






"""simple docstring"""





import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
  import optax
  from flax.training.common_utils import onehot

  from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
  from transformers.models.ta.modeling_flax_ta import shift_tokens_right






@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class      a						(					unittest.TestCase		):






    @slow
    def    UpperCamelCase       (       self     :							Tuple					)     ->		List[str]:
      lowerCamelCase_         =						FlaxMTaForConditionalGeneration.from_pretrained('google/mt5-small'					)
      lowerCamelCase_         =						AutoTokenizer.from_pretrained('google/mt5-small'					)

      lowerCamelCase_         =						tokenizer('Hello there'       ,						return_tensors='np'					).input_ids
      lowerCamelCase_         =						tokenizer('Hi I am'       ,						return_tensors='np'					).input_ids

      lowerCamelCase_         =						shift_tokens_right(lowerCamelCase__       ,						model.config.pad_token_id       ,						model.config.decoder_start_token_id					)

      lowerCamelCase_         =						model(lowerCamelCase__       ,						decoder_input_ids=lowerCamelCase__					).logits
      lowerCamelCase_         =						optax.softmax_cross_entropy(lowerCamelCase__       ,						onehot(lowerCamelCase__       ,						logits.shape[-1]					)					).mean()

      lowerCamelCase_         =						-(labels.shape[-1] * loss.item())

      lowerCamelCase_         =						-84.9_127
      self.assertTrue(abs(mtf_score - EXPECTED_SCORE					) < 1e-4					)











import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


SCREAMING_SNAKE_CASE_						   =       logging.getLogger(__name__)

@dataclass
class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	Optional[float]			=			field(
         default=0.0							,      metadata={"help": "The label smoothing epsilon to apply (if not zero)."}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Whether to SortishSamler or not."}   )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "whether to use adafactor"}   )
     __snake_case    :	Optional[float]			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Encoder layer dropout probability. Goes into model.config."}   )
     __snake_case    :	Optional[float]			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Decoder layer dropout probability. Goes into model.config."}   )
     __snake_case    :	Optional[float]			=			field(default=lowerCAmelCase_							,      metadata={"help": "Dropout probability. Goes into model.config."}   )
     __snake_case    :	Optional[float]			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Attention dropout probability. Goes into model.config."}   )
     __snake_case    :	Optional[str]			=			field(
         default="linear"							,      metadata={"help": F"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()   )}"}							,      )


