





'''simple docstring'''




from __future__ import annotations

import os
import tempfile
import unittest

from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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 (
      TFConvBertForMaskedLM,
      TFConvBertForMultipleChoice,
      TFConvBertForQuestionAnswering,
      TFConvBertForSequenceClassification,
      TFConvBertForTokenClassification,
      TFConvBertModel,
  )


class 					SCREAMING_SNAKE_CASE       :


     def __init__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase=13		,      _UpperCAmelCase=7		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      _UpperCAmelCase=99		,      _UpperCAmelCase=32		,      _UpperCAmelCase=2		,      _UpperCAmelCase=4		,      _UpperCAmelCase=37		,      _UpperCAmelCase="gelu"		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=512		,      _UpperCAmelCase=16		,      _UpperCAmelCase=2		,      _UpperCAmelCase=0.02		,      _UpperCAmelCase=3		,      _UpperCAmelCase=4		,      _UpperCAmelCase=None		,      ):

          '''simple docstring'''






          __A		:					Optional[int]						=			parent
          __A		:					str						=			13
          __A		:					List[Any]						=			7
          __A		:					List[str]						=			True
          __A		:					str						=			True
          __A		:					Optional[Any]						=			True
          __A		:					int						=			True
          __A		:					Dict						=			99
          __A		:					Dict						=			384
          __A		:					Any						=			2
          __A		:					int						=			4
          __A		:					Optional[Any]						=			37
          __A		:					Optional[int]						=			'gelu'
          __A		:					Dict						=			0.1
          __A		:					Optional[int]						=			0.1
          __A		:					Any						=			512
          __A		:					int						=			16
          __A		:					List[str]						=			2
          __A		:					str						=			0.02
          __A		:					Any						=			3
          __A		:					str						=			4
          __A		:					Union[str, Any]						=			128
          __A		:					int						=			2
          __A		:					List[Any]						=			9
          __A		:					List[Any]						=			1
          __A		:					List[Any]						=			None


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			ids_tensor([self.batch_size, self.seq_length]		,      self.vocab_size)

          __A		:					str						=			None
          if self.use_input_mask:
               __A		:					List[Any]						=			random_attention_mask([self.batch_size, self.seq_length])

          __A		:					Optional[Any]						=			None
          if self.use_token_type_ids:
               __A		:					Optional[Any]						=			ids_tensor([self.batch_size, self.seq_length]		,      self.type_vocab_size)

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

          __A		:					List[Any]						=			ConvBertConfig(
              vocab_size=self.vocab_size		,      hidden_size=self.hidden_size		,      num_hidden_layers=self.num_hidden_layers		,      num_attention_heads=self.num_attention_heads		,      intermediate_size=self.intermediate_size		,      hidden_act=self.hidden_act		,      hidden_dropout_prob=self.hidden_dropout_prob		,      attention_probs_dropout_prob=self.attention_probs_dropout_prob		,      max_position_embeddings=self.max_position_embeddings		,      type_vocab_size=self.type_vocab_size		,      initializer_range=self.initializer_range		,      return_dict=_UpperCAmelCase		,      )

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


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					int						=			TFConvBertModel(config=_UpperCAmelCase)
          __A		:					Optional[Any]						=			{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

          __A		:					Tuple						=			[input_ids, input_mask]
          __A		:					Any						=			model(_UpperCAmelCase)

          __A		:					Dict						=			model(_UpperCAmelCase)

          self.parent.assertEqual(result.last_hidden_state.shape		,      (self.batch_size, self.seq_length, self.hidden_size))


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					str						=			TFConvBertForMaskedLM(config=_UpperCAmelCase)
          __A		:					str						=			{
              'input_ids': input_ids,
              'attention_mask': input_mask,
              'token_type_ids': token_type_ids,
          }
          __A		:					str						=			model(_UpperCAmelCase)
          self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.seq_length, self.vocab_size))


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Optional[int]						=			self.num_labels
          __A		:					Any						=			TFConvBertForSequenceClassification(config=_UpperCAmelCase)
          __A		:					Optional[Any]						=			{
              'input_ids': input_ids,
              'attention_mask': input_mask,
              'token_type_ids': token_type_ids,
          }

