
'''simple docstring'''





import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import regex

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
from ...utils.generic import _is_jax, _is_numpy


UpperCAmelCase    					=   logging.get_logger(__name__)

UpperCAmelCase    					=   {
    "artists_file": "artists.json",
    "lyrics_file": "lyrics.json",
    "genres_file": "genres.json",
}

UpperCAmelCase    					=   {
    "artists_file": {
        "jukebox": "https://huggingface.co/ArthurZ/jukebox/blob/main/artists.json",
    },
    "genres_file": {
        "jukebox": "https://huggingface.co/ArthurZ/jukebox/blob/main/genres.json",
    },
    "lyrics_file": {
        "jukebox": "https://huggingface.co/ArthurZ/jukebox/blob/main/lyrics.json",
    },
}

UpperCAmelCase    					=   {
    "jukebox": 512,
}

class 						a   (				_SCREAMING_SNAKE_CASE    ):
				_snake_case		    = VOCAB_FILES_NAMES
				_snake_case		    = PRETRAINED_VOCAB_FILES_MAP
				_snake_case		    = PRETRAINED_LYRIC_TOKENS_SIZES
				_snake_case		    = ["input_ids", "attention_mask"]




				def __init__(							self	:				str,    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				List[str],    SCREAMING_SNAKE_CASE_	:				List[str]=["v3", "v2", "v2"],    SCREAMING_SNAKE_CASE_	:				List[str]=5_12,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=5,    SCREAMING_SNAKE_CASE_	:				Dict="<|endoftext|>",    **SCREAMING_SNAKE_CASE_	:				List[Any],    ):
										snake_case       :     Union[str, Any] =    AddedToken(A_,    lstrip=A_,    rstrip=A_      ) if isinstance(A_,    A_      ) else unk_token
										super().__init__(
										    unk_token=A_,    n_genres=A_,    version=A_,    max_n_lyric_tokens=A_,    **A_,    )
										snake_case       :     List[Any] =    version
										snake_case       :     List[str] =    max_n_lyric_tokens
										snake_case       :     int =    n_genres

										with open(A_,    encoding='''utf-8'''      ) as vocab_handle:
																snake_case       :     List[Any] =    json.load(A_      )

										with open(A_,    encoding='''utf-8'''      ) as vocab_handle:
																snake_case       :     Optional[Any] =    json.load(A_      )

										with open(A_,    encoding='''utf-8'''      ) as vocab_handle:
																snake_case       :     Tuple =    json.load(A_      )

										snake_case       :     List[Any] =    R'''[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+'''
										# In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
										if len(self.lyrics_encoder      ) == 79:
																snake_case       :     Any =    oov.replace(R'''\-\'''',    R'''\-+\''''      )

										snake_case       :     Optional[int] =    regex.compile(A_      )
										snake_case       :     List[str] =    {v: k for k, v in self.artists_encoder.items()}
										snake_case       :     List[str] =    {v: k for k, v in self.genres_encoder.items()}
										snake_case       :     List[Any] =    {v: k for k, v in self.lyrics_encoder.items()}




				@property
				def 	__snake_case							(							self	:				Optional[int]      ):
										return len(self.artists_encoder      ) + len(self.genres_encoder      ) + len(self.lyrics_encoder      )




				def 	__snake_case							(							self	:				int      ):
										return dict(self.artists_encoder,    self.genres_encoder,    self.lyrics_encoder      )




				def 	__snake_case							(							self	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				Any,    SCREAMING_SNAKE_CASE_	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				Tuple      ):
										snake_case       :     int =    [self.artists_encoder.get(A_,    0      ) for artist in list_artists]
										for genres in range(len(A_      )      ):
																snake_case       :     Tuple =    [self.genres_encoder.get(A_,    0      ) for genre in list_genres[genres]]
																snake_case       :     Union[str, Any] =    list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]      ))

										snake_case       :     List[Any] =    [[self.lyrics_encoder.get(A_,    0      ) for character in list_lyrics[0]], [], []]
										return artists_id, list_genres, lyric_ids




				def 	__snake_case							(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				Dict      ):
										return list(A_      )




				def 	__snake_case							(							self	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				Dict,    SCREAMING_SNAKE_CASE_	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				Any,    **SCREAMING_SNAKE_CASE_	:				str      ):
										snake_case,					snake_case,					snake_case       :     Tuple =    self.prepare_for_tokenization(A_,    A_,    A_      )
										snake_case       :     Union[str, Any] =    self._tokenize(A_      )
										return artist, genre, lyrics




