
"""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


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					     =	{
    '''artists_file''': '''artists.json''',
    '''lyrics_file''': '''lyrics.json''',
    '''genres_file''': '''genres.json''',
}

SCREAMING_SNAKE_CASE_					     =	{
    '''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''',
    },
}

SCREAMING_SNAKE_CASE_					     =	{
    '''jukebox''': 512,
}
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_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    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				snake_case_=["v3", "v2", "v2"]    ,				snake_case_=512    ,				snake_case_=5    ,				snake_case_="<|endoftext|>"    ,				**snake_case_    ,				)       ->  Optional[int]:
													__lowerCAmelCase     =  AddedToken(snake_case_    ,				lstrip=snake_case_    ,				rstrip=snake_case_				) if isinstance(snake_case_    ,				snake_case_				) else unk_token
													super().__init__(
													    unk_token=snake_case_    ,				n_genres=snake_case_    ,				version=snake_case_    ,				max_n_lyric_tokens=snake_case_    ,				**snake_case_    ,				)
													__lowerCAmelCase     =  version
													__lowerCAmelCase     =  max_n_lyric_tokens
													__lowerCAmelCase     =  n_genres

													with open(snake_case_    ,				encoding="""utf-8"""				) as vocab_handle:
																			__lowerCAmelCase     =  json.load(snake_case_				)

													with open(snake_case_    ,				encoding="""utf-8"""				) as vocab_handle:
																			__lowerCAmelCase     =  json.load(snake_case_				)

													with open(snake_case_    ,				encoding="""utf-8"""				) as vocab_handle:
																			__lowerCAmelCase     =  json.load(snake_case_				)

													__lowerCAmelCase     =  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:
																			__lowerCAmelCase     =  oov.replace(r"""\-'"""    ,				r"""\-+'"""				)

													__lowerCAmelCase     =  regex.compile(snake_case_				)
													__lowerCAmelCase     =  {v: k for k, v in self.artists_encoder.items()}
													__lowerCAmelCase     =  {v: k for k, v in self.genres_encoder.items()}
													__lowerCAmelCase     =  {v: k for k, v in self.lyrics_encoder.items()}




							@property
							def    A__						( self				)       ->  Any:
													return len(self.artists_encoder				) + len(self.genres_encoder				) + len(self.lyrics_encoder				)




							def    A__						( self				)       ->  Optional[Any]:
													return dict(self.artists_encoder    ,				self.genres_encoder    ,				self.lyrics_encoder				)




							def    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_				)       ->  Tuple:
													__lowerCAmelCase     =  [self.artists_encoder.get(snake_case_    ,				0				) for artist in list_artists]
													for genres in range(len(snake_case_				)				):
																			__lowerCAmelCase     =  [self.genres_encoder.get(snake_case_    ,				0				) for genre in list_genres[genres]]
																			__lowerCAmelCase     =  list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]				))

													__lowerCAmelCase     =  [[self.lyrics_encoder.get(snake_case_    ,				0				) for character in list_lyrics[0]], [], []]
													return artists_id, list_genres, lyric_ids




							def    A__						( self    ,				snake_case_				)       ->  Union[str, Any]:
													return list(snake_case_				)




							def    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				**snake_case_				)       ->  Union[str, Any]:
													__lowerCAmelCase   ,     __lowerCAmelCase   ,     __lowerCAmelCase     =  self.prepare_for_tokenization(snake_case_    ,				snake_case_    ,				snake_case_				)
													__lowerCAmelCase     =  self._tokenize(snake_case_				)
													return artist, genre, lyrics




							def    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				snake_case_ = False				)       ->  Tuple[str, str, str, Dict[str, Any]]:
													for idx in range(len(self.version				)				):
																			if self.version[idx] == "v3":
																									__lowerCAmelCase     =  artists[idx].lower()
																									__lowerCAmelCase     =  [genres[idx].lower()]
																			else:
																									__lowerCAmelCase     =  self._normalize(artists[idx]				) + """.v2"""
																									__lowerCAmelCase     =  [
																									    self._normalize(snake_case_				) + """.v2""" for genre in genres[idx].split("""_"""				)
																									]  # split is for the full dictionary with combined genres

