

import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
		    MODEL_MAPPING,
		    LevitForImageClassification,
		    LevitForImageClassificationWithTeacher,
		    LevitModel,
		)
		from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import LevitImageProcessor







class 					__lowercase  (						_UpperCamelCase				):





							"""simple docstring"""

							def       snake_case			(					self						)		->       str:
														A      :					List[Any]      		=					self.config_class(**self.inputs_dict						)
														self.parent.assertTrue(hasattr(__UpperCAmelCase    ,						'''hidden_sizes'''						)						)
														self.parent.assertTrue(hasattr(__UpperCAmelCase    ,						'''num_attention_heads'''						)						)







class 					__lowercase  :





							"""simple docstring"""

							def __init__(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase=13    ,						__UpperCAmelCase=64    ,						__UpperCAmelCase=3    ,						__UpperCAmelCase=3    ,						__UpperCAmelCase=2    ,						__UpperCAmelCase=1    ,						__UpperCAmelCase=16    ,						__UpperCAmelCase=[1_28, 2_56, 3_84]    ,						__UpperCAmelCase=[4, 6, 8]    ,						__UpperCAmelCase=[2, 3, 4]    ,						__UpperCAmelCase=[16, 16, 16]    ,						__UpperCAmelCase=0    ,						__UpperCAmelCase=[2, 2, 2]    ,						__UpperCAmelCase=[2, 2, 2]    ,						__UpperCAmelCase=0.0_2    ,						__UpperCAmelCase=True    ,						__UpperCAmelCase=True    ,						__UpperCAmelCase=2    ,						)		->       Tuple:
														A      :					int      		=					parent
														A      :					List[Any]      		=					batch_size
														A      :					Optional[int]      		=					image_size
														A      :					Tuple      		=					num_channels
														A      :					Optional[Any]      		=					kernel_size
														A      :					int      		=					stride
														A      :					Tuple      		=					padding
														A      :					str      		=					hidden_sizes
														A      :					int      		=					num_attention_heads
														A      :					Optional[Any]      		=					depths
														A      :					Any      		=					key_dim
														A      :					Dict      		=					drop_path_rate
														A      :					Optional[int]      		=					patch_size
														A      :					Optional[Any]      		=					attention_ratio
														A      :					Optional[int]      		=					mlp_ratio
														A      :					int      		=					initializer_range
														A      :					List[str]      		=					[
														    ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
														    ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
														]
														A      :					Optional[Any]      		=					is_training
														A      :					Any      		=					use_labels
														A      :					Any      		=					num_labels
														A      :					Optional[Any]      		=					initializer_range




							def       snake_case			(					self						)		->       str:
														A      :					int      		=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

														A      :					Union[str, Any]      		=					None
														if self.use_labels:
																					A      :					Tuple      		=					ids_tensor([self.batch_size]    ,						self.num_labels						)

														A      :					List[str]      		=					self.get_config()
														return config, pixel_values, labels




							def       snake_case			(					self						)		->       List[Any]:
														return LevitConfig(
														    image_size=self.image_size    ,						num_channels=self.num_channels    ,						kernel_size=self.kernel_size    ,						stride=self.stride    ,						padding=self.padding    ,						patch_size=self.patch_size    ,						hidden_sizes=self.hidden_sizes    ,						num_attention_heads=self.num_attention_heads    ,						depths=self.depths    ,						key_dim=self.key_dim    ,						drop_path_rate=self.drop_path_rate    ,						mlp_ratio=self.mlp_ratio    ,						attention_ratio=self.attention_ratio    ,						initializer_range=self.initializer_range    ,						down_ops=self.down_ops    ,						)




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase						)		->       Dict:
														A      :					Union[str, Any]      		=					LevitModel(config=__UpperCAmelCase						)
														model.to(__UpperCAmelCase						)
														model.eval()
														A      :					Tuple      		=					model(__UpperCAmelCase						)
														A      :					Dict      		=					(self.image_size, self.image_size)
														A      :					int      		=					image_size[0], image_size[1]
														for _ in range(4						):
																					A      :					Dict      		=					floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1						)
																					A      :					Any      		=					floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1						)
														self.parent.assertEqual(
														    result.last_hidden_state.shape    ,						(self.batch_size, ceil(height / 4						) * ceil(width / 4						), self.hidden_sizes[-1])    ,						)




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase						)		->       Tuple:
														A      :					int      		=					self.num_labels
														A      :					Union[str, Any]      		=					LevitForImageClassification(__UpperCAmelCase						)
														model.to(__UpperCAmelCase						)
														model.eval()
														A      :					Optional[int]      		=					model(__UpperCAmelCase    ,						labels=__UpperCAmelCase						)
														self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.num_labels)						)






							def       snake_case			(					self						)		->       List[str]:
														A      :					List[Any]      		=					self.prepare_config_and_inputs()
														A      :					Union[str, Any]      		=					config_and_inputs
														A      :					Optional[Any]      		=					{"pixel_values": pixel_values}
														return config, inputs_dict







