import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__						:						List[str]		  =			logging.get_logger(__name__)


lowerCAmelCase__						:						List[Any]		  =			{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

lowerCAmelCase__						:						Optional[int]		  =			{
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

lowerCAmelCase__						:						Any		  =			{'facebook/blenderbot_small-90M': 512}



def 						a_		(      lowerCamelCase ):
				UpperCAmelCase__    =							set()
				UpperCAmelCase__    =							word[0]
				for char in word[1:]:
								pairs.add((prev_char, char) )
								UpperCAmelCase__    =							char

				UpperCAmelCase__    =							set(lowerCamelCase )
				return pairs







class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				VOCAB_FILES_NAMES
					snake_case__       					=				PRETRAINED_VOCAB_FILES_MAP
					snake_case__       					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					snake_case__       					=				["input_ids", "attention_mask"]







					def __init__(       self     :			Tuple					,lowerCamelCase__     :			Union[str, Any]					,lowerCamelCase__     :			Dict					,lowerCamelCase__     :			Tuple="__start__"					,lowerCamelCase__     :			Tuple="__end__"					,lowerCamelCase__     :			Optional[int]="__unk__"					,lowerCamelCase__     :			List[Any]="__null__"					,**lowerCamelCase__     :			Optional[int]					,):
									super().__init__(unk_token=lowerCamelCase__					,bos_token=lowerCamelCase__					,eos_token=lowerCamelCase__					,pad_token=lowerCamelCase__					,**lowerCamelCase__    )

									with open(lowerCamelCase__					,encoding='utf-8'    ) as vocab_handle:
													UpperCAmelCase__    =							json.load(lowerCamelCase__    )
									UpperCAmelCase__    =							{v: k for k, v in self.encoder.items()}
									with open(lowerCamelCase__					,encoding='utf-8'    ) as merges_handle:
													UpperCAmelCase__    =							merges_handle.read().split('\n'    )[1:-1]
									UpperCAmelCase__    =							[tuple(merge.split()    ) for merge in merges]
									UpperCAmelCase__    =							dict(zip(lowerCamelCase__					,range(len(lowerCamelCase__    )    )    )    )
									UpperCAmelCase__    =							{}







					@property
					def 	__lowerCAmelCase		(       self     :			Optional[Any]    ):
									return len(self.encoder    )







					def 	__lowerCAmelCase		(       self     :			str    ):
									return dict(self.encoder					,**self.added_tokens_encoder    )







					def 	__lowerCAmelCase		(       self     :			Union[str, Any]					,lowerCamelCase__     :			str    ):
									if token in self.cache:
													return self.cache[token]
									UpperCAmelCase__    =							re.sub('([.,!?()])'					,R' \1'					,lowerCamelCase__    )
									UpperCAmelCase__    =							re.sub('(\')'					,R' \1 '					,lowerCamelCase__    )
									UpperCAmelCase__    =							re.sub(R'\s{2,}'					,' '					,lowerCamelCase__    )
									if "\n" in token:
													UpperCAmelCase__    =							token.replace('\n'					,' __newln__'    )

									UpperCAmelCase__    =							token.split(' '    )
									UpperCAmelCase__    =							[]
									for token in tokens:
													if not len(lowerCamelCase__    ):
																	continue

													UpperCAmelCase__    =							token.lower()
													UpperCAmelCase__    =							tuple(lowerCamelCase__    )
													UpperCAmelCase__    =							tuple(list(word[:-1]    ) + [word[-1] + '</w>']    )
													UpperCAmelCase__    =							get_pairs(lowerCamelCase__    )

													if not pairs:
																	words.append(lowerCamelCase__    )
																	continue

													while True:
																	UpperCAmelCase__    =							min(lowerCamelCase__					,key=lambda lowerCamelCase__    : self.bpe_ranks.get(lowerCamelCase__					,float('inf'    )    )    )
																	if bigram not in self.bpe_ranks:
																					break
																	UpperCAmelCase__   ,			UpperCAmelCase__    =							bigram
																	UpperCAmelCase__    =							[]
																	UpperCAmelCase__    =							0

																	while i < len(lowerCamelCase__    ):
																					try:
																									UpperCAmelCase__    =							word.index(lowerCamelCase__					,lowerCamelCase__    )
																									new_word.extend(word[i:j]    )
																									UpperCAmelCase__    =							j
																					except ValueError:
																									new_word.extend(word[i:]    )
																									break

																					if word[i] == first and i < len(lowerCamelCase__    ) - 1 and word[i + 1] == second:
																									new_word.append(first + second    )
																									i += 2
																					else:
																									new_word.append(word[i]    )
																									i += 1
																	UpperCAmelCase__    =							tuple(lowerCamelCase__    )
																	UpperCAmelCase__    =							new_word
																	if len(lowerCamelCase__    ) == 1:
																					break
																	else:
																					UpperCAmelCase__    =							get_pairs(lowerCamelCase__    )
													UpperCAmelCase__    =							'@@ '.join(lowerCamelCase__    )
													UpperCAmelCase__    =							word[:-4]

													UpperCAmelCase__    =							word
													words.append(lowerCamelCase__    )
									return " ".join(lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Dict					,lowerCamelCase__     :			str    ):
									UpperCAmelCase__    =							[]

									UpperCAmelCase__    =							re.findall(R'\S+\n?'					,lowerCamelCase__    )

									for token in words:
													split_tokens.extend(list(self.bpe(lowerCamelCase__    ).split(' '    )    )    )
									return split_tokens







					def 	__lowerCAmelCase		(       self     :			str					,lowerCamelCase__     :			str    ):
									UpperCAmelCase__    =							token.lower()
									return self.encoder.get(lowerCamelCase__					,self.encoder.get(self.unk_token    )    )







					def 	__lowerCAmelCase		(       self     :			List[Any]					,lowerCamelCase__     :			int    ):
									return self.decoder.get(lowerCamelCase__					,self.unk_token    )







					def 	__lowerCAmelCase		(       self     :			Optional[int]					,lowerCamelCase__     :			List[str]    ):
									UpperCAmelCase__    =							' '.join(lowerCamelCase__    ).replace('@@ '					,''    ).strip()
									return out_string







					def 	__lowerCAmelCase		(       self     :			List[str]					,lowerCamelCase__     :			str					,lowerCamelCase__     :			Optional[str] = None    ):
									if not os.path.isdir(lowerCamelCase__    ):
													logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''    )
													return
									UpperCAmelCase__    =							os.path.join(
									    lowerCamelCase__					,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )
									UpperCAmelCase__    =							os.path.join(
									    lowerCamelCase__					,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']    )

									with open(lowerCamelCase__					,'w'					,encoding='utf-8'    ) as f:
													f.write(json.dumps(self.encoder					,indent=2					,sort_keys=lowerCamelCase__					,ensure_ascii=lowerCamelCase__    ) + '\n'    )

									UpperCAmelCase__    =							0
									with open(lowerCamelCase__					,'w'					,encoding='utf-8'    ) as writer:
													writer.write('#version: 0.2\n'    )
													for bpe_tokens, token_index in sorted(self.bpe_ranks.items()					,key=lambda lowerCamelCase__    : kv[1]    ):
																	if index != token_index:
																					logger.warning(
																					    f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
																					    ' Please check that the tokenizer is not corrupted!'    )
																					UpperCAmelCase__    =							token_index
																	writer.write(' '.join(lowerCamelCase__    ) + '\n'    )
																	index += 1

									return vocab_file, merge_file




"""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)



