code: 
  
  
  
  """simple docstring"""
  
  
  
  
  import tempfile
  import unittest
  
  import numpy as np
  
  import transformers
  from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
  from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow
  
  from ...generation.test_flax_utils import FlaxGenerationTesterMixin
  from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
  
  
  if is_flax_available():
   import jax
   import jax.numpy as jnp
  
   from transformers.modeling_flax_pytorch_utils import (
       convert_pytorch_state_dict_to_flax,
       load_flax_weights_in_pytorch_model,
   )
   from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel
  
  if is_torch_available():
   import torch
  
  
  
  
  
  
  class 			UpperCAmelCase_						:
  
  
  
        def __init__(  self  :			str  ,			A  :			Union[str, Any]  ,			A  :			List[Any]=1_4  ,			A  :			List[str]=7  ,			A  :			Union[str, Any]=True  ,			A  :			Any=True  ,			A  :			int=False  ,			A  :			Dict=True  ,			A  :			Optional[int]=9_9  ,			A  :			List[Any]=3_2  ,			A  :			Union[str, Any]=4  ,			A  :			Tuple=4  ,			A  :			Optional[int]=[[["global", "local"], 2]]  ,			A  :			Any=3_7  ,			A  :			str="gelu"  ,			A  :			List[Any]=0.1  ,			A  :			Union[str, Any]=0.1  ,			A  :			Union[str, Any]=5_1_2  ,			A  :			Dict=7  ,			A  :			Tuple=0.02  ,			):
             _UpperCAmelCase    :			Optional[Any]              =					parent
             _UpperCAmelCase    :			Optional[int]              =					batch_size
             _UpperCAmelCase    :			Optional[Any]              =					seq_length
             _UpperCAmelCase    :			List[str]              =					is_training
             _UpperCAmelCase    :			Union[str, Any]              =					use_input_mask
             _UpperCAmelCase    :			List[Any]              =					use_token_type_ids
             _UpperCAmelCase    :			str              =					use_labels
             _UpperCAmelCase    :			Dict              =					vocab_size
             _UpperCAmelCase    :			int              =					hidden_size
             _UpperCAmelCase    :			int              =					num_hidden_layers
             _UpperCAmelCase    :			Optional[int]              =					num_attention_heads
             _UpperCAmelCase    :			Optional[Any]              =					attention_types
             _UpperCAmelCase    :			int              =					intermediate_size
             _UpperCAmelCase    :			str              =					hidden_act
             _UpperCAmelCase    :			Optional[int]              =					hidden_dropout_prob
             _UpperCAmelCase    :			Optional[int]              =					attention_probs_dropout_prob
             _UpperCAmelCase    :			Any              =					max_position_embeddings
             _UpperCAmelCase    :			List[str]              =					window_size
             _UpperCAmelCase    :			List[Any]              =					initializer_range
             _UpperCAmelCase    :			Dict              =					None
             _UpperCAmelCase    :			Tuple              =					vocab_size - 1
             _UpperCAmelCase    :			List[str]              =					vocab_size - 1
             _UpperCAmelCase    :			Union[str, Any]              =					vocab_size - 1
  
  
  
        def 		snake_case_      (  self  :			Tuple		):
             _UpperCAmelCase    :			Optional[Any]              =					ids_tensor([self.batch_size, self.seq_length]  ,			self.vocab_size		)
  
             _UpperCAmelCase    :			List[Any]              =					None
             if self.use_input_mask:
                  _UpperCAmelCase    :			Union[str, Any]              =					random_attention_mask([self.batch_size, self.seq_length]		)
  
             _UpperCAmelCase    :			int              =					GPTNeoConfig(
                 vocab_size=self.vocab_size  ,			hidden_size=self.hidden_size  ,			num_layers=self.num_hidden_layers  ,			num_heads=self.num_attention_heads  ,			max_position_embeddings=self.max_position_embeddings  ,			use_cache=A  ,			bos_token_id=self.bos_token_id  ,			eos_token_id=self.eos_token_id  ,			pad_token_id=self.pad_token_id  ,			window_size=self.window_size  ,			attention_types=self.attention_types  ,			)
  
             return (config, input_ids, input_mask)
  
