





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 				_UpperCamelCase       :

       def __init__(		self:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			List[str]		)							->			str:




              """simple docstring"""


              UpperCamelCase_ 							=	parent
              UpperCamelCase_ 							=	13
              UpperCamelCase_ 							=	7
              UpperCamelCase_ 							=	True
              UpperCamelCase_ 							=	True
              UpperCamelCase_ 							=	True
              UpperCamelCase_ 							=	True
              UpperCamelCase_ 							=	True
              UpperCamelCase_ 							=	99
              UpperCamelCase_ 							=	32
              UpperCamelCase_ 							=	4
              UpperCamelCase_ 							=	2
              UpperCamelCase_ 							=	4
              UpperCamelCase_ 							=	37
              UpperCamelCase_ 							=	"gelu"
              UpperCamelCase_ 							=	0.1
              UpperCamelCase_ 							=	0.1
              UpperCamelCase_ 							=	512
              UpperCamelCase_ 							=	16
              UpperCamelCase_ 							=	2
              UpperCamelCase_ 							=	0.02
              UpperCamelCase_ 							=	3
              UpperCamelCase_ 							=	4
              UpperCamelCase_ 							=	None
              UpperCamelCase_ 							=	self.vocab_size - 1
              UpperCamelCase_ 							=	self.vocab_size - 1
              UpperCamelCase_ 							=	self.vocab_size - 1

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




              """simple docstring"""


              UpperCamelCase_ 							=	ids_tensor([self.batch_size, self.seq_length]       , self.vocab_size		)

              UpperCamelCase_ 							=	None
              if self.use_input_mask:
                     UpperCamelCase_ 							=	random_attention_mask([self.batch_size, self.seq_length]		)

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

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

              UpperCamelCase_ 							=	None
              UpperCamelCase_ 							=	None
              UpperCamelCase_ 							=	None
              if self.use_labels:
                     UpperCamelCase_ 							=	ids_tensor([self.batch_size]       , self.type_sequence_label_size		)
                     UpperCamelCase_ 							=	ids_tensor([self.batch_size, self.seq_length]       , self.num_labels		)
                     UpperCamelCase_ 							=	ids_tensor([self.batch_size]       , self.num_choices		)

              UpperCamelCase_ 							=	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       , )

              UpperCamelCase_ 							=	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:			Any       , _SCREAMING_SNAKE_CASE:			Any       , _SCREAMING_SNAKE_CASE:			Dict       , _SCREAMING_SNAKE_CASE:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			Optional[int]       , _SCREAMING_SNAKE_CASE:			Optional[int]       , *_SCREAMING_SNAKE_CASE:			Any		)							->			List[Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	TFGPTJModel(config=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	{
                  "input_ids": input_ids,
                  "attention_mask": input_mask,
                  "token_type_ids": token_type_ids,
              }
              UpperCamelCase_ 							=	model(_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	[input_ids, None, input_mask]  # None is the input for 'past'
              UpperCamelCase_ 							=	model(_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	model(_SCREAMING_SNAKE_CASE		)

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

       def lowercase			(		self:			List[str]       , _SCREAMING_SNAKE_CASE:			str       , _SCREAMING_SNAKE_CASE:			Dict       , _SCREAMING_SNAKE_CASE:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			Tuple       , *_SCREAMING_SNAKE_CASE:			List[Any]		)							->			Any:




              """simple docstring"""


              UpperCamelCase_ 							=	TFGPTJModel(config=_SCREAMING_SNAKE_CASE		)

              # first forward pass
              UpperCamelCase_ 							=	model(_SCREAMING_SNAKE_CASE       , token_type_ids=_SCREAMING_SNAKE_CASE       , use_cache=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	model(_SCREAMING_SNAKE_CASE       , token_type_ids=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	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		)

              UpperCamelCase_							,			UpperCamelCase_ 							=	outputs.to_tuple()

              # create hypothetical next token and extent to next_input_ids
              UpperCamelCase_ 							=	ids_tensor((self.batch_size, 1)       , config.vocab_size		)
              UpperCamelCase_ 							=	ids_tensor([self.batch_size, 1]       , self.type_vocab_size		)

              # append to next input_ids and token_type_ids
              UpperCamelCase_ 							=	tf.concat([input_ids, next_tokens]       , axis=-1		)
              UpperCamelCase_ 							=	tf.concat([token_type_ids, next_token_types]       , axis=-1		)

