


"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
				from PIL import Image

				from transformers import AlignProcessor, EfficientNetImageProcessor





@require_vision
class      __A     (unittest.TestCase):





							'''simple docstring'''

							def 		lowerCAmelCase       (				self							:      Union[str, Any]       )					->int:



														"""simple docstring"""



														snake_case_           =		tempfile.mkdtemp()

														snake_case_           =		[
														    """[UNK]""",
														    """[CLS]""",
														    """[SEP]""",
														    """[PAD]""",
														    """[MASK]""",
														    """want""",
														    """##want""",
														    """##ed""",
														    """wa""",
														    """un""",
														    """runn""",
														    """##ing""",
														    """,""",
														    """low""",
														    """lowest""",
														]
														snake_case_           =		os.path.join(self.tmpdirname		,       VOCAB_FILES_NAMES["""vocab_file"""]       )
														with open(self.vocab_file		,       """w"""		,       encoding="""utf-8"""       ) as vocab_writer:
																					vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]       )       )

														snake_case_           =		{
														    """do_resize""": True,
														    """size""": 20,
														    """do_center_crop""": True,
														    """crop_size""": 18,
														    """do_normalize""": True,
														    """image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
														    """image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
														}
														snake_case_           =		os.path.join(self.tmpdirname		,       UpperCAmelCase_       )
														with open(self.image_processor_file		,       """w"""		,       encoding="""utf-8"""       ) as fp:
																					json.dump(UpperCAmelCase_		,       UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      List[str]		,       **UpperCAmelCase_							:      Dict       )					->Dict:



														"""simple docstring"""



														return BertTokenizer.from_pretrained(self.tmpdirname		,       **UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Optional[int]		,       **UpperCAmelCase_							:      Tuple       )					->str:



														"""simple docstring"""



														return BertTokenizerFast.from_pretrained(self.tmpdirname		,       **UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Optional[int]		,       **UpperCAmelCase_							:      Tuple       )					->Dict:



														"""simple docstring"""



														return EfficientNetImageProcessor.from_pretrained(self.tmpdirname		,       **UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      int       )					->Dict:



														"""simple docstring"""



														shutil.rmtree(self.tmpdirname       )



							def 		lowerCAmelCase       (				self							:      List[str]       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		[np.random.randint(255		,       size=(3, 30, 400)		,       dtype=np.uinta       )]
														snake_case_           =		[Image.fromarray(np.moveaxis(UpperCAmelCase_		,       0		,       -1       )       ) for x in image_inputs]
														return image_inputs



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->Optional[int]:



														"""simple docstring"""



														snake_case_           =		self.get_tokenizer()
														snake_case_           =		self.get_rust_tokenizer()
														snake_case_           =		self.get_image_processor()

														snake_case_           =		AlignProcessor(tokenizer=UpperCAmelCase_		,       image_processor=UpperCAmelCase_       )
														processor_slow.save_pretrained(self.tmpdirname       )
														snake_case_           =		AlignProcessor.from_pretrained(self.tmpdirname		,       use_fast=UpperCAmelCase_       )

														snake_case_           =		AlignProcessor(tokenizer=UpperCAmelCase_		,       image_processor=UpperCAmelCase_       )
														processor_fast.save_pretrained(self.tmpdirname       )
														snake_case_           =		AlignProcessor.from_pretrained(self.tmpdirname       )

														self.assertEqual(processor_slow.tokenizer.get_vocab()		,       tokenizer_slow.get_vocab()       )
														self.assertEqual(processor_fast.tokenizer.get_vocab()		,       tokenizer_fast.get_vocab()       )
														self.assertEqual(tokenizer_slow.get_vocab()		,       tokenizer_fast.get_vocab()       )
														self.assertIsInstance(processor_slow.tokenizer		,       UpperCAmelCase_       )
														self.assertIsInstance(processor_fast.tokenizer		,       UpperCAmelCase_       )

														self.assertEqual(processor_slow.image_processor.to_json_string()		,       image_processor.to_json_string()       )
														self.assertEqual(processor_fast.image_processor.to_json_string()		,       image_processor.to_json_string()       )
														self.assertIsInstance(processor_slow.image_processor		,       UpperCAmelCase_       )
														self.assertIsInstance(processor_fast.image_processor		,       UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      int       )					->int:



														"""simple docstring"""



														snake_case_           =		AlignProcessor(tokenizer=self.get_tokenizer()		,       image_processor=self.get_image_processor()       )
														processor.save_pretrained(self.tmpdirname       )

														snake_case_           =		self.get_tokenizer(bos_token="""(BOS)"""		,       eos_token="""(EOS)"""       )
														snake_case_           =		self.get_image_processor(do_normalize=UpperCAmelCase_		,       padding_value=1.0       )

														snake_case_           =		AlignProcessor.from_pretrained(
														    self.tmpdirname		,       bos_token="""(BOS)"""		,       eos_token="""(EOS)"""		,       do_normalize=UpperCAmelCase_		,       padding_value=1.0       )

														self.assertEqual(processor.tokenizer.get_vocab()		,       tokenizer_add_kwargs.get_vocab()       )
														self.assertIsInstance(processor.tokenizer		,       UpperCAmelCase_       )

														self.assertEqual(processor.image_processor.to_json_string()		,       image_processor_add_kwargs.to_json_string()       )
														self.assertIsInstance(processor.image_processor		,       UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Tuple       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		self.get_image_processor()
														snake_case_           =		self.get_tokenizer()