  
  
        def 		snake_case_      (  self  :			Any		):
             _UpperCAmelCase    :			List[str]              =					self.prepare_config_and_inputs()
             _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase    :			Dict              =					config_and_inputs
             _UpperCAmelCase    :			Tuple              =					{"input_ids": input_ids, "attention_mask": attention_mask}
             return config, inputs_dict
  
  
  
        def 		snake_case_      (  self  :			Optional[Any]  ,			A  :			Union[str, Any]  ,			A  :			Any  ,			A  :			Any  ,			A  :			List[str]		):
             _UpperCAmelCase    :			List[Any]              =					2_0
             _UpperCAmelCase    :			Optional[Any]              =					model_class_name(A		)
  
             _UpperCAmelCase    :			Dict              =					model.init_cache(input_ids.shape[0]  ,			A		)
             _UpperCAmelCase    :			Union[str, Any]              =					jnp.ones((input_ids.shape[0], max_decoder_length)  ,			dtype="i4"		)
  
             _UpperCAmelCase    :			Tuple              =					jnp.broadcast_to(
                 jnp.arange(input_ids.shape[-1] - 1		)[None, :]  ,			(input_ids.shape[0], input_ids.shape[-1] - 1)		)
             _UpperCAmelCase    :			Optional[Any]              =					model(
                 input_ids[:, :-1]  ,			attention_mask=A  ,			past_key_values=A  ,			position_ids=A  ,			)
  
             _UpperCAmelCase    :			str              =					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,			dtype="i4"		)
             _UpperCAmelCase    :			Union[str, Any]              =					model(
                 input_ids[:, -1:]  ,			attention_mask=A  ,			past_key_values=outputs_cache.past_key_values  ,			position_ids=A  ,			)
  
             _UpperCAmelCase    :			str              =					model(A		)
  
             _UpperCAmelCase    :			Union[str, Any]              =					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
             self.parent.assertTrue(diff < 1e-3  ,			msg=f'Max diff is {diff}'		)
  
  
  
        def 		snake_case_      (  self  :			str  ,			A  :			Optional[Any]  ,			A  :			str  ,			A  :			List[Any]  ,			A  :			List[Any]		):
             _UpperCAmelCase    :			List[str]              =					2_0
             _UpperCAmelCase    :			str              =					model_class_name(A		)
  
             _UpperCAmelCase    :			str              =					jnp.concatenate(
                 [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])		)]  ,			axis=-1  ,			)
  
             _UpperCAmelCase    :			Optional[int]              =					model.init_cache(input_ids.shape[0]  ,			A		)
             _UpperCAmelCase    :			Any              =					jnp.broadcast_to(
                 jnp.arange(input_ids.shape[-1] - 1		)[None, :]  ,			(input_ids.shape[0], input_ids.shape[-1] - 1)		)
  
             _UpperCAmelCase    :			Union[str, Any]              =					model(
                 input_ids[:, :-1]  ,			attention_mask=A  ,			past_key_values=A  ,			position_ids=A  ,			)
             _UpperCAmelCase    :			List[Any]              =					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,			dtype="i4"		)
             _UpperCAmelCase    :			Optional[Any]              =					model(
                 input_ids[:, -1:]  ,			past_key_values=outputs_cache.past_key_values  ,			attention_mask=A  ,			position_ids=A  ,			)
  
             _UpperCAmelCase    :			List[str]              =					model(A  ,			attention_mask=A		)
  
             _UpperCAmelCase    :			Optional[Any]              =					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
             self.parent.assertTrue(diff < 1e-3  ,			msg=f'Max diff is {diff}'		)
  
  
  
  
  
  
  
