





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							:
 def __init__(   self						:   List[Any]    ,  UpperCAmelCase						:   Union[str, Any]    ,  UpperCAmelCase						:   List[Any]=99    ,  UpperCAmelCase						:   List[Any]=13    ,  UpperCAmelCase						:   Optional[int]=7    ,  UpperCAmelCase						:   Optional[int]=9    ,  UpperCAmelCase						:   Optional[int]=True    ,  UpperCAmelCase						:   Tuple=True    ,  UpperCAmelCase						:   int=False    ,  UpperCAmelCase						:   Optional[Any]=32    ,  UpperCAmelCase						:   Tuple=5    ,  UpperCAmelCase						:   Optional[int]=4    ,  UpperCAmelCase						:   Optional[int]=37    ,  UpperCAmelCase						:   str=8    ,  UpperCAmelCase						:   Union[str, Any]=0.1    ,  UpperCAmelCase						:   Dict=0.0_0_2    ,  UpperCAmelCase						:   Tuple=1    ,  UpperCAmelCase						:   Tuple=0    ,  UpperCAmelCase						:   Union[str, Any]=0    ,  UpperCAmelCase						:   Union[str, Any]=None    ,  UpperCAmelCase						:   str=None    ,  )    ->			str:
     lowerCamelCase__					:   Dict      				=	parent
     lowerCamelCase__					:   Tuple      				=	batch_size
     lowerCamelCase__					:   Any      				=	encoder_seq_length
     lowerCamelCase__					:   Any      				=	decoder_seq_length
     # For common tests
     lowerCamelCase__					:   str      				=	self.decoder_seq_length
     lowerCamelCase__					:   Dict      				=	is_training
     lowerCamelCase__					:   Tuple      				=	use_attention_mask
     lowerCamelCase__					:   List[str]      				=	use_labels
     lowerCamelCase__					:   Tuple      				=	vocab_size
     lowerCamelCase__					:   Tuple      				=	hidden_size
     lowerCamelCase__					:   Optional[Any]      				=	num_hidden_layers
     lowerCamelCase__					:   List[str]      				=	num_attention_heads
     lowerCamelCase__					:   Optional[int]      				=	d_ff
     lowerCamelCase__					:   Tuple      				=	relative_attention_num_buckets
     lowerCamelCase__					:   List[str]      				=	dropout_rate
     lowerCamelCase__					:   str      				=	initializer_factor
     lowerCamelCase__					:   Tuple      				=	eos_token_id
     lowerCamelCase__					:   str      				=	pad_token_id
     lowerCamelCase__					:   Optional[int]      				=	decoder_start_token_id
     lowerCamelCase__					:   str      				=	None
     lowerCamelCase__					:   Optional[int]      				=	decoder_layers
 def   A_			(   self						:   List[Any]			)    ->			Any:
     return TaConfig.from_pretrained('google/umt5-base'			)
 def   A_			(   self						:   Dict    ,  UpperCAmelCase						:   Any    ,  UpperCAmelCase						:   Optional[Any]    ,  UpperCAmelCase						:   Tuple    ,  UpperCAmelCase						:   List[Any]=None    ,  UpperCAmelCase						:   Optional[int]=None    ,  UpperCAmelCase						:   Union[str, Any]=None    ,  UpperCAmelCase						:   List[str]=None    ,  UpperCAmelCase						:   int=None    ,  )    ->			Tuple:
     if attention_mask is None:
         lowerCamelCase__					:   Optional[Any]      				=	input_ids.ne(config.pad_token_id			)
     if decoder_attention_mask is None:
         lowerCamelCase__					:   List[Any]      				=	decoder_input_ids.ne(config.pad_token_id			)
     if head_mask is None:
         lowerCamelCase__					:   Optional[Any]      				=	torch.ones(config.num_hidden_layers    ,  config.num_attention_heads    ,  device=UpperCAmelCase			)
     if decoder_head_mask is None:
         lowerCamelCase__					:   Tuple      				=	torch.ones(config.num_decoder_layers    ,  config.num_attention_heads    ,  device=UpperCAmelCase			)
     if cross_attn_head_mask is None:
         lowerCamelCase__					:   Tuple      				=	torch.ones(
             config.num_decoder_layers    ,  config.num_attention_heads    ,  device=UpperCAmelCase			)
     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						:   int			)    ->			Optional[int]:
     lowerCamelCase__					:   Any      				=	ids_tensor([self.batch_size, self.encoder_seq_length]    ,  self.vocab_size			)
     lowerCamelCase__					:   Optional[int]      				=	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__					:   Dict      				=	input_ids.clamp(self.pad_token_id + 1			)
     lowerCamelCase__					:   Dict      				=	decoder_input_ids.clamp(self.pad_token_id + 1			)

