






from __future__ import annotations

import os
import tempfile
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow

from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import (
			    AutoTokenizer,
			    TFAutoModelForCausalLM,
			    TFAutoModelForSeqaSeqLM,
			    TFAutoModelForSpeechSeqaSeq,
			    TFAutoModelForVisionaSeq,
			    TFBartForConditionalGeneration,
			    TFLogitsProcessorList,
			    TFMinLengthLogitsProcessor,
			    tf_top_k_top_p_filtering,
			)

if is_tensorflow_text_available():
			import tensorflow_text as text




@require_tf
class 							snake_case_       (    unittest.TestCase      ):

						"""simple docstring"""



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




											'''simple docstring'''



											__a					:						Dict      =					tf.convert_to_tensor(
											    [
											        [
											            8.2_22_09_91,  # 3rd highest value; idx. 0
											            -0.5_62_00_44,
											            5.23_22_97_52,
											            4.0_38_63_93,
											            -6.8_79_83_78,
											            -0.54_78_58_02,
											            -3.2_01_21_53,
											            2.92_77_71_76,
											            1.88_17_19_53,
											            7.35_34_12_76,  # 5th highest value; idx. 9
											            8.43_20_78_33,  # 2nd highest value; idx. 10
											            -9.85_71_18_36,
											            -5.96_20_92_36,
											            -1.13_03_91_61,
											            -7.1_11_52_94,
											            -0.8_36_96_33,
											            -5.3_18_64_08,
											            7.06_42_74_07,
											            0.81_36_93_44,
											            -0.82_02_38_17,
											            -5.9_17_97_96,
											            0.58_81_34_43,
											            -6.99_77_84_38,
											            4.71_55_11_89,
											            -0.18_77_16_37,
											            7.44_02_07_59,  # 4th highest value; idx. 25
											            9.38_45_09_87,  # 1st highest value; idx. 26
											            2.12_66_29_41,
											            -9.32_56_20_38,
											            2.35_65_25_22,
											        ],  # cummulative prob of 5 highest values <= 0.6
											        [
											            0.58_42_55_18,
											            4.53_13_92_38,
											            -5.57_51_04_64,
											            -6.28_03_06_99,
											            -7.19_52_95_03,
											            -4.02_12_25_51,
											            1.39_33_70_37,
											            -6.06_70_70_57,
											            1.59_48_05_17,
											            -9.64_31_19,
											            0.03_90_77_99,
											            0.67_23_17_62,
											            -8.88_20_67_26,
											            6.27_11_59_22,  # 4th highest value; idx. 13
											            2.28_52_07_23,
											            4.82_76_75_06,
											            4.30_42_13_68,
											            8.8_27_53_13,  # 2nd highest value; idx. 17
											            5.44_02_99_58,  # 5th highest value; idx. 18
											            -4.4_73_57_94,
											            7.38_57_95_36,  # 3rd highest value; idx. 20
											            -2.91_05_16_63,
											            2.61_94_60_77,
											            -2.5_67_47_62,
											            -9.48_95_93_02,
											            -4.02_92_26_45,
											            -1.35_41_69_18,
											            9.67_70_23_23,  # 1st highest value; idx. 27
											            -5.89_47_85_53,
											            1.85_37_04_67,
											        ],  # cummulative prob of 5 highest values <= 0.6
											    ] ,					dtype=tf.floataa ,					)

											__a					:						Union[str, Any]      =					tf.convert_to_tensor(
											    [[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]] ,					dtype=tf.intaa ,					)  # expected non filtered idx as noted above

											__a					:						Any      =					tf.convert_to_tensor(
											    [8.22_20_99, 7.3_53_41_26, 8.43_20_78, 7.4_40_20_75, 9.3_84_51, 6.27_11_59, 8.82_75_31, 5.4_40_29_95, 7.3_85_79_56, 9.67_70_23] ,					dtype=tf.floataa ,					)  # expected non filtered values as noted above

											__a					:						Any      =					tf_top_k_top_p_filtering(__UpperCAmelCase ,					top_k=10 ,					top_p=0.6 ,					min_tokens_to_keep=4   )

