


"""simple docstring"""


import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


A     :   Optional[int]			    =			"sshleifer/mar_enro_6_3_student"
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							def 			snake_case				(     self	):
										super().setUp()

										__lowerCAmelCase				    =			cached_path(
										    "https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"    ,   extract_compressed_file=__a    ,   )
										__lowerCAmelCase				    =			f"{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"

							@slow
							@require_torch_gpu
							def 			snake_case				(     self	):
										MarianMTModel.from_pretrained(__a	)



							@slow
							@require_torch_gpu
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			{
										    "$MAX_LEN": 64,
										    "$BS": 64,
										    "$GAS": 1,
										    "$ENRO_DIR": self.data_dir,
										    "facebook/mbart-large-cc25": MARIAN_MODEL,
										    # "val_check_interval=0.25": "val_check_interval=1.0",
										    "--learning_rate=3e-5": "--learning_rate 3e-4",
										    "--num_train_epochs 6": "--num_train_epochs 1",
										}

										# Clean up bash script
										__lowerCAmelCase				    =			(self.test_file_dir / "train_mbart_cc25_enro.sh").open().read().split("finetune.py"	)[1].strip()
										__lowerCAmelCase				    =			bash_script.replace("\\\n"    ,   ""	).strip().replace("\"$@\""    ,   ""	)
										for k, v in env_vars_to_replace.items():
													__lowerCAmelCase				    =			bash_script.replace(__a    ,   str(__a	)	)
										__lowerCAmelCase				    =			self.get_auto_remove_tmp_dir()

										# bash_script = bash_script.replace("--fp16 ", "")
										__lowerCAmelCase				    =			f"\n            --output_dir {output_dir}\n            --tokenizer_name Helsinki-NLP/opus-mt-en-ro\n            --sortish_sampler\n            --do_predict\n            --gpus 1\n            --freeze_encoder\n            --n_train 40000\n            --n_val 500\n            --n_test 500\n            --fp16_opt_level O1\n            --num_sanity_val_steps 0\n            --eval_beams 2\n        ".split()
										# XXX: args.gpus > 1 : handle multi_gpu in the future

										__lowerCAmelCase				    =			["finetune.py"] + bash_script.split() + args
										with patch.object(__a    ,   "argv"    ,   __a	):
													__lowerCAmelCase				    =			argparse.ArgumentParser()
													__lowerCAmelCase				    =			pl.Trainer.add_argparse_args(__a	)
													__lowerCAmelCase				    =			SummarizationModule.add_model_specific_args(__a    ,   os.getcwd()	)
													__lowerCAmelCase				    =			parser.parse_args()
													__lowerCAmelCase				    =			main(__a	)

										# Check metrics
										__lowerCAmelCase				    =			load_json(model.metrics_save_path	)
										__lowerCAmelCase				    =			metrics["val"][0]
										__lowerCAmelCase				    =			metrics["val"][-1]
										self.assertEqual(len(metrics["val"]	)    ,   (args.max_epochs / args.val_check_interval)	)
										assert isinstance(last_step_stats[f"val_avg_{model.val_metric}"]    ,   __a	)

										self.assertGreater(last_step_stats["val_avg_gen_time"]    ,   0.0_1	)
										# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
										self.assertLessEqual(last_step_stats["val_avg_gen_time"]    ,   1.0	)

										# test learning requirements:

										# 1. BLEU improves over the course of training by more than 2 pts
										self.assertGreater(last_step_stats["val_avg_bleu"] - first_step_stats["val_avg_bleu"]    ,   2	)

										# 2. BLEU finishes above 17
										self.assertGreater(last_step_stats["val_avg_bleu"]    ,   17	)

										# 3. test BLEU and val BLEU within ~1.1 pt.
										self.assertLess(abs(metrics["val"][-1]["val_avg_bleu"] - metrics["test"][-1]["test_avg_bleu"]	)    ,   1.1	)

