




'''simple docstring'''





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		:  int   			=			logging.getLogger()
a		:  int   			=			torch.cuda.is_available()
a		:  Optional[Any]   			=			{
    '''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''': 4_2,
    '''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''': 1_2,
    '''max_target_length''': 1_2,
    '''val_max_target_length''': 1_2,
    '''test_max_target_length''': 1_2,
    '''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 UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_	):
				_a							:       Dict       					=			"""\n""".join(UpperCAmelCase_	)
				Path(UpperCAmelCase_	).open("""w"""	).writelines(UpperCAmelCase_	)


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


a		:  Optional[int]   			=			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks





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




class     UpperCamelCase					(  snake_case__					):



		@classmethod
		def 						a_       ( cls   ):







						'''simple docstring'''


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



		@slow
		@require_torch_gpu
		def 						a_       ( self   ):







						'''simple docstring'''


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



		def 						a_       ( self   ):







						'''simple docstring'''


						_a							:       str       					=			{"""student_encoder_layers""": 2, """student_decoder_layers""": 1, """no_teacher""": True}
						self._test_distiller_cli(__lowerCAmelCase   )



		def 						a_       ( self   ):







						'''simple docstring'''


						_a							:       Optional[int]       					=			{
						    """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""",
						}
						_a							:       str       					=			self._test_distiller_cli(__lowerCAmelCase		,    check_contents=__lowerCAmelCase   )

						_a							:       Tuple       					=			list(Path(model.output_dir   ).glob("""*.ckpt"""   )   )
						self.assertEqual(1		,    len(__lowerCAmelCase   )   )
						_a							:       List[Any]       					=			list(Path(model.output_dir   ).glob("""**/*.bin"""   )   )
						self.assertEqual(len(__lowerCAmelCase   )		,    2   )
						_a							:       str       					=			lmap(str.strip		,    Path(model.hparams.data_dir   ).joinpath("""test.source"""   ).open().readlines()   )
						_a							:       Any       					=			tempfile.mktemp()  # XXX: not being cleaned up
						generate_summaries_or_translations(__lowerCAmelCase		,    __lowerCAmelCase		,    str(model.output_dir / """best_tfmr"""   )   )
						self.assertTrue(Path(__lowerCAmelCase   ).exists()   )

						_a							:       Optional[Any]       					=			self.get_auto_remove_tmp_dir()
						convert_pl_to_hf(ckpts[0]		,    transformer_ckpts[0].parent		,    __lowerCAmelCase   )
						assert os.path.exists(os.path.join(__lowerCAmelCase		,    """pytorch_model.bin"""   )   )



		def 						a_       ( self   ):







						'''simple docstring'''


						_a							:       Tuple       					=			AutoModelForSeqaSeqLM.from_pretrained(__lowerCAmelCase   )
						_a							:       int       					=			model.dummy_inputs["""input_ids"""], model.dummy_inputs["""attention_mask"""]
						_a							:       Any       					=			torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]		,    dtype=torch.long		,    device=model.device   )
						_a							:       List[Any]       					=			target_ids[:, :-1].contiguous()  # Why this line?
						_a							:       Dict       					=			target_ids[:, 1:].clone()  # why clone?
						_a							:       List[Any]       					=			model(
						    __lowerCAmelCase		,    attention_mask=__lowerCAmelCase		,    decoder_input_ids=__lowerCAmelCase		,    labels=__lowerCAmelCase		,    use_cache=__lowerCAmelCase   ).loss

						_a							:       str       					=			model(__lowerCAmelCase		,    attention_mask=__lowerCAmelCase		,    decoder_input_ids=__lowerCAmelCase		,    use_cache=__lowerCAmelCase   ).logits

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



		def 						a_       ( self   ):







						'''simple docstring'''


						_a							:       Optional[int]       					=			{
						    """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""",
						}
						_a							:       Optional[int]       					=			self._test_distiller_cli(__lowerCAmelCase		,    check_contents=__lowerCAmelCase   )
						assert model.model.config.model_type == "mbart"

						_a							:       Optional[int]       					=			list(Path(model.output_dir   ).glob("""*.ckpt"""   )   )
						self.assertEqual(1		,    len(__lowerCAmelCase   )   )
						_a							:       List[str]       					=			list(Path(model.output_dir   ).glob("""**/*.bin"""   )   )
						_a							:       int       					=			list(Path(model.output_dir   ).glob("""best_tfmr/*"""   )   )
						assert len(__lowerCAmelCase   ) > 2
						self.assertEqual(len(__lowerCAmelCase   )		,    2   )



		def 						a_       ( self   ):







						'''simple docstring'''


