



import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel






class 						lowercase		:






	"""simple docstring"""


	def __init__(      self      :		Any		,    __UpperCAmelCase      :		List[str]		,    __UpperCAmelCase      :		str=99		,    __UpperCAmelCase      :		Any=13		,    __UpperCAmelCase      :		Dict=7		,    __UpperCAmelCase      :		str=9		,    __UpperCAmelCase      :		int=True		,    __UpperCAmelCase      :		Optional[Any]=True		,    __UpperCAmelCase      :		Tuple=False		,    __UpperCAmelCase      :		Optional[int]=32		,    __UpperCAmelCase      :		Any=5		,    __UpperCAmelCase      :		Optional[int]=4		,    __UpperCAmelCase      :		List[str]=37		,    __UpperCAmelCase      :		Union[str, Any]=8		,    __UpperCAmelCase      :		List[Any]=0.1		,    __UpperCAmelCase      :		List[Any]=0.002		,    __UpperCAmelCase      :		Optional[Any]=1		,    __UpperCAmelCase      :		Optional[Any]=0		,    __UpperCAmelCase      :		List[str]=0		,    __UpperCAmelCase      :		List[Any]=None		,    __UpperCAmelCase      :		Optional[Any]=None		,    )     ->					int:
							UpperCAmelCase_=  parent
							UpperCAmelCase_=  batch_size
							UpperCAmelCase_=  encoder_seq_length
							UpperCAmelCase_=  decoder_seq_length
							# For common tests
							UpperCAmelCase_=  self.decoder_seq_length
							UpperCAmelCase_=  is_training
							UpperCAmelCase_=  use_attention_mask
							UpperCAmelCase_=  use_labels
							UpperCAmelCase_=  vocab_size
							UpperCAmelCase_=  hidden_size
							UpperCAmelCase_=  num_hidden_layers
							UpperCAmelCase_=  num_attention_heads
							UpperCAmelCase_=  d_ff
							UpperCAmelCase_=  relative_attention_num_buckets
							UpperCAmelCase_=  dropout_rate
							UpperCAmelCase_=  initializer_factor
							UpperCAmelCase_=  eos_token_id
							UpperCAmelCase_=  pad_token_id
							UpperCAmelCase_=  decoder_start_token_id
							UpperCAmelCase_=  None
							UpperCAmelCase_=  decoder_layers




	def     _SCREAMING_SNAKE_CASE						(      self      :		Union[str, Any]	)     ->					List[Any]:
							return TaConfig.from_pretrained("""google/umt5-base"""	)




	def     _SCREAMING_SNAKE_CASE						(      self      :		List[Any]		,    __UpperCAmelCase      :		Any		,    __UpperCAmelCase      :		Dict		,    __UpperCAmelCase      :		Optional[int]		,    __UpperCAmelCase      :		List[str]=None		,    __UpperCAmelCase      :		Any=None		,    __UpperCAmelCase      :		Union[str, Any]=None		,    __UpperCAmelCase      :		str=None		,    __UpperCAmelCase      :		Optional[Any]=None		,    )     ->					Optional[Any]:
							if attention_mask is None:
													UpperCAmelCase_=  input_ids.ne(config.pad_token_id	)
							if decoder_attention_mask is None:
													UpperCAmelCase_=  decoder_input_ids.ne(config.pad_token_id	)
							if head_mask is None:
													UpperCAmelCase_=  torch.ones(config.num_hidden_layers		,    config.num_attention_heads		,    device=snake_case__	)
							if decoder_head_mask is None:
													UpperCAmelCase_=  torch.ones(config.num_decoder_layers		,    config.num_attention_heads		,    device=snake_case__	)
							if cross_attn_head_mask is None:
													UpperCAmelCase_=  torch.ones(
													    config.num_decoder_layers		,    config.num_attention_heads		,    device=snake_case__	)
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": decoder_attention_mask,
							    "head_mask": head_mask,
							    "decoder_head_mask": decoder_head_mask,
							    "cross_attn_head_mask": cross_attn_head_mask,
							}




	def     _SCREAMING_SNAKE_CASE						(      self      :		Union[str, Any]	)     ->					str:
							UpperCAmelCase_=  ids_tensor([self.batch_size, self.encoder_seq_length]		,    self.vocab_size	)
							UpperCAmelCase_=  ids_tensor([self.batch_size, self.decoder_seq_length]		,    self.vocab_size	)