              UpperCamelCase_ 							=	model(_SCREAMING_SNAKE_CASE       , token_type_ids=_SCREAMING_SNAKE_CASE		)["last_hidden_state"]
              UpperCamelCase_ 							=	model(_SCREAMING_SNAKE_CASE       , token_type_ids=_SCREAMING_SNAKE_CASE       , past_key_values=_SCREAMING_SNAKE_CASE		)[
                  "last_hidden_state"
              ]

              # select random slice
              UpperCamelCase_ 							=	int(ids_tensor((1,)       , shape_list(_SCREAMING_SNAKE_CASE		)[-1]		)		)
              UpperCamelCase_ 							=	output_from_no_past[:, -1, random_slice_idx]
              UpperCamelCase_ 							=	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 lowercase			(		self:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			Tuple       , _SCREAMING_SNAKE_CASE:			int       , _SCREAMING_SNAKE_CASE:			List[str]       , _SCREAMING_SNAKE_CASE:			Optional[int]       , *_SCREAMING_SNAKE_CASE:			str		)							->			Any:




              """simple docstring"""


              UpperCamelCase_ 							=	TFGPTJModel(config=_SCREAMING_SNAKE_CASE		)

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

              # first forward pass
              UpperCamelCase_							,			UpperCamelCase_ 							=	model(_SCREAMING_SNAKE_CASE       , attention_mask=_SCREAMING_SNAKE_CASE		).to_tuple()

              # create hypothetical next token and extent to next_input_ids
              UpperCamelCase_ 							=	ids_tensor((self.batch_size, 1)       , config.vocab_size		)

              # change a random masked slice from input_ids
              UpperCamelCase_ 							=	ids_tensor((1,)       , _SCREAMING_SNAKE_CASE		).numpy() + 1
              UpperCamelCase_ 							=	ids_tensor((self.batch_size, self.seq_length)       , config.vocab_size		)
              UpperCamelCase_ 							=	tf.range(self.seq_length		) == (self.seq_length - random_seq_idx_to_change)
              UpperCamelCase_ 							=	tf.transpose(
                  tf.broadcast_to(tf.expand_dims(_SCREAMING_SNAKE_CASE       , -1		)       , (self.seq_length, self.batch_size)		)		)
              UpperCamelCase_ 							=	tf.where(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)

              # append to next input_ids and attn_mask
              UpperCamelCase_ 							=	tf.concat([input_ids, next_tokens]       , axis=-1		)
              UpperCamelCase_ 							=	tf.concat([attn_mask, tf.ones((shape_list(_SCREAMING_SNAKE_CASE		)[0], 1)       , dtype=tf.intaa		)]       , axis=1		)

              # get two different outputs
              UpperCamelCase_ 							=	model(_SCREAMING_SNAKE_CASE       , attention_mask=_SCREAMING_SNAKE_CASE		)["last_hidden_state"]
              UpperCamelCase_ 							=	model(_SCREAMING_SNAKE_CASE       , past_key_values=_SCREAMING_SNAKE_CASE       , attention_mask=_SCREAMING_SNAKE_CASE		)[
                  "last_hidden_state"
              ]

              # select random slice
              UpperCamelCase_ 							=	int(ids_tensor((1,)       , shape_list(_SCREAMING_SNAKE_CASE		)[-1]		)		)
              UpperCamelCase_ 							=	output_from_no_past[:, -1, random_slice_idx]
              UpperCamelCase_ 							=	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 lowercase			(		self:			List[Any]       , _SCREAMING_SNAKE_CASE:			int       , _SCREAMING_SNAKE_CASE:			str       , _SCREAMING_SNAKE_CASE:			List[Any]       , _SCREAMING_SNAKE_CASE:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			Optional[Any]       , *_SCREAMING_SNAKE_CASE:			Dict		)							->			Optional[Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	TFGPTJModel(config=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	input_ids[:1, :]
              UpperCamelCase_ 							=	input_mask[:1, :]
              UpperCamelCase_ 							=	token_type_ids[:1, :]
              UpperCamelCase_ 							=	1

              # first forward pass
              UpperCamelCase_ 							=	model(_SCREAMING_SNAKE_CASE       , attention_mask=_SCREAMING_SNAKE_CASE       , token_type_ids=_SCREAMING_SNAKE_CASE       , use_cache=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_							,			UpperCamelCase_ 							=	outputs.to_tuple()

              # create hypothetical next token and extent to next_input_ids
              UpperCamelCase_ 							=	ids_tensor((self.batch_size, 3)       , config.vocab_size		)
              UpperCamelCase_ 							=	ids_tensor((self.batch_size, 3)       , 2		)
              UpperCamelCase_ 							=	ids_tensor((self.batch_size, 3)       , self.type_vocab_size		)