          __A		:					Dict						=			model(_UpperCAmelCase)
          self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.num_labels))


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Tuple						=			self.num_choices
          __A		:					List[str]						=			TFConvBertForMultipleChoice(config=_UpperCAmelCase)
          __A		:					int						=			tf.tile(tf.expand_dims(_UpperCAmelCase		,      1)		,      (1, self.num_choices, 1))
          __A		:					Optional[Any]						=			tf.tile(tf.expand_dims(_UpperCAmelCase		,      1)		,      (1, self.num_choices, 1))
          __A		:					List[Any]						=			tf.tile(tf.expand_dims(_UpperCAmelCase		,      1)		,      (1, self.num_choices, 1))
          __A		:					int						=			{
              'input_ids': multiple_choice_inputs_ids,
              'attention_mask': multiple_choice_input_mask,
              'token_type_ids': multiple_choice_token_type_ids,
          }
          __A		:					Optional[Any]						=			model(_UpperCAmelCase)
          self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.num_choices))


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					List[Any]						=			self.num_labels
          __A		:					List[Any]						=			TFConvBertForTokenClassification(config=_UpperCAmelCase)
          __A		:					str						=			{
              'input_ids': input_ids,
              'attention_mask': input_mask,
              'token_type_ids': token_type_ids,
          }
          __A		:					int						=			model(_UpperCAmelCase)
          self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.seq_length, self.num_labels))


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Optional[Any]						=			TFConvBertForQuestionAnswering(config=_UpperCAmelCase)
          __A		:					Any						=			{
              'input_ids': input_ids,
              'attention_mask': input_mask,
              'token_type_ids': token_type_ids,
          }

          __A		:					Union[str, Any]						=			model(_UpperCAmelCase)
          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 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Optional[int]						=			self.prepare_config_and_inputs()
          (
              (
              __A
          )					,(
              __A
          )					,(
              __A
          )					,(
              __A
          )					,(
              __A
          )					,(
              __A
          )					,(
              __A
          )					,
          )		:					Union[str, Any]						=			config_and_inputs
          __A		:					List[str]						=			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
          return config, inputs_dict




@require_tf
class 					SCREAMING_SNAKE_CASE       (a__   ,						a__   ,						unittest.TestCase				):
     lowerCAmelCase						     =			(
         (
             TFConvBertModel,
             TFConvBertForMaskedLM,
             TFConvBertForQuestionAnswering,
             TFConvBertForSequenceClassification,
             TFConvBertForTokenClassification,
             TFConvBertForMultipleChoice,
         )
         if is_tf_available()
         else ()
     )
     lowerCAmelCase						     =			(
         {
             '''feature-extraction''': TFConvBertModel,
             '''fill-mask''': TFConvBertForMaskedLM,
             '''question-answering''': TFConvBertForQuestionAnswering,
             '''text-classification''': TFConvBertForSequenceClassification,
             '''token-classification''': TFConvBertForTokenClassification,
             '''zero-shot''': TFConvBertForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     lowerCAmelCase						     =			False
     lowerCAmelCase						     =			False
     lowerCAmelCase						     =			False


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					int						=			TFConvBertModelTester(self)
          __A		:					str						=			ConfigTester(self		,      config_class=_UpperCAmelCase		,      hidden_size=37)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          self.config_tester.run_common_tests()


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Tuple						=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					str						=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Optional[Any]						=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_multiple_choice(*_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[Any]						=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					int						=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					int						=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase)


     @slow
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A					,__A		:					Union[str, Any]						=			self.model_tester.prepare_config_and_inputs_for_common()
          __A		:					List[str]						=			True
          __A		:					List[str]						=			True

          if hasattr(_UpperCAmelCase		,      'use_cache'):
               __A		:					List[Any]						=			True