						_a							:       int       					=			{
						    """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(__lowerCAmelCase   )



		def 						a_       ( self   ):







						'''simple docstring'''


						_a							:       Optional[int]       					=			{
						    """teacher""": T5_TINY,
						    """student""": T5_TINIER,
						    """model_name_or_path""": T5_TINIER,
						    """tokenizer_name""": T5_TINIER,
						}
						self._test_distiller_cli(__lowerCAmelCase   )



		def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase=True   ):







						'''simple docstring'''


						_a							:       str       					=			{
						    """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(__lowerCAmelCase   )
						_a							:       dict       					=			CHEAP_ARGS.copy()
						_a							:       Optional[int]       					=			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
						_a							:       int       					=			self.get_auto_remove_tmp_dir()

						args_d.update(data_dir=__lowerCAmelCase		,    output_dir=__lowerCAmelCase		,    **__lowerCAmelCase   )
						_a							:       Optional[Any]       					=			distill_main(argparse.Namespace(**__lowerCAmelCase   )   )
						if not check_contents:
										return model
						_a							:       Optional[Any]       					=			os.listdir(__lowerCAmelCase   )
						_a							:       List[str]       					=			{os.path.basename(__lowerCAmelCase   ) for p in contents}
						_a							:       List[Any]       					=			[p for p in contents if p.endswith("""ckpt"""   )]
						assert len(__lowerCAmelCase   ) > 0

						self.assertIn("""test_generations.txt"""		,    __lowerCAmelCase   )
						self.assertIn("""test_results.txt"""		,    __lowerCAmelCase   )

						_a							:       List[Any]       					=			load_json(model.metrics_save_path   )
						_a							:       List[Any]       					=			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}''']		,    __lowerCAmelCase   )
						_a							:       Union[str, Any]       					=			int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) + 1   )
						self.assertEqual(len(metrics["""val"""]   )		,    __lowerCAmelCase   )
						self.assertEqual(len(metrics["""test"""]   )		,    1   )
						return model






class     UpperCamelCase					(  snake_case__					):



		@parameterized.expand(
		    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]		,    )
		def 						a_       ( self		,    __lowerCAmelCase   ):







						'''simple docstring'''


						_a							:       dict       					=			CHEAP_ARGS.copy()
						_a							:       Any       					=			"""translation""" if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else """summarization"""
						_a							:       int       					=			0.1 if task == """translation""" else 0

						_a							:       Any       					=			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
						_a							:       List[str]       					=			self.get_auto_remove_tmp_dir()
						args_d.update(
						    data_dir=__lowerCAmelCase		,    model_name_or_path=__lowerCAmelCase		,    tokenizer_name=__lowerCAmelCase		,    train_batch_size=2		,    eval_batch_size=2		,    output_dir=__lowerCAmelCase		,    do_predict=__lowerCAmelCase		,    task=__lowerCAmelCase		,    src_lang="""en_XX"""		,    tgt_lang="""ro_RO"""		,    freeze_encoder=__lowerCAmelCase		,    freeze_embeds=__lowerCAmelCase		,    )
						assert "n_train" in args_d
						_a							:       Union[str, Any]       					=			argparse.Namespace(**__lowerCAmelCase   )
						_a							:       Any       					=			main(__lowerCAmelCase   )

						_a							:       Dict       					=			module.model.get_input_embeddings()
						assert not input_embeds.weight.requires_grad
						if model == T5_TINY:
										_a							:       Optional[Any]       					=			module.model.lm_head
										assert not lm_head.weight.requires_grad
										assert (lm_head.weight == input_embeds.weight).all().item()
						elif model == FSMT_TINY:
										_a							:       Optional[Any]       					=			module.model.model
										_a							:       Any       					=			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:
										_a							:       Optional[Any]       					=			module.model.model
										_a							:       Tuple       					=			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

						_a							:       Tuple       					=			load_json(module.output_dir / """text_batch.json"""   )
						assert isinstance(__lowerCAmelCase		,    __lowerCAmelCase   )
						assert len(__lowerCAmelCase   ) >= 4



		def 						a_       ( self   ):







						'''simple docstring'''


						_a							:       dict       					=			CHEAP_ARGS.copy()

						_a							:       Optional[int]       					=			"""summarization"""
						_a							:       Optional[int]       					=			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )

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

						# test models whose config includes the extra_model_args
						_a							:       Optional[Any]       					=			BART_TINY
						_a							:       Any       					=			self.get_auto_remove_tmp_dir()
						_a							:       str       					=			args_d.copy()
						args_da.update(
						    model_name_or_path=__lowerCAmelCase		,    output_dir=__lowerCAmelCase		,    )
						_a							:       List[Any]       					=			("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
						for p in extra_model_params:
										_a							:       List[str]       					=			0.5
						_a							:       Optional[Any]       					=			argparse.Namespace(**__lowerCAmelCase   )
						_a							:       Dict       					=			main(__lowerCAmelCase   )
						for p in extra_model_params:
										assert getattr(model.config		,    __lowerCAmelCase   ) == 0.5, f'''failed to override the model config for param {p}'''

