from __future__ import annotations

import unittest

from transformers import AutoTokenizer, GPTJConfig, is_tf_available
from transformers.testing_utils import require_tf, slow, tooslow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
from ...utils.test_modeling_tf_core import TFCoreModelTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers.models.gptj.modeling_tf_gptj import (
		    TFGPTJForCausalLM,
		    TFGPTJForQuestionAnswering,
		    TFGPTJForSequenceClassification,
		    TFGPTJModel,
		    shape_list,
		)






class 							_SCREAMING_SNAKE_CASE:
			def __init__(    self							,     lowerCAmelCase_    )	->Optional[int]:
								a  :    Any				=						parent
								a  :    int				=						13
								a  :    Any				=						7
								a  :    Dict				=						True
								a  :    Optional[Any]				=						True
								a  :    Any				=						True
								a  :    int				=						True
								a  :    Union[str, Any]				=						True
								a  :    Union[str, Any]				=						99
								a  :    List[Any]				=						32
								a  :    List[str]				=						4
								a  :    Optional[Any]				=						2
								a  :    List[Any]				=						4
								a  :    List[Any]				=						37
								a  :    List[Any]				=						'''gelu'''
								a  :    Optional[int]				=						0.1
								a  :    Any				=						0.1
								a  :    Union[str, Any]				=						5_12
								a  :    Union[str, Any]				=						16
								a  :    Any				=						2
								a  :    str				=						0.02
								a  :    str				=						3
								a  :    Any				=						4
								a  :    int				=						None
								a  :    str				=						self.vocab_size - 1
								a  :    int				=						self.vocab_size - 1
								a  :    str				=						self.vocab_size - 1
			def _lowercase(    self    )	->List[Any]:
								a  :    List[str]				=						ids_tensor([self.batch_size, self.seq_length]							,     self.vocab_size    )

								a  :    List[Any]				=						None
								if self.use_input_mask:
													a  :    Optional[int]				=						random_attention_mask([self.batch_size, self.seq_length]    )

								a  :    Tuple				=						None
								if self.use_token_type_ids:
													a  :    int				=						ids_tensor([self.batch_size, self.seq_length]							,     self.type_vocab_size    )

								a  :    List[Any]				=						None
								if self.use_mc_token_ids:
													a  :    Union[str, Any]				=						ids_tensor([self.batch_size, self.num_choices]							,     self.seq_length    )

								a  :    Any				=						None
								a  :    Any				=						None
								a  :    int				=						None
								if self.use_labels:
													a  :    Optional[int]				=						ids_tensor([self.batch_size]							,     self.type_sequence_label_size    )
													a  :    Tuple				=						ids_tensor([self.batch_size, self.seq_length]							,     self.num_labels    )
													a  :    int				=						ids_tensor([self.batch_size]							,     self.num_choices    )

								a  :    Optional[int]				=						GPTJConfig(
								    vocab_size=self.vocab_size							,     n_embd=self.hidden_size							,     n_layer=self.num_hidden_layers							,     n_head=self.num_attention_heads							,     intermediate_size=self.intermediate_size							,     hidden_act=self.hidden_act							,     hidden_dropout_prob=self.hidden_dropout_prob							,     attention_probs_dropout_prob=self.attention_probs_dropout_prob							,     n_positions=self.max_position_embeddings							,     type_vocab_size=self.type_vocab_size							,     initializer_range=self.initializer_range							,     bos_token_id=self.bos_token_id							,     eos_token_id=self.eos_token_id							,     pad_token_id=self.pad_token_id							,     rotary_dim=self.rotary_dim							,     return_dict=lowerCAmelCase_							,     )

								a  :    Dict				=						ids_tensor([self.num_hidden_layers, self.num_attention_heads]							,     2    )

