





import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
a__:      Optional[int]									=		logging.get_logger('transformers.models.encodec')

a__:      Union[str, Any]									=		{
    'quantizer.vq.layers.*._codebook.inited': 'quantizer.layers.*.codebook.inited',
    'quantizer.vq.layers.*._codebook.cluster_size': 'quantizer.layers.*.codebook.cluster_size',
    'quantizer.vq.layers.*._codebook.embed': 'quantizer.layers.*.codebook.embed',
    'quantizer.vq.layers.*._codebook.embed_avg': 'quantizer.layers.*.codebook.embed_avg',
}
a__:      List[str]									=		{
    'encoder.model.0.conv.conv': 'encoder.layers.0.conv',
    'encoder.model.1.block.1.conv.conv': 'encoder.layers.1.block.1.conv',
    'encoder.model.1.block.3.conv.conv': 'encoder.layers.1.block.3.conv',
    'encoder.model.1.shortcut.conv.conv': 'encoder.layers.1.shortcut.conv',
    'encoder.model.3.conv.conv': 'encoder.layers.3.conv',
    'encoder.model.4.block.1.conv.conv': 'encoder.layers.4.block.1.conv',
    'encoder.model.4.block.3.conv.conv': 'encoder.layers.4.block.3.conv',
    'encoder.model.4.shortcut.conv.conv': 'encoder.layers.4.shortcut.conv',
    'encoder.model.6.conv.conv': 'encoder.layers.6.conv',
    'encoder.model.7.block.1.conv.conv': 'encoder.layers.7.block.1.conv',
    'encoder.model.7.block.3.conv.conv': 'encoder.layers.7.block.3.conv',
    'encoder.model.7.shortcut.conv.conv': 'encoder.layers.7.shortcut.conv',
    'encoder.model.9.conv.conv': 'encoder.layers.9.conv',
    'encoder.model.10.block.1.conv.conv': 'encoder.layers.10.block.1.conv',
    'encoder.model.10.block.3.conv.conv': 'encoder.layers.10.block.3.conv',
    'encoder.model.10.shortcut.conv.conv': 'encoder.layers.10.shortcut.conv',
    'encoder.model.12.conv.conv': 'encoder.layers.12.conv',
    'encoder.model.13.lstm': 'encoder.layers.13.lstm',
    'encoder.model.15.conv.conv': 'encoder.layers.15.conv',
}
a__:      str									=		{
    'encoder.model.0.conv.norm': 'encoder.layers.0.norm',
    'encoder.model.1.block.1.conv.norm': 'encoder.layers.1.block.1.norm',
    'encoder.model.1.block.3.conv.norm': 'encoder.layers.1.block.3.norm',
    'encoder.model.1.shortcut.conv.norm': 'encoder.layers.1.shortcut.norm',
    'encoder.model.3.conv.norm': 'encoder.layers.3.norm',
    'encoder.model.4.block.1.conv.norm': 'encoder.layers.4.block.1.norm',
    'encoder.model.4.block.3.conv.norm': 'encoder.layers.4.block.3.norm',
    'encoder.model.4.shortcut.conv.norm': 'encoder.layers.4.shortcut.norm',
    'encoder.model.6.conv.norm': 'encoder.layers.6.norm',
    'encoder.model.7.block.1.conv.norm': 'encoder.layers.7.block.1.norm',
    'encoder.model.7.block.3.conv.norm': 'encoder.layers.7.block.3.norm',
    'encoder.model.7.shortcut.conv.norm': 'encoder.layers.7.shortcut.norm',
    'encoder.model.9.conv.norm': 'encoder.layers.9.norm',
    'encoder.model.10.block.1.conv.norm': 'encoder.layers.10.block.1.norm',
    'encoder.model.10.block.3.conv.norm': 'encoder.layers.10.block.3.norm',
    'encoder.model.10.shortcut.conv.norm': 'encoder.layers.10.shortcut.norm',
