






import inspect
import unittest
from typing import List

import numpy as np

from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
       import tensorflow as tf

       from transformers import (
           TFEfficientFormerForImageClassification,
           TFEfficientFormerForImageClassificationWithTeacher,
           TFEfficientFormerModel,
       )
       from transformers.models.efficientformer.modeling_tf_efficientformer import (
           TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
       )


if is_vision_available():
       from PIL import Image

       from transformers import EfficientFormerImageProcessor
class        a       :



      """simple docstring"""


      def __init__( self   :     Any						, __lowercase   :     Dict						, __lowercase   :     int = 13						, __lowercase   :     int = 64						, __lowercase   :     int = 2						, __lowercase   :     int = 3						, __lowercase   :     int = 3						, __lowercase   :     bool = True						, __lowercase   :     bool = True						, __lowercase   :     int = 128						, __lowercase   :     List[str]=[16, 32, 64, 128]						, __lowercase   :     int = 7						, __lowercase   :     int = 4						, __lowercase   :     int = 37						, __lowercase   :     str = "gelu"						, __lowercase   :     float = 0.1						, __lowercase   :     float = 0.1						, __lowercase   :     int = 10						, __lowercase   :     float = 0.02						, __lowercase   :     int = 2						, __lowercase   :     int = 1						, __lowercase   :     int = 128						, __lowercase   :     List[int] = [2, 2, 2, 2]						, __lowercase   :     int = 2						, __lowercase   :     int = 2						, )				-> int:
        __UpperCAmelCase				:      Optional[int]	 =    parent
        __UpperCAmelCase				:      Tuple	 =    batch_size
        __UpperCAmelCase				:      int	 =    image_size
        __UpperCAmelCase				:      Optional[int]	 =    patch_size
        __UpperCAmelCase				:      Any	 =    num_channels
        __UpperCAmelCase				:      List[str]	 =    is_training
        __UpperCAmelCase				:      Optional[Any]	 =    use_labels
        __UpperCAmelCase				:      List[Any]	 =    hidden_size
        __UpperCAmelCase				:      Optional[int]	 =    num_hidden_layers
        __UpperCAmelCase				:      Union[str, Any]	 =    num_attention_heads
        __UpperCAmelCase				:      Union[str, Any]	 =    intermediate_size
        __UpperCAmelCase				:      List[Any]	 =    hidden_act
        __UpperCAmelCase				:      Any	 =    hidden_dropout_prob
        __UpperCAmelCase				:      Dict	 =    attention_probs_dropout_prob
        __UpperCAmelCase				:      Union[str, Any]	 =    type_sequence_label_size
        __UpperCAmelCase				:      Dict	 =    initializer_range
        __UpperCAmelCase				:      str	 =    encoder_stride
        __UpperCAmelCase				:      List[Any]	 =    num_attention_outputs
        __UpperCAmelCase				:      str	 =    embed_dim
        __UpperCAmelCase				:      Tuple	 =    embed_dim + 1
        __UpperCAmelCase				:      Tuple	 =    resolution
        __UpperCAmelCase				:      Union[str, Any]	 =    depths
        __UpperCAmelCase				:      List[Any]	 =    hidden_sizes
        __UpperCAmelCase				:      Optional[Any]	 =    dim
        __UpperCAmelCase				:      Union[str, Any]	 =    mlp_expansion_ratio
      def       UpperCAmelCase ( self   :     str   )				-> int:
        __UpperCAmelCase				:      Optional[Any]	 =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

        __UpperCAmelCase				:      Any	 =    self.get_config()

        return config, pixel_values, labels
      def       UpperCAmelCase ( self   :     List[Any]   )				-> Optional[int]:
        return EfficientFormerConfig(
            image_size=self.image_size						, patch_size=self.patch_size						, num_channels=self.num_channels						, 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=__lowercase						, initializer_range=self.initializer_range						, encoder_stride=self.encoder_stride						, resolution=self.resolution						, depths=self.depths						, hidden_sizes=self.hidden_sizes						, dim=self.dim						, mlp_expansion_ratio=self.mlp_expansion_ratio						, )
      def       UpperCAmelCase ( self   :     Optional[int]						, __lowercase   :     int						, __lowercase   :     List[str]						, __lowercase   :     Optional[Any]   )				-> Tuple:
        __UpperCAmelCase				:      Tuple	 =    TFEfficientFormerModel(config=__lowercase   )
        __UpperCAmelCase				:      int	 =    model(__lowercase						, training=__lowercase   )
        self.parent.assertEqual(result.last_hidden_state.shape						, (self.batch_size, self.seq_length, self.hidden_size)   )
      def       UpperCAmelCase ( self   :     Union[str, Any]						, __lowercase   :     Optional[Any]						, __lowercase   :     Dict						, __lowercase   :     Any   )				-> Optional[int]:
        __UpperCAmelCase				:      Tuple	 =    self.type_sequence_label_size
        __UpperCAmelCase				:      Union[str, Any]	 =    TFEfficientFormerForImageClassification(__lowercase   )
        __UpperCAmelCase				:      List[str]	 =    model(__lowercase						, labels=__lowercase						, training=__lowercase   )
        self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.type_sequence_label_size)   )