								return (
								    config,
								    input_ids,
								    input_mask,
								    head_mask,
								    token_type_ids,
								    mc_token_ids,
								    sequence_labels,
								    token_labels,
								    choice_labels,
								)
			def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     *lowerCAmelCase_    )	->Dict:
								a  :    Dict				=						TFGPTJModel(config=lowerCAmelCase_    )
								a  :    Any				=						{
								    '''input_ids''': input_ids,
								    '''attention_mask''': input_mask,
								    '''token_type_ids''': token_type_ids,
								}
								a  :    int				=						model(lowerCAmelCase_    )

								a  :    Any				=						[input_ids, None, input_mask]  # None is the input for 'past'
								a  :    Tuple				=						model(lowerCAmelCase_    )

								a  :    Dict				=						model(lowerCAmelCase_    )

								self.parent.assertEqual(result.last_hidden_state.shape							,     (self.batch_size, self.seq_length, self.hidden_size)    )
			def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     *lowerCAmelCase_    )	->Optional[int]:
								a  :    Union[str, Any]				=						TFGPTJModel(config=lowerCAmelCase_    )

								# first forward pass
								a  :    Tuple				=						model(lowerCAmelCase_							,     token_type_ids=lowerCAmelCase_							,     use_cache=lowerCAmelCase_    )
								a  :    Any				=						model(lowerCAmelCase_							,     token_type_ids=lowerCAmelCase_    )
								a  :    Dict				=						model(lowerCAmelCase_							,     token_type_ids=lowerCAmelCase_							,     use_cache=lowerCAmelCase_    )

								self.parent.assertTrue(len(lowerCAmelCase_    ) == len(lowerCAmelCase_    )    )
								self.parent.assertTrue(len(lowerCAmelCase_    ) == len(lowerCAmelCase_    ) + 1    )

								a  :    List[str]				=						outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								a  :    Optional[int]				=						ids_tensor((self.batch_size, 1)							,     config.vocab_size    )
								a  :    Tuple				=						ids_tensor([self.batch_size, 1]							,     self.type_vocab_size    )

								# append to next input_ids and token_type_ids
								a  :    Optional[int]				=						tf.concat([input_ids, next_tokens]							,     axis=-1    )
								a  :    List[Any]				=						tf.concat([token_type_ids, next_token_types]							,     axis=-1    )

								a  :    Dict				=						model(lowerCAmelCase_							,     token_type_ids=lowerCAmelCase_    )['''last_hidden_state''']
								a  :    List[Any]				=						model(lowerCAmelCase_							,     token_type_ids=lowerCAmelCase_							,     past_key_values=lowerCAmelCase_    )[
								    '''last_hidden_state'''
								]

								# select random slice
								a  :    Optional[Any]				=						int(ids_tensor((1,)							,     shape_list(lowerCAmelCase_    )[-1]    )    )
								a  :    Union[str, Any]				=						output_from_no_past[:, -1, random_slice_idx]
								a  :    List[str]				=						output_from_past[:, 0, random_slice_idx]

								# test that outputs are equal for slice
								tf.debugging.assert_near(lowerCAmelCase_							,     lowerCAmelCase_							,     rtol=1e-6    )
			def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     *lowerCAmelCase_    )	->int:
								a  :    Tuple				=						TFGPTJModel(config=lowerCAmelCase_    )

								# create attention mask
								a  :    Union[str, Any]				=						self.seq_length // 2
								a  :    List[Any]				=						tf.ones((self.batch_size, half_seq_length)							,     dtype=tf.intaa    )
								a  :    str				=						tf.zeros((self.batch_size, self.seq_length - half_seq_length)							,     dtype=tf.intaa    )
								a  :    List[str]				=						tf.concat([attn_mask_begin, attn_mask_end]							,     axis=1    )

								# first forward pass
								a  :    int				=						model(lowerCAmelCase_							,     attention_mask=lowerCAmelCase_    ).to_tuple()

								# create hypothetical next token and extent to next_input_ids
								a  :    Optional[int]				=						ids_tensor((self.batch_size, 1)							,     config.vocab_size    )