    'encoder.model.12.conv.norm': 'encoder.layers.12.norm',
    'encoder.model.15.conv.norm': 'encoder.layers.15.norm',
}
a__:      List[Any]									=		{
    'decoder.model.0.conv.conv': 'decoder.layers.0.conv',
    'decoder.model.1.lstm': 'decoder.layers.1.lstm',
    'decoder.model.3.convtr.convtr': 'decoder.layers.3.conv',
    'decoder.model.4.block.1.conv.conv': 'decoder.layers.4.block.1.conv',
    'decoder.model.4.block.3.conv.conv': 'decoder.layers.4.block.3.conv',
    'decoder.model.4.shortcut.conv.conv': 'decoder.layers.4.shortcut.conv',
    'decoder.model.6.convtr.convtr': 'decoder.layers.6.conv',
    'decoder.model.7.block.1.conv.conv': 'decoder.layers.7.block.1.conv',
    'decoder.model.7.block.3.conv.conv': 'decoder.layers.7.block.3.conv',
    'decoder.model.7.shortcut.conv.conv': 'decoder.layers.7.shortcut.conv',
    'decoder.model.9.convtr.convtr': 'decoder.layers.9.conv',
    'decoder.model.10.block.1.conv.conv': 'decoder.layers.10.block.1.conv',
    'decoder.model.10.block.3.conv.conv': 'decoder.layers.10.block.3.conv',
    'decoder.model.10.shortcut.conv.conv': 'decoder.layers.10.shortcut.conv',
    'decoder.model.12.convtr.convtr': 'decoder.layers.12.conv',
    'decoder.model.13.block.1.conv.conv': 'decoder.layers.13.block.1.conv',
    'decoder.model.13.block.3.conv.conv': 'decoder.layers.13.block.3.conv',
    'decoder.model.13.shortcut.conv.conv': 'decoder.layers.13.shortcut.conv',
    'decoder.model.15.conv.conv': 'decoder.layers.15.conv',
}
a__:      Dict									=		{
    'decoder.model.0.conv.norm': 'decoder.layers.0.norm',
    'decoder.model.3.convtr.norm': 'decoder.layers.3.norm',
    'decoder.model.4.block.1.conv.norm': 'decoder.layers.4.block.1.norm',
    'decoder.model.4.block.3.conv.norm': 'decoder.layers.4.block.3.norm',
    'decoder.model.4.shortcut.conv.norm': 'decoder.layers.4.shortcut.norm',
    'decoder.model.6.convtr.norm': 'decoder.layers.6.norm',
    'decoder.model.7.block.1.conv.norm': 'decoder.layers.7.block.1.norm',
    'decoder.model.7.block.3.conv.norm': 'decoder.layers.7.block.3.norm',
    'decoder.model.7.shortcut.conv.norm': 'decoder.layers.7.shortcut.norm',
    'decoder.model.9.convtr.norm': 'decoder.layers.9.norm',
    'decoder.model.10.block.1.conv.norm': 'decoder.layers.10.block.1.norm',
    'decoder.model.10.block.3.conv.norm': 'decoder.layers.10.block.3.norm',
    'decoder.model.10.shortcut.conv.norm': 'decoder.layers.10.shortcut.norm',
    'decoder.model.12.convtr.norm': 'decoder.layers.12.norm',
    'decoder.model.13.block.1.conv.norm': 'decoder.layers.13.block.1.norm',
    'decoder.model.13.block.3.conv.norm': 'decoder.layers.13.block.3.norm',
    'decoder.model.13.shortcut.conv.norm': 'decoder.layers.13.shortcut.norm',
    'decoder.model.15.conv.norm': 'decoder.layers.15.norm',
}
a__:      List[str]									=		{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
a__:      Union[str, Any]									=		{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
a__:      Optional[int]									=		[]
a__:      int									=		[]


