




from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import TFLEDForConditionalGeneration, TFLEDModel





@require_tf
class 					snake_case_   :



				'''simple docstring'''
				lowerCamelCase       =  LEDConfig
				lowerCamelCase       =  {}
				lowerCamelCase       =  """gelu"""





				def __init__(      self							:   Optional[Any]      ,							__magic_name__							:   List[Any]      ,							__magic_name__							:   int=13      ,							__magic_name__							:   Union[str, Any]=7      ,							__magic_name__							:   List[Any]=True      ,							__magic_name__							:   List[Any]=False      ,							__magic_name__							:   str=99      ,							__magic_name__							:   Optional[int]=32      ,							__magic_name__							:   List[Any]=2      ,							__magic_name__							:   str=4      ,							__magic_name__							:   str=37      ,							__magic_name__							:   Any=0.1      ,							__magic_name__							:   List[Any]=0.1      ,							__magic_name__							:   Optional[Any]=20      ,							__magic_name__							:   Union[str, Any]=2      ,							__magic_name__							:   str=1      ,							__magic_name__							:   List[str]=0      ,							__magic_name__							:   List[Any]=4      ,							)							->			List[str]:
											lowerCamelCase_			:    Union[str, Any]   						=      parent
											lowerCamelCase_			:    List[Any]   						=      batch_size
											lowerCamelCase_			:    str   						=      seq_length
											lowerCamelCase_			:    List[str]   						=      is_training
											lowerCamelCase_			:    Dict   						=      use_labels
											lowerCamelCase_			:    int   						=      vocab_size
											lowerCamelCase_			:    Union[str, Any]   						=      hidden_size
											lowerCamelCase_			:    Any   						=      num_hidden_layers
											lowerCamelCase_			:    List[Any]   						=      num_attention_heads
											lowerCamelCase_			:    str   						=      intermediate_size

											lowerCamelCase_			:    Optional[Any]   						=      hidden_dropout_prob
											lowerCamelCase_			:    Optional[Any]   						=      attention_probs_dropout_prob
											lowerCamelCase_			:    Optional[Any]   						=      max_position_embeddings
											lowerCamelCase_			:    Optional[Any]   						=      eos_token_id
											lowerCamelCase_			:    str   						=      pad_token_id
											lowerCamelCase_			:    Any   						=      bos_token_id
											lowerCamelCase_			:    Union[str, Any]   						=      attention_window

											# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
											# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
											# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
											# because its local attention only attends to `self.attention_window` and one before and one after
											lowerCamelCase_			:    List[Any]   						=      self.attention_window + 2

											# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
											# the `test_attention_outputs` and `test_hidden_states_output` tests
											lowerCamelCase_			:    Optional[Any]   						=      (
											    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
											)





				def    __SCREAMING_SNAKE_CASE     (      self							:   Tuple							)							->			Dict:
											lowerCamelCase_			:    Any   						=      ids_tensor([self.batch_size, self.seq_length - 1]      ,							self.vocab_size							)
											lowerCamelCase_			:    List[str]   						=      tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							)      ,							1							)
											lowerCamelCase_			:    Tuple   						=      tf.concat([input_ids, eos_tensor]      ,							axis=1							)

											lowerCamelCase_			:    Optional[int]   						=      ids_tensor([self.batch_size, self.seq_length]      ,							self.vocab_size							)

											lowerCamelCase_			:    Optional[int]   						=      self.config_cls(
											    vocab_size=self.vocab_size      ,							d_model=self.hidden_size      ,							encoder_layers=self.num_hidden_layers      ,							decoder_layers=self.num_hidden_layers      ,							encoder_attention_heads=self.num_attention_heads      ,							decoder_attention_heads=self.num_attention_heads      ,							encoder_ffn_dim=self.intermediate_size      ,							decoder_ffn_dim=self.intermediate_size      ,							dropout=self.hidden_dropout_prob      ,							attention_dropout=self.attention_probs_dropout_prob      ,							max_position_embeddings=self.max_position_embeddings      ,							eos_token_ids=[2]      ,							bos_token_id=self.bos_token_id      ,							pad_token_id=self.pad_token_id      ,							decoder_start_token_id=self.pad_token_id      ,							attention_window=self.attention_window      ,							**self.config_updates      ,							)
											lowerCamelCase_			:    int   						=      prepare_led_inputs_dict(__lowercase      ,							__lowercase      ,							__lowercase							)
											lowerCamelCase_			:    Optional[Any]   						=      tf.concat(
											    [tf.zeros_like(__lowercase							)[:, :-1], tf.ones_like(__lowercase							)[:, -1:]]      ,							axis=-1      ,							)
											lowerCamelCase_			:    Any   						=      global_attention_mask
											return config, inputs_dict





