


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   :    Any     =		logging.get_logger(__name__)


UpperCAmelCase   :    List[str]     =		["model.decoder.embed_positions.weights"]






def 	__lowerCamelCase						(							lowerCamelCase__		:						Tuple      ):





			'''simple docstring'''




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






def 	__lowerCamelCase						(							lowerCamelCase__		:						OrderedDict  ,							lowerCamelCase__		:						int      ):





			'''simple docstring'''




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






def 	__lowerCamelCase						(							lowerCamelCase__		:						str      ):





			'''simple docstring'''




			if checkpoint == "small":
						# default config values
						lowerCamelCase    		=  1024
						lowerCamelCase    		=  24
						lowerCamelCase    		=  16
			elif checkpoint == "medium":
						lowerCamelCase    		=  1536
						lowerCamelCase    		=  48
						lowerCamelCase    		=  24
			elif checkpoint == "large":
						lowerCamelCase    		=  2048
						lowerCamelCase    		=  48
						lowerCamelCase    		=  32
			else:
						raise ValueError(f'Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'      )
			lowerCamelCase    		=  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__		:						str  ,							lowerCamelCase__		:						Dict=None  ,							lowerCamelCase__		:						str=None  ,							lowerCamelCase__		:						Optional[int]="cpu"      ):





			'''simple docstring'''




			lowerCamelCase    		=  MusicGen.get_pretrained(lowerCamelCase__  ,							device=lowerCamelCase__      )
			lowerCamelCase    		=  decoder_config_from_checkpoint(lowerCamelCase__      )

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

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

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

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

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

			# set the appropriate bos/pad token ids
			lowerCamelCase    		=  2048
			lowerCamelCase    		=  2048

			# set other default generation config params
			lowerCamelCase    		=  int(30 * audio_encoder.config.frame_rate      )
			lowerCamelCase    		=  True
			lowerCamelCase    		=  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   :    Optional[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."
		)

		UpperCAmelCase   :    Optional[int]     =		parser.parse_args()
		convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_pegasus import PegasusTokenizer
else:
		UpperCAmelCase   :    Union[str, Any]     =		None


UpperCAmelCase   :    Any     =		logging.get_logger(__name__)


UpperCAmelCase   :    Optional[Any]     =		"▁"

UpperCAmelCase   :    str     =		{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase   :    Dict     =		{
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"},
    "tokenizer_file": {
        "google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"
    },
}

UpperCAmelCase   :    List[Any]     =		{
    "google/pegasus-xsum": 5_12,
}







class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					str			       =		VOCAB_FILES_NAMES
		UpperCamelCase     :					int			       =		PRETRAINED_VOCAB_FILES_MAP
		UpperCamelCase     :					List[Any]			       =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCamelCase     :					Any			       =		PegasusTokenizer
		UpperCamelCase     :					Optional[Any]			       =		["input_ids", "attention_mask"]







		def __init__( self		,      A=None		,      A=None		,      A="<pad>"		,      A="</s>"		,      A="<unk>"		,      A="<mask_2>"		,      A="<mask_1>"		,      A=None		,      A=1_03		,      **A		,      )  -> Any:







					'''simple docstring'''







					lowerCamelCase    		=  offset

					if additional_special_tokens is not None:
								if not isinstance(A		,      A	):
											raise TypeError(
											    F'additional_special_tokens should be of type {type(A	)}, but is'
											    F' {type(A	)}'	)

								lowerCamelCase    		=  (
								    ([mask_token_sent] + additional_special_tokens)
								    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
								    else additional_special_tokens
								)
								# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
								additional_special_tokens_extended += [
								    F'<unk_{i}>' for i in range(len(A	)		,      self.offset - 1	)
								]

								if len(set(A	)	) != len(A	):
											raise ValueError(
											    """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
											    F' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'	)
								lowerCamelCase    		=  additional_special_tokens_extended
					else:
								lowerCamelCase    		=  [mask_token_sent] if mask_token_sent is not None else []
								additional_special_tokens += [F'<unk_{i}>' for i in range(2		,      self.offset	)]

					super().__init__(
					    A		,      tokenizer_file=A		,      pad_token=A		,      eos_token=A		,      unk_token=A		,      mask_token=A		,      mask_token_sent=A		,      offset=A		,      additional_special_tokens=A		,      **A		,      )
					lowerCamelCase    		=  vocab_file
					lowerCamelCase    		=  False if not self.vocab_file else True







		def 					__A   ( self		,      A	)  -> Dict:







					'''simple docstring'''







					lowerCamelCase    		=  set(self.all_special_ids	)  # call it once instead of inside list comp
					all_special_ids.remove(self.unk_token_id	)  # <unk> is only sometimes special

					if all_special_ids != set(range(len(self.additional_special_tokens	) + 3	)	):
								raise ValueError(
								    """There should be 3 special tokens: mask_token, pad_token, and eos_token +"""
								    F' {len(self.additional_special_tokens	)} additional_special_tokens, but got {all_special_ids}'	)

					return [1 if x in all_special_ids else 0 for x in seq]







		def 					__A   ( self		,      A		,      A = None		,      A = False	)  -> List[int]:







					'''simple docstring'''







					if already_has_special_tokens:
								return self._special_token_mask(A	)
					elif token_ids_a is None:
								return self._special_token_mask(A	) + [1]
					else:
								return self._special_token_mask(token_ids_a + token_ids_a	) + [1]







		def 					__A   ( self		,      A		,      A=None	)  -> List[int]:







					'''simple docstring'''







					if token_ids_a is None:
								return token_ids_a + [self.eos_token_id]
					# We don't expect to process pairs, but leave the pair logic for API consistency
					return token_ids_a + token_ids_a + [self.eos_token_id]







		def 					__A   ( self		,      A		,      A = None	)  -> Tuple[str]:







					'''simple docstring'''







					if not self.can_save_slow_tokenizer:
								raise ValueError(
								    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
								    """tokenizer."""	)

					if not os.path.isdir(A	):
								logger.error(F'Vocabulary path ({save_directory}) should be a directory'	)
								return
					lowerCamelCase    		=  os.path.join(
					    A		,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]	)

					if os.path.abspath(self.vocab_file	) != os.path.abspath(A	):
								copyfile(self.vocab_file		,      A	)

					return (out_vocab_file,)