        # test greyscale images
        __UpperCAmelCase				:      Dict	 =    1
        __UpperCAmelCase				:      Optional[Any]	 =    TFEfficientFormerForImageClassification(__lowercase   )

        __UpperCAmelCase				:      Dict	 =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
        __UpperCAmelCase				:      str	 =    model(__lowercase						, labels=__lowercase   )
        self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.type_sequence_label_size)   )




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


@require_tf
class        a       (					lowercase__				,			lowercase__				,			unittest.TestCase  ):



      """simple docstring"""


      a    :					Dict							=      (
          (
              TFEfficientFormerModel,
              TFEfficientFormerForImageClassificationWithTeacher,
              TFEfficientFormerForImageClassification,
          )
          if is_tf_available()
          else ()
      )
      a    :					int							=      (
          {
              'feature-extraction': TFEfficientFormerModel,
              'image-classification': (
                  TFEfficientFormerForImageClassification,
                  TFEfficientFormerForImageClassificationWithTeacher,
              ),
          }
          if is_tf_available()
          else {}
      )

      a    :					str							=      False

      a    :					Union[str, Any]							=      False
      a    :					Union[str, Any]							=      False
      a    :					str							=      False
      a    :					int							=      False
      def       UpperCAmelCase ( self   :     Tuple   )				-> Optional[Any]:
        __UpperCAmelCase				:      int	 =    TFEfficientFormerModelTester(self   )
        __UpperCAmelCase				:      Optional[int]	 =    ConfigTester(
            self						, config_class=__lowercase						, has_text_modality=__lowercase						, hidden_size=37   )
      def       UpperCAmelCase ( self   :     Optional[int]   )				-> Any:
        self.config_tester.run_common_tests()
      @unittest.skip(reason="""EfficientFormer does not use inputs_embeds"""   )
      def       UpperCAmelCase ( self   :     Any   )				-> str:
        pass
      @unittest.skip(reason="""EfficientFormer does not support input and output embeddings"""   )
      def       UpperCAmelCase ( self   :     Dict   )				-> Dict:
        pass
      def       UpperCAmelCase ( self   :     Tuple   )				-> Dict:
        __UpperCAmelCase ,					__UpperCAmelCase				:      Optional[int]	 =    self.model_tester.prepare_config_and_inputs_for_common()

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

          __UpperCAmelCase				:      Optional[int]	 =    ["""pixel_values"""]
          self.assertListEqual(arg_names[:1]						, __lowercase   )
      def       UpperCAmelCase ( self   :     List[Any]   )				-> str:
        def check_hidden_states_output(__lowercase   :     Optional[int]						, __lowercase   :     Union[str, Any]						, __lowercase   :     Optional[int]   ):
          __UpperCAmelCase				:      Optional[int]	 =    model_class(__lowercase   )

          __UpperCAmelCase				:      Any	 =    model(**self._prepare_for_class(__lowercase						, __lowercase   )						, training=__lowercase   )
          __UpperCAmelCase				:      Union[str, Any]	 =    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

          __UpperCAmelCase				:      int	 =    getattr(
              self.model_tester						, """expected_num_hidden_layers"""						, self.model_tester.num_hidden_layers + 1   )
          self.assertEqual(len(__lowercase   )						, __lowercase   )

          if hasattr(self.model_tester						, """encoder_seq_length"""   ):
            __UpperCAmelCase				:      List[str]	 =    self.model_tester.encoder_seq_length
            if hasattr(self.model_tester						, """chunk_length"""   ) and self.model_tester.chunk_length > 1:
              __UpperCAmelCase				:      Any	 =    seq_length * self.model_tester.chunk_length
          else:
            __UpperCAmelCase				:      str	 =    self.model_tester.seq_length

