'''simple docstring'''

from __future__ import annotations

from typing import Any



class 			UpperCAmelCase  :





       '''simple docstring'''



       def __init__(						self	,   __lowerCAmelCase = 6 )  ->  None:
        lowercase__				:    Node | None  =					None
        lowercase__				:    Node | None  =					None
        self.create_linked_list(__lowerCAmelCase )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase )  ->  None:
        lowercase__				:    List[str]  =					Node()
        lowercase__				:    List[Any]  =					current_node
        lowercase__				:    Dict  =					current_node
        lowercase__				:    Union[str, Any]  =					current_node
        for _ in range(1	,   __lowerCAmelCase ):
         lowercase__				:    int  =					Node()
         lowercase__				:    Any  =					current_node
         lowercase__				:    int  =					previous_node
         lowercase__				:    Optional[int]  =					current_node
        lowercase__				:    List[Any]  =					self.front
        lowercase__				:    Any  =					previous_node

       def _lowerCAmelCase(						self )  ->  bool:
        return (
            self.front == self.rear
            and self.front is not None
            and self.front.data is None
        )

       def _lowerCAmelCase(						self )  ->  Any | None:
        self.check_can_perform_operation()
        return self.front.data if self.front else None

       def _lowerCAmelCase(						self	,   __lowerCAmelCase )  ->  None:
        if self.rear is None:
         return

        self.check_is_full()
        if not self.is_empty():
         lowercase__				:    Optional[int]  =					self.rear.next
        if self.rear:
         lowercase__				:    str  =					data

       def _lowerCAmelCase(						self )  ->  Any:
        self.check_can_perform_operation()
        if self.rear is None or self.front is None:
         return None
        if self.front == self.rear:
         lowercase__				:    int  =					self.front.data
         lowercase__				:    List[Any]  =					None
         return data

        lowercase__				:    List[Any]  =					self.front
        lowercase__				:    Optional[Any]  =					old_front.next
        lowercase__				:    List[str]  =					old_front.data
        lowercase__				:    Dict  =					None
        return data

       def _lowerCAmelCase(						self )  ->  None:
        if self.is_empty():
         raise Exception('''Empty Queue''' )


       def _lowerCAmelCase(						self )  ->  None:
        if self.rear and self.rear.next == self.front:
         raise Exception('''Full Queue''' )



class 			UpperCAmelCase  :





       '''simple docstring'''



       def __init__(						self )  ->  None:
        lowercase__				:    Any | None  =					None
        lowercase__				:    Node | None  =					None
        lowercase__				:    Node | None  =					None


if __name__ == "__main__":
  import doctest

  doctest.testmod()


'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
  import tensorflow as tf

  from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM



@require_tf
@require_sentencepiece
@require_tokenizers
class 			UpperCAmelCase  (						unittest.TestCase					):





       '''simple docstring'''



       @slow
       def _lowerCAmelCase(						self )  ->  Dict:
        lowercase__				:    List[str]  =					TFAutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''' )
        lowercase__				:    Dict  =					AutoTokenizer.from_pretrained('''google/mt5-small''' )

        lowercase__				:    Any  =					tokenizer('''Hello there'''	,   return_tensors='''tf''' ).input_ids
        lowercase__				:    List[Any]  =					tokenizer('''Hi I am'''	,   return_tensors='''tf''' ).input_ids

        lowercase__				:    str  =					model(__lowerCAmelCase	,   labels=__lowerCAmelCase ).loss
        lowercase__				:    List[str]  =					-tf.math.reduce_mean(__lowerCAmelCase ).numpy()

        lowercase__				:    str  =					-2_1.2_2_8_1_6_8
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 2E-4 )