											__a					:						Dict      =					output[output != -float("inf"   )]
											__a					:						List[Any]      =					tf.cast(
											    tf.where(tf.not_equal(__UpperCAmelCase ,					tf.constant(-float("inf"   ) ,					dtype=tf.floataa   )   )   ) ,					dtype=tf.intaa ,					)

											tf.debugging.assert_near(__UpperCAmelCase ,					__UpperCAmelCase ,					rtol=1E-12   )
											tf.debugging.assert_equal(__UpperCAmelCase ,					__UpperCAmelCase   )




@require_tf
class 							snake_case_       (    unittest.TestCase		,							__UpperCamelCase      ):

						"""simple docstring"""



						if is_tf_available():
												snake_case__        =      {
												    """AutoModelForCausalLM""": TFAutoModelForCausalLM,
												    """AutoModelForSpeechSeq2Seq""": TFAutoModelForSpeechSeqaSeq,
												    """AutoModelForSeq2SeqLM""": TFAutoModelForSeqaSeqLM,
												    """AutoModelForVision2Seq""": TFAutoModelForVisionaSeq,
												    """LogitsProcessorList""": TFLogitsProcessorList,
												    """MinLengthLogitsProcessor""": TFMinLengthLogitsProcessor,
												    """create_tensor_fn""": tf.convert_to_tensor,
												    """floats_tensor""": floats_tensor,
												    """return_tensors""": """tf""",
												}



						@slow
						def        UpperCAmelCase__					(self:					Tuple   )    ->     int:




											'''simple docstring'''



											__a					:						Dict      =					TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"   )
											__a					:						Optional[Any]      =					2
											__a					:						Optional[int]      =					2




											class 							snake_case_       (    tf.Module      ):

																	"""simple docstring"""



																	def __init__(self:					str ,					__UpperCAmelCase:					Union[str, Any]   )    ->     Optional[Any]:




																						'''simple docstring'''



																						super(__UpperCAmelCase ,					self   ).__init__()
																						__a					:						List[str]      =					model



																	@tf.function(
																	    input_signature=(
																	        tf.TensorSpec((None, input_length) ,					tf.intaa ,					name="input_ids"   ),
																	        tf.TensorSpec((None, input_length) ,					tf.intaa ,					name="attention_mask"   ),
																	    ) ,					jit_compile=__UpperCAmelCase ,					)
																	def        UpperCAmelCase__					(self:					Union[str, Any] ,					__UpperCAmelCase:					Optional[int] ,					__UpperCAmelCase:					Any   )    ->     int:




																						'''simple docstring'''



																						__a					:						List[Any]      =					self.model.generate(
																						    input_ids=__UpperCAmelCase ,					attention_mask=__UpperCAmelCase ,					max_new_tokens=__UpperCAmelCase ,					return_dict_in_generate=__UpperCAmelCase ,					)
																						return {"sequences": outputs["sequences"]}


											__a					:						List[Any]      =					[[2, 0], [102, 103]]
											__a					:						Optional[Any]      =					[[1, 0], [1, 1]]
											__a					:						Optional[int]      =					DummyModel(model=__UpperCAmelCase   )
											with tempfile.TemporaryDirectory() as tmp_dir:
																tf.saved_model.save(__UpperCAmelCase ,					__UpperCAmelCase ,					signatures={"serving_default": dummy_model.serving}   )
																__a					:						Union[str, Any]      =					tf.saved_model.load(__UpperCAmelCase   ).signatures["serving_default"]
																for batch_size in range(1 ,					len(__UpperCAmelCase   ) + 1   ):
																					__a					:						List[Any]      =					{
																					    "input_ids": tf.constant(dummy_input_ids[:batch_size]   ),
																					    "attention_mask": tf.constant(dummy_attention_masks[:batch_size]   ),
																					}
																					__a					:						Optional[Any]      =					serving_func(**__UpperCAmelCase   )["sequences"]
																					__a					:						Optional[Any]      =					test_model.generate(**__UpperCAmelCase ,					max_new_tokens=__UpperCAmelCase   )
																					tf.debugging.assert_equal(__UpperCAmelCase ,					__UpperCAmelCase   )



