from __future__ import annotations

import unittest

from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import (
		    TFFunnelBaseModel,
		    TFFunnelForMaskedLM,
		    TFFunnelForMultipleChoice,
		    TFFunnelForPreTraining,
		    TFFunnelForQuestionAnswering,
		    TFFunnelForSequenceClassification,
		    TFFunnelForTokenClassification,
		    TFFunnelModel,
		)






class 		a__						:

					def __init__(    self      :       Optional[int],_A      :       Tuple,_A      :       List[Any]=13,_A      :       Tuple=7,_A      :       int=True,_A      :       List[str]=True,_A      :       Optional[int]=True,_A      :       Tuple=True,_A      :       str=99,_A      :       Optional[Any]=[1, 1, 2],_A      :       List[str]=1,_A      :       Tuple=32,_A      :       Any=4,_A      :       Optional[Any]=8,_A      :       Optional[Any]=37,_A      :       Any="gelu_new",_A      :       Tuple=0.1,_A      :       int=0.1,_A      :       Optional[int]=0.0,_A      :       Optional[int]=512,_A      :       Union[str, Any]=3,_A      :       Optional[Any]=0.02,_A      :       Optional[Any]=3,_A      :       List[Any]=4,_A      :       str=None,_A      :       str=False,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    parent
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    batch_size
							SCREAMING_SNAKE_CASE_  :		Any				      =    seq_length
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    is_training
							SCREAMING_SNAKE_CASE_  :		str				      =    use_input_mask
							SCREAMING_SNAKE_CASE_  :		int				      =    use_token_type_ids
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    use_labels
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    vocab_size
							SCREAMING_SNAKE_CASE_  :		Any				      =    block_sizes
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    num_decoder_layers
							SCREAMING_SNAKE_CASE_  :		Any				      =    d_model
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    n_head
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    d_head
							SCREAMING_SNAKE_CASE_  :		int				      =    d_inner
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    hidden_act
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    hidden_dropout
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    attention_dropout
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    activation_dropout
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    max_position_embeddings
							SCREAMING_SNAKE_CASE_  :		str				      =    type_vocab_size
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    2
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    num_labels
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    num_choices
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    scope
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    initializer_std

							# Used in the tests to check the size of the first attention layer
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    n_head
							# Used in the tests to check the size of the first hidden state
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    self.d_model
							# Used in the tests to check the number of output hidden states/attentions
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    sum(self.block_sizes    ) + (0 if base else self.num_decoder_layers)
							# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
							# the last hidden state of the first block (which is the first hidden state of the decoder).
							if not base:
									SCREAMING_SNAKE_CASE_  :		str				      =    self.num_hidden_layers + 2

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    ids_tensor([self.batch_size, self.seq_length],self.vocab_size    )

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    None
							if self.use_input_mask:
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    random_attention_mask([self.batch_size, self.seq_length]    )

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    None
							if self.use_token_type_ids:
									SCREAMING_SNAKE_CASE_  :		str				      =    ids_tensor([self.batch_size, self.seq_length],self.type_vocab_size    )

							SCREAMING_SNAKE_CASE_  :		str				      =    None
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    None
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    None
							if self.use_labels:
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    ids_tensor([self.batch_size],self.type_sequence_label_size    )
									SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    ids_tensor([self.batch_size, self.seq_length],self.num_labels    )
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    ids_tensor([self.batch_size],self.num_choices    )

							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    FunnelConfig(
							    vocab_size=self.vocab_size,block_sizes=self.block_sizes,num_decoder_layers=self.num_decoder_layers,d_model=self.d_model,n_head=self.n_head,d_head=self.d_head,d_inner=self.d_inner,hidden_act=self.hidden_act,hidden_dropout=self.hidden_dropout,attention_dropout=self.attention_dropout,activation_dropout=self.activation_dropout,max_position_embeddings=self.max_position_embeddings,type_vocab_size=self.type_vocab_size,initializer_std=self.initializer_std,)

							return (
							    config,
							    input_ids,
							    token_type_ids,
							    input_mask,
							    sequence_labels,
							    token_labels,
							    choice_labels,
							)

					def 				__UpperCamelCase   (    self      :       Any,_A      :       Optional[int],_A      :       int,_A      :       Tuple,_A      :       List[str],_A      :       Optional[Any],_A      :       Any,_A      :       Optional[int],):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    TFFunnelModel(config=_A    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    model(_A    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    [input_ids, input_mask]
							SCREAMING_SNAKE_CASE_  :		str				      =    model(_A    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    model(_A    )
							self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.d_model)    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    False
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    TFFunnelModel(config=_A    )
							SCREAMING_SNAKE_CASE_  :		int				      =    model(_A    )
							self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.d_model)    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    False
							SCREAMING_SNAKE_CASE_  :		Any				      =    TFFunnelModel(config=_A    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    model(_A    )
							self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.d_model)    )

