





"""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 			snake_case		:

      def __init__(			self   :List[str]						,							_lowerCamelCase   :Optional[int]						):
       __SCREAMING_SNAKE_CASE :			str												=       parent
       __SCREAMING_SNAKE_CASE :			Any												=       1_3
       __SCREAMING_SNAKE_CASE :			str												=       7
       __SCREAMING_SNAKE_CASE :			int												=       True
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       True
       __SCREAMING_SNAKE_CASE :			Dict												=       True
       __SCREAMING_SNAKE_CASE :			Any												=       True
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       True
       __SCREAMING_SNAKE_CASE :			int												=       9_9
       __SCREAMING_SNAKE_CASE :			Any												=       3_2
       __SCREAMING_SNAKE_CASE :			Tuple												=       4
       __SCREAMING_SNAKE_CASE :			int												=       2
       __SCREAMING_SNAKE_CASE :			Any												=       4
       __SCREAMING_SNAKE_CASE :			int												=       3_7
       __SCREAMING_SNAKE_CASE :			Dict												=       '''gelu'''
       __SCREAMING_SNAKE_CASE :			int												=       0.1
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       0.1
       __SCREAMING_SNAKE_CASE :			Tuple												=       5_1_2
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       1_6
       __SCREAMING_SNAKE_CASE :			List[Any]												=       2
       __SCREAMING_SNAKE_CASE :			Dict												=       0.0_2
       __SCREAMING_SNAKE_CASE :			List[str]												=       3
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       4
       __SCREAMING_SNAKE_CASE :			int												=       None
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       self.vocab_size - 1
       __SCREAMING_SNAKE_CASE :			str												=       self.vocab_size - 1
       __SCREAMING_SNAKE_CASE :			int												=       self.vocab_size - 1

      def 						SCREAMING_SNAKE_CASE_			(			self   :Union[str, Any]						):
       __SCREAMING_SNAKE_CASE :			Dict												=       ids_tensor([self.batch_size, self.seq_length]						,							self.vocab_size						)

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

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

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

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

       __SCREAMING_SNAKE_CASE :			Dict												=       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=_lowerCamelCase						,							)

       __SCREAMING_SNAKE_CASE :			Dict												=       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 						SCREAMING_SNAKE_CASE_			(			self   :List[Any]						,							_lowerCamelCase   :Any						,							_lowerCamelCase   :Tuple						,							_lowerCamelCase   :List[Any]						,							_lowerCamelCase   :int						,							_lowerCamelCase   :Optional[int]						,							*_lowerCamelCase   :Optional[int]						):
       __SCREAMING_SNAKE_CASE :			Tuple												=       TFGPTJModel(config=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       {
           '''input_ids''': input_ids,
           '''attention_mask''': input_mask,
           '''token_type_ids''': token_type_ids,
       }
       __SCREAMING_SNAKE_CASE :			List[Any]												=       model(_lowerCamelCase						)

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

       __SCREAMING_SNAKE_CASE :			Optional[int]												=       model(_lowerCamelCase						)

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

      def 						SCREAMING_SNAKE_CASE_			(			self   :Union[str, Any]						,							_lowerCamelCase   :Union[str, Any]						,							_lowerCamelCase   :str						,							_lowerCamelCase   :List[str]						,							_lowerCamelCase   :Union[str, Any]						,							_lowerCamelCase   :Tuple						,							*_lowerCamelCase   :int						):
       __SCREAMING_SNAKE_CASE :			int												=       TFGPTJModel(config=_lowerCamelCase						)

       # first forward pass
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       model(_lowerCamelCase						,							token_type_ids=_lowerCamelCase						,							use_cache=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			str												=       model(_lowerCamelCase						,							token_type_ids=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			str												=       model(_lowerCamelCase						,							token_type_ids=_lowerCamelCase						,							use_cache=_lowerCamelCase						)

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

       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			List[Any]												=       outputs.to_tuple()

