code: 
  
  
  """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   lowercase__							:
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE)     ->     Any:
        _lowerCamelCase  :				Optional[Any]      =		parent
        _lowerCamelCase  :				Optional[int]      =		13
        _lowerCamelCase  :				str      =		7
        _lowerCamelCase  :				Union[str, Any]      =		True
        _lowerCamelCase  :				Optional[Any]      =		True
        _lowerCamelCase  :				int      =		True
        _lowerCamelCase  :				Optional[int]      =		True
        _lowerCamelCase  :				Optional[Any]      =		True
        _lowerCamelCase  :				Tuple      =		99
        _lowerCamelCase  :				Optional[int]      =		32
        _lowerCamelCase  :				Tuple      =		4
        _lowerCamelCase  :				Union[str, Any]      =		2
        _lowerCamelCase  :				Optional[int]      =		4
        _lowerCamelCase  :				Union[str, Any]      =		37
        _lowerCamelCase  :				int      =		"""gelu"""
        _lowerCamelCase  :				Optional[int]      =		0.1
        _lowerCamelCase  :				Optional[Any]      =		0.1
        _lowerCamelCase  :				Dict      =		512
        _lowerCamelCase  :				Dict      =		16
        _lowerCamelCase  :				Optional[Any]      =		2
        _lowerCamelCase  :				Union[str, Any]      =		0.02
        _lowerCamelCase  :				str      =		3
        _lowerCamelCase  :				int      =		4
        _lowerCamelCase  :				List[str]      =		None
        _lowerCamelCase  :				Any      =		self.vocab_size - 1
        _lowerCamelCase  :				Union[str, Any]      =		self.vocab_size - 1
        _lowerCamelCase  :				Tuple      =		self.vocab_size - 1
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     str:
        _lowerCamelCase  :				Union[str, Any]      =		ids_tensor([self.batch_size, self.seq_length]			,      self.vocab_size)
  
        _lowerCamelCase  :				int      =		None
        if self.use_input_mask:
           _lowerCamelCase  :				Optional[Any]      =		random_attention_mask([self.batch_size, self.seq_length])
  
        _lowerCamelCase  :				Any      =		None
        if self.use_token_type_ids:
           _lowerCamelCase  :				str      =		ids_tensor([self.batch_size, self.seq_length]			,      self.type_vocab_size)
  
        _lowerCamelCase  :				List[str]      =		None
        if self.use_mc_token_ids:
           _lowerCamelCase  :				Tuple      =		ids_tensor([self.batch_size, self.num_choices]			,      self.seq_length)
  
        _lowerCamelCase  :				Union[str, Any]      =		None
        _lowerCamelCase  :				Dict      =		None
        _lowerCamelCase  :				Any      =		None
        if self.use_labels:
           _lowerCamelCase  :				int      =		ids_tensor([self.batch_size]			,      self.type_sequence_label_size)
           _lowerCamelCase  :				Optional[Any]      =		ids_tensor([self.batch_size, self.seq_length]			,      self.num_labels)
           _lowerCamelCase  :				Optional[Any]      =		ids_tensor([self.batch_size]			,      self.num_choices)
  
        _lowerCamelCase  :				List[Any]      =		GPTJConfig(
            vocab_size=self.vocab_size			,      n_embd=self.hidden_size			,      n_layer=self.num_hidden_layers			,      n_head=self.num_attention_heads			,      intermediate_size=self.intermediate_size			,      hidden_act=self.hidden_act			,      hidden_dropout_prob=self.hidden_dropout_prob			,      attention_probs_dropout_prob=self.attention_probs_dropout_prob			,      n_positions=self.max_position_embeddings			,      type_vocab_size=self.type_vocab_size			,      initializer_range=self.initializer_range			,      bos_token_id=self.bos_token_id			,      eos_token_id=self.eos_token_id			,      pad_token_id=self.pad_token_id			,      rotary_dim=self.rotary_dim			,      return_dict=SCREAMING_SNAKE_CASE			,      )
  