def 	UpperCamelCase__(       UpperCamelCase__			:       List[Any] ,     UpperCamelCase__			:       Optional[Any] ,     UpperCamelCase__			:       Tuple ,     UpperCamelCase__			:       str ,     UpperCamelCase__			:       List[Any]  )->str:







       for attribute in key.split('''.'''  ):
              A__											=						getattr(UpperCamelCase__ ,     UpperCamelCase__  )

       if weight_type is not None:
              A__											=						getattr(UpperCamelCase__ ,     UpperCamelCase__  ).shape
       else:
              A__											=						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":
              A__											=						value
       elif weight_type == "weight_g":
              A__											=						value
       elif weight_type == "weight_v":
              A__											=						value
       elif weight_type == "bias":
              A__											=						value
       elif weight_type == "running_mean":
              A__											=						value
       elif weight_type == "running_var":
              A__											=						value
       elif weight_type == "num_batches_tracked":
              A__											=						value
       elif weight_type == "weight_ih_l0":
              A__											=						value
       elif weight_type == "weight_hh_l0":
              A__											=						value
       elif weight_type == "bias_ih_l0":
              A__											=						value
       elif weight_type == "bias_hh_l0":
              A__											=						value
       elif weight_type == "weight_ih_l1":
              A__											=						value
       elif weight_type == "weight_hh_l1":
              A__											=						value
       elif weight_type == "bias_ih_l1":
              A__											=						value
       elif weight_type == "bias_hh_l1":
              A__											=						value
       else:
              A__											=						value

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


def 	UpperCamelCase__(       UpperCamelCase__			:       int ,     UpperCamelCase__			:       int  )->str:







       for key in ignore_keys:
              if key.endswith('''.*'''  ):
                     if name.startswith(key[:-1]  ):
                            return True
              elif ".*." in key:
                     A__											=						key.split('''.*.'''  )
                     if prefix in name and suffix in name:
                            return True
              elif key in name:
                     return True
       return False


def 	UpperCamelCase__(       UpperCamelCase__			:       int ,     UpperCamelCase__			:       Any ,     UpperCamelCase__			:       Optional[int]  )->str:
       A__											=						[]

       if model_name == "encodec_24khz" or "encodec_32khz":
              A__											=						MAPPING_24K
       elif model_name == "encodec_48khz":
              A__											=						MAPPING_48K
       else:
              raise ValueError(f"Unsupported model: {model_name}"  )

       for name, value in orig_dict.items():
              if should_ignore(UpperCamelCase__ ,     UpperCamelCase__  ):
                     logger.info(f"{name} was ignored"  )
                     continue

              A__											=						False
              for key, mapped_key in MAPPING.items():
                     if "*" in key:
                            A__											=						key.split('''.*.'''  )
                            if prefix in name and suffix in name:
                                   A__											=						suffix

                     if key in name:
                            # HACK otherwise .embed gets initialized with .embed_avg too
                            if key.endswith('''embed'''  ) and name.endswith('''embed_avg'''  ):
                                   continue

                            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 "weight_ih_l0" in name:
                                   A__											=						'''weight_ih_l0'''
                            elif "weight_hh_l0" in name:
                                   A__											=						'''weight_hh_l0'''
                            elif "bias_ih_l0" in name:
                                   A__											=						'''bias_ih_l0'''
                            elif "bias_hh_l0" in name:
                                   A__											=						'''bias_hh_l0'''
                            elif "weight_ih_l1" in name:
                                   A__											=						'''weight_ih_l1'''
                            elif "weight_hh_l1" in name:
                                   A__											=						'''weight_hh_l1'''
                            elif "bias_ih_l1" in name:
                                   A__											=						'''bias_ih_l1'''
                            elif "bias_hh_l1" in name:
                                   A__											=						'''bias_hh_l1'''
                            elif "bias" in name:
                                   A__											=						'''bias'''
                            elif "weight" in name:
                                   A__											=						'''weight'''
                            elif "running_mean" in name:
                                   A__											=						'''running_mean'''
                            elif "running_var" in name:
                                   A__											=						'''running_var'''
                            elif "num_batches_tracked" in name:
                                   A__											=						'''num_batches_tracked'''
                            else:
                                   A__											=						None
                            set_recursively(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )
                     continue
              if not is_used:
                     unused_weights.append(UpperCamelCase__  )

