





import unittest

from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase_  							=				get_tests_dir('fixtures/test_sentencepiece.model')






@require_sentencepiece
@require_tokenizers
class 					lowercase__ (			__lowerCamelCase    ,       unittest.TestCase       ):

   '''simple docstring'''


   a		:							Any     	=	XLNetTokenizer
   a		:							Dict     	=	XLNetTokenizerFast
   a		:							Dict     	=	True
   a		:							str     	=	True




   def    UpperCamelCase__						(					self							)				->   Optional[int]:






       """simple docstring"""
       super().setUp()

       # We have a SentencePiece fixture for testing
       UpperCamelCase__							:       int      =   XLNetTokenizer(__magic_name__,	keep_accents=__magic_name__							)
       tokenizer.sanitize_special_tokens()
       tokenizer.save_pretrained(self.tmpdirname							)




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






       """simple docstring"""
       UpperCamelCase__							:       str      =   '''<s>'''
       UpperCamelCase__							:       List[str]      =   1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(__magic_name__							),	__magic_name__							)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(__magic_name__							),	__magic_name__							)




   def    UpperCamelCase__						(					self							)				->   int:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   list(self.get_tokenizer().get_vocab().keys()							)

       self.assertEqual(vocab_keys[0],	'''<unk>'''							)
       self.assertEqual(vocab_keys[1],	'''<s>'''							)
       self.assertEqual(vocab_keys[-1],	'''<eod>'''							)
       self.assertEqual(len(__magic_name__							),	1006							)




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






       """simple docstring"""
       self.assertEqual(self.get_tokenizer().vocab_size,	1000							)




   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   XLNetTokenizer(__magic_name__,	keep_accents=__magic_name__							)

       UpperCamelCase__							:       str      =   tokenizer.tokenize('''This is a test'''							)
       self.assertListEqual(__magic_name__,	['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']							)

       self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__							),	[285, 46, 10, 170, 382]							)

       UpperCamelCase__							:       int      =   tokenizer.tokenize('''I was born in 92000, and this is falsé.'''							)
       self.assertListEqual(
           __magic_name__,	[
               SPIECE_UNDERLINE + '''I''',
               SPIECE_UNDERLINE + '''was''',
               SPIECE_UNDERLINE + '''b''',
               '''or''',
               '''n''',
               SPIECE_UNDERLINE + '''in''',
               SPIECE_UNDERLINE + '''''',
               '''9''',
               '''2''',
               '''0''',
               '''0''',
               '''0''',
               ''',''',
               SPIECE_UNDERLINE + '''and''',
               SPIECE_UNDERLINE + '''this''',
               SPIECE_UNDERLINE + '''is''',
               SPIECE_UNDERLINE + '''f''',
               '''al''',
               '''s''',
               '''é''',
               '''.''',
           ],	)
       UpperCamelCase__							:       int      =   tokenizer.convert_tokens_to_ids(__magic_name__							)
       self.assertListEqual(__magic_name__,	[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]							)

       UpperCamelCase__							:       Dict      =   tokenizer.convert_ids_to_tokens(__magic_name__							)
       self.assertListEqual(
           __magic_name__,	[
               SPIECE_UNDERLINE + '''I''',
               SPIECE_UNDERLINE + '''was''',
               SPIECE_UNDERLINE + '''b''',
               '''or''',
               '''n''',
               SPIECE_UNDERLINE + '''in''',
               SPIECE_UNDERLINE + '''''',
               '''<unk>''',
               '''2''',
               '''0''',
               '''0''',
               '''0''',
               ''',''',
               SPIECE_UNDERLINE + '''and''',
               SPIECE_UNDERLINE + '''this''',
               SPIECE_UNDERLINE + '''is''',
               SPIECE_UNDERLINE + '''f''',
               '''al''',
               '''s''',
               '''<unk>''',
               '''.''',
           ],	)




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






       """simple docstring"""
       UpperCamelCase__							:       Union[str, Any]      =   XLNetTokenizer(__magic_name__,	do_lower_case=__magic_name__							)
       UpperCamelCase__							:       str      =   tokenizer.tokenize('''I was born in 92000, and this is falsé.'''							)
       self.assertListEqual(
           __magic_name__,	[
               SPIECE_UNDERLINE + '''''',
               '''i''',
               SPIECE_UNDERLINE + '''was''',
               SPIECE_UNDERLINE + '''b''',
               '''or''',
               '''n''',
               SPIECE_UNDERLINE + '''in''',
               SPIECE_UNDERLINE + '''''',
               '''9''',
               '''2''',
               '''0''',
               '''0''',
               '''0''',
               ''',''',
               SPIECE_UNDERLINE + '''and''',
               SPIECE_UNDERLINE + '''this''',
               SPIECE_UNDERLINE + '''is''',
               SPIECE_UNDERLINE + '''f''',
               '''al''',
               '''se''',
               '''.''',
           ],	)
       self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''							),	['''▁he''', '''ll''', '''o''']							)




