'''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      __SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  def __init__( self      ,		snake_case_						):




       '''simple docstring'''

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


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   int   =      ids_tensor([self.batch_size, self.seq_length]      ,		self.vocab_size						)

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

       UpperCAmelCase_							:   Optional[Any]   =      None
       if self.use_token_type_ids:
            UpperCAmelCase_							:   Tuple   =      ids_tensor([self.batch_size, self.seq_length]      ,		self.type_vocab_size						)

       UpperCAmelCase_							:   Any   =      None
       if self.use_mc_token_ids:
            UpperCAmelCase_							:   Optional[int]   =      ids_tensor([self.batch_size, self.num_choices]      ,		self.seq_length						)

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

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

       UpperCAmelCase_							:   Union[str, 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 _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		*snake_case_						):




       '''simple docstring'''

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

       UpperCAmelCase_							:   int   =      [input_ids, None, input_mask]  # None is the input for 'past'
       UpperCAmelCase_							:   List[str]   =      model(_SCREAMING_SNAKE_CASE						)

       UpperCAmelCase_							:   Dict   =      model(_SCREAMING_SNAKE_CASE						)

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


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		*snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:   List[str]   =      TFGPTJModel(config=_SCREAMING_SNAKE_CASE						)

       # first forward pass
       UpperCAmelCase_							:   List[Any]   =      model(_SCREAMING_SNAKE_CASE      ,		token_type_ids=_SCREAMING_SNAKE_CASE      ,		use_cache=_SCREAMING_SNAKE_CASE						)
       UpperCAmelCase_							:   Any   =      model(_SCREAMING_SNAKE_CASE      ,		token_type_ids=_SCREAMING_SNAKE_CASE						)
       UpperCAmelCase_							:   Union[str, Any]   =      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_							:   str   =      outputs.to_tuple()

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

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

       UpperCAmelCase_							:   List[str]   =      model(_SCREAMING_SNAKE_CASE      ,		token_type_ids=_SCREAMING_SNAKE_CASE						)["last_hidden_state"]
       UpperCAmelCase_							:   Any   =      model(_SCREAMING_SNAKE_CASE      ,		token_type_ids=_SCREAMING_SNAKE_CASE      ,		past_key_values=_SCREAMING_SNAKE_CASE						)[
           "last_hidden_state"
       ]

       # select random slice
       UpperCAmelCase_							:   int   =      int(ids_tensor((1,)      ,		shape_list(_SCREAMING_SNAKE_CASE						)[-1]						)						)
       UpperCAmelCase_							:   Dict   =      output_from_no_past[:, -1, random_slice_idx]
       UpperCAmelCase_							:   Optional[Any]   =      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 _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		*snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:   Any   =      TFGPTJModel(config=_SCREAMING_SNAKE_CASE						)

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

       # first forward pass
       UpperCAmelCase_							:   int   =      model(_SCREAMING_SNAKE_CASE      ,		attention_mask=_SCREAMING_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,)      ,		_SCREAMING_SNAKE_CASE						).numpy() + 1
       UpperCAmelCase_							:   str   =      ids_tensor((self.batch_size, self.seq_length)      ,		config.vocab_size						)
       UpperCAmelCase_							:   Any   =      tf.range(self.seq_length						) == (self.seq_length - random_seq_idx_to_change)
       UpperCAmelCase_							:   List[Any]   =      tf.transpose(
           tf.broadcast_to(tf.expand_dims(_SCREAMING_SNAKE_CASE      ,		-1						)      ,		(self.seq_length, self.batch_size)						)						)
       UpperCAmelCase_							:   Optional[Any]   =      tf.where(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE						)

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

       # get two different outputs
       UpperCAmelCase_							:   List[Any]   =      model(_SCREAMING_SNAKE_CASE      ,		attention_mask=_SCREAMING_SNAKE_CASE						)["last_hidden_state"]
       UpperCAmelCase_							:   int   =      model(_SCREAMING_SNAKE_CASE      ,		past_key_values=_SCREAMING_SNAKE_CASE      ,		attention_mask=_SCREAMING_SNAKE_CASE						)[
           "last_hidden_state"
       ]

