from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class        snake_case (				a__       ):




					"""simple docstring"""

					snake_case__       					=				["""image_processor""", """tokenizer"""]
					snake_case__       					=				"""BlipImageProcessor"""
					snake_case__       					=				("""BertTokenizer""", """BertTokenizerFast""")







					def __init__(       self     :			int					,lowerCamelCase__     :			Union[str, Any]					,lowerCamelCase__     :			Tuple    ):
									UpperCAmelCase__    =							False
									super().__init__(lowerCAmelCase__					,lowerCAmelCase__    )
									UpperCAmelCase__    =							self.image_processor







					def __call__(       self     :			Tuple					,lowerCamelCase__     :			Optional[int] = None					,lowerCamelCase__     :			Optional[Any] = None					,lowerCamelCase__     :			int = True					,lowerCamelCase__     :			List[Any] = False					,lowerCamelCase__     :			Tuple = None					,lowerCamelCase__     :			Optional[int] = None					,lowerCamelCase__     :			List[Any] = 0					,lowerCamelCase__     :			Optional[int] = None					,lowerCamelCase__     :			Any = None					,lowerCamelCase__     :			Union[str, Any] = False					,lowerCamelCase__     :			List[Any] = False					,lowerCamelCase__     :			int = False					,lowerCamelCase__     :			Optional[int] = False					,lowerCamelCase__     :			Optional[Any] = False					,lowerCamelCase__     :			Optional[int] = True					,lowerCamelCase__     :			Union[str, Any] = None					,**lowerCamelCase__     :			Tuple					,):
									if images is None and text is None:
													raise ValueError('You have to specify either images or text.'    )

									# Get only text
									if images is None:
													UpperCAmelCase__    =							self.tokenizer
													UpperCAmelCase__    =							self.tokenizer(
													    text=lowerCAmelCase__					,add_special_tokens=lowerCAmelCase__					,padding=lowerCAmelCase__					,truncation=lowerCAmelCase__					,max_length=lowerCAmelCase__					,stride=lowerCAmelCase__					,pad_to_multiple_of=lowerCAmelCase__					,return_attention_mask=lowerCAmelCase__					,return_overflowing_tokens=lowerCAmelCase__					,return_special_tokens_mask=lowerCAmelCase__					,return_offsets_mapping=lowerCAmelCase__					,return_token_type_ids=lowerCAmelCase__					,return_length=lowerCAmelCase__					,verbose=lowerCAmelCase__					,return_tensors=lowerCAmelCase__					,**lowerCAmelCase__					,)
													return text_encoding

									# add pixel_values
									UpperCAmelCase__    =							self.image_processor(lowerCAmelCase__					,return_tensors=lowerCAmelCase__    )

									if text is not None:
													UpperCAmelCase__    =							self.tokenizer(
													    text=lowerCAmelCase__					,add_special_tokens=lowerCAmelCase__					,padding=lowerCAmelCase__					,truncation=lowerCAmelCase__					,max_length=lowerCAmelCase__					,stride=lowerCAmelCase__					,pad_to_multiple_of=lowerCAmelCase__					,return_attention_mask=lowerCAmelCase__					,return_overflowing_tokens=lowerCAmelCase__					,return_special_tokens_mask=lowerCAmelCase__					,return_offsets_mapping=lowerCAmelCase__					,return_token_type_ids=lowerCAmelCase__					,return_length=lowerCAmelCase__					,verbose=lowerCAmelCase__					,return_tensors=lowerCAmelCase__					,**lowerCAmelCase__					,)
									else:
													UpperCAmelCase__    =							None

									if text_encoding is not None:
													encoding_image_processor.update(lowerCAmelCase__    )

									return encoding_image_processor







					def 	__lowerCAmelCase		(       self     :			List[Any]					,*lowerCamelCase__     :			Dict					,**lowerCamelCase__     :			Union[str, Any]    ):
									return self.tokenizer.batch_decode(*lowerCAmelCase__					,**lowerCAmelCase__    )







					def 	__lowerCAmelCase		(       self     :			Any					,*lowerCamelCase__     :			int					,**lowerCamelCase__     :			int    ):
									return self.tokenizer.decode(*lowerCAmelCase__					,**lowerCAmelCase__    )







					@property
					def 	__lowerCAmelCase		(       self     :			str    ):
									UpperCAmelCase__    =							self.tokenizer.model_input_names
									UpperCAmelCase__    =							self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )




"""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[int]		  =			logging.get_logger(__name__)


lowerCAmelCase__						:						Optional[Any]		  =			['model.decoder.embed_positions.weights']



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



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



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



@torch.no_grad()
def 						a_		(      lowerCamelCase      ,    lowerCamelCase=None      ,    lowerCamelCase=None      ,    lowerCamelCase="cpu" ):
				UpperCAmelCase__    =							MusicGen.get_pretrained(lowerCamelCase      ,    device=lowerCamelCase )
				UpperCAmelCase__    =							decoder_config_from_checkpoint(lowerCamelCase )

				UpperCAmelCase__    =							fairseq_model.lm.state_dict()
				UpperCAmelCase__   ,			UpperCAmelCase__    =							rename_state_dict(
				    lowerCamelCase      ,    hidden_size=decoder_config.hidden_size )

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

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

				with torch.no_grad():
								UpperCAmelCase__    =							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
				UpperCAmelCase__    =							AutoTokenizer.from_pretrained('t5-base' )
				UpperCAmelCase__    =							AutoFeatureExtractor.from_pretrained('facebook/encodec_32khz'      ,    padding_side='left' )

				UpperCAmelCase__    =							MusicgenProcessor(feature_extractor=lowerCamelCase      ,    tokenizer=lowerCamelCase )

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

				# set other default generation config params
				UpperCAmelCase__    =							int(3_0 * audio_encoder.config.frame_rate )
				UpperCAmelCase__    =							True
				UpperCAmelCase__    =							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__":
						lowerCAmelCase__						:						Tuple		  =			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)