   def    UpperCamelCase__						(					self							)				->   Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:       Optional[int]      =   XLNetTokenizer(__magic_name__,	do_lower_case=__magic_name__							)
       UpperCamelCase__							:       List[Any]      =   tokenizer.tokenize('''I was born in 92000, and this is falsé.'''							)
       self.assertListEqual(
           __magic_name__,	[
               SPIECE_UNDERLINE + '''I''',
               SPIECE_UNDERLINE + '''was''',
               SPIECE_UNDERLINE + '''b''',
               '''or''',
               '''n''',
               SPIECE_UNDERLINE + '''in''',
               SPIECE_UNDERLINE + '''''',
               '''9''',
               '''2''',
               '''0''',
               '''0''',
               '''0''',
               ''',''',
               SPIECE_UNDERLINE + '''and''',
               SPIECE_UNDERLINE + '''this''',
               SPIECE_UNDERLINE + '''is''',
               SPIECE_UNDERLINE + '''f''',
               '''al''',
               '''se''',
               '''.''',
           ],	)




   @slow
   def    UpperCamelCase__						(					self							)				->   Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   XLNetTokenizer.from_pretrained('''xlnet-base-cased'''							)

       UpperCamelCase__							:       str      =   tokenizer.encode('''sequence builders''',	add_special_tokens=__magic_name__							)
       UpperCamelCase__							:       List[Any]      =   tokenizer.encode('''multi-sequence build''',	add_special_tokens=__magic_name__							)

       UpperCamelCase__							:       Union[str, Any]      =   tokenizer.build_inputs_with_special_tokens(__magic_name__							)
       UpperCamelCase__							:       List[str]      =   tokenizer.build_inputs_with_special_tokens(__magic_name__,	__magic_name__							)

       assert encoded_sentence == text + [4, 3]
       assert encoded_pair == text + [4] + text_a + [4, 3]




   @slow
   def    UpperCamelCase__						(					self							)				->   int:






       """simple docstring"""
       # fmt: off
       UpperCamelCase__							:       List[str]      =   {'''input_ids''': [[17, 21442, 270, 17, 10, 14645, 318, 34, 17, 4546, 3145, 787, 13, 7752, 22018, 23, 21, 17, 4546, 3145, 787, 13, 3352, 14431, 13, 5500, 11, 1176, 580, 13, 16819, 4797, 23, 17, 10, 17135, 658, 19, 457, 7932, 13, 184, 19, 3154, 17135, 6468, 19, 1404, 12269, 19, 4229, 5356, 16264, 46, 19, 17, 20545, 10395, 9, 9, 9, 11, 28, 6421, 9531, 20729, 17, 10, 353, 17022, 11, 21, 6421, 9531, 16949, 17, 10, 11509, 753, 11, 33, 95, 2421, 7385, 956, 14431, 2626, 25, 842, 7385, 4836, 21, 1429, 2272, 9855, 3120, 161, 24738, 19, 13203, 658, 218, 787, 21, 430, 18482, 847, 2637, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 322, 22178, 27, 1064, 22, 956, 13, 11101, 1429, 5854, 24313, 18953, 40, 422, 24366, 68, 1758, 37, 10483, 14257, 31, 207, 263, 21, 203, 3773, 25, 71, 9735, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 32, 2049, 3442, 17, 13894, 3380, 23, 95, 18, 17634, 2288, 9, 4, 3]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=__magic_name__,	model_name='''xlnet-base-cased''',	revision='''c841166438c31ec7ca9a106dee7bb312b73ae511''',	)








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

  UpperCAmelCase_  							=				'0.12'  # assumed parallelism: 8


if is_torch_available():
  import torch




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Dict	,     __UpperCAmelCase:    Tuple	,     __UpperCAmelCase:    Optional[Any]=None   )   ->			Union[str, Any]:
    if rng is None:
        UpperCamelCase__							:       Tuple      =   random.Random()

    UpperCamelCase__							:       List[str]      =   1
    for dim in shape:
        total_dims *= dim

    UpperCamelCase__							:       Tuple      =   []
    for _ in range(__UpperCAmelCase   ):
        values.append(rng.randint(0	,     vocab_size - 1   )   )

    UpperCamelCase__							:       Any      =   np.array(__UpperCAmelCase	,     dtype=jnp.intaa   ).reshape(__UpperCAmelCase   )

    return output




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[int]	,     __UpperCAmelCase:    Optional[int]=None   )   ->			Any:
    UpperCamelCase__							:       Any      =   ids_tensor(__UpperCAmelCase	,     vocab_size=2	,     rng=__UpperCAmelCase   )
    # make sure that at least one token is attended to for each batch
    UpperCamelCase__							:       Tuple      =   1
    return attn_mask