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




											'''simple docstring'''



											__a					:						str      =					TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"   )
											__a					:						Optional[int]      =					1
											__a					:						List[Any]      =					2




											class 							snake_case_       (    tf.Module      ):

																	"""simple docstring"""



																	def __init__(self:					Tuple ,					__UpperCAmelCase:					Optional[int]   )    ->     int:




																						'''simple docstring'''



																						super(__UpperCAmelCase ,					self   ).__init__()
																						__a					:						List[str]      =					model



																	@tf.function(
																	    input_signature=(
																	        tf.TensorSpec((batch_size, None) ,					tf.intaa ,					name="input_ids"   ),
																	        tf.TensorSpec((batch_size, None) ,					tf.intaa ,					name="attention_mask"   ),
																	    ) ,					jit_compile=__UpperCAmelCase ,					)
																	def        UpperCAmelCase__					(self:					Optional[Any] ,					__UpperCAmelCase:					Optional[Any] ,					__UpperCAmelCase:					Any   )    ->     Dict:




																						'''simple docstring'''



																						__a					:						str      =					self.model.generate(
																						    input_ids=__UpperCAmelCase ,					attention_mask=__UpperCAmelCase ,					max_new_tokens=__UpperCAmelCase ,					return_dict_in_generate=__UpperCAmelCase ,					)
																						return {"sequences": outputs["sequences"]}


											__a					:						Optional[Any]      =					[[2], [102, 103]]
											__a					:						Tuple      =					[[1], [1, 1]]
											__a					:						Any      =					DummyModel(model=__UpperCAmelCase   )
											with tempfile.TemporaryDirectory() as tmp_dir:
																tf.saved_model.save(__UpperCAmelCase ,					__UpperCAmelCase ,					signatures={"serving_default": dummy_model.serving}   )
																__a					:						str      =					tf.saved_model.load(__UpperCAmelCase   ).signatures["serving_default"]
																for input_row in range(len(__UpperCAmelCase   )   ):
																					__a					:						int      =					{
																					    "input_ids": tf.constant([dummy_input_ids[input_row]]   ),
																					    "attention_mask": tf.constant([dummy_attention_masks[input_row]]   ),
																					}
																					__a					:						Optional[int]      =					serving_func(**__UpperCAmelCase   )["sequences"]
																					__a					:						Dict      =					test_model.generate(**__UpperCAmelCase ,					max_new_tokens=__UpperCAmelCase   )
																					tf.debugging.assert_equal(__UpperCAmelCase ,					__UpperCAmelCase   )



						@slow
						@require_tensorflow_text
						def        UpperCAmelCase__					(self:					Dict   )    ->     Any:




											'''simple docstring'''



											with tempfile.TemporaryDirectory() as tmp_dir:
																# file needed to load the TF tokenizer
																hf_hub_download(repo_id="google/flan-t5-small" ,					filename="spiece.model" ,					local_dir=__UpperCAmelCase   )




																class 							snake_case_       (    tf.keras.layers.Layer      ):

																						"""simple docstring"""



																						def __init__(self:					Union[str, Any]   )    ->     Optional[int]:




																											'''simple docstring'''



																											super().__init__()
																											__a					:						List[str]      =					text.SentencepieceTokenizer(
																											    model=tf.io.gfile.GFile(os.path.join(__UpperCAmelCase ,					"spiece.model"   ) ,					"rb"   ).read()   )
																											__a					:						str      =					TFAutoModelForSeqaSeqLM.from_pretrained("hf-internal-testing/tiny-random-t5"   )



																						def        UpperCAmelCase__					(self:					Optional[int] ,					__UpperCAmelCase:					Any ,					*__UpperCAmelCase:					List[Any] ,					**__UpperCAmelCase:					Any   )    ->     Union[str, Any]:




																											'''simple docstring'''