				def    __SCREAMING_SNAKE_CASE     (      self							:   Union[str, Any]      ,							__magic_name__							:   List[str]      ,							__magic_name__							:   List[str]							)							->			Optional[Any]:
											lowerCamelCase_			:    int   						=      TFLEDModel(config=__lowercase							).get_decoder()
											lowerCamelCase_			:    Dict   						=      inputs_dict['''input_ids''']

											lowerCamelCase_			:    Optional[Any]   						=      input_ids[:1, :]
											lowerCamelCase_			:    List[Any]   						=      inputs_dict['''attention_mask'''][:1, :]
											lowerCamelCase_			:    List[Any]   						=      1

											# first forward pass
											lowerCamelCase_			:    List[Any]   						=      model(__lowercase      ,							attention_mask=__lowercase      ,							use_cache=__lowercase							)

											lowerCamelCase_			:    Any   						=      outputs.to_tuple()

											# create hypothetical next token and extent to next_input_ids
											lowerCamelCase_			:    List[Any]   						=      ids_tensor((self.batch_size, 3)      ,							config.vocab_size							)
											lowerCamelCase_			:    Union[str, Any]   						=      tf.cast(ids_tensor((self.batch_size, 3)      ,							2							)      ,							tf.inta							)

											# append to next input_ids and
											lowerCamelCase_			:    Dict   						=      tf.concat([input_ids, next_tokens]      ,							axis=-1							)
											lowerCamelCase_			:    int   						=      tf.concat([attention_mask, next_attn_mask]      ,							axis=-1							)

											lowerCamelCase_			:    str   						=      model(__lowercase      ,							attention_mask=__lowercase							)[0]
											lowerCamelCase_			:    List[str]   						=      model(__lowercase      ,							attention_mask=__lowercase      ,							past_key_values=__lowercase							)[0]

											self.parent.assertEqual(next_tokens.shape[1]      ,							output_from_past.shape[1]							)

											# select random slice
											lowerCamelCase_			:    List[str]   						=      int(ids_tensor((1,)      ,							output_from_past.shape[-1]							)							)
											lowerCamelCase_			:    Dict   						=      output_from_no_past[:, -3:, random_slice_idx]
											lowerCamelCase_			:    int   						=      output_from_past[:, :, random_slice_idx]

											# test that outputs are equal for slice
											tf.debugging.assert_near(__lowercase      ,							__lowercase      ,							rtol=1e-3							)



def        __a							(  __UpperCAmelCase		:						str   ,		__UpperCAmelCase		:						Any   ,		__UpperCAmelCase		:						List[Any]   ,		__UpperCAmelCase		:						Optional[Any]=None   ,		__UpperCAmelCase		:						Union[str, Any]=None   ,		__UpperCAmelCase		:						Union[str, Any]=None   ,		__UpperCAmelCase		:						Tuple=None   ,		) ->					Dict:
							"""simple docstring"""




							if attention_mask is None:
														lowerCamelCase_			:    Optional[int]   						=      tf.cast(tf.math.not_equal(__UpperCAmelCase   ,		config.pad_token_id      )   ,		tf.inta      )
							if decoder_attention_mask is None:
														lowerCamelCase_			:    str   						=      tf.concat(
														    [
														        tf.ones(decoder_input_ids[:, :1].shape   ,		dtype=tf.inta      ),
														        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]   ,		config.pad_token_id      )   ,		tf.inta      ),
														    ]   ,		axis=-1   ,		)
							if head_mask is None:
														lowerCamelCase_			:    Dict   						=      tf.ones((config.encoder_layers, config.encoder_attention_heads)      )
							if decoder_head_mask is None:
														lowerCamelCase_			:    List[str]   						=      tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
							return {
							    "input_ids": input_ids,
							    "attention_mask": attention_mask,
							    "decoder_input_ids": decoder_input_ids,
							    "decoder_attention_mask": decoder_attention_mask,
							    "head_mask": head_mask,
							    "decoder_head_mask": decoder_head_mask,
							}





