




import unittest

import requests

from transformers import MODEL_FOR_VISION_2_SEQ_MAPPING, TF_MODEL_FOR_VISION_2_SEQ_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
	from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
	__A						:		List[Any]          =						False


if is_vision_available():
	from PIL import Image
else:

	class 		lowercase_       :




						@staticmethod
						def     _lowercase						(   *_lowercase:		List[Any],     **_lowercase:		Optional[Any]):


										'''simple docstring'''


										pass

@is_pipeline_test
@require_vision
class 		lowercase_       (	unittest.TestCase				):
					__UpperCamelCase     	= MODEL_FOR_VISION_2_SEQ_MAPPING
					__UpperCamelCase     	= TF_MODEL_FOR_VISION_2_SEQ_MAPPING




					def     _lowercase						(   self:		Optional[Any],     _lowercase:		Union[str, Any],     _lowercase:		List[str],     _lowercase:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  pipeline("""image-to-text""",     model=_lowercase,     tokenizer=_lowercase,     image_processor=_lowercase)
									__lowerCAmelCase					=  [
									    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""),
									    """./tests/fixtures/tests_samples/COCO/000000039769.png""",
									]
									return pipe, examples




					def     _lowercase						(   self:		str,     _lowercase:		int,     _lowercase:		int):


									'''simple docstring'''


									__lowerCAmelCase					=  pipe(_lowercase)
									self.assertEqual(
									    _lowercase,     [
									        [{"""generated_text""": ANY(_lowercase)}],
									        [{"""generated_text""": ANY(_lowercase)}],
									    ],     )




					@require_tf
					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  pipeline("""image-to-text""",     model="""hf-internal-testing/tiny-random-vit-gpt2""",     framework="""tf""")
									__lowerCAmelCase					=  """./tests/fixtures/tests_samples/COCO/000000039769.png"""

									__lowerCAmelCase					=  pipe(_lowercase)
									self.assertEqual(
									    _lowercase,     [
									        {
									            """generated_text""": """growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"""
									        },
									    ],     )

									__lowerCAmelCase					=  pipe([image, image])
									self.assertEqual(
									    _lowercase,     [
									        [
									            {
									                """generated_text""": """growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"""
									            }
									        ],
									        [
									            {
									                """generated_text""": """growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"""
									            }
									        ],
									    ],     )

									__lowerCAmelCase					=  pipe(_lowercase,     max_new_tokens=1)
									self.assertEqual(
									    _lowercase,     [{"""generated_text""": """growth"""}],     )




					@require_torch
					def     _lowercase						(   self:		Any):


									'''simple docstring'''


									__lowerCAmelCase					=  pipeline("""image-to-text""",     model="""hf-internal-testing/tiny-random-vit-gpt2""")
									__lowerCAmelCase					=  """./tests/fixtures/tests_samples/COCO/000000039769.png"""

									__lowerCAmelCase					=  pipe(_lowercase)
									self.assertEqual(
									    _lowercase,     [
									        {
									            """generated_text""": """growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"""
									        },
									    ],     )

									__lowerCAmelCase					=  pipe([image, image])
									self.assertEqual(
									    _lowercase,     [
									        [
									            {
									                """generated_text""": """growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"""
									            }
									        ],
									        [
									            {
									                """generated_text""": """growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"""
									            }
									        ],
									    ],     )




					@require_torch
					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									__lowerCAmelCase					=  pipeline("""image-to-text""",     model="""hf-internal-testing/tiny-random-BlipForConditionalGeneration""")
									__lowerCAmelCase					=  """./tests/fixtures/tests_samples/COCO/000000039769.png"""
									__lowerCAmelCase					=  """a photo of"""

									__lowerCAmelCase					=  pipe(_lowercase,     prompt=_lowercase)
									self.assertTrue(outputs[0]["""generated_text"""].startswith(_lowercase))




					@slow
					@require_torch
					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  pipeline("""image-to-text""",     model="""ydshieh/vit-gpt2-coco-en""")
									__lowerCAmelCase					=  """./tests/fixtures/tests_samples/COCO/000000039769.png"""

									__lowerCAmelCase					=  pipe(_lowercase)
									self.assertEqual(_lowercase,     [{"""generated_text""": """a cat laying on a blanket next to a cat laying on a bed """}])

