


'''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 	lowerCAmelCase__			(			UpperCAmelCase__	):
			lowerCAmelCase :	str          =     VOCAB_FILES_NAMES
			lowerCAmelCase :	Optional[int]          =     PRETRAINED_VOCAB_FILES_MAP
			lowerCAmelCase :	Optional[int]          =     PRETRAINED_LYRIC_TOKENS_SIZES
			lowerCAmelCase :	int          =     ['input_ids', 'attention_mask']




			def __init__(		self  :     List[Any]	,  lowerCamelCase__  :     str	,  lowerCamelCase__  :     Optional[int]	,  lowerCamelCase__  :     List[Any]	,  lowerCamelCase__  :     int=["v3", "v2", "v2"]	,  lowerCamelCase__  :     Tuple=5_12	,  lowerCamelCase__  :     Any=5	,  lowerCamelCase__  :     Optional[Any]="<|endoftext|>"	,  **lowerCamelCase__  :     Dict	,  )   ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       List[str]								=      AddedToken(UpperCAmelCase__	,  lstrip=UpperCAmelCase__	,  rstrip=UpperCAmelCase__ ) if isinstance(UpperCAmelCase__	,  UpperCAmelCase__ ) else unk_token
					super().__init__(
					    unk_token=UpperCAmelCase__	,  n_genres=UpperCAmelCase__	,  version=UpperCAmelCase__	,  max_n_lyric_tokens=UpperCAmelCase__	,  **UpperCAmelCase__	,  )
					_UpperCAmelCase		:       Union[str, Any]								=      version
					_UpperCAmelCase		:       Union[str, Any]								=      max_n_lyric_tokens
					_UpperCAmelCase		:       List[str]								=      n_genres

					with open(UpperCAmelCase__	,  encoding="utf-8" ) as vocab_handle:
							_UpperCAmelCase		:       Tuple								=      json.load(UpperCAmelCase__ )

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

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

					_UpperCAmelCase		:       Optional[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		:       List[str]								=      regex.compile(UpperCAmelCase__ )
					_UpperCAmelCase		:       Tuple								=      {v: k for k, v in self.artists_encoder.items()}
					_UpperCAmelCase		:       Union[str, Any]								=      {v: k for k, v in self.genres_encoder.items()}
					_UpperCAmelCase		:       Tuple								=      {v: k for k, v in self.lyrics_encoder.items()}




			@property
			def 							lowerCAmelCase__    (		self  :     str )   ->str:



					'''simple docstring'''







					return len(self.artists_encoder ) + len(self.genres_encoder ) + len(self.lyrics_encoder )




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



					'''simple docstring'''







					return dict(self.artists_encoder	,  self.genres_encoder	,  self.lyrics_encoder )




			def 							lowerCAmelCase__    (		self  :     List[str]	,  lowerCamelCase__  :     Any	,  lowerCamelCase__  :     List[Any]	,  lowerCamelCase__  :     Optional[int] )   ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      [self.artists_encoder.get(UpperCAmelCase__	,  0 ) for artist in list_artists]
					for genres in range(len(UpperCAmelCase__ ) ):
							_UpperCAmelCase		:       Optional[int]								=      [self.genres_encoder.get(UpperCAmelCase__	,  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(UpperCAmelCase__	,  0 ) for character in list_lyrics[0]], [], []]
					return artists_id, list_genres, lyric_ids




			def 							lowerCAmelCase__    (		self  :     List[Any]	,  lowerCamelCase__  :     str )   ->List[Any]:



					'''simple docstring'''







					return list(UpperCAmelCase__ )




			def 							lowerCAmelCase__    (		self  :     Union[str, Any]	,  lowerCamelCase__  :     Any	,  lowerCamelCase__  :     Optional[int]	,  lowerCamelCase__  :     Union[str, Any]	,  **lowerCamelCase__  :     Optional[Any] )   ->Dict:



					'''simple docstring'''







					_UpperCAmelCase		:       Tuple								=      self.prepare_for_tokenization(UpperCAmelCase__	,  UpperCAmelCase__	,  UpperCAmelCase__ )
					_UpperCAmelCase		:       Optional[int]								=      self._tokenize(UpperCAmelCase__ )
					return artist, genre, lyrics




			def 							lowerCAmelCase__    (		self  :     Optional[Any]	,  lowerCamelCase__  :     str	,  lowerCamelCase__  :     str	,  lowerCamelCase__  :     str	,  lowerCamelCase__  :     bool = False )   ->str:



					'''simple docstring'''







					for idx in range(len(self.version ) ):
							if self.version[idx] == "v3":
									_UpperCAmelCase		:       List[str]								=      artists[idx].lower()
									_UpperCAmelCase		:       Optional[Any]								=      [genres[idx].lower()]
							else:
									_UpperCAmelCase		:       List[Any]								=      self._normalize(artists[idx] ) + '''.v2'''
									_UpperCAmelCase		:       int								=      [
									    self._normalize(UpperCAmelCase__ ) + '''.v2''' for genre in genres[idx].split("_" )
									]  # split is for the full dictionary with combined genres

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

					_UpperCAmelCase		:       int								=      self._run_strip_accents(UpperCAmelCase__ )
					_UpperCAmelCase		:       List[str]								=      lyrics.replace("\\"	,  "\n" )
					_UpperCAmelCase		:       List[str]								=      self.out_of_vocab.sub(""	,  UpperCAmelCase__ ), [], []
					return artists, genres, lyrics




			def 							lowerCAmelCase__    (		self  :     List[str]	,  lowerCamelCase__  :     str )   ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      unicodedata.normalize("NFD"	,  UpperCAmelCase__ )
					_UpperCAmelCase		:       Any								=      []
					for char in text:
							_UpperCAmelCase		:       int								=      unicodedata.category(UpperCAmelCase__ )
							if cat == "Mn":
									continue
							output.append(UpperCAmelCase__ )
					return "".join(UpperCAmelCase__ )




			def 							lowerCAmelCase__    (		self  :     Optional[Any]	,  lowerCamelCase__  :     str )   ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      (
					    [chr(UpperCAmelCase__ ) for i in range(ord("a" )	,  ord("z" ) + 1 )]
					    + [chr(UpperCAmelCase__ ) for i in range(ord("A" )	,  ord("Z" ) + 1 )]
					    + [chr(UpperCAmelCase__ ) for i in range(ord("0" )	,  ord("9" ) + 1 )]
					    + ['''.''']
					)
					_UpperCAmelCase		:       Optional[Any]								=      frozenset(UpperCAmelCase__ )
					_UpperCAmelCase		:       Dict								=      re.compile(R"_+" )
					_UpperCAmelCase		:       int								=      ''''''.join([c if c in accepted else "_" for c in text.lower()] )
					_UpperCAmelCase		:       List[Any]								=      pattern.sub("_"	,  UpperCAmelCase__ ).strip("_" )
					return text




			def 							lowerCAmelCase__    (		self  :     Dict	,  lowerCamelCase__  :     List[str] )   ->int:



					'''simple docstring'''







					return " ".join(UpperCAmelCase__ )




			def 							lowerCAmelCase__    (		self  :     Any	,  lowerCamelCase__  :     Dict	,  lowerCamelCase__  :     Optional[Union[str, TensorType]] = None	,  lowerCamelCase__  :     bool = False )   ->int:



					'''simple docstring'''







					if not isinstance(UpperCAmelCase__	,  UpperCAmelCase__ ):
							_UpperCAmelCase		:       List[str]								=      TensorType(UpperCAmelCase__ )

					# 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		:       Dict								=      tf.constant
							_UpperCAmelCase		:       List[str]								=      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		:       str								=      torch.tensor
							_UpperCAmelCase		:       List[Any]								=      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		:       Optional[int]								=      jnp.array
							_UpperCAmelCase		:       Optional[Any]								=      _is_jax
					else:
							_UpperCAmelCase		:       int								=      np.asarray
							_UpperCAmelCase		:       Union[str, Any]								=      _is_numpy

					# Do the tensor conversion in batch

					try:
							if prepend_batch_axis:
									_UpperCAmelCase		:       Optional[int]								=      [inputs]

							if not is_tensor(UpperCAmelCase__ ):
									_UpperCAmelCase		:       Union[str, Any]								=      as_tensor(UpperCAmelCase__ )
					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  :     Optional[int]	,  lowerCamelCase__  :     Dict	,  lowerCamelCase__  :     Any	,  lowerCamelCase__  :     Optional[Any]=""	,  lowerCamelCase__  :     int="pt" )   ->Dict:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      [0, 0, 0]
					_UpperCAmelCase		:       Optional[int]								=      [artist] * len(self.version )
					_UpperCAmelCase		:       Any								=      [genres] * len(self.version )

					_UpperCAmelCase		:       Dict								=      self.tokenize(UpperCAmelCase__	,  UpperCAmelCase__	,  UpperCAmelCase__ )
					_UpperCAmelCase		:       List[Any]								=      self._convert_token_to_id(UpperCAmelCase__	,  UpperCAmelCase__	,  UpperCAmelCase__ )