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

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

       __SCREAMING_SNAKE_CASE :			Optional[int]												=       model(_lowerCamelCase						,							token_type_ids=_lowerCamelCase						)['''last_hidden_state''']
       __SCREAMING_SNAKE_CASE :			Any												=       model(_lowerCamelCase						,							token_type_ids=_lowerCamelCase						,							past_key_values=_lowerCamelCase						)[
           '''last_hidden_state'''
       ]

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

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

      def 						SCREAMING_SNAKE_CASE_			(			self   :List[Any]						,							_lowerCamelCase   :Optional[int]						,							_lowerCamelCase   :Optional[int]						,							_lowerCamelCase   :Union[str, Any]						,							_lowerCamelCase   :Any						,							_lowerCamelCase   :Union[str, Any]						,							*_lowerCamelCase   :int						):
       __SCREAMING_SNAKE_CASE :			int												=       TFGPTJModel(config=_lowerCamelCase						)

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

       # first forward pass
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			List[str]												=       model(_lowerCamelCase						,							attention_mask=_lowerCamelCase						).to_tuple()

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

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

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

       # get two different outputs
       __SCREAMING_SNAKE_CASE :			List[Any]												=       model(_lowerCamelCase						,							attention_mask=_lowerCamelCase						)['''last_hidden_state''']
       __SCREAMING_SNAKE_CASE :			int												=       model(_lowerCamelCase						,							past_key_values=_lowerCamelCase						,							attention_mask=_lowerCamelCase						)[
           '''last_hidden_state'''
       ]

       # select random slice
       __SCREAMING_SNAKE_CASE :			List[str]												=       int(ids_tensor((1,)						,							shape_list(_lowerCamelCase						)[-1]						)						)
       __SCREAMING_SNAKE_CASE :			str												=       output_from_no_past[:, -1, random_slice_idx]
       __SCREAMING_SNAKE_CASE :			List[str]												=       output_from_past[:, 0, random_slice_idx]

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

      def 						SCREAMING_SNAKE_CASE_			(			self   :Tuple						,							_lowerCamelCase   :Union[str, Any]						,							_lowerCamelCase   :int						,							_lowerCamelCase   :str						,							_lowerCamelCase   :Optional[int]						,							_lowerCamelCase   :Optional[int]						,							*_lowerCamelCase   :Optional[int]						):
       __SCREAMING_SNAKE_CASE :			str												=       TFGPTJModel(config=_lowerCamelCase						)

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

       # first forward pass
       __SCREAMING_SNAKE_CASE :			Dict												=       model(_lowerCamelCase						,							attention_mask=_lowerCamelCase						,							token_type_ids=_lowerCamelCase						,							use_cache=_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Tuple												=       outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       __SCREAMING_SNAKE_CASE :			Any												=       ids_tensor((self.batch_size, 3)						,							config.vocab_size						)
       __SCREAMING_SNAKE_CASE :			int												=       ids_tensor((self.batch_size, 3)						,							2						)
       __SCREAMING_SNAKE_CASE :			Tuple												=       ids_tensor((self.batch_size, 3)						,							self.type_vocab_size						)

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

       __SCREAMING_SNAKE_CASE :			List[Any]												=       model(
           _lowerCamelCase						,							token_type_ids=_lowerCamelCase						,							attention_mask=_lowerCamelCase						)['''last_hidden_state''']
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       model(
           _lowerCamelCase						,							token_type_ids=_lowerCamelCase						,							attention_mask=_lowerCamelCase						,							past_key_values=_lowerCamelCase						,							)['''last_hidden_state''']
       self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]						)

       # select random slice
       __SCREAMING_SNAKE_CASE :			List[str]												=       int(ids_tensor((1,)						,							shape_list(_lowerCamelCase						)[-1]						)						)
       __SCREAMING_SNAKE_CASE :			str												=       output_from_no_past[:, -3:, random_slice_idx]
       __SCREAMING_SNAKE_CASE :			List[str]												=       output_from_past[:, :, random_slice_idx]

