

from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

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


if is_tf_available():
			import tensorflow as tf

			from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
			from PIL import Image

			from transformers import ViTImageProcessor






class      lowerCamelCase_      :




	def __init__(     self						:     str					,      __A						:     Tuple					,      __A						:     Union[str, Any]=13					,      __A						:     Union[str, Any]=30					,      __A						:     Optional[int]=2					,      __A						:     Optional[Any]=3					,      __A						:     Optional[Any]=True					,      __A						:     str=True					,      __A						:     Dict=32					,      __A						:     List[Any]=2					,      __A						:     Dict=4					,      __A						:     str=37					,      __A						:     Union[str, Any]="gelu"					,      __A						:     Optional[Any]=0.1					,      __A						:     Dict=0.1					,      __A						:     List[str]=10					,      __A						:     Optional[Any]=0.0_2					,      __A						:     Optional[int]=3					,      __A						:     Tuple=0.6					,      __A						:     Any=None					,      ):
		__A :			int		    =			parent
		__A :			str		    =			batch_size
		__A :			List[Any]		    =			image_size
		__A :			Optional[int]		    =			patch_size
		__A :			int		    =			num_channels
		__A :			Tuple		    =			is_training
		__A :			Optional[int]		    =			use_labels
		__A :			Union[str, Any]		    =			hidden_size
		__A :			Dict		    =			num_hidden_layers
		__A :			Dict		    =			num_attention_heads
		__A :			int		    =			intermediate_size
		__A :			Union[str, Any]		    =			hidden_act
		__A :			Dict		    =			hidden_dropout_prob
		__A :			List[str]		    =			attention_probs_dropout_prob
		__A :			Union[str, Any]		    =			type_sequence_label_size
		__A :			str		    =			initializer_range
		__A :			Any		    =			mask_ratio
		__A :			int		    =			scope

		# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
		# (we add 1 for the [CLS] token)
		__A :			str		    =			(image_size // patch_size) ** 2
		__A :			List[Any]		    =			int(math.ceil((1 - mask_ratio) * (num_patches + 1)							)							)




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		__A :			Optional[int]		    =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

		__A :			Union[str, Any]		    =			None
		if self.use_labels:
			__A :			Union[str, Any]		    =			ids_tensor([self.batch_size]					,      self.type_sequence_label_size							)

		__A :			Union[str, Any]		    =			self.get_config()

		return config, pixel_values, labels




	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		return ViTMAEConfig(
		    image_size=self.image_size					,      patch_size=self.patch_size					,      num_channels=self.num_channels					,      hidden_size=self.hidden_size					,      num_hidden_layers=self.num_hidden_layers					,      num_attention_heads=self.num_attention_heads					,      intermediate_size=self.intermediate_size					,      decoder_hidden_size=self.hidden_size					,      decoder_num_hidden_layers=self.num_hidden_layers					,      decoder_num_attention_heads=self.num_attention_heads					,      decoder_intermediate_size=self.intermediate_size					,      hidden_act=self.hidden_act					,      hidden_dropout_prob=self.hidden_dropout_prob					,      attention_probs_dropout_prob=self.attention_probs_dropout_prob					,      is_decoder=__A					,      initializer_range=self.initializer_range					,      mask_ratio=self.mask_ratio					,      )




	def        lowerCAmelCase_       (     self						:     List[Any]					,      __A						:     Tuple					,      __A						:     int					,      __A						:     Any							):
		__A :			int		    =			TFViTMAEModel(config=__A							)
		__A :			List[str]		    =			model(__A					,      training=__A							)
		self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size)							)




	def        lowerCAmelCase_       (     self						:     Dict					,      __A						:     str					,      __A						:     Union[str, Any]					,      __A						:     Union[str, Any]							):
		__A :			Optional[Any]		    =			TFViTMAEForPreTraining(__A							)
		__A :			Optional[Any]		    =			model(__A					,      training=__A							)
		# expected sequence length = num_patches
		__A :			Any		    =			(self.image_size // self.patch_size) ** 2
		__A :			str		    =			self.patch_size**2 * self.num_channels
		self.parent.assertEqual(result.logits.shape					,      (self.batch_size, num_patches, expected_num_channels)							)