          self.assertListEqual(
              list(hidden_states[-1].shape[-2:]   )						, [seq_length, self.model_tester.hidden_size]						, )

          if config.is_encoder_decoder:
            __UpperCAmelCase				:      str	 =    outputs.decoder_hidden_states

            self.asseretIsInstance(__lowercase						, (list, tuple)   )
            self.assertEqual(len(__lowercase   )						, __lowercase   )
            __UpperCAmelCase				:      Dict	 =    getattr(self.model_tester						, """seq_length"""						, __lowercase   )
            __UpperCAmelCase				:      str	 =    getattr(self.model_tester						, """decoder_seq_length"""						, __lowercase   )

            self.assertListEqual(
                list(hidden_states[-1].shape[-2:]   )						, [decoder_seq_length, self.model_tester.hidden_size]						, )

        __UpperCAmelCase ,					__UpperCAmelCase				:      List[Any]	 =    self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          __UpperCAmelCase				:      Optional[Any]	 =    True
          check_hidden_states_output(__lowercase						, __lowercase						, __lowercase   )

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          __UpperCAmelCase				:      Optional[int]	 =    True

          check_hidden_states_output(__lowercase						, __lowercase						, __lowercase   )
      def       UpperCAmelCase ( self   :     str						, __lowercase   :     str						, __lowercase   :     Optional[int]						, __lowercase   :     Union[str, Any]=False   )				-> Dict:
        __UpperCAmelCase				:      Optional[Any]	 =    super()._prepare_for_class(__lowercase						, __lowercase						, return_labels=__lowercase   )

        if return_labels:
          if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
            del inputs_dict["labels"]

        return inputs_dict
      def       UpperCAmelCase ( self   :     str   )				-> Optional[int]:
        __UpperCAmelCase				:      Union[str, Any]	 =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowercase   )
      @unittest.skip(reason="""EfficientFormer does not implement masked image modeling yet"""   )
      def       UpperCAmelCase ( self   :     str   )				-> Any:
        __UpperCAmelCase				:      Dict	 =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*__lowercase   )
      def       UpperCAmelCase ( self   :     Any   )				-> str:
        __UpperCAmelCase				:      Dict	 =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__lowercase   )
      @slow
      def       UpperCAmelCase ( self   :     int   )				-> int:
        for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          __UpperCAmelCase				:      Union[str, Any]	 =    TFEfficientFormerModel.from_pretrained(__lowercase   )
          self.assertIsNotNone(__lowercase   )
      def       UpperCAmelCase ( self   :     Optional[int]   )				-> Optional[Any]:
        __UpperCAmelCase ,					__UpperCAmelCase				:      List[str]	 =    self.model_tester.prepare_config_and_inputs_for_common()
        __UpperCAmelCase				:      Tuple	 =    True

        __UpperCAmelCase				:      Optional[Any]	 =    getattr(self.model_tester						, """seq_length"""						, __lowercase   )
        __UpperCAmelCase				:      Optional[Any]	 =    getattr(self.model_tester						, """encoder_seq_length"""						, __lowercase   )
        __UpperCAmelCase				:      Any	 =    getattr(self.model_tester						, """key_length"""						, __lowercase   )
        __UpperCAmelCase				:      Optional[Any]	 =    getattr(self.model_tester						, """chunk_length"""						, __lowercase   )

        if chunk_length is not None and hasattr(self.model_tester						, """num_hashes"""   ):
          __UpperCAmelCase				:      Dict	 =    encoder_seq_length * self.model_tester.num_hashes

        for model_class in self.all_model_classes:
          __UpperCAmelCase				:      List[str]	 =    True
          __UpperCAmelCase				:      List[Any]	 =    False
          __UpperCAmelCase				:      Dict	 =    True
          __UpperCAmelCase				:      Dict	 =    model_class(__lowercase   )

          __UpperCAmelCase				:      Optional[Any]	 =    model(**self._prepare_for_class(__lowercase						, __lowercase   )						, training=__lowercase   )
          __UpperCAmelCase				:      int	 =    outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
          self.assertEqual(len(__lowercase   )						, self.model_tester.num_attention_outputs   )

          # check that output_attentions also work using config
          del inputs_dict["output_attentions"]
          __UpperCAmelCase				:      Optional[int]	 =    True
          __UpperCAmelCase				:      Any	 =    model_class(__lowercase   )
          __UpperCAmelCase				:      str	 =    model(**self._prepare_for_class(__lowercase						, __lowercase   )						, training=__lowercase   )

