code: 
  
  from __future__ import annotations
  
  import unittest
  
  from transformers import AutoTokenizer, GPTJConfig, is_tf_available
  from transformers.testing_utils import require_tf, slow, tooslow
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
  from ...utils.test_modeling_tf_core import TFCoreModelTesterMixin
  
  
  if is_tf_available():
  			import tensorflow as tf
  
  			from transformers.models.gptj.modeling_tf_gptj import (
  			    TFGPTJForCausalLM,
  			    TFGPTJForQuestionAnswering,
  			    TFGPTJForSequenceClassification,
  			    TFGPTJModel,
  			    shape_list,
  			)
  
  class __UpperCamelCase    :
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					def __init__(							self							,			lowerCamelCase__	):
  						UpperCAmelCase__:  Union[str, Any]		       =   parent
  						UpperCAmelCase__:  List[Any]		       =   1_3
  						UpperCAmelCase__:  Tuple		       =   7
  						UpperCAmelCase__:  str		       =   True
  						UpperCAmelCase__:  Union[str, Any]		       =   True
  						UpperCAmelCase__:  int		       =   True
  						UpperCAmelCase__:  Union[str, Any]		       =   True
  						UpperCAmelCase__:  Any		       =   True
  						UpperCAmelCase__:  List[Any]		       =   9_9
  						UpperCAmelCase__:  Tuple		       =   3_2
  						UpperCAmelCase__:  int		       =   4
  						UpperCAmelCase__:  Optional[Any]		       =   2
  						UpperCAmelCase__:  Dict		       =   4
  						UpperCAmelCase__:  Optional[int]		       =   3_7
  						UpperCAmelCase__:  Tuple		       =   "gelu"
  						UpperCAmelCase__:  Dict		       =   0.1
  						UpperCAmelCase__:  int		       =   0.1
  						UpperCAmelCase__:  Optional[int]		       =   5_1_2
  						UpperCAmelCase__:  Union[str, Any]		       =   1_6
  						UpperCAmelCase__:  Optional[Any]		       =   2
  						UpperCAmelCase__:  Optional[int]		       =   0.02
  						UpperCAmelCase__:  List[str]		       =   3
  						UpperCAmelCase__:  Any		       =   4
  						UpperCAmelCase__:  Tuple		       =   None
  						UpperCAmelCase__:  List[Any]		       =   self.vocab_size - 1
  						UpperCAmelCase__:  Dict		       =   self.vocab_size - 1
  						UpperCAmelCase__:  int		       =   self.vocab_size - 1
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  List[str]		       =   ids_tensor([self.batch_size, self.seq_length]							,			self.vocab_size	)
  
  						UpperCAmelCase__:  Tuple		       =   None
  						if self.use_input_mask:
  							UpperCAmelCase__:  Dict		       =   random_attention_mask([self.batch_size, self.seq_length]	)
  
  						UpperCAmelCase__:  List[str]		       =   None
  						if self.use_token_type_ids:
  							UpperCAmelCase__:  Optional[Any]		       =   ids_tensor([self.batch_size, self.seq_length]							,			self.type_vocab_size	)
  
  						UpperCAmelCase__:  Optional[int]		       =   None
  						if self.use_mc_token_ids:
  							UpperCAmelCase__:  Union[str, Any]		       =   ids_tensor([self.batch_size, self.num_choices]							,			self.seq_length	)
  
  						UpperCAmelCase__:  List[str]		       =   None
  						UpperCAmelCase__:  Dict		       =   None
  						UpperCAmelCase__:  Tuple		       =   None
  						if self.use_labels:
  							UpperCAmelCase__:  List[str]		       =   ids_tensor([self.batch_size]							,			self.type_sequence_label_size	)
  							UpperCAmelCase__:  List[Any]		       =   ids_tensor([self.batch_size, self.seq_length]							,			self.num_labels	)
  							UpperCAmelCase__:  Union[str, Any]		       =   ids_tensor([self.batch_size]							,			self.num_choices	)
  
  						UpperCAmelCase__:  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__							,			)
  