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





							"""simple docstring"""

							UpperCAmelCase_						:		List[Any]								=	(
							    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
							    if is_torch_available()
							    else ()
							)
							UpperCAmelCase_						:		str								=	(
							    {
							        'feature-extraction': LevitModel,
							        'image-classification': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
							    }
							    if is_torch_available()
							    else {}
							)

							UpperCAmelCase_						:		Optional[Any]								=	False
							UpperCAmelCase_						:		Union[str, Any]								=	False
							UpperCAmelCase_						:		int								=	False
							UpperCAmelCase_						:		Union[str, Any]								=	False
							UpperCAmelCase_						:		List[str]								=	False




							def       snake_case			(					self						)		->       List[Any]:
														A      :					str      		=					LevitModelTester(self						)
														A      :					Any      		=					ConfigTester(self    ,						config_class=__UpperCAmelCase    ,						has_text_modality=__UpperCAmelCase    ,						hidden_size=37						)




							def       snake_case			(					self						)		->       Optional[int]:
														self.create_and_test_config_common_properties()
														self.config_tester.create_and_test_config_to_json_string()
														self.config_tester.create_and_test_config_to_json_file()
														self.config_tester.create_and_test_config_from_and_save_pretrained()
														self.config_tester.create_and_test_config_with_num_labels()
														self.config_tester.check_config_can_be_init_without_params()
														self.config_tester.check_config_arguments_init()




							def       snake_case			(					self						)		->       str:
														return




							@unittest.skip(reason='''Levit does not use inputs_embeds'''						)
							def       snake_case			(					self						)		->       Optional[Any]:
														pass




							@unittest.skip(reason='''Levit does not support input and output embeddings'''						)
							def       snake_case			(					self						)		->       Tuple:
														pass




							@unittest.skip(reason='''Levit does not output attentions'''						)
							def       snake_case			(					self						)		->       Optional[int]:
														pass




							def       snake_case			(					self						)		->       Optional[Any]:
														A      :					int      		=					self.model_tester.prepare_config_and_inputs_for_common()

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

																					A      :					Union[str, Any]      		=					["pixel_values"]
																					self.assertListEqual(arg_names[:1]    ,						__UpperCAmelCase						)




							def       snake_case			(					self						)		->       Any:
														def check_hidden_states_output(__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase						):
																					A      :					List[Any]      		=					model_class(__UpperCAmelCase						)
																					model.to(__UpperCAmelCase						)
																					model.eval()

																					with torch.no_grad():
																												A      :					Tuple      		=					model(**self._prepare_for_class(__UpperCAmelCase    ,						__UpperCAmelCase						)						)

																					A      :					int      		=					outputs.hidden_states

																					A      :					Optional[Any]      		=					len(self.model_tester.depths						) + 1
																					self.assertEqual(len(__UpperCAmelCase						)    ,						__UpperCAmelCase						)

																					A      :					int      		=					(self.model_tester.image_size, self.model_tester.image_size)
																					A      :					Union[str, Any]      		=					image_size[0], image_size[1]
																					for _ in range(4						):
																												A      :					List[str]      		=					floor(
																												    (
																												        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
																												        / self.model_tester.stride
																												    )
																												    + 1						)
																												A      :					Dict      		=					floor(
																												    (
																												        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
																												        / self.model_tester.stride
																												    )
																												    + 1						)
																					# verify the first hidden states (first block)
																					self.assertListEqual(
																					    list(hidden_states[0].shape[-2:]						)    ,						[
																					        height * width,
																					        self.model_tester.hidden_sizes[0],
																					    ]    ,						)

														A      :					Tuple      		=					self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					A      :					Tuple      		=					True
																					check_hidden_states_output(__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase						)

																					# check that output_hidden_states also work using config
																					del inputs_dict["output_hidden_states"]
																					A      :					Union[str, Any]      		=					True

																					check_hidden_states_output(__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase						)




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




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase=False						)		->       int:
														A      :					Tuple      		=					super()._prepare_for_class(__UpperCAmelCase    ,						__UpperCAmelCase    ,						return_labels=__UpperCAmelCase						)

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

														return inputs_dict




							def       snake_case			(					self						)		->       List[str]:
														A      :					Union[str, Any]      		=					self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*__UpperCAmelCase						)




							def       snake_case			(					self						)		->       Optional[Any]:
														A      :					str      		=					self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase						)




							def       snake_case			(					self						)		->       Tuple:
														if not self.model_tester.is_training:
																					return