          __UpperCAmelCase				:      Tuple	 =    outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
          self.assertEqual(len(__lowercase   )						, self.model_tester.num_attention_outputs   )

          if chunk_length is not None:
            self.assertListEqual(
                list(attentions[0].shape[-4:]   )						, [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]						, )
          else:
            self.assertListEqual(
                list(attentions[0].shape[-3:]   )						, [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]						, )




      def       UpperCAmelCase ( self   :     List[Any]   )				-> Any:
        # We use a simplified version of this test for EfficientFormer because it requires training=False
        # and Keras refuses to let us force that during functional construction
        __UpperCAmelCase ,					__UpperCAmelCase				:      str	 =    self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          # Prepare our model
          __UpperCAmelCase				:      int	 =    model_class(__lowercase   )
          # These are maximally general inputs for the model, with multiple None dimensions
          # Hopefully this will catch any conditionals that fail for flexible shapes
          __UpperCAmelCase				:      Tuple	 =    {
              key: tf.keras.Input(shape=val.shape[1:]						, dtype=val.dtype						, name=__lowercase   )
              for key, val in model.input_signature.items()
              if key in model.dummy_inputs
          }
          __UpperCAmelCase				:      str	 =    model(__lowercase   )
          self.assertTrue(outputs_dict is not None   )






def 							lowerCamelCase__ (       ):
  __UpperCAmelCase				:      str	 =    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
  return image







@require_tf
@require_vision
class        a       (					unittest.TestCase  ):



      """simple docstring"""


      @cached_property
      def       UpperCAmelCase ( self   :     Any   )				-> List[Any]:
        return (
            EfficientFormerImageProcessor.from_pretrained("""snap-research/efficientformer-l1-300"""   )
            if is_vision_available()
            else None
        )
      @slow
      def       UpperCAmelCase ( self   :     Optional[int]   )				-> Optional[int]:
        __UpperCAmelCase				:      Any	 =    TFEfficientFormerForImageClassification.from_pretrained("""snap-research/efficientformer-l1-300"""   )
        __UpperCAmelCase				:      Optional[Any]	 =    self.default_image_processor
        __UpperCAmelCase				:      Optional[Any]	 =    prepare_img()
        __UpperCAmelCase				:      Optional[Any]	 =    image_processor(images=__lowercase						, return_tensors="""tf"""   )
        # forward pass
        __UpperCAmelCase				:      Optional[Any]	 =    model(**__lowercase						, training=__lowercase   )
        # verify the logits
        __UpperCAmelCase				:      Any	 =    tf.TensorShape((1, 1000)   )
        self.assertEqual(outputs.logits.shape						, __lowercase   )
        __UpperCAmelCase				:      Optional[int]	 =    tf.constant([-0.0_555, 0.4_825, -0.0_852]   )
        self.assertTrue(np.allclose(outputs.logits[0, :3]						, __lowercase						, atol=1e-4   )   )




      @slow
      def       UpperCAmelCase ( self   :     Tuple   )				-> Dict:
        __UpperCAmelCase				:      Tuple	 =    TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
            """snap-research/efficientformer-l1-300"""   )
        __UpperCAmelCase				:      Dict	 =    self.default_image_processor
        __UpperCAmelCase				:      int	 =    prepare_img()
        __UpperCAmelCase				:      Dict	 =    image_processor(images=__lowercase						, return_tensors="""tf"""   )
        # forward pass
        __UpperCAmelCase				:      Any	 =    model(**__lowercase						, training=__lowercase   )
        # verify the logits
        __UpperCAmelCase				:      Any	 =    tf.TensorShape((1, 1000)   )
        self.assertEqual(outputs.logits.shape						, __lowercase   )
        __UpperCAmelCase				:      List[str]	 =    tf.constant([-0.1_312, 0.4_353, -1.0_499]   )
        self.assertTrue(np.allclose(outputs.logits[0, :3]						, __lowercase						, 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()
UpperCamelCase    							=      logging.get_logger(__name__)


UpperCamelCase    							=      ['model.decoder.embed_positions.weights']