								# change a random masked slice from input_ids
								a  :    Union[str, Any]				=						ids_tensor((1,)							,     lowerCAmelCase_    ).numpy() + 1
								a  :    Any				=						ids_tensor((self.batch_size, self.seq_length)							,     config.vocab_size    )
								a  :    str				=						tf.range(self.seq_length    ) == (self.seq_length - random_seq_idx_to_change)
								a  :    Dict				=						tf.transpose(
								    tf.broadcast_to(tf.expand_dims(lowerCAmelCase_							,     -1    )							,     (self.seq_length, self.batch_size)    )    )
								a  :    Tuple				=						tf.where(lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )

								# append to next input_ids and attn_mask
								a  :    Any				=						tf.concat([input_ids, next_tokens]							,     axis=-1    )
								a  :    Union[str, Any]				=						tf.concat([attn_mask, tf.ones((shape_list(lowerCAmelCase_    )[0], 1)							,     dtype=tf.intaa    )]							,     axis=1    )

								# get two different outputs
								a  :    str				=						model(lowerCAmelCase_							,     attention_mask=lowerCAmelCase_    )['''last_hidden_state''']
								a  :    Optional[int]				=						model(lowerCAmelCase_							,     past_key_values=lowerCAmelCase_							,     attention_mask=lowerCAmelCase_    )[
								    '''last_hidden_state'''
								]

								# select random slice
								a  :    Any				=						int(ids_tensor((1,)							,     shape_list(lowerCAmelCase_    )[-1]    )    )
								a  :    Dict				=						output_from_no_past[:, -1, random_slice_idx]
								a  :    List[str]				=						output_from_past[:, 0, random_slice_idx]

								# test that outputs are equal for slice
								tf.debugging.assert_near(lowerCAmelCase_							,     lowerCAmelCase_							,     rtol=1e-12    )
			def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     *lowerCAmelCase_    )	->int:
								a  :    List[Any]				=						TFGPTJModel(config=lowerCAmelCase_    )

								a  :    Optional[int]				=						input_ids[:1, :]
								a  :    Dict				=						input_mask[:1, :]
								a  :    Optional[Any]				=						token_type_ids[:1, :]
								a  :    List[str]				=						1

								# first forward pass
								a  :    Tuple				=						model(lowerCAmelCase_							,     attention_mask=lowerCAmelCase_							,     token_type_ids=lowerCAmelCase_							,     use_cache=lowerCAmelCase_    )

								a  :    Any				=						outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								a  :    Union[str, Any]				=						ids_tensor((self.batch_size, 3)							,     config.vocab_size    )
								a  :    Union[str, Any]				=						ids_tensor((self.batch_size, 3)							,     2    )
								a  :    Optional[int]				=						ids_tensor((self.batch_size, 3)							,     self.type_vocab_size    )

								# append to next input_ids and token_type_ids
								a  :    int				=						tf.concat([input_ids, next_tokens]							,     axis=-1    )
								a  :    Dict				=						tf.concat([input_mask, next_attn_mask]							,     axis=-1    )
								a  :    List[str]				=						tf.concat([token_type_ids, next_token_types]							,     axis=-1    )

								a  :    Union[str, Any]				=						model(
								    lowerCAmelCase_							,     token_type_ids=lowerCAmelCase_							,     attention_mask=lowerCAmelCase_    )['''last_hidden_state''']
								a  :    Dict				=						model(
								    lowerCAmelCase_							,     token_type_ids=lowerCAmelCase_							,     attention_mask=lowerCAmelCase_							,     past_key_values=lowerCAmelCase_							,     )['''last_hidden_state''']
								self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]    )

								# select random slice
								a  :    List[Any]				=						int(ids_tensor((1,)							,     shape_list(lowerCAmelCase_    )[-1]    )    )
								a  :    Dict				=						output_from_no_past[:, -3:, random_slice_idx]
								a  :    Optional[int]				=						output_from_past[:, :, random_slice_idx]