																											__a					:						Optional[Any]      =					self.tokenizer.tokenize(__UpperCAmelCase   )
																											__a      ,			__a					:						Tuple      =					text.pad_model_inputs(
																											    __UpperCAmelCase ,					max_seq_length=64 ,					pad_value=self.model.config.pad_token_id   )
																											__a					:						Union[str, Any]      =					self.model.generate(input_ids=__UpperCAmelCase ,					attention_mask=__UpperCAmelCase   )
																											return self.tokenizer.detokenize(__UpperCAmelCase   )


																__a					:						Any      =					CompleteSentenceTransformer()
																__a					:						Optional[Any]      =					tf.keras.layers.Input(shape=(1,) ,					dtype=tf.string ,					name="inputs"   )
																__a					:						Tuple      =					complete_model(__UpperCAmelCase   )
																__a					:						int      =					tf.keras.Model(__UpperCAmelCase ,					__UpperCAmelCase   )
																keras_model.save(__UpperCAmelCase   )



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




											'''simple docstring'''



											__a					:						Dict      =					{
											    "do_sample": True,
											    "num_beams": 1,
											    "top_p": 0.7,
											    "top_k": 10,
											    "temperature": 0.7,
											}
											__a					:						int      =					14

											__a					:						List[Any]      =					AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"   )
											__a					:						str      =					"Hello, my dog is cute and"
											__a					:						Optional[int]      =					tokenizer(__UpperCAmelCase ,					return_tensors="tf"   )
											__a					:						Optional[Any]      =					TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"   )

											__a					:						Any      =					638
											# forces the generation to happen on CPU, to avoid GPU-related quirks
											with tf.device(":/CPU:0"   ):
																tf.random.set_seed(0   )
																__a					:						Any      =					model.generate(**__UpperCAmelCase ,					eos_token_id=__UpperCAmelCase ,					**__UpperCAmelCase   )
											self.assertTrue(expectation == len(generated_tokens[0]   )   )

											__a					:						int      =					[638, 198]
											with tf.device(":/CPU:0"   ):
																tf.random.set_seed(0   )
																__a					:						Optional[Any]      =					model.generate(**__UpperCAmelCase ,					eos_token_id=__UpperCAmelCase ,					**__UpperCAmelCase   )
											self.assertTrue(expectation == len(generated_tokens[0]   )   )



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




											'''simple docstring'''



											__a					:						List[str]      =					AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart"   )
											__a					:						Dict      =					"Hugging Face is a technology company based in New York and Paris."
											__a					:						Tuple      =					bart_tokenizer(__UpperCAmelCase ,					return_tensors="tf"   ).input_ids
											__a					:						Tuple      =					TFBartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart"   )
											__a					:						int      =					bart_model.generate(__UpperCAmelCase   ).numpy()




											class 							snake_case_       (    __UpperCamelCase      ):

																	"""simple docstring"""



																	def        UpperCAmelCase__					(self:					Any ,					__UpperCAmelCase:					Union[str, Any] ,					__UpperCAmelCase:					List[Any]=None ,					**__UpperCAmelCase:					str   )    ->     List[Any]:




																						'''simple docstring'''



																						return super().call(__UpperCAmelCase ,					**__UpperCAmelCase   )


											__a					:						int      =					FakeBart.from_pretrained("hf-internal-testing/tiny-random-bart"   )
											__a					:						Tuple      =					bart_model.generate(__UpperCAmelCase ,					foo="bar"   ).numpy()
											self.assertTrue(np.array_equal(__UpperCAmelCase ,					__UpperCAmelCase   )   )




											class 							snake_case_       (    bart_model.model.encoder.__class__      ):

																	"""simple docstring"""



																	def        UpperCAmelCase__					(self:					str ,					__UpperCAmelCase:					Optional[Any] ,					**__UpperCAmelCase:					int   )    ->     Tuple:




																						'''simple docstring'''



																						return super().call(__UpperCAmelCase ,					**__UpperCAmelCase   )


											__a					:						Any      =					FakeEncoder(bart_model.config ,					bart_model.model.shared   )
											__a					:						List[Any]      =					fake_encoder

