
"""simple docstring"""







from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
       from fairscale.optim import OSS


lowerCamelCase__        =		logging.get_logger(__name__)

lowerCamelCase__        =		{
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    """constant""": get_constant_schedule,
    """constant_w_warmup""": get_constant_schedule_with_warmup,
}




class 			A__	(     _lowerCamelCase):

  def __init__(				self	,		_SCREAMING_SNAKE_CASE=None	,		_SCREAMING_SNAKE_CASE=None	,		*_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			):
   super().__init__(*_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			)

   if config is None:
    assert isinstance(self.model	,		_SCREAMING_SNAKE_CASE			), (
        "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
        f" {self.model.__class__}"
    )
    __lowerCAmelCase      :						Any						      =				self.model.config
   else:
    __lowerCAmelCase      :						Optional[int]						      =				config

   __lowerCAmelCase      :						Optional[int]						      =				data_args
   __lowerCAmelCase      :						str						      =				self.config.tgt_vocab_size if isinstance(self.config	,		_SCREAMING_SNAKE_CASE			) else self.config.vocab_size

   if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
    assert self.config.pad_token_id is not None, (
        "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
        " calculation or doing label smoothing."
    )

   if self.config.pad_token_id is None and self.config.eos_token_id is not None:
    logger.warning(
        f"The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"
        ' padding..'			)

   if self.args.label_smoothing == 0:
    __lowerCAmelCase      :						int						      =				torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id			)
   else:
    # dynamically import label_smoothed_nll_loss
    from utils import label_smoothed_nll_loss

    __lowerCAmelCase      :						Optional[int]						      =				label_smoothed_nll_loss

  def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
   if self.optimizer is None:
    __lowerCAmelCase      :						List[Any]						      =				['bias', 'LayerNorm.weight']
    __lowerCAmelCase      :						Tuple						      =				[
        {
            'params': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay			)],
            'weight_decay': self.args.weight_decay,
        },
        {
            'params': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay			)],
            'weight_decay': 0.0,
        },
    ]
    __lowerCAmelCase      :						Tuple						      =				Adafactor if self.args.adafactor else AdamW
    if self.args.adafactor:
     __lowerCAmelCase      :						Optional[Any]						      =				Adafactor
     __lowerCAmelCase      :						str						      =				{'scale_parameter': False, 'relative_step': False}
    else:
     __lowerCAmelCase      :						str						      =				AdamW
     __lowerCAmelCase      :						Optional[Any]						      =				{
         'betas': (self.args.adam_betaa, self.args.adam_betaa),
         'eps': self.args.adam_epsilon,
     }
    __lowerCAmelCase      :						str						      =				self.args.learning_rate
    if self.sharded_ddp:
     __lowerCAmelCase      :						Optional[int]						      =				OSS(
         params=_SCREAMING_SNAKE_CASE	,		optim=_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE	,		)
    else:
     __lowerCAmelCase      :						Any						      =				optimizer_cls(_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			)

   if self.lr_scheduler is None:
    __lowerCAmelCase      :						Optional[int]						      =				self._get_lr_scheduler(_SCREAMING_SNAKE_CASE			)
   else:  # ignoring --lr_scheduler
    logger.warning('scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'			)

  def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
   __lowerCAmelCase      :						int						      =				arg_to_scheduler[self.args.lr_scheduler]
   if self.args.lr_scheduler == "constant":
    __lowerCAmelCase      :						Optional[int]						      =				schedule_func(self.optimizer			)
   elif self.args.lr_scheduler == "constant_w_warmup":
    __lowerCAmelCase      :						Dict						      =				schedule_func(self.optimizer	,		num_warmup_steps=self.args.warmup_steps			)
   else:
    __lowerCAmelCase      :						Union[str, Any]						      =				schedule_func(
        self.optimizer	,		num_warmup_steps=self.args.warmup_steps	,		num_training_steps=_SCREAMING_SNAKE_CASE			)
   return scheduler