									__lowerCAmelCase					=  pipe([image, image])
									self.assertEqual(
									    _lowercase,     [
									        [{"""generated_text""": """a cat laying on a blanket next to a cat laying on a bed """}],
									        [{"""generated_text""": """a cat laying on a blanket next to a cat laying on a bed """}],
									    ],     )




					@slow
					@require_torch
					def     _lowercase						(   self:		Any):


									'''simple docstring'''


									__lowerCAmelCase					=  pipeline("""image-to-text""",     model="""Salesforce/blip-image-captioning-base""")
									__lowerCAmelCase					=  """https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"""
									__lowerCAmelCase					=  Image.open(requests.get(_lowercase,     stream=_lowercase).raw)

									__lowerCAmelCase					=  pipe(_lowercase)
									self.assertEqual(_lowercase,     [{"""generated_text""": """a pink pokemon pokemon with a blue shirt and a blue shirt"""}])




					@slow
					@require_torch
					def     _lowercase						(   self:		Union[str, Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  pipeline("""image-to-text""",     model="""microsoft/git-base-coco""")
									__lowerCAmelCase					=  """https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"""
									__lowerCAmelCase					=  Image.open(requests.get(_lowercase,     stream=_lowercase).raw)

									__lowerCAmelCase					=  pipe(_lowercase)
									self.assertEqual(_lowercase,     [{"""generated_text""": """a cartoon of a purple character."""}])




					@slow
					@require_torch
					def     _lowercase						(   self:		Tuple):


									'''simple docstring'''


									__lowerCAmelCase					=  pipeline("""image-to-text""",     model="""Salesforce/blip-image-captioning-base""")
									__lowerCAmelCase					=  """https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"""
									__lowerCAmelCase					=  Image.open(requests.get(_lowercase,     stream=_lowercase).raw)

									__lowerCAmelCase					=  """a photography of"""

									__lowerCAmelCase					=  pipe(_lowercase,     prompt=_lowercase)
									self.assertEqual(_lowercase,     [{"""generated_text""": """a photography of a volcano"""}])

									with self.assertRaises(_lowercase):
													__lowerCAmelCase					=  pipe([image, image],     prompt=[prompt, prompt])




					@slow
					@require_torch
					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


									__lowerCAmelCase					=  pipeline("""image-to-text""",     model="""microsoft/git-base-coco""")
									__lowerCAmelCase					=  """https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"""
									__lowerCAmelCase					=  Image.open(requests.get(_lowercase,     stream=_lowercase).raw)

									__lowerCAmelCase					=  """a photo of a"""

									__lowerCAmelCase					=  pipe(_lowercase,     prompt=_lowercase)
									self.assertEqual(_lowercase,     [{"""generated_text""": """a photo of a tent with a tent and a tent in the background."""}])

									with self.assertRaises(_lowercase):
													__lowerCAmelCase					=  pipe([image, image],     prompt=[prompt, prompt])




					@unittest.skipIf(
					    not is_torch_greater_or_equal_than_1_11,     reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`.""")
					@slow
					@require_torch
					def     _lowercase						(   self:		Union[str, Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  pipeline("""image-to-text""",     model="""google/pix2struct-ai2d-base""")
									__lowerCAmelCase					=  """https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"""
									__lowerCAmelCase					=  Image.open(requests.get(_lowercase,     stream=_lowercase).raw)

									__lowerCAmelCase					=  """What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"""

									__lowerCAmelCase					=  pipe(_lowercase,     prompt=_lowercase)
									self.assertEqual(_lowercase,     [{"""generated_text""": """ash cloud"""}])

									with self.assertRaises(_lowercase):
													__lowerCAmelCase					=  pipe([image, image],     prompt=[prompt, prompt])







					@slow
					@require_tf
					def     _lowercase						(   self:		Any):


									'''simple docstring'''


									__lowerCAmelCase					=  pipeline("""image-to-text""",     model="""ydshieh/vit-gpt2-coco-en""",     framework="""tf""")
									__lowerCAmelCase					=  """./tests/fixtures/tests_samples/COCO/000000039769.png"""

									__lowerCAmelCase					=  pipe(_lowercase)
									self.assertEqual(_lowercase,     [{"""generated_text""": """a cat laying on a blanket next to a cat laying on a bed """}])

									__lowerCAmelCase					=  pipe([image, image])
									self.assertEqual(
									    _lowercase,     [
									        [{"""generated_text""": """a cat laying on a blanket next to a cat laying on a bed """}],
									        [{"""generated_text""": """a cat laying on a blanket next to a cat laying on a bed """}],
									    ],     )













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"""