@require_tf
class 					snake_case_   (     snake_case__   ,	snake_case__   ,	unittest.TestCase	):



				'''simple docstring'''
				lowerCamelCase       =  (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
				lowerCamelCase       =  (TFLEDForConditionalGeneration,) if is_tf_available() else ()
				lowerCamelCase       =  (
				    {
				        """conversational""": TFLEDForConditionalGeneration,
				        """feature-extraction""": TFLEDModel,
				        """summarization""": TFLEDForConditionalGeneration,
				        """text2text-generation""": TFLEDForConditionalGeneration,
				        """translation""": TFLEDForConditionalGeneration,
				    }
				    if is_tf_available()
				    else {}
				)
				lowerCamelCase       =  True
				lowerCamelCase       =  False
				lowerCamelCase       =  False
				lowerCamelCase       =  False





				def    __SCREAMING_SNAKE_CASE     (      self							:   Optional[int]							)							->			List[str]:
											lowerCamelCase_			:    int   						=      TFLEDModelTester(self							)
											lowerCamelCase_			:    Any   						=      ConfigTester(self      ,							config_class=__lowercase							)





				def    __SCREAMING_SNAKE_CASE     (      self							:   Dict							)							->			str:
											self.config_tester.run_common_tests()





				def    __SCREAMING_SNAKE_CASE     (      self							:   str							)							->			Dict:
											lowerCamelCase_			:    Any   						=      self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.check_decoder_model_past_large_inputs(*__lowercase							)





				def    __SCREAMING_SNAKE_CASE     (      self							:   Optional[Any]							)							->			Tuple:
											lowerCamelCase_			:    List[str]   						=      self.model_tester.prepare_config_and_inputs_for_common()
											lowerCamelCase_			:    int   						=      tf.zeros_like(inputs_dict["attention_mask"]							)
											lowerCamelCase_			:    List[Any]   						=      2
											lowerCamelCase_			:    Optional[Any]   						=      tf.where(
											    tf.range(self.model_tester.seq_length							)[None, :] < num_global_attn_indices      ,							1      ,							inputs_dict["global_attention_mask"]      ,							)

											lowerCamelCase_			:    Tuple   						=      True
											lowerCamelCase_			:    Optional[Any]   						=      self.model_tester.seq_length
											lowerCamelCase_			:    List[str]   						=      self.model_tester.encoder_seq_length

											def check_decoder_attentions_output(__magic_name__							:   Any							):
																		lowerCamelCase_			:    Union[str, Any]   						=      outputs.decoder_attentions
																		self.assertEqual(len(__lowercase							)      ,							self.model_tester.num_hidden_layers							)
																		self.assertListEqual(
																		    list(decoder_attentions[0].shape[-3:]							)      ,							[self.model_tester.num_attention_heads, seq_length, seq_length]      ,							)

											def check_encoder_attentions_output(__magic_name__							:   List[str]							):
																		lowerCamelCase_			:    Any   						=      [t.numpy() for t in outputs.encoder_attentions]
																		lowerCamelCase_			:    Optional[int]   						=      [t.numpy() for t in outputs.encoder_global_attentions]
																		self.assertEqual(len(__lowercase							)      ,							self.model_tester.num_hidden_layers							)
																		self.assertEqual(len(__lowercase							)      ,							self.model_tester.num_hidden_layers							)
																		self.assertListEqual(
																		    list(attentions[0].shape[-3:]							)      ,							[self.model_tester.num_attention_heads, seq_length, seq_length]      ,							)
																		self.assertListEqual(
																		    list(global_attentions[0].shape[-3:]							)      ,							[self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]      ,							)

											for model_class in self.all_model_classes:
																		lowerCamelCase_			:    List[str]   						=      True
																		lowerCamelCase_			:    int   						=      False
																		lowerCamelCase_			:    Optional[Any]   						=      False
																		lowerCamelCase_			:    str   						=      model_class(__lowercase							)
																		lowerCamelCase_			:    Any   						=      model(self._prepare_for_class(__lowercase      ,							__lowercase							)							)
																		lowerCamelCase_			:    int   						=      len(__lowercase							)
																		self.assertEqual(config.output_hidden_states      ,							__lowercase							)
																		check_encoder_attentions_output(__lowercase							)

																		if self.is_encoder_decoder:
																									lowerCamelCase_			:    Optional[int]   						=      model_class(__lowercase							)
																									lowerCamelCase_			:    Union[str, Any]   						=      model(self._prepare_for_class(__lowercase      ,							__lowercase							)							)
																									self.assertEqual(config.output_hidden_states      ,							__lowercase							)
																									check_decoder_attentions_output(__lowercase							)