											# Normal generation still works (the output will be different because the encoder weights are different)
											__a					:						List[Any]      =					bart_model.generate(__UpperCAmelCase   ).numpy()
											with self.assertRaises(__UpperCAmelCase   ):
																# FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
																bart_model.generate(__UpperCAmelCase ,					foo="bar"   )













import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors




@require_tokenizers
class 							snake_case_       (    __UpperCamelCase		,							unittest.TestCase      ):

						"""simple docstring"""



						snake_case__        =      MvpTokenizer
						snake_case__        =      MvpTokenizerFast
						snake_case__        =      True
						snake_case__        =      filter_roberta_detectors



						def        UpperCAmelCase__					(self:					List[str]   )    ->     Any:




											'''simple docstring'''



											super().setUp()
											__a					:						Dict      =					[
											    "l",
											    "o",
											    "w",
											    "e",
											    "r",
											    "s",
											    "t",
											    "i",
											    "d",
											    "n",
											    "\u0120",
											    "\u0120l",
											    "\u0120n",
											    "\u0120lo",
											    "\u0120low",
											    "er",
											    "\u0120lowest",
											    "\u0120newer",
											    "\u0120wider",
											    "<unk>",
											]
											__a					:						Optional[int]      =					dict(zip(__UpperCAmelCase ,					range(len(__UpperCAmelCase   )   )   )   )
											__a					:						Dict      =					["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
											__a					:						Optional[int]      =					{"unk_token": "<unk>"}

											__a					:						Tuple      =					os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES["vocab_file"]   )
											__a					:						List[str]      =					os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES["merges_file"]   )
											with open(self.vocab_file ,					"w" ,					encoding="utf-8"   ) as fp:
																fp.write(json.dumps(__UpperCAmelCase   ) + "\n"   )
											with open(self.merges_file ,					"w" ,					encoding="utf-8"   ) as fp:
																fp.write("\n".join(__UpperCAmelCase   )   )



						def        UpperCAmelCase__					(self:					List[Any] ,					**__UpperCAmelCase:					Dict   )    ->     int:




											'''simple docstring'''



											kwargs.update(self.special_tokens_map   )
											return self.tokenizer_class.from_pretrained(self.tmpdirname ,					**__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					str ,					**__UpperCAmelCase:					Tuple   )    ->     Tuple:




											'''simple docstring'''



											kwargs.update(self.special_tokens_map   )
											return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,					**__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					Any ,					__UpperCAmelCase:					int   )    ->     List[str]:




											'''simple docstring'''



											return "lower newer", "lower newer"



						@cached_property
						def        UpperCAmelCase__					(self:					List[Any]   )    ->     Union[str, Any]:




											'''simple docstring'''



											return MvpTokenizer.from_pretrained("RUCAIBox/mvp"   )



						@cached_property
						def        UpperCAmelCase__					(self:					str   )    ->     List[Any]:




											'''simple docstring'''



											return MvpTokenizerFast.from_pretrained("RUCAIBox/mvp"   )



						@require_torch
						def        UpperCAmelCase__					(self:					Dict   )    ->     List[Any]:




											'''simple docstring'''



											__a					:						Union[str, Any]      =					["A long paragraph for summarization.", "Another paragraph for summarization."]
											__a					:						List[Any]      =					[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																__a					:						Optional[Any]      =					tokenizer(__UpperCAmelCase ,					max_length=len(__UpperCAmelCase   ) ,					padding=__UpperCAmelCase ,					return_tensors="pt"   )
																self.assertIsInstance(__UpperCAmelCase ,					__UpperCAmelCase   )

																self.assertEqual((2, 9) ,					batch.input_ids.shape   )
																self.assertEqual((2, 9) ,					batch.attention_mask.shape   )
																__a					:						Optional[int]      =					batch.input_ids.tolist()[0]
																self.assertListEqual(__UpperCAmelCase ,					__UpperCAmelCase   )
																# Test that special tokens are reset



						@require_torch
						def        UpperCAmelCase__					(self:					Optional[int]   )    ->     List[Any]:




											'''simple docstring'''