              # append to next input_ids and token_type_ids
              UpperCamelCase_ 							=	tf.concat([input_ids, next_tokens]       , axis=-1		)
              UpperCamelCase_ 							=	tf.concat([input_mask, next_attn_mask]       , axis=-1		)
              UpperCamelCase_ 							=	tf.concat([token_type_ids, next_token_types]       , axis=-1		)

              UpperCamelCase_ 							=	model(
                  _SCREAMING_SNAKE_CASE       , token_type_ids=_SCREAMING_SNAKE_CASE       , attention_mask=_SCREAMING_SNAKE_CASE		)["last_hidden_state"]
              UpperCamelCase_ 							=	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
              UpperCamelCase_ 							=	int(ids_tensor((1,)       , shape_list(_SCREAMING_SNAKE_CASE		)[-1]		)		)
              UpperCamelCase_ 							=	output_from_no_past[:, -3:, random_slice_idx]
              UpperCamelCase_ 							=	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 lowercase			(		self:			List[str]       , _SCREAMING_SNAKE_CASE:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			str       , _SCREAMING_SNAKE_CASE:			Dict       , _SCREAMING_SNAKE_CASE:			Any       , _SCREAMING_SNAKE_CASE:			Optional[Any]       , *_SCREAMING_SNAKE_CASE:			Dict		)							->			List[Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	TFGPTJForCausalLM(config=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	{
                  "input_ids": input_ids,
                  "attention_mask": input_mask,
                  "token_type_ids": token_type_ids,
              }
              UpperCamelCase_ 							=	model(_SCREAMING_SNAKE_CASE		)
              self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.seq_length, self.vocab_size)		)



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




              """simple docstring"""


              UpperCamelCase_ 							=	self.prepare_config_and_inputs()

              (
                  (
                  UpperCamelCase_
              )							,			(
                  UpperCamelCase_
              )							,			(
                  UpperCamelCase_
              )							,			(
                  UpperCamelCase_
              )							,			(
                  UpperCamelCase_
              )							,			(
                  UpperCamelCase_
              )							,			(
                  UpperCamelCase_
              )							,			(
                  UpperCamelCase_
              )							,			(
                  UpperCamelCase_
              )							,			
              ) 							=	config_and_inputs

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



@require_tf
class 				_UpperCamelCase       (						lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_	,    unittest.TestCase  ):
       _UpperCamelCase					:       str       = (
           (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
           if is_tf_available()
           else ()
       )

       _UpperCamelCase					:       Dict       = (TFGPTJForCausalLM,) if is_tf_available() else ()
       _UpperCamelCase					:       int       = (
           {
               '''feature-extraction''': TFGPTJModel,
               '''question-answering''': TFGPTJForQuestionAnswering,
               '''text-classification''': TFGPTJForSequenceClassification,
               '''text-generation''': TFGPTJForCausalLM,
               '''zero-shot''': TFGPTJForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       _UpperCamelCase					:       Tuple       = False
       _UpperCamelCase					:       int       = False
       _UpperCamelCase					:       Tuple       = False
       _UpperCamelCase					:       int       = False

       def lowercase			(		self:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			str       , _SCREAMING_SNAKE_CASE:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			str       , _SCREAMING_SNAKE_CASE:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			Dict		)							->			Tuple:




              """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 lowercase			(		self:			int		)							->			Optional[int]:




              """simple docstring"""


              UpperCamelCase_ 							=	TFGPTJModelTester(self		)
              UpperCamelCase_ 							=	ConfigTester(self       , config_class=_SCREAMING_SNAKE_CASE       , n_embd=37		)

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




              """simple docstring"""


              self.config_tester.run_common_tests()

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




              """simple docstring"""


              UpperCamelCase_ 							=	self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_gptj_model(*_SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			Tuple		)							->			List[str]:




              """simple docstring"""


              UpperCamelCase_ 							=	self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_gptj_model_past(*_SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			Any		)							->			Tuple:




              """simple docstring"""


              UpperCamelCase_ 							=	self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_gptj_model_attention_mask_past(*_SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			Optional[Any]		)							->			Optional[int]:




              """simple docstring"""


              UpperCamelCase_ 							=	self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_gptj_model_past_large_inputs(*_SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			Optional[int]		)							->			Tuple:




              """simple docstring"""