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

      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[int]						,							_lowerCamelCase   :List[str]						,							_lowerCamelCase   :Tuple						,							_lowerCamelCase   :Dict						,							_lowerCamelCase   :Dict						,							_lowerCamelCase   :Union[str, Any]						,							*_lowerCamelCase   :Tuple						):
       __SCREAMING_SNAKE_CASE :			Tuple												=       TFGPTJForCausalLM(config=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Any												=       {
           '''input_ids''': input_ids,
           '''attention_mask''': input_mask,
           '''token_type_ids''': token_type_ids,
       }
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       model(_lowerCamelCase						)
       self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.seq_length, self.vocab_size)						)



      def 						SCREAMING_SNAKE_CASE_			(			self   :Tuple						):
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       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
       )							,      
       ) :			str												=       config_and_inputs

       __SCREAMING_SNAKE_CASE :			Optional[int]												=       {
           '''input_ids''': input_ids,
           '''token_type_ids''': token_type_ids,
           '''attention_mask''': input_mask,
       }
       return config, inputs_dict







@require_tf
class 			snake_case		(   __UpperCAmelCase ,     __UpperCAmelCase ,     __UpperCAmelCase ,     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 						SCREAMING_SNAKE_CASE_			(			self   :Union[str, Any]						,							_lowerCamelCase   :Dict						,							_lowerCamelCase   :int						,							_lowerCamelCase   :Optional[Any]						,							_lowerCamelCase   :Union[str, Any]						,							_lowerCamelCase   :str						):
       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 						SCREAMING_SNAKE_CASE_			(			self   :int						):
       __SCREAMING_SNAKE_CASE :			List[str]												=       TFGPTJModelTester(self						)
       __SCREAMING_SNAKE_CASE :			Dict												=       ConfigTester(self						,							config_class=_lowerCamelCase						,							n_embd=3_7						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :int						):
       self.config_tester.run_common_tests()

      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[Any]						):
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_gptj_model(*_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Tuple						):
       __SCREAMING_SNAKE_CASE :			List[Any]												=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_gptj_model_past(*_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Dict						):
       __SCREAMING_SNAKE_CASE :			List[str]												=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_gptj_model_attention_mask_past(*_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :List[Any]						):
       __SCREAMING_SNAKE_CASE :			List[Any]												=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_gptj_model_past_large_inputs(*_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Any						):
       __SCREAMING_SNAKE_CASE :			List[Any]												=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_gptj_lm_head_model(*_lowerCamelCase						)

      @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 						SCREAMING_SNAKE_CASE_			(			self   :Any						):
       __SCREAMING_SNAKE_CASE :			str												=       TFGPTJModel.from_pretrained('''EleutherAI/gpt-j-6B'''						,							from_pt=_lowerCamelCase						)
       self.assertIsNotNone(_lowerCamelCase						)



      @unittest.skip(reason='''Currently, model embeddings are going to undergo a major refactor.'''						)
      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[Any]						):
       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 						SCREAMING_SNAKE_CASE_			(			self   :str						):
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       TFGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B'''						,							from_pt=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			List[Any]												=       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
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       [4_6_4, 3_2_9_0, 3_1_8, 2_5_7, 5_8_2, 3_3_8, 1_2_6_6, 1_5_4_5, 1_3, 6_3_2, 3_1_8, 2_5_7, 9_1_1_2, 1_5_1_8_5, 1_1, 2_9_0, 3_4_0, 3_1_8, 2_5_7, 1_5_4_5]
       # fmt: on
       __SCREAMING_SNAKE_CASE :			str												=       model.generate(_lowerCamelCase						,							do_sample=_lowerCamelCase						)
       self.assertListEqual(output_ids[0].numpy().tolist()						,							_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[int]						):
       __SCREAMING_SNAKE_CASE :			Dict												=       AutoTokenizer.from_pretrained('''EleutherAI/gpt-j-6B'''						,							revision='''float16'''						)
       __SCREAMING_SNAKE_CASE :			List[str]												=       TFGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B'''						,							revision='''float16'''						,							from_pt=_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			str												=       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 :			Dict												=       model.generate(**_lowerCamelCase						,							do_sample=_lowerCamelCase						,							seed=[4_2, 0]						)
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       tokenizer.decode(output_ids[0]						,							skip_special_tokens=_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			Dict												=       '''Today is a nice day and I’m going to go for a walk. I’'''
       self.assertEqual(_lowerCamelCase						,							_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[int]						):
       __SCREAMING_SNAKE_CASE :			Dict												=       TFGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B'''						,							revision='''float16'''						,							from_pt=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Dict												=       AutoTokenizer.from_pretrained('''EleutherAI/gpt-j-6B'''						,							revision='''float16'''						)