     lowerCamelCase__					:   Any      				=	self.get_config()
     lowerCamelCase__					:   Optional[int]      				=	config.num_attention_heads
     lowerCamelCase__					:   Optional[int]      				=	self.prepare_inputs_dict(UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase			)
     return config, input_dict
 def   A_			(   self						:   Optional[int]			)    ->			List[Any]:
     lowerCamelCase__				,							lowerCamelCase__					:   Any      				=	self.prepare_config_and_inputs()
     return config, inputs_dict
 def   A_			(   self						:   int			)    ->			List[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   A_			(   self						:   Optional[Any]			)    ->			Tuple:
     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						:   Union[str, Any]    ,  UpperCAmelCase						:   str    ,  UpperCAmelCase						:   Dict    ,  UpperCAmelCase						:   int    ,  UpperCAmelCase						:   str    ,  UpperCAmelCase						:   List[str]    ,  UpperCAmelCase						:   int    ,  )    ->			Dict:
     lowerCamelCase__					:   Optional[int]      				=	UMTaModel(config=UpperCAmelCase			)
     model.to(UpperCAmelCase			)
     model.eval()
     lowerCamelCase__					:   Union[str, Any]      				=	model(
         input_ids=UpperCAmelCase    ,  decoder_input_ids=UpperCAmelCase    ,  attention_mask=UpperCAmelCase    ,  decoder_attention_mask=UpperCAmelCase    ,  )
     lowerCamelCase__					:   Any      				=	model(input_ids=UpperCAmelCase    ,  decoder_input_ids=UpperCAmelCase			)
     lowerCamelCase__					:   List[str]      				=	result.last_hidden_state
     lowerCamelCase__					:   Tuple      				=	result.past_key_values
     lowerCamelCase__					:   Any      				=	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(UpperCAmelCase			)    ,  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						:   Dict    ,  UpperCAmelCase						:   Union[str, Any]    ,  UpperCAmelCase						:   Tuple    ,  UpperCAmelCase						:   Dict    ,  UpperCAmelCase						:   Tuple    ,  UpperCAmelCase						:   Optional[int]    ,  UpperCAmelCase						:   Any    ,  )    ->			List[Any]:
     lowerCamelCase__					:   List[Any]      				=	UMTaModel(config=UpperCAmelCase			).get_decoder().to(UpperCAmelCase			).eval()
     # first forward pass
     lowerCamelCase__					:   Any      				=	model(UpperCAmelCase    ,  use_cache=UpperCAmelCase			)
     lowerCamelCase__					:   List[Any]      				=	model(UpperCAmelCase			)
     lowerCamelCase__					:   Union[str, Any]      				=	model(UpperCAmelCase    ,  use_cache=UpperCAmelCase			)

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

     lowerCamelCase__				,							lowerCamelCase__					:   str      				=	outputs.to_tuple()

     # create hypothetical next token and extent to next_input_ids
     lowerCamelCase__					:   Optional[int]      				=	ids_tensor((self.batch_size, 1)    ,  config.vocab_size			)

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

     lowerCamelCase__					:   str      				=	model(UpperCAmelCase			)['last_hidden_state']
     lowerCamelCase__					:   Optional[Any]      				=	model(UpperCAmelCase    ,  past_key_values=UpperCAmelCase			)['last_hidden_state']