													if self.version[0] == "v2":
																			__lowerCAmelCase     =  regex.compile(r"""[^A-Za-z0-9.,:;!?\-'\"()\[\] \t\n]+"""				)
																			__lowerCAmelCase     =  """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+'\"()[] \t\n"""
																			__lowerCAmelCase     =  {vocab[index]: index + 1 for index in range(len(snake_case_				)				)}
																			__lowerCAmelCase     =  0
																			__lowerCAmelCase     =  len(snake_case_				) + 1
																			__lowerCAmelCase     =  self.vocab
																			__lowerCAmelCase     =  {v: k for k, v in self.vocab.items()}
																			__lowerCAmelCase     =  """"""
													else:
																			__lowerCAmelCase     =  regex.compile(r"""[^A-Za-z0-9.,:;!?\-+'\"()\[\] \t\n]+"""				)

													__lowerCAmelCase     =  self._run_strip_accents(snake_case_				)
													__lowerCAmelCase     =  lyrics.replace("""\\"""    ,				"""\n"""				)
													__lowerCAmelCase     =  self.out_of_vocab.sub(""""""    ,				snake_case_				), [], []
													return artists, genres, lyrics




							def    A__						( self    ,				snake_case_				)       ->  Tuple:
													__lowerCAmelCase     =  unicodedata.normalize("""NFD"""    ,				snake_case_				)
													__lowerCAmelCase     =  []
													for char in text:
																			__lowerCAmelCase     =  unicodedata.category(snake_case_				)
																			if cat == "Mn":
																									continue
																			output.append(snake_case_				)
													return "".join(snake_case_				)




							def    A__						( self    ,				snake_case_				)       ->  str:
													__lowerCAmelCase     =  (
													    [chr(snake_case_				) for i in range(ord("""a"""				)    ,				ord("""z"""				) + 1				)]
													    + [chr(snake_case_				) for i in range(ord("""A"""				)    ,				ord("""Z"""				) + 1				)]
													    + [chr(snake_case_				) for i in range(ord("""0"""				)    ,				ord("""9"""				) + 1				)]
													    + ["""."""]
													)
													__lowerCAmelCase     =  frozenset(snake_case_				)
													__lowerCAmelCase     =  re.compile(r"""_+"""				)
													__lowerCAmelCase     =  """""".join([c if c in accepted else """_""" for c in text.lower()]				)
													__lowerCAmelCase     =  pattern.sub("""_"""    ,				snake_case_				).strip("""_"""				)
													return text




							def    A__						( self    ,				snake_case_				)       ->  str:
													return " ".join(snake_case_				)




							def    A__						( self    ,				snake_case_    ,				snake_case_ = None    ,				snake_case_ = False				)       ->  Any:
													# Convert to TensorType
													if not isinstance(snake_case_    ,				snake_case_				):
																			__lowerCAmelCase     =  TensorType(snake_case_				)

													# 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

																			__lowerCAmelCase     =  tf.constant
																			__lowerCAmelCase     =  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

																			__lowerCAmelCase     =  torch.tensor
																			__lowerCAmelCase     =  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

																			__lowerCAmelCase     =  jnp.array
																			__lowerCAmelCase     =  _is_jax
													else:
																			__lowerCAmelCase     =  np.asarray
																			__lowerCAmelCase     =  _is_numpy

													# Do the tensor conversion in batch

													try:
																			if prepend_batch_axis:
																									__lowerCAmelCase     =  [inputs]

																			if not is_tensor(snake_case_				):
																									__lowerCAmelCase     =  as_tensor(snake_case_				)
													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    ,				snake_case_    ,				snake_case_    ,				snake_case_=""    ,				snake_case_="pt"				)       ->  BatchEncoding:
													__lowerCAmelCase     =  [0, 0, 0]
													__lowerCAmelCase     =  [artist] * len(self.version				)
													__lowerCAmelCase     =  [genres] * len(self.version				)

													__lowerCAmelCase   ,     __lowerCAmelCase   ,     __lowerCAmelCase     =  self.tokenize(snake_case_    ,				snake_case_    ,				snake_case_				)
													__lowerCAmelCase   ,     __lowerCAmelCase   ,     __lowerCAmelCase     =  self._convert_token_to_id(snake_case_    ,				snake_case_    ,				snake_case_				)