  						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							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			*lowerCamelCase__	):
  						UpperCAmelCase__:  Tuple		       =   TFGPTJModel(config=lowerCamelCase__	)
  						UpperCAmelCase__:  Optional[Any]		       =   {
  						    "input_ids": input_ids,
  						    "attention_mask": input_mask,
  						    "token_type_ids": token_type_ids,
  						}
  						UpperCAmelCase__:  Dict		       =   model(lowerCamelCase__	)
  
  						UpperCAmelCase__:  Union[str, Any]		       =   [input_ids, None, input_mask]  # None is the input for 'past'
  						UpperCAmelCase__:  str		       =   model(lowerCamelCase__	)
  
  						UpperCAmelCase__:  Optional[int]		       =   model(lowerCamelCase__	)
  
  						self.parent.assertEqual(result.last_hidden_state.shape							,			(self.batch_size, self.seq_length, self.hidden_size)	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			*lowerCamelCase__	):
  						UpperCAmelCase__:  Union[str, Any]		       =   TFGPTJModel(config=lowerCamelCase__	)
  
  						# first forward pass
  						UpperCAmelCase__:  int		       =   model(lowerCamelCase__							,			token_type_ids=lowerCamelCase__							,			use_cache=lowerCamelCase__	)
  						UpperCAmelCase__:  int		       =   model(lowerCamelCase__							,			token_type_ids=lowerCamelCase__	)
  						UpperCAmelCase__:  Optional[int]		       =   model(lowerCamelCase__							,			token_type_ids=lowerCamelCase__							,			use_cache=lowerCamelCase__	)
  
  						self.parent.assertTrue(len(lowerCamelCase__	) == len(lowerCamelCase__	)	)
  						self.parent.assertTrue(len(lowerCamelCase__	) == len(lowerCamelCase__	) + 1	)
  
  						UpperCAmelCase__       ,	UpperCAmelCase__:  Union[str, Any]		       =   outputs.to_tuple()
  
  						# create hypothetical next token and extent to next_input_ids
  						UpperCAmelCase__:  List[str]		       =   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__:  int		       =   tf.concat([input_ids, next_tokens]							,			axis=-1	)
  						UpperCAmelCase__:  str		       =   tf.concat([token_type_ids, next_token_types]							,			axis=-1	)
  
  						UpperCAmelCase__:  int		       =   model(lowerCamelCase__							,			token_type_ids=lowerCamelCase__	)["last_hidden_state"]
  						UpperCAmelCase__:  Optional[int]		       =   model(lowerCamelCase__							,			token_type_ids=lowerCamelCase__							,			past_key_values=lowerCamelCase__	)[
  						    "last_hidden_state"
  						]
  
  						# select random slice
  						UpperCAmelCase__:  Any		       =   int(ids_tensor((1,)							,			shape_list(lowerCamelCase__	)[-1]	)	)
  						UpperCAmelCase__:  Optional[Any]		       =   output_from_no_past[:, -1, random_slice_idx]
  						UpperCAmelCase__:  Union[str, Any]		       =   output_from_past[:, 0, random_slice_idx]
  
  						# test that outputs are equal for slice
  						tf.debugging.assert_near(lowerCamelCase__							,			lowerCamelCase__							,			rtol=1e-6	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			*lowerCamelCase__	):
  						UpperCAmelCase__:  Optional[Any]		       =   TFGPTJModel(config=lowerCamelCase__	)
  
  						# create attention mask
  						UpperCAmelCase__:  Any		       =   self.seq_length // 2
  						UpperCAmelCase__:  Union[str, Any]		       =   tf.ones((self.batch_size, half_seq_length)							,			dtype=tf.intaa	)
  						UpperCAmelCase__:  List[str]		       =   tf.zeros((self.batch_size, self.seq_length - half_seq_length)							,			dtype=tf.intaa	)
  						UpperCAmelCase__:  Any		       =   tf.concat([attn_mask_begin, attn_mask_end]							,			axis=1	)
  
