






import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
       import torch
       from torch import nn

       from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
       from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import DPTImageProcessor







class 		_A						:

  def __init__(    self						:	Optional[int]   ,				lowerCamelCase__						:	Optional[int]   ,				lowerCamelCase__						:	int=2   ,				lowerCamelCase__						:	Dict=32   ,				lowerCamelCase__						:	List[Any]=16   ,				lowerCamelCase__						:	str=3   ,				lowerCamelCase__						:	Optional[int]=True   ,				lowerCamelCase__						:	Union[str, Any]=True   ,				lowerCamelCase__						:	Optional[int]=32   ,				lowerCamelCase__						:	Tuple=4   ,				lowerCamelCase__						:	Optional[Any]=[0, 1, 2, 3]   ,				lowerCamelCase__						:	Any=4   ,				lowerCamelCase__						:	Optional[int]=37   ,				lowerCamelCase__						:	Any="gelu"   ,				lowerCamelCase__						:	Any=0.1   ,				lowerCamelCase__						:	Optional[int]=0.1   ,				lowerCamelCase__						:	List[Any]=0.02   ,				lowerCamelCase__						:	Dict=3   ,				lowerCamelCase__						:	Optional[Any]=[1, 3_84, 24, 24]   ,				lowerCamelCase__						:	Tuple=True   ,				lowerCamelCase__						:	Dict=None   ,				):
     """simple docstring"""

     __UpperCamelCase	:			Dict					=					parent
     __UpperCamelCase	:			int					=					batch_size
     __UpperCamelCase	:			Optional[int]					=					image_size
     __UpperCamelCase	:			Optional[int]					=					patch_size
     __UpperCamelCase	:			List[Any]					=					num_channels
     __UpperCamelCase	:			int					=					is_training
     __UpperCamelCase	:			Optional[Any]					=					use_labels
     __UpperCamelCase	:			str					=					hidden_size
     __UpperCamelCase	:			List[str]					=					num_hidden_layers
     __UpperCamelCase	:			int					=					backbone_out_indices
     __UpperCamelCase	:			Union[str, Any]					=					num_attention_heads
     __UpperCamelCase	:			Optional[int]					=					intermediate_size
     __UpperCamelCase	:			Any					=					hidden_act
     __UpperCamelCase	:			Optional[int]					=					hidden_dropout_prob
     __UpperCamelCase	:			Any					=					attention_probs_dropout_prob
     __UpperCamelCase	:			Any					=					initializer_range
     __UpperCamelCase	:			Optional[Any]					=					num_labels
     __UpperCamelCase	:			Tuple					=					backbone_featmap_shape
     __UpperCamelCase	:			Tuple					=					scope
     __UpperCamelCase	:			Optional[int]					=					is_hybrid
     # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
     __UpperCamelCase	:			Any					=					(image_size // patch_size) ** 2
     __UpperCamelCase	:			Any					=					num_patches + 1

  def    a					(    self						:	Optional[int]			):
     """simple docstring"""

     __UpperCamelCase	:			List[Any]					=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

     __UpperCamelCase	:			Tuple					=					None
     if self.use_labels:
        __UpperCamelCase	:			str					=					ids_tensor([self.batch_size, self.image_size, self.image_size]   ,				self.num_labels			)

     __UpperCamelCase	:			List[Any]					=					self.get_config()

     return config, pixel_values, labels

  def    a					(    self						:	Any			):
     """simple docstring"""

     __UpperCamelCase	:			Any					=					{
         """global_padding""": """same""",
         """layer_type""": """bottleneck""",
         """depths""": [3, 4, 9],
         """out_features""": ["""stage1""", """stage2""", """stage3"""],
         """embedding_dynamic_padding""": True,
         """hidden_sizes""": [96, 1_92, 3_84, 7_68],
         """num_groups""": 2,
     }

     return DPTConfig(
         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   ,				backbone_out_indices=self.backbone_out_indices   ,				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=lowerCamelCase__   ,				initializer_range=self.initializer_range   ,				is_hybrid=self.is_hybrid   ,				backbone_config=lowerCamelCase__   ,				backbone_featmap_shape=self.backbone_featmap_shape   ,				)

  def    a					(    self						:	List[str]   ,				lowerCamelCase__						:	Optional[int]   ,				lowerCamelCase__						:	str   ,				lowerCamelCase__						:	Any			):
     """simple docstring"""

     __UpperCamelCase	:			Optional[Any]					=					DPTModel(config=lowerCamelCase__			)
     model.to(lowerCamelCase__			)
     model.eval()
     __UpperCamelCase	:			Dict					=					model(lowerCamelCase__			)
     self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)			)

  def    a					(    self						:	Optional[Any]   ,				lowerCamelCase__						:	Optional[Any]   ,				lowerCamelCase__						:	Tuple   ,				lowerCamelCase__						:	List[str]			):
     """simple docstring"""

