





"""simple docstring"""


import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import (
      MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
      VideoMAEForPreTraining,
      VideoMAEForVideoClassification,
      VideoMAEModel,
  )
  from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from transformers import VideoMAEImageProcessor



class      __SCREAMING_SNAKE_CASE    :







  '''simple docstring'''


  def __init__(		self   :       Tuple					,   __a   :       Any					,   __a   :       int=13					,   __a   :       Any=10					,   __a   :       Tuple=3					,   __a   :       List[str]=2					,   __a   :       Tuple=2					,   __a   :       Optional[Any]=2					,   __a   :       Union[str, Any]=True					,   __a   :       Union[str, Any]=True					,   __a   :       Dict=32					,   __a   :       Tuple=5					,   __a   :       Union[str, Any]=4					,   __a   :       int=37					,   __a   :       List[Any]="gelu"					,   __a   :       Union[str, Any]=0.1					,   __a   :       Dict=0.1					,   __a   :       Optional[int]=10					,   __a   :       str=0.02					,   __a   :       List[Any]=0.9					,   __a   :       Optional[int]=None					,   )		->				Any:
        _UpperCamelCase      :	Optional[Any]						=       parent
        _UpperCamelCase      :	Any						=       batch_size
        _UpperCamelCase      :	List[Any]						=       image_size
        _UpperCamelCase      :	Any						=       num_channels
        _UpperCamelCase      :	int						=       patch_size
        _UpperCamelCase      :	Optional[int]						=       tubelet_size
        _UpperCamelCase      :	Any						=       num_frames
        _UpperCamelCase      :	Optional[int]						=       is_training
        _UpperCamelCase      :	Tuple						=       use_labels
        _UpperCamelCase      :	str						=       hidden_size
        _UpperCamelCase      :	str						=       num_hidden_layers
        _UpperCamelCase      :	Any						=       num_attention_heads
        _UpperCamelCase      :	Optional[Any]						=       intermediate_size
        _UpperCamelCase      :	Optional[Any]						=       hidden_act
        _UpperCamelCase      :	str						=       hidden_dropout_prob
        _UpperCamelCase      :	List[str]						=       attention_probs_dropout_prob
        _UpperCamelCase      :	Optional[Any]						=       type_sequence_label_size
        _UpperCamelCase      :	str						=       initializer_range
        _UpperCamelCase      :	Tuple						=       mask_ratio
        _UpperCamelCase      :	Union[str, Any]						=       scope

        # in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
        _UpperCamelCase      :	str						=       (image_size // patch_size) ** 2
        _UpperCamelCase      :	str						=       (num_frames // tubelet_size) * self.num_patches_per_frame

        # use this variable to define bool_masked_pos
        _UpperCamelCase      :	int						=       int(mask_ratio * self.seq_length	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				Dict:
        _UpperCamelCase      :	Dict						=       floats_tensor(
            [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]	)

        _UpperCamelCase      :	Optional[int]						=       None
        if self.use_labels:
              _UpperCamelCase      :	int						=       ids_tensor([self.batch_size]					,   self.type_sequence_label_size	)

        _UpperCamelCase      :	str						=       self.get_config()

        return config, pixel_values, labels

  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				int:
        return VideoMAEConfig(
            image_size=self.image_size					,   patch_size=self.patch_size					,   num_channels=self.num_channels					,   num_frames=self.num_frames					,   tubelet_size=self.tubelet_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					,   is_decoder=__a					,   initializer_range=self.initializer_range					,   )

  def    __SCREAMING_SNAKE_CASE				(		self   :       str					,   __a   :       Optional[int]					,   __a   :       Optional[int]					,   __a   :       Optional[Any]	)		->				Tuple:
        _UpperCamelCase      :	int						=       VideoMAEModel(config=__a	)
        model.to(__a	)
        model.eval()
        _UpperCamelCase      :	Tuple						=       model(__a	)
        self.parent.assertEqual(result.last_hidden_state.shape					,   (self.batch_size, self.seq_length, self.hidden_size)	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]					,   __a   :       Tuple					,   __a   :       List[Any]					,   __a   :       Optional[Any]	)		->				List[Any]:
        _UpperCamelCase      :	str						=       VideoMAEForPreTraining(__a	)
        model.to(__a	)
        model.eval()
        # important: each video needs to have the same number of masked patches
        # hence we define a single mask, which we then repeat for each example in the batch
        _UpperCamelCase      :	int						=       torch.ones((self.num_masks,)	)
        _UpperCamelCase      :	Dict						=       torch.cat([mask, torch.zeros(self.seq_length - mask.size(0	)	)]	)
        _UpperCamelCase      :	Tuple						=       mask.expand(self.batch_size					,   -1	).bool()