  						# first forward pass
  						UpperCAmelCase__       ,	UpperCAmelCase__:  Any		       =   model(lowerCamelCase__							,			attention_mask=lowerCamelCase__	).to_tuple()
  
  						# create hypothetical next token and extent to next_input_ids
  						UpperCAmelCase__:  int		       =   ids_tensor((self.batch_size, 1)							,			config.vocab_size	)
  
  						# change a random masked slice from input_ids
  						UpperCAmelCase__:  Optional[int]		       =   ids_tensor((1,)							,			lowerCamelCase__	).numpy() + 1
  						UpperCAmelCase__:  str		       =   ids_tensor((self.batch_size, self.seq_length)							,			config.vocab_size	)
  						UpperCAmelCase__:  Optional[Any]		       =   tf.range(self.seq_length	) == (self.seq_length - random_seq_idx_to_change)
  						UpperCAmelCase__:  Tuple		       =   tf.transpose(
  						    tf.broadcast_to(tf.expand_dims(lowerCamelCase__							,			-1	)							,			(self.seq_length, self.batch_size)	)	)
  						UpperCAmelCase__:  Tuple		       =   tf.where(lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	)
  
  						# append to next input_ids and attn_mask
  						UpperCAmelCase__:  int		       =   tf.concat([input_ids, next_tokens]							,			axis=-1	)
  						UpperCAmelCase__:  str		       =   tf.concat([attn_mask, tf.ones((shape_list(lowerCamelCase__	)[0], 1)							,			dtype=tf.intaa	)]							,			axis=1	)
  
  						# get two different outputs
  						UpperCAmelCase__:  Tuple		       =   model(lowerCamelCase__							,			attention_mask=lowerCamelCase__	)["last_hidden_state"]
  						UpperCAmelCase__:  List[str]		       =   model(lowerCamelCase__							,			past_key_values=lowerCamelCase__							,			attention_mask=lowerCamelCase__	)[
  						    "last_hidden_state"
  						]
  
  						# select random slice
  						UpperCAmelCase__:  Optional[int]		       =   int(ids_tensor((1,)							,			shape_list(lowerCamelCase__	)[-1]	)	)
  						UpperCAmelCase__:  Any		       =   output_from_no_past[:, -1, random_slice_idx]
  						UpperCAmelCase__:  Union[str, Any]		       =   output_from_past[:, 0, random_slice_idx]
  
  						# test that outputs are equal for slice
  						tf.debugging.assert_near(lowerCamelCase__							,			lowerCamelCase__							,			rtol=1e-12	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			*lowerCamelCase__	):
  						UpperCAmelCase__:  Optional[int]		       =   TFGPTJModel(config=lowerCamelCase__	)
  
  						UpperCAmelCase__:  Tuple		       =   input_ids[:1, :]
  						UpperCAmelCase__:  str		       =   input_mask[:1, :]
  						UpperCAmelCase__:  Tuple		       =   token_type_ids[:1, :]
  						UpperCAmelCase__:  Dict		       =   1
  
  						# first forward pass
  						UpperCAmelCase__:  List[Any]		       =   model(lowerCamelCase__							,			attention_mask=lowerCamelCase__							,			token_type_ids=lowerCamelCase__							,			use_cache=lowerCamelCase__	)
  
  						UpperCAmelCase__       ,	UpperCAmelCase__:  int		       =   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__:  str		       =   ids_tensor((self.batch_size, 3)							,			2	)
  						UpperCAmelCase__:  Dict		       =   ids_tensor((self.batch_size, 3)							,			self.type_vocab_size	)
  
  						# append to next input_ids and token_type_ids
  						UpperCAmelCase__:  Dict		       =   tf.concat([input_ids, next_tokens]							,			axis=-1	)
  						UpperCAmelCase__:  Tuple		       =   tf.concat([input_mask, next_attn_mask]							,			axis=-1	)
  						UpperCAmelCase__:  int		       =   tf.concat([token_type_ids, next_token_types]							,			axis=-1	)
  
