
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()
lowercase  :					str													=       logging.get_logger(__name__)


lowercase  :					Optional[int]													=       ['model.decoder.embed_positions.weights']




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			List[str])					->    Dict:






							'''simple docstring'''





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




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			OrderedDict						,  _lowerCamelCase :			int)					->    Tuple[Dict, Dict]:






							'''simple docstring'''





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




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str)					->    MusicgenDecoderConfig:






							'''simple docstring'''





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




@torch.no_grad()
def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Union[str, Any]						,  _lowerCamelCase :			Any=None						,  _lowerCamelCase :			List[str]=None						,  _lowerCamelCase :			List[Any]="cpu")					->    str:






							'''simple docstring'''





							__UpperCamelCase	:   Any			   =					MusicGen.get_pretrained(_lowerCamelCase						,  device=_lowerCamelCase)
							__UpperCamelCase	:   Union[str, Any]			   =					decoder_config_from_checkpoint(_lowerCamelCase)

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

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

							# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
							__UpperCamelCase   ,						__UpperCamelCase	:   Optional[Any]			   =					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	:   str			   =					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	:   Optional[int]			   =					torch.arange(0						,  8						,  dtype=torch.long).reshape(2						,  -1)
							__UpperCamelCase	:   Optional[Any]			   =					input_ids.reshape(2 * 4						,  -1)

							with torch.no_grad():
														__UpperCamelCase	:   Tuple			   =					model(input_ids=_lowerCamelCase						,  decoder_input_ids=_lowerCamelCase).logits

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

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

							__UpperCamelCase	:   Union[str, Any]			   =					MusicgenProcessor(feature_extractor=_lowerCamelCase						,  tokenizer=_lowerCamelCase)

							# set the appropriate bos/pad token ids
							__UpperCamelCase	:   Optional[int]			   =					2_048
							__UpperCamelCase	:   int			   =					2_048

							# set other default generation config params
							__UpperCamelCase	:   str			   =					int(30 * audio_encoder.config.frame_rate)
							__UpperCamelCase	:   Dict			   =					True
							__UpperCamelCase	:   Union[str, Any]			   =					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__":
		lowercase  :					Dict													=       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.'
		)

		lowercase  :					Any													=       parser.parse_args()
		convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)

import inspect
import unittest

import numpy as np

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
		import jax

		from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel




class  lowerCamelCase__							(				unittest.TestCase):




	'''simple docstring'''




	def __init__(					self		:List[str]     ,   a		:Any     ,   a		:List[Any]=1_3     ,   a		:Any=3_0     ,   a		:Dict=2     ,   a		:List[str]=3     ,   a		:Union[str, Any]=True     ,   a		:Optional[int]=True     ,   a		:Tuple=3_2     ,   a		:Any=5     ,   a		:List[str]=4     ,   a		:Optional[Any]=3_7     ,   a		:List[Any]="gelu"     ,   a		:Any=0.1     ,   a		:Any=0.1     ,   a		:List[str]=1_0     ,   a		:str=0.02     ,   )		-> Union[str, Any]:
								__UpperCamelCase	:   int			   =					parent
								__UpperCamelCase	:   List[Any]			   =					batch_size
								__UpperCamelCase	:   Optional[Any]			   =					image_size
								__UpperCamelCase	:   int			   =					patch_size
								__UpperCamelCase	:   Optional[Any]			   =					num_channels
								__UpperCamelCase	:   Optional[int]			   =					is_training
								__UpperCamelCase	:   Union[str, Any]			   =					use_labels
								__UpperCamelCase	:   Tuple			   =					hidden_size
								__UpperCamelCase	:   List[Any]			   =					num_hidden_layers
								__UpperCamelCase	:   List[Any]			   =					num_attention_heads
								__UpperCamelCase	:   Dict			   =					intermediate_size
								__UpperCamelCase	:   Optional[int]			   =					hidden_act
								__UpperCamelCase	:   List[Any]			   =					hidden_dropout_prob
								__UpperCamelCase	:   str			   =					attention_probs_dropout_prob
								__UpperCamelCase	:   List[str]			   =					type_sequence_label_size
								__UpperCamelCase	:   List[str]			   =					initializer_range

								# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
								__UpperCamelCase	:   Union[str, Any]			   =					(image_size // patch_size) ** 2
								__UpperCamelCase	:   Optional[int]			   =					num_patches + 1







	def    _lowerCamelCase				(					self		:Optional[int]						)		-> Union[str, Any]:
								__UpperCamelCase	:   Optional[int]			   =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

