code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import collections
  import types
  
  import numpy as np
  
  from ..utils import (
      add_end_docstrings,
      is_tensorflow_probability_available,
      is_tf_available,
      is_torch_available,
      requires_backends,
  )
  from .base import PIPELINE_INIT_ARGS, ArgumentHandler, Dataset, Pipeline, PipelineException
  
  
  if is_torch_available():
  	import torch
  
  	from ..models.auto.modeling_auto import (
  	    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
  	    MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
  	)
  
  if is_tf_available() and is_tensorflow_probability_available():
  	import tensorflow as tf
  	import tensorflow_probability as tfp
  
  	from ..models.auto.modeling_tf_auto import (
  	    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
  	    TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
  	)
  
  
  
  
  
  class 					_UpperCAmelCase							(		lowerCAmelCase_     ):
  		def __call__(						self,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,**__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					requires_backends(self,"""pandas"""    )
  					import pandas as pd
  
  					if table is None:
  								raise ValueError("""Keyword argument `table` cannot be None."""    )
  					elif query is None:
  								if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ) and table.get("""query"""    ) is not None and table.get("""table"""    ) is not None:
  											__lowerCAmelCase      		=       [table]
  								elif isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ) and len(__SCREAMING_SNAKE_CASE    ) > 0:
  											if not all(isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ) for d in table    ):
  														raise ValueError(
  														    f'Keyword argument `table` should be a list of dict, but is {(type(__SCREAMING_SNAKE_CASE    ) for d in table)}'    )
  
  											if table[0].get("""query"""    ) is not None and table[0].get("""table"""    ) is not None:
  														__lowerCAmelCase      		=       table
  											else:
  														raise ValueError(
  														    """If keyword argument `table` is a list of dictionaries, each dictionary should have a `table`"""
  														    f' and `query` key, but only dictionary has keys {table[0].keys()} `table` and `query` keys.'    )
  								elif Dataset is not None and isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ) or isinstance(__SCREAMING_SNAKE_CASE,types.GeneratorType    ):
  											return table
  								else:
  											raise ValueError(
  											    """Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but """
  											    f'is {type(__SCREAMING_SNAKE_CASE    )})'    )
  					else:
  								__lowerCAmelCase      		=       [{"""table""": table, """query""": query}]
  
  					for tqa_pipeline_input in tqa_pipeline_inputs:
  								if not isinstance(tqa_pipeline_input["""table"""],pd.DataFrame    ):
  											if tqa_pipeline_input["table"] is None:
  														raise ValueError("""Table cannot be None."""    )
  
  											__lowerCAmelCase      		=       pd.DataFrame(tqa_pipeline_input["""table"""]    )
  
  					return tqa_pipeline_inputs
  
  
  
  
  
  @add_end_docstrings(lowerCAmelCase_     )
  class 					_UpperCAmelCase							(		lowerCAmelCase_     ):
  		a     :     Optional[Any]     						="""table,query"""
  		def __init__(						self,__SCREAMING_SNAKE_CASE=TableQuestionAnsweringArgumentHandler(),*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					super().__init__(*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       args_parser
  
  					self.check_model_type(
  					    dict(
  					        TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items()
  					        + TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()    )
  					    if self.framework == """tf"""
  					    else dict(
  					        MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items() + MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()    )    )
  
  					__lowerCAmelCase      		=       bool(getattr(self.model.config,"""aggregation_labels""",__SCREAMING_SNAKE_CASE    )    ) and bool(
  					    getattr(self.model.config,"""num_aggregation_labels""",__SCREAMING_SNAKE_CASE    )    )
  					__lowerCAmelCase      		=       """tapas""" if hasattr(self.model.config,"""aggregation_labels"""    ) else None
  		def 						lowerCamelCase__    (						self,**__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					return self.model(**__SCREAMING_SNAKE_CASE    )
  		def 						lowerCamelCase__    (						self,**__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					if self.framework == "pt":
  								__lowerCAmelCase      		=       []
  								__lowerCAmelCase      		=       []
  								__lowerCAmelCase      		=       None
  								__lowerCAmelCase      		=       inputs["""input_ids"""].shape[0]
  