  						UpperCAmelCase__:  Union[str, Any]		       =   model(
  						    lowerCamelCase__							,			token_type_ids=lowerCamelCase__							,			attention_mask=lowerCamelCase__	)["last_hidden_state"]
  						UpperCAmelCase__:  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
  						UpperCAmelCase__:  int		       =   int(ids_tensor((1,)							,			shape_list(lowerCamelCase__	)[-1]	)	)
  						UpperCAmelCase__:  int		       =   output_from_no_past[:, -3:, random_slice_idx]
  						UpperCAmelCase__:  Optional[int]		       =   output_from_past[:, :, random_slice_idx]
  
  						# test that outputs are equal for slice
  						tf.debugging.assert_near(lowerCamelCase__							,			lowerCamelCase__							,			rtol=1e-3	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			*lowerCamelCase__	):
  						UpperCAmelCase__:  str		       =   TFGPTJForCausalLM(config=lowerCamelCase__	)
  						UpperCAmelCase__:  str		       =   {
  						    "input_ids": input_ids,
  						    "attention_mask": input_mask,
  						    "token_type_ids": token_type_ids,
  						}
  						UpperCAmelCase__:  List[str]		       =   model(lowerCamelCase__	)
  						self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.seq_length, self.vocab_size)	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Tuple		       =   self.prepare_config_and_inputs()
  
  						(
  						    (
  						    UpperCAmelCase__						
  						)       ,	(
  						    UpperCAmelCase__						
  						)       ,	(
  						    UpperCAmelCase__						
  						)       ,	(
  						    UpperCAmelCase__						
  						)       ,	(
  						    UpperCAmelCase__						
  						)       ,	(
  						    UpperCAmelCase__						
  						)       ,	(
  						    UpperCAmelCase__						
  						)       ,	(
  						    UpperCAmelCase__						
  						)       ,	(
  						    UpperCAmelCase__						
  						)       ,							
  						):  Dict		       =   config_and_inputs
  
  						UpperCAmelCase__:  Union[str, Any]		       =   {
  						    "input_ids": input_ids,
  						    "token_type_ids": token_type_ids,
  						    "attention_mask": input_mask,
  						}
  						return config, inputs_dict
  
  @require_tf
  class __UpperCamelCase    (							__UpperCAmelCase				,__UpperCAmelCase				,__UpperCAmelCase				,unittest.TestCase		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					__magic_name__     			=      (
  					    (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
  					    if is_tf_available()
  					    else ()
  					)
  
  					__magic_name__     			=      (TFGPTJForCausalLM,) if is_tf_available() else ()
  					__magic_name__     			=      (
  					    {
  					        """feature-extraction""": TFGPTJModel,
  					        """question-answering""": TFGPTJForQuestionAnswering,
  					        """text-classification""": TFGPTJForSequenceClassification,
  					        """text-generation""": TFGPTJForCausalLM,
  					        """zero-shot""": TFGPTJForSequenceClassification,
  					    }
  					    if is_tf_available()
  					    else {}
  					)
  					__magic_name__     			=      False
  					__magic_name__     			=      False
  					__magic_name__     			=      False
  					__magic_name__     			=      False
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	):
  
  
  
  						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	):
  						UpperCAmelCase__:  Union[str, Any]		       =   TFGPTJModelTester(self	)
  						UpperCAmelCase__:  List[Any]		       =   ConfigTester(self							,			config_class=lowerCamelCase__							,			n_embd=3_7	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						self.config_tester.run_common_tests()
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  str		       =   self.model_tester.prepare_config_and_inputs()
  						self.model_tester.create_and_check_gptj_model(*lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Tuple		       =   self.model_tester.prepare_config_and_inputs()
  						self.model_tester.create_and_check_gptj_model_past(*lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  int		       =   self.model_tester.prepare_config_and_inputs()
  						self.model_tester.create_and_check_gptj_model_attention_mask_past(*lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  List[str]		       =   self.model_tester.prepare_config_and_inputs()
  						self.model_tester.create_and_check_gptj_model_past_large_inputs(*lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Optional[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       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Optional[Any]		       =   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       _UpperCAmelCase						(							self	):
  						super().test_resize_token_embeddings()
  