		# test greyscale images
		__A :			Tuple		    =			1
		__A :			Optional[int]		    =			TFViTMAEForPreTraining(__A							)

		__A :			List[Any]		    =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
		__A :			List[str]		    =			model(__A					,      training=__A							)
		__A :			List[str]		    =			self.patch_size**2
		self.parent.assertEqual(result.logits.shape					,      (self.batch_size, num_patches, expected_num_channels)							)




	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		__A :			int		    =			self.prepare_config_and_inputs()
		((__A)    ,				(__A)    ,				(__A)) :			int		    =			config_and_inputs
		__A :			Optional[Any]		    =			{"""pixel_values""": pixel_values}
		return config, inputs_dict






@require_tf
class      lowerCamelCase_      (	_lowercase						,      _lowercase						,      unittest.TestCase	):
	_lowercase		:   Any     					=				(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
	_lowercase		:   Optional[Any]     					=				{'''feature-extraction''': TFViTMAEModel} if is_tf_available() else {}

	_lowercase		:   Optional[int]     					=				False
	_lowercase		:   Optional[int]     					=				False
	_lowercase		:   Tuple     					=				False
	_lowercase		:   List[str]     					=				False




	def        lowerCAmelCase_       (     self						:     Tuple							):
		__A :			Optional[Any]		    =			TFViTMAEModelTester(self							)
		__A :			Optional[Any]		    =			ConfigTester(self					,      config_class=__A					,      has_text_modality=__A					,      hidden_size=37							)




	def        lowerCAmelCase_       (     self						:     int							):
		self.config_tester.run_common_tests()




	@unittest.skip(reason="""ViTMAE does not use inputs_embeds"""							)
	def        lowerCAmelCase_       (     self						:     List[str]							):
		pass




	def        lowerCAmelCase_       (     self						:     str							):
		__A    ,				__A :			Optional[Any]		    =			self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			__A :			Optional[int]		    =			model_class(__A							)
			self.assertIsInstance(model.get_input_embeddings()					,      (tf.keras.layers.Layer)							)
			__A :			Dict		    =			model.get_output_embeddings()
			self.assertTrue(x is None or isinstance(__A					,      tf.keras.layers.Layer							)							)




	def        lowerCAmelCase_       (     self						:     Any							):
		__A    ,				__A :			Optional[int]		    =			self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			__A :			int		    =			model_class(__A							)
			__A :			Optional[Any]		    =			inspect.signature(model.call							)
			# signature.parameters is an OrderedDict => so arg_names order is deterministic
			__A :			Any		    =			[*signature.parameters.keys()]

			__A :			str		    =			["""pixel_values"""]
			self.assertListEqual(arg_names[:1]					,      __A							)




	def        lowerCAmelCase_       (     self						:     str							):
		__A :			Optional[int]		    =			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*__A							)




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		__A :			int		    =			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_pretraining(*__A							)




	def        lowerCAmelCase_       (     self						:     Tuple							):
		# make the mask reproducible
		np.random.seed(2							)

		__A    ,				__A :			Tuple		    =			self.model_tester.prepare_config_and_inputs_for_common()

		__A :			Union[str, Any]		    =			int((config.image_size // config.patch_size) ** 2							)
		__A :			Dict		    =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)

		for model_class in self.all_model_classes:
			__A :			str		    =			model_class(__A							)
			__A :			int		    =			self._prepare_for_class(__A					,      __A							)

			__A :			str		    =			model(__A					,      noise=__A							)

			__A :			Optional[int]		    =			copy.deepcopy(self._prepare_for_class(__A					,      __A							)							)
			__A :			Optional[Any]		    =			model(**__A					,      noise=__A							)
			__A :			int		    =			outputs_dict[0].numpy()
			__A :			Union[str, Any]		    =			outputs_keywords[0].numpy()

			self.assertLess(np.sum(np.abs(output_dict - output_keywords							)							)					,      1e-6							)




	def        lowerCAmelCase_       (     self						:     int							):
		# make the mask reproducible
		np.random.seed(2							)

