






import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate





class __lowerCamelCase					( snake_case__):





	"""simple docstring"""






	UpperCamelCase__								= CONFIG_NAME

	@register_to_config
	def __init__(				self			,       UpperCAmelCase = True			,       UpperCAmelCase = 8			,       UpperCAmelCase = "lanczos"			,       UpperCAmelCase = True			,       UpperCAmelCase = False			,       ):
							"""simple docstring"""







							super().__init__()

	@staticmethod
	def 			UpperCamelCase       (				UpperCAmelCase       ):
							"""simple docstring"""







							if images.ndim == 3:
													_UpperCAmelCase							   =     images[None, ...]
							_UpperCAmelCase							   =     (images * 255).round().astype('uint8'       )
							if images.shape[-1] == 1:
													# special case for grayscale (single channel) images
													_UpperCAmelCase							   =     [Image.fromarray(image.squeeze()			,       mode='L'       ) for image in images]
							else:
													_UpperCAmelCase							   =     [Image.fromarray(UpperCAmelCase       ) for image in images]

							return pil_images

	@staticmethod
	def 			UpperCamelCase       (				UpperCAmelCase       ):
							"""simple docstring"""







							if not isinstance(UpperCAmelCase			,       UpperCAmelCase       ):
													_UpperCAmelCase							   =     [images]
							_UpperCAmelCase							   =     [np.array(UpperCAmelCase       ).astype(np.floataa       ) / 2_55.0 for image in images]
							_UpperCAmelCase							   =     np.stack(UpperCAmelCase			,       axis=0       )

							return images

	@staticmethod
	def 			UpperCamelCase       (				UpperCAmelCase       ):
							"""simple docstring"""







							if images.ndim == 3:
													_UpperCAmelCase							   =     images[..., None]

							_UpperCAmelCase							   =     torch.from_numpy(images.transpose(0			,       3			,       1			,       2       )       )
							return images

	@staticmethod
	def 			UpperCamelCase       (				UpperCAmelCase       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     images.cpu().permute(0			,       2			,       3			,       1       ).float().numpy()
							return images

	@staticmethod
	def 			UpperCamelCase       (				UpperCAmelCase       ):
							"""simple docstring"""







							return 2.0 * images - 1.0

	@staticmethod
	def 			UpperCamelCase       (				UpperCAmelCase       ):
							"""simple docstring"""







							return (images / 2 + 0.5).clamp(0			,       1       )

	@staticmethod
	def 			UpperCamelCase       (				UpperCAmelCase       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     image.convert('RGB'       )
							return image

	def 			UpperCamelCase       (				self			,       UpperCAmelCase			,       UpperCAmelCase = None			,       UpperCAmelCase = None			,       ):
							"""simple docstring"""







							if height is None:
													_UpperCAmelCase							   =     image.height
							if width is None:
													_UpperCAmelCase							   =     image.width

							_UpperCAmelCase   ,					_UpperCAmelCase							   =     (
							    x - x % self.config.vae_scale_factor for x in (width, height)
							)  # resize to integer multiple of vae_scale_factor
							_UpperCAmelCase							   =     image.resize((width, height)			,       resample=PIL_INTERPOLATION[self.config.resample]       )
							return image

	def 			UpperCamelCase       (				self			,       UpperCAmelCase			,       UpperCAmelCase = None			,       UpperCAmelCase = None			,       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     (PIL.Image.Image, np.ndarray, torch.Tensor)
							if isinstance(UpperCAmelCase			,       UpperCAmelCase       ):
													_UpperCAmelCase							   =     [image]
							elif not (isinstance(UpperCAmelCase			,       UpperCAmelCase       ) and all(isinstance(UpperCAmelCase			,       UpperCAmelCase       ) for i in image       )):
													raise ValueError(
													    F"""Input is in incorrect format: {[type(UpperCAmelCase       ) for i in image]}. Currently, we only support {", ".join(UpperCAmelCase       )}"""       )

							if isinstance(image[0]			,       PIL.Image.Image       ):
													if self.config.do_convert_rgb:
																			_UpperCAmelCase							   =     [self.convert_to_rgb(UpperCAmelCase       ) for i in image]
													if self.config.do_resize:
																			_UpperCAmelCase							   =     [self.resize(UpperCAmelCase			,       UpperCAmelCase			,       UpperCAmelCase       ) for i in image]
													_UpperCAmelCase							   =     self.pil_to_numpy(UpperCAmelCase       )  # to np
													_UpperCAmelCase							   =     self.numpy_to_pt(UpperCAmelCase       )  # to pt