        _lowerCamelCase  :				Optional[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			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      *SCREAMING_SNAKE_CASE)     ->     Union[str, Any]:
        _lowerCamelCase  :				Optional[Any]      =		TFGPTJModel(config=SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				List[Any]      =		{
            """input_ids""": input_ids,
            """attention_mask""": input_mask,
            """token_type_ids""": token_type_ids,
        }
        _lowerCamelCase  :				List[Any]      =		model(SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				List[Any]      =		[input_ids, None, input_mask]  # None is the input for 'past'
        _lowerCamelCase  :				int      =		model(SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				Tuple      =		model(SCREAMING_SNAKE_CASE)
  
        self.parent.assertEqual(result.last_hidden_state.shape			,      (self.batch_size, self.seq_length, self.hidden_size))
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      *SCREAMING_SNAKE_CASE)     ->     List[Any]:
        _lowerCamelCase  :				Tuple      =		TFGPTJModel(config=SCREAMING_SNAKE_CASE)
  
        # first forward pass
        _lowerCamelCase  :				Tuple      =		model(SCREAMING_SNAKE_CASE			,      token_type_ids=SCREAMING_SNAKE_CASE			,      use_cache=SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Dict      =		model(SCREAMING_SNAKE_CASE			,      token_type_ids=SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Tuple      =		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)
  
        _lowerCamelCase      ,						_lowerCamelCase  :				Optional[int]      =		outputs.to_tuple()
  
        # create hypothetical next token and extent to next_input_ids
        _lowerCamelCase  :				Optional[int]      =		ids_tensor((self.batch_size, 1)			,      config.vocab_size)
        _lowerCamelCase  :				int      =		ids_tensor([self.batch_size, 1]			,      self.type_vocab_size)
  
        # append to next input_ids and token_type_ids
        _lowerCamelCase  :				str      =		tf.concat([input_ids, next_tokens]			,      axis=-1)
        _lowerCamelCase  :				Dict      =		tf.concat([token_type_ids, next_token_types]			,      axis=-1)
  
        _lowerCamelCase  :				Any      =		model(SCREAMING_SNAKE_CASE			,      token_type_ids=SCREAMING_SNAKE_CASE)["""last_hidden_state"""]
        _lowerCamelCase  :				List[Any]      =		model(SCREAMING_SNAKE_CASE			,      token_type_ids=SCREAMING_SNAKE_CASE			,      past_key_values=SCREAMING_SNAKE_CASE)[
            """last_hidden_state"""
        ]
  
        # select random slice
        _lowerCamelCase  :				Union[str, Any]      =		int(ids_tensor((1,)			,      shape_list(SCREAMING_SNAKE_CASE)[-1]))
        _lowerCamelCase  :				Any      =		output_from_no_past[:, -1, random_slice_idx]
        _lowerCamelCase  :				str      =		output_from_past[:, 0, random_slice_idx]
  
        # test that outputs are equal for slice
        tf.debugging.assert_near(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      rtol=1e-6)
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      *SCREAMING_SNAKE_CASE)     ->     str:
        _lowerCamelCase  :				Dict      =		TFGPTJModel(config=SCREAMING_SNAKE_CASE)
  
        # create attention mask
        _lowerCamelCase  :				Dict      =		self.seq_length // 2
        _lowerCamelCase  :				int      =		tf.ones((self.batch_size, half_seq_length)			,      dtype=tf.intaa)
        _lowerCamelCase  :				Optional[Any]      =		tf.zeros((self.batch_size, self.seq_length - half_seq_length)			,      dtype=tf.intaa)
        _lowerCamelCase  :				Dict      =		tf.concat([attn_mask_begin, attn_mask_end]			,      axis=1)
  
        # first forward pass
        _lowerCamelCase      ,						_lowerCamelCase  :				List[str]      =		model(SCREAMING_SNAKE_CASE			,      attention_mask=SCREAMING_SNAKE_CASE).to_tuple()
  
        # create hypothetical next token and extent to next_input_ids
        _lowerCamelCase  :				int      =		ids_tensor((self.batch_size, 1)			,      config.vocab_size)
  