          __A		:					str						=			getattr(self.model_tester		,      'encoder_seq_length'		,      self.model_tester.seq_length)
          __A		:					Union[str, Any]						=			getattr(self.model_tester		,      'key_length'		,      _UpperCAmelCase)

          for model_class in self.all_model_classes:
               __A		:					List[str]						=			self._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase)
               __A		:					Optional[int]						=			model_class(_UpperCAmelCase)
               __A		:					Optional[Any]						=			len(model(_UpperCAmelCase))

               with tempfile.TemporaryDirectory() as tmpdirname:
                    model.save_pretrained(_UpperCAmelCase		,      saved_model=_UpperCAmelCase)
                    __A		:					Union[str, Any]						=			os.path.join(_UpperCAmelCase		,      'saved_model'		,      '1')
                    __A		:					Tuple						=			tf.keras.models.load_model(_UpperCAmelCase)
                    __A		:					str						=			model(_UpperCAmelCase)

                    if self.is_encoder_decoder:
                         __A		:					Optional[int]						=			outputs['encoder_hidden_states']
                         __A		:					str						=			outputs['encoder_attentions']
                    else:
                         __A		:					List[Any]						=			outputs['hidden_states']
                         __A		:					Optional[Any]						=			outputs['attentions']

                    self.assertEqual(len(_UpperCAmelCase)		,      _UpperCAmelCase)

                    __A		:					str						=			getattr(
                        self.model_tester		,      'expected_num_hidden_layers'		,      self.model_tester.num_hidden_layers + 1)

                    self.assertEqual(len(_UpperCAmelCase)		,      _UpperCAmelCase)
                    self.assertListEqual(
                        list(output_hidden_states[0].shape[-2:])		,      [self.model_tester.seq_length, self.model_tester.hidden_size]		,      )

                    self.assertEqual(len(_UpperCAmelCase)		,      self.model_tester.num_hidden_layers)
                    self.assertListEqual(
                        list(output_attentions[0].shape[-3:])		,      [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]		,      )


     @slow
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Dict						=			TFConvBertModel.from_pretrained('YituTech/conv-bert-base')
          self.assertIsNotNone(_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A					,__A		:					Dict						=			self.model_tester.prepare_config_and_inputs_for_common()
          __A		:					Any						=			True
          __A		:					str						=			getattr(self.model_tester		,      'decoder_seq_length'		,      self.model_tester.seq_length)
          __A		:					Any						=			getattr(self.model_tester		,      'encoder_seq_length'		,      self.model_tester.seq_length)
          __A		:					int						=			getattr(self.model_tester		,      'key_length'		,      _UpperCAmelCase)
          __A		:					Tuple						=			getattr(self.model_tester		,      'key_length'		,      _UpperCAmelCase)

          def check_decoder_attentions_output(_UpperCAmelCase):
               __A		:					List[str]						=			len(_UpperCAmelCase)
               self.assertEqual(out_len % 2		,      0)
               __A		:					Any						=			outputs.decoder_attentions
               self.assertEqual(len(_UpperCAmelCase)		,      self.model_tester.num_hidden_layers)
               self.assertListEqual(
                   list(decoder_attentions[0].shape[-3:])		,      [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length]		,      )

          def check_encoder_attentions_output(_UpperCAmelCase):
               __A		:					str						=			[
                   t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
               ]
               self.assertEqual(len(_UpperCAmelCase)		,      self.model_tester.num_hidden_layers)
               self.assertListEqual(
                   list(attentions[0].shape[-3:])		,      [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]		,      )

          for model_class in self.all_model_classes:
               __A		:					Dict						=			True
               __A		:					Any						=			False
               __A		:					str						=			model_class(_UpperCAmelCase)
               __A		:					List[str]						=			model(self._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase))
               __A		:					List[str]						=			len(_UpperCAmelCase)
               self.assertEqual(config.output_hidden_states		,      _UpperCAmelCase)
               check_encoder_attentions_output(_UpperCAmelCase)

               if self.is_encoder_decoder:
                    __A		:					Union[str, Any]						=			model_class(_UpperCAmelCase)
                    __A		:					int						=			model(self._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase))
                    self.assertEqual(config.output_hidden_states		,      _UpperCAmelCase)
                    check_decoder_attentions_output(_UpperCAmelCase)

               # Check that output attentions can also be changed via the config
               del inputs_dict["output_attentions"]
               __A		:					int						=			True
               __A		:					Tuple						=			model_class(_UpperCAmelCase)
               __A		:					Dict						=			model(self._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase))
               self.assertEqual(config.output_hidden_states		,      _UpperCAmelCase)
               check_encoder_attentions_output(_UpperCAmelCase)