										# check lightning ckpt can be loaded and has a reasonable statedict
										__lowerCAmelCase				    =			os.listdir(__a	)
										__lowerCAmelCase				    =			[x for x in contents if x.endswith(".ckpt"	)][0]
										__lowerCAmelCase				    =			os.path.join(args.output_dir    ,   __a	)
										__lowerCAmelCase				    =			torch.load(__a    ,   map_location="cpu"	)
										__lowerCAmelCase				    =			"model.model.decoder.layers.0.encoder_attn_layer_norm.weight"
										assert expected_key in ckpt["state_dict"]
										assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

										# TODO: turn on args.do_predict when PL bug fixed.
										if args.do_predict:
													__lowerCAmelCase				    =			{os.path.basename(__a	) for p in contents}
													assert "test_generations.txt" in contents
													assert "test_results.txt" in contents
													# assert len(metrics["val"]) ==  desired_n_evals
													assert len(metrics["test"]	) == 1
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							@timeout_decorator.timeout(6_00	)
							@slow
							@require_torch_gpu
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			f"{self.test_file_dir_str}/test_data/wmt_en_ro"
										__lowerCAmelCase				    =			{
										    "--fp16_opt_level=O1": "",
										    "$MAX_LEN": 1_28,
										    "$BS": 16,
										    "$GAS": 1,
										    "$ENRO_DIR": data_dir,
										    "$m": "sshleifer/student_marian_en_ro_6_1",
										    "val_check_interval=0.25": "val_check_interval=1.0",
										}

										# Clean up bash script
										__lowerCAmelCase				    =			(
										    (self.test_file_dir / "distil_marian_no_teacher.sh").open().read().split("distillation.py"	)[1].strip()
										)
										__lowerCAmelCase				    =			bash_script.replace("\\\n"    ,   ""	).strip().replace("\"$@\""    ,   ""	)
										__lowerCAmelCase				    =			bash_script.replace("--fp16 "    ,   " "	)

										for k, v in env_vars_to_replace.items():
													__lowerCAmelCase				    =			bash_script.replace(__a    ,   str(__a	)	)
										__lowerCAmelCase				    =			self.get_auto_remove_tmp_dir()
										__lowerCAmelCase				    =			bash_script.replace("--fp16"    ,   ""	)
										__lowerCAmelCase				    =			6
										__lowerCAmelCase				    =			(
										    ["distillation.py"]
										    + bash_script.split()
										    + [
										        f"--output_dir={output_dir}",
										        "--gpus=1",
										        "--learning_rate=1e-3",
										        f"--num_train_epochs={epochs}",
										        "--warmup_steps=10",
										        "--val_check_interval=1.0",
										        "--do_predict",
										    ]
										)
										with patch.object(__a    ,   "argv"    ,   __a	):
													__lowerCAmelCase				    =			argparse.ArgumentParser()
													__lowerCAmelCase				    =			pl.Trainer.add_argparse_args(__a	)
													__lowerCAmelCase				    =			SummarizationDistiller.add_model_specific_args(__a    ,   os.getcwd()	)
													__lowerCAmelCase				    =			parser.parse_args()
													# assert args.gpus == gpus THIS BREAKS for multi_gpu

													__lowerCAmelCase				    =			distill_main(__a	)

										# Check metrics
										__lowerCAmelCase				    =			load_json(model.metrics_save_path	)
										__lowerCAmelCase				    =			metrics["val"][0]
										__lowerCAmelCase				    =			metrics["val"][-1]
										assert len(metrics["val"]	) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

										assert last_step_stats["val_avg_gen_time"] >= 0.0_1

										assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
										assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
										assert isinstance(last_step_stats[f"val_avg_{model.val_metric}"]    ,   __a	)

										# check lightning ckpt can be loaded and has a reasonable statedict
										__lowerCAmelCase				    =			os.listdir(__a	)
										__lowerCAmelCase				    =			[x for x in contents if x.endswith(".ckpt"	)][0]
										__lowerCAmelCase				    =			os.path.join(args.output_dir    ,   __a	)
										__lowerCAmelCase				    =			torch.load(__a    ,   map_location="cpu"	)
										__lowerCAmelCase				    =			"model.model.decoder.layers.0.encoder_attn_layer_norm.weight"
										assert expected_key in ckpt["state_dict"]
										assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

