'''simple docstring'''

import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a:					Any			  =  logging.get_logger(__name__)

__a:					List[str]			  =  {
    """facebook/encodec_24khz""": """https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json""",
    """facebook/encodec_48khz""": """https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json""",
}



class      UpperCAmelCase    (		a__   ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE   						=       "encodec"

       def __init__(		self      ,  __lowerCAmelCase=[1.5, 3.0, 6.0, 1_2.0, 2_4.0]      ,  __lowerCAmelCase=24000      ,  __lowerCAmelCase=1      ,  __lowerCAmelCase=False      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=128      ,  __lowerCAmelCase=32      ,  __lowerCAmelCase=1      ,  __lowerCAmelCase=[8, 5, 4, 2]      ,  __lowerCAmelCase="weight_norm"      ,  __lowerCAmelCase=7      ,  __lowerCAmelCase=7      ,  __lowerCAmelCase=3      ,  __lowerCAmelCase=2      ,  __lowerCAmelCase=True      ,  __lowerCAmelCase="reflect"      ,  __lowerCAmelCase=2      ,  __lowerCAmelCase=2      ,  __lowerCAmelCase=1.0      ,  __lowerCAmelCase=1024      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=True      ,  **__lowerCAmelCase      ,  )    ->    int:
        lowercase__							:       Tuple										=   target_bandwidths
        lowercase__							:       Dict										=   sampling_rate
        lowercase__							:       List[Any]										=   audio_channels
        lowercase__							:       Optional[Any]										=   normalize
        lowercase__							:       int										=   chunk_length_s
        lowercase__							:       Any										=   overlap
        lowercase__							:       Optional[Any]										=   hidden_size
        lowercase__							:       Union[str, Any]										=   num_filters
        lowercase__							:       Optional[int]										=   num_residual_layers
        lowercase__							:       Tuple										=   upsampling_ratios
        lowercase__							:       Any										=   norm_type
        lowercase__							:       Union[str, Any]										=   kernel_size
        lowercase__							:       Union[str, Any]										=   last_kernel_size
        lowercase__							:       Any										=   residual_kernel_size
        lowercase__							:       Tuple										=   dilation_growth_rate
        lowercase__							:       List[str]										=   use_causal_conv
        lowercase__							:       Optional[Any]										=   pad_mode
        lowercase__							:       List[str]										=   compress
        lowercase__							:       List[str]										=   num_lstm_layers
        lowercase__							:       Tuple										=   trim_right_ratio
        lowercase__							:       Optional[Any]										=   codebook_size
        lowercase__							:       List[Any]										=   codebook_dim if codebook_dim is not None else hidden_size
        lowercase__							:       Optional[int]										=   use_conv_shortcut

        if self.norm_type not in ["weight_norm", "time_group_norm"]:
         raise ValueError(
             F"""self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}"""					)

        super().__init__(**__a					)

       @property
       def _lowerCAmelCase(		self					)    ->    Optional[int]:
        if self.chunk_length_s is None:
         return None
        else:
         return int(self.chunk_length_s * self.sampling_rate					)

       @property
       def _lowerCAmelCase(		self					)    ->    Optional[int]:
        if self.chunk_length_s is None or self.overlap is None:
         return None
        else:
         return max(1      ,  int((1.0 - self.overlap) * self.chunk_length					)					)

       @property
       def _lowerCAmelCase(		self					)    ->    int:
        lowercase__							:       Tuple										=   np.prod(self.upsampling_ratios					)
        return math.ceil(self.sampling_rate / hop_length					)


       @property
       def _lowerCAmelCase(		self					)    ->    int:
        return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10)					)



"""simple docstring"""





import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
				_a								=						argparse.ArgumentParser(
				    description=(
				        """Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"""
				        """ Distillation"""
				    )
				)
				parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""])
				parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str)
				parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str)
				parser.add_argument("""--vocab_transform""", action="""store_true""")
				_a								=						parser.parse_args()

				if args.model_type == "bert":
								_a								=						BertForMaskedLM.from_pretrained(args.model_name)
								_a								=						"""bert"""
				else:
								raise ValueError("""args.model_type should be \"bert\".""")

				_a								=						model.state_dict()
				_a								=						{}

				for w in ["word_embeddings", "position_embeddings"]:
								_a								=						state_dict[F"""{prefix}.embeddings.{w}.weight"""]
				for w in ["weight", "bias"]:
								_a								=						state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""]

				_a								=						0
				for teacher_idx in [0, 2, 4, 7, 9, 11]:
								for w in ["weight", "bias"]:
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
												]

												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
												]

												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
												]
								std_idx += 1

				_a								=						state_dict["""cls.predictions.decoder.weight"""]
				_a								=						state_dict["""cls.predictions.bias"""]
				if args.vocab_transform:
								for w in ["weight", "bias"]:
												_a								=						state_dict[F"""cls.predictions.transform.dense.{w}"""]
												_a								=						state_dict[F"""cls.predictions.transform.LayerNorm.{w}"""]

				print(F"""N layers selected for distillation: {std_idx}""")
				print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

				print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
				torch.save(compressed_sd, args.dump_checkpoint)