@require_flax
class 					lowercase__ :

   '''simple docstring'''


   a		:							Optional[int]     	=	None
   a		:							Union[str, Any]     	=	()




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__							:       Tuple      =   self.model_tester.prepare_config_and_inputs_for_common()

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

       UpperCamelCase__							:       Tuple      =   jnp.ones_like(__magic_name__							)
       UpperCamelCase__							:       Any      =   attention_mask[:max_batch_size, :sequence_length]

       # generate max 5 tokens
       UpperCamelCase__							:       List[str]      =   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()`
           UpperCamelCase__							:       Tuple      =   config.eos_token_id
       return config, input_ids, attention_mask, max_length




   @is_pt_flax_cross_test
   def    UpperCamelCase__						(					self							)				->   List[str]:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       Optional[int]      =   self._get_input_ids_and_config()
       UpperCamelCase__							:       Optional[int]      =   False
       UpperCamelCase__							:       Optional[int]      =   max_length
       UpperCamelCase__							:       Dict      =   0

       for model_class in self.all_generative_model_classes:
           UpperCamelCase__							:       str      =   model_class(__magic_name__							)

           UpperCamelCase__							:       int      =   model_class.__name__[4:]  # Skip the "Flax" at the beginning
           UpperCamelCase__							:       List[str]      =   getattr(__magic_name__,	__magic_name__							)
           UpperCamelCase__							:       List[Any]      =   pt_model_class(__magic_name__							).eval()
           UpperCamelCase__							:       int      =   load_flax_weights_in_pytorch_model(__magic_name__,	flax_model.params							)

           UpperCamelCase__							:       Optional[int]      =   flax_model.generate(__magic_name__							).sequences
           UpperCamelCase__							:       Union[str, Any]      =   pt_model.generate(torch.tensor(__magic_name__,	dtype=torch.long							)							)

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

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




   def    UpperCamelCase__						(					self							)				->   Optional[int]:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       List[Any]      =   self._get_input_ids_and_config()
       UpperCamelCase__							:       Dict      =   False
       UpperCamelCase__							:       List[str]      =   max_length

       for model_class in self.all_generative_model_classes:
           UpperCamelCase__							:       Dict      =   model_class(__magic_name__							)

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

           UpperCamelCase__							:       int      =   jit(model.generate							)
           UpperCamelCase__							:       Optional[Any]      =   jit_generate(__magic_name__							).sequences

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




   def    UpperCamelCase__						(					self							)				->   Optional[int]:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       Union[str, Any]      =   self._get_input_ids_and_config()
       UpperCamelCase__							:       Optional[int]      =   True
       UpperCamelCase__							:       List[str]      =   max_length

       for model_class in self.all_generative_model_classes:
           UpperCamelCase__							:       Dict      =   model_class(__magic_name__							)

           UpperCamelCase__							:       Dict      =   model.generate(__magic_name__							).sequences
           self.assertEqual(generation_outputs.shape[-1],	__magic_name__							)

           UpperCamelCase__							:       Optional[Any]      =   jit(model.generate							)
           UpperCamelCase__							:       Optional[int]      =   jit_generate(__magic_name__							).sequences

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




   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       Optional[Any]      =   self._get_input_ids_and_config()
       UpperCamelCase__							:       List[str]      =   False
       UpperCamelCase__							:       Dict      =   max_length
       UpperCamelCase__							:       str      =   2

       for model_class in self.all_generative_model_classes:
           UpperCamelCase__							:       Optional[int]      =   model_class(__magic_name__							)

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

           UpperCamelCase__							:       str      =   jit(model.generate							)
           UpperCamelCase__							:       int      =   jit_generate(__magic_name__							).sequences

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




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






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       Any      =   self._get_input_ids_and_config()
       UpperCamelCase__							:       Tuple      =   False
       UpperCamelCase__							:       Union[str, Any]      =   max_length
       UpperCamelCase__							:       Optional[Any]      =   2
       UpperCamelCase__							:       List[Any]      =   2

       for model_class in self.all_generative_model_classes:
           UpperCamelCase__							:       List[str]      =   model_class(__magic_name__							)

           UpperCamelCase__							:       List[str]      =   model.generate(__magic_name__							).sequences
           self.assertEqual(generation_outputs.shape[0],	input_ids.shape[0] * config.num_return_sequences							)