													__lowerCAmelCase     =  [-INFINITY] * len(full_tokens[-1]				)
													__lowerCAmelCase     =  [
													    self.convert_to_tensors(
													        [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]]    ,				tensor_type=snake_case_				)
													    for i in range(len(self.version				)				)
													]
													return BatchEncoding({"""input_ids""": input_ids, """attention_masks""": attention_masks}				)




							def    A__						( self    ,				snake_case_    ,				snake_case_ = None				)       ->  Tuple[str]:
													if not os.path.isdir(snake_case_				):
																			logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
																			return

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

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

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

													return (artists_file, genres_file, lyrics_file)




							def    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_				)       ->  Tuple:
													__lowerCAmelCase     =  self.artists_decoder.get(snake_case_				)
													__lowerCAmelCase     =  [self.genres_decoder.get(snake_case_				) for genre in genres_index]
													__lowerCAmelCase     =  [self.lyrics_decoder.get(snake_case_				) for character in lyric_index]
													return artist, genres, lyrics


"""simple docstring"""
import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase=None						):
						# set parameter of one layer
						assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
						__lowerCAmelCase     =  nn.Parameter(_lowerCAmelCase						)
						if bias is not None:
												assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
												__lowerCAmelCase     =  nn.Parameter(_lowerCAmelCase						)

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# set torch weights for 1-to-1 comparison
						__lowerCAmelCase     =  np.asarray(weights[0]						)
						__lowerCAmelCase     =  np.asarray(weights[1]						)
						__lowerCAmelCase     =  np.asarray(weights[2]						)

						set_param(
						    torch_layer.self_attention.query_key ,    torch.tensor(_lowerCAmelCase						).transpose(1 ,    2						).contiguous().view(-1 ,    _lowerCAmelCase						) ,    )
						set_param(
						    torch_layer.self_attention.value ,    torch.tensor(_lowerCAmelCase						).transpose(1 ,    2						).contiguous().view(-1 ,    _lowerCAmelCase						) ,    )
						set_param(
						    torch_layer.output.dense ,    torch.tensor(_lowerCAmelCase						).view(-1 ,    _lowerCAmelCase						).contiguous().transpose(0 ,    1						) ,    )

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# set torch weights for 1-to-1 comparison
						__lowerCAmelCase     =  np.asarray(weights[0]						)
						__lowerCAmelCase     =  np.asarray(weights[1]						)
						__lowerCAmelCase     =  np.asarray(weights[2]						)
						__lowerCAmelCase     =  np.asarray(weights[3]						)

						set_param(
						    torch_layer.self_attention.query ,    torch.tensor(_lowerCAmelCase						).transpose(1 ,    2						).contiguous().view(-1 ,    _lowerCAmelCase						) ,    )
						set_param(
						    torch_layer.self_attention.key ,    torch.tensor(_lowerCAmelCase						).transpose(1 ,    2						).contiguous().view(-1 ,    _lowerCAmelCase						) ,    )
						set_param(
						    torch_layer.self_attention.value ,    torch.tensor(_lowerCAmelCase						).transpose(1 ,    2						).contiguous().view(-1 ,    _lowerCAmelCase						) ,    )
						set_param(
						    torch_layer.output.dense ,    torch.tensor(_lowerCAmelCase						).view(-1 ,    _lowerCAmelCase						).contiguous().transpose(0 ,    1						) ,    )

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# layernorm 1
						__lowerCAmelCase     =  weights[0][0][0]
						__lowerCAmelCase     =  np.asarray(layer_norm_a[0]						)
						__lowerCAmelCase     =  np.asarray(layer_norm_a[1]						)
						set_param(
						    torch_block.attention.layer_norm ,    torch.tensor(_lowerCAmelCase						) ,    torch.tensor(_lowerCAmelCase						) ,    )

						# lsh weights + output
						__lowerCAmelCase     =  weights[0][1]
						if len(_lowerCAmelCase						) < 4:
												set_layer_weights_in_torch_lsh(_lowerCAmelCase ,    torch_block.attention ,    _lowerCAmelCase						)
						else:
												set_layer_weights_in_torch_local(_lowerCAmelCase ,    torch_block.attention ,    _lowerCAmelCase						)

						# intermediate weighs
						__lowerCAmelCase     =  weights[2][0][1][2]

						# Chunked Feed Forward
						if len(_lowerCAmelCase						) == 4:
												__lowerCAmelCase     =  intermediate_weights[2]

