
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 		__lowerCamelCase     :



							def __init__(     self	,				__snake_case							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase:					List[str] 						=     parent
								UpperCAmelCase:					List[Any] 						=     1_3
								UpperCAmelCase:					Any 						=     7
								UpperCAmelCase:					List[str] 						=     True
								UpperCAmelCase:					Tuple 						=     True
								UpperCAmelCase:					List[Any] 						=     True
								UpperCAmelCase:					Optional[Any] 						=     True
								UpperCAmelCase:					Union[str, Any] 						=     True
								UpperCAmelCase:					List[Any] 						=     9_9
								UpperCAmelCase:					List[str] 						=     3_2
								UpperCAmelCase:					Any 						=     4
								UpperCAmelCase:					Any 						=     2
								UpperCAmelCase:					Optional[int] 						=     4
								UpperCAmelCase:					int 						=     3_7
								UpperCAmelCase:					Optional[int] 						=     "gelu"
								UpperCAmelCase:					int 						=     0.1
								UpperCAmelCase:					List[Any] 						=     0.1
								UpperCAmelCase:					Optional[int] 						=     5_1_2
								UpperCAmelCase:					List[Any] 						=     1_6
								UpperCAmelCase:					str 						=     2
								UpperCAmelCase:					List[str] 						=     0.02
								UpperCAmelCase:					int 						=     3
								UpperCAmelCase:					str 						=     4
								UpperCAmelCase:					Optional[int] 						=     None
								UpperCAmelCase:					str 						=     self.vocab_size - 1
								UpperCAmelCase:					int 						=     self.vocab_size - 1
								UpperCAmelCase:					Tuple 						=     self.vocab_size - 1



							def     A__ (     self							)						->     str:






								"""simple docstring"""



								UpperCAmelCase:					List[str] 						=     ids_tensor([self.batch_size, self.seq_length]	,				self.vocab_size							)

								UpperCAmelCase:					Tuple 						=     None
								if self.use_input_mask:
									UpperCAmelCase:					Union[str, Any] 						=     random_attention_mask([self.batch_size, self.seq_length]							)

								UpperCAmelCase:					Dict 						=     None
								if self.use_token_type_ids:
									UpperCAmelCase:					Dict 						=     ids_tensor([self.batch_size, self.seq_length]	,				self.type_vocab_size							)

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

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

								UpperCAmelCase:					Any 						=     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=__snake_case	,				)

								UpperCAmelCase:					List[str] 						=     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     A__ (     self	,				__snake_case	,				__snake_case	,				__snake_case	,				__snake_case	,				__snake_case	,				*__snake_case							)						->     int:






								"""simple docstring"""



								UpperCAmelCase:					List[str] 						=     TFGPTJModel(config=__snake_case							)
								UpperCAmelCase:					Optional[Any] 						=     {
								    "input_ids": input_ids,
								    "attention_mask": input_mask,
								    "token_type_ids": token_type_ids,
								}
								UpperCAmelCase:					Any 						=     model(__snake_case							)

								UpperCAmelCase:					Dict 						=     [input_ids, None, input_mask]  # None is the input for 'past'
								UpperCAmelCase:					Optional[int] 						=     model(__snake_case							)

								UpperCAmelCase:					Dict 						=     model(__snake_case							)

								self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, self.seq_length, self.hidden_size)							)



							def     A__ (     self	,				__snake_case	,				__snake_case	,				__snake_case	,				__snake_case	,				__snake_case	,				*__snake_case							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase:					int 						=     TFGPTJModel(config=__snake_case							)

								# first forward pass
								UpperCAmelCase:					Any 						=     model(__snake_case	,				token_type_ids=__snake_case	,				use_cache=__snake_case							)
								UpperCAmelCase:					Any 						=     model(__snake_case	,				token_type_ids=__snake_case							)
								UpperCAmelCase:					int 						=     model(__snake_case	,				token_type_ids=__snake_case	,				use_cache=__snake_case							)