     __UpperCamelCase	:			str					=					self.num_labels
     __UpperCamelCase	:			int					=					DPTForDepthEstimation(lowerCamelCase__			)
     model.to(lowerCamelCase__			)
     model.eval()
     __UpperCamelCase	:			Optional[int]					=					model(lowerCamelCase__			)
     self.parent.assertEqual(result.predicted_depth.shape   ,				(self.batch_size, self.image_size, self.image_size)			)

  def    a					(    self						:	str   ,				lowerCamelCase__						:	Dict   ,				lowerCamelCase__						:	Optional[Any]   ,				lowerCamelCase__						:	str			):
     """simple docstring"""

     __UpperCamelCase	:			List[Any]					=					self.num_labels
     __UpperCamelCase	:			str					=					DPTForSemanticSegmentation(lowerCamelCase__			)
     model.to(lowerCamelCase__			)
     model.eval()
     __UpperCamelCase	:			Any					=					model(lowerCamelCase__   ,				labels=lowerCamelCase__			)
     self.parent.assertEqual(
         result.logits.shape   ,				(self.batch_size, self.num_labels, self.image_size, self.image_size)			)

  def    a					(    self						:	int			):
     """simple docstring"""

     __UpperCamelCase	:			List[Any]					=					self.prepare_config_and_inputs()
     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	:			List[Any]					=					config_and_inputs
     __UpperCamelCase	:			Union[str, Any]					=					{"""pixel_values""": pixel_values}
     return config, inputs_dict







@require_torch
class 		_A						( UpperCAmelCase_	,				UpperCAmelCase_	,				unittest.TestCase ):
  lowercase_						:			Any						=					(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
  lowercase_						:			Optional[int]						=					(
      {
          '''depth-estimation''': DPTForDepthEstimation,
          '''feature-extraction''': DPTModel,
          '''image-segmentation''': DPTForSemanticSegmentation,
      }
      if is_torch_available()
      else {}
  )

  lowercase_						:			List[str]						=					False
  lowercase_						:			int						=					False
  lowercase_						:			Union[str, Any]						=					False

  def    a					(    self						:	int			):
     """simple docstring"""

     __UpperCamelCase	:			int					=					DPTModelTester(self			)
     __UpperCamelCase	:			int					=					ConfigTester(self   ,				config_class=lowerCamelCase__   ,				has_text_modality=lowerCamelCase__   ,				hidden_size=37			)

  def    a					(    self						:	str			):
     """simple docstring"""

     self.config_tester.run_common_tests()

  @unittest.skip(reason="""DPT does not use inputs_embeds"""			)
  def    a					(    self						:	Union[str, Any]			):
     """simple docstring"""

     pass

  def    a					(    self						:	Optional[int]			):
     """simple docstring"""

     __UpperCamelCase	,     __UpperCamelCase	:			Union[str, Any]					=					self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        __UpperCamelCase	:			Optional[int]					=					model_class(lowerCamelCase__			)
        self.assertIsInstance(model.get_input_embeddings()   ,				(nn.Module)			)
        __UpperCamelCase	:			List[str]					=					model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(lowerCamelCase__   ,				nn.Linear			)			)

  def    a					(    self						:	Dict			):
     """simple docstring"""

     __UpperCamelCase	,     __UpperCamelCase	:			int					=					self.model_tester.prepare_config_and_inputs_for_common()

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

        __UpperCamelCase	:			Tuple					=					["""pixel_values"""]
        self.assertListEqual(arg_names[:1]   ,				lowerCamelCase__			)

  def    a					(    self						:	Dict			):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowerCamelCase__			)

  def    a					(    self						:	Any			):
     """simple docstring"""

     __UpperCamelCase	:			List[Any]					=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_depth_estimation(*lowerCamelCase__			)

  def    a					(    self						:	Dict			):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase__			)

  def    a					(    self						:	str			):
     """simple docstring"""

     for model_class in self.all_model_classes:
        if model_class.__name__ == "DPTForDepthEstimation":
           continue

        __UpperCamelCase	,     __UpperCamelCase	:			Optional[int]					=					self.model_tester.prepare_config_and_inputs_for_common()
        __UpperCamelCase	:			str					=					True

        if model_class in get_values(lowerCamelCase__			):
           continue

        __UpperCamelCase	:			Tuple					=					model_class(lowerCamelCase__			)
        model.to(lowerCamelCase__			)
        model.train()
        __UpperCamelCase	:			List[Any]					=					self._prepare_for_class(lowerCamelCase__   ,				lowerCamelCase__   ,				return_labels=lowerCamelCase__			)
        __UpperCamelCase	:			Dict					=					model(**lowerCamelCase__			).loss
        loss.backward()

  def    a					(    self						:	Dict			):
     """simple docstring"""

     for model_class in self.all_model_classes:
        if model_class.__name__ == "DPTForDepthEstimation":
           continue