						# layernorm 2
						__lowerCAmelCase     =  np.asarray(intermediate_weights[0][0]						)
						__lowerCAmelCase     =  np.asarray(intermediate_weights[0][1]						)
						set_param(
						    torch_block.feed_forward.layer_norm ,    torch.tensor(_lowerCAmelCase						) ,    torch.tensor(_lowerCAmelCase						) ,    )

						# intermediate dense
						__lowerCAmelCase     =  np.asarray(intermediate_weights[1][0]						)
						__lowerCAmelCase     =  np.asarray(intermediate_weights[1][1]						)
						set_param(
						    torch_block.feed_forward.dense.dense ,    torch.tensor(_lowerCAmelCase						).transpose(0 ,    1						).contiguous() ,    torch.tensor(_lowerCAmelCase						) ,    )

						# intermediate out
						__lowerCAmelCase     =  np.asarray(intermediate_weights[4][0]						)
						__lowerCAmelCase     =  np.asarray(intermediate_weights[4][1]						)
						set_param(
						    torch_block.feed_forward.output.dense ,    torch.tensor(_lowerCAmelCase						).transpose(0 ,    1						).contiguous() ,    torch.tensor(_lowerCAmelCase						) ,    )

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# reformer model
						__lowerCAmelCase     =  torch_model.reformer

						# word embeds
						__lowerCAmelCase     =  np.asarray(weights[1]						)
						set_param(
						    torch_model_reformer.embeddings.word_embeddings ,    torch.tensor(_lowerCAmelCase						) ,    )

						if isinstance(weights[3] ,    _lowerCAmelCase						):
												__lowerCAmelCase     =  torch_model_reformer.embeddings.position_embeddings
												for emb_idx in range(len(position_embeddings.weights						)						):
																		__lowerCAmelCase     =  np.asarray(weights[3][emb_idx][0]						)
																		assert (
																		    position_embeddings.weights[emb_idx].shape == emb_weights.shape
																		), f"""{position_embeddings[emb_idx]} emb does not match"""
																		__lowerCAmelCase     =  nn.Parameter(torch.tensor(_lowerCAmelCase						)						)

						__lowerCAmelCase     =  weights[5]
						assert len(torch_model_reformer.encoder.layers						) * 4 == len(
						    _lowerCAmelCase						), "HF and trax model do not have the same number of layers"
						for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers						):
												__lowerCAmelCase     =  trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
												set_block_weights_in_torch(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						)

						# output layer norm
						__lowerCAmelCase     =  np.asarray(weights[7][0]						)
						__lowerCAmelCase     =  np.asarray(weights[7][1]						)
						set_param(
						    torch_model_reformer.encoder.layer_norm ,    torch.tensor(_lowerCAmelCase						) ,    torch.tensor(_lowerCAmelCase						) ,    )

						# output embeddings
						__lowerCAmelCase     =  np.asarray(weights[9][0]						)
						__lowerCAmelCase     =  np.asarray(weights[9][1]						)
						set_param(
						    torch_model.lm_head.decoder ,    torch.tensor(_lowerCAmelCase						).transpose(0 ,    1						).contiguous() ,    torch.tensor(_lowerCAmelCase						) ,    )

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# Initialise PyTorch model
						__lowerCAmelCase     =  ReformerConfig.from_json_file(_lowerCAmelCase						)
						print(f"""Building PyTorch model from configuration: {config}"""						)
						__lowerCAmelCase     =  ReformerModelWithLMHead(_lowerCAmelCase						)

						with open(_lowerCAmelCase ,    """rb"""						) as f:
												__lowerCAmelCase     =  pickle.load(_lowerCAmelCase						)["""weights"""]

						set_model_weights_in_torch(_lowerCAmelCase ,    _lowerCAmelCase ,    config.hidden_size						)

						# Save pytorch-model
						print(f"""Save PyTorch model to {pytorch_dump_path}"""						)
						torch.save(model.state_dict() ,    _lowerCAmelCase						)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE_					     =	argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--trax_model_pkl_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
			)
			parser.add_argument(
			    '''--config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained Reformer model. \n'''
			        '''This specifies the model architecture.'''
			    ),
			)
			parser.add_argument(
			    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			SCREAMING_SNAKE_CASE_					     =	parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