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

								UpperCAmelCase						,   UpperCAmelCase:					Any 						=     outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								UpperCAmelCase:					Optional[Any] 						=     ids_tensor((self.batch_size, 1)	,				config.vocab_size							)
								UpperCAmelCase:					Union[str, Any] 						=     ids_tensor([self.batch_size, 1]	,				self.type_vocab_size							)

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

								UpperCAmelCase:					Any 						=     model(__snake_case	,				token_type_ids=__snake_case							)["last_hidden_state"]
								UpperCAmelCase:					Tuple 						=     model(__snake_case	,				token_type_ids=__snake_case	,				past_key_values=__snake_case							)[
								    "last_hidden_state"
								]

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

								# test that outputs are equal for slice
								tf.debugging.assert_near(__snake_case	,				__snake_case	,				rtol=1e-6							)



							def     A__ (     self	,				__snake_case	,				__snake_case	,				__snake_case	,				__snake_case	,				__snake_case	,				*__snake_case							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase:					Optional[int] 						=     TFGPTJModel(config=__snake_case							)

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

								# first forward pass
								UpperCAmelCase						,   UpperCAmelCase:					Optional[Any] 						=     model(__snake_case	,				attention_mask=__snake_case							).to_tuple()

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

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

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

								# get two different outputs
								UpperCAmelCase:					Tuple 						=     model(__snake_case	,				attention_mask=__snake_case							)["last_hidden_state"]
								UpperCAmelCase:					Tuple 						=     model(__snake_case	,				past_key_values=__snake_case	,				attention_mask=__snake_case							)[
								    "last_hidden_state"
								]

								# select random slice
								UpperCAmelCase:					int 						=     int(ids_tensor((1,)	,				shape_list(__snake_case							)[-1]							)							)
								UpperCAmelCase:					Optional[int] 						=     output_from_no_past[:, -1, random_slice_idx]
								UpperCAmelCase:					int 						=     output_from_past[:, 0, random_slice_idx]

								# test that outputs are equal for slice
								tf.debugging.assert_near(__snake_case	,				__snake_case	,				rtol=1e-1_2							)



							def     A__ (     self	,				__snake_case	,				__snake_case	,				__snake_case	,				__snake_case	,				__snake_case	,				*__snake_case							)						->     Optional[Any]:






								"""simple docstring"""



								UpperCAmelCase:					int 						=     TFGPTJModel(config=__snake_case							)

								UpperCAmelCase:					List[str] 						=     input_ids[:1, :]
								UpperCAmelCase:					List[str] 						=     input_mask[:1, :]
								UpperCAmelCase:					Any 						=     token_type_ids[:1, :]
								UpperCAmelCase:					Tuple 						=     1

								# first forward pass
								UpperCAmelCase:					str 						=     model(__snake_case	,				attention_mask=__snake_case	,				token_type_ids=__snake_case	,				use_cache=__snake_case							)

								UpperCAmelCase						,   UpperCAmelCase:					int 						=     outputs.to_tuple()

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

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

								UpperCAmelCase:					Optional[int] 						=     model(
								    __snake_case	,				token_type_ids=__snake_case	,				attention_mask=__snake_case							)["last_hidden_state"]
								UpperCAmelCase:					int 						=     model(
								    __snake_case	,				token_type_ids=__snake_case	,				attention_mask=__snake_case	,				past_key_values=__snake_case	,				)["last_hidden_state"]
								self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]							)

								# select random slice
								UpperCAmelCase:					Union[str, Any] 						=     int(ids_tensor((1,)	,				shape_list(__snake_case							)[-1]							)							)
								UpperCAmelCase:					Optional[int] 						=     output_from_no_past[:, -3:, random_slice_idx]
								UpperCAmelCase:					Tuple 						=     output_from_past[:, :, random_slice_idx]

								# test that outputs are equal for slice
								tf.debugging.assert_near(__snake_case	,				__snake_case	,				rtol=1e-3							)



							def     A__ (     self	,				__snake_case	,				__snake_case	,				__snake_case	,				__snake_case	,				__snake_case	,				*__snake_case							)						->     str:






								"""simple docstring"""