        __UpperCamelCase	,     __UpperCamelCase	:			Optional[int]					=					self.model_tester.prepare_config_and_inputs_for_common()
        __UpperCamelCase	:			Optional[int]					=					False
        __UpperCamelCase	:			Union[str, Any]					=					True

        if model_class in get_values(lowerCamelCase__			) or not model_class.supports_gradient_checkpointing:
           continue
        __UpperCamelCase	:			str					=					model_class(lowerCamelCase__			)
        model.to(lowerCamelCase__			)
        model.gradient_checkpointing_enable()
        model.train()
        __UpperCamelCase	:			List[str]					=					self._prepare_for_class(lowerCamelCase__   ,				lowerCamelCase__   ,				return_labels=lowerCamelCase__			)
        __UpperCamelCase	:			Tuple					=					model(**lowerCamelCase__			).loss
        loss.backward()

  def    a					(    self						:	int			):
     """simple docstring"""

     __UpperCamelCase	,     __UpperCamelCase	:			Optional[Any]					=					self.model_tester.prepare_config_and_inputs_for_common()

     __UpperCamelCase	:			List[str]					=					_config_zero_init(lowerCamelCase__			)
     for model_class in self.all_model_classes:
        __UpperCamelCase	:			List[Any]					=					model_class(config=lowerCamelCase__			)
        # Skip the check for the backbone
        __UpperCamelCase	:			List[str]					=					[]
        for name, module in model.named_modules():
           if module.__class__.__name__ == "DPTViTHybridEmbeddings":
              __UpperCamelCase	:			Optional[Any]					=					[f'{name}.{key}' for key in module.state_dict().keys()]
              break

        for name, param in model.named_parameters():
           if param.requires_grad:
              if name in backbone_params:
                 continue
              self.assertIn(
                  ((param.data.mean() * 1e9).round() / 1e9).item()   ,				[0.0, 1.0]   ,				msg=f'Parameter {name} of model {model_class} seems not properly initialized'   ,				)

  @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""			)
  def    a					(    self						:	Union[str, Any]			):
     """simple docstring"""

     pass

  @slow
  def    a					(    self						:	int			):
     """simple docstring"""

     for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
        __UpperCamelCase	:			List[Any]					=					DPTModel.from_pretrained(lowerCamelCase__			)
        self.assertIsNotNone(lowerCamelCase__			)

  def    a					(    self						:	Union[str, Any]			):
     """simple docstring"""

     __UpperCamelCase	,     __UpperCamelCase	:			int					=					self.model_tester.prepare_config_and_inputs_for_common()
     __UpperCamelCase	:			Optional[int]					=					"""add"""
     with self.assertRaises(lowerCamelCase__			):
        __UpperCamelCase	:			Union[str, Any]					=					DPTForDepthEstimation(lowerCamelCase__			)







def 				__lowerCamelCase     (							)     ->  int:
   __UpperCamelCase	:			List[str]					=					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
   return image







@require_torch
@require_vision
@slow
class 		_A						( unittest.TestCase ):

  def    a					(    self						:	Union[str, Any]			):
     """simple docstring"""

     __UpperCamelCase	:			List[Any]					=					DPTImageProcessor.from_pretrained("""Intel/dpt-hybrid-midas"""			)
     __UpperCamelCase	:			Any					=					DPTForDepthEstimation.from_pretrained("""Intel/dpt-hybrid-midas"""			).to(lowerCamelCase__			)

     __UpperCamelCase	:			Optional[Any]					=					prepare_img()
     __UpperCamelCase	:			Union[str, Any]					=					image_processor(images=lowerCamelCase__   ,				return_tensors="""pt"""			).to(lowerCamelCase__			)

     # forward pass
     with torch.no_grad():
        __UpperCamelCase	:			int					=					model(**lowerCamelCase__			)
        __UpperCamelCase	:			Any					=					outputs.predicted_depth

     # verify the predicted depth
     __UpperCamelCase	:			Tuple					=					torch.Size((1, 3_84, 3_84)			)
     self.assertEqual(predicted_depth.shape   ,				lowerCamelCase__			)

     __UpperCamelCase	:			Optional[Any]					=					torch.tensor(
         [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]			).to(lowerCamelCase__			)

     self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_00   ,				lowerCamelCase__   ,				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)