											__a					:						int      =					["A long paragraph for summarization.", "Another paragraph for summarization."]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																__a					:						int      =					tokenizer(__UpperCAmelCase ,					padding=__UpperCAmelCase ,					return_tensors="pt"   )
																# check if input_ids are returned and no labels
																self.assertIn("input_ids" ,					__UpperCAmelCase   )
																self.assertIn("attention_mask" ,					__UpperCAmelCase   )
																self.assertNotIn("labels" ,					__UpperCAmelCase   )
																self.assertNotIn("decoder_attention_mask" ,					__UpperCAmelCase   )



						@require_torch
						def        UpperCAmelCase__					(self:					Any   )    ->     Union[str, Any]:




											'''simple docstring'''



											__a					:						List[Any]      =					[
											    "Summary of the text.",
											    "Another summary.",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																__a					:						int      =					tokenizer(text_target=__UpperCAmelCase ,					max_length=32 ,					padding="max_length" ,					return_tensors="pt"   )
																self.assertEqual(32 ,					targets["input_ids"].shape[1]   )



						@require_torch
						def        UpperCAmelCase__					(self:					int   )    ->     Union[str, Any]:




											'''simple docstring'''



											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																__a					:						str      =					tokenizer(
																    ["I am a small frog" * 1024, "I am a small frog"] ,					padding=__UpperCAmelCase ,					truncation=__UpperCAmelCase ,					return_tensors="pt"   )
																self.assertIsInstance(__UpperCAmelCase ,					__UpperCAmelCase   )
																self.assertEqual(batch.input_ids.shape ,					(2, 1024)   )



						@require_torch
						def        UpperCAmelCase__					(self:					str   )    ->     Any:




											'''simple docstring'''



											__a					:						Optional[int]      =					["A long paragraph for summarization."]
											__a					:						Tuple      =					[
											    "Summary of the text.",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																__a					:						Dict      =					tokenizer(__UpperCAmelCase ,					text_target=__UpperCAmelCase ,					return_tensors="pt"   )
																__a					:						str      =					inputs["input_ids"]
																__a					:						Optional[Any]      =					inputs["labels"]
																self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()   )
																self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()   )
																self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()   )
																self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()   )



						def        UpperCAmelCase__					(self:					Dict   )    ->     str:




											'''simple docstring'''



											pass



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




											'''simple docstring'''



											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'   ):
																					__a					:						Tuple      =					self.rust_tokenizer_class.from_pretrained(__UpperCAmelCase ,					**__UpperCAmelCase   )
																					__a					:						List[Any]      =					self.tokenizer_class.from_pretrained(__UpperCAmelCase ,					**__UpperCAmelCase   )
																					__a					:						Tuple      =					"A, <mask> AllenNLP sentence."
																					__a					:						Dict      =					tokenizer_r.encode_plus(__UpperCAmelCase ,					add_special_tokens=__UpperCAmelCase ,					return_token_type_ids=__UpperCAmelCase   )
																					__a					:						List[Any]      =					tokenizer_p.encode_plus(__UpperCAmelCase ,					add_special_tokens=__UpperCAmelCase ,					return_token_type_ids=__UpperCAmelCase   )

																					# token_type_ids should put 0 everywhere
																					self.assertEqual(sum(tokens_r["token_type_ids"]   ) ,					sum(tokens_p["token_type_ids"]   )   )

																					# attention_mask should put 1 everywhere, so sum over length should be 1
																					self.assertEqual(
																					    sum(tokens_r["attention_mask"]   ) / len(tokens_r["attention_mask"]   ) ,					sum(tokens_p["attention_mask"]   ) / len(tokens_p["attention_mask"]   ) ,					)

																					__a					:						Optional[int]      =					tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]   )
																					__a					:						Union[str, Any]      =					tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]   )

																					# Rust correctly handles the space before the mask while python doesnt
																					self.assertSequenceEqual(tokens_p["input_ids"] ,					[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]   )
																					self.assertSequenceEqual(tokens_r["input_ids"] ,					[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]   )

																					self.assertSequenceEqual(
																					    __UpperCAmelCase ,					["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]   )
																					self.assertSequenceEqual(
																					    __UpperCAmelCase ,					["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]   )





