






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)

UpperCAmelCase     				=			logging.getLogger()
UpperCAmelCase     				=			torch.cuda.is_available()
UpperCAmelCase     				=			{
    '''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 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   "\n".join(A_							)
	Path(A_							).open('w'							).writelines(A_							)


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


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






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







class 	A_       (      UpperCamelCase__       ):




	'''simple docstring'''


	@classmethod
	def      SCREAMING_SNAKE_CASE__						(	cls	):
		logging.disable(logging.CRITICAL	)  # remove noisy download output from tracebacks
		return cls





	@slow
	@require_torch_gpu
	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   list_models()
		lowercase					  =   "sshleifer"
		lowercase					  =   [x.modelId for x in model_list if x.modelId.startswith(__lowerCamelCase	)]
		lowercase					  =   ["sshleifer/blenderbot-3B", "sshleifer/blenderbot-90M"]
		lowercase					  =   []
		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      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   {"student_encoder_layers": 2, "student_decoder_layers": 1, "no_teacher": True}
		self._test_distiller_cli(__lowerCamelCase	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   {
		    "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",
		}
		lowercase					  =   self._test_distiller_cli(__lowerCamelCase		,   check_contents=__lowerCamelCase	)

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

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

		lowercase					  =   model(__lowerCamelCase		,   attention_mask=__lowerCamelCase		,   decoder_input_ids=__lowerCamelCase		,   use_cache=__lowerCamelCase	).logits

		lowercase					  =   nn.functional.log_softmax(__lowerCamelCase		,   dim=-1	)
		lowercase					  =   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      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   {
		    "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",
		}
		lowercase					  =   self._test_distiller_cli(__lowerCamelCase		,   check_contents=__lowerCamelCase	)
		assert model.model.config.model_type == "mbart"

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





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   {
		    "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      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   {
		    "teacher": T5_TINY,
		    "student": T5_TINIER,
		    "model_name_or_path": T5_TINIER,
		    "tokenizer_name": T5_TINIER,
		}
		self._test_distiller_cli(__lowerCamelCase	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case=True	):
		lowercase					  =   {
		    "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	)
		lowercase					  =   CHEAP_ARGS.copy()
		lowercase					  =   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)
		lowercase					  =   self.get_auto_remove_tmp_dir()

		args_d.update(data_dir=__lowerCamelCase		,   output_dir=__lowerCamelCase		,   **__lowerCamelCase	)
		lowercase					  =   distill_main(argparse.Namespace(**__lowerCamelCase	)	)
		if not check_contents:
			return model
		lowercase					  =   os.listdir(__lowerCamelCase	)
		lowercase					  =   {os.path.basename(__lowerCamelCase	) for p in contents}
		lowercase					  =   [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	)

		lowercase					  =   load_json(model.metrics_save_path	)
		lowercase					  =   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	)
		lowercase					  =   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 	A_       (      UpperCamelCase__       ):




	'''simple docstring'''


	@parameterized.expand(
	    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]		,   )
	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case	):
		lowercase					  =   CHEAP_ARGS.copy()
		lowercase					  =   "translation" if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else "summarization"
		lowercase					  =   0.1 if task == "translation" else 0

		lowercase					  =   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)
		lowercase					  =   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
		lowercase					  =   argparse.Namespace(**__lowerCamelCase	)
		lowercase					  =   main(__lowerCamelCase	)

		lowercase					  =   module.model.get_input_embeddings()
		assert not input_embeds.weight.requires_grad
		if model == T5_TINY:
			lowercase					  =   module.model.lm_head
			assert not lm_head.weight.requires_grad
			assert (lm_head.weight == input_embeds.weight).all().item()
		elif model == FSMT_TINY:
			lowercase					  =   module.model.model
			lowercase					  =   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:
			lowercase					  =   module.model.model
			lowercase					  =   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