               # Check attention is always last and order is fine
               __A		:					Any						=			True
               __A		:					str						=			True
               __A		:					Union[str, Any]						=			model_class(_UpperCAmelCase)
               __A		:					Union[str, Any]						=			model(self._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase))

               self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)		,      len(_UpperCAmelCase))
               self.assertEqual(model.config.output_hidden_states		,      _UpperCAmelCase)
               check_encoder_attentions_output(_UpperCAmelCase)




@require_tf
class 					SCREAMING_SNAKE_CASE       (unittest.TestCase				):


     @slow
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Tuple						=			TFConvBertModel.from_pretrained('YituTech/conv-bert-base')
          __A		:					str						=			tf.constant([[0, 1, 2, 3, 4, 5]])
          __A		:					Optional[int]						=			model(_UpperCAmelCase)[0]

          __A		:					List[Any]						=			[1, 6, 768]
          self.assertEqual(output.shape		,      _UpperCAmelCase)

          __A		:					Tuple						=			tf.constant(
              [
                  [
                      [-0.03475493, -0.4686034, -0.30638832],
                      [0.22637248, -0.26988646, -0.7423424],
                      [0.10324868, -0.45013508, -0.58280784],
                  ]
              ])
          tf.debugging.assert_near(output[:, :3, :3]		,      _UpperCAmelCase		,      atol=1e-4)






import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple

import torch
from audiocraft.models import MusicGen

from transformers import (
    AutoFeatureExtractor,
    AutoTokenizer,
    EncodecModel,
    MusicgenDecoderConfig,
    MusicgenForConditionalGeneration,
    MusicgenProcessor,
    TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_   :      Optional[Any]			=   logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_   :      Any			=   ['''model.decoder.embed_positions.weights''']



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    int:
		if "emb" in name:
				__lowercase											=     name.replace('emb'						,   'model.decoder.embed_tokens'							)
		if "transformer" in name:
				__lowercase											=     name.replace('transformer'						,   'model.decoder'							)
		if "cross_attention" in name:
				__lowercase											=     name.replace('cross_attention'						,   'encoder_attn'							)
		if "linear1" in name:
				__lowercase											=     name.replace('linear1'						,   'fc1'							)
		if "linear2" in name:
				__lowercase											=     name.replace('linear2'						,   'fc2'							)
		if "norm1" in name:
				__lowercase											=     name.replace('norm1'						,   'self_attn_layer_norm'							)
		if "norm_cross" in name:
				__lowercase											=     name.replace('norm_cross'						,   'encoder_attn_layer_norm'							)
		if "norm2" in name:
				__lowercase											=     name.replace('norm2'						,   'final_layer_norm'							)
		if "out_norm" in name:
				__lowercase											=     name.replace('out_norm'						,   'model.decoder.layer_norm'							)
		if "linears" in name:
				__lowercase											=     name.replace('linears'						,   'lm_heads'							)
		if "condition_provider.conditioners.description.output_proj" in name:
				__lowercase											=     name.replace('condition_provider.conditioners.description.output_proj'						,   'enc_to_dec_proj'							)
		return name



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    Tuple[Dict, Dict]:
		__lowercase											=     list(state_dict.keys()							)
		__lowercase											=     {}
		for key in keys:
				__lowercase											=     state_dict.pop(snake_case							)
				__lowercase											=     rename_keys(snake_case							)
				if "in_proj_weight" in key:
						# split fused qkv proj
						__lowercase											=     val[:hidden_size, :]
						__lowercase											=     val[hidden_size : 2 * hidden_size, :]
						__lowercase											=     val[-hidden_size:, :]
				elif "enc_to_dec_proj" in key:
						__lowercase											=     val
				else:
						__lowercase											=     val
		return state_dict, enc_dec_proj_state_dict



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    MusicgenDecoderConfig:
		if checkpoint == "small":
				# default config values
				__lowercase											=     1_024
				__lowercase											=     24
				__lowercase											=     16
		elif checkpoint == "medium":
				__lowercase											=     1_536
				__lowercase											=     48
				__lowercase											=     24
		elif checkpoint == "large":
				__lowercase											=     2_048
				__lowercase											=     48
				__lowercase											=     32
		else:
				raise ValueError(F"Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."							)
		__lowercase											=     MusicgenDecoderConfig(
		    hidden_size=snake_case						,   ffn_dim=hidden_size * 4						,   num_hidden_layers=snake_case						,   num_attention_heads=snake_case						,   )
		return config