  								__lowerCAmelCase      		=       inputs["""input_ids"""].to(self.device    )
  								__lowerCAmelCase      		=       inputs["""attention_mask"""].to(self.device    )
  								__lowerCAmelCase      		=       inputs["""token_type_ids"""].to(self.device    )
  								__lowerCAmelCase      		=       None
  
  								for index in range(__SCREAMING_SNAKE_CASE    ):
  											# If sequences have already been processed, the token type IDs will be created according to the previous
  											# answer.
  											if prev_answers is not None:
  														__lowerCAmelCase      		=       token_type_ids_example[:, 3]  # shape (seq_len,)
  														__lowerCAmelCase      		=       np.zeros_like(prev_labels_example.cpu().numpy()    )  # shape (seq_len,)
  
  														__lowerCAmelCase      		=       token_type_ids[index]  # shape (seq_len, 7)
  														for i in range(model_labels.shape[0]    ):
  																	__lowerCAmelCase      		=       token_type_ids_example[:, 0].tolist()[i]
  																	__lowerCAmelCase      		=       token_type_ids_example[:, 1].tolist()[i] - 1
  																	__lowerCAmelCase      		=       token_type_ids_example[:, 2].tolist()[i] - 1
  
  																	if row_id >= 0 and col_id >= 0 and segment_id == 1:
  																				__lowerCAmelCase      		=       int(prev_answers[(col_id, row_id)]    )
  
  														__lowerCAmelCase      		=       torch.from_numpy(__SCREAMING_SNAKE_CASE    ).type(torch.long    ).to(self.device    )
  
  											__lowerCAmelCase      		=       input_ids[index]
  											__lowerCAmelCase      		=       attention_mask[index]  # shape (seq_len,)
  											__lowerCAmelCase      		=       token_type_ids[index]  # shape (seq_len, 7)
  											__lowerCAmelCase      		=       self.model(
  											    input_ids=input_ids_example.unsqueeze(0    ),attention_mask=attention_mask_example.unsqueeze(0    ),token_type_ids=token_type_ids_example.unsqueeze(0    ),)
  											__lowerCAmelCase      		=       outputs.logits
  
  											if self.aggregate:
  														all_aggregations.append(outputs.logits_aggregation    )
  
  											all_logits.append(__SCREAMING_SNAKE_CASE    )
  
  											__lowerCAmelCase      		=       torch.distributions.Bernoulli(logits=__SCREAMING_SNAKE_CASE    )
  											__lowerCAmelCase      		=       dist_per_token.probs * attention_mask_example.type(torch.floataa    ).to(
  											    dist_per_token.probs.device    )
  
  											__lowerCAmelCase      		=       collections.defaultdict(__SCREAMING_SNAKE_CASE    )
  											for i, p in enumerate(probabilities.squeeze().tolist()    ):
  														__lowerCAmelCase      		=       token_type_ids_example[:, 0].tolist()[i]
  														__lowerCAmelCase      		=       token_type_ids_example[:, 1].tolist()[i] - 1
  														__lowerCAmelCase      		=       token_type_ids_example[:, 2].tolist()[i] - 1
  														if col >= 0 and row >= 0 and segment_id == 1:
  																	coords_to_probs[(col, row)].append(__SCREAMING_SNAKE_CASE    )
  
  											__lowerCAmelCase      		=       {key: np.array(coords_to_probs[key]    ).mean() > 0.5 for key in coords_to_probs}
  
  								__lowerCAmelCase      		=       torch.cat(tuple(__SCREAMING_SNAKE_CASE    ),0    )
  
  								return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(__SCREAMING_SNAKE_CASE    ),0    ))
  					else:
  								__lowerCAmelCase      		=       []
  								__lowerCAmelCase      		=       []
  								__lowerCAmelCase      		=       None
  								__lowerCAmelCase      		=       inputs["""input_ids"""].shape[0]
  