  @require_flax
  class 			UpperCAmelCase_						(       _UpperCamelCase ,    _UpperCamelCase ,    unittest.TestCase		):
        __SCREAMING_SNAKE_CASE		:     Any				  =				(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
        __SCREAMING_SNAKE_CASE		:     str				  =				(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()
  
  
  
        def 		snake_case_      (  self  :			Optional[int]		):
             _UpperCAmelCase    :			List[str]              =					FlaxGPTNeoModelTester(self		)
  
  
  
        def 		snake_case_      (  self  :			Any		):
             for model_class_name in self.all_model_classes:
                  _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase    :			List[str]              =					self.model_tester.prepare_config_and_inputs()
                  self.model_tester.check_use_cache_forward(A  ,			A  ,			A  ,			A		)
  
  
  
        def 		snake_case_      (  self  :			Tuple		):
             for model_class_name in self.all_model_classes:
                  _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase    :			List[str]              =					self.model_tester.prepare_config_and_inputs()
                  self.model_tester.check_use_cache_forward_with_attn_mask(
                      A  ,			A  ,			A  ,			A		)
  
  
  
        @slow
        def 		snake_case_      (  self  :			Any		):
             _UpperCAmelCase    :			Any              =					GPTaTokenizer.from_pretrained("gpt2"  ,			pad_token="<|endoftext|>"  ,			padding_side="left"		)
             _UpperCAmelCase    :			Optional[int]              =					tokenizer(["Hello this is a long string", "Hey"]  ,			return_tensors="np"  ,			padding=A  ,			truncation=A		)
  
             _UpperCAmelCase    :			Optional[Any]              =					FlaxGPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-125M"		)
             _UpperCAmelCase    :			Optional[int]              =					False
             _UpperCAmelCase    :			Optional[Any]              =					model.config.eos_token_id
  
             _UpperCAmelCase    :			str              =					jax.jit(model.generate		)
  
             _UpperCAmelCase    :			Any              =					jit_generate(
                 inputs["input_ids"]  ,			attention_mask=inputs["attention_mask"]  ,			pad_token_id=tokenizer.pad_token_id		).sequences
  
             _UpperCAmelCase    :			Tuple              =					tokenizer.batch_decode(A  ,			skip_special_tokens=A		)
  
             _UpperCAmelCase    :			List[str]              =					[
                 "Hello this is a long string of text.\n\nI'm trying to get the text of the",
                 "Hey, I'm a little late to the party. I'm going to",
             ]
  
             self.assertListEqual(A  ,			A		)
  
  
  
        @is_pt_flax_cross_test
        def 		snake_case_      (  self  :			Optional[int]		):
             _UpperCAmelCase       , _UpperCAmelCase    :			Optional[Any]              =					self.model_tester.prepare_config_and_inputs_for_common()
  
             for model_class in self.all_model_classes:
                  with self.subTest(model_class.__name__		):
                       # prepare inputs
                       _UpperCAmelCase    :			str              =					self._prepare_for_class(A  ,			A		)
                       _UpperCAmelCase    :			List[Any]              =					{k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}
  
                       # load corresponding PyTorch class
                       _UpperCAmelCase    :			List[Any]              =					model_class.__name__[4:]  # Skip the "Flax" at the beginning
                       _UpperCAmelCase    :			Optional[int]              =					getattr(A  ,			A		)
  
                       _UpperCAmelCase       , _UpperCAmelCase    :			Dict              =					pt_inputs["input_ids"].shape
                       _UpperCAmelCase    :			int              =					np.random.randint(0  ,			seq_length - 1  ,			size=(batch_size,)		)
                       for batch_idx, start_index in enumerate(A		):
                            _UpperCAmelCase    :			Optional[Any]              =					0
                            _UpperCAmelCase    :			Any              =					1
                            _UpperCAmelCase    :			Optional[int]              =					0
                            _UpperCAmelCase    :			Any              =					1
                       _UpperCAmelCase    :			Dict              =					pt_model_class(A		).eval()
                       _UpperCAmelCase    :			List[Any]              =					model_class(A  ,			dtype=jnp.floataa		)
  
