

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  __UpperCAmelCase					(	unittest.TestCase						):



   @slow
   def 	__magic_name__   (							self							:					Union[str, Any]						):
     UpperCAmelCase	:							str						=			FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''						)
     UpperCAmelCase	:							Dict						=			AutoTokenizer.from_pretrained('''google/mt5-small'''						)

     UpperCAmelCase	:							Optional[Any]						=			tokenizer('''Hello there''',		return_tensors='''np'''						).input_ids
     UpperCAmelCase	:							List[Any]						=			tokenizer('''Hi I am''',		return_tensors='''np'''						).input_ids

     UpperCAmelCase	:							List[str]						=			shift_tokens_right(__A,		model.config.pad_token_id,		model.config.decoder_start_token_id						)

     UpperCAmelCase	:							Dict						=			model(__A,		decoder_input_ids=__A						).logits
     UpperCAmelCase	:							List[str]						=			optax.softmax_cross_entropy(__A,		onehot(__A,		logits.shape[-1]						)						).mean()

     UpperCAmelCase	:							List[Any]						=			-(labels.shape[-1] * loss.item())

     UpperCAmelCase	:							Tuple						=			-8_4.9_1_2_7
     self.assertTrue(abs(mtf_score - EXPECTED_SCORE						) < 1E-4						)








import logging
import os

from .state import PartialState





class  __UpperCAmelCase					(	logging.LoggerAdapter						):



   @staticmethod
   def 	__magic_name__   (							__A							:					str						):
     UpperCAmelCase	:							Dict						=			PartialState()
     return not main_process_only or (main_process_only and state.is_main_process)





   def 	__magic_name__   (							self							:					Union[str, Any],		__A							:					Union[str, Any],		__A							:					Union[str, Any],		*__A							:					Optional[int],		**__A							:					Tuple						):
     if PartialState._shared_state == {}:
       raise RuntimeError(
           '''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'''						)
     UpperCAmelCase	:							List[str]						=			kwargs.pop('''main_process_only''',		__A						)
     UpperCAmelCase	:							int						=			kwargs.pop('''in_order''',		__A						)

     if self.isEnabledFor(__A						):
       if self._should_log(__A						):
         UpperCAmelCase			,						UpperCAmelCase	:							Dict						=			self.process(__A,		__A						)
         self.logger.log(__A,		__A,		*__A,		**__A						)

       elif in_order:
         UpperCAmelCase	:							Dict						=			PartialState()
         for i in range(state.num_processes						):
           if i == state.process_index:
             UpperCAmelCase			,						UpperCAmelCase	:							Optional[int]						=			self.process(__A,		__A						)
             self.logger.log(__A,		__A,		*__A,		**__A						)
           state.wait_for_everyone()






def       a__					(		UpperCAmelCase							:      str    ,			UpperCAmelCase							:      str = None      )  ->						Dict:
  if log_level is None:
    UpperCAmelCase	:							Union[str, Any]						=			os.environ.get('''ACCELERATE_LOG_LEVEL'''    ,			UpperCAmelCase      )
  UpperCAmelCase	:							Tuple						=			logging.getLogger(UpperCAmelCase      )
  if log_level is not None:
    logger.setLevel(log_level.upper()      )
    logger.root.setLevel(log_level.upper()      )
  return MultiProcessAdapter(UpperCAmelCase    ,			{}      )