																		# Check that output attentions can also be changed via the config
																		del inputs_dict["output_attentions"]
																		lowerCamelCase_			:    Any   						=      True
																		lowerCamelCase_			:    Union[str, Any]   						=      model_class(__lowercase							)
																		lowerCamelCase_			:    Tuple   						=      model(self._prepare_for_class(__lowercase      ,							__lowercase							)							)
																		self.assertEqual(config.output_hidden_states      ,							__lowercase							)
																		check_encoder_attentions_output(__lowercase							)

																		# Check attention is always last and order is fine
																		lowerCamelCase_			:    Optional[Any]   						=      True
																		lowerCamelCase_			:    Optional[int]   						=      True
																		lowerCamelCase_			:    Dict   						=      model_class(__lowercase							)
																		lowerCamelCase_			:    Union[str, Any]   						=      model(self._prepare_for_class(__lowercase      ,							__lowercase							)							)

																		self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)      ,							len(__lowercase							)							)
																		self.assertEqual(model.config.output_hidden_states      ,							__lowercase							)
																		check_encoder_attentions_output(__lowercase							)





				@unittest.skip("LED keeps using potentially symbolic tensors in conditionals and breaks tracing."							)
				def    __SCREAMING_SNAKE_CASE     (      self							:   int							)							->			Dict:
											pass





				def    __SCREAMING_SNAKE_CASE     (      self							:   int							)							->			List[Any]:
											# TODO: Head-masking not yet implement
											pass



def        __a							(  __UpperCAmelCase		:						Union[str, Any]      ) ->					int:
							"""simple docstring"""




							return tf.constant(__UpperCAmelCase   ,		dtype=tf.intaa      )


snake_case_  :      Union[str, Any]		=		1E-4





@slow
@require_tf
class 					snake_case_   (     unittest.TestCase	):



				'''simple docstring'''





				def    __SCREAMING_SNAKE_CASE     (      self							:   List[str]							)							->			Tuple:
											lowerCamelCase_			:    Dict   						=      TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"							).led

											# change to intended input here
											lowerCamelCase_			:    List[str]   						=      _long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]							)
											lowerCamelCase_			:    Dict   						=      _long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]							)
											lowerCamelCase_			:    Optional[int]   						=      prepare_led_inputs_dict(model.config      ,							__lowercase      ,							__lowercase							)
											lowerCamelCase_			:    Dict   						=      model(**__lowercase							)[0]
											lowerCamelCase_			:    Optional[int]   						=      (1, 1024, 768)
											self.assertEqual(output.shape      ,							__lowercase							)
											# change to expected output here
											lowerCamelCase_			:    Dict   						=      tf.convert_to_tensor(
											    [[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]]      ,							)
											tf.debugging.assert_near(output[:, :3, :3]      ,							__lowercase      ,							atol=1e-3							)





				def    __SCREAMING_SNAKE_CASE     (      self							:   Tuple							)							->			Union[str, Any]:
											lowerCamelCase_			:    Any   						=      TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"							)

											# change to intended input here
											lowerCamelCase_			:    Union[str, Any]   						=      _long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]							)
											lowerCamelCase_			:    Any   						=      _long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]							)
											lowerCamelCase_			:    Tuple   						=      prepare_led_inputs_dict(model.config      ,							__lowercase      ,							__lowercase							)
											lowerCamelCase_			:    List[Any]   						=      model(**__lowercase							)[0]
											lowerCamelCase_			:    str   						=      (1, 1024, model.config.vocab_size)
											self.assertEqual(output.shape      ,							__lowercase							)
											# change to expected output here
											lowerCamelCase_			:    List[str]   						=      tf.convert_to_tensor(
											    [[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]]      ,							)
											tf.debugging.assert_near(output[:, :3, :3]      ,							__lowercase      ,							atol=1e-3      ,							rtol=1e-3							)








"""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


_UpperCamelCase					      =   'sshleifer/mar_enro_6_3_student'
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""




				def        __lowercase	(  self	:List[Any]      ):
											super().setUp()