              UpperCamelCase_ 							=	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 lowercase			(		self:			Optional[int]		)							->			List[Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	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 lowercase			(		self:			Tuple		)							->			Optional[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 				_UpperCamelCase       (						unittest.TestCase  ):

       def lowercase			(		self:			List[str]		)							->			Tuple:




              """simple docstring"""


              UpperCamelCase_ 							=	TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"       , from_pt=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	tf.convert_to_tensor([[464, 3290]]       , 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_ 							=	[464, 3290, 318, 257, 582, 338, 1266, 1545, 13, 632, 318, 257, 9112, 15185, 11, 290, 340, 318, 257, 1545]
              # fmt: on
              UpperCamelCase_ 							=	model.generate(_SCREAMING_SNAKE_CASE       , do_sample=_SCREAMING_SNAKE_CASE		)
              self.assertListEqual(output_ids[0].numpy().tolist()       , _SCREAMING_SNAKE_CASE		)

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




              """simple docstring"""


              UpperCamelCase_ 							=	AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B"       , revision="float16"		)
              UpperCamelCase_ 							=	TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"       , revision="float16"       , from_pt=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	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_ 							=	model.generate(**_SCREAMING_SNAKE_CASE       , do_sample=_SCREAMING_SNAKE_CASE       , seed=[42, 0]		)
              UpperCamelCase_ 							=	tokenizer.decode(output_ids[0]       , skip_special_tokens=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	"Today is a nice day and I’m going to go for a walk. I’"
              self.assertEqual(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)

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




              """simple docstring"""


              UpperCamelCase_ 							=	TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"       , revision="float16"       , from_pt=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B"       , revision="float16"		)

              UpperCamelCase_ 							=	"left"

              # Define PAD Token = EOS Token = 50256
              UpperCamelCase_ 							=	tokenizer.eos_token
              UpperCamelCase_ 							=	model.config.eos_token_id

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




              """simple docstring"""


              UpperCamelCase_							,			UpperCamelCase_							,			UpperCamelCase_							,			UpperCamelCase_ 							=	self._get_beam_search_test_objects()

              UpperCamelCase_ 							=	tokenizer(_SCREAMING_SNAKE_CASE       , return_tensors="tf"       , padding=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	model.generate(**_SCREAMING_SNAKE_CASE       , do_sample=_SCREAMING_SNAKE_CASE       , num_beams=2		)
              UpperCamelCase_ 							=	tokenizer.batch_decode(_SCREAMING_SNAKE_CASE       , skip_special_tokens=_SCREAMING_SNAKE_CASE		)
              self.assertListEqual(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)

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




              """simple docstring"""


              UpperCamelCase_							,			UpperCamelCase_							,			UpperCamelCase_							,			UpperCamelCase_ 							=	self._get_beam_search_test_objects()

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



       def lowercase			(		self:			Tuple		)							->			Any:




              """simple docstring"""


              UpperCamelCase_							,			UpperCamelCase_							,			UpperCamelCase_							,			UpperCamelCase_ 							=	self._get_beam_search_test_objects()

              UpperCamelCase_ 							=	tokenizer(_SCREAMING_SNAKE_CASE       , return_tensors="tf"       , padding=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	tf.function(model.generate       , jit_compile=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	xla_generate(**_SCREAMING_SNAKE_CASE       , do_sample=_SCREAMING_SNAKE_CASE       , num_beams=2		)
              UpperCamelCase_ 							=	tokenizer.batch_decode(_SCREAMING_SNAKE_CASE       , skip_special_tokens=_SCREAMING_SNAKE_CASE		)
              self.assertListEqual(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)






def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					for index, item in enumerate(_UpperCAmelCase				):
										if item == target:
															return index
					return -1





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					if not (0 <= high < len(_UpperCAmelCase				) and 0 <= low < len(_UpperCAmelCase				)):
										raise Exception('Invalid upper or lower bound!'				)
					if high < low:
										return -1
					if sequence[low] == target:
										return low
					if sequence[high] == target:
										return high
					return rec_linear_search(_UpperCAmelCase       ,			low + 1       ,			high - 1       ,			_UpperCAmelCase				)


if __name__ == "__main__":
		__magic_name__       =     input('''Enter numbers separated by comma:\n''').strip()
		__magic_name__       =     [int(item.strip()) for item in user_input.split(''',''')]

		__magic_name__       =     int(input('''Enter a single number to be found in the list:\n''').strip())
		__magic_name__       =     linear_search(sequence, target)
		if result != -1:
				print(F"""linear_search({sequence}, {target}) = {result}""")
		else:
				print(F"""{target} was not found in {sequence}""")