        _UpperCamelCase      :	Dict						=       model(__a					,   __a	)
        # model only returns predictions for masked patches
        _UpperCamelCase      :	List[Any]						=       mask.sum().item()
        _UpperCamelCase      :	int						=       3 * self.tubelet_size * self.patch_size**2
        self.parent.assertEqual(result.logits.shape					,   (self.batch_size, num_masked_patches, decoder_num_labels)	)


  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]	)		->				Optional[Any]:
        _UpperCamelCase      :	Optional[Any]						=       self.prepare_config_and_inputs()
        _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase      :	Tuple						=       config_and_inputs
        _UpperCamelCase      :	Any						=       {"pixel_values": pixel_values}
        return config, inputs_dict





@require_torch
class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase		,  _UpperCamelCase		,  unittest.TestCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Optional[int]	     =					(
      (VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
  )
  SCREAMING_SNAKE_CASE__							:Dict	     =					(
      {"feature-extraction": VideoMAEModel, "video-classification": VideoMAEForVideoClassification}
      if is_torch_available()
      else {}
  )

  SCREAMING_SNAKE_CASE__							:Dict	     =					False
  SCREAMING_SNAKE_CASE__							:Any	     =					False
  SCREAMING_SNAKE_CASE__							:Dict	     =					False
  SCREAMING_SNAKE_CASE__							:Optional[int]	     =					False

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]	)		->				List[str]:
        _UpperCamelCase      :	str						=       VideoMAEModelTester(self	)
        _UpperCamelCase      :	List[str]						=       ConfigTester(self					,   config_class=__a					,   has_text_modality=__a					,   hidden_size=37	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       str					,   __a   :       Optional[int]					,   __a   :       List[Any]					,   __a   :       Optional[int]=False	)		->				List[Any]:
        _UpperCamelCase      :	Optional[Any]						=       copy.deepcopy(__a	)

        if model_class == VideoMAEForPreTraining:
              # important: each video needs to have the same number of masked patches
              # hence we define a single mask, which we then repeat for each example in the batch
              _UpperCamelCase      :	Any						=       torch.ones((self.model_tester.num_masks,)	)
              _UpperCamelCase      :	int						=       torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0	)	)]	)
              _UpperCamelCase      :	int						=       mask.expand(self.model_tester.batch_size					,   -1	).bool()
              _UpperCamelCase      :	Optional[Any]						=       bool_masked_pos.to(__a	)

        if return_labels:
              if model_class in [
                  *get_values(__a	),
              ]:
                    _UpperCamelCase      :	List[str]						=       torch.zeros(
                        self.model_tester.batch_size					,   dtype=torch.long					,   device=__a	)

        return inputs_dict

  def    __SCREAMING_SNAKE_CASE				(		self   :       Any	)		->				Union[str, Any]:
        self.config_tester.run_common_tests()

  @unittest.skip(reason="VideoMAE does not use inputs_embeds"	)
  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				List[Any]:
        pass

  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				Union[str, Any]:
        _UpperCamelCase,  _UpperCamelCase      :	str						=       self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              _UpperCamelCase      :	Tuple						=       model_class(__a	)
              self.assertIsInstance(model.get_input_embeddings()					,   (nn.Module)	)
              _UpperCamelCase      :	List[Any]						=       model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(__a					,   nn.Linear	)	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				Optional[Any]:
        _UpperCamelCase,  _UpperCamelCase      :	Optional[int]						=       self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              _UpperCamelCase      :	int						=       model_class(__a	)
              _UpperCamelCase      :	Optional[Any]						=       inspect.signature(model.forward	)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              _UpperCamelCase      :	List[Any]						=       [*signature.parameters.keys()]

              _UpperCamelCase      :	Optional[Any]						=       ["pixel_values"]
              self.assertListEqual(arg_names[:1]					,   __a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				Optional[Any]:
        _UpperCamelCase      :	Optional[Any]						=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Any	)		->				Optional[Any]:
        _UpperCamelCase      :	Optional[int]						=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*__a	)

  @slow
  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				List[Any]:
        for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              _UpperCamelCase      :	List[Any]						=       VideoMAEModel.from_pretrained(__a	)
              self.assertIsNotNone(__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Any	)		->				Optional[Any]:
        if not self.has_attentions:
              pass

        else:
              _UpperCamelCase,  _UpperCamelCase      :	Any						=       self.model_tester.prepare_config_and_inputs_for_common()
              _UpperCamelCase      :	Any						=       True

              for model_class in self.all_model_classes:
                    _UpperCamelCase      :	Any						=       self.model_tester.seq_length - self.model_tester.num_masks
                    _UpperCamelCase      :	Union[str, Any]						=       (
                        num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
                    )