@torch.no_grad()
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case=None						,   snake_case=None						,   snake_case="cpu"							)		->    Optional[int]:
		__lowercase											=     MusicGen.get_pretrained(snake_case						,   device=snake_case							)
		__lowercase											=     decoder_config_from_checkpoint(snake_case							)

		__lowercase											=     fairseq_model.lm.state_dict()
		__lowercase						,      __lowercase											=     rename_state_dict(
		    snake_case						,   hidden_size=decoder_config.hidden_size							)

		__lowercase											=     TaEncoderModel.from_pretrained('t5-base'							)
		__lowercase											=     EncodecModel.from_pretrained('facebook/encodec_32khz'							)
		__lowercase											=     MusicgenForCausalLM(snake_case							).eval()

		# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
		__lowercase						,      __lowercase											=     decoder.load_state_dict(snake_case						,   strict=snake_case							)

		for key in missing_keys.copy():
				if key.startswith(('text_encoder', 'audio_encoder')							) or key in EXPECTED_MISSING_KEYS:
						missing_keys.remove(snake_case							)

		if len(snake_case							) > 0:
				raise ValueError(F"Missing key(s) in state_dict: {missing_keys}"							)

		if len(snake_case							) > 0:
				raise ValueError(F"Unexpected key(s) in state_dict: {unexpected_keys}"							)

		# init the composite model
		__lowercase											=     MusicgenForConditionalGeneration(text_encoder=snake_case						,   audio_encoder=snake_case						,   decoder=snake_case							)

		# load the pre-trained enc-dec projection (from the decoder state dict)
		model.enc_to_dec_proj.load_state_dict(snake_case							)

		# check we can do a forward pass
		__lowercase											=     torch.arange(0						,   8						,   dtype=torch.long							).reshape(2						,   -1							)
		__lowercase											=     input_ids.reshape(2 * 4						,   -1							)

		with torch.no_grad():
				__lowercase											=     model(input_ids=snake_case						,   decoder_input_ids=snake_case							).logits

		if logits.shape != (8, 1, 2_048):
				raise ValueError('Incorrect shape for logits'							)

		# now construct the processor
		__lowercase											=     AutoTokenizer.from_pretrained('t5-base'							)
		__lowercase											=     AutoFeatureExtractor.from_pretrained('facebook/encodec_32khz'						,   padding_side='left'							)

		__lowercase											=     MusicgenProcessor(feature_extractor=snake_case						,   tokenizer=snake_case							)

		# set the appropriate bos/pad token ids
		__lowercase											=     2_048
		__lowercase											=     2_048

		# set other default generation config params
		__lowercase											=     int(30 * audio_encoder.config.frame_rate							)
		__lowercase											=     True
		__lowercase											=     3.0

		if pytorch_dump_folder is not None:
				Path(snake_case							).mkdir(exist_ok=snake_case							)
				logger.info(F"Saving model {checkpoint} to {pytorch_dump_folder}"							)
				model.save_pretrained(snake_case							)
				processor.save_pretrained(snake_case							)

		if repo_id:
				logger.info(F"Pushing model {checkpoint} to {repo_id}"							)
				model.push_to_hub(snake_case							)
				processor.push_to_hub(snake_case							)


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_   :      Optional[int]			=   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--checkpoint''',
		    default='''small''',
		    type=str,
		    help='''Checkpoint size of the MusicGen model you\'d like to convert. Can be one of: `[\'small\', \'medium\', \'large\']`.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder''',
		    required=True,
		    default=None,
		    type=str,
		    help='''Path to the output PyTorch model directory.''',
		)
		parser.add_argument(
		    '''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
		)
		parser.add_argument(
		    '''--device''', default='''cpu''', type=str, help='''Torch device to run the conversion, either cpu or cuda.'''
		)

		SCREAMING_SNAKE_CASE_   :      Any			=   parser.parse_args()
		convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)