							elif isinstance(image[0]			,       np.ndarray       ):
													_UpperCAmelCase							   =     np.concatenate(UpperCAmelCase			,       axis=0       ) if image[0].ndim == 4 else np.stack(UpperCAmelCase			,       axis=0       )
													_UpperCAmelCase							   =     self.numpy_to_pt(UpperCAmelCase       )
													_UpperCAmelCase   ,					_UpperCAmelCase   ,					_UpperCAmelCase   ,					_UpperCAmelCase							   =     image.shape
													if self.config.do_resize and (
													    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
													):
																			raise ValueError(
																			    F"""Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"""
																			    F"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""       )

							elif isinstance(image[0]			,       torch.Tensor       ):
													_UpperCAmelCase							   =     torch.cat(UpperCAmelCase			,       axis=0       ) if image[0].ndim == 4 else torch.stack(UpperCAmelCase			,       axis=0       )
													_UpperCAmelCase   ,					_UpperCAmelCase   ,					_UpperCAmelCase   ,					_UpperCAmelCase							   =     image.shape

													# don't need any preprocess if the image is latents
													if channel == 4:
																			return image

													if self.config.do_resize and (
													    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
													):
																			raise ValueError(
																			    F"""Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"""
																			    F"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""       )

        # expected range [0,1], normalize to [-1,1]
							_UpperCAmelCase							   =     self.config.do_normalize
							if image.min() < 0:
													warnings.warn(
													    'Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '
													    F"""when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"""			,       UpperCAmelCase			,       )
													_UpperCAmelCase							   =     False

							if do_normalize:
													_UpperCAmelCase							   =     self.normalize(UpperCAmelCase       )

							return image

	def 			UpperCamelCase       (				self			,       UpperCAmelCase			,       UpperCAmelCase = "pil"			,       UpperCAmelCase = None			,       ):
							"""simple docstring"""







							if not isinstance(UpperCAmelCase			,       torch.Tensor       ):
													raise ValueError(
													    F"""Input for postprocessing is in incorrect format: {type(UpperCAmelCase       )}. We only support pytorch tensor"""       )
							if output_type not in ["latent", "pt", "np", "pil"]:
													_UpperCAmelCase							   =     (
													    F"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
													    '`pil`, `np`, `pt`, `latent`'
													)
													deprecate('Unsupported output_type'			,       '1.0.0'			,       UpperCAmelCase			,       standard_warn=UpperCAmelCase       )
													_UpperCAmelCase							   =     'np'

							if output_type == "latent":
													return image

							if do_denormalize is None:
													_UpperCAmelCase							   =     [self.config.do_normalize] * image.shape[0]

							_UpperCAmelCase							   =     torch.stack(
							    [self.denormalize(image[i]       ) if do_denormalize[i] else image[i] for i in range(image.shape[0]       )]       )

							if output_type == "pt":
													return image

							_UpperCAmelCase							   =     self.pt_to_numpy(UpperCAmelCase       )

							if output_type == "np":
													return image

							if output_type == "pil":
													return self.numpy_to_pil(UpperCAmelCase       )





class __lowerCamelCase					( snake_case__):





	"""simple docstring"""






	UpperCamelCase__								= CONFIG_NAME

	@register_to_config
	def __init__(				self			,       UpperCAmelCase = True			,       UpperCAmelCase = 8			,       UpperCAmelCase = "lanczos"			,       UpperCAmelCase = True			,       ):
							"""simple docstring"""







							super().__init__()

	@staticmethod
	def 			UpperCamelCase       (				UpperCAmelCase       ):
							"""simple docstring"""







							if images.ndim == 3:
													_UpperCAmelCase							   =     images[None, ...]
							_UpperCAmelCase							   =     (images * 255).round().astype('uint8'       )
							if images.shape[-1] == 1:
													# special case for grayscale (single channel) images
													_UpperCAmelCase							   =     [Image.fromarray(image.squeeze()			,       mode='L'       ) for image in images]
							else:
													_UpperCAmelCase							   =     [Image.fromarray(image[:, :, :3]       ) for image in images]

							return pil_images

	@staticmethod
	def 			UpperCamelCase       (				UpperCAmelCase       ):
							"""simple docstring"""







							return image[:, :, 1] * 2**8 + image[:, :, 2]

