



import dataclasses
import enum
import functools
import math
import re

# The following script is adapted from the script of TaPas.
# Original: https://github.com/google-research/tapas/master/wikisql_utils.py
from typing import Any, List, Text


A__     				=	"""none"""
A__     				=	"""none"""


def 		_UpperCAmelCase    (				snake_case     ,						snake_case      ):





					"""simple docstring"""

					_lowerCAmelCase      =   value.split(snake_case      )
					return len(snake_case      ) > 1 and any((len(snake_case      ) == 3 for x in split)      )


def 		_UpperCAmelCase    (				snake_case      ):





					"""simple docstring"""

					if isinstance(snake_case     ,						snake_case      ):
										return value
					if isinstance(snake_case     ,						snake_case      ):
										return float(snake_case      )
					if not isinstance(snake_case     ,						snake_case      ):
										raise ValueError("""Argument value is not a string. Can't parse it as float"""      )
					_lowerCAmelCase      =   value

					try:
										# Example: 1,000.7
										if "." in sanitized and "," in sanitized:
															return float(sanitized.replace(""","""     ,						""""""      )      )
										# 1,000
										if "," in sanitized and _split_thousands(""","""     ,						snake_case      ):
															return float(sanitized.replace(""","""     ,						""""""      )      )
										# 5,5556
										if "," in sanitized and sanitized.count(""","""      ) == 1 and not _split_thousands(""","""     ,						snake_case      ):
															return float(sanitized.replace(""","""     ,						"""."""      )      )
										# 0.0.0.1
										if sanitized.count("""."""      ) > 1:
															return float(sanitized.replace("""."""     ,						""""""      )      )
										# 0,0,0,1
										if sanitized.count(""","""      ) > 1:
															return float(sanitized.replace(""","""     ,						""""""      )      )
										return float(snake_case      )
					except ValueError:
										# Avoid adding the sanitized value in the error message.
										raise ValueError("""Unable to convert value to float"""      )


def 		_UpperCAmelCase    (				snake_case      ):





					"""simple docstring"""

					if answer is None:
										return None
					try:
										_lowerCAmelCase      =   convert_to_float(snake_case      )
										if isinstance(snake_case     ,						snake_case      ) and math.isnan(snake_case      ):
															return None
										return value
					except ValueError:
										return answer.lower()


A__     				=	{
    """text""": lambda x: x,
    """real""": convert_to_float,
}





class  __lowerCAmelCase			(					enum.Enum	):
				__lowerCamelCase			=    0
				__lowerCamelCase			=    1
				__lowerCamelCase			=    2
				__lowerCamelCase			=    3
				__lowerCamelCase			=    4
				__lowerCamelCase			=    5





class  __lowerCAmelCase			(					enum.Enum	):
				__lowerCamelCase			=    0
				__lowerCamelCase			=    1
				__lowerCamelCase			=    2





@dataclasses.dataclass
class  __lowerCAmelCase			:
				__lowerCamelCase			=    42
				__lowerCamelCase			=    42
				__lowerCamelCase			=    42

A__     				=	re.compile(R"""\w+|[^\w\s]+""", re.UNICODE | re.MULTILINE | re.DOTALL)


def 		_UpperCAmelCase    (				snake_case      ):





					"""simple docstring"""

					return list(_TOKENIZER.findall(x.lower()      )      )


def 		_UpperCAmelCase    (				snake_case     ,						snake_case     ,						snake_case      ):





					"""simple docstring"""

					if operator == _Operator.EQUALS:
										return src == tgt
					elif operator == _Operator.GREATER:
										return src > tgt
					elif operator == _Operator.LESSER:
										return src < tgt
					raise ValueError(F'Unknown operator: {operator}'      )


def 		_UpperCAmelCase    (				snake_case     ,						snake_case     ,						snake_case      ):





					"""simple docstring"""

					_lowerCAmelCase      =   table["""types"""]
					return _TYPE_CONVERTER[types[column]](snake_case      )


def 		_UpperCAmelCase    (				snake_case      ):





					"""simple docstring"""

					return isinstance(snake_case     ,						snake_case      )


def 		_UpperCAmelCase    (				snake_case     ,						snake_case     ,						snake_case      ):





					"""simple docstring"""

					for cond in conditions:
										_lowerCAmelCase      =   row[cond.column]

										_lowerCAmelCase      =   _parse_value(snake_case     ,						cond.column     ,						cond.cmp_value      )

										if _is_string(snake_case      ) and _is_string(snake_case      ):
															_lowerCAmelCase      =   _normalize_for_match(snake_case      )
															_lowerCAmelCase      =   _normalize_for_match(snake_case      )

										if not isinstance(snake_case     ,						type(snake_case      )      ):
															raise ValueError("""Type difference {} != {}""".format(type(snake_case      )     ,						type(snake_case      )      )      )

										if not _compare(cond.operator     ,						snake_case     ,						snake_case      ):
															return False
					return True


def 		_UpperCAmelCase    (				snake_case     ,						snake_case     ,						snake_case      ):





					"""simple docstring"""

					if not answer_coordinates:
										if aggregation_op == _Aggregation.COUNT:
															return 0.0
										else:
															return EMPTY_ANSWER_AGG

    # Count can support non numeric answers.
					if aggregation_op == _Aggregation.COUNT:
										return float(len(snake_case      )      )

