


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 						lowerCamelCase_							:



					'''simple docstring'''







					def __init__(					self			,							snake_case_			,							snake_case_=9_9			,							snake_case_=1_3			,							snake_case_=7			,							snake_case_=9			,							snake_case_=True			,							snake_case_=True			,							snake_case_=False			,							snake_case_=3_2			,							snake_case_=5			,							snake_case_=4			,							snake_case_=3_7			,							snake_case_=8			,							snake_case_=0.1			,							snake_case_=0.0_0_2			,							snake_case_=1			,							snake_case_=0			,							snake_case_=0			,							snake_case_=None			,							snake_case_=None			,							) ->      List[Any]:







								'''simple docstring'''

								__lowercase			       = parent
								__lowercase			       = batch_size
								__lowercase			       = encoder_seq_length
								__lowercase			       = decoder_seq_length
								# For common tests
								__lowercase			       = self.decoder_seq_length
								__lowercase			       = is_training
								__lowercase			       = use_attention_mask
								__lowercase			       = use_labels
								__lowercase			       = vocab_size
								__lowercase			       = hidden_size
								__lowercase			       = num_hidden_layers
								__lowercase			       = num_attention_heads
								__lowercase			       = d_ff
								__lowercase			       = relative_attention_num_buckets
								__lowercase			       = dropout_rate
								__lowercase			       = initializer_factor
								__lowercase			       = eos_token_id
								__lowercase			       = pad_token_id
								__lowercase			       = decoder_start_token_id
								__lowercase			       = None
								__lowercase			       = decoder_layers






					def A   (					self    ) ->      Union[str, Any]:







								'''simple docstring'''

								return TaConfig.from_pretrained('''google/umt5-base'''    )






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_=None			,							snake_case_=None			,							snake_case_=None			,							snake_case_=None			,							snake_case_=None			,							) ->      str:







								'''simple docstring'''

								if attention_mask is None:
											__lowercase			       = input_ids.ne(config.pad_token_id    )
								if decoder_attention_mask is None:
											__lowercase			       = decoder_input_ids.ne(config.pad_token_id    )
								if head_mask is None:
											__lowercase			       = torch.ones(config.num_hidden_layers			,							config.num_attention_heads			,							device=snake_case_    )
								if decoder_head_mask is None:
											__lowercase			       = torch.ones(config.num_decoder_layers			,							config.num_attention_heads			,							device=snake_case_    )
								if cross_attn_head_mask is None:
											__lowercase			       = 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 A   (					self    ) ->      Tuple:







								'''simple docstring'''

								__lowercase			       = ids_tensor([self.batch_size, self.encoder_seq_length]			,							self.vocab_size    )
								__lowercase			       = 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
								__lowercase			       = input_ids.clamp(self.pad_token_id + 1    )
								__lowercase			       = decoder_input_ids.clamp(self.pad_token_id + 1    )

								__lowercase			       = self.get_config()
								__lowercase			       = config.num_attention_heads
								__lowercase			       = self.prepare_inputs_dict(snake_case_			,							snake_case_			,							snake_case_    )
								return config, input_dict






					def A   (					self    ) ->      Optional[Any]:







								'''simple docstring'''

								__lowercase       ,			__lowercase			       = self.prepare_config_and_inputs()
								return config, inputs_dict






					def A   (					self    ) ->      List[Any]:







								'''simple docstring'''

								return TaConfig(
								    vocab_size=1_6_6			,							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 A   (					self    ) ->      List[Any]:







								'''simple docstring'''

								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 A   (					self			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_			,							) ->      Union[str, Any]:







								'''simple docstring'''

								__lowercase			       = UMTaModel(config=snake_case_    )
								model.to(snake_case_    )
								model.eval()
								__lowercase			       = model(
								    input_ids=snake_case_			,							decoder_input_ids=snake_case_			,							attention_mask=snake_case_			,							decoder_attention_mask=snake_case_			,							)
								__lowercase			       = model(input_ids=snake_case_			,							decoder_input_ids=snake_case_    )
								__lowercase			       = result.last_hidden_state
								__lowercase			       = result.past_key_values
								__lowercase			       = 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 A   (					self			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_			,							) ->      Tuple:







								'''simple docstring'''

								__lowercase			       = UMTaModel(config=snake_case_    ).get_decoder().to(snake_case_    ).eval()
								# first forward pass
								__lowercase			       = model(snake_case_			,							use_cache=snake_case_    )
								__lowercase			       = model(snake_case_    )
								__lowercase			       = 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    )

								__lowercase       ,			__lowercase			       = outputs.to_tuple()

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

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

								__lowercase			       = model(snake_case_    )['''last_hidden_state''']
								__lowercase			       = model(snake_case_			,							past_key_values=snake_case_    )['''last_hidden_state''']