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


@torch.no_grad()
def 	UpperCamelCase__(       UpperCamelCase__			:       Tuple ,     UpperCamelCase__			:       str ,     UpperCamelCase__			:       str ,     UpperCamelCase__			:       Optional[int]=None ,     UpperCamelCase__			:       Optional[Any]=None ,     )->Any:







       if config_path is not None:
              A__											=						EncodecConfig.from_pretrained(UpperCamelCase__  )
       else:
              A__											=						EncodecConfig()

       if model_name == "encodec_24khz":
              pass  # config is already correct
       elif model_name == "encodec_32khz":
              A__											=						[8, 5, 4, 4]
              A__											=						[2.2]
              A__											=						64
              A__											=						3_20_00
              A__											=						20_48
              A__											=						False
              A__											=						False
              A__											=						False
       elif model_name == "encodec_48khz":
              A__											=						[8, 5, 4, 2]
              A__											=						[3.0, 6.0, 12.0, 24.0]
              A__											=						4_80_00
              A__											=						2
              A__											=						False
              A__											=						'''time_group_norm'''
              A__											=						True
              A__											=						1.0
              A__											=						0.01
       else:
              raise ValueError(f"Unknown model name: {model_name}"  )

       A__											=						EncodecModel(UpperCamelCase__  )

       A__											=						EncodecFeatureExtractor(
           feature_size=config.audio_channels ,     sampling_rate=config.sampling_rate ,     chunk_length_s=config.chunk_length_s ,     overlap=config.overlap ,     )
       feature_extractor.save_pretrained(UpperCamelCase__  )

       A__											=						torch.load(UpperCamelCase__  )
       if "best_state" in original_checkpoint:
              # we might have a training state saved, in which case discard the yaml results and just retain the weights
              A__											=						original_checkpoint['''best_state''']
       recursively_load_weights(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )
       model.save_pretrained(UpperCamelCase__  )

       if repo_id:
              print('''Pushing to the hub...'''  )
              feature_extractor.push_to_hub(UpperCamelCase__  )
              model.push_to_hub(UpperCamelCase__  )


if __name__ == "__main__":
       a__:      Union[str, Any]									=		argparse.ArgumentParser()
       parser.add_argument(
           '--model',
           default='encodec_24khz',
           type=str,
           help='The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.',
       )
       parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
       parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
       parser.add_argument(
           '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
       )
       parser.add_argument(
           '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
       )

       a__:      Optional[int]									=		parser.parse_args()
       convert_checkpoint(
           args.model,
           args.checkpoint_path,
           args.pytorch_dump_folder_path,
           args.config_path,
           args.push_to_hub,
       )






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Optional[Any]						=      """facebook/bart-large-mnli"""
    a__	:						int						=      (
        """This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which """
        """should be the text to classify, and `labels`, which should be the list of labels to use for classification. """
        """It returns the most likely label in the list of provided `labels` for the input text."""
    )
    a__	:						Optional[Any]						=      """text_classifier"""
    a__	:						Any						=      AutoTokenizer
    a__	:						str						=      AutoModelForSequenceClassification

    a__	:						str						=      ["""text""", ["""text"""]]
    a__	:						Optional[int]						=      ["""text"""]







    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        super().setup()
        __UpperCamelCase     :int			  =		self.model.config
        __UpperCamelCase     :Optional[Any]			  =		-1
        for idx, label in config.idalabel.items():
            if label.lower().startswith('''entail'''):
                __UpperCamelCase     :List[Any]			  =		int(__lowercase)
        if self.entailment_id == -1:
            raise ValueError('''Could not determine the entailment ID from the model config, please pass it at init.''')







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase)       ->	Union[str, Any]:
        __UpperCamelCase     :Any			  =		labels
        return self.pre_processor(
            [text] * len(__lowercase) , [f"""This example is {label}""" for label in labels] , return_tensors='''pt''' , padding='''max_length''' , )







    def 			UpperCamelCase__   (	self , __lowercase)       ->	Optional[Any]:
        __UpperCamelCase     :List[Any]			  =		outputs.logits
        __UpperCamelCase     :Any			  =		torch.argmax(logits[:, 2]).item()
        return self._labels[label_id]