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Optional[int]							)     ->  Union[str, Any]:
   if "emb" in name:
      __UpperCamelCase	:			Any					=					name.replace("""emb"""					,							"""model.decoder.embed_tokens"""							)
   if "transformer" in name:
      __UpperCamelCase	:			str					=					name.replace("""transformer"""					,							"""model.decoder"""							)
   if "cross_attention" in name:
      __UpperCamelCase	:			List[str]					=					name.replace("""cross_attention"""					,							"""encoder_attn"""							)
   if "linear1" in name:
      __UpperCamelCase	:			Optional[int]					=					name.replace("""linear1"""					,							"""fc1"""							)
   if "linear2" in name:
      __UpperCamelCase	:			Optional[Any]					=					name.replace("""linear2"""					,							"""fc2"""							)
   if "norm1" in name:
      __UpperCamelCase	:			Tuple					=					name.replace("""norm1"""					,							"""self_attn_layer_norm"""							)
   if "norm_cross" in name:
      __UpperCamelCase	:			List[str]					=					name.replace("""norm_cross"""					,							"""encoder_attn_layer_norm"""							)
   if "norm2" in name:
      __UpperCamelCase	:			Tuple					=					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	:			Optional[int]					=					name.replace("""linears"""					,							"""lm_heads"""							)
   if "condition_provider.conditioners.description.output_proj" in name:
      __UpperCamelCase	:			Tuple					=					name.replace("""condition_provider.conditioners.description.output_proj"""					,							"""enc_to_dec_proj"""							)
   return name


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


def 				__lowerCamelCase     (							__lowerCAmelCase							:					str							)     ->  MusicgenDecoderConfig:
   if checkpoint == "small":
      # default config values
      __UpperCamelCase	:			int					=					1024
      __UpperCamelCase	:			Union[str, Any]					=					24
      __UpperCamelCase	:			int					=					16
   elif checkpoint == "medium":
      __UpperCamelCase	:			List[Any]					=					1536
      __UpperCamelCase	:			Dict					=					48
      __UpperCamelCase	:			Dict					=					24
   elif checkpoint == "large":
      __UpperCamelCase	:			List[Any]					=					2048
      __UpperCamelCase	:			str					=					48
      __UpperCamelCase	:			Optional[int]					=					32
   else:
      raise ValueError(f'Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'							)
   __UpperCamelCase	:			Any					=					MusicgenDecoderConfig(
       hidden_size=__lowerCAmelCase					,							ffn_dim=hidden_size * 4					,							num_hidden_layers=__lowerCAmelCase					,							num_attention_heads=__lowerCAmelCase					,							)
   return config


@torch.no_grad()
def 				__lowerCamelCase     (							__lowerCAmelCase							:					List[str]					,							__lowerCAmelCase							:					Optional[Any]=None					,							__lowerCAmelCase							:					Tuple=None					,							__lowerCAmelCase							:					str="cpu"							)     ->  Optional[int]:
   __UpperCamelCase	:			str					=					MusicGen.get_pretrained(__lowerCAmelCase					,							device=__lowerCAmelCase							)
   __UpperCamelCase	:			int					=					decoder_config_from_checkpoint(__lowerCAmelCase							)

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

   __UpperCamelCase	:			List[Any]					=					TaEncoderModel.from_pretrained("""t5-base"""							)
   __UpperCamelCase	:			Dict					=					EncodecModel.from_pretrained("""facebook/encodec_32khz"""							)
   __UpperCamelCase	:			List[str]					=					MusicgenForCausalLM(__lowerCAmelCase							).eval()

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

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

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

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

   # init the composite model
   __UpperCamelCase	:			Tuple					=					MusicgenForConditionalGeneration(text_encoder=__lowerCAmelCase					,							audio_encoder=__lowerCAmelCase					,							decoder=__lowerCAmelCase							)

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

   # 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	:			int					=					model(input_ids=__lowerCAmelCase					,							decoder_input_ids=__lowerCAmelCase							).logits

   if logits.shape != (8, 1, 2048):
      raise ValueError("""Incorrect shape for logits"""							)

   # now construct the processor
   __UpperCamelCase	:			Union[str, Any]					=					AutoTokenizer.from_pretrained("""t5-base"""							)
   __UpperCamelCase	:			List[Any]					=					AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz"""					,							padding_side="""left"""							)

   __UpperCamelCase	:			Any					=					MusicgenProcessor(feature_extractor=__lowerCAmelCase					,							tokenizer=__lowerCAmelCase							)

   # set the appropriate bos/pad token ids
   __UpperCamelCase	:			Tuple					=					2048
   __UpperCamelCase	:			int					=					2048

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

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

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


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

       UpperCamelCase    							=      parser.parse_args()
       convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)