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





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   CHEAP_ARGS.copy()

		lowercase					  =   "summarization"
		lowercase					  =   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
		lowercase					  =   BART_TINY
		lowercase					  =   self.get_auto_remove_tmp_dir()
		lowercase					  =   args_d.copy()
		args_da.update(
		    model_name_or_path=__lowerCamelCase		,   output_dir=__lowerCamelCase		,   )
		lowercase					  =   ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
		for p in extra_model_params:
			lowercase					  =   0.5
		lowercase					  =   argparse.Namespace(**__lowerCamelCase	)
		lowercase					  =   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
		lowercase					  =   T5_TINY
		lowercase					  =   self.get_auto_remove_tmp_dir()
		lowercase					  =   args_d.copy()
		args_da.update(
		    model_name_or_path=__lowerCamelCase		,   output_dir=__lowerCamelCase		,   )
		lowercase					  =   "encoder_layerdrop"
		lowercase					  =   0.5
		lowercase					  =   argparse.Namespace(**__lowerCamelCase	)
		with pytest.raises(__lowerCamelCase	) as excinfo:
			lowercase					  =   main(__lowerCamelCase	)
		assert str(excinfo.value	) == F'''model config doesn\'t have a `{unsupported_param}` attribute'''





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   CHEAP_ARGS.copy()

		lowercase					  =   "summarization"
		lowercase					  =   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)

		lowercase					  =   BART_TINY
		lowercase					  =   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
		lowercase					  =   argparse.ArgumentParser()
		lowercase					  =   pl.Trainer.add_argparse_args(__lowerCamelCase	)
		lowercase					  =   SummarizationModule.add_model_specific_args(__lowerCamelCase		,   os.getcwd()	)
		lowercase					  =   {"--help": True}

		# --help test
		with pytest.raises(__lowerCamelCase	) as excinfo:
			with CaptureStdout() as cs:
				lowercase					  =   parser.parse_args(__lowerCamelCase	)
			assert False, "--help is expected to sys.exit"
		assert excinfo.type == SystemExit
		lowercase					  =   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
		lowercase					  =   "non_existing_scheduler"
		lowercase					  =   {F'''--lr_scheduler={unsupported_param}'''}
		with pytest.raises(__lowerCamelCase	) as excinfo:
			with CaptureStderr() as cs:
				lowercase					  =   parser.parse_args(__lowerCamelCase	)
			assert False, "invalid argument is expected to sys.exit"
		assert excinfo.type == SystemExit
		lowercase					  =   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
		lowercase					  =   "cosine"
		lowercase					  =   args_d.copy()
		lowercase					  =   supported_param
		lowercase					  =   argparse.Namespace(**__lowerCamelCase	)
		lowercase					  =   main(__lowerCamelCase	)
		assert (
		    getattr(model.hparams		,   'lr_scheduler'	) == supported_param
		), F'''lr_scheduler={supported_param} shouldn\'t fail'''



from string import ascii_uppercase






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE = False			,							__SCREAMING_SNAKE_CASE = ascii_uppercase							):
	lowercase					  =   keyword.upper()
	lowercase					  =   plaintext.upper()
	lowercase					  =   set(__SCREAMING_SNAKE_CASE							)

	# create a list of unique characters in the keyword - their order matters
	# it determines how we will map plaintext characters to the ciphertext
	lowercase					  =   []
	for char in keyword:
		if char in alphabet_set and char not in unique_chars:
			unique_chars.append(__SCREAMING_SNAKE_CASE							)
    # the number of those unique characters will determine the number of rows
	lowercase					  =   len(__SCREAMING_SNAKE_CASE							)

	# create a shifted version of the alphabet
	lowercase					  =   unique_chars + [
	    char for char in alphabet if char not in unique_chars
	]

	# create a modified alphabet by splitting the shifted alphabet into rows
	lowercase					  =   [
	    shifted_alphabet[k : k + num_unique_chars_in_keyword]
	    for k in range(0			,							26			,							__SCREAMING_SNAKE_CASE							)
	]

	# map the alphabet characters to the modified alphabet characters
	# going 'vertically' through the modified alphabet - consider columns first
	lowercase					  =   {}
	lowercase					  =   0
	for column in range(__SCREAMING_SNAKE_CASE							):
		for row in modified_alphabet:
			# if current row (the last one) is too short, break out of loop
			if len(__SCREAMING_SNAKE_CASE							) <= column:
				break

			# map current letter to letter in modified alphabet
			lowercase					  =   row[column]
			letter_index += 1

	if verbose:
		print(__SCREAMING_SNAKE_CASE							)
	# create the encrypted text by mapping the plaintext to the modified alphabet
	return "".join(mapping[char] if char in mapping else char for char in plaintext							)


if __name__ == "__main__":
	# example use
	print(mixed_keyword('''college''', '''UNIVERSITY'''))

