





"""simple docstring"""




import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


a		:					List[str]	      =     get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
		from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

a		:					int	      =     256047
a		:					str	      =     256145


@require_sentencepiece
@require_tokenizers
class 			__UpperCamelCase							(		a__						,			unittest.TestCase		):
							lowerCamelCase  :   List[Any]    =NllbTokenizer
							lowerCamelCase  :   Any    =NllbTokenizerFast
							lowerCamelCase  :   Any    =True
							lowerCamelCase  :   List[str]    =True
							lowerCamelCase  :   List[str]    ={}






							def        __a  (  self )      ->					List[str]:
								super().setUp()

								# We have a SentencePiece fixture for testing
								a :						List[Any]      =							NllbTokenizer(lowerCAmelCase__ ,   keep_accents=lowerCAmelCase__ )
								tokenizer.save_pretrained(self.tmpdirname )






							def        __a  (  self )      ->					List[Any]:
								a :						str      =							NllbTokenizer(lowerCAmelCase__ ,   keep_accents=lowerCAmelCase__ )

								a :						List[str]      =							tokenizer.tokenize("This is a test" )
								self.assertListEqual(lowerCAmelCase__ ,   ["▁This", "▁is", "▁a", "▁t", "est"] )

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) ,   [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] ,   )

								a :						List[Any]      =							tokenizer.tokenize("I was born in 92000, and this is falsé." )
								self.assertListEqual(
								    lowerCAmelCase__ ,   [
								        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",
								        ".",
								    ] ,   )
								a :						Union[str, Any]      =							tokenizer.convert_tokens_to_ids(lowerCAmelCase__ )
								self.assertListEqual(
								    lowerCAmelCase__ ,   [
								        value + tokenizer.fairseq_offset
								        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
								    ] ,   )

								a :						Tuple      =							tokenizer.convert_ids_to_tokens(lowerCAmelCase__ )
								self.assertListEqual(
								    lowerCAmelCase__ ,   [
								        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        __a  (  self )      ->					Optional[Any]:
								a :						Any      =							(self.rust_tokenizer_class, "hf-internal-testing/tiny-random-nllb", {})
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
										a :						Any      =							self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__ ,   **lowerCAmelCase__ )
										a :						int      =							self.tokenizer_class.from_pretrained(lowerCAmelCase__ ,   **lowerCAmelCase__ )

										a :						Optional[Any]      =							tempfile.mkdtemp()

										a :						str      =							tokenizer_r.save_pretrained(lowerCAmelCase__ )
										a :						Dict      =							tokenizer_p.save_pretrained(lowerCAmelCase__ )

										# Checks it save with the same files + the tokenizer.json file for the fast one
										self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )
										a :						List[str]      =							tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f )
										self.assertSequenceEqual(lowerCAmelCase__ ,   lowerCAmelCase__ )

										# Checks everything loads correctly in the same way
										a :						List[str]      =							tokenizer_r.from_pretrained(lowerCAmelCase__ )
										a :						Tuple      =							tokenizer_p.from_pretrained(lowerCAmelCase__ )

										# Check special tokens are set accordingly on Rust and Python
										for key in tokenizer_pp.special_tokens_map:
											self.assertTrue(hasattr(lowerCAmelCase__ ,   lowerCAmelCase__ ) )

										shutil.rmtree(lowerCAmelCase__ )

										# Save tokenizer rust, legacy_format=True
										a :						Dict      =							tempfile.mkdtemp()

										a :						Optional[Any]      =							tokenizer_r.save_pretrained(lowerCAmelCase__ ,   legacy_format=lowerCAmelCase__ )
										a :						Optional[Any]      =							tokenizer_p.save_pretrained(lowerCAmelCase__ )

										# Checks it save with the same files
										self.assertSequenceEqual(lowerCAmelCase__ ,   lowerCAmelCase__ )

										# Checks everything loads correctly in the same way
										a :						Optional[int]      =							tokenizer_r.from_pretrained(lowerCAmelCase__ )
										a :						str      =							tokenizer_p.from_pretrained(lowerCAmelCase__ )

										# Check special tokens are set accordingly on Rust and Python
										for key in tokenizer_pp.special_tokens_map:
											self.assertTrue(hasattr(lowerCAmelCase__ ,   lowerCAmelCase__ ) )

										shutil.rmtree(lowerCAmelCase__ )