					def 				__UpperCamelCase   (    self      :       Tuple,_A      :       Union[str, Any],_A      :       Any,_A      :       Optional[Any],_A      :       List[Any],_A      :       List[str],_A      :       Optional[int],_A      :       int,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    TFFunnelBaseModel(config=_A    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
							SCREAMING_SNAKE_CASE_  :		str				      =    model(_A    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    [input_ids, input_mask]
							SCREAMING_SNAKE_CASE_  :		Dict				      =    model(_A    )

							SCREAMING_SNAKE_CASE_  :		Dict				      =    model(_A    )
							self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, 2, self.d_model)    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    False
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    TFFunnelBaseModel(config=_A    )
							SCREAMING_SNAKE_CASE_  :		Dict				      =    model(_A    )
							self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, 3, self.d_model)    )

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    False
							SCREAMING_SNAKE_CASE_  :		Any				      =    TFFunnelBaseModel(config=_A    )
							SCREAMING_SNAKE_CASE_  :		str				      =    model(_A    )
							self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, 2, self.d_model)    )

					def 				__UpperCamelCase   (    self      :       Dict,_A      :       Tuple,_A      :       Dict,_A      :       List[Any],_A      :       str,_A      :       int,_A      :       Optional[int],_A      :       Optional[Any],):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    TFFunnelForPreTraining(config=_A    )
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    model(_A    )
							self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length)    )

					def 				__UpperCamelCase   (    self      :       Any,_A      :       List[Any],_A      :       Tuple,_A      :       Tuple,_A      :       Dict,_A      :       Any,_A      :       Optional[int],_A      :       Union[str, Any],):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    TFFunnelForMaskedLM(config=_A    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
							SCREAMING_SNAKE_CASE_  :		str				      =    model(_A    )
							self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.vocab_size)    )

					def 				__UpperCamelCase   (    self      :       Union[str, Any],_A      :       List[Any],_A      :       Optional[int],_A      :       Optional[int],_A      :       List[str],_A      :       Optional[int],_A      :       Optional[int],_A      :       List[Any],):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self.num_labels
							SCREAMING_SNAKE_CASE_  :		int				      =    TFFunnelForSequenceClassification(config=_A    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    model(_A    )
							self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels)    )

					def 				__UpperCamelCase   (    self      :       int,_A      :       List[Any],_A      :       Dict,_A      :       Dict,_A      :       List[str],_A      :       Tuple,_A      :       str,_A      :       str,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    self.num_choices
							SCREAMING_SNAKE_CASE_  :		int				      =    TFFunnelForMultipleChoice(config=_A    )
							SCREAMING_SNAKE_CASE_  :		str				      =    tf.tile(tf.expand_dims(_A,1    ),(1, self.num_choices, 1)    )
							SCREAMING_SNAKE_CASE_  :		Dict				      =    tf.tile(tf.expand_dims(_A,1    ),(1, self.num_choices, 1)    )
							SCREAMING_SNAKE_CASE_  :		Dict				      =    tf.tile(tf.expand_dims(_A,1    ),(1, self.num_choices, 1)    )
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    {
							    "input_ids": multiple_choice_inputs_ids,
							    "attention_mask": multiple_choice_input_mask,
							    "token_type_ids": multiple_choice_token_type_ids,
							}
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    model(_A    )
							self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_choices)    )

					def 				__UpperCamelCase   (    self      :       Optional[Any],_A      :       str,_A      :       str,_A      :       List[str],_A      :       Union[str, Any],_A      :       Dict,_A      :       List[Any],_A      :       int,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    self.num_labels
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    TFFunnelForTokenClassification(config=_A    )
							SCREAMING_SNAKE_CASE_  :		str				      =    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
							SCREAMING_SNAKE_CASE_  :		str				      =    model(_A    )
							self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.num_labels)    )

					def 				__UpperCamelCase   (    self      :       Optional[Any],_A      :       Dict,_A      :       List[str],_A      :       str,_A      :       List[str],_A      :       List[str],_A      :       str,_A      :       Dict,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    TFFunnelForQuestionAnswering(config=_A    )
							SCREAMING_SNAKE_CASE_  :		int				      =    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    model(_A    )
							self.parent.assertEqual(result.start_logits.shape,(self.batch_size, self.seq_length)    )
							self.parent.assertEqual(result.end_logits.shape,(self.batch_size, self.seq_length)    )