								UpperCAmelCase:					Optional[int] 						=     TFGPTJForCausalLM(config=__snake_case							)
								UpperCAmelCase:					Tuple 						=     {
								    "input_ids": input_ids,
								    "attention_mask": input_mask,
								    "token_type_ids": token_type_ids,
								}
								UpperCAmelCase:					Optional[Any] 						=     model(__snake_case							)
								self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.seq_length, self.vocab_size)							)




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






								"""simple docstring"""



								UpperCAmelCase:					Optional[int] 						=     self.prepare_config_and_inputs()

								(
								    (
								    UpperCAmelCase
								)						,   (
								    UpperCAmelCase
								)						,   (
								    UpperCAmelCase
								)						,   (
								    UpperCAmelCase
								)						,   (
								    UpperCAmelCase
								)						,   (
								    UpperCAmelCase
								)						,   (
								    UpperCAmelCase
								)						,   (
								    UpperCAmelCase
								)						,   (
								    UpperCAmelCase
								)						,   
								):					Any 						=     config_and_inputs

								UpperCAmelCase:					Any 						=     {
								    "input_ids": input_ids,
								    "token_type_ids": token_type_ids,
								    "attention_mask": input_mask,
								}
								return config, inputs_dict





@require_tf
class 		__lowerCamelCase     (   lowercase							, lowercase							, lowercase							, unittest.TestCase		):
							lowerCamelCase__:				List[str]      =							(
							    (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
							    if is_tf_available()
							    else ()
							)

							lowerCamelCase__:				Tuple      =							(TFGPTJForCausalLM,) if is_tf_available() else ()
							lowerCamelCase__:				Any      =							(
							    {
							        '''feature-extraction''': TFGPTJModel,
							        '''question-answering''': TFGPTJForQuestionAnswering,
							        '''text-classification''': TFGPTJForSequenceClassification,
							        '''text-generation''': TFGPTJForCausalLM,
							        '''zero-shot''': TFGPTJForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)
							lowerCamelCase__:				int      =							False
							lowerCamelCase__:				Union[str, Any]      =							False
							lowerCamelCase__:				Optional[int]      =							False
							lowerCamelCase__:				Dict      =							False



							def     A__ (     self	,				__snake_case	,				__snake_case	,				__snake_case	,				__snake_case	,				__snake_case							)						->     str:






								"""simple docstring"""



								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     A__ (     self							)						->     List[str]:






								"""simple docstring"""



								UpperCAmelCase:					int 						=     TFGPTJModelTester(self							)
								UpperCAmelCase:					int 						=     ConfigTester(self	,				config_class=__snake_case	,				n_embd=3_7							)



							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								self.config_tester.run_common_tests()



							def     A__ (     self							)						->     str:






								"""simple docstring"""



								UpperCAmelCase:					Dict 						=     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_gptj_model(*__snake_case							)



							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase:					List[str] 						=     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_gptj_model_past(*__snake_case							)



							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_gptj_model_attention_mask_past(*__snake_case							)



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






								"""simple docstring"""



								UpperCAmelCase:					Optional[int] 						=     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_gptj_model_past_large_inputs(*__snake_case							)



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






								"""simple docstring"""



								UpperCAmelCase:					Dict 						=     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_gptj_lm_head_model(*__snake_case							)



							@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     A__ (     self							)						->     List[Any]:






								"""simple docstring"""



								UpperCAmelCase:					int 						=     TFGPTJModel.from_pretrained("EleutherAI/gpt-j-6B"	,				from_pt=__snake_case							)
								self.assertIsNotNone(__snake_case							)




							@unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor."							)
							def     A__ (     self							)						->     int:






								"""simple docstring"""



								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 		__lowerCamelCase     (   unittest.TestCase		):



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






								"""simple docstring"""