  def 							__lowerCamelCase							(				self			):
   if isinstance(self.train_dataset	,		torch.utils.data.IterableDataset			):
    return None
   elif is_torch_tpu_available():
    return get_tpu_sampler(self.train_dataset			)
   else:
    if self.args.sortish_sampler:
     self.train_dataset.make_sortish_sampler(
         self.args.per_device_train_batch_size	,		distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)	,		)

    return (
        RandomSampler(self.train_dataset			)
        if self.args.local_rank == -1
        else DistributedSampler(self.train_dataset			)
    )

  def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
   if self.args.label_smoothing == 0:
    if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
     # force training to ignore pad token
     __lowerCAmelCase      :						Dict						      =				model(**_SCREAMING_SNAKE_CASE	,		use_cache=_SCREAMING_SNAKE_CASE			)[0]
     __lowerCAmelCase      :						Union[str, Any]						      =				self.loss_fn(logits.view(-1	,		logits.shape[-1]			)	,		labels.view(-1			)			)
    else:
     # compute usual loss via models
     __lowerCAmelCase      :						Optional[int]						      =				model(**_SCREAMING_SNAKE_CASE	,		labels=_SCREAMING_SNAKE_CASE	,		use_cache=_SCREAMING_SNAKE_CASE			)[:2]
   else:
    # compute label smoothed loss
    __lowerCAmelCase      :						List[str]						      =				model(**_SCREAMING_SNAKE_CASE	,		use_cache=_SCREAMING_SNAKE_CASE			)[0]
    __lowerCAmelCase      :						List[str]						      =				torch.nn.functional.log_softmax(_SCREAMING_SNAKE_CASE	,		dim=-1			)
    __lowerCAmelCase      :						Optional[Any]						      =				self.loss_fn(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		self.args.label_smoothing	,		ignore_index=self.config.pad_token_id			)
   return loss, logits

  def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
   __lowerCAmelCase      :						Optional[Any]						      =				inputs.pop('labels'			)
   __lowerCAmelCase      :						Union[str, Any]						      =				self._compute_loss(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
   return loss

  def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = None	,		):
   __lowerCAmelCase      :						Optional[Any]						      =				self._prepare_inputs(_SCREAMING_SNAKE_CASE			)

   __lowerCAmelCase      :						Tuple						      =				{
       'max_length': self.data_args.val_max_target_length
       if self.data_args is not None
       else self.config.max_length,
       'num_beams': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
   }

   if self.args.predict_with_generate and not self.args.prediction_loss_only:
    __lowerCAmelCase      :						str						      =				self.model.generate(
        inputs['input_ids']	,		attention_mask=inputs['attention_mask']	,		**_SCREAMING_SNAKE_CASE	,		)
    # in case the batch is shorter than max length, the output should be padded
    if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
     __lowerCAmelCase      :						Optional[int]						      =				self._pad_tensors_to_max_len(_SCREAMING_SNAKE_CASE	,		gen_kwargs['max_length']			)

   __lowerCAmelCase      :						Optional[int]						      =				inputs.pop('labels'			)
   with torch.no_grad():
    # compute loss on predict data
    __lowerCAmelCase      :						Union[str, Any]						      =				self._compute_loss(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)

   __lowerCAmelCase      :						int						      =				loss.mean().detach()
   if self.args.prediction_loss_only:
    return (loss, None, None)

   __lowerCAmelCase      :						Optional[int]						      =				generated_tokens if self.args.predict_with_generate else logits

   if labels.shape[-1] < gen_kwargs["max_length"]:
    __lowerCAmelCase      :						str						      =				self._pad_tensors_to_max_len(_SCREAMING_SNAKE_CASE	,		gen_kwargs['max_length']			)

   return (loss, logits, labels)







  def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
   # If PAD token is not defined at least EOS token has to be defined
   __lowerCAmelCase      :						Union[str, Any]						      =				self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

   if pad_token_id is None:
    raise ValueError(
        'Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'
        f" padded to `max_length`={max_length}"			)