  @require_tf
  @tooslow
  # Marked as @tooslow due to GPU OOM -- but still useful to run locally. Requires ~39GB of RAM.
  class __UpperCamelCase    (							unittest.TestCase		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  str		       =   TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"							,			from_pt=lowerCamelCase__	)
  						UpperCAmelCase__:  Tuple		       =   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__:  Dict		       =   [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__:  Union[str, Any]		       =   model.generate(lowerCamelCase__							,			do_sample=lowerCamelCase__	)
  						self.assertListEqual(output_ids[0].numpy().tolist()							,			lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  int		       =   AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B"							,			revision="float16"	)
  						UpperCAmelCase__:  Optional[int]		       =   TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"							,			revision="float16"							,			from_pt=lowerCamelCase__	)
  
  						UpperCAmelCase__:  int		       =   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__:  List[Any]		       =   model.generate(**lowerCamelCase__							,			do_sample=lowerCamelCase__							,			seed=[4_2, 0]	)
  						UpperCAmelCase__:  Tuple		       =   tokenizer.decode(output_ids[0]							,			skip_special_tokens=lowerCamelCase__	)
  
  						UpperCAmelCase__:  List[Any]		       =   "Today is a nice day and I’m going to go for a walk. I’"
  						self.assertEqual(lowerCamelCase__							,			lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Union[str, Any]		       =   TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"							,			revision="float16"							,			from_pt=lowerCamelCase__	)
  						UpperCAmelCase__:  List[str]		       =   AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B"							,			revision="float16"	)
  
  						UpperCAmelCase__:  str		       =   "left"
  
  						# Define PAD Token = EOS Token = 50256
  						UpperCAmelCase__:  Optional[Any]		       =   tokenizer.eos_token
  						UpperCAmelCase__:  List[Any]		       =   model.config.eos_token_id
  
  						# use different length sentences to test batching
  						UpperCAmelCase__:  Optional[Any]		       =   [
  						    "Hello, my dog is a little",
  						    "Today, I",
  						]
  						UpperCAmelCase__:  List[str]		       =   [
  						    "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	):
  						UpperCAmelCase__       ,	UpperCAmelCase__       ,	UpperCAmelCase__       ,	UpperCAmelCase__:  Any		       =   self._get_beam_search_test_objects()
  
  						UpperCAmelCase__:  List[Any]		       =   tokenizer(lowerCamelCase__							,			return_tensors="tf"							,			padding=lowerCamelCase__	)
  						UpperCAmelCase__:  List[Any]		       =   model.generate(**lowerCamelCase__							,			do_sample=lowerCamelCase__							,			num_beams=2	)
  						UpperCAmelCase__:  Dict		       =   tokenizer.batch_decode(lowerCamelCase__							,			skip_special_tokens=lowerCamelCase__	)
  						self.assertListEqual(lowerCamelCase__							,			lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__       ,	UpperCAmelCase__       ,	UpperCAmelCase__       ,	UpperCAmelCase__:  int		       =   self._get_beam_search_test_objects()
  
  						UpperCAmelCase__:  Union[str, Any]		       =   tokenizer(lowerCamelCase__							,			return_tensors="tf"							,			padding=lowerCamelCase__	)
  						UpperCAmelCase__:  List[str]		       =   tokenizer(sentences[0]							,			return_tensors="tf"	)
  						UpperCAmelCase__:  List[Any]		       =   model.generate(**lowerCamelCase__							,			do_sample=lowerCamelCase__							,			num_beams=2	)
  						UpperCAmelCase__:  int		       =   (
  						    shape_list(inputs_non_padded["input_ids"]	)[-1]
  						    - tf.reduce_sum(tf.cast(inputs["attention_mask"][-1]							,			tf.intaa	)	).numpy()
  						)
  						UpperCAmelCase__:  str		       =   tokenizer(sentences[1]							,			return_tensors="tf"	)
  						UpperCAmelCase__:  str		       =   model.generate(
  						    **lowerCamelCase__							,			do_sample=lowerCamelCase__							,			num_beams=2							,			max_length=model.config.max_length - num_paddings	)
  						UpperCAmelCase__:  Tuple		       =   tokenizer.decode(output_non_padded[0]							,			skip_special_tokens=lowerCamelCase__	)
  						UpperCAmelCase__:  Optional[int]		       =   tokenizer.decode(output_padded[0]							,			skip_special_tokens=lowerCamelCase__	)
  						self.assertListEqual(lowerCamelCase__							,			[non_padded_sentence, padded_sentence]	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__       ,	UpperCAmelCase__       ,	UpperCAmelCase__       ,	UpperCAmelCase__:  List[str]		       =   self._get_beam_search_test_objects()
  