										# Save tokenizer rust, legacy_format=False
										a :						Optional[Any]      =							tempfile.mkdtemp()

										a :						List[Any]      =							tokenizer_r.save_pretrained(lowerCAmelCase__ ,   legacy_format=lowerCAmelCase__ )
										a :						Any      =							tokenizer_p.save_pretrained(lowerCAmelCase__ )

										# Checks it saved the tokenizer.json file
										self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )

										# Checks everything loads correctly in the same way
										a :						Tuple      =							tokenizer_r.from_pretrained(lowerCAmelCase__ )
										a :						Tuple      =							tokenizer_p.from_pretrained(lowerCAmelCase__ )

										# Check special tokens are set accordingly on Rust and Python
										for key in tokenizer_pp.special_tokens_map:
											self.assertTrue(hasattr(lowerCAmelCase__ ,   lowerCAmelCase__ ) )

										shutil.rmtree(lowerCAmelCase__ )






							@require_torch
							def        __a  (  self )      ->					Union[str, Any]:
								if not self.test_seqaseq:
									return

								a :						str      =							self.get_tokenizers()
								for tokenizer in tokenizers:
									with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
										# Longer text that will definitely require truncation.
										a :						str      =							[
										    " UN Chief Says There Is No Military Solution in Syria",
										    " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"
										    " Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"
										    " will only worsen the violence and misery for millions of people.",
										]
										a :						Union[str, Any]      =							[
										    "Şeful ONU declară că nu există o soluţie militară în Siria",
										    "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"
										    " Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi"
										    " că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
										]
										try:
											a :						int      =							tokenizer.prepare_seqaseq_batch(
											    src_texts=lowerCAmelCase__ ,   tgt_texts=lowerCAmelCase__ ,   max_length=3 ,   max_target_length=10 ,   return_tensors="pt" ,   src_lang="eng_Latn" ,   tgt_lang="ron_Latn" ,   )
										except NotImplementedError:
											return
										self.assertEqual(batch.input_ids.shape[1] ,   3 )
										self.assertEqual(batch.labels.shape[1] ,   10 )
										# max_target_length will default to max_length if not specified
										a :						int      =							tokenizer.prepare_seqaseq_batch(
										    lowerCAmelCase__ ,   tgt_texts=lowerCAmelCase__ ,   max_length=3 ,   return_tensors="pt" )
										self.assertEqual(batch.input_ids.shape[1] ,   3 )
										self.assertEqual(batch.labels.shape[1] ,   3 )

										a :						str      =							tokenizer.prepare_seqaseq_batch(
										    src_texts=lowerCAmelCase__ ,   max_length=3 ,   max_target_length=10 ,   return_tensors="pt" )
										self.assertEqual(batch_encoder_only.input_ids.shape[1] ,   3 )
										self.assertEqual(batch_encoder_only.attention_mask.shape[1] ,   3 )
										self.assertNotIn("decoder_input_ids" ,   lowerCAmelCase__ )






							@unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece." )
							def        __a  (  self )      ->					Dict:
								pass






							def        __a  (  self )      ->					Union[str, Any]:
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
										a :						Dict      =							[AddedToken("<special>" ,   lstrip=lowerCAmelCase__ )]

										a :						List[str]      =							self.rust_tokenizer_class.from_pretrained(
										    lowerCAmelCase__ ,   additional_special_tokens=lowerCAmelCase__ ,   **lowerCAmelCase__ )
										a :						Any      =							tokenizer_r.encode("Hey this is a <special> token" )

										a :						Any      =							tokenizer_r.encode("<special>" ,   add_special_tokens=lowerCAmelCase__ )[0]

										self.assertTrue(special_token_id in r_output )

										if self.test_slow_tokenizer:
											a :						int      =							self.rust_tokenizer_class.from_pretrained(
											    lowerCAmelCase__ ,   additional_special_tokens=lowerCAmelCase__ ,   **lowerCAmelCase__ ,   )
											a :						int      =							self.tokenizer_class.from_pretrained(
											    lowerCAmelCase__ ,   additional_special_tokens=lowerCAmelCase__ ,   **lowerCAmelCase__ )

											a :						Any      =							tokenizer_p.encode("Hey this is a <special> token" )

											a :						Dict      =							tokenizer_cr.encode("Hey this is a <special> token" )

											self.assertEqual(lowerCAmelCase__ ,   lowerCAmelCase__ )
											self.assertEqual(lowerCAmelCase__ ,   lowerCAmelCase__ )
											self.assertTrue(special_token_id in p_output )
											self.assertTrue(special_token_id in cr_output )