					# If we have just one answer, if float returns it or try a conversion.
					_lowerCAmelCase      =   [table["""rows"""][i][j] for (i, j) in answer_coordinates]
					if len(snake_case      ) == 1:
										try:
															return convert_to_float(values[0]      )
										except ValueError as e:
															if aggregation_op != _Aggregation.NONE:
																				raise e

					if aggregation_op == _Aggregation.NONE:
										return None

					# Other aggregation only support numeric values. Bail out if we have strings.
					if not all((isinstance(snake_case     ,						(int, float)      ) for v in values)      ):
										return None

					if aggregation_op == _Aggregation.SUM:
										return float(sum(snake_case      )      )
					elif aggregation_op == _Aggregation.AVERAGE:
										return sum(snake_case      ) / len(snake_case      )
					else:
										raise ValueError(F'Unknown aggregation: {aggregation_op}'      )


def 		_UpperCAmelCase    (				snake_case     ,						snake_case      ):





					"""simple docstring"""

					_lowerCAmelCase      =   sql_query["""agg"""]
					if aggregation_op_index >= 3:
										_lowerCAmelCase      =   _Aggregation(snake_case      )
					else:
										_lowerCAmelCase      =   _Aggregation.NONE

					_lowerCAmelCase      =   sql_query["""sel"""]
					_lowerCAmelCase      =   [
					    _Condition(snake_case     ,						_Operator(snake_case      )     ,						snake_case      )
					    for column, operator, cmp_value in zip(
					        sql_query["""conds"""]["""column_index"""]     ,						sql_query["""conds"""]["""operator_index"""]     ,						sql_query["""conds"""]["""condition"""]      )
					]

					_lowerCAmelCase      =   []
					for row in range(len(table["""rows"""]      )      ):
										if _respect_conditions(snake_case     ,						table["""rows"""][row]     ,						snake_case      ):
															indices.append((row, target_column)      )

					if not indices:
										return [], aggregation_op

					if len(snake_case      ) == 1:
										return indices, aggregation_op

					# Parsing of MIN/MAX.
					if aggregation_op_index in (1, 2):
										_lowerCAmelCase      =   {2: min, 1: max}
										_lowerCAmelCase      =   [(table["""rows"""][i][j], index) for index, (i, j) in enumerate(snake_case      )]
										_lowerCAmelCase      =   functools.reduce(operators[sql_query["""agg"""]]     ,						snake_case      )

										_lowerCAmelCase      =   [indices[reduced[1]]]
										return ret, _Aggregation.NONE

					return indices, aggregation_op


def 		_UpperCAmelCase    (				snake_case     ,						snake_case     ,						snake_case      ):





					"""simple docstring"""

					if float_answer is not None:
										return [str(snake_case      )]
					return [str(table["""real_rows"""][r][c]      ) for r, c in answer_coordinates]


def 		_UpperCAmelCase    (				snake_case     ,						snake_case      ):





					"""simple docstring"""

					_lowerCAmelCase     ,     _lowerCAmelCase      =   _get_answer_coordinates(snake_case     ,						snake_case      )
					_lowerCAmelCase      =   _get_float_answer(snake_case     ,						snake_case     ,						snake_case      )
					_lowerCAmelCase      =   _get_answer_text(snake_case     ,						snake_case     ,						snake_case      )
					# keep the original data the same with TaPas
					if len(snake_case      ) == 0:
										_lowerCAmelCase      =   [EMPTY_ANSWER]
					return answer_text








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__     				=	torch.cuda.is_available()
A__     				=	[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
A__     				=	["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
A__     				=	{
    """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-0_8,
    """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 		_UpperCAmelCase    (				snake_case     ,						snake_case      ):





					"""simple docstring"""

					_lowerCAmelCase      =   """\n""".join(snake_case      )
					Path(snake_case      ).open("""w"""      ).writelines(snake_case      )


def 		_UpperCAmelCase    (				snake_case      ):





					"""simple docstring"""

					for split in ["train", "val", "test"]:
										_dump_articles(os.path.join(snake_case     ,						F'{split}.source'      )     ,						snake_case      )
										_dump_articles(os.path.join(snake_case     ,						F'{split}.target'      )     ,						snake_case      )
					return tmp_dir





class  __lowerCAmelCase			(					lowerCamelCase__	):




				@classmethod
				def 							snake_case       (							cls		):



									"""simple docstring"""





									return cls




				@require_torch_multi_gpu
				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   {
									    """no_teacher""": True,
									    """freeze_encoder""": True,
									    """gpus""": 2,
									    """overwrite_output_dir""": True,
									    """sortish_sampler""": True,
									}
									self._test_distiller_cli_fork(_snake_case						,    check_contents=_snake_case		)




				def 							snake_case       (							self						,    _snake_case						,    _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(_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=_snake_case						,    output_dir=_snake_case						,    **_snake_case		)

									def convert(_snake_case						,    _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(_snake_case						,    _snake_case		) for k, v in args_d.items()) if len(_snake_case		)]
									_lowerCAmelCase      =   [sys.executable, F'{self.test_file_dir}/distillation.py'] + cli_args
									execute_subprocess_async(_snake_case						,    env=self.get_env()		)

									_lowerCAmelCase      =   os.listdir(_snake_case		)
									_lowerCAmelCase      =   {os.path.basename(_snake_case		) for p in contents}
									_lowerCAmelCase      =   [p for p in contents if p.endswith("""ckpt"""		)]
									assert len(_snake_case		) > 0

									self.assertIn("""test_generations.txt"""						,    _snake_case		)
									self.assertIn("""test_results.txt"""						,    _snake_case		)

									# get the following from the module, (we don't have access to `model` here)
									_lowerCAmelCase      =   os.path.join(_snake_case						,    """metrics.json"""		)
									_lowerCAmelCase      =   """rouge2"""

									_lowerCAmelCase      =   load_json(_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(_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}']						,    _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"""]		)						,    _snake_case		)