  								__lowerCAmelCase      		=       inputs["""input_ids"""]
  								__lowerCAmelCase      		=       inputs["""attention_mask"""]
  								__lowerCAmelCase      		=       inputs["""token_type_ids"""].numpy()
  								__lowerCAmelCase      		=       None
  
  								for index in range(__SCREAMING_SNAKE_CASE    ):
  											# If sequences have already been processed, the token type IDs will be created according to the previous
  											# answer.
  											if prev_answers is not None:
  														__lowerCAmelCase      		=       token_type_ids_example[:, 3]  # shape (seq_len,)
  														__lowerCAmelCase      		=       np.zeros_like(__SCREAMING_SNAKE_CASE,dtype=np.intaa    )  # shape (seq_len,)
  
  														__lowerCAmelCase      		=       token_type_ids[index]  # shape (seq_len, 7)
  														for i in range(model_labels.shape[0]    ):
  																	__lowerCAmelCase      		=       token_type_ids_example[:, 0].tolist()[i]
  																	__lowerCAmelCase      		=       token_type_ids_example[:, 1].tolist()[i] - 1
  																	__lowerCAmelCase      		=       token_type_ids_example[:, 2].tolist()[i] - 1
  
  																	if row_id >= 0 and col_id >= 0 and segment_id == 1:
  																				__lowerCAmelCase      		=       int(prev_answers[(col_id, row_id)]    )
  
  														__lowerCAmelCase      		=       model_labels
  
  											__lowerCAmelCase      		=       input_ids[index]
  											__lowerCAmelCase      		=       attention_mask[index]  # shape (seq_len,)
  											__lowerCAmelCase      		=       token_type_ids[index]  # shape (seq_len, 7)
  											__lowerCAmelCase      		=       self.model(
  											    input_ids=np.expand_dims(__SCREAMING_SNAKE_CASE,axis=0    ),attention_mask=np.expand_dims(__SCREAMING_SNAKE_CASE,axis=0    ),token_type_ids=np.expand_dims(__SCREAMING_SNAKE_CASE,axis=0    ),)
  											__lowerCAmelCase      		=       outputs.logits
  
  											if self.aggregate:
  														all_aggregations.append(outputs.logits_aggregation    )
  
  											all_logits.append(__SCREAMING_SNAKE_CASE    )
  
  											__lowerCAmelCase      		=       tfp.distributions.Bernoulli(logits=__SCREAMING_SNAKE_CASE    )
  											__lowerCAmelCase      		=       dist_per_token.probs_parameter() * tf.cast(__SCREAMING_SNAKE_CASE,tf.floataa    )
  
  											__lowerCAmelCase      		=       collections.defaultdict(__SCREAMING_SNAKE_CASE    )
  											__lowerCAmelCase      		=       token_type_ids_example
  											for i, p in enumerate(tf.squeeze(__SCREAMING_SNAKE_CASE    ).numpy().tolist()    ):
  														__lowerCAmelCase      		=       token_type_ids_example[:, 0].tolist()[i]
  														__lowerCAmelCase      		=       token_type_ids_example[:, 1].tolist()[i] - 1
  														__lowerCAmelCase      		=       token_type_ids_example[:, 2].tolist()[i] - 1
  														if col >= 0 and row >= 0 and segment_id == 1:
  																	coords_to_probs[(col, row)].append(__SCREAMING_SNAKE_CASE    )
  
  											__lowerCAmelCase      		=       {key: np.array(coords_to_probs[key]    ).mean() > 0.5 for key in coords_to_probs}
  
  								__lowerCAmelCase      		=       tf.concat(tuple(__SCREAMING_SNAKE_CASE    ),0    )
  
  								return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(__SCREAMING_SNAKE_CASE    ),0    ))
  		def __call__(						self,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       self._args_parser(*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE    )
  
  					__lowerCAmelCase      		=       super().__call__(__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE    )
  					if len(__SCREAMING_SNAKE_CASE    ) == 1:
  								return results[0]
  					return results
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,**__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       {}
  					if padding is not None:
  								__lowerCAmelCase      		=       padding
  					if truncation is not None:
  								__lowerCAmelCase      		=       truncation
  