                    _UpperCamelCase      :	Union[str, Any]						=       True
                    _UpperCamelCase      :	Optional[Any]						=       False
                    _UpperCamelCase      :	Dict						=       True
                    _UpperCamelCase      :	Optional[Any]						=       model_class(__a	)
                    model.to(__a	)
                    model.eval()
                    with torch.no_grad():
                          _UpperCamelCase      :	int						=       model(**self._prepare_for_class(__a					,   __a	)	)
                    _UpperCamelCase      :	str						=       outputs.attentions
                    self.assertEqual(len(__a	)					,   self.model_tester.num_hidden_layers	)

                    # check that output_attentions also work using config
                    del inputs_dict["output_attentions"]
                    _UpperCamelCase      :	Tuple						=       True
                    _UpperCamelCase      :	Any						=       model_class(__a	)
                    model.to(__a	)
                    model.eval()
                    with torch.no_grad():
                          _UpperCamelCase      :	Any						=       model(**self._prepare_for_class(__a					,   __a	)	)
                    _UpperCamelCase      :	int						=       outputs.attentions
                    self.assertEqual(len(__a	)					,   self.model_tester.num_hidden_layers	)

                    self.assertListEqual(
                        list(attentions[0].shape[-3:]	)					,   [self.model_tester.num_attention_heads, seq_len, seq_len]					,   )
                    _UpperCamelCase      :	Optional[Any]						=       len(__a	)

                    # Check attention is always last and order is fine
                    _UpperCamelCase      :	Optional[Any]						=       True
                    _UpperCamelCase      :	Union[str, Any]						=       True
                    _UpperCamelCase      :	str						=       model_class(__a	)
                    model.to(__a	)
                    model.eval()
                    with torch.no_grad():
                          _UpperCamelCase      :	List[str]						=       model(**self._prepare_for_class(__a					,   __a	)	)

                    self.assertEqual(out_len + 1					,   len(__a	)	)

                    _UpperCamelCase      :	Any						=       outputs.attentions

                    self.assertEqual(len(__a	)					,   self.model_tester.num_hidden_layers	)
                    self.assertListEqual(
                        list(self_attentions[0].shape[-3:]	)					,   [self.model_tester.num_attention_heads, seq_len, seq_len]					,   )

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				Optional[Any]:
        def check_hidden_states_output(__a   :       Union[str, Any]					,   __a   :       Optional[Any]					,   __a   :       Union[str, Any]	):
              _UpperCamelCase      :	Tuple						=       model_class(__a	)
              model.to(__a	)
              model.eval()

              with torch.no_grad():
                    _UpperCamelCase      :	List[str]						=       model(**self._prepare_for_class(__a					,   __a	)	)

              _UpperCamelCase      :	Union[str, Any]						=       outputs.hidden_states
              _UpperCamelCase      :	Optional[Any]						=       self.model_tester.num_hidden_layers + 1
              self.assertEqual(len(__a	)					,   __a	)

              _UpperCamelCase      :	List[str]						=       self.model_tester.seq_length - self.model_tester.num_masks
              _UpperCamelCase      :	Tuple						=       num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length

              self.assertListEqual(
                  list(hidden_states[0].shape[-2:]	)					,   [seq_length, self.model_tester.hidden_size]					,   )

        _UpperCamelCase,  _UpperCamelCase      :	Any						=       self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              _UpperCamelCase      :	int						=       True
              check_hidden_states_output(__a					,   __a					,   __a	)

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              _UpperCamelCase      :	List[str]						=       True

              check_hidden_states_output(__a					,   __a					,   __a	)


  @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."	)
  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]	)		->				List[str]:
        pass





def 					lowercase__    (					)					->		List[str]:




      """simple docstring"""

      _UpperCamelCase      :	int						=       hf_hub_download(
          repo_id="hf-internal-testing/spaghetti-video"		,filename="eating_spaghetti.npy"		,repo_type="dataset"			)
      _UpperCamelCase      :	Dict						=       np.load(lowercase_			)
      return list(lowercase_			)



@require_torch
@require_vision
class      __SCREAMING_SNAKE_CASE    (						unittest.TestCase   ):







  '''simple docstring'''


  @cached_property
  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				int:
        # logits were tested with a different mean and std, so we use the same here
        return (
            VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]					,   image_std=[0.5, 0.5, 0.5]	)
            if is_vision_available()
            else None
        )

  @slow
  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				Optional[int]:
        _UpperCamelCase      :	Any						=       VideoMAEForVideoClassification.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics"	).to(
            __a	)

        _UpperCamelCase      :	List[Any]						=       self.default_image_processor
        _UpperCamelCase      :	Optional[int]						=       prepare_video()
        _UpperCamelCase      :	Dict						=       image_processor(__a					,   return_tensors="pt"	).to(__a	)