										# TODO: turn on args.do_predict when PL bug fixed.
										if args.do_predict:
													__lowerCAmelCase				    =			{os.path.basename(__a	) for p in contents}
													assert "test_generations.txt" in contents
													assert "test_results.txt" in contents
													# assert len(metrics["val"]) ==  desired_n_evals
													assert len(metrics["test"]	) == 1







'''simple docstring'''



import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
  from transformers import WhisperFeatureExtractor

if is_torch_available():
  import torch

UpperCAmelCase :       int			   =     random.Random()







def    a__       (  a__      ,    a__=1.0      ,    a__=None      ,    a__=None						):




     """simple docstring"""


     if rng is None:
          __SCREAMING_SNAKE_CASE     =      global_rng

     __SCREAMING_SNAKE_CASE     =      []
     for batch_idx in range(shape[0]						):
          values.append([]						)
          for _ in range(shape[1]						):
               values[-1].append(rng.random() * scale						)

     return values







@require_torch
@require_torchaudio
class 						lowerCAmelCase__   (      unittest.TestCase					):



 """simple docstring"""



 def __init__(		self					:       str					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       str=7					,							__SCREAMING_SNAKE_CASE					:       List[str]=400					,							__SCREAMING_SNAKE_CASE					:       Any=2_000					,							__SCREAMING_SNAKE_CASE					:       List[str]=10					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=160					,							__SCREAMING_SNAKE_CASE					:       List[str]=8					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=0.0					,							__SCREAMING_SNAKE_CASE					:       Dict=4_000					,							__SCREAMING_SNAKE_CASE					:       Optional[int]=False					,							__SCREAMING_SNAKE_CASE					:       List[Any]=True					,							)  -> str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      parent
      __SCREAMING_SNAKE_CASE     =      batch_size
      __SCREAMING_SNAKE_CASE     =      min_seq_length
      __SCREAMING_SNAKE_CASE     =      max_seq_length
      __SCREAMING_SNAKE_CASE     =      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
      __SCREAMING_SNAKE_CASE     =      padding_value
      __SCREAMING_SNAKE_CASE     =      sampling_rate
      __SCREAMING_SNAKE_CASE     =      return_attention_mask
      __SCREAMING_SNAKE_CASE     =      do_normalize
      __SCREAMING_SNAKE_CASE     =      feature_size
      __SCREAMING_SNAKE_CASE     =      chunk_length
      __SCREAMING_SNAKE_CASE     =      hop_length
 def       UpperCAmelCase__		(		self					:       Dict			)  -> Dict:


      """simple docstring"""




      return {
          "feature_size": self.feature_size,
          "hop_length": self.hop_length,
          "chunk_length": self.chunk_length,
          "padding_value": self.padding_value,
          "sampling_rate": self.sampling_rate,
          "return_attention_mask": self.return_attention_mask,
          "do_normalize": self.do_normalize,
      }


 def       UpperCAmelCase__		(		self					:       Union[str, Any]					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]=False					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]=False			)  -> Union[str, Any]:


      """simple docstring"""




      def _flatten(__SCREAMING_SNAKE_CASE					:       Dict			):
           return list(itertools.chain(*__SCREAMING_SNAKE_CASE			)			)

      if equal_length:
           __SCREAMING_SNAKE_CASE     =      [floats_list((self.max_seq_length, self.feature_size)			) for _ in range(self.batch_size			)]
      else:
           # make sure that inputs increase in size
           __SCREAMING_SNAKE_CASE     =      [
               floats_list((x, self.feature_size)			)
               for x in range(self.min_seq_length					,							self.max_seq_length					,							self.seq_length_diff			)
           ]
      if numpify:
           __SCREAMING_SNAKE_CASE     =      [np.asarray(__SCREAMING_SNAKE_CASE			) for x in speech_inputs]
      return speech_inputs







@require_torch
@require_torchaudio
class 						lowerCAmelCase__   (      a ,					unittest.TestCase					):



