






'''simple docstring'''

import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
UpperCAmelCase__						:Optional[int]          =			logging.get_logger(__name__)

UpperCAmelCase__						:str          =			{
    """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""",
    """encoder.layer_norm_for_extract""": """layer_norm_for_extract""",
    """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""",
    """label_embs_concat""": """label_embeddings_concat""",
    """mask_emb""": """masked_spec_embed""",
    """spk_proj""": """speaker_proj""",
}
UpperCAmelCase__						:Optional[int]          =			[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """label_embeddings_concat""",
    """speaker_proj""",
    """layer_norm_for_extract""",
]
def     __lowercase  (_lowercase,						_lowercase,						_lowercase,						_lowercase,						_lowercase     )   ->     Any:


					"""simple docstring"""
					for attribute in key.split("""."""     ):
										__lowerCamelCase						:       Union[str, Any]				   =				getattr(lowerCamelCase_,						lowerCamelCase_     )

					if weight_type is not None:
										__lowerCamelCase						:       List[Any]				   =				getattr(lowerCamelCase_,						lowerCamelCase_     ).shape
					else:
										__lowerCamelCase						:       Optional[int]				   =				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":
										__lowerCamelCase						:       int				   =				value
					elif weight_type == "weight_g":
										__lowerCamelCase						:       Optional[int]				   =				value
					elif weight_type == "weight_v":
										__lowerCamelCase						:       List[Any]				   =				value
					elif weight_type == "bias":
										__lowerCamelCase						:       Tuple				   =				value
					else:
										__lowerCamelCase						:       Any				   =				value

					logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."     )
def     __lowercase  (_lowercase,						_lowercase     )   ->     Optional[int]:


					"""simple docstring"""
					__lowerCamelCase						:       str				   =				[]
					__lowerCamelCase						:       Optional[int]				   =				fairseq_model.state_dict()

					__lowerCamelCase						:       Any				   =				hf_model.unispeech_sat.feature_extractor

					for name, value in fairseq_dict.items():
										__lowerCamelCase						:       Optional[Any]				   =				False
										if "conv_layers" in name:
															load_conv_layer(
															    lowerCamelCase_,						lowerCamelCase_,						lowerCamelCase_,						lowerCamelCase_,						hf_model.config.feat_extract_norm == """group""",						)
															__lowerCamelCase						:       List[Any]				   =				True
										else:
															for key, mapped_key in MAPPING.items():
																				__lowerCamelCase						:       List[str]				   =				"""unispeech_sat.""" + 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]:
																									if "layer_norm_for_extract" in name and (".".join(name.split("""."""     )[:-1]     ) != key):
																														# special case since naming is very similar
																														continue
																									__lowerCamelCase						:       int				   =				True
																									if "*" in mapped_key:
																														__lowerCamelCase						:       Optional[Any]				   =				name.split(lowerCamelCase_     )[0].split("""."""     )[-2]
																														__lowerCamelCase						:       Union[str, Any]				   =				mapped_key.replace("""*""",						lowerCamelCase_     )
																									if "weight_g" in name:
																														__lowerCamelCase						:       Union[str, Any]				   =				"""weight_g"""
																									elif "weight_v" in name:
																														__lowerCamelCase						:       Union[str, Any]				   =				"""weight_v"""
																									elif "bias" in name:
																														__lowerCamelCase						:       List[Any]				   =				"""bias"""
																									elif "weight" in name:
																														# TODO: don't match quantizer.weight_proj
																														__lowerCamelCase						:       List[Any]				   =				"""weight"""
																									else:
																														__lowerCamelCase						:       Any				   =				None
																									set_recursively(lowerCamelCase_,						lowerCamelCase_,						lowerCamelCase_,						lowerCamelCase_,						lowerCamelCase_     )
																				continue
										if not is_used:
															unused_weights.append(lowerCamelCase_     )

					logger.warning(f"Unused weights: {unused_weights}"     )
def     __lowercase  (_lowercase,						_lowercase,						_lowercase,						_lowercase,						_lowercase     )   ->     Union[str, Any]:


					"""simple docstring"""
					__lowerCamelCase						:       int				   =				full_name.split("""conv_layers."""     )[-1]
					__lowerCamelCase						:       Any				   =				name.split("""."""     )
					__lowerCamelCase						:       str				   =				int(items[0]     )
					__lowerCamelCase						:       List[Any]				   =				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."     )
															__lowerCamelCase						:       Any				   =				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."     )
															__lowerCamelCase						:       Union[str, Any]				   =				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[layer_id].layer_norm.bias.data.shape} was found."     )
															__lowerCamelCase						:       List[str]				   =				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[layer_id].layer_norm.weight.data.shape} was found."     )
															__lowerCamelCase						:       Tuple				   =				value
															logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."     )
					else:
										unused_weights.append(lowerCamelCase_     )
@torch.no_grad()
def     __lowercase  (_lowercase,						_lowercase,						_lowercase=None,						_lowercase=None,						_lowercase=True     )   ->     int:


					"""simple docstring"""
					if config_path is not None:
										__lowerCamelCase						:       List[Any]				   =				UniSpeechSatConfig.from_pretrained(lowerCamelCase_     )
					else:
										__lowerCamelCase						:       Any				   =				UniSpeechSatConfig()

					__lowerCamelCase						:       Optional[Any]				   =				""""""

					if is_finetuned:
										__lowerCamelCase						:       List[Any]				   =				UniSpeechSatForCTC(lowerCamelCase_     )
					else:
										__lowerCamelCase						:       Union[str, Any]				   =				UniSpeechSatForPreTraining(lowerCamelCase_     )

					__lowerCamelCase   ,      __lowerCamelCase   ,      __lowerCamelCase						:       Tuple				   =				fairseq.checkpoint_utils.load_model_ensemble_and_task(
					    [checkpoint_path],						arg_overrides={"""data""": """/""".join(dict_path.split("""/"""     )[:-1]     )}     )
					__lowerCamelCase						:       Dict				   =				model[0].eval()

					recursively_load_weights(lowerCamelCase_,						lowerCamelCase_     )

					hf_wavavec.save_pretrained(lowerCamelCase_     )


if __name__ == "__main__":
			UpperCAmelCase__						:Optional[int]          =			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__						:List[Any]          =			parser.parse_args()
			convert_unispeech_sat_checkpoint(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
			)











'''simple docstring'''

from __future__ import annotations

from typing import Any
def     __lowercase  (_lowercase     )   ->     int:


     """simple docstring"""
     if not postfix_notation:
          return 0

     __lowerCamelCase						:       Optional[int]				   =				{"""+""", """-""", """*""", """/"""}
     __lowerCamelCase						:       list[Any]				   =				[]

     for token in postfix_notation:
          if token in operations:
               __lowerCamelCase   ,      __lowerCamelCase						:       List[Any]				   =				stack.pop(), stack.pop()
               if token == "+":
                    stack.append(a + b     )
               elif token == "-":
                    stack.append(a - b     )
               elif token == "*":
                    stack.append(a * b     )
               else:
                    if a * b < 0 and a % b != 0:
                         stack.append(a // b + 1     )
                    else:
                         stack.append(a // b     )
          else:
               stack.append(int(_lowercase     )     )

     return stack.pop()


if __name__ == "__main__":
   import doctest

   doctest.testmod()



