"""simple docstring"""




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    a_   :




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




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




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]	,    snake_case__    :  Any	,    snake_case__    :  Dict	,    snake_case__    :  Optional[int]	,    snake_case__    :  List[str]=None	,    snake_case__    :  Any=None	,    snake_case__    :  Union[str, Any]=None	,    snake_case__    :  str=None	,    snake_case__    :  Optional[Any]=None	,    ):
							if attention_mask is None:
									lowerCAmelCase__   = input_ids.ne(config.pad_token_id  )
							if decoder_attention_mask is None:
									lowerCAmelCase__   = decoder_input_ids.ne(config.pad_token_id  )
							if head_mask is None:
									lowerCAmelCase__   = torch.ones(config.num_hidden_layers	,    config.num_attention_heads	,    device=snake_case__  )
							if decoder_head_mask is None:
									lowerCAmelCase__   = torch.ones(config.num_decoder_layers	,    config.num_attention_heads	,    device=snake_case__  )
							if cross_attn_head_mask is None:
									lowerCAmelCase__   = 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]  ):
							lowerCAmelCase__   = ids_tensor([self.batch_size, self.encoder_seq_length]	,    self.vocab_size  )
							lowerCAmelCase__   = 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
							lowerCAmelCase__   = input_ids.clamp(self.pad_token_id + 1  )
							lowerCAmelCase__   = decoder_input_ids.clamp(self.pad_token_id + 1  )

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




					def     _SCREAMING_SNAKE_CASE    (						self    :  int  ):
							lowerCAmelCase__ ,       lowerCAmelCase__   = self.prepare_config_and_inputs()
							return config, inputs_dict




					def     _SCREAMING_SNAKE_CASE    (						self    :  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]  ):
							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]	,    snake_case__    :  Tuple	,    snake_case__    :  Any	,    snake_case__    :  str	,    snake_case__    :  str	,    snake_case__    :  List[str]	,    snake_case__    :  Dict	,    ):
							lowerCAmelCase__   = UMTaModel(config=snake_case__  )
							model.to(snake_case__  )
							model.eval()
							lowerCAmelCase__   = model(
							    input_ids=snake_case__	,    decoder_input_ids=snake_case__	,    attention_mask=snake_case__	,    decoder_attention_mask=snake_case__	,    )
							lowerCAmelCase__   = model(input_ids=snake_case__	,    decoder_input_ids=snake_case__  )
							lowerCAmelCase__   = result.last_hidden_state
							lowerCAmelCase__   = result.past_key_values
							lowerCAmelCase__   = 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]	,    snake_case__    :  Optional[Any]	,    snake_case__    :  str	,    snake_case__    :  Union[str, Any]	,    snake_case__    :  List[Any]	,    snake_case__    :  Tuple	,    snake_case__    :  List[str]	,    ):
							lowerCAmelCase__   = UMTaModel(config=snake_case__  ).get_decoder().to(snake_case__  ).eval()
							# first forward pass
							lowerCAmelCase__   = model(snake_case__	,    use_cache=snake_case__  )
							lowerCAmelCase__   = model(snake_case__  )
							lowerCAmelCase__   = 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  )

							lowerCAmelCase__ ,       lowerCAmelCase__   = outputs.to_tuple()

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

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

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

							# select random slice
							lowerCAmelCase__   = ids_tensor((1,)	,    output_from_past.shape[-1]  ).item()
							lowerCAmelCase__   = output_from_no_past[:, -1, random_slice_idx].detach()
							lowerCAmelCase__   = 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]	,    snake_case__    :  Any	,    snake_case__    :  Dict	,    ):
							lowerCAmelCase__   = UMTaModel(config=snake_case__  ).to(snake_case__  ).half().eval()
							lowerCAmelCase__   = model(**snake_case__  )["""last_hidden_state"""]
							self.parent.assertFalse(torch.isnan(snake_case__  ).any().item()  )

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




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							lowerCAmelCase__   = UMTaModelTester(self  )




					@unittest.skip("""Test has a segmentation fault on torch 1.8.0"""  )
					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]  ):
							lowerCAmelCase__   = self.model_tester.prepare_config_and_inputs()
							lowerCAmelCase__   = 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]  ):
							lowerCAmelCase__   = 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]  ):
							lowerCAmelCase__   = ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
							lowerCAmelCase__   = self.model_tester.prepare_config_and_inputs()
							lowerCAmelCase__   = config_and_inputs[0]
							lowerCAmelCase__   = UMTaForConditionalGeneration(snake_case__  ).eval()
							model.to(snake_case__  )

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

									lowerCAmelCase__   = 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
									lowerCAmelCase__   = 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]  ):
							pass

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




					@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  ):
							lowerCAmelCase__   = UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""	,    return_dict=snake_case__  ).to(snake_case__  )
							lowerCAmelCase__   = AutoTokenizer.from_pretrained("""google/umt5-small"""	,    use_fast=snake_case__	,    legacy=snake_case__  )
							lowerCAmelCase__   = [
							    """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>.""",
							]
							lowerCAmelCase__   = tokenizer(snake_case__	,    return_tensors="""pt"""	,    padding=snake_case__  ).input_ids
							# fmt: off
							lowerCAmelCase__   = torch.tensor(
							    [
							        [ 38530, 210703, 256299, 1410, 256298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
							        [   826, 321, 671, 25922, 256299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
							        [  1460, 339, 312, 19014, 10620, 758, 256299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
							        [   517, 256299, 14869, 281, 301, 256298, 275, 119983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
							        [   320, 256299, 14869, 281, 2234, 289, 2275, 333,61391, 289, 256298, 543, 256297, 168714, 329, 256296,274, 1],
							    ]  )
							# fmt: on
							torch.testing.assert_allclose(snake_case__	,    snake_case__  )

							lowerCAmelCase__   = model.generate(input_ids.to(snake_case__  )  )
							lowerCAmelCase__   = [
							    """<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>""",
							]
							lowerCAmelCase__   = tokenizer.batch_decode(snake_case__  )
							self.assertEqual(snake_case__	,    snake_case__  )





"""simple docstring"""




def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		return abs(lowerCamelCase__  ) if a == 0 else greatest_common_divisor(b % a      ,	lowerCamelCase__  )







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		while y:  # --> when y=0 then loop will terminate and return x as final GCD.
				lowerCAmelCase__ ,       lowerCAmelCase__   = y, x % y
		return abs(lowerCamelCase__  )







def 				_UpperCAmelCase		(							):





		"""simple docstring"""




		try:
				lowerCAmelCase__   = input("""Enter two integers separated by comma (,): """  ).split(""","""  )
				lowerCAmelCase__   = int(nums[0]  )
				lowerCAmelCase__   = int(nums[1]  )
				print(
				    f"""greatest_common_divisor({num_a}, {num_a}) = """
				    f"""{greatest_common_divisor(lowerCamelCase__      ,	lowerCamelCase__  )}"""  )
				print(f"""By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(lowerCamelCase__      ,	lowerCamelCase__  )}"""  )
		except (IndexError, UnboundLocalError, ValueError):
				print("""Wrong input"""  )


if __name__ == "__main__":
			main()