		__A    ,				__A :			List[str]		    =			self.model_tester.prepare_config_and_inputs_for_common()

		__A :			str		    =			int((config.image_size // config.patch_size) ** 2							)
		__A :			Union[str, Any]		    =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)

		def prepare_numpy_arrays(__A						:     Union[str, Any]							):
			__A :			Union[str, Any]		    =			{}
			for k, v in inputs_dict.items():
				if tf.is_tensor(__A							):
					__A :			Dict		    =			v.numpy()
				else:
					__A :			str		    =			np.array(__A							)

			return inputs_np_dict

		for model_class in self.all_model_classes:
			__A :			Dict		    =			model_class(__A							)

			__A :			Optional[Any]		    =			self._prepare_for_class(__A					,      __A							)
			__A :			Union[str, Any]		    =			prepare_numpy_arrays(__A							)

			__A :			List[str]		    =			model(__A					,      noise=__A							)
			__A :			Union[str, Any]		    =			model(**__A					,      noise=__A							)
			self.assert_outputs_same(__A					,      __A							)




	def        lowerCAmelCase_       (     self						:     Dict					,      __A						:     Union[str, Any]					,      __A						:     Union[str, Any]					,      __A						:     Dict							):
		# make masks reproducible
		np.random.seed(2							)

		__A :			Tuple		    =			int((tf_model.config.image_size // tf_model.config.patch_size) ** 2							)
		__A :			List[str]		    =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)
		__A :			Optional[Any]		    =			tf.constant(__A							)

		# Add `noise` argument.
		# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
		__A :			List[Any]		    =			tf_noise

		super().check_pt_tf_models(__A					,      __A					,      __A							)




	def        lowerCAmelCase_       (     self						:     Dict							):
		# make mask reproducible
		np.random.seed(2							)

		__A    ,				__A :			Union[str, Any]		    =			self.model_tester.prepare_config_and_inputs_for_common()

		__A :			Optional[int]		    =			{
		    module_member
		    for model_class in self.all_model_classes
		    for module in (import_module(model_class.__module__							),)
		    for module_member_name in dir(__A							)
		    if module_member_name.endswith("""MainLayer"""							)
		    # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
		    and module_member_name[: -len("""MainLayer"""							)] == model_class.__name__[: -len("""Model"""							)]
		    for module_member in (getattr(__A					,      __A							),)
		    if isinstance(__A					,      __A							)
		    and tf.keras.layers.Layer in module_member.__bases__
		    and getattr(__A					,      """_keras_serializable"""					,      __A							)
		}

		__A :			Optional[int]		    =			int((config.image_size // config.patch_size) ** 2							)
		__A :			Optional[Any]		    =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)
		__A :			List[str]		    =			tf.convert_to_tensor(__A							)
		inputs_dict.update({"""noise""": noise}							)

		for main_layer_class in tf_main_layer_classes:
			__A :			Union[str, Any]		    =			main_layer_class(__A							)

			__A :			Tuple		    =			{
			    name: tf.keras.Input(tensor.shape[1:]					,      dtype=tensor.dtype							) for name, tensor in inputs_dict.items()
			}

			__A :			List[str]		    =			tf.keras.Model(__A					,      outputs=main_layer(__A							)							)
			__A :			Union[str, Any]		    =			model(__A							)

			with tempfile.TemporaryDirectory() as tmpdirname:
				__A :			List[str]		    =			os.path.join(__A					,      """keras_model.h5"""							)
				model.save(__A							)
				__A :			List[Any]		    =			tf.keras.models.load_model(
				    __A					,      custom_objects={main_layer_class.__name__: main_layer_class}							)
				assert isinstance(__A					,      tf.keras.Model							)
				__A :			str		    =			model(__A							)
				self.assert_outputs_same(__A					,      __A							)




	@slow
	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		# make mask reproducible
		np.random.seed(2							)