@require_torch
@require_sentencepiece
@require_tokenizers
class 			__UpperCamelCase							(		unittest.TestCase		):
							lowerCamelCase  :   str    ="""facebook/nllb-200-distilled-600M"""
							lowerCamelCase  :   int    =[
							    """ UN Chief Says There Is No Military Solution in Syria""",
							    """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
							]
							lowerCamelCase  :   str    =[
							    """Şeful ONU declară că nu există o soluţie militară în Siria""",
							    """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
							    """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
							    """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
							]
							lowerCamelCase  :   Tuple    =[
							    25_6047,
							    1_6297,
							    13_4408,
							    8165,
							    24_8066,
							    1_4734,
							    950,
							    1135,
							    10_5721,
							    3573,
							    83,
							    2_7352,
							    108,
							    4_9486,
							    2,
							]






							@classmethod
							def        __a  (  cls )      ->					str:
								a :						NllbTokenizer      =							NllbTokenizer.from_pretrained(
								    cls.checkpoint_name ,   src_lang="eng_Latn" ,   tgt_lang="ron_Latn" )
								a :						int      =							1
								return cls






							def        __a  (  self )      ->					Any:
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Arab"] ,   25_6001 )
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Latn"] ,   25_6002 )
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["fra_Latn"] ,   25_6057 )






							def        __a  (  self )      ->					Union[str, Any]:
								a :						Union[str, Any]      =							self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
								self.assertListEqual(self.expected_src_tokens ,   lowerCAmelCase__ )






							def        __a  (  self )      ->					Optional[int]:
								self.assertIn(lowerCAmelCase__ ,   self.tokenizer.all_special_ids )
								# fmt: off
								a :						Any      =							[RO_CODE, 4254, 9_8068, 11_2923, 3_9072, 3909, 713, 10_2767, 26, 1_7314, 3_5642, 1_4683, 3_3118, 2022, 6_6987, 2, 25_6047]
								# fmt: on

								a :						Tuple      =							self.tokenizer.decode(lowerCAmelCase__ ,   skip_special_tokens=lowerCAmelCase__ )
								a :						List[Any]      =							self.tokenizer.decode(generated_ids[1:] ,   skip_special_tokens=lowerCAmelCase__ )
								self.assertEqual(lowerCAmelCase__ ,   lowerCAmelCase__ )
								self.assertNotIn(self.tokenizer.eos_token ,   lowerCAmelCase__ )






							def        __a  (  self )      ->					Optional[int]:
								a :						Any      =							["this is gunna be a long sentence " * 20]
								assert isinstance(src_text[0] ,   lowerCAmelCase__ )
								a :						List[str]      =							10
								a :						int      =							self.tokenizer(lowerCAmelCase__ ,   max_length=lowerCAmelCase__ ,   truncation=lowerCAmelCase__ ).input_ids[0]
								self.assertEqual(ids[-1] ,   2 )
								self.assertEqual(ids[0] ,   lowerCAmelCase__ )
								self.assertEqual(len(lowerCAmelCase__ ) ,   lowerCAmelCase__ )






							def        __a  (  self )      ->					Dict:
								self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"] ) ,   [25_6203, 3] )






							def        __a  (  self )      ->					Tuple:
								a :						Any      =							tempfile.mkdtemp()
								a :						Union[str, Any]      =							self.tokenizer.fairseq_tokens_to_ids
								self.tokenizer.save_pretrained(lowerCAmelCase__ )
								a :						List[Any]      =							NllbTokenizer.from_pretrained(lowerCAmelCase__ )
								self.assertDictEqual(new_tok.fairseq_tokens_to_ids ,   lowerCAmelCase__ )






							@require_torch
							def        __a  (  self )      ->					Optional[int]:
								a :						Any      =							self.tokenizer(
								    self.src_text ,   text_target=self.tgt_text ,   padding=lowerCAmelCase__ ,   truncation=lowerCAmelCase__ ,   max_length=len(self.expected_src_tokens ) ,   return_tensors="pt" ,   )
								a :						Union[str, Any]      =							shift_tokens_right(
								    batch["labels"] ,   self.tokenizer.pad_token_id ,   self.tokenizer.lang_code_to_id["ron_Latn"] )

								self.assertIsInstance(lowerCAmelCase__ ,   lowerCAmelCase__ )

