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   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def __init__(							self :       int   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  []







  def    __A     (							self :       Dict  ,     __magic_name__ :       int  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       Optional[int]  ,     **__magic_name__ :       Optional[int]   )  ->	Any:
        self.events.append("on_init_end"   )







  def    __A     (							self :       Optional[Any]  ,     __magic_name__ :       str  ,     __magic_name__ :       Tuple  ,     __magic_name__ :       List[str]  ,     **__magic_name__ :       Dict   )  ->	int:
        self.events.append("on_train_begin"   )







  def    __A     (							self :       str  ,     __magic_name__ :       List[Any]  ,     __magic_name__ :       Dict  ,     __magic_name__ :       Any  ,     **__magic_name__ :       Optional[int]   )  ->	Optional[Any]:
        self.events.append("on_train_end"   )







  def    __A     (							self :       Dict  ,     __magic_name__ :       int  ,     __magic_name__ :       int  ,     __magic_name__ :       List[str]  ,     **__magic_name__ :       Optional[Any]   )  ->	Union[str, Any]:
        self.events.append("on_epoch_begin"   )







  def    __A     (							self :       Union[str, Any]  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       str  ,     __magic_name__ :       Optional[int]  ,     **__magic_name__ :       int   )  ->	Optional[Any]:
        self.events.append("on_epoch_end"   )







  def    __A     (							self :       List[Any]  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       Any  ,     **__magic_name__ :       List[Any]   )  ->	Tuple:
        self.events.append("on_step_begin"   )







  def    __A     (							self :       Union[str, Any]  ,     __magic_name__ :       Dict  ,     __magic_name__ :       Dict  ,     __magic_name__ :       str  ,     **__magic_name__ :       Tuple   )  ->	List[Any]:
        self.events.append("on_step_end"   )







  def    __A     (							self :       Any  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       List[str]  ,     **__magic_name__ :       Any   )  ->	List[Any]:
        self.events.append("on_evaluate"   )







  def    __A     (							self :       str  ,     __magic_name__ :       str  ,     __magic_name__ :       int  ,     __magic_name__ :       List[str]  ,     **__magic_name__ :       Optional[int]   )  ->	Optional[int]:
        self.events.append("on_predict"   )







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       Tuple  ,     **__magic_name__ :       Any   )  ->	Any:
        self.events.append("on_save"   )







  def    __A     (							self :       Union[str, Any]  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       Tuple  ,     **__magic_name__ :       Dict   )  ->	Optional[Any]:
        self.events.append("on_log"   )







  def    __A     (							self :       Dict  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       Optional[Any]  ,     **__magic_name__ :       Optional[int]   )  ->	Any:
        self.events.append("on_prediction_step"   )



@require_torch
class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  def    __A     (							self :       List[str]   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  tempfile.mkdtemp()







  def    __A     (							self :       Dict   )  ->	Optional[int]:
        shutil.rmtree(self.output_dir   )







  def    __A     (							self :       List[str]  ,     __magic_name__ :       Optional[Any]=0  ,     __magic_name__ :       Dict=0  ,     __magic_name__ :       List[str]=64  ,     __magic_name__ :       str=64  ,     __magic_name__ :       Dict=None  ,     __magic_name__ :       Optional[Any]=False  ,     **__magic_name__ :       List[Any]   )  ->	Optional[Any]:
        # 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.
        SCREAMING_SNAKE_CASE_          =  RegressionDataset(length=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  RegressionDataset(length=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  RegressionModelConfig(a=__magic_name__  ,     b=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  RegressionPreTrainedModel(__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  TrainingArguments(self.output_dir  ,     disable_tqdm=__magic_name__  ,     report_to=[]  ,     **__magic_name__   )
        return Trainer(
            __magic_name__  ,     __magic_name__  ,     train_dataset=__magic_name__  ,     eval_dataset=__magic_name__  ,     callbacks=__magic_name__  ,     )







  def    __A     (							self :       Dict  ,     __magic_name__ :       List[str]  ,     __magic_name__ :       Dict   )  ->	Union[str, Any]:
        self.assertEqual(len(__magic_name__   )  ,     len(__magic_name__   )   )

        # Order doesn't matter
        SCREAMING_SNAKE_CASE_          =  sorted(__magic_name__  ,     key=lambda __magic_name__   : cb.__name__ if isinstance(__magic_name__  ,     __magic_name__   ) else cb.__class__.__name__   )
        SCREAMING_SNAKE_CASE_          =  sorted(__magic_name__  ,     key=lambda __magic_name__   : cb.__name__ if isinstance(__magic_name__  ,     __magic_name__   ) else cb.__class__.__name__   )

        for cba, cba in zip(__magic_name__  ,     __magic_name__   ):
              if isinstance(__magic_name__  ,     __magic_name__   ) and isinstance(__magic_name__  ,     __magic_name__   ):
                    self.assertEqual(__magic_name__  ,     __magic_name__   )
              elif isinstance(__magic_name__  ,     __magic_name__   ) and not isinstance(__magic_name__  ,     __magic_name__   ):
                    self.assertEqual(__magic_name__  ,     cba.__class__   )
              elif not isinstance(__magic_name__  ,     __magic_name__   ) and isinstance(__magic_name__  ,     __magic_name__   ):
                    self.assertEqual(cba.__class__  ,     __magic_name__   )
              else:
                    self.assertEqual(__magic_name__  ,     __magic_name__   )