	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							if images.ndim == 3:
													_UpperCAmelCase							   =     images[None, ...]
							_UpperCAmelCase							   =     images[:, :, :, 3:]
							if images.shape[-1] == 6:
													_UpperCAmelCase							   =     (images_depth * 255).round().astype('uint8'       )
													_UpperCAmelCase							   =     [
													    Image.fromarray(self.rgblike_to_depthmap(UpperCAmelCase       )			,       mode='I;16'       ) for image_depth in images_depth
													]
							elif images.shape[-1] == 4:
													_UpperCAmelCase							   =     (images_depth * 6_55_35.0).astype(np.uintaa       )
													_UpperCAmelCase							   =     [Image.fromarray(UpperCAmelCase			,       mode='I;16'       ) for image_depth in images_depth]
							else:
													raise Exception('Not supported'       )

							return pil_images

	def 			UpperCamelCase       (				self			,       UpperCAmelCase			,       UpperCAmelCase = "pil"			,       UpperCAmelCase = None			,       ):
							"""simple docstring"""







							if not isinstance(UpperCAmelCase			,       torch.Tensor       ):
													raise ValueError(
													    F"""Input for postprocessing is in incorrect format: {type(UpperCAmelCase       )}. We only support pytorch tensor"""       )
							if output_type not in ["latent", "pt", "np", "pil"]:
													_UpperCAmelCase							   =     (
													    F"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
													    '`pil`, `np`, `pt`, `latent`'
													)
													deprecate('Unsupported output_type'			,       '1.0.0'			,       UpperCAmelCase			,       standard_warn=UpperCAmelCase       )
													_UpperCAmelCase							   =     'np'

							if do_denormalize is None:
													_UpperCAmelCase							   =     [self.config.do_normalize] * image.shape[0]

							_UpperCAmelCase							   =     torch.stack(
							    [self.denormalize(image[i]       ) if do_denormalize[i] else image[i] for i in range(image.shape[0]       )]       )

							_UpperCAmelCase							   =     self.pt_to_numpy(UpperCAmelCase       )

							if output_type == "np":
													if image.shape[-1] == 6:
																			_UpperCAmelCase							   =     np.stack([self.rgblike_to_depthmap(im[:, :, 3:]       ) for im in image]			,       axis=0       )
													else:
																			_UpperCAmelCase							   =     image[:, :, :, 3:]
													return image[:, :, :, :3], image_depth

							if output_type == "pil":
													return self.numpy_to_pil(UpperCAmelCase       ), self.numpy_to_depth(UpperCAmelCase       )
							else:
													raise Exception(F"""This type {output_type} is not supported"""       )








import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

_a       =    logging.getLogger()
_a       =    torch.cuda.is_available()
_a       =    {
    '''max_tokens_per_batch''': None,
    '''supervise_forward''': True,
    '''normalize_hidden''': True,
    '''label_smoothing''': 0.2,
    '''eval_max_gen_length''': None,
    '''eval_beams''': 1,
    '''val_metric''': '''loss''',
    '''save_top_k''': 1,
    '''adafactor''': True,
    '''early_stopping_patience''': 2,
    '''logger_name''': '''default''',
    '''length_penalty''': 0.5,
    '''cache_dir''': '''''',
    '''task''': '''summarization''',
    '''num_workers''': 2,
    '''alpha_hid''': 0,
    '''freeze_embeds''': True,
    '''enc_only''': False,
    '''tgt_suffix''': '''''',
    '''resume_from_checkpoint''': None,
    '''sortish_sampler''': True,
    '''student_decoder_layers''': 1,
    '''val_check_interval''': 1.0,
    '''output_dir''': '''''',
    '''fp16''': False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    '''no_teacher''': False,
    '''fp16_opt_level''': '''O1''',
    '''gpus''': 1 if CUDA_AVAILABLE else 0,
    '''n_tpu_cores''': 0,
    '''max_grad_norm''': 1.0,
    '''do_train''': True,
    '''do_predict''': True,
    '''accumulate_grad_batches''': 1,
    '''server_ip''': '''''',
    '''server_port''': '''''',
    '''seed''': 42,
    '''model_name_or_path''': '''sshleifer/bart-tiny-random''',
    '''config_name''': '''''',
    '''tokenizer_name''': '''facebook/bart-large''',
    '''do_lower_case''': False,
    '''learning_rate''': 0.3,
    '''lr_scheduler''': '''linear''',
    '''weight_decay''': 0.0,
    '''adam_epsilon''': 1e-08,
    '''warmup_steps''': 0,
    '''max_epochs''': 1,
    '''train_batch_size''': 2,
    '''eval_batch_size''': 2,
    '''max_source_length''': 12,
    '''max_target_length''': 12,
    '''val_max_target_length''': 12,
    '''test_max_target_length''': 12,
    '''fast_dev_run''': False,
    '''no_cache''': False,
    '''n_train''': -1,
    '''n_val''': -1,
    '''n_test''': -1,
    '''student_encoder_layers''': 1,
    '''freeze_encoder''': False,
    '''auto_scale_batch_size''': False,
    '''overwrite_output_dir''': False,
    '''student''': None,
}