								# test that outputs are equal for slice
								tf.debugging.assert_near(lowerCAmelCase_							,     lowerCAmelCase_							,     rtol=1e-3    )
			def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     *lowerCAmelCase_    )	->Any:
								a  :    Optional[Any]				=						TFGPTJForCausalLM(config=lowerCAmelCase_    )
								a  :    Dict				=						{
								    '''input_ids''': input_ids,
								    '''attention_mask''': input_mask,
								    '''token_type_ids''': token_type_ids,
								}
								a  :    Optional[Any]				=						model(lowerCAmelCase_    )
								self.parent.assertEqual(result.logits.shape							,     (self.batch_size, self.seq_length, self.vocab_size)    )


			def _lowercase(    self    )	->List[Any]:
								a  :    int				=						self.prepare_config_and_inputs()

								(
								    a							
								)  :    Tuple				=						config_and_inputs

								a  :    List[Any]				=						{
								    '''input_ids''': input_ids,
								    '''token_type_ids''': token_type_ids,
								    '''attention_mask''': input_mask,
								}
								return config, inputs_dict






@require_tf
class 							_SCREAMING_SNAKE_CASE(   snake_case__       ,      snake_case__       ,      snake_case__       ,      unittest.TestCase	):
			UpperCamelCase_				:						Optional[int] 			=   (
			    (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
			    if is_tf_available()
			    else ()
			)

			UpperCamelCase_				:						str 			=   (TFGPTJForCausalLM,) if is_tf_available() else ()
			UpperCamelCase_				:						Dict 			=   (
			    {
			        'feature-extraction': TFGPTJModel,
			        'question-answering': TFGPTJForQuestionAnswering,
			        'text-classification': TFGPTJForSequenceClassification,
			        'text-generation': TFGPTJForCausalLM,
			        'zero-shot': TFGPTJForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			UpperCamelCase_				:						Union[str, Any] 			=   False
			UpperCamelCase_				:						List[Any] 			=   False
			UpperCamelCase_				:						List[Any] 			=   False
			UpperCamelCase_				:						str 			=   False
			def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_    )	->List[str]:
								if (
								    pipeline_test_casse_name == "QAPipelineTests"
								    and tokenizer_name is not None
								    and not tokenizer_name.endswith('''Fast'''    )
								):
													# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
													# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
													# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
													return True

								return False
			def _lowercase(    self    )	->str:
								a  :    Dict				=						TFGPTJModelTester(self    )
								a  :    List[str]				=						ConfigTester(self							,     config_class=lowerCAmelCase_							,     n_embd=37    )
			def _lowercase(    self    )	->str:
								self.config_tester.run_common_tests()
			def _lowercase(    self    )	->Optional[int]:
								a  :    Optional[Any]				=						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_gptj_model(*lowerCAmelCase_    )
			def _lowercase(    self    )	->Optional[Any]:
								a  :    Optional[int]				=						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_gptj_model_past(*lowerCAmelCase_    )
			def _lowercase(    self    )	->Optional[Any]:
								a  :    Union[str, Any]				=						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_gptj_model_attention_mask_past(*lowerCAmelCase_    )
			def _lowercase(    self    )	->str:
								a  :    Any				=						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_gptj_model_past_large_inputs(*lowerCAmelCase_    )
			def _lowercase(    self    )	->Dict:
								a  :    Optional[Any]				=						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_gptj_lm_head_model(*lowerCAmelCase_    )
			@slow
			@unittest.skipIf(
			    not is_tf_available() or len(tf.config.list_physical_devices('''GPU'''    )    ) > 0							,     '''skip testing on GPU for now to avoid GPU OOM.'''							,     )
			def _lowercase(    self    )	->Any:
								a  :    Optional[Any]				=						TFGPTJModel.from_pretrained('''EleutherAI/gpt-j-6B'''							,     from_pt=lowerCAmelCase_    )
								self.assertIsNotNone(lowerCAmelCase_    )


			@unittest.skip(reason='''Currently, model embeddings are going to undergo a major refactor.'''    )
			def _lowercase(    self    )	->List[Any]:
								super().test_resize_token_embeddings()






