





"""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()
__magic_name__       :					List[Any]						=			logging.get_logger(__name__)


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

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						if "emb" in name:
												UpperCamelCase				:		Tuple				   =			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				:		str				   =			name.replace("""cross_attention"""							,     """encoder_attn"""     )
						if "linear1" in name:
												UpperCamelCase				:		Optional[int]				   =			name.replace("""linear1"""							,     """fc1"""     )
						if "linear2" in name:
												UpperCamelCase				:		int				   =			name.replace("""linear2"""							,     """fc2"""     )
						if "norm1" in name:
												UpperCamelCase				:		List[Any]				   =			name.replace("""norm1"""							,     """self_attn_layer_norm"""     )
						if "norm_cross" in name:
												UpperCamelCase				:		int				   =			name.replace("""norm_cross"""							,     """encoder_attn_layer_norm"""     )
						if "norm2" in name:
												UpperCamelCase				:		str				   =			name.replace("""norm2"""							,     """final_layer_norm"""     )
						if "out_norm" in name:
												UpperCamelCase				:		Tuple				   =			name.replace("""out_norm"""							,     """model.decoder.layer_norm"""     )
						if "linears" in name:
												UpperCamelCase				:		str				   =			name.replace("""linears"""							,     """lm_heads"""     )
						if "condition_provider.conditioners.description.output_proj" in name:
												UpperCamelCase				:		Dict				   =			name.replace("""condition_provider.conditioners.description.output_proj"""							,     """enc_to_dec_proj"""     )
						return name

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

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						if checkpoint == "small":
												# default config values
												UpperCamelCase				:		Tuple				   =			1024
												UpperCamelCase				:		Dict				   =			24
												UpperCamelCase				:		Optional[int]				   =			16
						elif checkpoint == "medium":
												UpperCamelCase				:		Optional[Any]				   =			1536
												UpperCamelCase				:		int				   =			48
												UpperCamelCase				:		int				   =			24
						elif checkpoint == "large":
												UpperCamelCase				:		Any				   =			2048
												UpperCamelCase				:		List[Any]				   =			48
												UpperCamelCase				:		str				   =			32
						else:
												raise ValueError(f"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."""     )
						UpperCamelCase				:		Optional[int]				   =			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      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE=None							,     SCREAMING_SNAKE_CASE=None							,     SCREAMING_SNAKE_CASE="cpu"     ):
						UpperCamelCase				:		Optional[Any]				   =			MusicGen.get_pretrained(SCREAMING_SNAKE_CASE							,     device=SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		Tuple				   =			decoder_config_from_checkpoint(SCREAMING_SNAKE_CASE     )

						UpperCamelCase				:		Optional[Any]				   =			fairseq_model.lm.state_dict()
						UpperCamelCase   ,						UpperCamelCase				:		Optional[int]				   =			rename_state_dict(
						    SCREAMING_SNAKE_CASE							,     hidden_size=decoder_config.hidden_size     )

						UpperCamelCase				:		int				   =			TaEncoderModel.from_pretrained("""t5-base"""     )
						UpperCamelCase				:		List[str]				   =			EncodecModel.from_pretrained("""facebook/encodec_32khz"""     )
						UpperCamelCase				:		Optional[int]				   =			MusicgenForCausalLM(SCREAMING_SNAKE_CASE     ).eval()

						# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
						UpperCamelCase   ,						UpperCamelCase				:		Tuple				   =			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				:		Tuple				   =			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				:		Tuple				   =			input_ids.reshape(2 * 4							,     -1     )

						with torch.no_grad():
												UpperCamelCase				:		Any				   =			model(input_ids=SCREAMING_SNAKE_CASE							,     decoder_input_ids=SCREAMING_SNAKE_CASE     ).logits

						if logits.shape != (8, 1, 2048):
												raise ValueError("""Incorrect shape for logits"""     )

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

						UpperCamelCase				:		int				   =			MusicgenProcessor(feature_extractor=SCREAMING_SNAKE_CASE							,     tokenizer=SCREAMING_SNAKE_CASE     )

						# set the appropriate bos/pad token ids
						UpperCamelCase				:		Optional[int]				   =			2048
						UpperCamelCase				:		Tuple				   =			2048

						# set other default generation config params
						UpperCamelCase				:		Optional[int]				   =			int(30 * audio_encoder.config.frame_rate     )
						UpperCamelCase				:		int				   =			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__":
		__magic_name__       :					Any						=			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."""
		)