                       _UpperCAmelCase    :			Any              =					convert_pytorch_state_dict_to_flax(pt_model.state_dict()  ,			A		)
                       _UpperCAmelCase    :			Any              =					fx_state
  
                       with torch.no_grad():
                            _UpperCAmelCase    :			str              =					pt_model(**A		).to_tuple()
  
                       _UpperCAmelCase    :			str              =					fx_model(**A		).to_tuple()
                       self.assertEqual(len(A		)  ,			len(A		)  ,			"Output lengths differ between Flax and PyTorch"		)
                       for fx_output, pt_output in zip(A  ,			A		):
                            self.assert_almost_equals(fx_output[:, -1]  ,			pt_output[:, -1].numpy()  ,			4e-2		)
  
                       with tempfile.TemporaryDirectory() as tmpdirname:
                            pt_model.save_pretrained(A		)
                            _UpperCAmelCase    :			str              =					model_class.from_pretrained(A  ,			from_pt=A		)
  
                       _UpperCAmelCase    :			Tuple              =					fx_model_loaded(**A		).to_tuple()
                       self.assertEqual(
                           len(A		)  ,			len(A		)  ,			"Output lengths differ between Flax and PyTorch"		)
                       for fx_output_loaded, pt_output in zip(A  ,			A		):
                            self.assert_almost_equals(fx_output_loaded[:, -1]  ,			pt_output[:, -1].numpy()  ,			4e-2		)
  
  
  
        @is_pt_flax_cross_test
        def 		snake_case_      (  self  :			Tuple		):
             _UpperCAmelCase       , _UpperCAmelCase    :			int              =					self.model_tester.prepare_config_and_inputs_for_common()
             for model_class in self.all_model_classes:
                  with self.subTest(model_class.__name__		):
                       # prepare inputs
                       _UpperCAmelCase    :			Any              =					self._prepare_for_class(A  ,			A		)
                       _UpperCAmelCase    :			int              =					{k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}
  
                       # load corresponding PyTorch class
                       _UpperCAmelCase    :			int              =					model_class.__name__[4:]  # Skip the "Flax" at the beginning
                       _UpperCAmelCase    :			Tuple              =					getattr(A  ,			A		)
  
                       _UpperCAmelCase    :			List[str]              =					pt_model_class(A		).eval()
                       _UpperCAmelCase    :			Union[str, Any]              =					model_class(A  ,			dtype=jnp.floataa		)
  
                       _UpperCAmelCase    :			Dict              =					load_flax_weights_in_pytorch_model(A  ,			fx_model.params		)
                       _UpperCAmelCase       , _UpperCAmelCase    :			Optional[Any]              =					pt_inputs["input_ids"].shape
                       _UpperCAmelCase    :			List[str]              =					np.random.randint(0  ,			seq_length - 1  ,			size=(batch_size,)		)
                       for batch_idx, start_index in enumerate(A		):
                            _UpperCAmelCase    :			str              =					0
                            _UpperCAmelCase    :			List[Any]              =					1
                            _UpperCAmelCase    :			Optional[int]              =					0
                            _UpperCAmelCase    :			Optional[int]              =					1
  
                       # make sure weights are tied in PyTorch
                       pt_model.tie_weights()
  
                       with torch.no_grad():
                            _UpperCAmelCase    :			Optional[Any]              =					pt_model(**A		).to_tuple()
  
                       _UpperCAmelCase    :			List[Any]              =					fx_model(**A		).to_tuple()
                       self.assertEqual(len(A		)  ,			len(A		)  ,			"Output lengths differ between Flax and PyTorch"		)
                       for fx_output, pt_output in zip(A  ,			A		):
                            self.assert_almost_equals(fx_output[:, -1]  ,			pt_output[:, -1].numpy()  ,			4e-2		)
  
                       with tempfile.TemporaryDirectory() as tmpdirname:
                            fx_model.save_pretrained(A		)
                            _UpperCAmelCase    :			Any              =					pt_model_class.from_pretrained(A  ,			from_flax=A		)
  
                       with torch.no_grad():
                            _UpperCAmelCase    :			Union[str, Any]              =					pt_model_loaded(**A		).to_tuple()
  
                       self.assertEqual(
                           len(A		)  ,			len(A		)  ,			"Output lengths differ between Flax and PyTorch"		)
                       for fx_output, pt_output in zip(A  ,			A		):
                            self.assert_almost_equals(fx_output[:, -1]  ,			pt_output[:, -1].numpy()  ,			4e-2		)
  