								self.assertEqual((2, 15) ,   batch.input_ids.shape )
								self.assertEqual((2, 15) ,   batch.attention_mask.shape )
								a :						Optional[Any]      =							batch.input_ids.tolist()[0]
								self.assertListEqual(self.expected_src_tokens ,   lowerCAmelCase__ )
								self.assertEqual(lowerCAmelCase__ ,   batch.decoder_input_ids[0, 0] )  # EOS
								# Test that special tokens are reset
								self.assertEqual(self.tokenizer.prefix_tokens ,   [EN_CODE] )
								self.assertEqual(self.tokenizer.suffix_tokens ,   [self.tokenizer.eos_token_id] )






							def        __a  (  self )      ->					int:
								a :						str      =							self.tokenizer(self.src_text ,   padding=lowerCAmelCase__ ,   truncation=lowerCAmelCase__ ,   max_length=3 ,   return_tensors="pt" )
								a :						Dict      =							self.tokenizer(
								    text_target=self.tgt_text ,   padding=lowerCAmelCase__ ,   truncation=lowerCAmelCase__ ,   max_length=10 ,   return_tensors="pt" )
								a :						List[str]      =							targets["input_ids"]
								a :						str      =							shift_tokens_right(
								    lowerCAmelCase__ ,   self.tokenizer.pad_token_id ,   decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang] ,   )

								self.assertEqual(batch.input_ids.shape[1] ,   3 )
								self.assertEqual(batch.decoder_input_ids.shape[1] ,   10 )






							@require_torch
							def        __a  (  self )      ->					List[Any]:
								a :						Dict      =							self.tokenizer._build_translation_inputs(
								    "A test" ,   return_tensors="pt" ,   src_lang="eng_Latn" ,   tgt_lang="fra_Latn" )

								self.assertEqual(
								    nested_simplify(lowerCAmelCase__ ) ,   {
								        # A, test, EOS, en_XX
								        "input_ids": [[25_6047, 70, 7356, 2]],
								        "attention_mask": [[1, 1, 1, 1]],
								        # ar_AR
								        "forced_bos_token_id": 25_6057,
								    } ,   )






							@require_torch
							def        __a  (  self )      ->					Optional[int]:
								a :						int      =							True
								a :						List[Any]      =							self.tokenizer(
								    "UN Chief says there is no military solution in Syria" ,   src_lang="eng_Latn" ,   tgt_lang="fra_Latn" )
								self.assertEqual(
								    inputs.input_ids ,   [1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2, 25_6047] )

								a :						Union[str, Any]      =							False
								a :						Optional[Any]      =							self.tokenizer(
								    "UN Chief says there is no military solution in Syria" ,   src_lang="eng_Latn" ,   tgt_lang="fra_Latn" )
								self.assertEqual(
								    inputs.input_ids ,   [25_6047, 1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2] )









"""simple docstring"""




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

		a		:					Optional[Any]	      =     '''0.12'''  # assumed parallelism: 8


if is_torch_available():
		import torch






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       List[str]       ,			_lowercase		:       Any       ,			_lowercase		:       Union[str, Any]=None   )					->Union[str, Any]:






	'''simple docstring'''






	if rng is None:
		a :						Dict      =							random.Random()

	a :						Any      =							1
	for dim in shape:
		total_dims *= dim

	a :						Dict      =							[]
	for _ in range(_lowercase   ):
		values.append(rng.randint(0       ,			vocab_size - 1   )   )

	a :						Dict      =							np.array(_lowercase       ,			dtype=jnp.intaa   ).reshape(_lowercase   )

	return output






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       List[str]       ,			_lowercase		:       int=None   )					->Optional[Any]:






	'''simple docstring'''






	a :						str      =							ids_tensor(_lowercase       ,			vocab_size=2       ,			rng=_lowercase   )
	# make sure that at least one token is attended to for each batch
	a :						Union[str, Any]      =							1
	return attn_mask


@require_flax
class 			__UpperCamelCase							:
							lowerCamelCase  :   Dict    =None
							lowerCamelCase  :   List[Any]    =()






							def        __a  (  self )      ->					Union[str, Any]:
								a,			a :						List[Any]      =							self.model_tester.prepare_config_and_inputs_for_common()

								# cut to half length & take max batch_size 3
								a :						Any      =							2
								a :						List[Any]      =							inputs["input_ids"].shape[-1] // 2
								a :						Union[str, Any]      =							inputs["input_ids"][:max_batch_size, :sequence_length]

								a :						Optional[Any]      =							jnp.ones_like(lowerCAmelCase__ )
								a :						Any      =							attention_mask[:max_batch_size, :sequence_length]