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




			def 							lowerCAmelCase__    (		self  :     List[Any]	,  lowerCamelCase__  :     str	,  lowerCamelCase__  :     Optional[str] = None )   ->str:



					'''simple docstring'''







					if not os.path.isdir(UpperCAmelCase__ ):
							logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
							return

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

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

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

					return (artists_file, genres_file, lyrics_file)






			def 							lowerCAmelCase__    (		self  :     Union[str, Any]	,  lowerCamelCase__  :     List[Any]	,  lowerCamelCase__  :     Optional[int]	,  lowerCamelCase__  :     List[str] )   ->str:



					'''simple docstring'''







					_UpperCAmelCase		:       List[str]								=      self.artists_decoder.get(UpperCAmelCase__ )
					_UpperCAmelCase		:       int								=      [self.genres_decoder.get(UpperCAmelCase__ ) for genre in genres_index]
					_UpperCAmelCase		:       int								=      [self.lyrics_decoder.get(UpperCAmelCase__ ) for character in lyric_index]
					return artist, genres, lyrics






'''simple docstring'''


import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
						import jax.numpy as jnp

						from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
						    FlaxRobertaPreLayerNormForCausalLM,
						    FlaxRobertaPreLayerNormForMaskedLM,
						    FlaxRobertaPreLayerNormForMultipleChoice,
						    FlaxRobertaPreLayerNormForQuestionAnswering,
						    FlaxRobertaPreLayerNormForSequenceClassification,
						    FlaxRobertaPreLayerNormForTokenClassification,
						    FlaxRobertaPreLayerNormModel,
						)






class 	lowerCAmelCase__			(			unittest.TestCase	):




			def __init__(		self  :     int	,  lowerCamelCase__  :     str	,  lowerCamelCase__  :     str=13	,  lowerCamelCase__  :     Dict=7	,  lowerCamelCase__  :     str=True	,  lowerCamelCase__  :     List[str]=True	,  lowerCamelCase__  :     Dict=True	,  lowerCamelCase__  :     int=True	,  lowerCamelCase__  :     Tuple=99	,  lowerCamelCase__  :     Optional[int]=32	,  lowerCamelCase__  :     str=5	,  lowerCamelCase__  :     List[Any]=4	,  lowerCamelCase__  :     Any=37	,  lowerCamelCase__  :     List[Any]="gelu"	,  lowerCamelCase__  :     int=0.1	,  lowerCamelCase__  :     Tuple=0.1	,  lowerCamelCase__  :     Optional[int]=5_12	,  lowerCamelCase__  :     Any=16	,  lowerCamelCase__  :     Optional[Any]=2	,  lowerCamelCase__  :     Optional[Any]=0.0_2	,  lowerCamelCase__  :     Optional[int]=4	,  )   ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      parent
					_UpperCAmelCase		:       Optional[int]								=      batch_size
					_UpperCAmelCase		:       List[Any]								=      seq_length
					_UpperCAmelCase		:       Dict								=      is_training
					_UpperCAmelCase		:       int								=      use_attention_mask
					_UpperCAmelCase		:       List[Any]								=      use_token_type_ids
					_UpperCAmelCase		:       int								=      use_labels
					_UpperCAmelCase		:       str								=      vocab_size
					_UpperCAmelCase		:       Tuple								=      hidden_size
					_UpperCAmelCase		:       Dict								=      num_hidden_layers
					_UpperCAmelCase		:       List[Any]								=      num_attention_heads
					_UpperCAmelCase		:       Tuple								=      intermediate_size
					_UpperCAmelCase		:       List[Any]								=      hidden_act
					_UpperCAmelCase		:       Union[str, Any]								=      hidden_dropout_prob
					_UpperCAmelCase		:       List[str]								=      attention_probs_dropout_prob
					_UpperCAmelCase		:       Optional[int]								=      max_position_embeddings
					_UpperCAmelCase		:       Tuple								=      type_vocab_size
					_UpperCAmelCase		:       int								=      type_sequence_label_size
					_UpperCAmelCase		:       List[str]								=      initializer_range
					_UpperCAmelCase		:       Union[str, Any]								=      num_choices




			def 							lowerCAmelCase__    (		self  :     int )   ->Any:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      ids_tensor([self.batch_size, self.seq_length]	,  self.vocab_size )

					_UpperCAmelCase		:       Any								=      None
					if self.use_attention_mask:
							_UpperCAmelCase		:       Optional[Any]								=      random_attention_mask([self.batch_size, self.seq_length] )

