






"""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


lowerCamelCase_       			=   logging.get_logger(__name__)

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

lowerCamelCase_       			=   {
    '''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''',
    },
}

lowerCamelCase_       			=   {
    '''jukebox''': 512,
}







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       VOCAB_FILES_NAMES
	__magic_name__			   =       PRETRAINED_VOCAB_FILES_MAP
	__magic_name__			   =       PRETRAINED_LYRIC_TOKENS_SIZES
	__magic_name__			   =       ['''input_ids''', '''attention_mask''']





	def __init__(   self      :				Optional[int]			,			lowerCAmelCase_      :				Dict			,			lowerCAmelCase_      :				List[Any]			,			lowerCAmelCase_      :				Optional[Any]			,			lowerCAmelCase_      :				Union[str, Any]=["v3", "v2", "v2"]			,			lowerCAmelCase_      :				Optional[int]=512			,			lowerCAmelCase_      :				Tuple=5			,			lowerCAmelCase_      :				List[Any]="<|endoftext|>"			,			**lowerCAmelCase_      :				List[Any]			,			)		->    Dict:
				UpperCAmelCase_  :				Optional[int]      =      AddedToken(lowerCAmelCase_			,			lstrip=lowerCAmelCase_			,			rstrip=lowerCAmelCase_		) if isinstance(lowerCAmelCase_			,			lowerCAmelCase_		) else unk_token
				super().__init__(
				    unk_token=lowerCAmelCase_			,			n_genres=lowerCAmelCase_			,			version=lowerCAmelCase_			,			max_n_lyric_tokens=lowerCAmelCase_			,			**lowerCAmelCase_			,			)
				UpperCAmelCase_  :				int      =      version
				UpperCAmelCase_  :				Union[str, Any]      =      max_n_lyric_tokens
				UpperCAmelCase_  :				Tuple      =      n_genres

				with open(lowerCAmelCase_			,			encoding="utf-8"		) as vocab_handle:
							UpperCAmelCase_  :				Union[str, Any]      =      json.load(lowerCAmelCase_		)

				with open(lowerCAmelCase_			,			encoding="utf-8"		) as vocab_handle:
							UpperCAmelCase_  :				Any      =      json.load(lowerCAmelCase_		)

				with open(lowerCAmelCase_			,			encoding="utf-8"		) as vocab_handle:
							UpperCAmelCase_  :				Optional[Any]      =      json.load(lowerCAmelCase_		)

				UpperCAmelCase_  :				Union[str, 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:
							UpperCAmelCase_  :				Optional[Any]      =      oov.replace(R"\-'"			,			R"\-+'"		)

				UpperCAmelCase_  :				str      =      regex.compile(lowerCAmelCase_		)
				UpperCAmelCase_  :				Any      =      {v: k for k, v in self.artists_encoder.items()}
				UpperCAmelCase_  :				List[str]      =      {v: k for k, v in self.genres_encoder.items()}
				UpperCAmelCase_  :				Dict      =      {v: k for k, v in self.lyrics_encoder.items()}





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    str:
				return len(self.artists_encoder		) + len(self.genres_encoder		) + len(self.lyrics_encoder		)





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





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]			,			lowerCAmelCase_      :				str			,			lowerCAmelCase_      :				List[str]			,			lowerCAmelCase_      :				Optional[int]		)		->    str:
				UpperCAmelCase_  :				str      =      [self.artists_encoder.get(lowerCAmelCase_			,			0		) for artist in list_artists]
				for genres in range(len(lowerCAmelCase_		)		):
							UpperCAmelCase_  :				Tuple      =      [self.genres_encoder.get(lowerCAmelCase_			,			0		) for genre in list_genres[genres]]
							UpperCAmelCase_  :				Optional[int]      =      list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]		))

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





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple			,			lowerCAmelCase_      :				Tuple		)		->    Any:
				return list(lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]			,			lowerCAmelCase_      :				List[str]			,			lowerCAmelCase_      :				Any			,			lowerCAmelCase_      :				List[str]			,			**lowerCAmelCase_      :				List[str]		)		->    Tuple:
				UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  :				str      =      self.prepare_for_tokenization(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				Optional[Any]      =      self._tokenize(lowerCAmelCase_		)
				return artist, genre, lyrics





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

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

				UpperCAmelCase_  :				Optional[int]      =      self._run_strip_accents(lowerCAmelCase_		)
				UpperCAmelCase_  :				Dict      =      lyrics.replace("\\"			,			"\n"		)
				UpperCAmelCase_  :				str      =      self.out_of_vocab.sub(""			,			lowerCAmelCase_		), [], []
				return artists, genres, lyrics