							# we need to clamp the input ids here to avoid having pad token in between
							# this is because for NllbMoe the position_ids are prepared such that
							# all pad tokens have pos id = 2 and rest are between 2..seq_length
							# and the seq_length here is seq_length - num_pad_tokens
							# but when using past, there is no way of knowing if the past input ids had
							# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
							# position_ids being off by num_pad_tokens in past input
							UpperCAmelCase_=  input_ids.clamp(self.pad_token_id + 1	)
							UpperCAmelCase_=  decoder_input_ids.clamp(self.pad_token_id + 1	)

							UpperCAmelCase_=  self.get_config()
							UpperCAmelCase_=  config.num_attention_heads
							UpperCAmelCase_=  self.prepare_inputs_dict(snake_case__		,    snake_case__		,    snake_case__	)
							return config, input_dict




	def     _SCREAMING_SNAKE_CASE						(      self      :		int	)     ->					Any:
							UpperCAmelCase_,  UpperCAmelCase_=  self.prepare_config_and_inputs()
							return config, inputs_dict




	def     _SCREAMING_SNAKE_CASE						(      self      :		Union[str, Any]	)     ->					Union[str, Any]:
							return TaConfig(
							    vocab_size=166		,    d_model=self.hidden_size		,    d_ff=self.d_ff		,    d_kv=self.hidden_size // self.num_attention_heads		,    num_layers=self.num_hidden_layers		,    num_decoder_layers=self.decoder_layers		,    num_heads=self.num_attention_heads		,    relative_attention_num_buckets=self.relative_attention_num_buckets		,    dropout_rate=self.dropout_rate		,    initializer_factor=self.initializer_factor		,    eos_token_id=self.eos_token_id		,    bos_token_id=self.pad_token_id		,    pad_token_id=self.pad_token_id		,    decoder_start_token_id=self.decoder_start_token_id		,    )




	def     _SCREAMING_SNAKE_CASE						(      self      :		Optional[Any]	)     ->					int:
							return TaConfig(
							    vocab_size=self.vocab_size		,    d_model=self.hidden_size		,    d_ff=self.d_ff		,    d_kv=self.hidden_size // self.num_attention_heads		,    num_layers=self.num_hidden_layers		,    num_decoder_layers=self.decoder_layers		,    num_heads=self.num_attention_heads		,    relative_attention_num_buckets=self.relative_attention_num_buckets		,    dropout_rate=self.dropout_rate		,    initializer_factor=self.initializer_factor		,    eos_token_id=self.eos_token_id		,    bos_token_id=self.pad_token_id		,    pad_token_id=self.pad_token_id		,    decoder_start_token_id=self.decoder_start_token_id		,    )




	def     _SCREAMING_SNAKE_CASE						(      self      :		Union[str, Any]		,    __UpperCAmelCase      :		Tuple		,    __UpperCAmelCase      :		Any		,    __UpperCAmelCase      :		str		,    __UpperCAmelCase      :		str		,    __UpperCAmelCase      :		List[str]		,    __UpperCAmelCase      :		Dict		,    )     ->					Optional[int]:
							UpperCAmelCase_=  UMTaModel(config=snake_case__	)
							model.to(snake_case__	)
							model.eval()
							UpperCAmelCase_=  model(
							    input_ids=snake_case__		,    decoder_input_ids=snake_case__		,    attention_mask=snake_case__		,    decoder_attention_mask=snake_case__		,    )
							UpperCAmelCase_=  model(input_ids=snake_case__		,    decoder_input_ids=snake_case__	)
							UpperCAmelCase_=  result.last_hidden_state
							UpperCAmelCase_=  result.past_key_values
							UpperCAmelCase_=  result.encoder_last_hidden_state

							self.parent.assertEqual(encoder_output.size()		,    (self.batch_size, self.encoder_seq_length, self.hidden_size)	)
							self.parent.assertEqual(decoder_output.size()		,    (self.batch_size, self.decoder_seq_length, self.hidden_size)	)
							# There should be `num_layers` key value embeddings stored in decoder_past
							self.parent.assertEqual(len(snake_case__	)		,    config.num_layers	)
							# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
							self.parent.assertEqual(len(decoder_past[0]	)		,    4	)




	def     _SCREAMING_SNAKE_CASE						(      self      :		List[str]		,    __UpperCAmelCase      :		Optional[Any]		,    __UpperCAmelCase      :		str		,    __UpperCAmelCase      :		Union[str, Any]		,    __UpperCAmelCase      :		List[Any]		,    __UpperCAmelCase      :		Tuple		,    __UpperCAmelCase      :		List[str]		,    )     ->					List[Any]:
							UpperCAmelCase_=  UMTaModel(config=snake_case__	).get_decoder().to(snake_case__	).eval()
							# first forward pass
							UpperCAmelCase_=  model(snake_case__		,    use_cache=snake_case__	)
							UpperCAmelCase_=  model(snake_case__	)
							UpperCAmelCase_=  model(snake_case__		,    use_cache=snake_case__	)

							self.parent.assertTrue(len(snake_case__	) == len(snake_case__	)	)
							self.parent.assertTrue(len(snake_case__	) == len(snake_case__	) + 1	)