        # change a random masked slice from input_ids
        _lowerCamelCase  :				Any      =		ids_tensor((1,)			,      SCREAMING_SNAKE_CASE).numpy() + 1
        _lowerCamelCase  :				Union[str, Any]      =		ids_tensor((self.batch_size, self.seq_length)			,      config.vocab_size)
        _lowerCamelCase  :				Union[str, Any]      =		tf.range(self.seq_length) == (self.seq_length - random_seq_idx_to_change)
        _lowerCamelCase  :				Optional[int]      =		tf.transpose(
            tf.broadcast_to(tf.expand_dims(SCREAMING_SNAKE_CASE			,      -1)			,      (self.seq_length, self.batch_size)))
        _lowerCamelCase  :				str      =		tf.where(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
  
        # append to next input_ids and attn_mask
        _lowerCamelCase  :				int      =		tf.concat([input_ids, next_tokens]			,      axis=-1)
        _lowerCamelCase  :				Optional[Any]      =		tf.concat([attn_mask, tf.ones((shape_list(SCREAMING_SNAKE_CASE)[0], 1)			,      dtype=tf.intaa)]			,      axis=1)
  
        # get two different outputs
        _lowerCamelCase  :				List[str]      =		model(SCREAMING_SNAKE_CASE			,      attention_mask=SCREAMING_SNAKE_CASE)["""last_hidden_state"""]
        _lowerCamelCase  :				Dict      =		model(SCREAMING_SNAKE_CASE			,      past_key_values=SCREAMING_SNAKE_CASE			,      attention_mask=SCREAMING_SNAKE_CASE)[
            """last_hidden_state"""
        ]
  
        # select random slice
        _lowerCamelCase  :				Optional[int]      =		int(ids_tensor((1,)			,      shape_list(SCREAMING_SNAKE_CASE)[-1]))
        _lowerCamelCase  :				str      =		output_from_no_past[:, -1, random_slice_idx]
        _lowerCamelCase  :				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-1_2)
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      *SCREAMING_SNAKE_CASE)     ->     str:
        _lowerCamelCase  :				int      =		TFGPTJModel(config=SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				Dict      =		input_ids[:1, :]
        _lowerCamelCase  :				Optional[int]      =		input_mask[:1, :]
        _lowerCamelCase  :				Dict      =		token_type_ids[:1, :]
        _lowerCamelCase  :				Any      =		1
  
        # first forward pass
        _lowerCamelCase  :				List[Any]      =		model(SCREAMING_SNAKE_CASE			,      attention_mask=SCREAMING_SNAKE_CASE			,      token_type_ids=SCREAMING_SNAKE_CASE			,      use_cache=SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase      ,						_lowerCamelCase  :				int      =		outputs.to_tuple()
  
        # create hypothetical next token and extent to next_input_ids
        _lowerCamelCase  :				Dict      =		ids_tensor((self.batch_size, 3)			,      config.vocab_size)
        _lowerCamelCase  :				Tuple      =		ids_tensor((self.batch_size, 3)			,      2)
        _lowerCamelCase  :				List[Any]      =		ids_tensor((self.batch_size, 3)			,      self.type_vocab_size)
  
        # append to next input_ids and token_type_ids
        _lowerCamelCase  :				Tuple      =		tf.concat([input_ids, next_tokens]			,      axis=-1)
        _lowerCamelCase  :				List[str]      =		tf.concat([input_mask, next_attn_mask]			,      axis=-1)
        _lowerCamelCase  :				Optional[int]      =		tf.concat([token_type_ids, next_token_types]			,      axis=-1)
  
        _lowerCamelCase  :				List[Any]      =		model(
            SCREAMING_SNAKE_CASE			,      token_type_ids=SCREAMING_SNAKE_CASE			,      attention_mask=SCREAMING_SNAKE_CASE)["""last_hidden_state"""]
        _lowerCamelCase  :				Union[str, Any]      =		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
        _lowerCamelCase  :				Optional[int]      =		int(ids_tensor((1,)			,      shape_list(SCREAMING_SNAKE_CASE)[-1]))
        _lowerCamelCase  :				Optional[int]      =		output_from_no_past[:, -3:, random_slice_idx]
        _lowerCamelCase  :				Optional[Any]      =		output_from_past[:, :, random_slice_idx]
  