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any			,			lowerCAmelCase_      :				Optional[Any]		)		->    int:
				UpperCAmelCase_  :				Dict      =      unicodedata.normalize("NFD"			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				List[Any]      =      []
				for char in text:
							UpperCAmelCase_  :				str      =      unicodedata.category(lowerCAmelCase_		)
							if cat == "Mn":
										continue
							output.append(lowerCAmelCase_		)
				return "".join(lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]			,			lowerCAmelCase_      :				str		)		->    str:
				UpperCAmelCase_  :				int      =      (
				    [chr(lowerCAmelCase_		) for i in range(ord("a"		)			,			ord("z"		) + 1		)]
				    + [chr(lowerCAmelCase_		) for i in range(ord("A"		)			,			ord("Z"		) + 1		)]
				    + [chr(lowerCAmelCase_		) for i in range(ord("0"		)			,			ord("9"		) + 1		)]
				    + ["."]
				)
				UpperCAmelCase_  :				Union[str, Any]      =      frozenset(lowerCAmelCase_		)
				UpperCAmelCase_  :				int      =      re.compile(R"_+"		)
				UpperCAmelCase_  :				str      =      "".join([c if c in accepted else "_" for c in text.lower()]		)
				UpperCAmelCase_  :				Tuple      =      pattern.sub("_"			,			lowerCAmelCase_		).strip("_"		)
				return text





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]			,			lowerCAmelCase_      :				List[str]		)		->    str:
				return " ".join(lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]			,			lowerCAmelCase_      :				List[Any]			,			lowerCAmelCase_      :				Optional[Union[str, TensorType]] = None			,			lowerCAmelCase_      :				bool = False		)		->    Tuple:
				# Convert to TensorType
				if not isinstance(lowerCAmelCase_			,			lowerCAmelCase_		):
							UpperCAmelCase_  :				Any      =      TensorType(lowerCAmelCase_		)

				# 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

							UpperCAmelCase_  :				List[str]      =      tf.constant
							UpperCAmelCase_  :				List[Any]      =      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

							UpperCAmelCase_  :				List[str]      =      torch.tensor
							UpperCAmelCase_  :				int      =      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

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

				# Do the tensor conversion in batch

				try:
							if prepend_batch_axis:
										UpperCAmelCase_  :				List[Any]      =      [inputs]

							if not is_tensor(lowerCAmelCase_		):
										UpperCAmelCase_  :				Dict      =      as_tensor(lowerCAmelCase_		)
				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      :				Dict			,			lowerCAmelCase_      :				Optional[int]			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				Any=""			,			lowerCAmelCase_      :				Optional[Any]="pt"		)		->    BatchEncoding:
				UpperCAmelCase_  :				Optional[int]      =      [0, 0, 0]
				UpperCAmelCase_  :				Optional[int]      =      [artist] * len(self.version		)
				UpperCAmelCase_  :				List[str]      =      [genres] * len(self.version		)

				UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  :				List[str]      =      self.tokenize(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_		)
				UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  :				List[Any]      =      self._convert_token_to_id(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_		)

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





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

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

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

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

				return (artists_file, genres_file, lyrics_file)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]			,			lowerCAmelCase_      :				Dict			,			lowerCAmelCase_      :				Any			,			lowerCAmelCase_      :				Optional[int]		)		->    Optional[int]:
				UpperCAmelCase_  :				List[Any]      =      self.artists_decoder.get(lowerCAmelCase_		)
				UpperCAmelCase_  :				Optional[Any]      =      [self.genres_decoder.get(lowerCAmelCase_		) for genre in genres_index]
				UpperCAmelCase_  :				List[str]      =      [self.lyrics_decoder.get(lowerCAmelCase_		) for character in lyric_index]
				return artist, genres, lyrics











"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

lowerCamelCase_       			=   '''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
lowerCamelCase_       			=   [
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
lowerCamelCase_       			=   dict(zip(vocab, range(len(vocab))))
lowerCamelCase_       			=   ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
			lowerCamelCase_       			=   Path(tmpdirname)
			lowerCamelCase_       			=   build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
			lowerCamelCase_       			=   build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
			lowerCamelCase_       			=   build_dir / VOCAB_FILES_NAMES['''merges_file''']
			with open(src_vocab_file, '''w''') as fp:
						fp.write(json.dumps(vocab_tokens))
			with open(tgt_vocab_file, '''w''') as fp:
						fp.write(json.dumps(vocab_tokens))
			with open(merges_file, '''w''') as fp:
						fp.write('''\n'''.join(merges))

			lowerCamelCase_       			=   FSMTTokenizer(
			    langs=['''en''', '''ru'''],
			    src_vocab_size=len(vocab),
			    tgt_vocab_size=len(vocab),
			    src_vocab_file=src_vocab_file,
			    tgt_vocab_file=tgt_vocab_file,
			    merges_file=merges_file,
			)

lowerCamelCase_       			=   FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1000,
    tgt_vocab_size=1000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

lowerCamelCase_       			=   FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

# Test
lowerCamelCase_       			=   tokenizer(['''Making tiny model'''], return_tensors='''pt''')
lowerCamelCase_       			=   tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f'Generated {mname_tiny}')

# Upload
# transformers-cli upload tiny-wmt19-en-ru