		__A    ,				__A :			Tuple		    =			self.model_tester.prepare_config_and_inputs_for_common()

		__A :			List[str]		    =			int((config.image_size // config.patch_size) ** 2							)
		__A :			Optional[int]		    =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)

		for model_class in self.all_model_classes:
			__A :			Any		    =			model_class(__A							)
			__A :			Dict		    =			self._prepare_for_class(__A					,      __A							)
			__A :			Optional[Any]		    =			model(__A					,      noise=__A							)

			if model_class.__name__ == "TFViTMAEModel":
				__A :			List[str]		    =			outputs.last_hidden_state.numpy()
				__A :			Optional[Any]		    =			0
			else:
				__A :			List[str]		    =			outputs.logits.numpy()
				__A :			List[str]		    =			0

			with tempfile.TemporaryDirectory() as tmpdirname:
				model.save_pretrained(__A					,      saved_model=__A							)
				__A :			List[str]		    =			model_class.from_pretrained(__A							)
				__A :			str		    =			model(__A					,      noise=__A							)

				if model_class.__name__ == "TFViTMAEModel":
					__A :			Dict		    =			after_outputs["""last_hidden_state"""].numpy()
					__A :			List[str]		    =			0
				else:
					__A :			Optional[int]		    =			after_outputs["""logits"""].numpy()
					__A :			Dict		    =			0

				__A :			Optional[int]		    =			np.amax(np.abs(out_a - out_a							)							)
				self.assertLessEqual(__A					,      1e-5							)




	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		# make mask reproducible
		np.random.seed(2							)

		__A    ,				__A :			List[Any]		    =			self.model_tester.prepare_config_and_inputs_for_common()

		__A :			List[str]		    =			int((config.image_size // config.patch_size) ** 2							)
		__A :			Tuple		    =			np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)

		for model_class in self.all_model_classes:
			__A :			Tuple		    =			model_class(__A							)
			__A :			Optional[int]		    =			self._prepare_for_class(__A					,      __A							)

			__A :			Tuple		    =			model(__A					,      noise=__A							)
			__A :			int		    =			model.get_config()
			# make sure that returned config is jsonifiable, which is required by keras
			json.dumps(__A							)
			__A :			Dict		    =			model_class.from_config(model.get_config()							)
			# make sure it also accepts a normal config
			__A :			Tuple		    =			model_class.from_config(model.config							)
			__A :			Union[str, Any]		    =			new_model(__A							)  # Build model
			new_model.set_weights(model.get_weights()							)
			__A :			Optional[int]		    =			new_model(__A					,      noise=__A							)

			self.assert_outputs_same(__A					,      __A							)




	@unittest.skip(
	    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""							)
	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		pass




	@unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""							)
	def        lowerCAmelCase_       (     self						:     int							):
		pass




	@slow
	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		__A :			Dict		    =			TFViTMAEModel.from_pretrained("""google/vit-base-patch16-224"""							)
		self.assertIsNotNone(__A							)






def 							__SCREAMING_SNAKE_CASE     (   )					->				Optional[Any]:
	__A :			Any		    =			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
	return image







@require_tf
@require_vision
class      lowerCamelCase_      (	unittest.TestCase	):




	@cached_property
	def        lowerCAmelCase_       (     self						:     int							):
		return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""							) if is_vision_available() else None




	@slow
	def        lowerCAmelCase_       (     self						:     int							):
		# make random mask reproducible across the PT and TF model
		np.random.seed(2							)

		__A :			List[Any]		    =			TFViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""							)

		__A :			List[Any]		    =			self.default_image_processor
		__A :			Union[str, Any]		    =			prepare_img()
		__A :			Union[str, Any]		    =			image_processor(images=__A					,      return_tensors="""tf"""							)

		# prepare a noise vector that will be also used for testing the TF model
		# (this way we can ensure that the PT and TF models operate on the same inputs)
		__A :			Dict		    =			ViTMAEConfig()
		__A :			List[str]		    =			int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2							)
		__A :			List[Any]		    =			np.random.uniform(size=(1, num_patches)							)

		# forward pass
		__A :			int		    =			model(**__A					,      noise=__A							)

		# verify the logits
		__A :			Optional[Any]		    =			tf.convert_to_tensor([1, 196, 768]							)
		self.assertEqual(outputs.logits.shape					,      __A							)

		__A :			Optional[Any]		    =			tf.convert_to_tensor(
		    [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]]							)

		tf.debugging.assert_near(outputs.logits[0, :3, :3]					,      __A					,      atol=1e-4							)












"""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




