



import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
							import os

							import jax.numpy as jnp
							from jax import jit

							from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
							from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

							__snake_case       :	str            ='0.12'  # assumed parallelism: 8


if is_torch_available():
							import torch






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Optional[Any] ,lowerCamelCase_	:	Optional[Any] ,lowerCamelCase_	:	Any=None):





				'''simple docstring'''

				if rng is None:
								lowerCAmelCase__  :       str				=  random.Random()

				lowerCAmelCase__  :       str				=  1
				for dim in shape:
								total_dims *= dim

				lowerCAmelCase__  :       Union[str, Any]				=  []
				for _ in range(lowerCamelCase_):
								values.append(rng.randint(0 ,vocab_size - 1))

				lowerCAmelCase__  :       Union[str, Any]				=  np.array(lowerCamelCase_ ,dtype=jnp.intaa).reshape(lowerCamelCase_)

				return output






def 			lowerCAmelCase__	(					lowerCamelCase_	:	int ,lowerCamelCase_	:	List[Any]=None):





				'''simple docstring'''

				lowerCAmelCase__  :       Dict				=  ids_tensor(lowerCamelCase_ ,vocab_size=2 ,rng=lowerCamelCase_)
				# make sure that at least one token is attended to for each batch
				lowerCAmelCase__  :       Optional[Any]				=  1
				return attn_mask



@require_flax
class  lowerCamelCase__    :




		'''simple docstring'''



		snake_case_			=None
		snake_case_			=()



		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						lowerCAmelCase__		,	lowerCAmelCase__  :       Any				=  self.model_tester.prepare_config_and_inputs_for_common()

						# cut to half length & take max batch_size 3
						lowerCAmelCase__  :       Optional[int]				=  2
						lowerCAmelCase__  :       Tuple				=  inputs['''input_ids'''].shape[-1] // 2
						lowerCAmelCase__  :       int				=  inputs['''input_ids'''][:max_batch_size, :sequence_length]

						lowerCAmelCase__  :       List[str]				=  jnp.ones_like(__lowerCamelCase				)
						lowerCAmelCase__  :       Tuple				=  attention_mask[:max_batch_size, :sequence_length]

						# generate max 5 tokens
						lowerCAmelCase__  :       Any				=  input_ids.shape[-1] + 5
						if config.eos_token_id is not None and config.pad_token_id is None:
										# hack to allow generate for models such as GPT2 as is done in `generate()`
										lowerCAmelCase__  :       List[str]				=  config.eos_token_id
						return config, input_ids, attention_mask, max_length



		@is_pt_flax_cross_test
		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__  :       Union[str, Any]				=  self._get_input_ids_and_config()
						lowerCAmelCase__  :       int				=  False
						lowerCAmelCase__  :       Union[str, Any]				=  max_length
						lowerCAmelCase__  :       Union[str, Any]				=  0

						for model_class in self.all_generative_model_classes:
										lowerCAmelCase__  :       Tuple				=  model_class(__lowerCamelCase				)

										lowerCAmelCase__  :       Tuple				=  model_class.__name__[4:]  # Skip the "Flax" at the beginning
										lowerCAmelCase__  :       Optional[int]				=  getattr(__lowerCamelCase	,__lowerCamelCase				)
										lowerCAmelCase__  :       Optional[int]				=  pt_model_class(__lowerCamelCase				).eval()
										lowerCAmelCase__  :       Optional[Any]				=  load_flax_weights_in_pytorch_model(__lowerCamelCase	,flax_model.params				)

										lowerCAmelCase__  :       str				=  flax_model.generate(__lowerCamelCase				).sequences
										lowerCAmelCase__  :       Any				=  pt_model.generate(torch.tensor(__lowerCamelCase	,dtype=torch.long				)				)

										if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
														lowerCAmelCase__  :       Any				=  flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

										self.assertListEqual(pt_generation_outputs.numpy().tolist()	,flax_generation_outputs.tolist()				)



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

						"""simple docstring"""






						lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__  :       Dict				=  self._get_input_ids_and_config()
						lowerCAmelCase__  :       Optional[Any]				=  False
						lowerCAmelCase__  :       str				=  max_length

						for model_class in self.all_generative_model_classes:
										lowerCAmelCase__  :       Optional[Any]				=  model_class(__lowerCamelCase				)

										lowerCAmelCase__  :       Union[str, Any]				=  model.generate(__lowerCamelCase				).sequences
										self.assertEqual(generation_outputs.shape[-1]	,__lowerCamelCase				)

										lowerCAmelCase__  :       Optional[Any]				=  jit(model.generate				)
										lowerCAmelCase__  :       Optional[int]				=  jit_generate(__lowerCamelCase				).sequences

										self.assertListEqual(generation_outputs.tolist()	,jit_generation_outputs.tolist()				)