 """simple docstring"""



 lowerCAmelCase__  					=      WhisperFeatureExtractor if is_speech_available() else None
 def       UpperCAmelCase__		(		self					:       str			)  -> Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      WhisperFeatureExtractionTester(self			)
 def       UpperCAmelCase__		(		self					:       str			)  -> List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.feature_extraction_class(**self.feat_extract_dict			)

      with tempfile.TemporaryDirectory() as tmpdirname:
           __SCREAMING_SNAKE_CASE     =      feat_extract_first.save_pretrained(__SCREAMING_SNAKE_CASE			)[0]
           check_json_file_has_correct_format(__SCREAMING_SNAKE_CASE			)
           __SCREAMING_SNAKE_CASE     =      self.feature_extraction_class.from_pretrained(__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      feat_extract_first.to_dict()
      __SCREAMING_SNAKE_CASE     =      feat_extract_second.to_dict()
      __SCREAMING_SNAKE_CASE     =      feat_extract_first.mel_filters
      __SCREAMING_SNAKE_CASE     =      feat_extract_second.mel_filters
      self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)			)
      self.assertEqual(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       List[str]			)  -> List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.feature_extraction_class(**self.feat_extract_dict			)

      with tempfile.TemporaryDirectory() as tmpdirname:
           __SCREAMING_SNAKE_CASE     =      os.path.join(__SCREAMING_SNAKE_CASE					,							"""feat_extract.json"""			)
           feat_extract_first.to_json_file(__SCREAMING_SNAKE_CASE			)
           __SCREAMING_SNAKE_CASE     =      self.feature_extraction_class.from_json_file(__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      feat_extract_first.to_dict()
      __SCREAMING_SNAKE_CASE     =      feat_extract_second.to_dict()
      __SCREAMING_SNAKE_CASE     =      feat_extract_first.mel_filters
      __SCREAMING_SNAKE_CASE     =      feat_extract_second.mel_filters
      self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)			)
      self.assertEqual(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
      # create three inputs of length 800, 1000, and 1200
      __SCREAMING_SNAKE_CASE     =      [floats_list((1, x)			)[0] for x in range(800					,							1_400					,							200			)]
      __SCREAMING_SNAKE_CASE     =      [np.asarray(__SCREAMING_SNAKE_CASE			) for speech_input in speech_inputs]

      # Test feature size
      __SCREAMING_SNAKE_CASE     =      feature_extractor(__SCREAMING_SNAKE_CASE					,							padding="""max_length"""					,							return_tensors="""np"""			).input_features
      self.assertTrue(input_features.ndim == 3			)
      self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames			)
      self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size			)

      # Test not batched input
      __SCREAMING_SNAKE_CASE     =      feature_extractor(speech_inputs[0]					,							return_tensors="""np"""			).input_features
      __SCREAMING_SNAKE_CASE     =      feature_extractor(np_speech_inputs[0]					,							return_tensors="""np"""			).input_features
      self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							atol=1E-3			)			)

      # Test batched
      __SCREAMING_SNAKE_CASE     =      feature_extractor(__SCREAMING_SNAKE_CASE					,							return_tensors="""np"""			).input_features
      __SCREAMING_SNAKE_CASE     =      feature_extractor(__SCREAMING_SNAKE_CASE					,							return_tensors="""np"""			).input_features
      for enc_seq_a, enc_seq_a in zip(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			):
           self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							atol=1E-3			)			)

      # Test 2-D numpy arrays are batched.
      __SCREAMING_SNAKE_CASE     =      [floats_list((1, x)			)[0] for x in (800, 800, 800)]
      __SCREAMING_SNAKE_CASE     =      np.asarray(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      feature_extractor(__SCREAMING_SNAKE_CASE					,							return_tensors="""np"""			).input_features
      __SCREAMING_SNAKE_CASE     =      feature_extractor(__SCREAMING_SNAKE_CASE					,							return_tensors="""np"""			).input_features
      for enc_seq_a, enc_seq_a in zip(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			):
           self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							atol=1E-3			)			)