       __SCREAMING_SNAKE_CASE :			List[Any]												=       '''left'''

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

       # use different length sentences to test batching
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       [
           '''Hello, my dog is a little''',
           '''Today, I''',
       ]
       __SCREAMING_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 						SCREAMING_SNAKE_CASE_			(			self   :int						):
       # Confirms that we get the expected results with left-padded beam search
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			List[str]												=       self._get_beam_search_test_objects()

       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       tokenizer(_lowerCamelCase						,							return_tensors='''tf'''						,							padding=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			List[str]												=       model.generate(**_lowerCamelCase						,							do_sample=_lowerCamelCase						,							num_beams=2						)
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       tokenizer.batch_decode(_lowerCamelCase						,							skip_special_tokens=_lowerCamelCase						)
       self.assertListEqual(_lowerCamelCase						,							_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :int						):
       # Confirms that left-padding is working properly
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			str												=       self._get_beam_search_test_objects()

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



      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[int]						):
       # Confirms that XLA is working properly
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       self._get_beam_search_test_objects()

       __SCREAMING_SNAKE_CASE :			int												=       tokenizer(_lowerCamelCase						,							return_tensors='''tf'''						,							padding=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Any												=       tf.function(model.generate						,							jit_compile=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			List[Any]												=       xla_generate(**_lowerCamelCase						,							do_sample=_lowerCamelCase						,							num_beams=2						)
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       tokenizer.batch_decode(_lowerCamelCase						,							skip_special_tokens=_lowerCamelCase						)
       self.assertListEqual(_lowerCamelCase						,							_lowerCamelCase						)








"""simple docstring"""





import math




def 		lowerCAmelCase_	(  lowercase_   :							int		):

 '''simple docstring'''






 if 1 < number < 4:
  # 2 and 3 are primes
  return True
 elif number < 2 or number % 2 == 0 or number % 3 == 0:
  # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
  return False

 # All primes number are in format of 6k +/- 1
 for i in range(5 ,      int(math.sqrt(lowercase_		) + 1		) ,      6		):
  if number % i == 0 or number % (i + 2) == 0:
   return False
 return True




def 		lowerCAmelCase_	(  lowercase_   :							int = 6008_5147_5143		):

 '''simple docstring'''






 try:
  __SCREAMING_SNAKE_CASE :			Any												=       int(lowercase_		)
 except (TypeError, ValueError):
  raise TypeError('''Parameter n must be int or castable to int.'''		)
 if n <= 0:
  raise ValueError('''Parameter n must be greater than or equal to one.'''		)
 __SCREAMING_SNAKE_CASE :			List[str]												=       0
 if is_prime(lowercase_		):
  return n
 while n % 2 == 0:
  n //= 2
 if is_prime(lowercase_		):
  return n
 for i in range(3 ,      int(math.sqrt(lowercase_		)		) + 1 ,      2		):
  if n % i == 0:
   if is_prime(n // i		):
    __SCREAMING_SNAKE_CASE :			Optional[int]												=       n // i
    break
   elif is_prime(lowercase_		):
    __SCREAMING_SNAKE_CASE :			Any												=       i
 return max_number


if __name__ == "__main__":
    print(f'{solution() = }')

