




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


lowercase__							:	int											=		"sshleifer/mar_enro_6_3_student"
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						def 			snake_case__		(		self				:	Tuple		):






							"""simple docstring"""






							super().setUp()

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






						@slow
						@require_torch_gpu
						def 			snake_case__		(		self				:	Any		):






							"""simple docstring"""






							MarianMTModel.from_pretrained(__lowercase		)






						@slow
						@require_torch_gpu
						def 			snake_case__		(		self				:	Optional[Any]		):






							"""simple docstring"""






							snake_case_						       =	{
							    "$MAX_LEN": 64,
							    "$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
							snake_case_						       =	(self.test_file_dir / "train_mbart_cc25_enro.sh").open().read().split("finetune.py"		)[1].strip()
							snake_case_						       =	bash_script.replace("\\\n"			,      ""		).strip().replace("\"$@\""			,      ""		)
							for k, v in env_vars_to_replace.items():
								snake_case_						       =	bash_script.replace(__lowercase			,      str(__lowercase		)		)
							snake_case_						       =	self.get_auto_remove_tmp_dir()

							# bash_script = bash_script.replace("--fp16 ", "")
							snake_case_						       =	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

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

							# Check metrics
							snake_case_						       =	load_json(model.metrics_save_path		)
							snake_case_						       =	metrics["val"][0]
							snake_case_						       =	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}"]			,      __lowercase		)

							self.assertGreater(last_step_stats["val_avg_gen_time"]			,      0.01		)
							# 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
							snake_case_						       =	os.listdir(__lowercase		)
							snake_case_						       =	[x for x in contents if x.endswith(".ckpt"		)][0]
							snake_case_						       =	os.path.join(args.output_dir			,      __lowercase		)
							snake_case_						       =	torch.load(__lowercase			,      map_location="cpu"		)
							snake_case_						       =	"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:
								snake_case_						       =	{os.path.basename(__lowercase		) 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  (							UpperCAmelCase__     ):







						'''simple docstring'''





						@timeout_decorator.timeout(6_00		)
						@slow
						@require_torch_gpu
						def 			snake_case__		(		self				:	Optional[Any]		):






							"""simple docstring"""






							snake_case_						       =	f"{self.test_file_dir_str}/test_data/wmt_en_ro"
							snake_case_						       =	{
							    "--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
							snake_case_						       =	(
							    (self.test_file_dir / "distil_marian_no_teacher.sh").open().read().split("distillation.py"		)[1].strip()
							)
							snake_case_						       =	bash_script.replace("\\\n"			,      ""		).strip().replace("\"$@\""			,      ""		)
							snake_case_						       =	bash_script.replace("--fp16 "			,      " "		)

							for k, v in env_vars_to_replace.items():
								snake_case_						       =	bash_script.replace(__lowercase			,      str(__lowercase		)		)
							snake_case_						       =	self.get_auto_remove_tmp_dir()
							snake_case_						       =	bash_script.replace("--fp16"			,      ""		)
							snake_case_						       =	6
							snake_case_						       =	(
							    ["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(__lowercase			,      "argv"			,      __lowercase		):
								snake_case_						       =	argparse.ArgumentParser()
								snake_case_						       =	pl.Trainer.add_argparse_args(__lowercase		)
								snake_case_						       =	SummarizationDistiller.add_model_specific_args(__lowercase			,      os.getcwd()		)
								snake_case_						       =	parser.parse_args()
								# assert args.gpus == gpus THIS BREAKS for multi_gpu

								snake_case_						       =	distill_main(__lowercase		)

							# Check metrics
							snake_case_						       =	load_json(model.metrics_save_path		)
							snake_case_						       =	metrics["val"][0]
							snake_case_						       =	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"] >= 0.01

							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}"]			,      __lowercase		)

							# check lightning ckpt can be loaded and has a reasonable statedict
							snake_case_						       =	os.listdir(__lowercase		)
							snake_case_						       =	[x for x in contents if x.endswith(".ckpt"		)][0]
							snake_case_						       =	os.path.join(args.output_dir			,      __lowercase		)
							snake_case_						       =	torch.load(__lowercase			,      map_location="cpu"		)
							snake_case_						       =	"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:
								snake_case_						       =	{os.path.basename(__lowercase		) 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






import warnings
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


lowercase__							:	Tuple											=		logging.get_logger(__name__)
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						lowerCAmelCase_							=  ['''input_values''', '''attention_mask''']






						def __init__(		self				:	List[Any]			,      __lowercase				:	int = 1			,      __lowercase				:	int = 1_60_00			,      __lowercase				:	float = 0.0			,      __lowercase				:	bool = False			,      __lowercase				:	int = 80			,      __lowercase				:	int = 16			,      __lowercase				:	int = 64			,      __lowercase				:	str = "hann_window"			,      __lowercase				:	float = 1.0			,      __lowercase				:	float = 80			,      __lowercase				:	float = 76_00			,      __lowercase				:	float = 1E-10			,      __lowercase				:	int = 2			,      __lowercase				:	bool = True			,      **__lowercase				:	Union[str, Any]			,      ):