        # test that outputs are equal for slice
        tf.debugging.assert_near(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      rtol=1e-3)
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      *SCREAMING_SNAKE_CASE)     ->     Union[str, Any]:
        _lowerCamelCase  :				Optional[Any]      =		TFGPTJForCausalLM(config=SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Dict      =		{
            """input_ids""": input_ids,
            """attention_mask""": input_mask,
            """token_type_ids""": token_type_ids,
        }
        _lowerCamelCase  :				int      =		model(SCREAMING_SNAKE_CASE)
        self.parent.assertEqual(result.logits.shape			,      (self.batch_size, self.seq_length, self.vocab_size))
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     List[str]:
        _lowerCamelCase  :				Optional[Any]      =		self.prepare_config_and_inputs()
  
        (
            (
            _lowerCamelCase
        )      ,						(
            _lowerCamelCase
        )      ,						(
            _lowerCamelCase
        )      ,						(
            _lowerCamelCase
        )      ,						(
            _lowerCamelCase
        )      ,						(
            _lowerCamelCase
        )      ,						(
            _lowerCamelCase
        )      ,						(
            _lowerCamelCase
        )      ,						(
            _lowerCamelCase
        )      ,						
        )  :				Optional[Any]      =		config_and_inputs
  
        _lowerCamelCase  :				str      =		{
            """input_ids""": input_ids,
            """token_type_ids""": token_type_ids,
            """attention_mask""": input_mask,
        }
        return config, inputs_dict
  
  
  
  
  
  
  
  @require_tf
  class   lowercase__							(				A_   ,A_   ,A_   ,unittest.TestCase							):
     __UpperCAmelCase       					=				(
         (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
         if is_tf_available()
         else ()
     )
  
     __UpperCAmelCase       					=				(TFGPTJForCausalLM,) if is_tf_available() else ()
     __UpperCAmelCase       					=				(
         {
             '''feature-extraction''': TFGPTJModel,
             '''question-answering''': TFGPTJForQuestionAnswering,
             '''text-classification''': TFGPTJForSequenceClassification,
             '''text-generation''': TFGPTJForCausalLM,
             '''zero-shot''': TFGPTJForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     __UpperCAmelCase       					=				False
     __UpperCAmelCase       					=				False
     __UpperCAmelCase       					=				False
     __UpperCAmelCase       					=				False
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     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 		UpperCamelCase_     (       self)     ->     str:
        _lowerCamelCase  :				Optional[Any]      =		TFGPTJModelTester(self)
        _lowerCamelCase  :				List[str]      =		ConfigTester(self			,      config_class=SCREAMING_SNAKE_CASE			,      n_embd=37)
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Optional[Any]:
        self.config_tester.run_common_tests()
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Tuple:
        _lowerCamelCase  :				Any      =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_gptj_model(*SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Optional[Any]:
        _lowerCamelCase  :				Optional[int]      =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_gptj_model_past(*SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Optional[Any]:
        _lowerCamelCase  :				Union[str, 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)     ->     Any:
        _lowerCamelCase  :				List[str]      =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_gptj_model_past_large_inputs(*SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Union[str, Any]:
        _lowerCamelCase  :				int      =		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)     ->     Tuple:
        _lowerCamelCase  :				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)     ->     int:
        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   lowercase__							(				unittest.TestCase							):
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Tuple:
        _lowerCamelCase  :				List[str]      =		TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""			,      from_pt=SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Union[str, Any]      =		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
        _lowerCamelCase  :				Union[str, Any]      =		[464, 3290, 318, 257, 582, 338, 1266, 1545, 13, 632, 318, 257, 9112, 1_5185, 11, 290, 340, 318, 257, 1545]
        # fmt: on
        _lowerCamelCase  :				str      =		model.generate(SCREAMING_SNAKE_CASE			,      do_sample=SCREAMING_SNAKE_CASE)
        self.assertListEqual(output_ids[0].numpy().tolist()			,      SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Dict:
        _lowerCamelCase  :				Optional[int]      =		AutoTokenizer.from_pretrained("""EleutherAI/gpt-j-6B"""			,      revision="""float16""")
        _lowerCamelCase  :				Any      =		TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""			,      revision="""float16"""			,      from_pt=SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				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"""):
           _lowerCamelCase  :				List[str]      =		model.generate(**SCREAMING_SNAKE_CASE			,      do_sample=SCREAMING_SNAKE_CASE			,      seed=[42, 0])
        _lowerCamelCase  :				Optional[Any]      =		tokenizer.decode(output_ids[0]			,      skip_special_tokens=SCREAMING_SNAKE_CASE)
  