								# select random slice
								__lowercase			       = ids_tensor((1,)			,							output_from_past.shape[-1]    ).item()
								__lowercase			       = output_from_no_past[:, -1, random_slice_idx].detach()
								__lowercase			       = 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 A   (					self			,							snake_case_			,							snake_case_			,							) ->      Union[str, Any]:







								'''simple docstring'''

								__lowercase			       = UMTaModel(config=snake_case_    ).to(snake_case_    ).half().eval()
								__lowercase			       = model(**snake_case_    )['''last_hidden_state''']
								self.parent.assertFalse(torch.isnan(snake_case_    ).any().item()    )




@require_torch
class 						lowerCamelCase_							(					lowerCAmelCase__  ,  lowerCAmelCase__  ,  lowerCAmelCase__  ,  unittest.TestCase					):



					'''simple docstring'''







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






					def A   (					self    ) ->      int:







								'''simple docstring'''

								__lowercase			       = UMTaModelTester(self    )






					@unittest.skip('''Test has a segmentation fault on torch 1.8.0'''    )
					def A   (					self    ) ->      int:







								'''simple docstring'''

								__lowercase			       = self.model_tester.prepare_config_and_inputs()
								__lowercase			       = 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 A   (					self    ) ->      Any:







								'''simple docstring'''

								__lowercase			       = self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model_fpaa_forward(*snake_case_    )






					def A   (					self    ) ->      Tuple:







								'''simple docstring'''

								__lowercase			       = ['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
								__lowercase			       = self.model_tester.prepare_config_and_inputs()
								__lowercase			       = config_and_inputs[0]
								__lowercase			       = UMTaForConditionalGeneration(snake_case_    ).eval()
								model.to(snake_case_    )

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

											__lowercase			       = 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
											__lowercase			       = 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 A   (					self    ) ->      Any:







								'''simple docstring'''

								pass




@require_torch
@require_sentencepiece
@require_tokenizers
class 						lowerCamelCase_							(					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 A   (					self    ) ->      List[Any]:







								'''simple docstring'''

								__lowercase			       = UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''			,							return_dict=snake_case_    ).to(snake_case_    )
								__lowercase			       = AutoTokenizer.from_pretrained('''google/umt5-small'''			,							use_fast=snake_case_			,							legacy=snake_case_    )
								__lowercase			       = [
								    '''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>.''',
								]
								__lowercase			       = tokenizer(snake_case_			,							return_tensors='''pt'''			,							padding=snake_case_    ).input_ids
								# fmt: off
								__lowercase			       = torch.tensor(
								    [
								        [ 3_8_5_3_0, 2_1_0_7_0_3, 2_5_6_2_9_9, 1_4_1_0, 2_5_6_2_9_8, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
								        [   8_2_6, 3_2_1, 6_7_1, 2_5_9_2_2, 2_5_6_2_9_9, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
								        [  1_4_6_0, 3_3_9, 3_1_2, 1_9_0_1_4, 1_0_6_2_0, 7_5_8, 2_5_6_2_9_9, 2_3_5_5,2_7_4, 1, 0, 0, 0, 0, 0, 0,0, 0],
								        [   5_1_7, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 3_0_1, 2_5_6_2_9_8, 2_7_5, 1_1_9_9_8_3,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
								        [   3_2_0, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 2_2_3_4, 2_8_9, 2_2_7_5, 3_3_3,6_1_3_9_1, 2_8_9, 2_5_6_2_9_8, 5_4_3, 2_5_6_2_9_7, 1_6_8_7_1_4, 3_2_9, 2_5_6_2_9_6,2_7_4, 1],
								    ]    )
								# fmt: on
								torch.testing.assert_allclose(snake_case_			,							snake_case_    )

								__lowercase			       = model.generate(input_ids.to(snake_case_    )    )
								__lowercase			       = [
								    '''<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>''',
								]
								__lowercase			       = tokenizer.batch_decode(snake_case_    )
								self.assertEqual(snake_case_			,							snake_case_    )






a			:			Any												=					[sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(100000)]


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = 0
			while number:
						# Increased Speed Slightly by checking every 5 digits together.
						sum_of_digits_squared += DIGITS_SQUARED[number % 10_00_00]
						number //= 10_00_00

			return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
a			:			list[bool | None]												=					[None] * 10000000
a			:			int												=					True
a			:			Any												=					False


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			if CHAINS[number - 1] is not None:
						return CHAINS[number - 1]  # type: ignore

			__lowercase			       = chain(next_number(_UpperCamelCase	)	)
			__lowercase			       = number_chain

			while number < 10_00_00_00:
						__lowercase			       = number_chain
						number *= 10

			return number_chain


def 		lowercase_	(  _UpperCamelCase = 10_00_00_00	):


			'''simple docstring'''







			for i in range(1							,      _UpperCamelCase	):
						if CHAINS[i] is None:
									chain(i + 1	)

			return CHAINS[:number].count(_UpperCamelCase	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(f'''{solution() = }''')