		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__  :       Tuple				=  self._get_input_ids_and_config()
						lowerCAmelCase__  :       List[Any]				=  True
						lowerCAmelCase__  :       Tuple				=  max_length

						for model_class in self.all_generative_model_classes:
										lowerCAmelCase__  :       Any				=  model_class(__lowerCamelCase				)

										lowerCAmelCase__  :       int				=  model.generate(__lowerCamelCase				).sequences
										self.assertEqual(generation_outputs.shape[-1]	,__lowerCamelCase				)

										lowerCAmelCase__  :       str				=  jit(model.generate				)
										lowerCAmelCase__  :       Optional[int]				=  jit_generate(__lowerCamelCase				).sequences

										self.assertListEqual(generation_outputs.tolist()	,jit_generation_outputs.tolist()				)



		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__  :       int				=  self._get_input_ids_and_config()
						lowerCAmelCase__  :       Optional[Any]				=  False
						lowerCAmelCase__  :       List[str]				=  max_length
						lowerCAmelCase__  :       List[Any]				=  2

						for model_class in self.all_generative_model_classes:
										lowerCAmelCase__  :       List[str]				=  model_class(__lowerCamelCase				)

										lowerCAmelCase__  :       List[Any]				=  model.generate(__lowerCamelCase				).sequences
										self.assertEqual(generation_outputs.shape[-1]	,__lowerCamelCase				)

										lowerCAmelCase__  :       List[str]				=  jit(model.generate				)
										lowerCAmelCase__  :       Optional[Any]				=  jit_generate(__lowerCamelCase				).sequences

										self.assertListEqual(generation_outputs.tolist()	,jit_generation_outputs.tolist()				)



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

						"""simple docstring"""






						lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__  :       Tuple				=  self._get_input_ids_and_config()
						lowerCAmelCase__  :       str				=  False
						lowerCAmelCase__  :       Optional[Any]				=  max_length
						lowerCAmelCase__  :       str				=  2
						lowerCAmelCase__  :       int				=  2

						for model_class in self.all_generative_model_classes:
										lowerCAmelCase__  :       Optional[Any]				=  model_class(__lowerCamelCase				)

										lowerCAmelCase__  :       List[Any]				=  model.generate(__lowerCamelCase				).sequences
										self.assertEqual(generation_outputs.shape[0]	,input_ids.shape[0] * config.num_return_sequences				)



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

						"""simple docstring"""






						lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__  :       Optional[Any]				=  self._get_input_ids_and_config()
						lowerCAmelCase__  :       Tuple				=  True
						lowerCAmelCase__  :       Any				=  max_length
						lowerCAmelCase__  :       Optional[int]				=  0.8
						lowerCAmelCase__  :       Any				=  10
						lowerCAmelCase__  :       int				=  0.3
						lowerCAmelCase__  :       Optional[int]				=  1
						lowerCAmelCase__  :       Tuple				=  8
						lowerCAmelCase__  :       Optional[Any]				=  9

						for model_class in self.all_generative_model_classes:
										lowerCAmelCase__  :       Any				=  model_class(__lowerCamelCase				)

										lowerCAmelCase__  :       int				=  model.generate(__lowerCamelCase				).sequences
										self.assertEqual(generation_outputs.shape[-1]	,__lowerCamelCase				)

										lowerCAmelCase__  :       Any				=  jit(model.generate				)
										lowerCAmelCase__  :       Any				=  jit_generate(__lowerCamelCase				).sequences

										self.assertListEqual(generation_outputs.tolist()	,jit_generation_outputs.tolist()				)



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__  :       Optional[int]				=  self._get_input_ids_and_config()
						lowerCAmelCase__  :       int				=  max_length
						lowerCAmelCase__  :       Optional[int]				=  1
						lowerCAmelCase__  :       Dict				=  8
						lowerCAmelCase__  :       Tuple				=  9

						for model_class in self.all_generative_model_classes:
										lowerCAmelCase__  :       Optional[Any]				=  model_class(__lowerCamelCase				)

										lowerCAmelCase__  :       Optional[int]				=  model.generate(__lowerCamelCase				).sequences
										self.assertEqual(generation_outputs.shape[-1]	,__lowerCamelCase				)

										lowerCAmelCase__  :       Optional[Any]				=  jit(model.generate				)
										lowerCAmelCase__  :       Dict				=  jit_generate(__lowerCamelCase				).sequences

										self.assertListEqual(generation_outputs.tolist()	,jit_generation_outputs.tolist()				)