        _lowerCamelCase  :				Any      =		"""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)     ->     List[str]:
        _lowerCamelCase  :				List[Any]      =		TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""			,      revision="""float16"""			,      from_pt=SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Tuple      =		AutoTokenizer.from_pretrained("""EleutherAI/gpt-j-6B"""			,      revision="""float16""")
  
        _lowerCamelCase  :				Tuple      =		"""left"""
  
        # Define PAD Token = EOS Token = 50256
        _lowerCamelCase  :				Dict      =		tokenizer.eos_token
        _lowerCamelCase  :				List[str]      =		model.config.eos_token_id
  
        # use different length sentences to test batching
        _lowerCamelCase  :				List[Any]      =		[
            """Hello, my dog is a little""",
            """Today, I""",
        ]
        _lowerCamelCase  :				List[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 		UpperCamelCase_     (       self)     ->     int:
        # Confirms that we get the expected results with left-padded beam search
        _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase  :				Optional[Any]      =		self._get_beam_search_test_objects()
  
        _lowerCamelCase  :				List[Any]      =		tokenizer(SCREAMING_SNAKE_CASE			,      return_tensors="""tf"""			,      padding=SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				int      =		model.generate(**SCREAMING_SNAKE_CASE			,      do_sample=SCREAMING_SNAKE_CASE			,      num_beams=2)
        _lowerCamelCase  :				int      =		tokenizer.batch_decode(SCREAMING_SNAKE_CASE			,      skip_special_tokens=SCREAMING_SNAKE_CASE)
        self.assertListEqual(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     List[str]:
        # Confirms that left-padding is working properly
        _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase  :				int      =		self._get_beam_search_test_objects()
  
        _lowerCamelCase  :				int      =		tokenizer(SCREAMING_SNAKE_CASE			,      return_tensors="""tf"""			,      padding=SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				str      =		tokenizer(sentences[0]			,      return_tensors="""tf""")
        _lowerCamelCase  :				Optional[Any]      =		model.generate(**SCREAMING_SNAKE_CASE			,      do_sample=SCREAMING_SNAKE_CASE			,      num_beams=2)
        _lowerCamelCase  :				int      =		(
            shape_list(inputs_non_padded["""input_ids"""])[-1]
            - tf.reduce_sum(tf.cast(inputs["""attention_mask"""][-1]			,      tf.intaa)).numpy()
        )
        _lowerCamelCase  :				Tuple      =		tokenizer(sentences[1]			,      return_tensors="""tf""")
        _lowerCamelCase  :				str      =		model.generate(
            **SCREAMING_SNAKE_CASE			,      do_sample=SCREAMING_SNAKE_CASE			,      num_beams=2			,      max_length=model.config.max_length - num_paddings)
        _lowerCamelCase  :				Tuple      =		tokenizer.decode(output_non_padded[0]			,      skip_special_tokens=SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				List[str]      =		tokenizer.decode(output_padded[0]			,      skip_special_tokens=SCREAMING_SNAKE_CASE)
        self.assertListEqual(SCREAMING_SNAKE_CASE			,      [non_padded_sentence, padded_sentence])
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Dict:
        # Confirms that XLA is working properly
        _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase  :				Any      =		self._get_beam_search_test_objects()
  