				def 	__snake_case							(							self	:				int,    SCREAMING_SNAKE_CASE_	:				Any,    SCREAMING_SNAKE_CASE_	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				Union[str, Any] = False      ):
										for idx in range(len(self.version      )      ):
																if self.version[idx] == "v3":
																						snake_case       :     Any =    artists[idx].lower()
																						snake_case       :     Optional[int] =    [genres[idx].lower()]
																else:
																						snake_case       :     str =    self._normalize(artists[idx]      ) + '''.v2'''
																						snake_case       :     int =    [
																						    self._normalize(A_      ) + '''.v2''' for genre in genres[idx].split('''_'''      )
																						]  # split is for the full dictionary with combined genres

										if self.version[0] == "v2":
																snake_case       :     Tuple =    regex.compile(R'''[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+'''      )
																snake_case       :     Optional[Any] =    '''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+\'\"()[] \t\n'''
																snake_case       :     int =    {vocab[index]: index + 1 for index in range(len(A_      )      )}
																snake_case       :     List[str] =    0
																snake_case       :     Union[str, Any] =    len(A_      ) + 1
																snake_case       :     Any =    self.vocab
																snake_case       :     List[str] =    {v: k for k, v in self.vocab.items()}
																snake_case       :     Any =    ''''''
										else:
																snake_case       :     Optional[Any] =    regex.compile(R'''[^A-Za-z0-9.,:;!?\-+\'\"()\[\] \t\n]+'''      )

										snake_case       :     Optional[int] =    self._run_strip_accents(A_      )
										snake_case       :     Union[str, Any] =    lyrics.replace('''\\''',    '''\n'''      )
										snake_case       :     Optional[int] =    self.out_of_vocab.sub('''''',    A_      ), [], []
										return artists, genres, lyrics




				def 	__snake_case							(							self	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				Tuple      ):
										snake_case       :     int =    unicodedata.normalize('''NFD''',    A_      )
										snake_case       :     int =    []
										for char in text:
																snake_case       :     Tuple =    unicodedata.category(A_      )
																if cat == "Mn":
																						continue
																output.append(A_      )
										return "".join(A_      )




				def 	__snake_case							(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				Tuple      ):
										snake_case       :     Any =    (
										    [chr(A_      ) for i in range(ord('''a'''      ),    ord('''z'''      ) + 1      )]
										    + [chr(A_      ) for i in range(ord('''A'''      ),    ord('''Z'''      ) + 1      )]
										    + [chr(A_      ) for i in range(ord('''0'''      ),    ord('''9'''      ) + 1      )]
										    + ['''.''']
										)
										snake_case       :     Dict =    frozenset(A_      )
										snake_case       :     List[Any] =    re.compile(R'''_+'''      )
										snake_case       :     List[Any] =    ''''''.join([c if c in accepted else '''_''' for c in text.lower()]      )
										snake_case       :     List[Any] =    pattern.sub('''_''',    A_      ).strip('''_'''      )
										return text




				def 	__snake_case							(							self	:				str,    SCREAMING_SNAKE_CASE_	:				int      ):
										return " ".join(A_      )




				def 	__snake_case							(							self	:				Dict,    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				Optional[int] = None,    SCREAMING_SNAKE_CASE_	:				Optional[Any] = False      ):
										# Convert to TensorType
										if not isinstance(A_,    A_      ):
																snake_case       :     int =    TensorType(A_      )

										# Get a function reference for the correct framework
										if tensor_type == TensorType.TENSORFLOW:
																if not is_tf_available():
																						raise ImportError(
																						    '''Unable to convert output to TensorFlow tensors format, TensorFlow is not installed.'''      )
																import tensorflow as tf

																snake_case       :     Union[str, Any] =    tf.constant
																snake_case       :     Dict =    tf.is_tensor
										elif tensor_type == TensorType.PYTORCH:
																if not is_torch_available():
																						raise ImportError('''Unable to convert output to PyTorch tensors format, PyTorch is not installed.'''      )
																import torch

																snake_case       :     List[Any] =    torch.tensor
																snake_case       :     List[str] =    torch.is_tensor
										elif tensor_type == TensorType.JAX:
																if not is_flax_available():
																						raise ImportError('''Unable to convert output to JAX tensors format, JAX is not installed.'''      )
																import jax.numpy as jnp  # noqa: F811

																snake_case       :     Optional[int] =    jnp.array
																snake_case       :     List[str] =    _is_jax
										else:
																snake_case       :     Any =    np.asarray
																snake_case       :     Optional[int] =    _is_numpy