						# test models whose config doesn't include the extra_model_args
						_a							:       Optional[int]       					=			T5_TINY
						_a							:       int       					=			self.get_auto_remove_tmp_dir()
						_a							:       List[Any]       					=			args_d.copy()
						args_da.update(
						    model_name_or_path=__lowerCAmelCase		,    output_dir=__lowerCAmelCase		,    )
						_a							:       Tuple       					=			"""encoder_layerdrop"""
						_a							:       Optional[Any]       					=			0.5
						_a							:       int       					=			argparse.Namespace(**__lowerCAmelCase   )
						with pytest.raises(__lowerCAmelCase   ) as excinfo:
										_a							:       int       					=			main(__lowerCAmelCase   )
						assert str(excinfo.value   ) == f'''model config doesn\'t have a `{unsupported_param}` attribute'''



		def 						a_       ( self   ):







						'''simple docstring'''


						_a							:       dict       					=			CHEAP_ARGS.copy()

						_a							:       str       					=			"""summarization"""
						_a							:       Optional[Any]       					=			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )

						_a							:       Dict       					=			BART_TINY
						_a							:       Union[str, Any]       					=			self.get_auto_remove_tmp_dir()

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

						# emulate finetune.py
						_a							:       Optional[Any]       					=			argparse.ArgumentParser()
						_a							:       int       					=			pl.Trainer.add_argparse_args(__lowerCAmelCase   )
						_a							:       int       					=			SummarizationModule.add_model_specific_args(__lowerCAmelCase		,    os.getcwd()   )
						_a							:       int       					=			{"""--help""": True}

						# --help test
						with pytest.raises(__lowerCAmelCase   ) as excinfo:
										with CaptureStdout() as cs:
														_a							:       str       					=			parser.parse_args(__lowerCAmelCase   )
										assert False, "--help is expected to sys.exit"
						assert excinfo.type == SystemExit
						_a							:       Optional[int]       					=			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
						_a							:       Union[str, Any]       					=			"""non_existing_scheduler"""
						_a							:       List[Any]       					=			{f'''--lr_scheduler={unsupported_param}'''}
						with pytest.raises(__lowerCAmelCase   ) as excinfo:
										with CaptureStderr() as cs:
														_a							:       Optional[Any]       					=			parser.parse_args(__lowerCAmelCase   )
										assert False, "invalid argument is expected to sys.exit"
						assert excinfo.type == SystemExit
						_a							:       Any       					=			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
						_a							:       Dict       					=			"""cosine"""
						_a							:       List[Any]       					=			args_d.copy()
						_a							:       List[Any]       					=			supported_param
						_a							:       Union[str, Any]       					=			argparse.Namespace(**__lowerCAmelCase   )
						_a							:       Optional[Any]       					=			main(__lowerCAmelCase   )
						assert (
						    getattr(model.hparams		,    """lr_scheduler"""   ) == supported_param
						), f'''lr_scheduler={supported_param} shouldn\'t fail'''








'''simple docstring'''





from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like





def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_=3	):
    _a							:       Optional[Any]       					=			int(mask / 2	)
    # copy image size
    _a							:       Optional[int]       					=			zeros_like(UpperCAmelCase_	)
    for i in range(UpperCAmelCase_      ,  gray_img.shape[0] - bd	):
        for j in range(UpperCAmelCase_      ,  gray_img.shape[1] - bd	):
            # get mask according with mask
            _a							:       List[str]       					=			ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]	)
            # calculate mask median
            _a							:       Optional[Any]       					=			sort(UpperCAmelCase_	)[inta(divide((multiply(UpperCAmelCase_      ,  UpperCAmelCase_	))      ,  2	) + 1	)]
            _a							:       int       					=			median
    return median_img


if __name__ == "__main__":
  # read original image
  a		:  List[Any]   			=			imread('''../image_data/lena.jpg''')
  # turn image in gray scale value
  a		:  Optional[Any]   			=			cvtColor(img, COLOR_BGR2GRAY)

  # get values with two different mask size
  a		:  str   			=			median_filter(gray, 3)
  a		:  Optional[Any]   			=			median_filter(gray, 5)

  # show result images
  imshow('''median filter with 3x3 mask''', medianaxa)
  imshow('''median filter with 5x5 mask''', medianaxa)
  waitKey(0)