        _lowerCamelCase  :				List[Any]      =		tokenizer(SCREAMING_SNAKE_CASE			,      return_tensors="""tf"""			,      padding=SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				List[Any]      =		tf.function(model.generate			,      jit_compile=SCREAMING_SNAKE_CASE)
        _lowerCamelCase  :				Optional[int]      =		xla_generate(**SCREAMING_SNAKE_CASE			,      do_sample=SCREAMING_SNAKE_CASE			,      num_beams=2)
        _lowerCamelCase  :				List[str]      =		tokenizer.batch_decode(SCREAMING_SNAKE_CASE			,      skip_special_tokens=SCREAMING_SNAKE_CASE)
        self.assertListEqual(SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)
  
  
style_context: 
  
  
  """simple docstring"""
  from typing import List, Optional, Tuple
  
  from tokenizers import pre_tokenizers
  
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  from .tokenization_clip import CLIPTokenizer
  
  
  UpperCAmelCase   =	logging.get_logger(__name__)
  
  UpperCAmelCase   =	{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}
  
  UpperCAmelCase   =	{
      """vocab_file""": {
          """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json""",
      },
      """merges_file""": {
          """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt""",
      },
      """tokenizer_file""": {
          """openai/clip-vit-base-patch32""": (
              """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json"""
          ),
      },
  }
  
  UpperCAmelCase   =	{
      """openai/clip-vit-base-patch32""": 77,
  }
  
  
  
  
  class   lowercase__							(				A_							):
     __UpperCAmelCase       					=				VOCAB_FILES_NAMES
     __UpperCAmelCase       					=				PRETRAINED_VOCAB_FILES_MAP
     __UpperCAmelCase       					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCAmelCase       					=				['''input_ids''', '''attention_mask''']
     __UpperCAmelCase       					=				CLIPTokenizer
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE="<|endoftext|>"			,      SCREAMING_SNAKE_CASE="<|startoftext|>"			,      SCREAMING_SNAKE_CASE="<|endoftext|>"			,      SCREAMING_SNAKE_CASE="<|endoftext|>"			,      **SCREAMING_SNAKE_CASE			,      )     ->     List[Any]:
        super().__init__(
            SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      tokenizer_file=SCREAMING_SNAKE_CASE			,      unk_token=SCREAMING_SNAKE_CASE			,      bos_token=SCREAMING_SNAKE_CASE			,      eos_token=SCREAMING_SNAKE_CASE			,      pad_token=SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE			,      )
  
        if not isinstance(self.backend_tokenizer.pre_tokenizer			,      pre_tokenizers.Sequence):
           raise ValueError(
               """The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been"""
               """ heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using"""
               """ to be compatible with this version.The easiest way to do so is"""
               """ `CLIPTokenizerFast.from_pretrained(\"path_to_local_folder_or_hub_repo, from_slow=True)`. If you want"""
               """ to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of"""
               """ transformers.""")
  
        self._wrap_decode_method_backend_tokenizer()
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Union[str, Any]:
        _lowerCamelCase  :				str      =		self.backend_tokenizer.decode
  
        def new_decode_method(*SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE):
           _lowerCamelCase  :				Any      =		orig_decode_method(*SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE)
           _lowerCamelCase  :				Optional[int]      =		text.replace(self.backend_tokenizer.model.end_of_word_suffix			,      """ """).strip()
           return text
  
        _lowerCamelCase  :				List[str]      =		new_decode_method
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE = None)     ->     List[int]:
        _lowerCamelCase  :				str      =		[self.bos_token_id]
        _lowerCamelCase  :				Union[str, Any]      =		[self.eos_token_id]
  
        if token_ids_a is None:
           return bos_token + token_ids_a + eos_token
        return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE = None)     ->     List[int]:
        _lowerCamelCase  :				Any      =		[self.bos_token_id]
        _lowerCamelCase  :				int      =		[self.eos_token_id]
  
        if token_ids_a is None:
           return len(bos_token + token_ids_a + eos_token) * [0]
        return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token) * [0]
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE = None)     ->     Tuple[str]:
        _lowerCamelCase  :				Dict      =		self._tokenizer.model.save(SCREAMING_SNAKE_CASE			,      name=SCREAMING_SNAKE_CASE)
        return tuple(SCREAMING_SNAKE_CASE)
  
  