							"""simple docstring"""






							super().__init__(feature_size=__lowercase			,      sampling_rate=__lowercase			,      padding_value=__lowercase			,      **__lowercase		)
							snake_case_						       =	do_normalize
							snake_case_						       =	return_attention_mask

							snake_case_						       =	num_mel_bins
							snake_case_						       =	hop_length
							snake_case_						       =	win_length
							snake_case_						       =	win_function
							snake_case_						       =	frame_signal_scale
							snake_case_						       =	fmin
							snake_case_						       =	fmax
							snake_case_						       =	mel_floor
							snake_case_						       =	reduction_factor

							snake_case_						       =	win_length * sampling_rate // 10_00
							snake_case_						       =	hop_length * sampling_rate // 10_00
							snake_case_						       =	optimal_fft_length(self.sample_size		)
							snake_case_						       =	(self.n_fft // 2) + 1

							snake_case_						       =	window_function(window_length=self.sample_size			,      name=self.win_function			,      periodic=__lowercase		)

							snake_case_						       =	mel_filter_bank(
							    num_frequency_bins=self.n_freqs			,      num_mel_filters=self.num_mel_bins			,      min_frequency=self.fmin			,      max_frequency=self.fmax			,      sampling_rate=self.sampling_rate			,      norm="slaney"			,      mel_scale="slaney"			,      )

							if frame_signal_scale != 1.0:
								warnings.warn(
								    "The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers"			,      __lowercase			,      )
							if reduction_factor != 2.0:
								warnings.warn(
								    "The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"			,      __lowercase			,      )






						@staticmethod
						# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
						def 			snake_case__		(		__lowercase				:	List[np.ndarray]			,      __lowercase				:	List[np.ndarray]			,      __lowercase				:	float = 0.0		):






							"""simple docstring"""






							if attention_mask is not None:
								snake_case_						       =	np.array(__lowercase			,      np.intaa		)
								snake_case_						       =	[]

								for vector, length in zip(__lowercase			,      attention_mask.sum(-1		)		):
									snake_case_						       =	(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7		)
									if length < normed_slice.shape[0]:
										snake_case_						       =	padding_value

									normed_input_values.append(__lowercase		)
							else:
								snake_case_						       =	[(x - x.mean()) / np.sqrt(x.var() + 1E-7		) for x in input_values]

							return normed_input_values






						def 			snake_case__		(		self				:	str			,      __lowercase				:	np.ndarray			,      ):






							"""simple docstring"""






							snake_case_						       =	spectrogram(
							    __lowercase			,      window=self.window			,      frame_length=self.sample_size			,      hop_length=self.sample_stride			,      fft_length=self.n_fft			,      mel_filters=self.mel_filters			,      mel_floor=self.mel_floor			,      log_mel="log10"			,      )
							return log_mel_spec.T






						def __call__(		self				:	Optional[int]			,      __lowercase				:	Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None			,      __lowercase				:	Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None			,      __lowercase				:	Union[bool, str, PaddingStrategy] = False			,      __lowercase				:	Optional[int] = None			,      __lowercase				:	bool = False			,      __lowercase				:	Optional[int] = None			,      __lowercase				:	Optional[bool] = None			,      __lowercase				:	Optional[Union[str, TensorType]] = None			,      __lowercase				:	Optional[int] = None			,      **__lowercase				:	List[str]			,      ):






							"""simple docstring"""






							if audio is None and audio_target is None:
								raise ValueError("You must provide either `audio` or `audio_target` values."		)

							if sampling_rate is not None:
								if sampling_rate != self.sampling_rate:
									raise ValueError(
									    f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
									    f" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"
									    f" {self.sampling_rate} and not {sampling_rate}."		)
							else:
								logger.warning(
								    "It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
								    "Failing to do so can result in silent errors that might be hard to debug."		)

							if audio is not None:
								snake_case_						       =	self._process_audio(
								    __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      **__lowercase			,      )
							else:
								snake_case_						       =	None

							if audio_target is not None:
								snake_case_						       =	self._process_audio(
								    __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      __lowercase			,      **__lowercase			,      )