  
  
        @slow
        def 		snake_case_      (  self  :			Tuple		):
             for model_class_name in self.all_model_classes:
                  _UpperCAmelCase    :			Dict              =					model_class_name.from_pretrained("EleutherAI/gpt-neo-125M"		)
                  _UpperCAmelCase    :			Optional[Any]              =					model(np.ones((1, 1)		)		)
                  self.assertIsNotNone(A		)
  
style_context: 
  
  
  
  """simple docstring"""
  
  
  
  
  import os
  import sys
  from pathlib import Path
  
  import torch
  
  from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
  from utils import load_json
  
  
  _lowerCAmelCase       :      Optional[Any]							=							torch.cuda.is_available()
  _lowerCAmelCase       :      str							=							[" Sam ate lunch today.", "Sams lunch ingredients."]
  _lowerCAmelCase       :      Dict							=							["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
  _lowerCAmelCase       :      Union[str, Any]							=							{
      "max_tokens_per_batch": None,
      "supervise_forward": True,
      "normalize_hidden": True,
      "label_smoothing": 0.2,
      "eval_max_gen_length": None,
      "eval_beams": 1,
      "val_metric": "loss",
      "save_top_k": 1,
      "adafactor": True,
      "early_stopping_patience": 2,
      "logger_name": "default",
      "length_penalty": 0.5,
      "cache_dir": "",
      "task": "summarization",
      "num_workers": 2,
      "alpha_hid": 0,
      "freeze_embeds": True,
      "enc_only": False,
      "tgt_suffix": "",
      "resume_from_checkpoint": None,
      "sortish_sampler": True,
      "student_decoder_layers": 1,
      "val_check_interval": 1.0,
      "output_dir": "",
      "fp16": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
      "no_teacher": False,
      "fp16_opt_level": "O1",
      "gpus": 1 if CUDA_AVAILABLE else 0,
      "n_tpu_cores": 0,
      "max_grad_norm": 1.0,
      "do_train": True,
      "do_predict": True,
      "accumulate_grad_batches": 1,
      "server_ip": "",
      "server_port": "",
      "seed": 42,
      "model_name_or_path": "sshleifer/bart-tiny-random",
      "config_name": "",
      "tokenizer_name": "facebook/bart-large",
      "do_lower_case": False,
      "learning_rate": 0.3,
      "lr_scheduler": "linear",
      "weight_decay": 0.0,
      "adam_epsilon": 1E-08,
      "warmup_steps": 0,
      "max_epochs": 1,
      "train_batch_size": 2,
      "eval_batch_size": 2,
      "max_source_length": 12,
      "max_target_length": 12,
      "val_max_target_length": 12,
      "test_max_target_length": 12,
      "fast_dev_run": False,
      "no_cache": False,
      "n_train": -1,
      "n_val": -1,
      "n_test": -1,
      "student_encoder_layers": 1,
      "freeze_encoder": False,
      "auto_scale_batch_size": False,
      "overwrite_output_dir": False,
      "student": None,
  }
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Path		,						SCREAMING_SNAKE_CASE__			:		list       )						->     Dict:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Tuple              =					"\n".join(SCREAMING_SNAKE_CASE__       )
       Path(SCREAMING_SNAKE_CASE__       ).open("w"       ).writelines(SCREAMING_SNAKE_CASE__       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		List[Any]       )						->     Union[str, Any]:
  
  
  
  
  