@require_tf
@tooslow
# Marked as @tooslow due to GPU OOM -- but still useful to run locally. Requires ~39GB of RAM.
class 							_SCREAMING_SNAKE_CASE(   unittest.TestCase	):
			def _lowercase(    self    )	->Any:
								a  :    Optional[int]				=						TFGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B'''							,     from_pt=lowerCAmelCase_    )
								a  :    Optional[Any]				=						tf.convert_to_tensor([[4_64, 32_90]]							,     dtype=tf.intaa    )  # The dog
								# fmt: off
								# The dog is a man's best friend. It is a loyal companion, and it is a friend
								a  :    List[Any]				=						[4_64, 32_90, 3_18, 2_57, 5_82, 3_38, 12_66, 15_45, 13, 6_32, 3_18, 2_57, 91_12, 1_51_85, 11, 2_90, 3_40, 3_18, 2_57, 15_45]
								# fmt: on
								a  :    Any				=						model.generate(lowerCAmelCase_							,     do_sample=lowerCAmelCase_    )
								self.assertListEqual(output_ids[0].numpy().tolist()							,     lowerCAmelCase_    )
			def _lowercase(    self    )	->Optional[Any]:
								a  :    Tuple				=						AutoTokenizer.from_pretrained('''EleutherAI/gpt-j-6B'''							,     revision='''float16'''    )
								a  :    Union[str, Any]				=						TFGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B'''							,     revision='''float16'''							,     from_pt=lowerCAmelCase_    )

								a  :    Optional[int]				=						tokenizer('''Today is a nice day and'''							,     return_tensors='''tf'''    )
								# forces the generation to happen on CPU, to avoid GPU-related quirks
								with tf.device(''':/CPU:0'''    ):
													a  :    Dict				=						model.generate(**lowerCAmelCase_							,     do_sample=lowerCAmelCase_							,     seed=[42, 0]    )
								a  :    List[str]				=						tokenizer.decode(output_ids[0]							,     skip_special_tokens=lowerCAmelCase_    )

								a  :    Union[str, Any]				=						'''Today is a nice day and I’m going to go for a walk. I’'''
								self.assertEqual(lowerCAmelCase_							,     lowerCAmelCase_    )
			def _lowercase(    self    )	->Optional[int]:
								a  :    Optional[Any]				=						TFGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B'''							,     revision='''float16'''							,     from_pt=lowerCAmelCase_    )
								a  :    Optional[int]				=						AutoTokenizer.from_pretrained('''EleutherAI/gpt-j-6B'''							,     revision='''float16'''    )

								a  :    List[Any]				=						'''left'''

								# Define PAD Token = EOS Token = 50256
								a  :    Optional[Any]				=						tokenizer.eos_token
								a  :    List[str]				=						model.config.eos_token_id

								# use different length sentences to test batching
								a  :    Any				=						[
								    '''Hello, my dog is a little''',
								    '''Today, I''',
								]
								a  :    Tuple				=						[
								    '''Hello, my dog is a little over a year old and has been diagnosed with hip dysplasia''',
								    '''Today, I’m going to be talking about a topic that’''',
								]
								return model, tokenizer, sentences, expected_output_sentences
			def _lowercase(    self    )	->Tuple:
								# Confirms that we get the expected results with left-padded beam search
								a  :    Optional[Any]				=						self._get_beam_search_test_objects()

								a  :    Tuple				=						tokenizer(lowerCAmelCase_							,     return_tensors='''tf'''							,     padding=lowerCAmelCase_    )
								a  :    Dict				=						model.generate(**lowerCAmelCase_							,     do_sample=lowerCAmelCase_							,     num_beams=2    )
								a  :    Optional[Any]				=						tokenizer.batch_decode(lowerCAmelCase_							,     skip_special_tokens=lowerCAmelCase_    )
								self.assertListEqual(lowerCAmelCase_							,     lowerCAmelCase_    )
			def _lowercase(    self    )	->str:
								# Confirms that left-padding is working properly
								a  :    Optional[Any]				=						self._get_beam_search_test_objects()