      # Test truncation required
      __SCREAMING_SNAKE_CASE     =      [floats_list((1, x)			)[0] for x in range(200					,							(feature_extractor.n_samples + 500)					,							200			)]
      __SCREAMING_SNAKE_CASE     =      [np.asarray(__SCREAMING_SNAKE_CASE			) for speech_input in speech_inputs]

      __SCREAMING_SNAKE_CASE     =      [x[: feature_extractor.n_samples] for x in speech_inputs]
      __SCREAMING_SNAKE_CASE     =      [np.asarray(__SCREAMING_SNAKE_CASE			) for speech_input in speech_inputs_truncated]

      __SCREAMING_SNAKE_CASE     =      feature_extractor(__SCREAMING_SNAKE_CASE					,							return_tensors="""np"""			).input_features
      __SCREAMING_SNAKE_CASE     =      feature_extractor(__SCREAMING_SNAKE_CASE					,							return_tensors="""np"""			).input_features
      for enc_seq_a, enc_seq_a in zip(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			):
           self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							atol=1E-3			)			)
 def       UpperCAmelCase__		(		self					:       Dict			)  -> Optional[int]:


      """simple docstring"""




      import torch

      __SCREAMING_SNAKE_CASE     =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
      __SCREAMING_SNAKE_CASE     =      np.random.rand(100					,							32			).astype(np.floataa			)
      __SCREAMING_SNAKE_CASE     =      np_speech_inputs.tolist()

      for inputs in [py_speech_inputs, np_speech_inputs]:
           __SCREAMING_SNAKE_CASE     =      feature_extractor.pad([{"""input_features""": inputs}]					,							return_tensors="""np"""			)
           self.assertTrue(np_processed.input_features.dtype == np.floataa			)
           __SCREAMING_SNAKE_CASE     =      feature_extractor.pad([{"""input_features""": inputs}]					,							return_tensors="""pt"""			)
           self.assertTrue(pt_processed.input_features.dtype == torch.floataa			)
 def       UpperCAmelCase__		(		self					:       str					,							__SCREAMING_SNAKE_CASE					:       Tuple			)  -> Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      load_dataset("""hf-internal-testing/librispeech_asr_dummy"""					,							"""clean"""					,							split="""validation"""			)
      # automatic decoding with librispeech
      __SCREAMING_SNAKE_CASE     =      ds.sort("""id"""			).select(range(__SCREAMING_SNAKE_CASE			)			)[:num_samples]["""audio"""]

      return [x["array"] for x in speech_samples]
 def       UpperCAmelCase__		(		self					:       Tuple			)  -> int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      torch.tensor(
          [
              0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
              0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
              0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
              -0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
          ]			)
      # fmt: on

      __SCREAMING_SNAKE_CASE     =      self._load_datasamples(1			)
      __SCREAMING_SNAKE_CASE     =      WhisperFeatureExtractor()
      __SCREAMING_SNAKE_CASE     =      feature_extractor(__SCREAMING_SNAKE_CASE					,							return_tensors="""pt"""			).input_features
      self.assertEqual(input_features.shape					,							(1, 80, 3_000)			)
      self.assertTrue(torch.allclose(input_features[0, 0, :30]					,							__SCREAMING_SNAKE_CASE					,							atol=1E-4			)			)


 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
      __SCREAMING_SNAKE_CASE     =      self._load_datasamples(1			)[0]
      __SCREAMING_SNAKE_CASE     =      ((audio - audio.min()) / (audio.max() - audio.min())) * 65_535  # Rescale to [0, 65535] to show issue
      __SCREAMING_SNAKE_CASE     =      feat_extract.zero_mean_unit_var_norm([audio]					,							attention_mask=__SCREAMING_SNAKE_CASE			)[0]

      self.assertTrue(np.all(np.mean(__SCREAMING_SNAKE_CASE			) < 1E-3			)			)
      self.assertTrue(np.all(np.abs(np.var(__SCREAMING_SNAKE_CASE			) - 1			) < 1E-3			)			)






