



"""simple docstring"""




import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
 import jax.numpy as jnp

 from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
     FlaxRobertaPreLayerNormForCausalLM,
     FlaxRobertaPreLayerNormForMaskedLM,
     FlaxRobertaPreLayerNormForMultipleChoice,
     FlaxRobertaPreLayerNormForQuestionAnswering,
     FlaxRobertaPreLayerNormForSequenceClassification,
     FlaxRobertaPreLayerNormForTokenClassification,
     FlaxRobertaPreLayerNormModel,
 )






class       UpperCAmelCase_    (							unittest.TestCase		):



      def __init__(	self	:      List[str]	,      A	:      Union[str, Any]	,      A	:      Any=1_3	,      A	:      str=7	,      A	:      Union[str, Any]=True	,      A	:      Optional[int]=True	,      A	:      Tuple=True	,      A	:      Any=True	,      A	:      List[Any]=9_9	,      A	:      Union[str, Any]=3_2	,      A	:      List[str]=5	,      A	:      Any=4	,      A	:      Tuple=3_7	,      A	:      Optional[Any]="gelu"	,      A	:      str=0.1	,      A	:      Union[str, Any]=0.1	,      A	:      List[Any]=5_1_2	,      A	:      Dict=1_6	,      A	:      Dict=2	,      A	:      str=0.02	,      A	:      Optional[int]=4	,      ):
           _UpperCAmelCase     :      int														=       parent
           _UpperCAmelCase     :      Optional[Any]														=       batch_size
           _UpperCAmelCase     :      Union[str, Any]														=       seq_length
           _UpperCAmelCase     :      Dict														=       is_training
           _UpperCAmelCase     :      Optional[Any]														=       use_attention_mask
           _UpperCAmelCase     :      Tuple														=       use_token_type_ids
           _UpperCAmelCase     :      Union[str, Any]														=       use_labels
           _UpperCAmelCase     :      Any														=       vocab_size
           _UpperCAmelCase     :      Any														=       hidden_size
           _UpperCAmelCase     :      List[Any]														=       num_hidden_layers
           _UpperCAmelCase     :      List[str]														=       num_attention_heads
           _UpperCAmelCase     :      int														=       intermediate_size
           _UpperCAmelCase     :      Optional[int]														=       hidden_act
           _UpperCAmelCase     :      List[Any]														=       hidden_dropout_prob
           _UpperCAmelCase     :      List[Any]														=       attention_probs_dropout_prob
           _UpperCAmelCase     :      List[str]														=       max_position_embeddings
           _UpperCAmelCase     :      int														=       type_vocab_size
           _UpperCAmelCase     :      Union[str, Any]														=       type_sequence_label_size
           _UpperCAmelCase     :      int														=       initializer_range
           _UpperCAmelCase     :      Dict														=       num_choices



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Tuple														=       ids_tensor([self.batch_size, self.seq_length]	,      self.vocab_size		)

           _UpperCAmelCase     :      str														=       None
           if self.use_attention_mask:
                _UpperCAmelCase     :      Dict														=       random_attention_mask([self.batch_size, self.seq_length]		)

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

           _UpperCAmelCase     :      List[str]														=       RobertaPreLayerNormConfig(
               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	,      is_decoder=A	,      initializer_range=self.initializer_range	,      )

           return config, input_ids, token_type_ids, attention_mask



      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase     :      List[Any]														=       self.prepare_config_and_inputs()
           _UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase     :      int														=       config_and_inputs
           _UpperCAmelCase     :      str														=       {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
           return config, inputs_dict



      def 		snake_case_   (	self	:      int		):
           _UpperCAmelCase     :      Optional[Any]														=       self.prepare_config_and_inputs()
           _UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase     :      List[Any]														=       config_and_inputs

           _UpperCAmelCase     :      Dict														=       True
           _UpperCAmelCase     :      str														=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
           _UpperCAmelCase     :      Optional[int]														=       ids_tensor([self.batch_size, self.seq_length]	,      vocab_size=2		)

           return (
               config,
               input_ids,
               token_type_ids,
               encoder_hidden_states,
               encoder_attention_mask,
           )







@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class       UpperCAmelCase_    (							_UpperCamelCase			,     unittest.TestCase		):
      __SCREAMING_SNAKE_CASE		:  Tuple						=					True

      __SCREAMING_SNAKE_CASE		:  Optional[Any]						=					(
          (
              FlaxRobertaPreLayerNormModel,
              FlaxRobertaPreLayerNormForCausalLM,
              FlaxRobertaPreLayerNormForMaskedLM,
              FlaxRobertaPreLayerNormForSequenceClassification,
              FlaxRobertaPreLayerNormForTokenClassification,
              FlaxRobertaPreLayerNormForMultipleChoice,
              FlaxRobertaPreLayerNormForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )



      def 		snake_case_   (	self	:      int		):
           _UpperCAmelCase     :      Union[str, Any]														=       FlaxRobertaPreLayerNormModelTester(self		)