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase      )->    Dict:





						"""simple docstring"""



						_UpperCAmelCase							   =     '\n'.join(__lowerCAmelCase      )
						Path(__lowerCAmelCase      ).open('w'      ).writelines(__lowerCAmelCase      )


_a       =    [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
_a       =    ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
_a       =    '''patrickvonplaten/t5-tiny-random'''
_a       =    '''sshleifer/t5-tinier-random'''
_a       =    '''sshleifer/bart-tiny-random'''
_a       =    '''sshleifer/tiny-mbart'''
_a       =    '''sshleifer/tiny-marian-en-de'''
_a       =    '''stas/tiny-wmt19-en-de'''


_a       =    logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks


def __A       ( __lowerCAmelCase      )->    Tuple:





						"""simple docstring"""



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





class __lowerCamelCase					( snake_case__):





	"""simple docstring"""






	@classmethod
	def 			UpperCamelCase       (				cls       ):
							"""simple docstring"""







							logging.disable(logging.CRITICAL       )  # remove noisy download output from tracebacks
							return cls

	@slow
	@require_torch_gpu
	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     list_models()
							_UpperCAmelCase							   =     'sshleifer'
							_UpperCAmelCase							   =     [x.modelId for x in model_list if x.modelId.startswith(UpperCAmelCase       )]
							_UpperCAmelCase							   =     ['sshleifer/blenderbot-3B', 'sshleifer/blenderbot-90M']
							_UpperCAmelCase							   =     []
							for m in model_ids:
													if m in allowed_to_be_broken:
																			continue
													try:
																			AutoConfig.from_pretrained(UpperCAmelCase       )
													except Exception:
																			failures.append(UpperCAmelCase       )
							assert not failures, F"""The following models could not be loaded through AutoConfig: {failures}"""

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     {'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
							self._test_distiller_cli(UpperCAmelCase       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     {
							    'student_encoder_layers': 2,
							    'student_decoder_layers': 1,
							    'max_epochs': 4,
							    'val_check_interval': 0.25,
							    'alpha_hid': 2.0,
							    'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
							}
							_UpperCAmelCase							   =     self._test_distiller_cli(UpperCAmelCase			,       check_contents=UpperCAmelCase       )

							_UpperCAmelCase							   =     list(Path(model.output_dir       ).glob('*.ckpt'       )       )
							self.assertEqual(1			,       len(UpperCAmelCase       )       )
							_UpperCAmelCase							   =     list(Path(model.output_dir       ).glob('**/*.bin'       )       )
							self.assertEqual(len(UpperCAmelCase       )			,       2       )
							_UpperCAmelCase							   =     lmap(str.strip			,       Path(model.hparams.data_dir       ).joinpath('test.source'       ).open().readlines()       )
							_UpperCAmelCase							   =     tempfile.mktemp()  # XXX: not being cleaned up
							generate_summaries_or_translations(UpperCAmelCase			,       UpperCAmelCase			,       str(model.output_dir / 'best_tfmr'       )       )
							self.assertTrue(Path(UpperCAmelCase       ).exists()       )