  						UpperCAmelCase__:  Optional[Any]		       =   tokenizer(lowerCamelCase__							,			return_tensors="tf"							,			padding=lowerCamelCase__	)
  						UpperCAmelCase__:  Dict		       =   tf.function(model.generate							,			jit_compile=lowerCamelCase__	)
  						UpperCAmelCase__:  Optional[Any]		       =   xla_generate(**lowerCamelCase__							,			do_sample=lowerCamelCase__							,			num_beams=2	)
  						UpperCAmelCase__:  int		       =   tokenizer.batch_decode(lowerCamelCase__							,			skip_special_tokens=lowerCamelCase__	)
  						self.assertListEqual(lowerCamelCase__							,			lowerCamelCase__	)
style_context: 
  
  from __future__ import annotations
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	assert n > 1 and a > 0 and greatest_common_divisor(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			) == 1
  	((UpperCAmelCase__)       ,	(UpperCAmelCase__)       ,	(UpperCAmelCase__)):  Optional[Any]		       =   extended_gcd(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)  # Implemented below
  	UpperCAmelCase__:  List[str]		       =   (b * s) % n
  	return x
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	((UpperCAmelCase__)       ,	(UpperCAmelCase__)):  int		       =   extended_euclid(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)  # Implemented below
  	if b < 0:
  		UpperCAmelCase__:  List[str]		       =   (b % n + n) % n
  	return b
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	UpperCAmelCase__:  Union[str, Any]		       =   invert_modulo(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__:  Any		       =   (b * s) % n
  	return x
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	assert a >= 0 and b >= 0
  
  	if b == 0:
  		UpperCAmelCase__       ,	UpperCAmelCase__       ,	UpperCAmelCase__:  List[Any]		       =   a, 1, 0
  	else:
  		((UpperCAmelCase__)       ,	(UpperCAmelCase__)       ,	(UpperCAmelCase__)):  Tuple		       =   extended_gcd(SCREAMING_SNAKE_CASE  ,a % b			)
  		UpperCAmelCase__:  Union[str, Any]		       =   q
  		UpperCAmelCase__:  Union[str, Any]		       =   p - q * (a // b)
  
  	assert a % d == 0 and b % d == 0
  	assert d == a * x + b * y
  
  	return (d, x, y)
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	if b == 0:
  		return (1, 0)
  	((UpperCAmelCase__)       ,	(UpperCAmelCase__)):  List[str]		       =   extended_euclid(SCREAMING_SNAKE_CASE  ,a % b			)
  	UpperCAmelCase__:  Optional[Any]		       =   a // b
  	return (y, x - k * y)
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	if a < b:
  		UpperCAmelCase__       ,	UpperCAmelCase__:  str		       =   b, a
  
  	while a % b != 0:
  		UpperCAmelCase__       ,	UpperCAmelCase__:  Union[str, Any]		       =   b, a % b
  
  	return b
  
  
  if __name__ == "__main__":
  					from doctest import testmod
  
  					testmod(name="""modular_division""", verbose=True)
  					testmod(name="""modular_division2""", verbose=True)
  					testmod(name="""invert_modulo""", verbose=True)
  					testmod(name="""extended_gcd""", verbose=True)
  					testmod(name="""extended_euclid""", verbose=True)
  					testmod(name="""greatest_common_divisor""", verbose=True)