple							  =	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.'''
																					)
																					UpperCAmelCase  :	Union[str, Any]							  =	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.'''
																					)
																					UpperCAmelCase  :	int							  =	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."
																					)
																					UpperCAmelCase  :	List[str]							  =	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.'''
																					)
																					UpperCAmelCase  :	List[str]							  =	value
																					logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
							else:
														unused_weights.append(_lowercase	)



@torch.no_grad()
def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase=None			,       _lowercase=None			,       _lowercase=True	)   ->    Union[str, Any]:
							if config_path is not None:
														UpperCAmelCase  :	Tuple							  =	UniSpeechConfig.from_pretrained(_lowercase	)
							else:
														UpperCAmelCase  :	Any							  =	UniSpeechConfig()

							if is_finetuned:
														if dict_path:
																					UpperCAmelCase  :	Optional[Any]							  =	Dictionary.load_from_json(_lowercase	)

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

																					# fairseq has the <pad> and <s> switched
																					UpperCAmelCase  :	List[Any]							  =	4_2
																					UpperCAmelCase  :	Dict							  =	4_3
																					with open(_lowercase			,       """w"""			,       encoding="""utf-8"""	) as vocab_handle:
																												json.dump(_lowercase			,       _lowercase	)
																					UpperCAmelCase  :	str							  =	WavaVecaPhonemeCTCTokenizer(
																					    _lowercase			,       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=_lowercase			,       )
																					UpperCAmelCase  :	Optional[int]							  =	True if config.feat_extract_norm == """layer""" else False
																					UpperCAmelCase  :	List[str]							  =	WavaVecaFeatureExtractor(
																					    feature_size=1			,       sampling_rate=1_6_0_0_0			,       padding_value=0			,       do_normalize=_lowercase			,       return_attention_mask=_lowercase			,       )
																					UpperCAmelCase  :	Dict							  =	WavaVecaProcessor(feature_extractor=_lowercase			,       tokenizer=_lowercase	)
																					processor.save_pretrained(_lowercase	)

														UpperCAmelCase  :	List[Any]							  =	UniSpeechForCTC(_lowercase	)
							else:
														UpperCAmelCase  :	Union[str, Any]							  =	UniSpeechForPreTraining(_lowercase	)

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

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

							recursively_load_weights(_lowercase			,       _lowercase			,       _lowercase	)

							hf_unispeech.save_pretrained(_lowercase	)


if __name__ == "__main__":
							a     :    List[str]      						=					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"""
							)
							a     :    Any      						=					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'''




a     :    List[Any]      						=					"""Alexander Joslin"""

import operator as op

from .stack import Stack



def    __lowerCamelCase						(      _lowercase	)   ->    int:
							UpperCAmelCase  :	Dict							  =	{"""*""": op.mul, """/""": op.truediv, """+""": op.add, """-""": op.sub}

							UpperCAmelCase  :	Stack[int]							  =	Stack()
							UpperCAmelCase  :	Stack[str]							  =	Stack()

							for i in equation:
														if i.isdigit():
																					# RULE 1
																					operand_stack.push(int(_lowercase	)	)
														elif i in operators:
																					# RULE 2
																					operator_stack.push(_lowercase	)
														elif i == ")":
																					# RULE 4
																					UpperCAmelCase  :	List[Any]							  =	operator_stack.peek()
																					operator_stack.pop()
																					UpperCAmelCase  :	str							  =	operand_stack.peek()
																					operand_stack.pop()
																					UpperCAmelCase  :	str							  =	operand_stack.peek()
																					operand_stack.pop()

																					UpperCAmelCase  :	List[Any]							  =	operators[opr](_lowercase			,       _lowercase	)
																					operand_stack.push(_lowercase	)

    # RULE 5
							return operand_stack.peek()


if __name__ == "__main__":
							a     :    Tuple      						=					"""(5 + ((4 * 2) * (2 + 3)))"""
							# answer = 45
							print(F'''{equation} = {dijkstras_two_stack_algorithm(equation)}''')