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

						"""simple docstring"""






						lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__  :       Tuple				=  self._get_input_ids_and_config()
						lowerCAmelCase__  :       List[str]				=  max_length
						lowerCAmelCase__  :       Optional[Any]				=  2
						lowerCAmelCase__  :       Any				=  1
						lowerCAmelCase__  :       Union[str, Any]				=  8
						lowerCAmelCase__  :       List[str]				=  9

						for model_class in self.all_generative_model_classes:
										lowerCAmelCase__  :       Optional[int]				=  model_class(__lowerCamelCase				)

										lowerCAmelCase__  :       List[Any]				=  model.generate(__lowerCamelCase				).sequences
										self.assertEqual(generation_outputs.shape[-1]	,__lowerCamelCase				)

										lowerCAmelCase__  :       List[str]				=  jit(model.generate				)
										lowerCAmelCase__  :       List[Any]				=  jit_generate(__lowerCamelCase				).sequences

										self.assertListEqual(generation_outputs.tolist()	,jit_generation_outputs.tolist()				)



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

						"""simple docstring"""






						lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__  :       List[Any]				=  self._get_input_ids_and_config()

						# pad attention mask on the left
						lowerCAmelCase__  :       Optional[int]				=  attention_mask.at[(0, 0)].set(0				)

						lowerCAmelCase__  :       Tuple				=  False
						lowerCAmelCase__  :       List[Any]				=  max_length

						for model_class in self.all_generative_model_classes:
										lowerCAmelCase__  :       int				=  model_class(__lowerCamelCase				)

										lowerCAmelCase__  :       Tuple				=  model.generate(__lowerCamelCase	,attention_mask=__lowerCamelCase				).sequences
										self.assertEqual(generation_outputs.shape[-1]	,__lowerCamelCase				)

										lowerCAmelCase__  :       Any				=  jit(model.generate				)
										lowerCAmelCase__  :       Tuple				=  jit_generate(__lowerCamelCase	,attention_mask=__lowerCamelCase				).sequences

										self.assertListEqual(generation_outputs.tolist()	,jit_generation_outputs.tolist()				)



		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__  :       Union[str, Any]				=  self._get_input_ids_and_config()

						# pad attention mask on the left
						lowerCAmelCase__  :       List[str]				=  attention_mask.at[(0, 0)].set(0				)

						lowerCAmelCase__  :       Dict				=  True
						lowerCAmelCase__  :       Union[str, Any]				=  max_length

						for model_class in self.all_generative_model_classes:
										lowerCAmelCase__  :       List[str]				=  model_class(__lowerCamelCase				)

										lowerCAmelCase__  :       int				=  model.generate(__lowerCamelCase	,attention_mask=__lowerCamelCase				).sequences
										self.assertEqual(generation_outputs.shape[-1]	,__lowerCamelCase				)

										lowerCAmelCase__  :       Union[str, Any]				=  jit(model.generate				)
										lowerCAmelCase__  :       str				=  jit_generate(__lowerCamelCase	,attention_mask=__lowerCamelCase				).sequences

										self.assertListEqual(generation_outputs.tolist()	,jit_generation_outputs.tolist()				)



		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__  :       Optional[Any]				=  self._get_input_ids_and_config()

						# pad attention mask on the left
						lowerCAmelCase__  :       int				=  attention_mask.at[(0, 0)].set(0				)

						lowerCAmelCase__  :       Optional[Any]				=  2
						lowerCAmelCase__  :       Dict				=  max_length

						for model_class in self.all_generative_model_classes:
										lowerCAmelCase__  :       Optional[int]				=  model_class(__lowerCamelCase				)

										lowerCAmelCase__  :       str				=  model.generate(__lowerCamelCase	,attention_mask=__lowerCamelCase				).sequences
										self.assertEqual(generation_outputs.shape[-1]	,__lowerCamelCase				)

										lowerCAmelCase__  :       List[str]				=  jit(model.generate				)
										lowerCAmelCase__  :       List[Any]				=  jit_generate(__lowerCamelCase	,attention_mask=__lowerCamelCase				).sequences

										self.assertListEqual(generation_outputs.tolist()	,jit_generation_outputs.tolist()				)



@require_flax
class  lowerCamelCase__    (							unittest.TestCase):




		'''simple docstring'''



		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert'''				)
						lowerCAmelCase__  :       Optional[int]				=  FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''				)

						lowerCAmelCase__  :       Any				=  '''Hello world'''
						lowerCAmelCase__  :       int				=  tokenizer(__lowerCamelCase	,return_tensors='''np'''				).input_ids

						# typos are quickly detected (the correct argument is `do_sample`)
						with self.assertRaisesRegex(__lowerCamelCase	,'''do_samples'''				):
										model.generate(__lowerCamelCase	,do_samples=__lowerCamelCase				)