       '''simple docstring'''
  
       for split in ["train", "val", "test"]:
            _dump_articles(os.path.join(SCREAMING_SNAKE_CASE__		,						f'{split}.source'       )		,						SCREAMING_SNAKE_CASE__       )
            _dump_articles(os.path.join(SCREAMING_SNAKE_CASE__		,						f'{split}.target'       )		,						SCREAMING_SNAKE_CASE__       )
       return tmp_dir
  
  
  
  
  
  
  class 			UpperCAmelCase_						(       _UpperCamelCase		):
  
  
  
        @classmethod
        def 		snake_case_      (  cls  :			Optional[int]		):
             return cls
  
  
  
        @require_torch_multi_gpu
        def 		snake_case_      (  self  :			Union[str, Any]		):
             _UpperCAmelCase    :			Dict              =					{
                 "no_teacher": True,
                 "freeze_encoder": True,
                 "gpus": 2,
                 "overwrite_output_dir": True,
                 "sortish_sampler": True,
             }
             self._test_distiller_cli_fork(A  ,			check_contents=A		)
  
  
  
        def 		snake_case_      (  self  :			List[str]  ,			A  :			List[Any]  ,			A  :			Optional[Any]=True		):
             _UpperCAmelCase    :			int              =					{
                 "label_smoothing": 0.0,
                 "early_stopping_patience": -1,
                 "train_batch_size": 1,
                 "eval_batch_size": 2,
                 "max_epochs": 2,
                 "alpha_mlm": 0.2,
                 "alpha_ce": 0.8,
                 "do_predict": True,
                 "model_name_or_path": "sshleifer/tinier_bart",
                 "teacher": CHEAP_ARGS["model_name_or_path"],
                 "val_check_interval": 0.5,
             }
             default_updates.update(A		)
             _UpperCAmelCase    :			dict              =					CHEAP_ARGS.copy()
             _UpperCAmelCase    :			Union[str, Any]              =					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
             _UpperCAmelCase    :			List[Any]              =					self.get_auto_remove_tmp_dir()
             args_d.update(data_dir=A  ,			output_dir=A  ,			**A		)
  
             def convert(A  :			List[Any]  ,			A  :			Optional[int]		):
                  if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
                       return ""
                  if v is False or v is None:
                       return ""
                  if v is True:  # or len(str(v))==0:
                       return f'--{k}'
                  return f'--{k}={v}'
  
             _UpperCAmelCase    :			Tuple              =					[x for x in (convert(A  ,			A		) for k, v in args_d.items()) if len(A		)]
             _UpperCAmelCase    :			Union[str, Any]              =					[sys.executable, f'{self.test_file_dir}/distillation.py'] + cli_args
             execute_subprocess_async(A  ,			env=self.get_env()		)
  
             _UpperCAmelCase    :			int              =					os.listdir(A		)
             _UpperCAmelCase    :			List[Any]              =					{os.path.basename(A		) for p in contents}
             _UpperCAmelCase    :			List[Any]              =					[p for p in contents if p.endswith("ckpt"		)]
             assert len(A		) > 0
  
             self.assertIn("test_generations.txt"  ,			A		)
             self.assertIn("test_results.txt"  ,			A		)
  
             # get the following from the module, (we don't have access to `model` here)
             _UpperCAmelCase    :			List[Any]              =					os.path.join(A  ,			"metrics.json"		)
             _UpperCAmelCase    :			str              =					"rouge2"
  
             _UpperCAmelCase    :			List[Any]              =					load_json(A		)
             # {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
             print(A		)
             _UpperCAmelCase    :			List[str]              =					metrics["val"][-1]
             self.assertGreaterEqual(last_step_stats["val_avg_gen_time"]  ,			0.01		)
             self.assertIsInstance(last_step_stats[f'val_avg_{val_metric}']  ,			A		)
             self.assertEqual(len(metrics["test"]		)  ,			1		)
             _UpperCAmelCase    :			Dict              =					int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) / 2 + 1		)
             self.assertEqual(len(metrics["val"]		)  ,			A		)
  