


"""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_       :       Optional[Any]      						=  logging.get_logger(__name__)

lowerCamelCase_       :       List[Any]      						=  {
    """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    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 def __init__(		self			,   __A=None			,   __A=None			,   *__A			,   **__A						) ->					Optional[Any]:
     super().__init__(*__A			,   **__A						)

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

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

         a					=label_smoothed_nll_loss


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					List[str]:
     if self.optimizer is None:
         a					=['''bias''', '''LayerNorm.weight''']
         a					=[
             {
                 '''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,
             },
         ]
         a					=Adafactor if self.args.adafactor else AdamW
         if self.args.adafactor:
             a					=Adafactor
             a					={'''scale_parameter''': False, '''relative_step''': False}
         else:
             a					=AdamW
             a					={
                 '''betas''': (self.args.adam_betaa, self.args.adam_betaa),
                 '''eps''': self.args.adam_epsilon,
             }
         a					=self.args.learning_rate
         if self.sharded_ddp:
             a					=OSS(
                 params=__A			,   optim=__A			,   **__A			,   )
         else:
             a					=optimizer_cls(__A			,   **__A						)

     if self.lr_scheduler is None:
         a					=self._get_lr_scheduler(__A						)
     else:  # ignoring --lr_scheduler
         logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'''						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					Tuple:
     a					=arg_to_scheduler[self.args.lr_scheduler]
     if self.args.lr_scheduler == "constant":
         a					=schedule_func(self.optimizer						)
     elif self.args.lr_scheduler == "constant_w_warmup":
         a					=schedule_func(self.optimizer			,   num_warmup_steps=self.args.warmup_steps						)
     else:
         a					=schedule_func(
             self.optimizer			,   num_warmup_steps=self.args.warmup_steps			,   num_training_steps=__A						)
     return scheduler


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[torch.utils.data.Sampler]:
     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 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A						) ->					Optional[int]:
     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
             a					=model(**__A			,   use_cache=__A						)[0]
             a					=self.loss_fn(logits.view(-1			,   logits.shape[-1]						)			,   labels.view(-1						)						)
         else:
             # compute usual loss via models
             a		,				a					=model(**__A			,   labels=__A			,   use_cache=__A						)[:2]
     else:
         # compute label smoothed loss
         a					=model(**__A			,   use_cache=__A						)[0]
         a					=torch.nn.functional.log_softmax(__A			,   dim=-1						)
         a		,				a					=self.loss_fn(__A			,   __A			,   self.args.label_smoothing			,   ignore_index=self.config.pad_token_id						)
     return loss, logits


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					Optional[Any]:
     a					=inputs.pop('''labels'''						)
     a		,				a					=self._compute_loss(__A			,   __A			,   __A						)
     return loss


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A = None			,   ) ->					Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
     a					=self._prepare_inputs(__A						)

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

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

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

     a					=generated_tokens if self.args.predict_with_generate else logits

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

     return (loss, logits, labels)





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					List[Any]:
     # If PAD token is not defined at least EOS token has to be defined
     a					=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}'''						)

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



"""simple docstring"""







import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
       import tensorflow as tf

if is_tensorflow_text_available():
       from transformers.models.bert import TFBertTokenizer


lowerCamelCase_       :       str      						=  ["""bert-base-uncased""", """bert-base-cased"""]
lowerCamelCase_       :       List[str]      						=  """hf-internal-testing/tiny-bert-tf-only"""

if is_tf_available():






       class 				__A    (	tf.keras.Model      ):






        """simple docstring"""




        def __init__(		self			,   __A						) ->					Dict:
            super().__init__()
            a					=tokenizer
            a					=AutoConfig.from_pretrained(__A						)
            a					=TFAutoModel.from_config(__A						)





        def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					int:
            a					=self.tokenizer(__A						)
            a					=self.bert(**__A						)
            return out["pooler_output"]






@require_tf
@require_tensorflow_text
class 				__A    (	unittest.TestCase      ):






 """simple docstring"""




 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     super().setUp()

     a					=[
         BertTokenizer.from_pretrained(__A						) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
     ]  # repeat for when fast_bert_tokenizer=false
     a					=[TFBertTokenizer.from_pretrained(__A						) for checkpoint in TOKENIZER_CHECKPOINTS] + [
         TFBertTokenizer.from_pretrained(__A			,   use_fast_bert_tokenizer=__A						)
         for checkpoint in TOKENIZER_CHECKPOINTS
     ]
     assert len(self.tokenizers						) == len(self.tf_tokenizers						)

     a					=[
         '''This is a straightforward English test sentence.''',
         '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
         '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
         '''And some much more rare Chinese: 齉 堃 齉堃''',
         '''Je vais aussi écrire en français pour tester les accents''',
         '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
     ]
     a					=list(zip(self.test_sentences			,   self.test_sentences[::-1]						)						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     for tokenizer, tf_tokenizer in zip(self.tokenizers			,   self.tf_tokenizers						):
         for test_inputs in (self.test_sentences, self.paired_sentences):
             a					=tokenizer(__A			,   return_tensors='''tf'''			,   padding='''longest'''						)
             a					=tf_tokenizer(__A						)

             for key in python_outputs.keys():
                 self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape						)						)
                 self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]			,   tf.intaa						) == tf_outputs[key]						)						)


 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     for tf_tokenizer in self.tf_tokenizers:
         a					=tf_tokenizer(self.paired_sentences						)
         a					=tf_tokenizer(
             text=[sentence[0] for sentence in self.paired_sentences]			,   text_pair=[sentence[1] for sentence in self.paired_sentences]			,   )
         for key in merged_outputs.keys():
             self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]			,   tf.intaa						) == separated_outputs[key]						)						)


 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     for tf_tokenizer in self.tf_tokenizers:
         a					=tf.function(__A						)
         for test_inputs in (self.test_sentences, self.paired_sentences):
             a					=tf.constant(__A						)
             a					=compiled_tokenizer(__A						)
             a					=tf_tokenizer(__A						)

             for key in eager_outputs.keys():
                 self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]						)						)





 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
     for tf_tokenizer in self.tf_tokenizers:
         a					=ModelToSave(tokenizer=__A						)
         a					=tf.convert_to_tensor(self.test_sentences						)
         a					=model(__A						)  # Build model with some sample inputs
         with TemporaryDirectory() as tempdir:
             a					=Path(__A						) / '''saved.model'''
             model.save(__A						)
             a					=tf.keras.models.load_model(__A						)
         a					=loaded_model(__A						)
         # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
         self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output						)						)			,   1E-5						)