




'''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


A     =logging.get_logger(__name__)

A     ={
    '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  (	__a ):
       def __init__(	self						:	List[Any]   ,				lowercase						:	int=None   ,				lowercase						:	Any=None   ,				*lowercase						:	int   ,				**lowercase						:	str				):



           '''simple docstring'''

           super().__init__(*lowercase   ,				**lowercase				)

           if config is None:
               assert isinstance(self.model   ,				lowercase				), (
                   "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
                   f" {self.model.__class__}"
               )
               UpperCAmelCase			       =		self.model.config
           else:
               UpperCAmelCase			       =		config

           UpperCAmelCase			       =		data_args
           UpperCAmelCase			       =		self.config.tgt_vocab_size if isinstance(self.config   ,				lowercase				) 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:
               UpperCAmelCase			       =		torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id				)
           else:
               # dynamically import label_smoothed_nll_loss
               from utils import label_smoothed_nll_loss

               UpperCAmelCase			       =		label_smoothed_nll_loss
       def     A      (	self						:	Optional[Any]   ,				lowercase						:	int				):



           '''simple docstring'''

           if self.optimizer is None:
               UpperCAmelCase			       =		['''bias''', '''LayerNorm.weight''']
               UpperCAmelCase			       =		[
                   {
                       '''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,
                   },
               ]
               UpperCAmelCase			       =		Adafactor if self.args.adafactor else AdamW
               if self.args.adafactor:
                   UpperCAmelCase			       =		Adafactor
                   UpperCAmelCase			       =		{'''scale_parameter''': False, '''relative_step''': False}
               else:
                   UpperCAmelCase			       =		AdamW
                   UpperCAmelCase			       =		{
                       '''betas''': (self.args.adam_betaa, self.args.adam_betaa),
                       '''eps''': self.args.adam_epsilon,
                   }
               UpperCAmelCase			       =		self.args.learning_rate
               if self.sharded_ddp:
                   UpperCAmelCase			       =		OSS(
                       params=lowercase   ,				optim=lowercase   ,				**lowercase   ,				)
               else:
                   UpperCAmelCase			       =		optimizer_cls(lowercase   ,				**lowercase				)

           if self.lr_scheduler is None:
               UpperCAmelCase			       =		self._get_lr_scheduler(lowercase				)
           else:  # ignoring --lr_scheduler
               logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'''				)
       def     A      (	self						:	Optional[Any]   ,				lowercase						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		arg_to_scheduler[self.args.lr_scheduler]
           if self.args.lr_scheduler == "constant":
               UpperCAmelCase			       =		schedule_func(self.optimizer				)
           elif self.args.lr_scheduler == "constant_w_warmup":
               UpperCAmelCase			       =		schedule_func(self.optimizer   ,				num_warmup_steps=self.args.warmup_steps				)
           else:
               UpperCAmelCase			       =		schedule_func(
                   self.optimizer   ,				num_warmup_steps=self.args.warmup_steps   ,				num_training_steps=lowercase				)
           return scheduler
       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           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     A      (	self						:	int   ,				lowercase						:	str   ,				lowercase						:	Any   ,				lowercase						:	Any				):



           '''simple docstring'''

           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
                   UpperCAmelCase			       =		model(**lowercase   ,				use_cache=lowercase				)[0]
                   UpperCAmelCase			       =		self.loss_fn(logits.view(-1   ,				logits.shape[-1]				)   ,				labels.view(-1				)				)
               else:
                   # compute usual loss via models
                   UpperCAmelCase       ,     UpperCAmelCase			       =		model(**lowercase   ,				labels=lowercase   ,				use_cache=lowercase				)[:2]
           else:
               # compute label smoothed loss
               UpperCAmelCase			       =		model(**lowercase   ,				use_cache=lowercase				)[0]
               UpperCAmelCase			       =		torch.nn.functional.log_softmax(lowercase   ,				dim=-1				)
               UpperCAmelCase       ,     UpperCAmelCase			       =		self.loss_fn(lowercase   ,				lowercase   ,				self.args.label_smoothing   ,				ignore_index=self.config.pad_token_id				)
           return loss, logits
       def     A      (	self						:	Tuple   ,				lowercase						:	Any   ,				lowercase						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		inputs.pop('''labels'''				)
           UpperCAmelCase       ,     UpperCAmelCase			       =		self._compute_loss(lowercase   ,				lowercase   ,				lowercase				)
           return loss
       def     A      (	self						:	str   ,				lowercase						:	nn.Module   ,				lowercase						:	Dict[str, Union[torch.Tensor, Any]]   ,				lowercase						:	bool   ,				lowercase						:	Optional[List[str]] = None   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		self._prepare_inputs(lowercase				)

           UpperCAmelCase			       =		{
               '''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:
               UpperCAmelCase			       =		self.model.generate(
                   inputs['''input_ids''']   ,				attention_mask=inputs['''attention_mask''']   ,				**lowercase   ,				)
               # in case the batch is shorter than max length, the output should be padded
               if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
                   UpperCAmelCase			       =		self._pad_tensors_to_max_len(lowercase   ,				gen_kwargs['''max_length''']				)

           UpperCAmelCase			       =		inputs.pop('''labels'''				)
           with torch.no_grad():
               # compute loss on predict data
               UpperCAmelCase       ,     UpperCAmelCase			       =		self._compute_loss(lowercase   ,				lowercase   ,				lowercase				)