        # forward pass
        with torch.no_grad():
              _UpperCamelCase      :	int						=       model(**__a	)

        # verify the logits
        _UpperCamelCase      :	Tuple						=       torch.Size((1, 400)	)
        self.assertEqual(outputs.logits.shape					,   __a	)

        _UpperCamelCase      :	Optional[Any]						=       torch.tensor([0.36_69, -0.06_88, -0.24_21]	).to(__a	)

        self.assertTrue(torch.allclose(outputs.logits[0, :3]					,   __a					,   atol=1e-4	)	)


  @slow
  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]	)		->				int:
        _UpperCamelCase      :	List[Any]						=       VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base-short"	).to(__a	)

        _UpperCamelCase      :	Tuple						=       self.default_image_processor
        _UpperCamelCase      :	List[Any]						=       prepare_video()
        _UpperCamelCase      :	Any						=       image_processor(__a					,   return_tensors="pt"	).to(__a	)

        # add boolean mask, indicating which patches to mask
        _UpperCamelCase      :	List[str]						=       hf_hub_download(repo_id="hf-internal-testing/bool-masked-pos"					,   filename="bool_masked_pos.pt"	)
        _UpperCamelCase      :	int						=       torch.load(__a	)

        # forward pass
        with torch.no_grad():
              _UpperCamelCase      :	str						=       model(**__a	)

        # verify the logits
        _UpperCamelCase      :	Dict						=       torch.Size([1, 1408, 1536]	)
        _UpperCamelCase      :	List[Any]						=       torch.tensor(
            [[0.79_94, 0.96_12, 0.85_08], [0.74_01, 0.89_58, 0.83_02], [0.58_62, 0.74_68, 0.73_25]]					,   device=__a	)
        self.assertEqual(outputs.logits.shape					,   __a	)
        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]					,   __a					,   atol=1e-4	)	)

        # verify the loss (`config.norm_pix_loss` = `True`)
        _UpperCamelCase      :	int						=       torch.tensor([0.51_42]					,   device=__a	)
        self.assertTrue(torch.allclose(outputs.loss					,   __a					,   atol=1e-4	)	)

        # verify the loss (`config.norm_pix_loss` = `False`)
        _UpperCamelCase      :	List[Any]						=       VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base-short"					,   norm_pix_loss=__a	).to(
            __a	)

        with torch.no_grad():
              _UpperCamelCase      :	List[str]						=       model(**__a	)

        _UpperCamelCase      :	Tuple						=       torch.tensor(torch.tensor([0.64_69]	)					,   device=__a	)
        self.assertTrue(torch.allclose(outputs.loss					,   __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__				  =   logging.get_logger(__name__)


lowerCamelCase__				  =   ["model.decoder.embed_positions.weights"]




def 					lowercase__    (					lowercase_			)					->		Optional[Any]:




      """simple docstring"""

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




def 					lowercase__    (					lowercase_		,lowercase_			)					->		Tuple[Dict, Dict]:




      """simple docstring"""

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




def 					lowercase__    (					lowercase_			)					->		MusicgenDecoderConfig:




      """simple docstring"""

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




@torch.no_grad()
def 					lowercase__    (					lowercase_		,lowercase_=None		,lowercase_=None		,lowercase_="cpu"			)					->		List[str]:




      """simple docstring"""

      _UpperCamelCase      :	str						=       MusicGen.get_pretrained(lowercase_		,device=lowercase_			)
      _UpperCamelCase      :	Union[str, Any]						=       decoder_config_from_checkpoint(lowercase_			)

      _UpperCamelCase      :	Optional[int]						=       fairseq_model.lm.state_dict()
      _UpperCamelCase,  _UpperCamelCase      :	Optional[Any]						=       rename_state_dict(
          lowercase_		,hidden_size=decoder_config.hidden_size			)

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

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

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

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

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

      # init the composite model
      _UpperCamelCase      :	str						=       MusicgenForConditionalGeneration(text_encoder=lowercase_		,audio_encoder=lowercase_		,decoder=lowercase_			)

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

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

      with torch.no_grad():
            _UpperCamelCase      :	Tuple						=       model(input_ids=lowercase_		,decoder_input_ids=lowercase_			).logits

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

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

      _UpperCamelCase      :	Optional[int]						=       MusicgenProcessor(feature_extractor=lowercase_		,tokenizer=lowercase_			)

      # set the appropriate bos/pad token ids
      _UpperCamelCase      :	str						=       2_048
      _UpperCamelCase      :	str						=       2_048

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

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

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


if __name__ == "__main__":
  lowerCamelCase__				  =   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__				  =   parser.parse_args()
  convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
