






lowerCamelCase       :       List[str]        =				[4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
lowerCamelCase       :       List[str]        =				[3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
lowerCamelCase       :       List[Any]        =				{
    0: 'Sunday',
    1: 'Monday',
    2: 'Tuesday',
    3: 'Wednesday',
    4: 'Thursday',
    5: 'Friday',
    6: 'Saturday',
}
def 					snake_case_						(   lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  List[Any]				,				lowerCAmelCase_				:  Union[str, Any]					):
							assert len(str(_lowercase					)					) > 2, "year should be in YYYY format"
							assert 1 <= month <= 12, "month should be between 1 to 12"
							assert 1 <= day <= 31, "day should be between 1 to 31"

							# Doomsday algorithm:
							__lowercase					:							Dict			=   year // 100
							__lowercase					:							List[Any]			=   (5 * (century % 4) + 2) % 7
							__lowercase					:							Optional[int]			=   year % 100
							__lowercase					:							Union[str, Any]			=   centurian % 12
							__lowercase					:							Tuple			=   (
							    (centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
							) % 7
							__lowercase					:							List[str]			=   (
							    DOOMSDAY_NOT_LEAP[month - 1]
							    if (year % 4 != 0) or (centurian == 0 and (year % 400) == 0)
							    else DOOMSDAY_LEAP[month - 1]
							)
							__lowercase					:							List[str]			=   (dooms_day + day - day_anchor) % 7
							return WEEK_DAY_NAMES[week_day]


if __name__ == "__main__":
	import doctest

	doctest.testmod()
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 					lowercase__						(   unittest.TestCase):


						@slow
						def        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Any							=							TFAutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							AutoTokenizer.from_pretrained('''google/mt5-small'''						)

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							tokenizer('''Hello there'''    ,		return_tensors='''tf'''						).input_ids
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							tokenizer('''Hi I am'''    ,		return_tensors='''tf'''						).input_ids

												SCREAMING_SNAKE_CASE							:    List[Any]							=							model(UpperCamelCase__    ,		labels=UpperCamelCase__						).loss
												SCREAMING_SNAKE_CASE							:    int							=							-tf.math.reduce_mean(UpperCamelCase__						).numpy()

												SCREAMING_SNAKE_CASE							:    List[str]							=							-21.22_8168
												self.assertTrue(abs(mtf_score - EXPECTED_SCORE						) < 2E-4						)





