
'''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_      =  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   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE				):
    """simple docstring"""






    snake_case_       :			Optional[Any]   	=  """\n""".join(__UpperCAmelCase				)
    Path(__UpperCAmelCase				).open("w"				).writelines(__UpperCAmelCase				)


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   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE				):
    """simple docstring"""






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





 @slow
 @require_torch_gpu
 def 				snake_case__						(			self):
     snake_case_       :			Any   	=  list_models()
     snake_case_       :			Optional[Any]   	=  """sshleifer"""
     snake_case_       :			Dict   	=  [x.modelId for x in model_list if x.modelId.startswith(UpperCAmelCase_)]
     snake_case_       :			int   	=  ["""sshleifer/blenderbot-3B""", """sshleifer/blenderbot-90M"""]
     snake_case_       :			Union[str, Any]   	=  []
     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 				snake_case__						(			self):
     snake_case_       :			Any   	=  {"""student_encoder_layers""": 2, """student_decoder_layers""": 1, """no_teacher""": True}
     self._test_distiller_cli(UpperCAmelCase_)





 def 				snake_case__						(			self):
     snake_case_       :			Optional[Any]   	=  {
         """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""",
     }
     snake_case_       :			Union[str, Any]   	=  self._test_distiller_cli(UpperCAmelCase_       ,				check_contents=UpperCAmelCase_)

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

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

     snake_case_       :			Tuple   	=  model(UpperCAmelCase_       ,				attention_mask=UpperCAmelCase_       ,				decoder_input_ids=UpperCAmelCase_       ,				use_cache=UpperCAmelCase_).logits

     snake_case_       :			Union[str, Any]   	=  nn.functional.log_softmax(UpperCAmelCase_       ,				dim=-1)
     snake_case_       :			Optional[Any]   	=  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 				snake_case__						(			self):
     snake_case_       :			Optional[Any]   	=  {
         """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""",
     }
     snake_case_       :			str   	=  self._test_distiller_cli(UpperCAmelCase_       ,				check_contents=UpperCAmelCase_)
     assert model.model.config.model_type == "mbart"

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





 def 				snake_case__						(			self):
     snake_case_       :			Union[str, Any]   	=  {
         """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 				snake_case__						(			self):
     snake_case_       :			Optional[Any]   	=  {
         """teacher""": T5_TINY,
         """student""": T5_TINIER,
         """model_name_or_path""": T5_TINIER,
         """tokenizer_name""": T5_TINIER,
     }
     self._test_distiller_cli(UpperCAmelCase_)





 def 				snake_case__						(			self       ,				lowercase_       ,				lowercase_=True):
     snake_case_       :			List[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(UpperCAmelCase_)
     snake_case_       :			dict   	=  CHEAP_ARGS.copy()
     snake_case_       :			List[str]   	=  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
     snake_case_       :			Tuple   	=  self.get_auto_remove_tmp_dir()

     args_d.update(data_dir=UpperCAmelCase_       ,				output_dir=UpperCAmelCase_       ,				**UpperCAmelCase_)
     snake_case_       :			Optional[int]   	=  distill_main(argparse.Namespace(**UpperCAmelCase_))
     if not check_contents:
         return model
     snake_case_       :			Tuple   	=  os.listdir(UpperCAmelCase_)
     snake_case_       :			str   	=  {os.path.basename(UpperCAmelCase_) for p in contents}
     snake_case_       :			List[str]   	=  [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_)

     snake_case_       :			Any   	=  load_json(model.metrics_save_path)
     snake_case_       :			Union[str, 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}']       ,				UpperCAmelCase_)
     snake_case_       :			Optional[int]   	=  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       UpperCAmelCase_ (		snake_case__    ):





 @parameterized.expand(
     [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]       ,				)
 def 				snake_case__						(			self       ,				lowercase_):
     snake_case_       :			dict   	=  CHEAP_ARGS.copy()
     snake_case_       :			Optional[Any]   	=  """translation""" if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else """summarization"""
     snake_case_       :			str   	=  0.1 if task == """translation""" else 0

     snake_case_       :			Tuple   	=  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
     snake_case_       :			Any   	=  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
     snake_case_       :			List[str]   	=  argparse.Namespace(**UpperCAmelCase_)
     snake_case_       :			Optional[int]   	=  main(UpperCAmelCase_)

     snake_case_       :			Union[str, Any]   	=  module.model.get_input_embeddings()
     assert not input_embeds.weight.requires_grad
     if model == T5_TINY:
         snake_case_       :			str   	=  module.model.lm_head
         assert not lm_head.weight.requires_grad
         assert (lm_head.weight == input_embeds.weight).all().item()
     elif model == FSMT_TINY:
         snake_case_       :			Optional[Any]   	=  module.model.model
         snake_case_       :			Optional[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:
         snake_case_       :			List[str]   	=  module.model.model
         snake_case_       :			List[str]   	=  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

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





 def 				snake_case__						(			self):
     snake_case_       :			dict   	=  CHEAP_ARGS.copy()

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





 def 				snake_case__						(			self):
     snake_case_       :			dict   	=  CHEAP_ARGS.copy()

     snake_case_       :			Tuple   	=  """summarization"""
     snake_case_       :			List[str]   	=  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())

     snake_case_       :			Optional[int]   	=  BART_TINY
     snake_case_       :			Tuple   	=  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
     snake_case_       :			Dict   	=  argparse.ArgumentParser()
     snake_case_       :			List[Any]   	=  pl.Trainer.add_argparse_args(UpperCAmelCase_)
     snake_case_       :			Tuple   	=  SummarizationModule.add_model_specific_args(UpperCAmelCase_       ,				os.getcwd())
     snake_case_       :			Union[str, Any]   	=  {"""--help""": True}

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









"""simple docstring"""







import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils





class    UpperCamelCase     (			unittest.TestCase    ):




	"""simple docstring"""






	@patch("""diffusers.utils.hub_utils.get_full_repo_name"""					)
	def 					lowerCamelCase__ (			self							,UpperCAmelCase_					):
			_lowercase							:      List[Any]			=					"""full_repo_name"""
			with TemporaryDirectory() as tmpdir:
					# Dummy args values
					_lowercase							:      Optional[Any]			=					Mock()
					_lowercase							:      int			=					tmpdir
					_lowercase							:      int			=					0
					_lowercase							:      Optional[Any]			=					"""hub_token"""
					_lowercase							:      Optional[Any]			=					"""dataset_name"""
					_lowercase							:      List[Any]			=					0.01
					_lowercase							:      Tuple			=					10_00_00
					_lowercase							:      Optional[Any]			=					1_00_00
					_lowercase							:      Optional[int]			=					0.01
					_lowercase							:      Tuple			=					0.02
					_lowercase							:      int			=					0.03
					_lowercase							:      Optional[int]			=					0.0005
					_lowercase							:      Tuple			=					0.000001
					_lowercase							:      int			=					1
					_lowercase							:      List[str]			=					10
					_lowercase							:      Optional[Any]			=					0.001
					_lowercase							:      int			=					0.1
					_lowercase							:      Optional[Any]			=					0.2
					_lowercase							:      Tuple			=					True

					# Model card mush be rendered and saved
					diffusers.utils.hub_utils.create_model_card(UpperCAmelCase_							,model_name="""model_name"""					)
					self.assertTrue((Path(UpperCAmelCase_					) / """README.md""").is_file()					)