							_UpperCAmelCase							   =     self.get_auto_remove_tmp_dir()
							convert_pl_to_hf(ckpts[0]			,       transformer_ckpts[0].parent			,       UpperCAmelCase       )
							assert os.path.exists(os.path.join(UpperCAmelCase			,       'pytorch_model.bin'       )       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     AutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase       )
							_UpperCAmelCase   ,					_UpperCAmelCase							   =     model.dummy_inputs['input_ids'], model.dummy_inputs['attention_mask']
							_UpperCAmelCase							   =     torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]			,       dtype=torch.long			,       device=model.device       )
							_UpperCAmelCase							   =     target_ids[:, :-1].contiguous()  # Why this line?
							_UpperCAmelCase							   =     target_ids[:, 1:].clone()  # why clone?
							_UpperCAmelCase							   =     model(
							    UpperCAmelCase			,       attention_mask=UpperCAmelCase			,       decoder_input_ids=UpperCAmelCase			,       labels=UpperCAmelCase			,       use_cache=UpperCAmelCase       ).loss

							_UpperCAmelCase							   =     model(UpperCAmelCase			,       attention_mask=UpperCAmelCase			,       decoder_input_ids=UpperCAmelCase			,       use_cache=UpperCAmelCase       ).logits

							_UpperCAmelCase							   =     nn.functional.log_softmax(UpperCAmelCase			,       dim=-1       )
							_UpperCAmelCase   ,					_UpperCAmelCase							   =     label_smoothed_nll_loss(
							    UpperCAmelCase			,       UpperCAmelCase			,       0.1			,       ignore_index=model.config.pad_token_id       )
							with self.assertRaises(UpperCAmelCase       ):
													# TODO: understand why this breaks
													self.assertEqual(UpperCAmelCase			,       UpperCAmelCase       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     {
							    'student_encoder_layers': 2,
							    'student_decoder_layers': 1,
							    'num_train_epochs': 4,
							    'val_check_interval': 0.25,
							    'alpha_hid': 2.0,
							    'task': 'translation',
							    'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
							    'tokenizer_name': MBART_TINY,
							    'teacher': MBART_TINY,
							    'src_lang': 'en_XX',
							    'tgt_lang': 'ro_RO',
							}
							_UpperCAmelCase							   =     self._test_distiller_cli(UpperCAmelCase			,       check_contents=UpperCAmelCase       )
							assert model.model.config.model_type == "mbart"

							_UpperCAmelCase							   =     list(Path(model.output_dir       ).glob('*.ckpt'       )       )
							self.assertEqual(1			,       len(UpperCAmelCase       )       )
							_UpperCAmelCase							   =     list(Path(model.output_dir       ).glob('**/*.bin'       )       )
							_UpperCAmelCase							   =     list(Path(model.output_dir       ).glob('best_tfmr/*'       )       )
							assert len(UpperCAmelCase       ) > 2
							self.assertEqual(len(UpperCAmelCase       )			,       2       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     {
							    'student_encoder_layers': 1,
							    'student_decoder_layers': 1,
							    'alpha_hid': 2.0,
							    'teacher': T5_TINY,
							    'model_name_or_path': T5_TINY,
							    'tokenizer_name': T5_TINY,
							}
							self._test_distiller_cli(UpperCAmelCase       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     {
							    'teacher': T5_TINY,
							    'student': T5_TINIER,
							    'model_name_or_path': T5_TINIER,
							    'tokenizer_name': T5_TINIER,
							}
							self._test_distiller_cli(UpperCAmelCase       )

	def 			UpperCamelCase       (				self			,       UpperCAmelCase			,       UpperCAmelCase=True       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     {
							    'label_smoothing': 0.0,
							    'early_stopping_patience': -1,
							    'train_batch_size': 1,
							    'eval_batch_size': 2,
							    'max_epochs': 2,
							    'alpha_mlm': 0.2,
							    'alpha_ce': 0.8,
							    'do_predict': True,
							    'model_name_or_path': 'sshleifer/tinier_bart',
							    'teacher': CHEAP_ARGS['model_name_or_path'],
							    'val_check_interval': 0.5,
							}
							default_updates.update(UpperCAmelCase       )
							_UpperCAmelCase							   =     CHEAP_ARGS.copy()
							_UpperCAmelCase							   =     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
							_UpperCAmelCase							   =     self.get_auto_remove_tmp_dir()

							args_d.update(data_dir=UpperCAmelCase			,       output_dir=UpperCAmelCase			,       **UpperCAmelCase       )
							_UpperCAmelCase							   =     distill_main(argparse.Namespace(**UpperCAmelCase       )       )
							if not check_contents:
													return model
							_UpperCAmelCase							   =     os.listdir(UpperCAmelCase       )
							_UpperCAmelCase							   =     {os.path.basename(UpperCAmelCase       ) for p in contents}
							_UpperCAmelCase							   =     [p for p in contents if p.endswith('ckpt'       )]
							assert len(UpperCAmelCase       ) > 0

							self.assertIn('test_generations.txt'			,       UpperCAmelCase       )
							self.assertIn('test_results.txt'			,       UpperCAmelCase       )