										# Do the tensor conversion in batch

										try:
																if prepend_batch_axis:
																						snake_case       :     List[str] =    [inputs]

																if not is_tensor(A_      ):
																						snake_case       :     Any =    as_tensor(A_      )
										except:  # noqa E722
																raise ValueError(
																    '''Unable to create tensor, you should probably activate truncation and/or padding '''
																    '''with \'padding=True\' \'truncation=True\' to have batched tensors with the same length.'''      )

										return inputs




				def __call__(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				List[str],    SCREAMING_SNAKE_CASE_	:				Any,    SCREAMING_SNAKE_CASE_	:				Optional[int]="",    SCREAMING_SNAKE_CASE_	:				Optional[Any]="pt"      ):
										snake_case       :     Optional[int] =    [0, 0, 0]
										snake_case       :     Optional[int] =    [artist] * len(self.version      )
										snake_case       :     str =    [genres] * len(self.version      )

										snake_case,					snake_case,					snake_case       :     int =    self.tokenize(A_,    A_,    A_      )
										snake_case,					snake_case,					snake_case       :     int =    self._convert_token_to_id(A_,    A_,    A_      )

										snake_case       :     str =    [-INFINITY] * len(full_tokens[-1]      )
										snake_case       :     Optional[int] =    [
										    self.convert_to_tensors(
										        [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]],    tensor_type=A_      )
										    for i in range(len(self.version      )      )
										]
										return BatchEncoding({'''input_ids''': input_ids, '''attention_masks''': attention_masks}      )




				def 	__snake_case							(							self	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				Any,    SCREAMING_SNAKE_CASE_	:				Tuple = None      ):
										if not os.path.isdir(A_      ):
																logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
																return

										snake_case       :     Tuple =    os.path.join(
										    A_,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''artists_file''']      )
										with open(A_,    '''w''',    encoding='''utf-8'''      ) as f:
																f.write(json.dumps(self.artists_encoder,    ensure_ascii=A_      )      )

										snake_case       :     Optional[Any] =    os.path.join(
										    A_,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''genres_file''']      )
										with open(A_,    '''w''',    encoding='''utf-8'''      ) as f:
																f.write(json.dumps(self.genres_encoder,    ensure_ascii=A_      )      )

										snake_case       :     Optional[Any] =    os.path.join(
										    A_,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''lyrics_file''']      )
										with open(A_,    '''w''',    encoding='''utf-8'''      ) as f:
																f.write(json.dumps(self.lyrics_encoder,    ensure_ascii=A_      )      )

										return (artists_file, genres_file, lyrics_file)




				def 	__snake_case							(							self	:				Dict,    SCREAMING_SNAKE_CASE_	:				Tuple,    SCREAMING_SNAKE_CASE_	:				Dict,    SCREAMING_SNAKE_CASE_	:				Tuple      ):
										snake_case       :     Dict =    self.artists_decoder.get(A_      )
										snake_case       :     Dict =    [self.genres_decoder.get(A_      ) for genre in genres_index]
										snake_case       :     Any =    [self.lyrics_decoder.get(A_      ) for character in lyric_index]
										return artist, genres, lyrics








'''simple docstring'''





import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


UpperCAmelCase    					=   logging.get_logger(__name__)
logging.set_verbosity_info()

def      A (       A_							:					str    ,							A_							:					str       ):
						if "xprophetnet" in prophetnet_checkpoint_path:
												snake_case       :     Any =    XLMProphetNetForConditionalGenerationOld.from_pretrained(A_       )
												snake_case,					snake_case       :     Tuple =    XLMProphetNetForConditionalGeneration.from_pretrained(
												    A_    ,							output_loading_info=A_       )
						else:
												snake_case       :     Union[str, Any] =    ProphetNetForConditionalGenerationOld.from_pretrained(A_       )
												snake_case,					snake_case       :     List[Any] =    ProphetNetForConditionalGeneration.from_pretrained(
												    A_    ,							output_loading_info=A_       )

