


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


snake_case							:       Union[str, Any]     			=      ['''model.decoder.embed_positions.weights''']






def 							__lowercase   ( __lowerCAmelCase							:				Optional[int]   ):
							if "emb" in name:
														a__             =     name.replace('emb'					,    'model.decoder.embed_tokens'   )
							if "transformer" in name:
														a__             =     name.replace('transformer'					,    'model.decoder'   )
							if "cross_attention" in name:
														a__             =     name.replace('cross_attention'					,    'encoder_attn'   )
							if "linear1" in name:
														a__             =     name.replace('linear1'					,    'fc1'   )
							if "linear2" in name:
														a__             =     name.replace('linear2'					,    'fc2'   )
							if "norm1" in name:
														a__             =     name.replace('norm1'					,    'self_attn_layer_norm'   )
							if "norm_cross" in name:
														a__             =     name.replace('norm_cross'					,    'encoder_attn_layer_norm'   )
							if "norm2" in name:
														a__             =     name.replace('norm2'					,    'final_layer_norm'   )
							if "out_norm" in name:
														a__             =     name.replace('out_norm'					,    'model.decoder.layer_norm'   )
							if "linears" in name:
														a__             =     name.replace('linears'					,    'lm_heads'   )
							if "condition_provider.conditioners.description.output_proj" in name:
														a__             =     name.replace('condition_provider.conditioners.description.output_proj'					,    'enc_to_dec_proj'   )
							return name






def 							__lowercase   ( __lowerCAmelCase							:				OrderedDict					,    __lowerCAmelCase							:				int   ):
							a__             =     list(state_dict.keys()   )
							a__             =     {}
							for key in keys:
														a__             =     state_dict.pop(__lowerCAmelCase   )
														a__             =     rename_keys(__lowerCAmelCase   )
														if "in_proj_weight" in key:
																					# split fused qkv proj
																					a__             =     val[:hidden_size, :]
																					a__             =     val[hidden_size : 2 * hidden_size, :]
																					a__             =     val[-hidden_size:, :]
														elif "enc_to_dec_proj" in key:
																					a__             =     val
														else:
																					a__             =     val
							return state_dict, enc_dec_proj_state_dict






def 							__lowercase   ( __lowerCAmelCase							:				str   ):
							if checkpoint == "small":
														# default config values
														a__             =     1_0_2_4
														a__             =     2_4
														a__             =     1_6
							elif checkpoint == "medium":
														a__             =     1_5_3_6
														a__             =     4_8
														a__             =     2_4
							elif checkpoint == "large":
														a__             =     2_0_4_8
														a__             =     4_8
														a__             =     3_2
							else:
														raise ValueError(F'Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'   )
							a__             =     MusicgenDecoderConfig(
							    hidden_size=__lowerCAmelCase					,    ffn_dim=hidden_size * 4					,    num_hidden_layers=__lowerCAmelCase					,    num_attention_heads=__lowerCAmelCase					,    )
							return config






@torch.no_grad()
def 							__lowercase   ( __lowerCAmelCase							:				str					,    __lowerCAmelCase							:				Dict=None					,    __lowerCAmelCase							:				Optional[int]=None					,    __lowerCAmelCase							:				Dict="cpu"   ):
							a__             =     MusicGen.get_pretrained(__lowerCAmelCase					,    device=__lowerCAmelCase   )
							a__             =     decoder_config_from_checkpoint(__lowerCAmelCase   )

							a__             =     fairseq_model.lm.state_dict()
							a__      ,					a__             =     rename_state_dict(
							    __lowerCAmelCase					,    hidden_size=decoder_config.hidden_size   )

							a__             =     TaEncoderModel.from_pretrained('t5-base'   )
							a__             =     EncodecModel.from_pretrained('facebook/encodec_32khz'   )
							a__             =     MusicgenForCausalLM(__lowerCAmelCase   ).eval()

							# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
							a__      ,					a__             =     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
							a__             =     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
							a__             =     torch.arange(0					,    8					,    dtype=torch.long   ).reshape(2					,    -1   )
							a__             =     input_ids.reshape(2 * 4					,    -1   )

							with torch.no_grad():
														a__             =     model(input_ids=__lowerCAmelCase					,    decoder_input_ids=__lowerCAmelCase   ).logits

							if logits.shape != (8, 1, 2_0_4_8):
														raise ValueError('Incorrect shape for logits'   )

							# now construct the processor
							a__             =     AutoTokenizer.from_pretrained('t5-base'   )
							a__             =     AutoFeatureExtractor.from_pretrained('facebook/encodec_32khz'					,    padding_side='left'   )

							a__             =     MusicgenProcessor(feature_extractor=__lowerCAmelCase					,    tokenizer=__lowerCAmelCase   )

							# set the appropriate bos/pad token ids
							a__             =     2_0_4_8
							a__             =     2_0_4_8

							# set other default generation config params
							a__             =     int(3_0 * audio_encoder.config.frame_rate   )
							a__             =     True
							a__             =     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__":
						snake_case							:       List[str]     			=      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.'''
						)

						snake_case							:       Union[str, Any]     			=      parser.parse_args()
						convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)








from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case							:       Union[str, Any]     			=      logging.get_logger(__name__)

snake_case							:       str     			=      {
    '''google/mobilenet_v2_1.4_224''': '''https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json''',
    '''google/mobilenet_v2_1.0_224''': '''https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v2_0.75_160''': '''https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json''',
    '''google/mobilenet_v2_0.35_96''': '''https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json''',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}







class        snake_case_   (lowerCamelCase_      ):
					UpperCAmelCase__					:    str          =			'''mobilenet_v2'''
					def __init__(      self   :Optional[Any]			,__snake_case   :List[Any]=3			,__snake_case   :Any=2_24			,__snake_case   :Dict=1.0			,__snake_case   :str=8			,__snake_case   :Union[str, Any]=8			,__snake_case   :Optional[Any]=6			,__snake_case   :str=32			,__snake_case   :Tuple=True			,__snake_case   :Union[str, Any]=True			,__snake_case   :Any="relu6"			,__snake_case   :List[str]=True			,__snake_case   :Dict=0.8			,__snake_case   :Optional[int]=0.02			,__snake_case   :Tuple=0.0_01			,__snake_case   :Dict=2_55			,**__snake_case   :Dict			,)					->    Any:
												super().__init__(**__snake_case      )

												if depth_multiplier <= 0:
																			raise ValueError('depth_multiplier must be greater than zero.'      )

												a__             =     num_channels
												a__             =     image_size
												a__             =     depth_multiplier
												a__             =     depth_divisible_by
												a__             =     min_depth
												a__             =     expand_ratio
												a__             =     output_stride
												a__             =     first_layer_is_expansion
												a__             =     finegrained_output
												a__             =     hidden_act
												a__             =     tf_padding
												a__             =     classifier_dropout_prob
												a__             =     initializer_range
												a__             =     layer_norm_eps
												a__             =     semantic_loss_ignore_index







class        snake_case_   (lowerCamelCase_      ):
					UpperCAmelCase__					:    Tuple          =			version.parse('''1.11'''      )
					@property
					def  lowerCamelCase__(      self   :Any      )					->    Mapping[str, Mapping[int, str]]:
												return OrderedDict([('pixel_values', {0: 'batch'})]      )
					@property
					def  lowerCamelCase__(      self   :List[str]      )					->    Mapping[str, Mapping[int, str]]:
												if self.task == "image-classification":
																			return OrderedDict([('logits', {0: 'batch'})]      )
												else:
																			return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]      )



					@property
					def  lowerCamelCase__(      self   :Any      )					->    float:
												return 1E-4