   __lowerCAmelCase      :						Union[str, Any]						      =				pad_token_id * torch.ones(
       (tensor.shape[0], max_length)	,		dtype=tensor.dtype	,		device=tensor.device			)
   __lowerCAmelCase      :						Dict						      =				tensor
   return padded_tensor

"""simple docstring"""







import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from requests.exceptions import HTTPError

from transformers.utils import (
    CONFIG_NAME,
    FLAX_WEIGHTS_NAME,
    TF2_WEIGHTS_NAME,
    TRANSFORMERS_CACHE,
    WEIGHTS_NAME,
    cached_file,
    get_file_from_repo,
    has_file,
)


lowerCamelCase__        =		"""hf-internal-testing/tiny-random-bert"""
lowerCamelCase__        =		os.path.join(TRANSFORMERS_CACHE, """models--hf-internal-testing--tiny-random-bert""")
lowerCamelCase__        =		"""9b8c223d42b2188cb49d29af482996f9d0f3e5a6"""




class 			A__	(     unittest.TestCase):

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Union[str, Any]						      =				cached_file(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
			# Should have downloaded the file in here
			self.assertTrue(os.path.isdir(_SCREAMING_SNAKE_CASE			)			)
			# Cache should contain at least those three subfolders:
			for subfolder in ["blobs", "refs", "snapshots"]:
				self.assertTrue(os.path.isdir(os.path.join(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)			)			)
			with open(os.path.join(_SCREAMING_SNAKE_CASE	,		'refs'	,		'main'			)			) as f:
				__lowerCAmelCase      :						List[Any]						      =				f.read()
			self.assertEqual(_SCREAMING_SNAKE_CASE	,		os.path.join(_SCREAMING_SNAKE_CASE	,		'snapshots'	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)			)
			self.assertTrue(os.path.isfile(_SCREAMING_SNAKE_CASE			)			)

			# File is cached at the same place the second time.
			__lowerCAmelCase      :						Union[str, Any]						      =				cached_file(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
			self.assertEqual(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)

			# Using a specific revision to test the full commit hash.
			__lowerCAmelCase      :						Union[str, Any]						      =				cached_file(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		revision='9b8c223'			)
			self.assertEqual(_SCREAMING_SNAKE_CASE	,		os.path.join(_SCREAMING_SNAKE_CASE	,		'snapshots'	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)			)

		def 							__lowerCamelCase							(				self			):
			with self.assertRaisesRegex(_SCREAMING_SNAKE_CASE	,		'is not a valid model identifier'			):
				__lowerCAmelCase      :						Optional[Any]						      =				cached_file('tiny-random-bert'	,		_SCREAMING_SNAKE_CASE			)

			with self.assertRaisesRegex(_SCREAMING_SNAKE_CASE	,		'is not a valid git identifier'			):
				__lowerCAmelCase      :						str						      =				cached_file(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		revision='aaaa'			)

			with self.assertRaisesRegex(_SCREAMING_SNAKE_CASE	,		'does not appear to have a file named'			):
				__lowerCAmelCase      :						Optional[Any]						      =				cached_file(_SCREAMING_SNAKE_CASE	,		'conf'			)

		def 							__lowerCamelCase							(				self			):
			with self.assertRaisesRegex(_SCREAMING_SNAKE_CASE	,		'does not appear to have a file named'			):
				__lowerCAmelCase      :						Optional[int]						      =				cached_file(_SCREAMING_SNAKE_CASE	,		'conf'			)

			with open(os.path.join(_SCREAMING_SNAKE_CASE	,		'refs'	,		'main'			)			) as f:
				__lowerCAmelCase      :						Tuple						      =				f.read()
			self.assertTrue(os.path.isfile(os.path.join(_SCREAMING_SNAKE_CASE	,		'.no_exist'	,		_SCREAMING_SNAKE_CASE	,		'conf'			)			)			)

