




"""simple docstring"""

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			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  Any        =			parent
					snake_case						:  str        =			13
					snake_case						:  Tuple        =			7
					snake_case						:  Any        =			True
					snake_case						:  List[str]        =			True
					snake_case						:  str        =			True
					snake_case						:  Tuple        =			True
					snake_case						:  Optional[Any]        =			True
					snake_case						:  Any        =			99
					snake_case						:  Any        =			32
					snake_case						:  List[str]        =			4
					snake_case						:  Dict        =			2
					snake_case						:  List[str]        =			4
					snake_case						:  int        =			37
					snake_case						:  str        =			"gelu"
					snake_case						:  int        =			0.1
					snake_case						:  Tuple        =			0.1
					snake_case						:  Optional[int]        =			512
					snake_case						:  Any        =			16
					snake_case						:  Dict        =			2
					snake_case						:  str        =			0.02
					snake_case						:  Any        =			3
					snake_case						:  List[str]        =			4
					snake_case						:  int        =			None
					snake_case						:  Union[str, Any]        =			self.vocab_size - 1
					snake_case						:  List[str]        =			self.vocab_size - 1
					snake_case						:  Optional[int]        =			self.vocab_size - 1


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Optional[Any]        =			ids_tensor([self.batch_size, self.seq_length]			,	self.vocab_size							)

					snake_case						:  List[str]        =			None
					if self.use_input_mask:
							snake_case						:  List[Any]        =			random_attention_mask([self.batch_size, self.seq_length]							)

					snake_case						:  List[Any]        =			None
					if self.use_token_type_ids:
							snake_case						:  Any        =			ids_tensor([self.batch_size, self.seq_length]			,	self.type_vocab_size							)

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

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

					snake_case						:  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=SCREAMING_SNAKE_CASE			,	)

					snake_case						:  Any        =			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 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	*SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  Optional[int]        =			TFGPTJModel(config=SCREAMING_SNAKE_CASE							)
					snake_case						:  Optional[int]        =			{
					    "input_ids": input_ids,
					    "attention_mask": input_mask,
					    "token_type_ids": token_type_ids,
					}
					snake_case						:  Any        =			model(SCREAMING_SNAKE_CASE							)

					snake_case						:  Any        =			[input_ids, None, input_mask]  # None is the input for 'past'
					snake_case						:  str        =			model(SCREAMING_SNAKE_CASE							)

					snake_case						:  int        =			model(SCREAMING_SNAKE_CASE							)

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


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	*SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  Dict        =			TFGPTJModel(config=SCREAMING_SNAKE_CASE							)

					# first forward pass
					snake_case						:  Tuple        =			model(SCREAMING_SNAKE_CASE			,	token_type_ids=SCREAMING_SNAKE_CASE			,	use_cache=SCREAMING_SNAKE_CASE							)
					snake_case						:  int        =			model(SCREAMING_SNAKE_CASE			,	token_type_ids=SCREAMING_SNAKE_CASE							)
					snake_case						:  List[str]        =			model(SCREAMING_SNAKE_CASE			,	token_type_ids=SCREAMING_SNAKE_CASE			,	use_cache=SCREAMING_SNAKE_CASE							)

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

					snake_case     ,			snake_case						:  int        =			outputs.to_tuple()

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

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

					snake_case						:  int        =			model(SCREAMING_SNAKE_CASE			,	token_type_ids=SCREAMING_SNAKE_CASE							)["last_hidden_state"]
					snake_case						:  Dict        =			model(SCREAMING_SNAKE_CASE			,	token_type_ids=SCREAMING_SNAKE_CASE			,	past_key_values=SCREAMING_SNAKE_CASE							)[
					    "last_hidden_state"
					]

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

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


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	*SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  Tuple        =			TFGPTJModel(config=SCREAMING_SNAKE_CASE							)

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

					# first forward pass
					snake_case     ,			snake_case						:  Optional[int]        =			model(SCREAMING_SNAKE_CASE			,	attention_mask=SCREAMING_SNAKE_CASE							).to_tuple()