       # select random slice
       UpperCAmelCase_							:   List[Any]   =      int(ids_tensor((1,)      ,		shape_list(_SCREAMING_SNAKE_CASE						)[-1]						)						)
       UpperCAmelCase_							:   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(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		rtol=1E-12						)


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		*snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:   List[Any]   =      TFGPTJModel(config=_SCREAMING_SNAKE_CASE						)

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

       # first forward pass
       UpperCAmelCase_							:   List[str]   =      model(_SCREAMING_SNAKE_CASE      ,		attention_mask=_SCREAMING_SNAKE_CASE      ,		token_type_ids=_SCREAMING_SNAKE_CASE      ,		use_cache=_SCREAMING_SNAKE_CASE						)

       UpperCAmelCase_							:   Any   =      outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       UpperCAmelCase_							:   Dict   =      ids_tensor((self.batch_size, 3)      ,		config.vocab_size						)
       UpperCAmelCase_							:   List[str]   =      ids_tensor((self.batch_size, 3)      ,		2						)
       UpperCAmelCase_							:   Union[str, Any]   =      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_							:   List[Any]   =      tf.concat([input_mask, next_attn_mask]      ,		axis=-1						)
       UpperCAmelCase_							:   Optional[int]   =      tf.concat([token_type_ids, next_token_types]      ,		axis=-1						)

       UpperCAmelCase_							:   Tuple   =      model(
           _SCREAMING_SNAKE_CASE      ,		token_type_ids=_SCREAMING_SNAKE_CASE      ,		attention_mask=_SCREAMING_SNAKE_CASE						)["last_hidden_state"]
       UpperCAmelCase_							:   int   =      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_							:   Dict   =      int(ids_tensor((1,)      ,		shape_list(_SCREAMING_SNAKE_CASE						)[-1]						)						)
       UpperCAmelCase_							:   str   =      output_from_no_past[:, -3:, random_slice_idx]
       UpperCAmelCase_							:   int   =      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 _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		*snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:   Optional[Any]   =      TFGPTJForCausalLM(config=_SCREAMING_SNAKE_CASE						)
       UpperCAmelCase_							:   str   =      {
           "input_ids": input_ids,
           "attention_mask": input_mask,
           "token_type_ids": token_type_ids,
       }
       UpperCAmelCase_							:   Dict   =      model(_SCREAMING_SNAKE_CASE						)
       self.parent.assertEqual(result.logits.shape      ,		(self.batch_size, self.seq_length, self.vocab_size)						)






  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Union[str, Any]   =      self.prepare_config_and_inputs()

       (
           UpperCAmelCase_				
       )							:   Optional[Any]   =      config_and_inputs

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







@require_tf
class      __SCREAMING_SNAKE_CASE						(       SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	unittest.TestCase       ):







  '''simple docstring'''






  lowerCamelCase_		:Union[str, Any]						    =		(
      (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
      if is_tf_available()
      else ()
  )

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


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		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 _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Dict   =      TFGPTJModelTester(self						)
       UpperCAmelCase_							:   Dict   =      ConfigTester(self      ,		config_class=_SCREAMING_SNAKE_CASE      ,		n_embd=3_7						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       self.config_tester.run_common_tests()


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   str   =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_gptj_model(*_SCREAMING_SNAKE_CASE						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Dict   =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_gptj_model_past(*_SCREAMING_SNAKE_CASE						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Any   =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_gptj_model_attention_mask_past(*_SCREAMING_SNAKE_CASE						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Tuple   =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_gptj_model_past_large_inputs(*_SCREAMING_SNAKE_CASE						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Tuple   =      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 _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Optional[int]   =      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 _UpperCamelCase     ( 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      __SCREAMING_SNAKE_CASE						(       unittest.TestCase       ):







  '''simple docstring'''