								__UpperCamelCase	:   Tuple			   =					ViTConfig(
								    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=a     ,   initializer_range=self.initializer_range     ,   )

								return config, pixel_values







	def    _lowerCamelCase				(					self		:Tuple     ,   a		:Optional[int]     ,   a		:Optional[int]						)		-> Optional[int]:
								__UpperCamelCase	:   Optional[Any]			   =					FlaxViTModel(config=a						)
								__UpperCamelCase	:   int			   =					model(a						)
								# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
								__UpperCamelCase	:   Any			   =					(self.image_size, self.image_size)
								__UpperCamelCase	:   Optional[int]			   =					(self.patch_size, self.patch_size)
								__UpperCamelCase	:   Optional[int]			   =					(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
								self.parent.assertEqual(result.last_hidden_state.shape     ,   (self.batch_size, num_patches + 1, self.hidden_size)						)







	def    _lowerCamelCase				(					self		:Dict     ,   a		:List[str]     ,   a		:Any						)		-> str:
								__UpperCamelCase	:   Dict			   =					self.type_sequence_label_size
								__UpperCamelCase	:   Dict			   =					FlaxViTForImageClassification(config=a						)
								__UpperCamelCase	:   Tuple			   =					model(a						)
								self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.type_sequence_label_size)						)

								# test greyscale images
								__UpperCamelCase	:   Dict			   =					1
								__UpperCamelCase	:   Optional[Any]			   =					FlaxViTForImageClassification(a						)

								__UpperCamelCase	:   List[str]			   =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
								__UpperCamelCase	:   str			   =					model(a						)







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




@require_flax
class  lowerCamelCase__							(				__lowercase     ,						unittest.TestCase):




	'''simple docstring'''




	_A								=     (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()







	def    _lowerCamelCase				(					self		:Optional[int]						)		-> None:
								__UpperCamelCase	:   int			   =					FlaxViTModelTester(self						)
								__UpperCamelCase	:   Any			   =					ConfigTester(self     ,   config_class=a     ,   has_text_modality=a     ,   hidden_size=3_7						)







	def    _lowerCamelCase				(					self		:List[Any]						)		-> str:
								self.config_tester.run_common_tests()







	def    _lowerCamelCase				(					self		:List[Any]						)		-> List[str]:
								__UpperCamelCase	:   int			   =					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*a						)







	def    _lowerCamelCase				(					self		:List[str]						)		-> Union[str, Any]:
								__UpperCamelCase	:   Any			   =					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*a						)







	def    _lowerCamelCase				(					self		:Tuple						)		-> Any:
								__UpperCamelCase   ,						__UpperCamelCase	:   Optional[Any]			   =					self.model_tester.prepare_config_and_inputs_for_common()

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

															__UpperCamelCase	:   Any			   =					["pixel_values"]
															self.assertListEqual(arg_names[:1]     ,   a						)







	def    _lowerCamelCase				(					self		:str						)		-> Any:
								__UpperCamelCase   ,						__UpperCamelCase	:   List[str]			   =					self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__						):
																						__UpperCamelCase	:   List[Any]			   =					self._prepare_for_class(a     ,   a						)
																						__UpperCamelCase	:   List[Any]			   =					model_class(a						)

																						@jax.jit
																						def model_jitted(a		:List[str]     ,   **a		:List[Any]						):
																													return model(pixel_values=a     ,   **a						)

																						with self.subTest("JIT Enabled"						):
																													__UpperCamelCase	:   int			   =					model_jitted(**a						).to_tuple()

																						with self.subTest("JIT Disabled"						):
																													with jax.disable_jit():
																																				__UpperCamelCase	:   Optional[Any]			   =					model_jitted(**a						).to_tuple()

																						self.assertEqual(len(a						)     ,   len(a						)						)
																						for jitted_output, output in zip(a     ,   a						):
																													self.assertEqual(jitted_output.shape     ,   output.shape						)







	@slow
	def    _lowerCamelCase				(					self		:Dict						)		-> str:
								for model_class_name in self.all_model_classes:
															__UpperCamelCase	:   List[Any]			   =					model_class_name.from_pretrained("google/vit-base-patch16-224"						)
															__UpperCamelCase	:   Optional[int]			   =					model(np.ones((1, 3, 2_2_4, 2_2_4)						)						)
															self.assertIsNotNone(a						)