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

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

					# append to next input_ids and attn_mask
					snake_case						:  Optional[Any]        =			tf.concat([input_ids, next_tokens]			,	axis=-1							)
					snake_case						:  Optional[int]        =			tf.concat([attn_mask, tf.ones((shape_list(SCREAMING_SNAKE_CASE							)[0], 1)			,	dtype=tf.intaa							)]			,	axis=1							)

					# get two different outputs
					snake_case						:  List[Any]        =			model(SCREAMING_SNAKE_CASE			,	attention_mask=SCREAMING_SNAKE_CASE							)["last_hidden_state"]
					snake_case						:  Union[str, Any]        =			model(SCREAMING_SNAKE_CASE			,	past_key_values=SCREAMING_SNAKE_CASE			,	attention_mask=SCREAMING_SNAKE_CASE							)[
					    "last_hidden_state"
					]

					# select random slice
					snake_case						:  Optional[Any]        =			int(ids_tensor((1,)			,	shape_list(SCREAMING_SNAKE_CASE							)[-1]							)							)
					snake_case						:  Dict        =			output_from_no_past[:, -1, random_slice_idx]
					snake_case						:  str        =			output_from_past[:, 0, random_slice_idx]

					# test that outputs are equal for slice
					tf.debugging.assert_near(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	rtol=1E-12							)


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	*SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  Dict        =			TFGPTJModel(config=SCREAMING_SNAKE_CASE							)

					snake_case						:  Optional[int]        =			input_ids[:1, :]
					snake_case						:  Dict        =			input_mask[:1, :]
					snake_case						:  Tuple        =			token_type_ids[:1, :]
					snake_case						:  Optional[Any]        =			1

					# first forward pass
					snake_case						:  Tuple        =			model(SCREAMING_SNAKE_CASE			,	attention_mask=SCREAMING_SNAKE_CASE			,	token_type_ids=SCREAMING_SNAKE_CASE			,	use_cache=SCREAMING_SNAKE_CASE							)

					snake_case     ,			snake_case						:  Any        =			outputs.to_tuple()

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

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

					snake_case						:  int        =			model(
					    SCREAMING_SNAKE_CASE			,	token_type_ids=SCREAMING_SNAKE_CASE			,	attention_mask=SCREAMING_SNAKE_CASE							)["last_hidden_state"]
					snake_case						:  Dict        =			model(
					    SCREAMING_SNAKE_CASE			,	token_type_ids=SCREAMING_SNAKE_CASE			,	attention_mask=SCREAMING_SNAKE_CASE			,	past_key_values=SCREAMING_SNAKE_CASE			,	)["last_hidden_state"]
					self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]							)

					# select random slice
					snake_case						:  Tuple        =			int(ids_tensor((1,)			,	shape_list(SCREAMING_SNAKE_CASE							)[-1]							)							)
					snake_case						:  int        =			output_from_no_past[:, -3:, random_slice_idx]
					snake_case						:  Optional[Any]        =			output_from_past[:, :, random_slice_idx]

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


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	*SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







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



			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  List[Any]        =			self.prepare_config_and_inputs()

					(
					    (
					    snake_case
					)     ,			(
					    snake_case
					)     ,			(
					    snake_case
					)     ,			(
					    snake_case
					)     ,			(
					    snake_case
					)     ,			(
					    snake_case
					)     ,			(
					    snake_case
					)     ,			(
					    snake_case
					)     ,			(
					    snake_case
					)     ,			
					)						:  int        =			config_and_inputs

					snake_case						:  Union[str, Any]        =			{
					    "input_ids": input_ids,
					    "token_type_ids": token_type_ids,
					    "attention_mask": input_mask,
					}
					return config, inputs_dict