  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Any   =      TFGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'      ,		from_pt=_SCREAMING_SNAKE_CASE						)
       UpperCAmelCase_							:   Dict   =      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_							:   Optional[int]   =      [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_							:   List[str]   =      model.generate(_SCREAMING_SNAKE_CASE      ,		do_sample=_SCREAMING_SNAKE_CASE						)
       self.assertListEqual(output_ids[0].numpy().tolist()      ,		_SCREAMING_SNAKE_CASE						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   str   =      AutoTokenizer.from_pretrained('EleutherAI/gpt-j-6B'      ,		revision='float16'						)
       UpperCAmelCase_							:   str   =      TFGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'      ,		revision='float16'      ,		from_pt=_SCREAMING_SNAKE_CASE						)

       UpperCAmelCase_							:   Any   =      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_							:   int   =      model.generate(**_SCREAMING_SNAKE_CASE      ,		do_sample=_SCREAMING_SNAKE_CASE      ,		seed=[4_2, 0]						)
       UpperCAmelCase_							:   List[str]   =      tokenizer.decode(output_ids[0]      ,		skip_special_tokens=_SCREAMING_SNAKE_CASE						)

       UpperCAmelCase_							:   List[str]   =      "Today is a nice day and I’m going to go for a walk. I’"
       self.assertEqual(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Optional[Any]   =      TFGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'      ,		revision='float16'      ,		from_pt=_SCREAMING_SNAKE_CASE						)
       UpperCAmelCase_							:   Dict   =      AutoTokenizer.from_pretrained('EleutherAI/gpt-j-6B'      ,		revision='float16'						)

       UpperCAmelCase_							:   Union[str, Any]   =      "left"

       # Define PAD Token = EOS Token = 50256
       UpperCAmelCase_							:   Dict   =      tokenizer.eos_token
       UpperCAmelCase_							:   Any   =      model.config.eos_token_id

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




       '''simple docstring'''

       UpperCAmelCase_							:   Any   =      self._get_beam_search_test_objects()

       UpperCAmelCase_							:   Dict   =      tokenizer(_SCREAMING_SNAKE_CASE      ,		return_tensors='tf'      ,		padding=_SCREAMING_SNAKE_CASE						)
       UpperCAmelCase_							:   Tuple   =      model.generate(**_SCREAMING_SNAKE_CASE      ,		do_sample=_SCREAMING_SNAKE_CASE      ,		num_beams=2						)
       UpperCAmelCase_							:   str   =      tokenizer.batch_decode(_SCREAMING_SNAKE_CASE      ,		skip_special_tokens=_SCREAMING_SNAKE_CASE						)
       self.assertListEqual(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   List[Any]   =      self._get_beam_search_test_objects()

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






  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   int   =      self._get_beam_search_test_objects()

       UpperCAmelCase_							:   str   =      tokenizer(_SCREAMING_SNAKE_CASE      ,		return_tensors='tf'      ,		padding=_SCREAMING_SNAKE_CASE						)
       UpperCAmelCase_							:   Optional[Any]   =      tf.function(model.generate      ,		jit_compile=_SCREAMING_SNAKE_CASE						)
       UpperCAmelCase_							:   Optional[Any]   =      xla_generate(**_SCREAMING_SNAKE_CASE      ,		do_sample=_SCREAMING_SNAKE_CASE      ,		num_beams=2						)
       UpperCAmelCase_							:   Tuple   =      tokenizer.batch_decode(_SCREAMING_SNAKE_CASE      ,		skip_special_tokens=_SCREAMING_SNAKE_CASE						)
       self.assertListEqual(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE						)














from __future__ import annotations

from collections.abc import Generator

def    lowerCAmelCase__		(			_a				:	int			):
		if not isinstance(_a     ,					_a			) or n < 1:
				raise Exception("Sequence only defined for positive integers"			)

		yield n
		while n != 1:
				if n % 2 == 0:
						n //= 2
				else:
						snake_case_ :							List[str]         =				3 * n + 1
				yield n

def    lowerCAmelCase__		(			):
		snake_case_ :							Optional[int]         =				43
		snake_case_ :							Union[str, Any]         =				tuple(collatz_sequence(_a			)			)
		print(_a			)
		print(F'''Collatz sequence from {n} took {len(_a			)} steps.'''			)


if __name__ == "__main__":
							main()