  def    __A     (							self :       int  ,     __magic_name__ :       List[str]   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  ["on_init_end", "on_train_begin"]
        SCREAMING_SNAKE_CASE_          =  0
        SCREAMING_SNAKE_CASE_          =  len(trainer.get_eval_dataloader()   )
        SCREAMING_SNAKE_CASE_          =  ["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(__magic_name__   ):
                    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    __A     (							self :       Union[str, Any]   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  self.get_trainer()
        SCREAMING_SNAKE_CASE_          =  DEFAULT_CALLBACKS.copy() + [ProgressCallback]
        self.check_callbacks_equality(trainer.callback_handler.callbacks  ,     __magic_name__   )

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

        # TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
        SCREAMING_SNAKE_CASE_          =  self.get_trainer(disable_tqdm=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  DEFAULT_CALLBACKS.copy() + [PrinterCallback]
        self.check_callbacks_equality(trainer.callback_handler.callbacks  ,     __magic_name__   )







  def    __A     (							self :       Optional[Any]   )  ->	Tuple:
        SCREAMING_SNAKE_CASE_          =  DEFAULT_CALLBACKS.copy() + [ProgressCallback]
        SCREAMING_SNAKE_CASE_          =  self.get_trainer()

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

        SCREAMING_SNAKE_CASE_          =  self.get_trainer()
        SCREAMING_SNAKE_CASE_          =  trainer.pop_callback(__magic_name__   )
        self.assertEqual(cb.__class__  ,     __magic_name__   )
        self.check_callbacks_equality(trainer.callback_handler.callbacks  ,     __magic_name__   )

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

        # We can also add, pop, or remove by instance
        SCREAMING_SNAKE_CASE_          =  self.get_trainer()
        SCREAMING_SNAKE_CASE_          =  trainer.callback_handler.callbacks[0]
        trainer.remove_callback(__magic_name__   )
        expected_callbacks.remove(__magic_name__   )
        self.check_callbacks_equality(trainer.callback_handler.callbacks  ,     __magic_name__   )

        SCREAMING_SNAKE_CASE_          =  self.get_trainer()
        SCREAMING_SNAKE_CASE_          =  trainer.callback_handler.callbacks[0]
        SCREAMING_SNAKE_CASE_          =  trainer.pop_callback(__magic_name__   )
        self.assertEqual(__magic_name__  ,     __magic_name__   )
        self.check_callbacks_equality(trainer.callback_handler.callbacks  ,     __magic_name__   )

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







  def    __A     (							self :       List[Any]   )  ->	Optional[int]:
        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=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  self.get_trainer(callbacks=[MyTestTrainerCallback]   )
        trainer.train()
        SCREAMING_SNAKE_CASE_          =  trainer.callback_handler.callbacks[-2].events
        self.assertEqual(__magic_name__  ,     self.get_expected_events(__magic_name__   )   )

        # Independent log/save/eval
        SCREAMING_SNAKE_CASE_          =  self.get_trainer(callbacks=[MyTestTrainerCallback]  ,     logging_steps=5   )
        trainer.train()
        SCREAMING_SNAKE_CASE_          =  trainer.callback_handler.callbacks[-2].events
        self.assertEqual(__magic_name__  ,     self.get_expected_events(__magic_name__   )   )

        SCREAMING_SNAKE_CASE_          =  self.get_trainer(callbacks=[MyTestTrainerCallback]  ,     save_steps=5   )
        trainer.train()
        SCREAMING_SNAKE_CASE_          =  trainer.callback_handler.callbacks[-2].events
        self.assertEqual(__magic_name__  ,     self.get_expected_events(__magic_name__   )   )

        SCREAMING_SNAKE_CASE_          =  self.get_trainer(callbacks=[MyTestTrainerCallback]  ,     eval_steps=5  ,     evaluation_strategy="steps"   )
        trainer.train()
        SCREAMING_SNAKE_CASE_          =  trainer.callback_handler.callbacks[-2].events
        self.assertEqual(__magic_name__  ,     self.get_expected_events(__magic_name__   )   )

        SCREAMING_SNAKE_CASE_          =  self.get_trainer(callbacks=[MyTestTrainerCallback]  ,     evaluation_strategy="epoch"   )
        trainer.train()
        SCREAMING_SNAKE_CASE_          =  trainer.callback_handler.callbacks[-2].events
        self.assertEqual(__magic_name__  ,     self.get_expected_events(__magic_name__   )   )

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

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



from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase = None				):
      if version.parse(hfh.__version__				).release < version.parse("0.11.0"				).release:
            # old versions of hfh don't url-encode the file path
            SCREAMING_SNAKE_CASE_          =  quote(__UpperCamelCase				)
      return hfh.hf_hub_url(__UpperCamelCase     ,      __UpperCamelCase     ,      repo_type="dataset"     ,      revision=__UpperCamelCase				)


