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 __snake_case							:




   def __init__(       self     :							Dict			,      _lowercase     :							Tuple ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= parent
     SCREAMING_SNAKE_CASE__     				= 13
     SCREAMING_SNAKE_CASE__     				= 7
     SCREAMING_SNAKE_CASE__     				= True
     SCREAMING_SNAKE_CASE__     				= True
     SCREAMING_SNAKE_CASE__     				= True
     SCREAMING_SNAKE_CASE__     				= True
     SCREAMING_SNAKE_CASE__     				= True
     SCREAMING_SNAKE_CASE__     				= 99
     SCREAMING_SNAKE_CASE__     				= 32
     SCREAMING_SNAKE_CASE__     				= 4
     SCREAMING_SNAKE_CASE__     				= 2
     SCREAMING_SNAKE_CASE__     				= 4
     SCREAMING_SNAKE_CASE__     				= 37
     SCREAMING_SNAKE_CASE__     				= """gelu"""
     SCREAMING_SNAKE_CASE__     				= 0.1
     SCREAMING_SNAKE_CASE__     				= 0.1
     SCREAMING_SNAKE_CASE__     				= 5_12
     SCREAMING_SNAKE_CASE__     				= 16
     SCREAMING_SNAKE_CASE__     				= 2
     SCREAMING_SNAKE_CASE__     				= 0.02
     SCREAMING_SNAKE_CASE__     				= 3
     SCREAMING_SNAKE_CASE__     				= 4
     SCREAMING_SNAKE_CASE__     				= None
     SCREAMING_SNAKE_CASE__     				= self.vocab_size - 1
     SCREAMING_SNAKE_CASE__     				= self.vocab_size - 1
     SCREAMING_SNAKE_CASE__     				= self.vocab_size - 1




   def       __a		(       self     :							int ):



     """simple docstring"""






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

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

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

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

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

     SCREAMING_SNAKE_CASE__     				= 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=_lowercase			,      )

     SCREAMING_SNAKE_CASE__     				= 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     :							List[Any]			,      _lowercase     :							List[str]			,      _lowercase     :							Optional[int]			,      _lowercase     :							Optional[Any]			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							Union[str, Any]			,      *_lowercase     :							Tuple ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= TFGPTJModel(config=_lowercase )
     SCREAMING_SNAKE_CASE__     				= {
         """input_ids""": input_ids,
         """attention_mask""": input_mask,
         """token_type_ids""": token_type_ids,
     }
     SCREAMING_SNAKE_CASE__     				= model(_lowercase )

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

     SCREAMING_SNAKE_CASE__     				= model(_lowercase )

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




   def       __a		(       self     :							Union[str, Any]			,      _lowercase     :							Optional[int]			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							Dict			,      _lowercase     :							Dict			,      _lowercase     :							Optional[Any]			,      *_lowercase     :							Any ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= TFGPTJModel(config=_lowercase )

     # first forward pass
     SCREAMING_SNAKE_CASE__     				= model(_lowercase			,      token_type_ids=_lowercase			,      use_cache=_lowercase )
     SCREAMING_SNAKE_CASE__     				= model(_lowercase			,      token_type_ids=_lowercase )
     SCREAMING_SNAKE_CASE__     				= model(_lowercase			,      token_type_ids=_lowercase			,      use_cache=_lowercase )

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

     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= outputs.to_tuple()

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

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

     SCREAMING_SNAKE_CASE__     				= model(_lowercase			,      token_type_ids=_lowercase )["""last_hidden_state"""]
     SCREAMING_SNAKE_CASE__     				= model(_lowercase			,      token_type_ids=_lowercase			,      past_key_values=_lowercase )[
         """last_hidden_state"""
     ]

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

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




   def       __a		(       self     :							Optional[Any]			,      _lowercase     :							Optional[int]			,      _lowercase     :							List[str]			,      _lowercase     :							str			,      _lowercase     :							Optional[Any]			,      _lowercase     :							int			,      *_lowercase     :							Tuple ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= TFGPTJModel(config=_lowercase )

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

     # first forward pass
     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= model(_lowercase			,      attention_mask=_lowercase ).to_tuple()

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

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

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

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

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

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




   def       __a		(       self     :							Dict			,      _lowercase     :							Any			,      _lowercase     :							int			,      _lowercase     :							Dict			,      _lowercase     :							Dict			,      _lowercase     :							Union[str, Any]			,      *_lowercase     :							List[str] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= TFGPTJModel(config=_lowercase )

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

     # first forward pass
     SCREAMING_SNAKE_CASE__     				= model(_lowercase			,      attention_mask=_lowercase			,      token_type_ids=_lowercase			,      use_cache=_lowercase )

     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= outputs.to_tuple()

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

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

     SCREAMING_SNAKE_CASE__     				= model(
         _lowercase			,      token_type_ids=_lowercase			,      attention_mask=_lowercase )["""last_hidden_state"""]
     SCREAMING_SNAKE_CASE__     				= model(
         _lowercase			,      token_type_ids=_lowercase			,      attention_mask=_lowercase			,      past_key_values=_lowercase			,      )["""last_hidden_state"""]
     self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1] )