								if inputs is None:
									return inputs_target
								else:
									snake_case_						       =	inputs_target["input_values"]
									snake_case_						       =	inputs_target.get("attention_mask"		)
									if decoder_attention_mask is not None:
										snake_case_						       =	decoder_attention_mask

							return inputs






						def 			snake_case__		(		self				:	Dict			,      __lowercase				:	Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]			,      __lowercase				:	bool = False			,      __lowercase				:	Union[bool, str, PaddingStrategy] = False			,      __lowercase				:	Optional[int] = None			,      __lowercase				:	bool = False			,      __lowercase				:	Optional[int] = None			,      __lowercase				:	Optional[bool] = None			,      __lowercase				:	Optional[Union[str, TensorType]] = None			,      **__lowercase				:	Optional[Any]			,      ):






							"""simple docstring"""






							snake_case_						       =	isinstance(__lowercase			,      np.ndarray		) and len(speech.shape		) > 1
							if is_batched_numpy and len(speech.shape		) > 2:
								raise ValueError(f"Only mono-channel audio is supported for input to {self}"		)
							snake_case_						       =	is_batched_numpy or (
							    isinstance(__lowercase			,      (list, tuple)		) and (isinstance(speech[0]			,      (np.ndarray, tuple, list)		))
							)

							if is_batched:
								snake_case_						       =	[np.asarray(__lowercase			,      dtype=np.floataa		) for speech in speech]
							elif not is_batched and not isinstance(__lowercase			,      np.ndarray		):
								snake_case_						       =	np.asarray(__lowercase			,      dtype=np.floataa		)
							elif isinstance(__lowercase			,      np.ndarray		) and speech.dtype is np.dtype(np.floataa		):
								snake_case_						       =	speech.astype(np.floataa		)

							# always return batch
							if not is_batched:
								snake_case_						       =	[speech]

							# needed to make pad() work on spectrogram inputs
							snake_case_						       =	self.feature_size

							# convert into correct format for padding
							if is_target:
								snake_case_						       =	[self._extract_mel_features(__lowercase		) for waveform in speech]
								snake_case_						       =	BatchFeature({"input_values": features}		)
								snake_case_						       =	self.num_mel_bins
							else:
								snake_case_						       =	BatchFeature({"input_values": speech}		)

							snake_case_						       =	self.pad(
							    __lowercase			,      padding=__lowercase			,      max_length=__lowercase			,      truncation=__lowercase			,      pad_to_multiple_of=__lowercase			,      return_attention_mask=__lowercase			,      **__lowercase			,      )

							snake_case_						       =	feature_size_hack

							# convert input values to correct format
							snake_case_						       =	padded_inputs["input_values"]
							if not isinstance(input_values[0]			,      np.ndarray		):
								snake_case_						       =	[np.asarray(__lowercase			,      dtype=np.floataa		) for array in input_values]
							elif (
							    not isinstance(__lowercase			,      np.ndarray		)
							    and isinstance(input_values[0]			,      np.ndarray		)
							    and input_values[0].dtype is np.dtype(np.floataa		)
							):
								snake_case_						       =	[array.astype(np.floataa		) for array in input_values]
							elif isinstance(__lowercase			,      np.ndarray		) and input_values.dtype is np.dtype(np.floataa		):
								snake_case_						       =	input_values.astype(np.floataa		)

							# convert attention_mask to correct format
							snake_case_						       =	padded_inputs.get("attention_mask"		)
							if attention_mask is not None:
								snake_case_						       =	[np.asarray(__lowercase			,      dtype=np.intaa		) for array in attention_mask]

							# zero-mean and unit-variance normalization
							if not is_target and self.do_normalize:
								snake_case_						       =	(
								    attention_mask
								    if self._get_padding_strategies(__lowercase			,      max_length=__lowercase		) is not PaddingStrategy.DO_NOT_PAD
								    else None
								)
								snake_case_						       =	self.zero_mean_unit_var_norm(
								    padded_inputs["input_values"]			,      attention_mask=__lowercase			,      padding_value=self.padding_value		)

							if return_tensors is not None:
								snake_case_						       =	padded_inputs.convert_to_tensors(__lowercase		)

							return padded_inputs






						def 			snake_case__		(		self				:	Tuple		):






							"""simple docstring"""






							snake_case_						       =	super().to_dict()

							# Don't serialize these as they are derived from the other properties.
							snake_case_						       =	["window", "mel_filters", "sample_size", "sample_stride", "n_fft", "n_freqs"]
							for name in names:
								if name in output:
									del output[name]

							return output