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

           UpperCAmelCase			       =		generated_tokens if self.args.predict_with_generate else logits

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

           return (loss, logits, labels)







       def     A      (	self						:	List[str]   ,				lowercase						:	List[Any]   ,				lowercase						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		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}"				)

           UpperCAmelCase			       =		pad_token_id * torch.ones(
               (tensor.shape[0], max_length)   ,				dtype=tensor.dtype   ,				device=tensor.device				)
           UpperCAmelCase			       =		tensor
           return padded_tensor









import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / "utils"))

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


_snake_case		=  get_tests_dir("fixtures")



class   UpperCAmelCase_    (       unittest.TestCase):







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    mock.Mock()
														_lowerCAmelCase     :							int    =    500
														_lowerCAmelCase     :							Tuple    =    {}
														_lowerCAmelCase     :							str    =    HTTPError
														_lowerCAmelCase     :							Union[str, Any]    =    {}

														# Download this model to make sure it's in the cache.
														_lowerCAmelCase     :							Tuple    =    ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit")
														# Under the mock environment we get a 500 error when trying to reach the model.
														with mock.patch("requests.Session.request",			return_value=__a) as mock_head:
																					_lowerCAmelCase     :							Optional[int]    =    ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit")
																					# This check we did call the fake head request
																					mock_head.assert_called()







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    ViTImageProcessor.from_pretrained(
														    "https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json")







							def    snake_case__      (   self):

														'''simple docstring'''

														with self.assertRaises(__a):
																					# config is in subfolder, the following should not work without specifying the subfolder
																					_lowerCAmelCase     :							int    =    AutoImageProcessor.from_pretrained("hf-internal-testing/stable-diffusion-all-variants")

														_lowerCAmelCase     :							Optional[Any]    =    AutoImageProcessor.from_pretrained(
														    "hf-internal-testing/stable-diffusion-all-variants",			subfolder="feature_extractor")

														self.assertIsNotNone(__a)



@is_staging_test
class   UpperCAmelCase_    (       unittest.TestCase):







							@classmethod
							def    snake_case__      (   cls):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    TOKEN
														HfFolder.save_token(__a)







							@classmethod
							def    snake_case__      (   cls):

														'''simple docstring'''

														try:
																					delete_repo(token=cls._token,			repo_id="test-image-processor")
														except HTTPError:
																					pass

														try:
																					delete_repo(token=cls._token,			repo_id="valid_org/test-image-processor-org")
														except HTTPError:
																					pass

														try:
																					delete_repo(token=cls._token,			repo_id="test-dynamic-image-processor")
														except HTTPError:
																					pass







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    ViTImageProcessor.from_pretrained(__a)
														image_processor.push_to_hub("test-image-processor",			use_auth_token=self._token)

														_lowerCAmelCase     :							str    =    ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor")
														for k, v in image_processor.__dict__.items():
																					self.assertEqual(__a,			getattr(__a,			__a))

														# Reset repo
														delete_repo(token=self._token,			repo_id="test-image-processor")

														# Push to hub via save_pretrained
														with tempfile.TemporaryDirectory() as tmp_dir:
																					image_processor.save_pretrained(
																					    __a,			repo_id="test-image-processor",			push_to_hub=__a,			use_auth_token=self._token)

														_lowerCAmelCase     :							Optional[int]    =    ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor")
														for k, v in image_processor.__dict__.items():
																					self.assertEqual(__a,			getattr(__a,			__a))







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Any    =    ViTImageProcessor.from_pretrained(__a)
														image_processor.push_to_hub("valid_org/test-image-processor",			use_auth_token=self._token)

														_lowerCAmelCase     :							Tuple    =    ViTImageProcessor.from_pretrained("valid_org/test-image-processor")
														for k, v in image_processor.__dict__.items():
																					self.assertEqual(__a,			getattr(__a,			__a))

														# Reset repo
														delete_repo(token=self._token,			repo_id="valid_org/test-image-processor")

														# Push to hub via save_pretrained
														with tempfile.TemporaryDirectory() as tmp_dir:
																					image_processor.save_pretrained(
																					    __a,			repo_id="valid_org/test-image-processor-org",			push_to_hub=__a,			use_auth_token=self._token)

														_lowerCAmelCase     :							Optional[int]    =    ViTImageProcessor.from_pretrained("valid_org/test-image-processor-org")
														for k, v in image_processor.__dict__.items():
																					self.assertEqual(__a,			getattr(__a,			__a))







							def    snake_case__      (   self):

														'''simple docstring'''

														CustomImageProcessor.register_for_auto_class()
														_lowerCAmelCase     :							List[str]    =    CustomImageProcessor.from_pretrained(__a)

														image_processor.push_to_hub("test-dynamic-image-processor",			use_auth_token=self._token)

														# This has added the proper auto_map field to the config
														self.assertDictEqual(
														    image_processor.auto_map,			{"AutoImageProcessor": "custom_image_processing.CustomImageProcessor"},			)

														_lowerCAmelCase     :							Tuple    =    AutoImageProcessor.from_pretrained(
														    f"{USER}/test-dynamic-image-processor",			trust_remote_code=__a)
														# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
														self.assertEqual(new_image_processor.__class__.__name__,			"CustomImageProcessor")