						# arbitrary arguments that will not be used anywhere are also not accepted
						with self.assertRaisesRegex(__lowerCamelCase	,'''foo'''				):
										lowerCAmelCase__  :       List[str]				=  {'''foo''': '''bar'''}
										model.generate(__lowerCamelCase	,**__lowerCamelCase				)






import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

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


__snake_case       :	Union[str, Any]            =logging.get_logger(__name__)

__snake_case       :	Dict            ={'vocab_file': 'sentencepiece.model'}

__snake_case       :	Optional[Any]            ={
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
}

__snake_case       :	int            ={
    'google/rembert': 2_5_6,
}



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			=VOCAB_FILES_NAMES
		snake_case_			=PRETRAINED_VOCAB_FILES_MAP
		snake_case_			=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



		def __init__(self	,__lowerCamelCase	,__lowerCamelCase=False	,__lowerCamelCase=True	,__lowerCamelCase=True	,__lowerCamelCase="[CLS]"	,__lowerCamelCase="[SEP]"	,__lowerCamelCase="[UNK]"	,__lowerCamelCase="[SEP]"	,__lowerCamelCase="[PAD]"	,__lowerCamelCase="[CLS]"	,__lowerCamelCase="[MASK]"	,**__lowerCamelCase	,)  ->    Union[str, Any]:

						"""simple docstring"""






						super().__init__(
						    do_lower_case=__lowerCamelCase	,remove_space=__lowerCamelCase	,keep_accents=__lowerCamelCase	,bos_token=__lowerCamelCase	,eos_token=__lowerCamelCase	,unk_token=__lowerCamelCase	,sep_token=__lowerCamelCase	,pad_token=__lowerCamelCase	,cls_token=__lowerCamelCase	,mask_token=__lowerCamelCase	,**__lowerCamelCase	,)

						lowerCAmelCase__  :       int				=  do_lower_case
						lowerCAmelCase__  :       Optional[Any]				=  remove_space
						lowerCAmelCase__  :       Any				=  keep_accents
						lowerCAmelCase__  :       Dict				=  vocab_file

						lowerCAmelCase__  :       List[str]				=  spm.SentencePieceProcessor()
						self.sp_model.Load(__lowerCamelCase				)



		@property
		def 	lowerCAmelCase__   (self				)  ->    List[str]:

						"""simple docstring"""






						return len(self.sp_model				)



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

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  {self.convert_ids_to_tokens(__lowerCamelCase				): i for i in range(self.vocab_size				)}
						vocab.update(self.added_tokens_encoder				)
						return vocab



		def __getstate__(self				)  ->    Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Any				=  self.__dict__.copy()
						lowerCAmelCase__  :       Dict				=  None
						return state



		def __setstate__(self	,__lowerCamelCase				)  ->    int:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  d
						lowerCAmelCase__  :       List[str]				=  spm.SentencePieceProcessor()
						self.sp_model.Load(self.vocab_file				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase=False				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       str				=  self.sp_model.EncodeAsPieces(__lowerCamelCase				)
						return pieces



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Dict:

						"""simple docstring"""






						return self.sp_model.PieceToId(__lowerCamelCase				)



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

						"""simple docstring"""






						return self.sp_model.IdToPiece(__lowerCamelCase				)



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

						"""simple docstring"""






						lowerCAmelCase__  :       Any				=  self.sp_model.decode_pieces(__lowerCamelCase				)
						return out_string



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase = None				)  ->    List[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       str				=  [self.sep_token_id]
						lowerCAmelCase__  :       int				=  [self.cls_token_id]
						if token_ids_a is None:
										return cls + token_ids_a + sep
						return cls + token_ids_a + sep + token_ids_a + sep



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase = None	,__lowerCamelCase = False				)  ->    List[int]:

						"""simple docstring"""






						if already_has_special_tokens:
										if token_ids_a is not None:
														raise ValueError(
														    '''You should not supply a second sequence if the provided sequence of '''
														    '''ids is already formatted with special tokens for the model.'''				)
										return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

						if token_ids_a is not None:
										return [1] + ([0] * len(__lowerCamelCase				)) + [1] + ([0] * len(__lowerCamelCase				)) + [1]
						return [1] + ([0] * len(__lowerCamelCase				)) + [1]



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase = None				)  ->    List[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  [self.sep_token_id]
						lowerCAmelCase__  :       Union[str, Any]				=  [self.cls_token_id]

						if token_ids_a is None:
										return len(cls + token_ids_a + sep				) * [0]
						return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase = None				)  ->    Tuple[str]:

						"""simple docstring"""






						if not os.path.isdir(__lowerCamelCase				):
										logger.error('''Vocabulary path ({}) should be a directory'''.format(__lowerCamelCase				)				)
										return
						lowerCAmelCase__  :       Union[str, Any]				=  os.path.join(
						    __lowerCamelCase	,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

						if os.path.abspath(self.vocab_file				) != os.path.abspath(__lowerCamelCase				):
										copyfile(self.vocab_file	,__lowerCamelCase				)

						return (out_vocab_file,)