			__lowerCAmelCase      :						List[Any]						      =				cached_file(_SCREAMING_SNAKE_CASE	,		'conf'	,		_raise_exceptions_for_missing_entries=_SCREAMING_SNAKE_CASE			)
			self.assertIsNone(_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						List[str]						      =				cached_file(_SCREAMING_SNAKE_CASE	,		'conf'	,		local_files_only=_SCREAMING_SNAKE_CASE	,		_raise_exceptions_for_missing_entries=_SCREAMING_SNAKE_CASE			)
			self.assertIsNone(_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						List[Any]						      =				mock.Mock()
			__lowerCAmelCase      :						Tuple						      =				5_00
			__lowerCAmelCase      :						List[Any]						      =				{}
			__lowerCAmelCase      :						Dict						      =				HTTPError
			__lowerCAmelCase      :						str						      =				{}

			# Under the mock environment we get a 500 error when trying to reach the tokenizer.
			with mock.patch('requests.Session.request'	,		return_value=_SCREAMING_SNAKE_CASE			) as mock_head:
				__lowerCAmelCase      :						Optional[Any]						      =				cached_file(_SCREAMING_SNAKE_CASE	,		'conf'	,		_raise_exceptions_for_connection_errors=_SCREAMING_SNAKE_CASE			)
				self.assertIsNone(_SCREAMING_SNAKE_CASE			)
				# This check we did call the fake head request
				mock_head.assert_called()

		def 							__lowerCamelCase							(				self			):
			self.assertTrue(has_file('hf-internal-testing/tiny-bert-pt-only'	,		_SCREAMING_SNAKE_CASE			)			)
			self.assertFalse(has_file('hf-internal-testing/tiny-bert-pt-only'	,		_SCREAMING_SNAKE_CASE			)			)
			self.assertFalse(has_file('hf-internal-testing/tiny-bert-pt-only'	,		_SCREAMING_SNAKE_CASE			)			)

		def 							__lowerCamelCase							(				self			):
			# `get_file_from_repo` returns None if the file does not exist
			self.assertIsNone(get_file_from_repo('bert-base-cased'	,		'ahah.txt'			)			)

			# The function raises if the repository does not exist.
			with self.assertRaisesRegex(_SCREAMING_SNAKE_CASE	,		'is not a valid model identifier'			):
				get_file_from_repo('bert-base-case'	,		_SCREAMING_SNAKE_CASE			)

			# The function raises if the revision does not exist.
			with self.assertRaisesRegex(_SCREAMING_SNAKE_CASE	,		'is not a valid git identifier'			):
				get_file_from_repo('bert-base-cased'	,		_SCREAMING_SNAKE_CASE	,		revision='ahaha'			)

			__lowerCAmelCase      :						Union[str, Any]						      =				get_file_from_repo('bert-base-cased'	,		_SCREAMING_SNAKE_CASE			)
			# The name is the cached name which is not very easy to test, so instead we load the content.
			__lowerCAmelCase      :						List[Any]						      =				json.loads(open(_SCREAMING_SNAKE_CASE	,		'r'			).read()			)
			self.assertEqual(config['hidden_size']	,		7_68			)







		def 							__lowerCamelCase							(				self			):
			with tempfile.TemporaryDirectory() as tmp_dir:
				__lowerCAmelCase      :						str						      =				Path(_SCREAMING_SNAKE_CASE			) / 'a.txt'
				filename.touch()
				self.assertEqual(get_file_from_repo(_SCREAMING_SNAKE_CASE	,		'a.txt'			)	,		str(_SCREAMING_SNAKE_CASE			)			)

				self.assertIsNone(get_file_from_repo(_SCREAMING_SNAKE_CASE	,		'b.txt'			)			)