  					__lowerCAmelCase      		=       {}
  					if sequential is not None:
  								__lowerCAmelCase      		=       sequential
  					return preprocess_params, forward_params, {}
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=True,__SCREAMING_SNAKE_CASE=None    ):
  
  
  
  					'''simple docstring'''
  
  					if truncation is None:
  								if self.type == "tapas":
  											__lowerCAmelCase      		=       """drop_rows_to_fit"""
  								else:
  											__lowerCAmelCase      		=       """do_not_truncate"""
  
  					__lowerCAmelCase		, __lowerCAmelCase      		=       pipeline_input["""table"""], pipeline_input["""query"""]
  					if table.empty:
  								raise ValueError("""table is empty"""    )
  					if query is None or query == "":
  								raise ValueError("""query is empty"""    )
  					__lowerCAmelCase      		=       self.tokenizer(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,return_tensors=self.framework,truncation=__SCREAMING_SNAKE_CASE,padding=__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       table
  					return inputs
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=False    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       model_inputs.pop("""table"""    )
  
  					if self.type == "tapas":
  								if sequential:
  											__lowerCAmelCase      		=       self.sequential_inference(**__SCREAMING_SNAKE_CASE    )
  								else:
  											__lowerCAmelCase      		=       self.batch_inference(**__SCREAMING_SNAKE_CASE    )
  					else:
  								__lowerCAmelCase      		=       self.model.generate(**__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       {"""model_inputs""": model_inputs, """table""": table, """outputs""": outputs}
  					return model_outputs
  
  
  
  
  
  
  
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       model_outputs["""model_inputs"""]
  					__lowerCAmelCase      		=       model_outputs["""table"""]
  					__lowerCAmelCase      		=       model_outputs["""outputs"""]
  					if self.type == "tapas":
  								if self.aggregate:
  											__lowerCAmelCase		, __lowerCAmelCase      		=       outputs[:2]
  											__lowerCAmelCase      		=       self.tokenizer.convert_logits_to_predictions(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )
  											__lowerCAmelCase		, __lowerCAmelCase      		=       predictions
  											__lowerCAmelCase      		=       {i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(__SCREAMING_SNAKE_CASE    )}
  
  											__lowerCAmelCase      		=       self.model.config.no_aggregation_label_index
  											__lowerCAmelCase      		=       {
  											    i: aggregators[i] + """ > """ for i, pred in enumerate(__SCREAMING_SNAKE_CASE    ) if pred != no_agg_label_index
  											}
  								else:
  											__lowerCAmelCase      		=       outputs[0]
  											__lowerCAmelCase      		=       self.tokenizer.convert_logits_to_predictions(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )
  											__lowerCAmelCase      		=       predictions[0]
  											__lowerCAmelCase      		=       {}
  											__lowerCAmelCase      		=       {}
  								__lowerCAmelCase      		=       []
  								for index, coordinates in enumerate(__SCREAMING_SNAKE_CASE    ):
  											__lowerCAmelCase      		=       [table.iat[coordinate] for coordinate in coordinates]
  											__lowerCAmelCase      		=       aggregators.get(__SCREAMING_SNAKE_CASE,""""""    )
  											__lowerCAmelCase      		=       aggregators_prefix.get(__SCREAMING_SNAKE_CASE,""""""    )
  											__lowerCAmelCase      		=       {
  											    """answer""": aggregator_prefix + """, """.join(__SCREAMING_SNAKE_CASE    ),
  											    """coordinates""": coordinates,
  											    """cells""": [table.iat[coordinate] for coordinate in coordinates],
  											}
  											if aggregator:
  														__lowerCAmelCase      		=       aggregator
  
  											answers.append(__SCREAMING_SNAKE_CASE    )
  								if len(__SCREAMING_SNAKE_CASE    ) == 0:
  											raise PipelineException("""Empty answer"""    )
  					else:
  								__lowerCAmelCase      		=       [{"""answer""": answer} for answer in self.tokenizer.batch_decode(__SCREAMING_SNAKE_CASE,skip_special_tokens=__SCREAMING_SNAKE_CASE    )]
  
  					return answers if len(__SCREAMING_SNAKE_CASE    ) > 1 else answers[0]
  