@require_tf
class      lowerCamelCase__      (					lowerCamelCase_			,					lowerCamelCase_			,					lowerCamelCase_			,					unittest.TestCase     ):
			a__	: Dict        =       (
			    (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
			    if is_tf_available()
			    else ()
			)

			a__	: Union[str, Any]        =       (TFGPTJForCausalLM,) if is_tf_available() else ()
			a__	: str        =       (
			    {
			        """feature-extraction""": TFGPTJModel,
			        """question-answering""": TFGPTJForQuestionAnswering,
			        """text-classification""": TFGPTJForSequenceClassification,
			        """text-generation""": TFGPTJForCausalLM,
			        """zero-shot""": TFGPTJForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			a__	: List[Any]        =       False
			a__	: Any        =       False
			a__	: List[str]        =       False
			a__	: Dict        =       False


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""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 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  List[Any]        =			TFGPTJModelTester(self							)
					snake_case						:  Dict        =			ConfigTester(self			,	config_class=SCREAMING_SNAKE_CASE			,	n_embd=37							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					self.config_tester.run_common_tests()


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  List[Any]        =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_gptj_model(*SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  List[Any]        =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_gptj_model_past(*SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Optional[Any]        =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_gptj_model_attention_mask_past(*SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Dict        =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_gptj_model_past_large_inputs(*SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Any        =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_gptj_lm_head_model(*SCREAMING_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 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Dict        =			TFGPTJModel.from_pretrained("EleutherAI/gpt-j-6B"			,	from_pt=SCREAMING_SNAKE_CASE							)
					self.assertIsNotNone(SCREAMING_SNAKE_CASE							)



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



					"""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 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Tuple        =			TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"			,	from_pt=SCREAMING_SNAKE_CASE							)
					snake_case						:  Optional[Any]        =			tf.convert_to_tensor([[464, 3_290]]			,	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
					snake_case						:  List[Any]        =			[464, 3_290, 318, 257, 582, 338, 1_266, 1_545, 13, 632, 318, 257, 9_112, 15_185, 11, 290, 340, 318, 257, 1_545]
					# fmt: on
					snake_case						:  int        =			model.generate(SCREAMING_SNAKE_CASE			,	do_sample=SCREAMING_SNAKE_CASE							)
					self.assertListEqual(output_ids[0].numpy().tolist()			,	SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







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

					snake_case						:  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"							):
							snake_case						:  Union[str, Any]        =			model.generate(**SCREAMING_SNAKE_CASE			,	do_sample=SCREAMING_SNAKE_CASE			,	seed=[42, 0]							)
					snake_case						:  Tuple        =			tokenizer.decode(output_ids[0]			,	skip_special_tokens=SCREAMING_SNAKE_CASE							)

					snake_case						:  Dict        =			"Today is a nice day and I’m going to go for a walk. I’"
					self.assertEqual(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







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

					snake_case						:  List[str]        =			"left"

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

					# use different length sentences to test batching
					snake_case						:  int        =			[
					    "Hello, my dog is a little",
					    "Today, I",
					]
					snake_case						:  Any        =			[
					    "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 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case     ,			snake_case     ,			snake_case     ,			snake_case						:  Optional[Any]        =			self._get_beam_search_test_objects()

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


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







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

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



			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







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

					snake_case						:  Tuple        =			tokenizer(SCREAMING_SNAKE_CASE			,	return_tensors="tf"			,	padding=SCREAMING_SNAKE_CASE							)
					snake_case						:  List[Any]        =			tf.function(model.generate			,	jit_compile=SCREAMING_SNAKE_CASE							)
					snake_case						:  Dict        =			xla_generate(**SCREAMING_SNAKE_CASE			,	do_sample=SCREAMING_SNAKE_CASE			,	num_beams=2							)
					snake_case						:  Optional[Any]        =			tokenizer.batch_decode(SCREAMING_SNAKE_CASE			,	skip_special_tokens=SCREAMING_SNAKE_CASE							)
					self.assertListEqual(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)







"""simple docstring"""

def 					UpperCamelCase__       (						lowercase__					:       int				):
		if not isinstance(lowercase__    ,       lowercase__				):
				raise ValueError("double_factorial() only accepts integral values"				)
		if n < 0:
				raise ValueError("double_factorial() not defined for negative values"				)
		return 1 if n <= 1 else n * double_factorial(n - 2				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()