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






							@is_pt_flax_cross_test
							def        __a  (  self )      ->					List[Any]:
								a,			a,			a,			a :						int      =							self._get_input_ids_and_config()
								a :						Dict      =							False
								a :						Tuple      =							max_length
								a :						List[Any]      =							0

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

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

									a :						Optional[Any]      =							flax_model.generate(lowerCAmelCase__ ).sequences
									a :						Tuple      =							pt_model.generate(torch.tensor(lowerCAmelCase__ ,   dtype=torch.long ) )

									if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
										a :						List[str]      =							flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

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






							def        __a  (  self )      ->					Optional[Any]:
								a,			a,			a,			a :						Optional[Any]      =							self._get_input_ids_and_config()
								a :						List[str]      =							False
								a :						Tuple      =							max_length

								for model_class in self.all_generative_model_classes:
									a :						Dict      =							model_class(lowerCAmelCase__ )

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

									a :						Dict      =							jit(model.generate )
									a :						int      =							jit_generate(lowerCAmelCase__ ).sequences

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






							def        __a  (  self )      ->					Any:
								a,			a,			a,			a :						List[Any]      =							self._get_input_ids_and_config()
								a :						Dict      =							True
								a :						Any      =							max_length

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

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

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

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






							def        __a  (  self )      ->					Dict:
								a,			a,			a,			a :						str      =							self._get_input_ids_and_config()
								a :						str      =							False
								a :						Union[str, Any]      =							max_length
								a :						Optional[int]      =							2

								for model_class in self.all_generative_model_classes:
									a :						List[Any]      =							model_class(lowerCAmelCase__ )

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

									a :						Union[str, Any]      =							jit(model.generate )
									a :						Optional[int]      =							jit_generate(lowerCAmelCase__ ).sequences

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






							def        __a  (  self )      ->					Optional[Any]:
								a,			a,			a,			a :						Optional[Any]      =							self._get_input_ids_and_config()
								a :						Optional[Any]      =							False
								a :						Tuple      =							max_length
								a :						List[Any]      =							2
								a :						Any      =							2

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

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






							def        __a  (  self )      ->					Optional[int]:
								a,			a,			a,			a :						List[str]      =							self._get_input_ids_and_config()
								a :						Optional[int]      =							True
								a :						Dict      =							max_length
								a :						Dict      =							0.8
								a :						Any      =							10
								a :						Union[str, Any]      =							0.3
								a :						List[Any]      =							1
								a :						Optional[int]      =							8
								a :						List[Any]      =							9

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

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

									a :						Tuple      =							jit(model.generate )
									a :						Dict      =							jit_generate(lowerCAmelCase__ ).sequences

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






							def        __a  (  self )      ->					Optional[int]:
								a,			a,			a,			a :						Dict      =							self._get_input_ids_and_config()
								a :						Dict      =							max_length
								a :						List[str]      =							1
								a :						List[str]      =							8
								a :						List[str]      =							9

								for model_class in self.all_generative_model_classes:
									a :						Dict      =							model_class(lowerCAmelCase__ )

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

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

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






							def        __a  (  self )      ->					int:
								a,			a,			a,			a :						Tuple      =							self._get_input_ids_and_config()
								a :						List[Any]      =							max_length
								a :						Dict      =							2
								a :						List[Any]      =							1
								a :						List[str]      =							8
								a :						Any      =							9

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

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

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

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






							def        __a  (  self )      ->					Optional[int]:
								a,			a,			a,			a :						List[str]      =							self._get_input_ids_and_config()

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

								a :						Any      =							False
								a :						Union[str, Any]      =							max_length

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

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

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

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






							def        __a  (  self )      ->					List[str]:
								a,			a,			a,			a :						Union[str, Any]      =							self._get_input_ids_and_config()

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

								a :						Dict      =							True
								a :						List[Any]      =							max_length

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

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

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

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






							def        __a  (  self )      ->					str:
								a,			a,			a,			a :						Dict      =							self._get_input_ids_and_config()

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

								a :						int      =							2
								a :						Any      =							max_length

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

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

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

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


@require_flax
class 			__UpperCamelCase							(		unittest.TestCase		):






							def        __a  (  self )      ->					List[Any]:
								a :						Any      =							AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert" )
								a :						Tuple      =							FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only" )

								a :						int      =							"Hello world"
								a :						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" ):
									a :						str      =							{"foo": "bar"}
									model.generate(lowerCAmelCase__ ,   **lowerCAmelCase__ )