					_UpperCAmelCase		:       int								=      None
					if self.use_token_type_ids:
							_UpperCAmelCase		:       List[Any]								=      ids_tensor([self.batch_size, self.seq_length]	,  self.type_vocab_size )

					_UpperCAmelCase		:       Tuple								=      RobertaPreLayerNormConfig(
					    vocab_size=self.vocab_size	,  hidden_size=self.hidden_size	,  num_hidden_layers=self.num_hidden_layers	,  num_attention_heads=self.num_attention_heads	,  intermediate_size=self.intermediate_size	,  hidden_act=self.hidden_act	,  hidden_dropout_prob=self.hidden_dropout_prob	,  attention_probs_dropout_prob=self.attention_probs_dropout_prob	,  max_position_embeddings=self.max_position_embeddings	,  type_vocab_size=self.type_vocab_size	,  is_decoder=lowerCamelCase__	,  initializer_range=self.initializer_range	,  )

					return config, input_ids, token_type_ids, attention_mask




			def 							lowerCAmelCase__    (		self  :     Dict )   ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:       List[Any]								=      self.prepare_config_and_inputs()
					_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase		:       int								=      config_and_inputs
					_UpperCAmelCase		:       Optional[Any]								=      {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
					return config, inputs_dict






			def 							lowerCAmelCase__    (		self  :     int )   ->Dict:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      self.prepare_config_and_inputs()
					_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase		:       Union[str, Any]								=      config_and_inputs

					_UpperCAmelCase		:       List[Any]								=      True
					_UpperCAmelCase		:       List[str]								=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
					_UpperCAmelCase		:       int								=      ids_tensor([self.batch_size, self.seq_length]	,  vocab_size=2 )

					return (
					    config,
					    input_ids,
					    token_type_ids,
					    encoder_hidden_states,
					    encoder_attention_mask,
					)






@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 	lowerCAmelCase__			(			UpperCAmelCase__    ,       unittest.TestCase	):
			lowerCAmelCase :	Tuple          =     True

			lowerCAmelCase :	Tuple          =     (
			    (
			        FlaxRobertaPreLayerNormModel,
			        FlaxRobertaPreLayerNormForCausalLM,
			        FlaxRobertaPreLayerNormForMaskedLM,
			        FlaxRobertaPreLayerNormForSequenceClassification,
			        FlaxRobertaPreLayerNormForTokenClassification,
			        FlaxRobertaPreLayerNormForMultipleChoice,
			        FlaxRobertaPreLayerNormForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)




			def 							lowerCAmelCase__    (		self  :     Tuple )   ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      FlaxRobertaPreLayerNormModelTester(self )






			@slow
			def 							lowerCAmelCase__    (		self  :     Optional[int] )   ->int:



					'''simple docstring'''







					for model_class_name in self.all_model_classes:
							_UpperCAmelCase		:       Any								=      model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"	,  from_pt=lowerCamelCase__ )
							_UpperCAmelCase		:       Tuple								=      model(np.ones((1, 1) ) )
							self.assertIsNotNone(lowerCamelCase__ )






@require_flax
class 	lowerCAmelCase__			(			unittest.TestCase	):




			@slow
			def 							lowerCAmelCase__    (		self  :     Tuple )   ->str:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[int]								=      FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"	,  from_pt=lowerCamelCase__ )

					_UpperCAmelCase		:       str								=      np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]	,  dtype=jnp.intaa )
					_UpperCAmelCase		:       Tuple								=      model(lowerCamelCase__ )[0]
					_UpperCAmelCase		:       int								=      [1, 11, 5_02_65]
					self.assertEqual(list(output.shape )	,  lowerCamelCase__ )
					# compare the actual values for a slice.
					_UpperCAmelCase		:       int								=      np.array(
					    [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]	,  dtype=np.floataa )
					self.assertTrue(np.allclose(output[:, :3, :3]	,  lowerCamelCase__	,  atol=1E-4 ) )






			@slow
			def 							lowerCAmelCase__    (		self  :     Optional[Any] )   ->Dict:



					'''simple docstring'''







					_UpperCAmelCase		:       Any								=      FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"	,  from_pt=lowerCamelCase__ )

					_UpperCAmelCase		:       List[Any]								=      np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]	,  dtype=jnp.intaa )
					_UpperCAmelCase		:       Optional[Any]								=      model(lowerCamelCase__ )[0]
					# compare the actual values for a slice.
					_UpperCAmelCase		:       str								=      np.array(
					    [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]	,  dtype=np.floataa )
					self.assertTrue(np.allclose(output[:, :3, :3]	,  lowerCamelCase__	,  atol=1E-4 ) )