														snake_case_           =		AlignProcessor(tokenizer=UpperCAmelCase_		,       image_processor=UpperCAmelCase_       )

														snake_case_           =		self.prepare_image_inputs()

														snake_case_           =		image_processor(UpperCAmelCase_		,       return_tensors="""np"""       )
														snake_case_           =		processor(images=UpperCAmelCase_		,       return_tensors="""np"""       )

														for key in input_image_proc.keys():
																					self.assertAlmostEqual(input_image_proc[key].sum()		,       input_processor[key].sum()		,       delta=1E-2       )



							def 		lowerCAmelCase       (				self							:      Any       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		self.get_image_processor()
														snake_case_           =		self.get_tokenizer()

														snake_case_           =		AlignProcessor(tokenizer=UpperCAmelCase_		,       image_processor=UpperCAmelCase_       )

														snake_case_           =		"""lower newer"""

														snake_case_           =		processor(text=UpperCAmelCase_       )

														snake_case_           =		tokenizer(UpperCAmelCase_		,       padding="""max_length"""		,       max_length=64       )

														for key in encoded_tok.keys():
																					self.assertListEqual(encoded_tok[key]		,       encoded_processor[key]       )



							def 		lowerCAmelCase       (				self							:      Dict       )					->Optional[Any]:



														"""simple docstring"""



														snake_case_           =		self.get_image_processor()
														snake_case_           =		self.get_tokenizer()

														snake_case_           =		AlignProcessor(tokenizer=UpperCAmelCase_		,       image_processor=UpperCAmelCase_       )

														snake_case_           =		"""lower newer"""
														snake_case_           =		self.prepare_image_inputs()

														snake_case_           =		processor(text=UpperCAmelCase_		,       images=UpperCAmelCase_       )

														self.assertListEqual(list(inputs.keys()       )		,       ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]       )

														# test if it raises when no input is passed
														with pytest.raises(UpperCAmelCase_       ):
																					processor()



							def 		lowerCAmelCase       (				self							:      int       )					->int:



														"""simple docstring"""



														snake_case_           =		self.get_image_processor()
														snake_case_           =		self.get_tokenizer()

														snake_case_           =		AlignProcessor(tokenizer=UpperCAmelCase_		,       image_processor=UpperCAmelCase_       )

														snake_case_           =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

														snake_case_           =		processor.batch_decode(UpperCAmelCase_       )
														snake_case_           =		tokenizer.batch_decode(UpperCAmelCase_       )

														self.assertListEqual(UpperCAmelCase_		,       UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      int       )					->Union[str, Any]:



														"""simple docstring"""



														snake_case_           =		self.get_image_processor()
														snake_case_           =		self.get_tokenizer()

														snake_case_           =		AlignProcessor(tokenizer=UpperCAmelCase_		,       image_processor=UpperCAmelCase_       )

														snake_case_           =		"""lower newer"""
														snake_case_           =		self.prepare_image_inputs()

														snake_case_           =		processor(text=UpperCAmelCase_		,       images=UpperCAmelCase_       )

														self.assertListEqual(list(inputs.keys()       )		,       processor.model_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()
__SCREAMING_SNAKE_CASE							:    Union[str, Any]		=    logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE							:    int		=    ['model.decoder.embed_positions.weights']







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







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







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				MusicgenDecoderConfig:
							if checkpoint == "small":
														# default config values
														snake_case_           =		1_024
														snake_case_           =		24
														snake_case_           =		16
							elif checkpoint == "medium":
														snake_case_           =		1_536
														snake_case_           =		48
														snake_case_           =		24
							elif checkpoint == "large":
														snake_case_           =		2_048
														snake_case_           =		48
														snake_case_           =		32
							else:
														raise ValueError(f"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."""				)
							snake_case_           =		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 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE=None				, _SCREAMING_SNAKE_CASE=None				, _SCREAMING_SNAKE_CASE="cpu"				)    ->				Tuple:
							snake_case_           =		MusicGen.get_pretrained(_SCREAMING_SNAKE_CASE				, device=_SCREAMING_SNAKE_CASE				)
							snake_case_           =		decoder_config_from_checkpoint(_SCREAMING_SNAKE_CASE				)

							snake_case_           =		fairseq_model.lm.state_dict()
							snake_case_       ,						snake_case_           =		rename_state_dict(
							    _SCREAMING_SNAKE_CASE				, hidden_size=decoder_config.hidden_size				)

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

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

							with torch.no_grad():
														snake_case_           =		model(input_ids=_SCREAMING_SNAKE_CASE				, decoder_input_ids=_SCREAMING_SNAKE_CASE				).logits

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

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

							snake_case_           =		MusicgenProcessor(feature_extractor=_SCREAMING_SNAKE_CASE				, tokenizer=_SCREAMING_SNAKE_CASE				)

							# set the appropriate bos/pad token ids
							snake_case_           =		2_048
							snake_case_           =		2_048

							# set other default generation config params
							snake_case_           =		int(30 * audio_encoder.config.frame_rate				)
							snake_case_           =		True
							snake_case_           =		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__":
				__SCREAMING_SNAKE_CASE							:    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.'
				)

				__SCREAMING_SNAKE_CASE							:    int		=    parser.parse_args()
				convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)