					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    self.prepare_config_and_inputs()
							(
							    (
							    SCREAMING_SNAKE_CASE_
							)      ,    (
							    SCREAMING_SNAKE_CASE_
							)      ,    (
							    SCREAMING_SNAKE_CASE_
							)      ,    (
							    SCREAMING_SNAKE_CASE_
							)      ,    (
							    SCREAMING_SNAKE_CASE_
							)      ,    (
							    SCREAMING_SNAKE_CASE_
							)      ,    (
							    SCREAMING_SNAKE_CASE_
							)      ,    
							)  :		Tuple				      =    config_and_inputs
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
							return config, inputs_dict







@require_tf
class 		a__						(					A__    ,		A__    ,		unittest.TestCase					):
					A	       =							(
					    (
					        TFFunnelModel,
					        TFFunnelForMaskedLM,
					        TFFunnelForPreTraining,
					        TFFunnelForQuestionAnswering,
					        TFFunnelForTokenClassification,
					    )
					    if is_tf_available()
					    else ()
					)
					A	       =							(
					    {
					        'feature-extraction': (TFFunnelBaseModel, TFFunnelModel),
					        'fill-mask': TFFunnelForMaskedLM,
					        'question-answering': TFFunnelForQuestionAnswering,
					        'text-classification': TFFunnelForSequenceClassification,
					        'token-classification': TFFunnelForTokenClassification,
					        'zero-shot': TFFunnelForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					A	       =							False
					A	       =							False

					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    TFFunnelModelTester(self    )
							SCREAMING_SNAKE_CASE_  :		int				      =    ConfigTester(self,config_class=_A    )

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							self.config_tester.run_common_tests()

					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_A    )

					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_pretraining(*_A    )

					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*_A    )

					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*_A    )






					def 				__UpperCamelCase   (    self      :       Any    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*_A    )







@require_tf
class 		a__						(					A__    ,		unittest.TestCase					):
					A	       =							(
					    (TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
					)
					A	       =							False
					A	       =							False

					def 				__UpperCamelCase   (    self      :       Any    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    TFFunnelModelTester(self,base=_A    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    ConfigTester(self,config_class=_A    )

					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






							self.config_tester.run_common_tests()

					def 				__UpperCamelCase   (    self      :       Any    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_base_model(*_A    )

					def 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*_A    )






					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_multiple_choice(*_A    )

"""simple docstring"""
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()
lowercase__					       =	logging.get_logger('transformers.models.encodec')

lowercase__					       =	{
    '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',
}
lowercase__					       =	{
    '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',
}
lowercase__					       =	{
    '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',
}
lowercase__					       =	{
    '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',
}
lowercase__					       =	{
    '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',
}
lowercase__					       =	{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
lowercase__					       =	{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
lowercase__					       =	[]
lowercase__					       =	[]







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->int:
  for attribute in key.split('.'   ):
    a__:     str										=	getattr(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )

  if weight_type is not None:
    a__:     List[str]										=	getattr(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ).shape
  else:
    a__:     Optional[Any]										=	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__:     str										=	value
  elif weight_type == "weight_g":
    a__:     int										=	value
  elif weight_type == "weight_v":
    a__:     Tuple										=	value
  elif weight_type == "bias":
    a__:     Dict										=	value
  elif weight_type == "running_mean":
    a__:     Any										=	value
  elif weight_type == "running_var":
    a__:     Tuple										=	value
  elif weight_type == "num_batches_tracked":
    a__:     List[str]										=	value
  elif weight_type == "weight_ih_l0":
    a__:     List[Any]										=	value
  elif weight_type == "weight_hh_l0":
    a__:     List[Any]										=	value
  elif weight_type == "bias_ih_l0":
    a__:     List[Any]										=	value
  elif weight_type == "bias_hh_l0":
    a__:     List[Any]										=	value
  elif weight_type == "weight_ih_l1":
    a__:     int										=	value
  elif weight_type == "weight_hh_l1":
    a__:     str										=	value
  elif weight_type == "bias_ih_l1":
    a__:     Union[str, Any]										=	value
  elif weight_type == "bias_hh_l1":
    a__:     Any										=	value
  else:
    a__:     Union[str, Any]										=	value

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







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Dict:
  for key in ignore_keys:
    if key.endswith('.*'   ):
      if name.startswith(key[:-1]   ):
        return True
    elif ".*." in key:
      a__							,    a__:     Optional[Any]										=	key.split('.*.'   )
      if prefix in name and suffix in name:
        return True
    elif key in name:
      return True
  return False