     # select random slice
     SCREAMING_SNAKE_CASE__     				= int(ids_tensor((1,)			,      shape_list(_lowercase )[-1] ) )
     SCREAMING_SNAKE_CASE__     				= output_from_no_past[:, -3:, random_slice_idx]
     SCREAMING_SNAKE_CASE__     				= output_from_past[:, :, random_slice_idx]

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




   def       __a		(       self     :							Any			,      _lowercase     :							Optional[int]			,      _lowercase     :							List[Any]			,      _lowercase     :							List[Any]			,      _lowercase     :							List[str]			,      _lowercase     :							List[str]			,      *_lowercase     :							int ):



     """simple docstring"""






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




   def       __a		(       self     :							Union[str, Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.prepare_config_and_inputs()

     (
         (
         SCREAMING_SNAKE_CASE__
     )				,	(
         SCREAMING_SNAKE_CASE__
     )				,	(
         SCREAMING_SNAKE_CASE__
     )				,	(
         SCREAMING_SNAKE_CASE__
     )				,	(
         SCREAMING_SNAKE_CASE__
     )				,	(
         SCREAMING_SNAKE_CASE__
     )				,	(
         SCREAMING_SNAKE_CASE__
     )				,	(
         SCREAMING_SNAKE_CASE__
     )				,	(
         SCREAMING_SNAKE_CASE__
     )				,	
     )     				= config_and_inputs

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







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

   lowerCAmelCase_								=				(TFGPTJForCausalLM,) if is_tf_available() else ()
   lowerCAmelCase_								=				(
       {
           "feature-extraction": TFGPTJModel,
           "question-answering": TFGPTJForQuestionAnswering,
           "text-classification": TFGPTJForSequenceClassification,
           "text-generation": TFGPTJForCausalLM,
           "zero-shot": TFGPTJForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   lowerCAmelCase_								=				False
   lowerCAmelCase_								=				False
   lowerCAmelCase_								=				False
   lowerCAmelCase_								=				False




   def       __a		(       self     :							str			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							Optional[int]			,      _lowercase     :							Tuple			,      _lowercase     :							List[Any] ):



     """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     :							Any ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= TFGPTJModelTester(self )
     SCREAMING_SNAKE_CASE__     				= ConfigTester(self			,      config_class=_lowercase			,      n_embd=37 )




   def       __a		(       self     :							Union[str, Any] ):



     """simple docstring"""






     self.config_tester.run_common_tests()




   def       __a		(       self     :							Optional[int] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_gptj_model(*_lowercase )




   def       __a		(       self     :							Union[str, Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_gptj_model_past(*_lowercase )




   def       __a		(       self     :							int ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_gptj_model_attention_mask_past(*_lowercase )




   def       __a		(       self     :							Any ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_gptj_model_past_large_inputs(*_lowercase )




   def       __a		(       self     :							Union[str, Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_gptj_lm_head_model(*_lowercase )




   @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     :							Optional[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= TFGPTJModel.from_pretrained("""EleutherAI/gpt-j-6B"""			,      from_pt=_lowercase )
     self.assertIsNotNone(_lowercase )




   @unittest.skip(reason="""Currently, model embeddings are going to undergo a major refactor.""" )
   def       __a		(       self     :							List[str] ):



     """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 __snake_case							(		unittest.TestCase						):




   def       __a		(       self     :							List[str] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""			,      from_pt=_lowercase )
     SCREAMING_SNAKE_CASE__     				= 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
     SCREAMING_SNAKE_CASE__     				= [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
     SCREAMING_SNAKE_CASE__     				= model.generate(_lowercase			,      do_sample=_lowercase )
     self.assertListEqual(output_ids[0].numpy().tolist()			,      _lowercase )




   def       __a		(       self     :							Any ):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__     				= 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""" ):
       SCREAMING_SNAKE_CASE__     				= model.generate(**_lowercase			,      do_sample=_lowercase			,      seed=[42, 0] )
     SCREAMING_SNAKE_CASE__     				= tokenizer.decode(output_ids[0]			,      skip_special_tokens=_lowercase )

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




   def       __a		(       self     :							int ):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__     				= """left"""

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

     # use different length sentences to test batching
     SCREAMING_SNAKE_CASE__     				= [
         """Hello, my dog is a little""",
         """Today, I""",
     ]
     SCREAMING_SNAKE_CASE__     				= [
         """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     :							str ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= self._get_beam_search_test_objects()

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




   def       __a		(       self     :							Dict ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= self._get_beam_search_test_objects()

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




   def       __a		(       self     :							Dict ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= self._get_beam_search_test_objects()

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

from __future__ import annotations



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			str      )				->   bool:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= 42
  SCREAMING_SNAKE_CASE__     				= list(__UpperCamelCase      )
  SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= _vector[:-1], int(_vector[-1]      )
  SCREAMING_SNAKE_CASE__     				= [int(__UpperCamelCase      ) for digit in __vector]

  vector.reverse()
  for i, digit in enumerate(__UpperCamelCase      ):
    if i & 1 == 0:
      SCREAMING_SNAKE_CASE__     				= digit * 2
      if doubled > 9:
        doubled -= 9
      check_digit += doubled
    else:
      check_digit += digit

  return check_digit % 10 == 0


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   assert is_luhn('''79927398713''')
   assert not is_luhn('''79927398714''')