							UpperCAmelCase_,  UpperCAmelCase_=  outputs.to_tuple()

							# create hypothetical next token and extent to next_input_ids
							UpperCAmelCase_=  ids_tensor((self.batch_size, 1)		,    config.vocab_size	)

							# append to next input_ids and
							UpperCAmelCase_=  torch.cat([input_ids, next_tokens]		,    dim=-1	)

							UpperCAmelCase_=  model(snake_case__	)["""last_hidden_state"""]
							UpperCAmelCase_=  model(snake_case__		,    past_key_values=snake_case__	)["""last_hidden_state"""]

							# select random slice
							UpperCAmelCase_=  ids_tensor((1,)		,    output_from_past.shape[-1]	).item()
							UpperCAmelCase_=  output_from_no_past[:, -1, random_slice_idx].detach()
							UpperCAmelCase_=  output_from_past[:, 0, random_slice_idx].detach()

							# test that outputs are equal for slice
							self.parent.assertTrue(torch.allclose(snake_case__		,    snake_case__		,    atol=1E-3	)	)




	def     _SCREAMING_SNAKE_CASE						(      self      :		Optional[int]		,    __UpperCAmelCase      :		Any		,    __UpperCAmelCase      :		Dict		,    )     ->					Any:
							UpperCAmelCase_=  UMTaModel(config=snake_case__	).to(snake_case__	).half().eval()
							UpperCAmelCase_=  model(**snake_case__	)["""last_hidden_state"""]
							self.parent.assertFalse(torch.isnan(snake_case__	).any().item()	)






@require_torch
class 						lowercase		(					__UpperCamelCase					,       __UpperCamelCase					,       __UpperCamelCase					,       unittest.TestCase):






	"""simple docstring"""


	a__ :   str						  =				(
	    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
	)
	a__ :   Optional[Any]						  =				(UMTaForConditionalGeneration,) if is_torch_available() else ()
	a__ :   Any						  =				(
	    {
	        "conversational": UMTaForConditionalGeneration,
	        "feature-extraction": UMTaModel,
	        "summarization": UMTaForConditionalGeneration,
	        "text2text-generation": UMTaForConditionalGeneration,
	        "translation": UMTaForConditionalGeneration,
	        "question-answering": UMTaForQuestionAnswering,
	    }
	    if is_torch_available()
	    else {}
	)
	a__ :   int						  =				True
	a__ :   Optional[Any]						  =				False
	a__ :   Optional[int]						  =				False
	a__ :   Any						  =				True
	a__ :   Any						  =				True
	# The small UMT5 model needs higher percentages for CPU/MP tests
	a__ :   Dict						  =				[0.8, 0.9]




	def     _SCREAMING_SNAKE_CASE						(      self      :		Dict	)     ->					List[str]:
							UpperCAmelCase_=  UMTaModelTester(self	)




	@unittest.skip("""Test has a segmentation fault on torch 1.8.0"""	)
	def     _SCREAMING_SNAKE_CASE						(      self      :		List[str]	)     ->					Union[str, Any]:
							UpperCAmelCase_=  self.model_tester.prepare_config_and_inputs()
							UpperCAmelCase_=  UMTaModel(config_and_inputs[0]	).to(snake_case__	)
							with tempfile.TemporaryDirectory() as tmpdirname:
													torch.onnx.export(
													    snake_case__		,    (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])		,    F"""{tmpdirname}/t5_test.onnx"""		,    export_params=snake_case__		,    opset_version=9		,    input_names=["""input_ids""", """decoder_input_ids"""]		,    )




	@unittest.skipIf(torch_device == """cpu"""		,    """Cant do half precision"""	)
	def     _SCREAMING_SNAKE_CASE						(      self      :		Optional[int]	)     ->					Optional[int]:
							UpperCAmelCase_=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model_fpaa_forward(*snake_case__	)




	def     _SCREAMING_SNAKE_CASE						(      self      :		List[Any]	)     ->					List[Any]:
							UpperCAmelCase_=  ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
							UpperCAmelCase_=  self.model_tester.prepare_config_and_inputs()
							UpperCAmelCase_=  config_and_inputs[0]
							UpperCAmelCase_=  UMTaForConditionalGeneration(snake_case__	).eval()
							model.to(snake_case__	)