								UpperCAmelCase:					List[str] 						=     TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"	,				from_pt=__snake_case							)
								UpperCAmelCase:					List[str] 						=     tf.convert_to_tensor([[4_6_4, 3_2_9_0]]	,				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
								UpperCAmelCase:					Union[str, Any] 						=     [4_6_4, 3_2_9_0, 3_1_8, 2_5_7, 5_8_2, 3_3_8, 1_2_6_6, 1_5_4_5, 1_3, 6_3_2, 3_1_8, 2_5_7, 9_1_1_2, 1_5_1_8_5, 1_1, 2_9_0, 3_4_0, 3_1_8, 2_5_7, 1_5_4_5]
								# fmt: on
								UpperCAmelCase:					Optional[Any] 						=     model.generate(__snake_case	,				do_sample=__snake_case							)
								self.assertListEqual(output_ids[0].numpy().tolist()	,				__snake_case							)



							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase:					str 						=     AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B"	,				revision="float16"							)
								UpperCAmelCase:					List[Any] 						=     TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"	,				revision="float16"	,				from_pt=__snake_case							)

								UpperCAmelCase:					Dict 						=     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"							):
									UpperCAmelCase:					Any 						=     model.generate(**__snake_case	,				do_sample=__snake_case	,				seed=[4_2, 0]							)
								UpperCAmelCase:					List[str] 						=     tokenizer.decode(output_ids[0]	,				skip_special_tokens=__snake_case							)

								UpperCAmelCase:					str 						=     "Today is a nice day and I’m going to go for a walk. I’"
								self.assertEqual(__snake_case	,				__snake_case							)



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






								"""simple docstring"""



								UpperCAmelCase:					Optional[int] 						=     TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"	,				revision="float16"	,				from_pt=__snake_case							)
								UpperCAmelCase:					str 						=     AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B"	,				revision="float16"							)

								UpperCAmelCase:					Dict 						=     "left"

								# Define PAD Token = EOS Token = 50256
								UpperCAmelCase:					Optional[Any] 						=     tokenizer.eos_token
								UpperCAmelCase:					Tuple 						=     model.config.eos_token_id

								# use different length sentences to test batching
								UpperCAmelCase:					Any 						=     [
								    "Hello, my dog is a little",
								    "Today, I",
								]
								UpperCAmelCase:					int 						=     [
								    "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     A__ (     self							)						->     Optional[Any]:






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase:					str 						=     self._get_beam_search_test_objects()

								UpperCAmelCase:					List[str] 						=     tokenizer(__snake_case	,				return_tensors="tf"	,				padding=__snake_case							)
								UpperCAmelCase:					Tuple 						=     model.generate(**__snake_case	,				do_sample=__snake_case	,				num_beams=2							)
								UpperCAmelCase:					List[str] 						=     tokenizer.batch_decode(__snake_case	,				skip_special_tokens=__snake_case							)
								self.assertListEqual(__snake_case	,				__snake_case							)



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






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase:					Union[str, Any] 						=     self._get_beam_search_test_objects()

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




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






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase:					Any 						=     self._get_beam_search_test_objects()

								UpperCAmelCase:					str 						=     tokenizer(__snake_case	,				return_tensors="tf"	,				padding=__snake_case							)
								UpperCAmelCase:					List[str] 						=     tf.function(model.generate	,				jit_compile=__snake_case							)
								UpperCAmelCase:					int 						=     xla_generate(**__snake_case	,				do_sample=__snake_case	,				num_beams=2							)
								UpperCAmelCase:					List[Any] 						=     tokenizer.batch_decode(__snake_case	,				skip_special_tokens=__snake_case							)
								self.assertListEqual(__snake_case	,				__snake_case							)


def      __UpperCAmelCase    (  snake_case_							:		str				):



	'''simple docstring'''



	UpperCAmelCase:					Tuple 						=     str(snake_case_				).strip()
	if not bin_string:
		raise ValueError("Empty string was passed to the function"				)
	UpperCAmelCase:					List[Any] 						=     bin_string[0] == "-"
	if is_negative:
		UpperCAmelCase:					Optional[int] 						=     bin_string[1:]
	if not all(char in "01" for char in bin_string				):
		raise ValueError("Non-binary value was passed to the function"				)
	UpperCAmelCase:					List[str] 						=     0
	for char in bin_string:
		UpperCAmelCase:					Union[str, Any] 						=     2 * decimal_number + int(snake_case_				)
	return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
	from doctest import testmod

	testmod()