							_UpperCAmelCase							   =     load_json(model.metrics_save_path       )
							_UpperCAmelCase							   =     metrics['val'][-1]
							self.assertGreaterEqual(last_step_stats['val_avg_gen_time']			,       0.01       )
							self.assertGreaterEqual(1.0			,       last_step_stats['val_avg_gen_time']       )
							self.assertIsInstance(last_step_stats[F"""val_avg_{model.val_metric}"""]			,       UpperCAmelCase       )
							_UpperCAmelCase							   =     int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) + 1       )
							self.assertEqual(len(metrics['val']       )			,       UpperCAmelCase       )
							self.assertEqual(len(metrics['test']       )			,       1       )
							return model





class __lowerCamelCase					( snake_case__):





	"""simple docstring"""






	@parameterized.expand(
	    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]			,       )
	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     CHEAP_ARGS.copy()
							_UpperCAmelCase							   =     'translation' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else 'summarization'
							_UpperCAmelCase							   =     0.1 if task == 'translation' else 0

							_UpperCAmelCase							   =     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
							_UpperCAmelCase							   =     self.get_auto_remove_tmp_dir()
							args_d.update(
							    data_dir=UpperCAmelCase			,       model_name_or_path=UpperCAmelCase			,       tokenizer_name=UpperCAmelCase			,       train_batch_size=2			,       eval_batch_size=2			,       output_dir=UpperCAmelCase			,       do_predict=UpperCAmelCase			,       task=UpperCAmelCase			,       src_lang='en_XX'			,       tgt_lang='ro_RO'			,       freeze_encoder=UpperCAmelCase			,       freeze_embeds=UpperCAmelCase			,       )
							assert "n_train" in args_d
							_UpperCAmelCase							   =     argparse.Namespace(**UpperCAmelCase       )
							_UpperCAmelCase							   =     main(UpperCAmelCase       )

							_UpperCAmelCase							   =     module.model.get_input_embeddings()
							assert not input_embeds.weight.requires_grad
							if model == T5_TINY:
													_UpperCAmelCase							   =     module.model.lm_head
													assert not lm_head.weight.requires_grad
													assert (lm_head.weight == input_embeds.weight).all().item()
							elif model == FSMT_TINY:
													_UpperCAmelCase							   =     module.model.model
													_UpperCAmelCase							   =     fsmt.decoder.embed_positions
													assert not embed_pos.weight.requires_grad
													assert not fsmt.decoder.embed_tokens.weight.requires_grad
													# check that embeds are not the same
													assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
							else:
													_UpperCAmelCase							   =     module.model.model
													_UpperCAmelCase							   =     bart.decoder.embed_positions
													assert not embed_pos.weight.requires_grad
													assert not bart.shared.weight.requires_grad
													# check that embeds are the same
													assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
													assert bart.decoder.embed_tokens == bart.shared

							_UpperCAmelCase							   =     load_json(module.output_dir / 'text_batch.json'       )
							assert isinstance(UpperCAmelCase			,       UpperCAmelCase       )
							assert len(UpperCAmelCase       ) >= 4

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     CHEAP_ARGS.copy()

							_UpperCAmelCase							   =     'summarization'
							_UpperCAmelCase							   =     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )

							args_d.update(
							    data_dir=UpperCAmelCase			,       tokenizer_name=UpperCAmelCase			,       train_batch_size=2			,       eval_batch_size=2			,       do_predict=UpperCAmelCase			,       task=UpperCAmelCase			,       src_lang='en_XX'			,       tgt_lang='ro_RO'			,       freeze_encoder=UpperCAmelCase			,       freeze_embeds=UpperCAmelCase			,       )