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->List[str]:
  a__:     List[Any]										=	[]

  if model_name == "encodec_24khz" or "encodec_32khz":
    a__:     Optional[int]										=	MAPPING_24K
  elif model_name == "encodec_48khz":
    a__:     List[Any]										=	MAPPING_48K
  else:
    raise ValueError(F'Unsupported model: {model_name}'   )

  for name, value in orig_dict.items():
    if should_ignore(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ):
      logger.info(F'{name} was ignored'   )
      continue

    a__:     int										=	False
    for key, mapped_key in MAPPING.items():
      if "*" in key:
        a__							,    a__:     str										=	key.split('.*.'   )
        if prefix in name and suffix in name:
          a__:     List[str]										=	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__:     List[str]										=	True
        if "*" in mapped_key:
          a__:     List[str]										=	name.split(_SCREAMING_SNAKE_CASE   )[0].split('.'   )[-2]
          a__:     str										=	mapped_key.replace('*'		,     _SCREAMING_SNAKE_CASE   )
        if "weight_g" in name:
          a__:     int										=	'weight_g'
        elif "weight_v" in name:
          a__:     Dict										=	'weight_v'
        elif "weight_ih_l0" in name:
          a__:     int										=	'weight_ih_l0'
        elif "weight_hh_l0" in name:
          a__:     Union[str, Any]										=	'weight_hh_l0'
        elif "bias_ih_l0" in name:
          a__:     Optional[Any]										=	'bias_ih_l0'
        elif "bias_hh_l0" in name:
          a__:     Optional[int]										=	'bias_hh_l0'
        elif "weight_ih_l1" in name:
          a__:     Dict										=	'weight_ih_l1'
        elif "weight_hh_l1" in name:
          a__:     Optional[Any]										=	'weight_hh_l1'
        elif "bias_ih_l1" in name:
          a__:     List[str]										=	'bias_ih_l1'
        elif "bias_hh_l1" in name:
          a__:     Optional[Any]										=	'bias_hh_l1'
        elif "bias" in name:
          a__:     List[str]										=	'bias'
        elif "weight" in name:
          a__:     Any										=	'weight'
        elif "running_mean" in name:
          a__:     Dict										=	'running_mean'
        elif "running_var" in name:
          a__:     Dict										=	'running_var'
        elif "num_batches_tracked" in name:
          a__:     Dict										=	'num_batches_tracked'
        else:
          a__:     List[str]										=	None
        set_recursively(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
      continue
    if not is_used:
      unused_weights.append(_SCREAMING_SNAKE_CASE   )

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







@torch.no_grad()
def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=None		,     _SCREAMING_SNAKE_CASE=None		,     )	->int:
  if config_path is not None:
    a__:     Dict										=	EncodecConfig.from_pretrained(_SCREAMING_SNAKE_CASE   )
  else:
    a__:     Tuple										=	EncodecConfig()

  if model_name == "encodec_24khz":
    pass  # config is already correct
  elif model_name == "encodec_32khz":
    a__:     Any										=	[8, 5, 4, 4]
    a__:     List[str]										=	[2.2]
    a__:     List[Any]										=	64
    a__:     Dict										=	32000
    a__:     Union[str, Any]										=	2048
    a__:     Union[str, Any]										=	False
    a__:     Any										=	False
    a__:     Optional[Any]										=	False
  elif model_name == "encodec_48khz":
    a__:     Optional[int]										=	[8, 5, 4, 2]
    a__:     Union[str, Any]										=	[3.0, 6.0, 12.0, 24.0]
    a__:     List[str]										=	48000
    a__:     Tuple										=	2
    a__:     Optional[Any]										=	False
    a__:     Optional[int]										=	'time_group_norm'
    a__:     Union[str, Any]										=	True
    a__:     Dict										=	1.0
    a__:     str										=	0.01
  else:
    raise ValueError(F'Unknown model name: {model_name}'   )

  a__:     Optional[int]										=	EncodecModel(_SCREAMING_SNAKE_CASE   )

  a__:     List[str]										=	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(_SCREAMING_SNAKE_CASE   )

  a__:     int										=	torch.load(_SCREAMING_SNAKE_CASE   )
  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__:     str										=	original_checkpoint['best_state']
  recursively_load_weights(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
  model.save_pretrained(_SCREAMING_SNAKE_CASE   )

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


if __name__ == "__main__":
 lowercase__					       =	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.'
 )

 lowercase__					       =	parser.parse_args()
 convert_checkpoint(
     args.model,
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.push_to_hub,
 )