   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       Optional[int]      =   self._get_input_ids_and_config()
       UpperCamelCase__							:       List[str]      =   True
       UpperCamelCase__							:       Any      =   max_length
       UpperCamelCase__							:       Union[str, Any]      =   0.8
       UpperCamelCase__							:       Dict      =   10
       UpperCamelCase__							:       Any      =   0.3
       UpperCamelCase__							:       str      =   1
       UpperCamelCase__							:       Union[str, Any]      =   8
       UpperCamelCase__							:       Union[str, Any]      =   9

       for model_class in self.all_generative_model_classes:
           UpperCamelCase__							:       int      =   model_class(__magic_name__							)

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

           UpperCamelCase__							:       Any      =   jit(model.generate							)
           UpperCamelCase__							:       List[Any]      =   jit_generate(__magic_name__							).sequences

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




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






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       Optional[int]      =   self._get_input_ids_and_config()
       UpperCamelCase__							:       Any      =   max_length
       UpperCamelCase__							:       str      =   1
       UpperCamelCase__							:       Dict      =   8
       UpperCamelCase__							:       List[Any]      =   9

       for model_class in self.all_generative_model_classes:
           UpperCamelCase__							:       Union[str, Any]      =   model_class(__magic_name__							)

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

           UpperCamelCase__							:       Dict      =   jit(model.generate							)
           UpperCamelCase__							:       int      =   jit_generate(__magic_name__							).sequences

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




   def    UpperCamelCase__						(					self							)				->   Optional[int]:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       int      =   self._get_input_ids_and_config()
       UpperCamelCase__							:       Dict      =   max_length
       UpperCamelCase__							:       List[Any]      =   2
       UpperCamelCase__							:       Dict      =   1
       UpperCamelCase__							:       int      =   8
       UpperCamelCase__							:       List[Any]      =   9

       for model_class in self.all_generative_model_classes:
           UpperCamelCase__							:       Union[str, Any]      =   model_class(__magic_name__							)

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

           UpperCamelCase__							:       Optional[Any]      =   jit(model.generate							)
           UpperCamelCase__							:       Any      =   jit_generate(__magic_name__							).sequences

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




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






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       Optional[Any]      =   self._get_input_ids_and_config()

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

       UpperCamelCase__							:       Tuple      =   False
       UpperCamelCase__							:       str      =   max_length

       for model_class in self.all_generative_model_classes:
           UpperCamelCase__							:       int      =   model_class(__magic_name__							)

           UpperCamelCase__							:       List[Any]      =   model.generate(__magic_name__,	attention_mask=__magic_name__							).sequences
           self.assertEqual(generation_outputs.shape[-1],	__magic_name__							)

           UpperCamelCase__							:       str      =   jit(model.generate							)
           UpperCamelCase__							:       Optional[Any]      =   jit_generate(__magic_name__,	attention_mask=__magic_name__							).sequences

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




   def    UpperCamelCase__						(					self							)				->   Tuple:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       List[str]      =   self._get_input_ids_and_config()

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

       UpperCamelCase__							:       Any      =   True
       UpperCamelCase__							:       Union[str, Any]      =   max_length

       for model_class in self.all_generative_model_classes:
           UpperCamelCase__							:       Union[str, Any]      =   model_class(__magic_name__							)

           UpperCamelCase__							:       Optional[Any]      =   model.generate(__magic_name__,	attention_mask=__magic_name__							).sequences
           self.assertEqual(generation_outputs.shape[-1],	__magic_name__							)

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

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




   def    UpperCamelCase__						(					self							)				->   int:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       Optional[int]      =   self._get_input_ids_and_config()

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

       UpperCamelCase__							:       Optional[Any]      =   2
       UpperCamelCase__							:       List[str]      =   max_length

       for model_class in self.all_generative_model_classes:
           UpperCamelCase__							:       int      =   model_class(__magic_name__							)

           UpperCamelCase__							:       Any      =   model.generate(__magic_name__,	attention_mask=__magic_name__							).sequences
           self.assertEqual(generation_outputs.shape[-1],	__magic_name__							)

           UpperCamelCase__							:       Tuple      =   jit(model.generate							)
           UpperCamelCase__							:       int      =   jit_generate(__magic_name__,	attention_mask=__magic_name__							).sequences

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






@require_flax
class 					lowercase__ (			unittest.TestCase       ):

   '''simple docstring'''


   def    UpperCamelCase__						(					self							)				->   Tuple:






       """simple docstring"""
       UpperCamelCase__							:       List[str]      =   AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert'''							)
       UpperCamelCase__							:       Any      =   FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''							)

       UpperCamelCase__							:       int      =   '''Hello world'''
       UpperCamelCase__							:       Tuple      =   tokenizer(__magic_name__,	return_tensors='''np'''							).input_ids

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

       # arbitrary arguments that will not be used anywhere are also not accepted
       with self.assertRaisesRegex(__magic_name__,	'''foo'''							):
           UpperCamelCase__							:       Optional[Any]      =   {'''foo''': '''bar'''}
           model.generate(__magic_name__,	**__magic_name__							)