							UpperCAmelCase_=  {
							    """head_mask""": torch.zeros(config.num_layers		,    config.num_heads		,    device=snake_case__	),
							    """decoder_head_mask""": torch.zeros(config.num_decoder_layers		,    config.num_heads		,    device=snake_case__	),
							    """cross_attn_head_mask""": torch.zeros(config.num_decoder_layers		,    config.num_heads		,    device=snake_case__	),
							}

							for attn_name, (name, mask) in zip(snake_case__		,    head_masking.items()	):
													UpperCAmelCase_=  {name: mask}
													# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
													if name == "head_mask":
																			UpperCAmelCase_=  torch.ones(
																			    config.num_decoder_layers		,    config.num_heads		,    device=snake_case__	)

													UpperCAmelCase_=  model.generate(
													    config_and_inputs[1]["""input_ids"""]		,    num_beams=1		,    max_length=3		,    output_attentions=snake_case__		,    return_dict_in_generate=snake_case__		,    **snake_case__		,    )
													# We check the state of decoder_attentions and cross_attentions just from the last step
													UpperCAmelCase_=  out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
													self.assertEqual(sum([w.sum().item() for w in attn_weights]	)		,    0.0	)




	@unittest.skip("""Does not work on the tiny model as we keep hitting edge cases."""	)
	def     _SCREAMING_SNAKE_CASE						(      self      :		Optional[Any]	)     ->					int:
							pass






@require_torch
@require_sentencepiece
@require_tokenizers
class 						lowercase		(					unittest.TestCase):






	"""simple docstring"""


	@slow
	@unittest.skip(
	    """Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged"""	)
	def     _SCREAMING_SNAKE_CASE						(      self      :		Tuple	)     ->					Union[str, Any]:
							UpperCAmelCase_=  UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""		,    return_dict=snake_case__	).to(snake_case__	)
							UpperCAmelCase_=  AutoTokenizer.from_pretrained("""google/umt5-small"""		,    use_fast=snake_case__		,    legacy=snake_case__	)
							UpperCAmelCase_=  [
							    """Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
							    """No se como puedo <extra_id_0>.""",
							    """This is the reason why we <extra_id_0> them.""",
							    """The <extra_id_0> walks in <extra_id_1>, seats""",
							    """A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
							]
							UpperCAmelCase_=  tokenizer(snake_case__		,    return_tensors="""pt"""		,    padding=snake_case__	).input_ids
							# fmt: off
							UpperCAmelCase_=  torch.tensor(
							    [
							        [ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
							        [   826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
							        [  1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
							        [   517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
							        [   320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
							    ]	)
							# fmt: on
							torch.testing.assert_allclose(snake_case__		,    snake_case__	)

							UpperCAmelCase_=  model.generate(input_ids.to(snake_case__	)	)
							UpperCAmelCase_=  [
							    """<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
							    """<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
							    """<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
							    """<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
							    """<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
							]
							UpperCAmelCase_=  tokenizer.batch_decode(snake_case__	)
							self.assertEqual(snake_case__		,    snake_case__	)





"""simple docstring"""




import math
from datetime import datetime, timedelta







def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = year % 19
		lowerCAmelCase__   = year % 4
		lowerCAmelCase__   = year % 7
		lowerCAmelCase__   = math.floor(year / 100  )
		lowerCAmelCase__   = math.floor((13 + 8 * leap_day_inhibits) / 25  )
		lowerCAmelCase__   = leap_day_inhibits / 4
		lowerCAmelCase__   = (
		    15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
		) % 30
		lowerCAmelCase__   = (4 + leap_day_inhibits - leap_day_reinstall_number) % 7

		# days to be added to March 21
		lowerCAmelCase__   = (19 * metonic_cycle + secular_moon_shift) % 30

		# PHM -> Paschal Full Moon
		lowerCAmelCase__   = (
		    2 * julian_leap_year
		    + 4 * non_leap_year
		    + 6 * days_to_add
		    + century_starting_point
		) % 7

		if days_to_add == 29 and days_from_phm_to_sunday == 6:
				return datetime(lowerCamelCase__      ,	4      ,	19  )
		elif days_to_add == 28 and days_from_phm_to_sunday == 6:
				return datetime(lowerCamelCase__      ,	4      ,	18  )
		else:
				return datetime(lowerCamelCase__      ,	3      ,	22  ) + timedelta(
				    days=int(days_to_add + days_from_phm_to_sunday  )  )


if __name__ == "__main__":
			for year in (19_94, 20_00, 20_10, 20_21, 20_23):
						__lowerCAmelCase     :				List[str]    				=    "will be" if year > datetime.now().year else "was"
						print(F"Easter in {year} {tense} {gauss_easter(year)}")




