
"""simple docstring"""




import shutil
import tempfile
import unittest
from unittest.mock import patch

from transformers import (
    DefaultFlowCallback,
    IntervalStrategy,
    PrinterCallback,
    ProgressCallback,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    is_torch_available,
)
from transformers.testing_utils import require_torch


if is_torch_available():
   from transformers.trainer import DEFAULT_CALLBACKS

   from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel

class 						a__		( a_		):
   def __init__(     self							):
          lowercase       :							Optional[Any]									=				[]
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       **_a							):
          self.events.append("on_init_end"							)
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       **_a							):
          self.events.append("on_train_begin"							)
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       **_a							):
          self.events.append("on_train_end"							)
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       **_a							):
          self.events.append("on_epoch_begin"							)
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       **_a							):
          self.events.append("on_epoch_end"							)
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       **_a							):
          self.events.append("on_step_begin"							)
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       **_a							):
          self.events.append("on_step_end"							)
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       **_a							):
          self.events.append("on_evaluate"							)
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       **_a							):
          self.events.append("on_predict"							)
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       **_a							):
          self.events.append("on_save"							)
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       **_a							):
          self.events.append("on_log"							)

   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a      ,       **_a							):
          self.events.append("on_prediction_step"							)


@require_torch
class 						a__		( unittest.TestCase		):
   def      __magic_name__     (     self							):
          lowercase       :							List[str]									=				tempfile.mkdtemp()
   def      __magic_name__     (     self							):
          shutil.rmtree(self.output_dir							)
   def      __magic_name__     (     self      ,       _a=0      ,       _a=0      ,       _a=64      ,       _a=64      ,       _a=None      ,       _a=False      ,       **_a							):
          # disable_tqdm in TrainingArguments has a flaky default since it depends on the level of logging. We make sure
          # its set to False since the tests later on depend on its value.
          lowercase       :							Optional[int]									=				RegressionDataset(length=_a							)
          lowercase       :							Any									=				RegressionDataset(length=_a							)
          lowercase       :							List[str]									=				RegressionModelConfig(a=_a      ,       b=_a							)
          lowercase       :							Dict									=				RegressionPreTrainedModel(_a							)

          lowercase       :							Tuple									=				TrainingArguments(self.output_dir      ,       disable_tqdm=_a      ,       report_to=[]      ,       **_a							)
          return Trainer(
              _a      ,       _a      ,       train_dataset=_a      ,       eval_dataset=_a      ,       callbacks=_a      ,       )
   def      __magic_name__     (     self      ,       _a      ,       _a							):
          self.assertEqual(len(_a							)      ,       len(_a							)							)

          # Order doesn't matter
          lowercase       :							Optional[int]									=				sorted(_a      ,       key=lambda _a							: cb.__name__ if isinstance(_a      ,       _a							) else cb.__class__.__name__							)
          lowercase       :							Dict									=				sorted(_a      ,       key=lambda _a							: cb.__name__ if isinstance(_a      ,       _a							) else cb.__class__.__name__							)

          for cba, cba in zip(_a      ,       _a							):
                 if isinstance(_a      ,       _a							) and isinstance(_a      ,       _a							):
                        self.assertEqual(_a      ,       _a							)
                 elif isinstance(_a      ,       _a							) and not isinstance(_a      ,       _a							):
                        self.assertEqual(_a      ,       cba.__class__							)
                 elif not isinstance(_a      ,       _a							) and isinstance(_a      ,       _a							):
                        self.assertEqual(cba.__class__      ,       _a							)
                 else:
                        self.assertEqual(_a      ,       _a							)
   def      __magic_name__     (     self      ,       _a							):
          lowercase       :							int									=				["on_init_end", "on_train_begin"]
          lowercase       :							Tuple									=				0
          lowercase       :							Tuple									=				len(trainer.get_eval_dataloader()							)
          lowercase       :							str									=				["on_prediction_step"] * len(trainer.get_eval_dataloader()							) + ["on_log", "on_evaluate"]
          for _ in range(trainer.state.num_train_epochs							):
                 expected_events.append("on_epoch_begin"							)
                 for _ in range(_a							):
                        step += 1
                        expected_events += ["on_step_begin", "on_step_end"]
                        if step % trainer.args.logging_steps == 0:
                               expected_events.append("on_log"							)
                        if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
                               expected_events += evaluation_events.copy()
                        if step % trainer.args.save_steps == 0:
                               expected_events.append("on_save"							)
                 expected_events.append("on_epoch_end"							)
                 if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
                        expected_events += evaluation_events.copy()
          expected_events += ["on_log", "on_train_end"]
          return expected_events
   def      __magic_name__     (     self							):
          lowercase       :							Optional[int]									=				self.get_trainer()
          lowercase       :							Optional[Any]									=				DEFAULT_CALLBACKS.copy() + [ProgressCallback]
          self.check_callbacks_equality(trainer.callback_handler.callbacks      ,       _a							)

          # Callbacks passed at init are added to the default callbacks
          lowercase       :							Tuple									=				self.get_trainer(callbacks=[MyTestTrainerCallback]							)
          expected_callbacks.append(_a							)
          self.check_callbacks_equality(trainer.callback_handler.callbacks      ,       _a							)

          # TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
          lowercase       :							Optional[Any]									=				self.get_trainer(disable_tqdm=_a							)
          lowercase       :							List[Any]									=				DEFAULT_CALLBACKS.copy() + [PrinterCallback]
          self.check_callbacks_equality(trainer.callback_handler.callbacks      ,       _a							)
   def      __magic_name__     (     self							):
          lowercase       :							Optional[Any]									=				DEFAULT_CALLBACKS.copy() + [ProgressCallback]
          lowercase       :							int									=				self.get_trainer()

          # We can add, pop, or remove by class name
          trainer.remove_callback(_a							)
          expected_callbacks.remove(_a							)
          self.check_callbacks_equality(trainer.callback_handler.callbacks      ,       _a							)

          lowercase       :							Union[str, Any]									=				self.get_trainer()
          lowercase       :							str									=				trainer.pop_callback(_a							)
          self.assertEqual(cb.__class__      ,       _a							)
          self.check_callbacks_equality(trainer.callback_handler.callbacks      ,       _a							)

          trainer.add_callback(_a							)
          expected_callbacks.insert(0      ,       _a							)
          self.check_callbacks_equality(trainer.callback_handler.callbacks      ,       _a							)

          # We can also add, pop, or remove by instance
          lowercase       :							Any									=				self.get_trainer()
          lowercase       :							str									=				trainer.callback_handler.callbacks[0]
          trainer.remove_callback(_a							)
          expected_callbacks.remove(_a							)
          self.check_callbacks_equality(trainer.callback_handler.callbacks      ,       _a							)

          lowercase       :							List[str]									=				self.get_trainer()
          lowercase       :							List[Any]									=				trainer.callback_handler.callbacks[0]
          lowercase       :							Union[str, Any]									=				trainer.pop_callback(_a							)
          self.assertEqual(_a      ,       _a							)
          self.check_callbacks_equality(trainer.callback_handler.callbacks      ,       _a							)

          trainer.add_callback(_a							)
          expected_callbacks.insert(0      ,       _a							)
          self.check_callbacks_equality(trainer.callback_handler.callbacks      ,       _a							)

   def      __magic_name__     (     self							):
          import warnings

          # XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
          warnings.simplefilter(action="ignore"      ,       category=_a							)

          lowercase       :							Union[str, Any]									=				self.get_trainer(callbacks=[MyTestTrainerCallback]							)
          trainer.train()
          lowercase       :							Any									=				trainer.callback_handler.callbacks[-2].events
          self.assertEqual(_a      ,       self.get_expected_events(_a							)							)

          # Independent log/save/eval
          lowercase       :							Any									=				self.get_trainer(callbacks=[MyTestTrainerCallback]      ,       logging_steps=5							)
          trainer.train()
          lowercase       :							int									=				trainer.callback_handler.callbacks[-2].events
          self.assertEqual(_a      ,       self.get_expected_events(_a							)							)

          lowercase       :							List[str]									=				self.get_trainer(callbacks=[MyTestTrainerCallback]      ,       save_steps=5							)
          trainer.train()
          lowercase       :							Tuple									=				trainer.callback_handler.callbacks[-2].events
          self.assertEqual(_a      ,       self.get_expected_events(_a							)							)

          lowercase       :							Any									=				self.get_trainer(callbacks=[MyTestTrainerCallback]      ,       eval_steps=5      ,       evaluation_strategy="steps"							)
          trainer.train()
          lowercase       :							List[Any]									=				trainer.callback_handler.callbacks[-2].events
          self.assertEqual(_a      ,       self.get_expected_events(_a							)							)

          lowercase       :							Dict									=				self.get_trainer(callbacks=[MyTestTrainerCallback]      ,       evaluation_strategy="epoch"							)
          trainer.train()
          lowercase       :							Dict									=				trainer.callback_handler.callbacks[-2].events
          self.assertEqual(_a      ,       self.get_expected_events(_a							)							)

          # A bit of everything
          lowercase       :							int									=				self.get_trainer(
              callbacks=[MyTestTrainerCallback]      ,       logging_steps=3      ,       save_steps=10      ,       eval_steps=5      ,       evaluation_strategy="steps"      ,       )
          trainer.train()
          lowercase       :							Optional[Any]									=				trainer.callback_handler.callbacks[-2].events
          self.assertEqual(_a      ,       self.get_expected_events(_a							)							)

          # warning should be emitted for duplicated callbacks
          with patch("transformers.trainer_callback.logger.warning"							) as warn_mock:
                 lowercase       :							Optional[Any]									=				self.get_trainer(
                     callbacks=[MyTestTrainerCallback, MyTestTrainerCallback]      ,       )
                 assert str(_a							) in warn_mock.call_args[0][0]




"""simple docstring"""




# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

_A     :			Dict  						=			"""facebook/wmt19-en-de"""
_A     :			str  						=			FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_A     :			List[Any]  						=			FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

_A     :			int  						=			FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")

# Test
_A     :			Dict  						=			tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_A     :			List[Any]  						=			tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
_A     :			List[Any]  						=			"""tiny-wmt19-en-de"""
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F"Generated {mname_tiny}")

# Upload
# transformers-cli upload tiny-wmt19-en-de