								a  :    Optional[Any]				=						tokenizer(lowerCAmelCase_							,     return_tensors='''tf'''							,     padding=lowerCAmelCase_    )
								a  :    str				=						tokenizer(sentences[0]							,     return_tensors='''tf'''    )
								a  :    str				=						model.generate(**lowerCAmelCase_							,     do_sample=lowerCAmelCase_							,     num_beams=2    )
								a  :    List[Any]				=						(
								    shape_list(inputs_non_padded['''input_ids''']    )[-1]
								    - tf.reduce_sum(tf.cast(inputs['''attention_mask'''][-1]							,     tf.intaa    )    ).numpy()
								)
								a  :    List[str]				=						tokenizer(sentences[1]							,     return_tensors='''tf'''    )
								a  :    Union[str, Any]				=						model.generate(
								    **lowerCAmelCase_							,     do_sample=lowerCAmelCase_							,     num_beams=2							,     max_length=model.config.max_length - num_paddings    )
								a  :    Optional[int]				=						tokenizer.decode(output_non_padded[0]							,     skip_special_tokens=lowerCAmelCase_    )
								a  :    str				=						tokenizer.decode(output_padded[0]							,     skip_special_tokens=lowerCAmelCase_    )
								self.assertListEqual(lowerCAmelCase_							,     [non_padded_sentence, padded_sentence]    )


			def _lowercase(    self    )	->Union[str, Any]:
								# Confirms that XLA is working properly
								a  :    Optional[Any]				=						self._get_beam_search_test_objects()

								a  :    int				=						tokenizer(lowerCAmelCase_							,     return_tensors='''tf'''							,     padding=lowerCAmelCase_    )
								a  :    Tuple				=						tf.function(model.generate							,     jit_compile=lowerCAmelCase_    )
								a  :    Tuple				=						xla_generate(**lowerCAmelCase_							,     do_sample=lowerCAmelCase_							,     num_beams=2    )
								a  :    List[Any]				=						tokenizer.batch_decode(lowerCAmelCase_							,     skip_special_tokens=lowerCAmelCase_    )
								self.assertListEqual(lowerCAmelCase_							,     lowerCAmelCase_    )
import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property






class 							_SCREAMING_SNAKE_CASE(   unittest.TestCase	):
   @cached_property
   def _lowercase(    self    )	->List[str]:
        return BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''    )
   @cached_property
   def _lowercase(    self    )	->str:
        return BlenderbotTokenizerFast.from_pretrained('''facebook/blenderbot-3B'''    )
   def _lowercase(    self    )	->str:
        a  :    Optional[int]				=						self.tokenizer_ab
        a  :    List[Any]				=						''' I am a small frog.'''
        a  :    Optional[Any]				=						tok([src_text]							,     padding=lowerCAmelCase_							,     truncation=lowerCAmelCase_    )['''input_ids''']
        a  :    int				=						tok.batch_decode(lowerCAmelCase_							,     skip_special_tokens=lowerCAmelCase_							,     clean_up_tokenization_spaces=lowerCAmelCase_    )[0]
        assert src_text == decoded
   def _lowercase(    self    )	->Dict:
        a  :    List[Any]				=						self.rust_tokenizer_ab
        a  :    str				=						''' I am a small frog.'''
        a  :    Tuple				=						tok([src_text]							,     padding=lowerCAmelCase_							,     truncation=lowerCAmelCase_    )['''input_ids''']
        a  :    Optional[Any]				=						tok.batch_decode(lowerCAmelCase_							,     skip_special_tokens=lowerCAmelCase_							,     clean_up_tokenization_spaces=lowerCAmelCase_    )[0]
        assert src_text == decoded
   def _lowercase(    self    )	->Tuple:
        assert self.tokenizer_ab.add_prefix_space
        assert self.tokenizer_ab([''' Sam''', '''Sam''']    ).input_ids == [[55_02, 2], [55_02, 2]]


   def _lowercase(    self    )	->Optional[Any]:
        assert self.rust_tokenizer_ab.add_prefix_space
        assert self.rust_tokenizer_ab([''' Sam''', '''Sam''']    ).input_ids == [[55_02, 2], [55_02, 2]]