      @slow
      def 		snake_case_   (	self	:      Dict		):
           for model_class_name in self.all_model_classes:
                _UpperCAmelCase     :      str														=       model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"	,      from_pt=A		)
                _UpperCAmelCase     :      Tuple														=       model(np.ones((1, 1)		)		)
                self.assertIsNotNone(A		)







@require_flax
class       UpperCAmelCase_    (							unittest.TestCase		):



      @slow
      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Optional[int]														=       FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"	,      from_pt=A		)

           _UpperCAmelCase     :      Dict														=       np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]	,      dtype=jnp.intaa		)
           _UpperCAmelCase     :      Optional[Any]														=       model(A		)[0]
           _UpperCAmelCase     :      List[str]														=       [1, 1_1, 5_0_2_6_5]
           self.assertEqual(list(output.shape		)	,      A		)
           # compare the actual values for a slice.
           _UpperCAmelCase     :      Optional[Any]														=       np.array(
               [[[40.4_880, 18.0_199, -5.2_367], [-1.8_877, -4.0_885, 10.7_085], [-2.2_613, -5.6_110, 7.2_665]]]	,      dtype=np.floataa		)
           self.assertTrue(np.allclose(output[:, :3, :3]	,      A	,      atol=1e-4		)		)



      @slow
      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Optional[Any]														=       FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"	,      from_pt=A		)

           _UpperCAmelCase     :      Any														=       np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]	,      dtype=jnp.intaa		)
           _UpperCAmelCase     :      Tuple														=       model(A		)[0]
           # compare the actual values for a slice.
           _UpperCAmelCase     :      int														=       np.array(
               [[[0.0_208, -0.0_356, 0.0_237], [-0.1_569, -0.0_411, -0.2_626], [0.1_879, 0.0_125, -0.0_089]]]	,      dtype=np.floataa		)
           self.assertTrue(np.allclose(output[:, :3, :3]	,      A	,      atol=1e-4		)		)





"""simple docstring"""




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


_lowerCAmelCase							:   List[str]				=				["model.decoder.embed_positions.weights"]

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]							)    ->  List[str]:





     '''simple docstring'''

     if "emb" in name:
          _UpperCAmelCase     :      int														=       name.replace("emb"		,    "model.decoder.embed_tokens"							)
     if "transformer" in name:
          _UpperCAmelCase     :      List[str]														=       name.replace("transformer"		,    "model.decoder"							)
     if "cross_attention" in name:
          _UpperCAmelCase     :      Any														=       name.replace("cross_attention"		,    "encoder_attn"							)
     if "linear1" in name:
          _UpperCAmelCase     :      List[str]														=       name.replace("linear1"		,    "fc1"							)
     if "linear2" in name:
          _UpperCAmelCase     :      Any														=       name.replace("linear2"		,    "fc2"							)
     if "norm1" in name:
          _UpperCAmelCase     :      List[str]														=       name.replace("norm1"		,    "self_attn_layer_norm"							)
     if "norm_cross" in name:
          _UpperCAmelCase     :      List[Any]														=       name.replace("norm_cross"		,    "encoder_attn_layer_norm"							)
     if "norm2" in name:
          _UpperCAmelCase     :      Dict														=       name.replace("norm2"		,    "final_layer_norm"							)
     if "out_norm" in name:
          _UpperCAmelCase     :      List[str]														=       name.replace("out_norm"		,    "model.decoder.layer_norm"							)
     if "linears" in name:
          _UpperCAmelCase     :      Dict														=       name.replace("linears"		,    "lm_heads"							)
     if "condition_provider.conditioners.description.output_proj" in name:
          _UpperCAmelCase     :      Optional[Any]														=       name.replace("condition_provider.conditioners.description.output_proj"		,    "enc_to_dec_proj"							)
     return name

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		OrderedDict		,    SCREAMING_SNAKE_CASE__      :		int							)    ->  Tuple[Dict, Dict]:





     '''simple docstring'''

     _UpperCAmelCase     :      Tuple														=       list(state_dict.keys()							)
     _UpperCAmelCase     :      Dict														=       {}
     for key in keys:
          _UpperCAmelCase     :      Optional[Any]														=       state_dict.pop(SCREAMING_SNAKE_CASE__							)
          _UpperCAmelCase     :      Any														=       rename_keys(SCREAMING_SNAKE_CASE__							)
          if "in_proj_weight" in key:
               # split fused qkv proj
               _UpperCAmelCase     :      Optional[int]														=       val[:hidden_size, :]
               _UpperCAmelCase     :      str														=       val[hidden_size : 2 * hidden_size, :]
               _UpperCAmelCase     :      List[Any]														=       val[-hidden_size:, :]
          elif "enc_to_dec_proj" in key:
               _UpperCAmelCase     :      Union[str, Any]														=       val
          else:
               _UpperCAmelCase     :      int														=       val
     return state_dict, enc_dec_proj_state_dict

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str							)    ->  MusicgenDecoderConfig:





     '''simple docstring'''

     if checkpoint == "small":
          # default config values
          _UpperCAmelCase     :      Union[str, Any]														=       1_024
          _UpperCAmelCase     :      Tuple														=       24
          _UpperCAmelCase     :      List[str]														=       16
     elif checkpoint == "medium":
          _UpperCAmelCase     :      List[str]														=       1_536
          _UpperCAmelCase     :      List[str]														=       48
          _UpperCAmelCase     :      str														=       24
     elif checkpoint == "large":
          _UpperCAmelCase     :      Dict														=       2_048
          _UpperCAmelCase     :      Optional[int]														=       48
          _UpperCAmelCase     :      Tuple														=       32
     else:
          raise ValueError(f'Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'							)
     _UpperCAmelCase     :      Union[str, Any]														=       MusicgenDecoderConfig(
         hidden_size=SCREAMING_SNAKE_CASE__		,    ffn_dim=hidden_size * 4		,    num_hidden_layers=SCREAMING_SNAKE_CASE__		,    num_attention_heads=SCREAMING_SNAKE_CASE__		,    )
     return config

@torch.no_grad()
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		List[Any]		,    SCREAMING_SNAKE_CASE__      :		int=None		,    SCREAMING_SNAKE_CASE__      :		Tuple=None		,    SCREAMING_SNAKE_CASE__      :		Dict="cpu"							)    ->  Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase     :      Tuple														=       MusicGen.get_pretrained(SCREAMING_SNAKE_CASE__		,    device=SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      str														=       decoder_config_from_checkpoint(SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase     :      Optional[int]														=       fairseq_model.lm.state_dict()
     _UpperCAmelCase							,			_UpperCAmelCase     :      Union[str, Any]														=       rename_state_dict(
         SCREAMING_SNAKE_CASE__		,    hidden_size=decoder_config.hidden_size							)

     _UpperCAmelCase     :      Tuple														=       TaEncoderModel.from_pretrained("t5-base"							)
     _UpperCAmelCase     :      List[str]														=       EncodecModel.from_pretrained("facebook/encodec_32khz"							)
     _UpperCAmelCase     :      Union[str, Any]														=       MusicgenForCausalLM(SCREAMING_SNAKE_CASE__							).eval()

     # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
     _UpperCAmelCase							,			_UpperCAmelCase     :      Optional[Any]														=       decoder.load_state_dict(SCREAMING_SNAKE_CASE__		,    strict=SCREAMING_SNAKE_CASE__							)

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

     if len(SCREAMING_SNAKE_CASE__							) > 0:
          raise ValueError(f'Missing key(s) in state_dict: {missing_keys}'							)

     if len(SCREAMING_SNAKE_CASE__							) > 0:
          raise ValueError(f'Unexpected key(s) in state_dict: {unexpected_keys}'							)

     # init the composite model
     _UpperCAmelCase     :      Dict														=       MusicgenForConditionalGeneration(text_encoder=SCREAMING_SNAKE_CASE__		,    audio_encoder=SCREAMING_SNAKE_CASE__		,    decoder=SCREAMING_SNAKE_CASE__							)

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

     # check we can do a forward pass
     _UpperCAmelCase     :      int														=       torch.arange(0		,    8		,    dtype=torch.long							).reshape(2		,    -1							)
     _UpperCAmelCase     :      List[Any]														=       input_ids.reshape(2 * 4		,    -1							)

     with torch.no_grad():
          _UpperCAmelCase     :      List[str]														=       model(input_ids=SCREAMING_SNAKE_CASE__		,    decoder_input_ids=SCREAMING_SNAKE_CASE__							).logits

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

     # now construct the processor
     _UpperCAmelCase     :      Optional[int]														=       AutoTokenizer.from_pretrained("t5-base"							)
     _UpperCAmelCase     :      str														=       AutoFeatureExtractor.from_pretrained("facebook/encodec_32khz"		,    padding_side="left"							)

     _UpperCAmelCase     :      Optional[Any]														=       MusicgenProcessor(feature_extractor=SCREAMING_SNAKE_CASE__		,    tokenizer=SCREAMING_SNAKE_CASE__							)

     # set the appropriate bos/pad token ids
     _UpperCAmelCase     :      Optional[int]														=       2_048
     _UpperCAmelCase     :      Union[str, Any]														=       2_048

     # set other default generation config params
     _UpperCAmelCase     :      Optional[int]														=       int(30 * audio_encoder.config.frame_rate							)
     _UpperCAmelCase     :      Union[str, Any]														=       True
     _UpperCAmelCase     :      Dict														=       3.0

     if pytorch_dump_folder is not None:
          Path(SCREAMING_SNAKE_CASE__							).mkdir(exist_ok=SCREAMING_SNAKE_CASE__							)
          logger.info(f'Saving model {checkpoint} to {pytorch_dump_folder}'							)
          model.save_pretrained(SCREAMING_SNAKE_CASE__							)
          processor.save_pretrained(SCREAMING_SNAKE_CASE__							)

     if repo_id:
          logger.info(f'Pushing model {checkpoint} to {repo_id}'							)
          model.push_to_hub(SCREAMING_SNAKE_CASE__							)
          processor.push_to_hub(SCREAMING_SNAKE_CASE__							)


if __name__ == "__main__":
 _lowerCAmelCase							:   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."
 )

 _lowerCAmelCase							:   List[str]				=				parser.parse_args()
 convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