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




import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		get_tests_dir("""fixtures/test_sentencepiece.model""")
SCREAMING_SNAKE_CASE			:						List[Any]				  =		get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

SCREAMING_SNAKE_CASE			:						Any				  =		"""pt""" if is_torch_available() else """tf"""
@require_sentencepiece
@require_tokenizers
class  A_			(							a_      ,							unittest.TestCase			):
  _SCREAMING_SNAKE_CASE     					=      CamembertTokenizer
  _SCREAMING_SNAKE_CASE     					=      CamembertTokenizerFast
  _SCREAMING_SNAKE_CASE     					=      True
  _SCREAMING_SNAKE_CASE     					=      True

  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   super().setUp()

   # We have a SentencePiece fixture for testing
   __a							=	CamembertTokenizer(__SCREAMING_SNAKE_CASE							)
   tokenizer.save_pretrained(self.tmpdirname							)

  def 		_UpperCAmelCase (		self			:   Any							):
   __a							=	"<pad>"
   __a							=	1

   self.assertEqual(self.get_tokenizer()._convert_token_to_id(__SCREAMING_SNAKE_CASE							)  ,				__SCREAMING_SNAKE_CASE							)
   self.assertEqual(self.get_tokenizer()._convert_id_to_token(__SCREAMING_SNAKE_CASE							)  ,				__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Any							):
   __a							=	list(self.get_tokenizer().get_vocab().keys()							)

   self.assertEqual(vocab_keys[0]  ,				"<s>NOTUSED"							)
   self.assertEqual(vocab_keys[1]  ,				"<pad>"							)
   self.assertEqual(vocab_keys[-1]  ,				"<mask>"							)
   self.assertEqual(len(__SCREAMING_SNAKE_CASE							)  ,				10_04							)

  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   self.assertEqual(self.get_tokenizer().vocab_size  ,				10_05							)

  def 		_UpperCAmelCase (		self			:   int							):
   __a							=	CamembertTokenizer(__SCREAMING_SNAKE_CASE							)
   tokenizer.save_pretrained(self.tmpdirname							)
   __a							=	CamembertTokenizerFast.from_pretrained(self.tmpdirname							)

   __a							=	"I was born in 92000, and this is falsé."

   __a							=	tokenizer.encode(__SCREAMING_SNAKE_CASE							)
   __a							=	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE							)
   self.assertListEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

   __a							=	tokenizer.encode(__SCREAMING_SNAKE_CASE  ,				add_special_tokens=__SCREAMING_SNAKE_CASE							)
   __a							=	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE  ,				add_special_tokens=__SCREAMING_SNAKE_CASE							)
   self.assertListEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

   # <unk> tokens are not the same for `rust` than for `slow`.
   # Because spm gives back raw token instead of `unk` in EncodeAsPieces
   # tokens = tokenizer.tokenize(sequence)
   __a							=	tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE							)
   __a							=	rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE							)
   self.assertListEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   List[Any]							):
   if not self.test_rust_tokenizer:
    return

   __a							=	self.get_tokenizer()
   __a							=	self.get_rust_tokenizer()

   __a							=	"I was born in 92000, and this is falsé."

   __a							=	tokenizer.tokenize(__SCREAMING_SNAKE_CASE							)
   __a							=	rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE							)
   self.assertListEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

   __a							=	tokenizer.encode(__SCREAMING_SNAKE_CASE  ,				add_special_tokens=__SCREAMING_SNAKE_CASE							)
   __a							=	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE  ,				add_special_tokens=__SCREAMING_SNAKE_CASE							)
   self.assertListEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

   __a							=	self.get_rust_tokenizer()
   __a							=	tokenizer.encode(__SCREAMING_SNAKE_CASE							)
   __a							=	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE							)
   self.assertListEqual(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

  @slow
  def 		_UpperCAmelCase (		self			:   str							):
   # fmt: off
   __a							=	{"input_ids": [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 9, 6]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
   # fmt: on

   # camembert is a french model. So we also use french texts.
   __a							=	[
       "Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
       "utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
       "À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
       "pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
       "telles que la traduction et la synthèse de texte.",
   ]

   self.tokenizer_integration_test_util(
       expected_encoding=__SCREAMING_SNAKE_CASE  ,				model_name="camembert-base"  ,				revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"  ,				sequences=__SCREAMING_SNAKE_CASE  ,				)