						snake_case       :     Union[str, Any] =    ['''key_proj''', '''value_proj''', '''query_proj''']

						snake_case       :     str =    {
						    '''self_attn''': '''ngram_self_attn''',
						    '''cross_attn''': '''encoder_attn''',
						    '''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
						    '''feed_forward_layer_norm''': '''final_layer_norm''',
						    '''feed_forward''': '''''',
						    '''intermediate''': '''fc1''',
						    '''output''': '''fc2''',
						    '''key_proj''': '''k_proj''',
						    '''query_proj''': '''q_proj''',
						    '''value_proj''': '''v_proj''',
						    '''word_embeddings''': '''embed_tokens''',
						    '''embeddings_layer_norm''': '''emb_layer_norm''',
						    '''relative_pos_embeddings''': '''relative_linear''',
						    '''ngram_embeddings''': '''ngram_input_embed''',
						    '''position_embeddings''': '''embed_positions''',
						}

						for key in loading_info["missing_keys"]:
												snake_case       :     Optional[Any] =    key.split('''.'''       )

												if attributes[0] == "lm_head":
																		snake_case       :     Optional[int] =    prophet
																		snake_case       :     Union[str, Any] =    prophet_old
												else:
																		snake_case       :     Optional[int] =    prophet.prophetnet
																		snake_case       :     Any =    prophet_old.model

												snake_case       :     Optional[Any] =    False
												for attribute in attributes:
																		if attribute in mapping:
																								snake_case       :     List[str] =    mapping[attribute]
																								if not hasattr(A_    ,							A_       ) and len(A_       ) > 0:
																														snake_case       :     str =    attribute
																		elif hasattr(A_    ,							A_       ):
																								snake_case       :     Any =    attribute

																		if attribute == "weight":
																								assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
																								snake_case       :     Optional[Any] =    old_model.weight
																								logger.info(F"""{attribute} is initialized."""       )
																								snake_case       :     Tuple =    True
																								break
																		elif attribute == "bias":
																								assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
																								snake_case       :     List[str] =    old_model.bias
																								logger.info(F"""{attribute} is initialized"""       )
																								snake_case       :     Tuple =    True
																								break
																		elif attribute in special_keys and hasattr(A_    ,							'''in_proj_weight'''       ):
																								snake_case       :     Union[str, Any] =    old_model.in_proj_weight.shape[0] // 3
																								snake_case       :     Any =    getattr(A_    ,							A_       )
																								param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
																								param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
																								if attribute == "query_proj":
																														snake_case       :     Tuple =    nn.Parameter(old_model.in_proj_weight[:embed_dim, :]       )
																														snake_case       :     List[Any] =    nn.Parameter(old_model.in_proj_bias[:embed_dim]       )

																								elif attribute == "key_proj":
																														snake_case       :     str =    nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]       )
																														snake_case       :     Any =    nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]       )
																								elif attribute == "value_proj":
																														snake_case       :     Tuple =    nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]       )
																														snake_case       :     List[str] =    nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]       )
																								snake_case       :     Optional[Any] =    True
																								break
																		elif attribute == "position_embeddings":
																								assert (
																								    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
																								), "Hidden size has to match"
																								assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
																								snake_case       :     List[Any] =    nn.Parameter(old_model.embed_positions.weight[:512, :]       )
																								snake_case       :     Any =    True
																								break

																		if attribute.isdigit():
																								snake_case       :     Optional[Any] =    model[int(A_       )]
																								snake_case       :     List[str] =    old_model[int(A_       )]
																		else:
																								snake_case       :     Optional[Any] =    getattr(A_    ,							A_       )

																								if old_attribute == "":
																														snake_case       :     Union[str, Any] =    old_model
																								else:
																														if not hasattr(A_    ,							A_       ):
																																				raise ValueError(F"""{old_model} does not have {old_attribute}"""       )
																														snake_case       :     Tuple =    getattr(A_    ,							A_       )

												if not is_key_init:
																		raise ValueError(F"""{key} was not correctly initialized!"""       )

						print(F"""Saving model to {pytorch_dump_folder_path}"""       )
						prophet.save_pretrained(A_       )


if __name__ == "__main__":
						UpperCAmelCase    					=   argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--prophetnet_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						UpperCAmelCase    					=   parser.parse_args()
						convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)