														A      :					Dict      		=					self.model_tester.prepare_config_and_inputs_for_common()
														A      :					Optional[Any]      		=					True

														for model_class in self.all_model_classes:
																					# LevitForImageClassificationWithTeacher supports inference-only
																					if (
																					    model_class in get_values(__UpperCAmelCase						)
																					    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
																					):
																												continue
																					A      :					List[str]      		=					model_class(__UpperCAmelCase						)
																					model.to(__UpperCAmelCase						)
																					model.train()
																					A      :					Union[str, Any]      		=					self._prepare_for_class(__UpperCAmelCase    ,						__UpperCAmelCase    ,						return_labels=__UpperCAmelCase						)
																					A      :					Tuple      		=					model(**__UpperCAmelCase						).loss
																					loss.backward()




							def       snake_case			(					self						)		->       int:
														A      :					Optional[int]      		=					self.model_tester.prepare_config_and_inputs_for_common()
														if not self.model_tester.is_training:
																					return

														A      :					Optional[int]      		=					False
														A      :					Union[str, Any]      		=					True

														for model_class in self.all_model_classes:
																					if model_class in get_values(__UpperCAmelCase						) or not model_class.supports_gradient_checkpointing:
																												continue
																					# LevitForImageClassificationWithTeacher supports inference-only
																					if model_class.__name__ == "LevitForImageClassificationWithTeacher":
																												continue
																					A      :					List[Any]      		=					model_class(__UpperCAmelCase						)
																					model.gradient_checkpointing_enable()
																					model.to(__UpperCAmelCase						)
																					model.train()
																					A      :					Tuple      		=					self._prepare_for_class(__UpperCAmelCase    ,						__UpperCAmelCase    ,						return_labels=__UpperCAmelCase						)
																					A      :					List[Any]      		=					model(**__UpperCAmelCase						).loss
																					loss.backward()




							def       snake_case			(					self						)		->       int:
														A      :					Dict      		=					self.model_tester.prepare_config_and_inputs_for_common()

														A      :					str      		=					[
														    {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
														    {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
														    {"title": "regression", "num_labels": 1, "dtype": torch.float},
														]

														for model_class in self.all_model_classes:
																					if (
																					    model_class
																					    not in [
																					        *get_values(__UpperCAmelCase						),
																					    ]
																					    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
																					):
																												continue

																					for problem_type in problem_types:
																												with self.subTest(msg=f'Testing {model_class} with {problem_type["title"]}'						):
																																			A      :					List[Any]      		=					problem_type["title"]
																																			A      :					Dict      		=					problem_type["num_labels"]

																																			A      :					Optional[int]      		=					model_class(__UpperCAmelCase						)
																																			model.to(__UpperCAmelCase						)
																																			model.train()

																																			A      :					List[Any]      		=					self._prepare_for_class(__UpperCAmelCase    ,						__UpperCAmelCase    ,						return_labels=__UpperCAmelCase						)

																																			if problem_type["num_labels"] > 1:
																																										A      :					Tuple      		=					inputs["labels"].unsqueeze(1						).repeat(1    ,						problem_type['''num_labels''']						)

																																			A      :					int      		=					inputs["labels"].to(problem_type['''dtype''']						)

																																			# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																																			# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																																			# they have the same size." which is a symptom something in wrong for the regression problem.
																																			# See https://github.com/huggingface/transformers/issues/11780
																																			with warnings.catch_warnings(record=__UpperCAmelCase						) as warning_list:
																																										A      :					Dict      		=					model(**__UpperCAmelCase						).loss
																																			for w in warning_list:
																																										if "Using a target size that is different to the input size" in str(w.message						):
																																																	raise ValueError(
																																																	    f'Something is going wrong in the regression problem: intercepted {w.message}'						)

																																			loss.backward()






							@slow
							def       snake_case			(					self						)		->       int:
														for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					A      :					str      		=					LevitModel.from_pretrained(__UpperCAmelCase						)
																					self.assertIsNotNone(__UpperCAmelCase						)






def        snake_case__		(    ):
							A      :					Optional[int]      		=					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
							return image







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





							"""simple docstring"""

							@cached_property
							def       snake_case			(					self						)		->       Dict:
														return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)






							@slow
							def       snake_case			(					self						)		->       List[Any]:
														A      :					str      		=					LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(
														    __UpperCAmelCase						)

														A      :					Optional[Any]      		=					self.default_image_processor
														A      :					str      		=					prepare_img()
														A      :					Optional[int]      		=					image_processor(images=__UpperCAmelCase    ,						return_tensors='''pt'''						).to(__UpperCAmelCase						)

														# forward pass
														with torch.no_grad():
																					A      :					Optional[int]      		=					model(**__UpperCAmelCase						)

														# verify the logits
														A      :					Union[str, Any]      		=					torch.Size((1, 10_00)						)
														self.assertEqual(outputs.logits.shape    ,						__UpperCAmelCase						)

														A      :					Any      		=					torch.tensor([1.0_4_4_8, -0.3_7_4_5, -1.8_3_1_7]						).to(__UpperCAmelCase						)

														self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,						__UpperCAmelCase    ,						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)