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




				@slow
				@require_torch_gpu
				def        __lowercase	(  self	:int      ):
											MarianMTModel.from_pretrained(__lowercase      )




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

											# bash_script = bash_script.replace("--fp16 ", "")
											__lowerCamelCase   :   List[Any]       =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   :   str       =['''finetune.py'''] + bash_script.split() + args
											with patch.object(__lowercase    ,     '''argv'''    ,     __lowercase      ):
																		__lowerCamelCase   :   str       =argparse.ArgumentParser()
																		__lowerCamelCase   :   Dict       =pl.Trainer.add_argparse_args(__lowercase      )
																		__lowerCamelCase   :   List[Any]       =SummarizationModule.add_model_specific_args(__lowercase    ,     os.getcwd()      )
																		__lowerCamelCase   :   int       =parser.parse_args()
																		__lowerCamelCase   :   Tuple       =main(__lowercase      )

											# Check metrics
											__lowerCamelCase   :   Union[str, Any]       =load_json(model.metrics_save_path      )
											__lowerCamelCase   :   Any       =metrics['''val'''][0]
											__lowerCamelCase   :   int       =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
											__lowerCamelCase   :   Any       =os.listdir(__lowercase      )
											__lowerCamelCase   :   Tuple       =[x for x in contents if x.endswith('''.ckpt'''      )][0]
											__lowerCamelCase   :   int       =os.path.join(args.output_dir    ,     __lowercase      )
											__lowerCamelCase   :   List[str]       =torch.load(__lowercase    ,     map_location='''cpu'''      )
											__lowerCamelCase   :   str       ='''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   :   str       ={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 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""




				@timeout_decorator.timeout(600      )
				@slow
				@require_torch_gpu
				def        __lowercase	(  self	:str      ):
											__lowerCamelCase   :   int       =f'{self.test_file_dir_str}/test_data/wmt_en_ro'
											__lowerCamelCase   :   List[Any]       ={
											    '''--fp16_opt_level=O1''': '''''',
											    '''$MAX_LEN''': 128,
											    '''$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   :   Any       =(
											    (self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split('''distillation.py'''      )[1].strip()
											)
											__lowerCamelCase   :   Optional[Any]       =bash_script.replace('''\\\n'''    ,     ''''''      ).strip().replace('''"$@"'''    ,     ''''''      )
											__lowerCamelCase   :   Union[str, Any]       =bash_script.replace('''--fp16 '''    ,     ''' '''      )

											for k, v in env_vars_to_replace.items():
																		__lowerCamelCase   :   str       =bash_script.replace(__lowercase    ,     str(__lowercase      )      )
											__lowerCamelCase   :   Any       =self.get_auto_remove_tmp_dir()
											__lowerCamelCase   :   Optional[int]       =bash_script.replace('''--fp16'''    ,     ''''''      )
											__lowerCamelCase   :   Tuple       =6
											__lowerCamelCase   :   str       =(
											    ['''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      ):
																		__lowerCamelCase   :   int       =argparse.ArgumentParser()
																		__lowerCamelCase   :   int       =pl.Trainer.add_argparse_args(__lowercase      )
																		__lowerCamelCase   :   Optional[int]       =SummarizationDistiller.add_model_specific_args(__lowercase    ,     os.getcwd()      )
																		__lowerCamelCase   :   Union[str, Any]       =parser.parse_args()
																		# assert args.gpus == gpus THIS BREAKS for multi_gpu

																		__lowerCamelCase   :   Union[str, Any]       =distill_main(__lowercase      )

											# Check metrics
											__lowerCamelCase   :   Dict       =load_json(model.metrics_save_path      )
											__lowerCamelCase   :   List[str]       =metrics['''val'''][0]
											__lowerCamelCase   :   Tuple       =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
											__lowerCamelCase   :   int       =os.listdir(__lowercase      )
											__lowerCamelCase   :   str       =[x for x in contents if x.endswith('''.ckpt'''      )][0]
											__lowerCamelCase   :   Union[str, Any]       =os.path.join(args.output_dir    ,     __lowercase      )
											__lowerCamelCase   :   Optional[int]       =torch.load(__lowercase    ,     map_location='''cpu'''      )
											__lowerCamelCase   :   int       ='''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   :   int       ={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