  
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
  
  
  _a      :				Optional[Any]											=					torch.cuda.is_available()
  _a      :				Dict											=					[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
  _a      :				str											=					["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
  _a      :				Optional[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""": 4_2,
      """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""": 1_2,
      """max_target_length""": 1_2,
      """val_max_target_length""": 1_2,
      """test_max_target_length""": 1_2,
      """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      _lowerCAmelCase		(		lowercase      ,			lowercase			)      ->      Any:
  			__lowerCAmelCase      		=       """\n""".join(lowercase			)
  			Path(lowercase			).open("""w"""			).writelines(lowercase			)
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase			)      ->      Optional[int]:
  			for split in ["train", "val", "test"]:
  						_dump_articles(os.path.join(lowercase      ,			f'{split}.source'			)      ,			lowercase			)
  						_dump_articles(os.path.join(lowercase      ,			f'{split}.target'			)      ,			lowercase			)
  			return tmp_dir
  
  
  
  
  
  
  
  class 					_UpperCAmelCase							(		lowerCAmelCase_     ):
  		@classmethod
  		def 						lowerCamelCase__    (						cls    ):
  
  
  
  					'''simple docstring'''
  
  					return cls
  		@require_torch_multi_gpu
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       {
  					    """no_teacher""": True,
  					    """freeze_encoder""": True,
  					    """gpus""": 2,
  					    """overwrite_output_dir""": True,
  					    """sortish_sampler""": True,
  					}
  					self._test_distiller_cli_fork(__SCREAMING_SNAKE_CASE,check_contents=__SCREAMING_SNAKE_CASE    )
  
  
  
  
  
  
  
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=True    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       {
  					    """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(__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       CHEAP_ARGS.copy()
  					__lowerCAmelCase      		=       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
  					__lowerCAmelCase      		=       self.get_auto_remove_tmp_dir()
  					args_d.update(data_dir=__SCREAMING_SNAKE_CASE,output_dir=__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE    )
  
  					def convert(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
  								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}'
  
  					__lowerCAmelCase      		=       [x for x in (convert(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ) for k, v in args_d.items()) if len(__SCREAMING_SNAKE_CASE    )]
  					__lowerCAmelCase      		=       [sys.executable, f'{self.test_file_dir}/distillation.py'] + cli_args
  					execute_subprocess_async(__SCREAMING_SNAKE_CASE,env=self.get_env()    )
  
  					__lowerCAmelCase      		=       os.listdir(__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       {os.path.basename(__SCREAMING_SNAKE_CASE    ) for p in contents}
  					__lowerCAmelCase      		=       [p for p in contents if p.endswith("""ckpt"""    )]
  					assert len(__SCREAMING_SNAKE_CASE    ) > 0
  
  					self.assertIn("""test_generations.txt""",__SCREAMING_SNAKE_CASE    )
  					self.assertIn("""test_results.txt""",__SCREAMING_SNAKE_CASE    )
  
  					# get the following from the module, (we don't have access to `model` here)
  					__lowerCAmelCase      		=       os.path.join(__SCREAMING_SNAKE_CASE,"""metrics.json"""    )
  					__lowerCAmelCase      		=       """rouge2"""
  
  					__lowerCAmelCase      		=       load_json(__SCREAMING_SNAKE_CASE    )
  					# {'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(__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       metrics["""val"""][-1]
  					self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""],0.01    )
  					self.assertIsInstance(last_step_stats[f'val_avg_{val_metric}'],__SCREAMING_SNAKE_CASE    )
  					self.assertEqual(len(metrics["""test"""]    ),1    )
  					__lowerCAmelCase      		=       int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1    )
  					self.assertEqual(len(metrics["""val"""]    ),__SCREAMING_SNAKE_CASE    )
  
  