							# test models whose config includes the extra_model_args
							_UpperCAmelCase							   =     BART_TINY
							_UpperCAmelCase							   =     self.get_auto_remove_tmp_dir()
							_UpperCAmelCase							   =     args_d.copy()
							args_da.update(
							    model_name_or_path=UpperCAmelCase			,       output_dir=UpperCAmelCase			,       )
							_UpperCAmelCase							   =     ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
							for p in extra_model_params:
													_UpperCAmelCase							   =     0.5
							_UpperCAmelCase							   =     argparse.Namespace(**UpperCAmelCase       )
							_UpperCAmelCase							   =     main(UpperCAmelCase       )
							for p in extra_model_params:
													assert getattr(model.config			,       UpperCAmelCase       ) == 0.5, F"""failed to override the model config for param {p}"""

							# test models whose config doesn't include the extra_model_args
							_UpperCAmelCase							   =     T5_TINY
							_UpperCAmelCase							   =     self.get_auto_remove_tmp_dir()
							_UpperCAmelCase							   =     args_d.copy()
							args_da.update(
							    model_name_or_path=UpperCAmelCase			,       output_dir=UpperCAmelCase			,       )
							_UpperCAmelCase							   =     'encoder_layerdrop'
							_UpperCAmelCase							   =     0.5
							_UpperCAmelCase							   =     argparse.Namespace(**UpperCAmelCase       )
							with pytest.raises(UpperCAmelCase       ) as excinfo:
													_UpperCAmelCase							   =     main(UpperCAmelCase       )
							assert str(excinfo.value       ) == F"""model config doesn't have a `{unsupported_param}` attribute"""

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     CHEAP_ARGS.copy()

							_UpperCAmelCase							   =     'summarization'
							_UpperCAmelCase							   =     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )

							_UpperCAmelCase							   =     BART_TINY
							_UpperCAmelCase							   =     self.get_auto_remove_tmp_dir()

							args_d.update(
							    data_dir=UpperCAmelCase			,       model_name_or_path=UpperCAmelCase			,       output_dir=UpperCAmelCase			,       tokenizer_name=UpperCAmelCase			,       train_batch_size=2			,       eval_batch_size=2			,       do_predict=UpperCAmelCase			,       task=UpperCAmelCase			,       src_lang='en_XX'			,       tgt_lang='ro_RO'			,       freeze_encoder=UpperCAmelCase			,       freeze_embeds=UpperCAmelCase			,       )

							# emulate finetune.py
							_UpperCAmelCase							   =     argparse.ArgumentParser()
							_UpperCAmelCase							   =     pl.Trainer.add_argparse_args(UpperCAmelCase       )
							_UpperCAmelCase							   =     SummarizationModule.add_model_specific_args(UpperCAmelCase			,       os.getcwd()       )
							_UpperCAmelCase							   =     {'--help': True}

							# --help test
							with pytest.raises(UpperCAmelCase       ) as excinfo:
													with CaptureStdout() as cs:
																			_UpperCAmelCase							   =     parser.parse_args(UpperCAmelCase       )
													assert False, "--help is expected to sys.exit"
							assert excinfo.type == SystemExit
							_UpperCAmelCase							   =     lightning_base.arg_to_scheduler_metavar
							assert expected in cs.out, "--help is expected to list the supported schedulers"

							# --lr_scheduler=non_existing_scheduler test
							_UpperCAmelCase							   =     'non_existing_scheduler'
							_UpperCAmelCase							   =     {F"""--lr_scheduler={unsupported_param}"""}
							with pytest.raises(UpperCAmelCase       ) as excinfo:
													with CaptureStderr() as cs:
																			_UpperCAmelCase							   =     parser.parse_args(UpperCAmelCase       )
													assert False, "invalid argument is expected to sys.exit"
							assert excinfo.type == SystemExit
							_UpperCAmelCase							   =     F"""invalid choice: '{unsupported_param}'"""
							assert expected in cs.err, F"""should have bailed on invalid choice of scheduler {unsupported_param}"""

							# --lr_scheduler=existing_scheduler test
							_UpperCAmelCase							   =     'cosine'
							_UpperCAmelCase							   =     args_d.copy()
							_UpperCAmelCase							   =     supported_param
							_UpperCAmelCase							   =     argparse.Namespace(**UpperCAmelCase       )
							_UpperCAmelCase							   =     main(UpperCAmelCase       )
							assert (
							    getattr(model.hparams			,       'lr_scheduler'       ) == supported_param
							), F"""lr_scheduler={supported_param} shouldn't fail"""