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

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







 def   A_			(   self						:   int    ,  UpperCAmelCase						:   int    ,  UpperCAmelCase						:   Union[str, Any]    ,  )    ->			Dict:
     lowerCamelCase__					:   List[str]      				=	UMTaModel(config=UpperCAmelCase			).to(UpperCAmelCase			).half().eval()
     lowerCamelCase__					:   Tuple      				=	model(**UpperCAmelCase			)['last_hidden_state']
     self.parent.assertFalse(torch.isnan(UpperCAmelCase			).any().item()			)


@require_torch
class      lowerCAmelCase							(      __UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase,						unittest.TestCase    ):
 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						:   Optional[Any]			)    ->			Union[str, Any]:
     lowerCamelCase__					:   Union[str, Any]      				=	UMTaModelTester(self			)
 @unittest.skip('Test has a segmentation fault on torch 1.8.0'			)
 def   A_			(   self						:   Optional[Any]			)    ->			Tuple:
     lowerCamelCase__					:   Any      				=	self.model_tester.prepare_config_and_inputs()
     lowerCamelCase__					:   List[str]      				=	UMTaModel(config_and_inputs[0]			).to(UpperCAmelCase			)
     with tempfile.TemporaryDirectory() as tmpdirname:
         torch.onnx.export(
             UpperCAmelCase    ,  (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])    ,  F"""{tmpdirname}/t5_test.onnx"""    ,  export_params=UpperCAmelCase    ,  opset_version=9    ,  input_names=['input_ids', 'decoder_input_ids']    ,  )
 @unittest.skipIf(torch_device == 'cpu'    ,  'Cant do half precision'			)
 def   A_			(   self						:   List[str]			)    ->			List[Any]:
     lowerCamelCase__					:   List[Any]      				=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model_fpaa_forward(*UpperCAmelCase			)
 def   A_			(   self						:   Dict			)    ->			int:
     lowerCamelCase__					:   Optional[int]      				=	['encoder_attentions', 'decoder_attentions', 'cross_attentions']
     lowerCamelCase__					:   Tuple      				=	self.model_tester.prepare_config_and_inputs()
     lowerCamelCase__					:   Any      				=	config_and_inputs[0]
     lowerCamelCase__					:   str      				=	UMTaForConditionalGeneration(UpperCAmelCase			).eval()
     model.to(UpperCAmelCase			)

     lowerCamelCase__					:   Dict      				=	{
         'head_mask': torch.zeros(config.num_layers    ,  config.num_heads    ,  device=UpperCAmelCase			),
         'decoder_head_mask': torch.zeros(config.num_decoder_layers    ,  config.num_heads    ,  device=UpperCAmelCase			),
         'cross_attn_head_mask': torch.zeros(config.num_decoder_layers    ,  config.num_heads    ,  device=UpperCAmelCase			),
     }

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

         lowerCamelCase__					:   List[Any]      				=	model.generate(
             config_and_inputs[1]['input_ids']    ,  num_beams=1    ,  max_length=3    ,  output_attentions=UpperCAmelCase    ,  return_dict_in_generate=UpperCAmelCase    ,  **UpperCAmelCase    ,  )
         # We check the state of decoder_attentions and cross_attentions just from the last step
         lowerCamelCase__					:   Optional[int]      				=	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						:   List[Any]			)    ->			Tuple:
     pass


@require_torch
@require_sentencepiece
@require_tokenizers
class      lowerCAmelCase							(      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   A_			(   self						:   str			)    ->			Union[str, Any]:
     lowerCamelCase__					:   List[str]      				=	UMTaForConditionalGeneration.from_pretrained('google/umt5-small'    ,  return_dict=UpperCAmelCase			).to(UpperCAmelCase			)
     lowerCamelCase__					:   Tuple      				=	AutoTokenizer.from_pretrained('google/umt5-small'    ,  use_fast=UpperCAmelCase    ,  legacy=UpperCAmelCase			)
     lowerCamelCase__					:   str      				=	[
         '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__					:   List[Any]      				=	tokenizer(UpperCAmelCase    ,  return_tensors='pt'    ,  padding=UpperCAmelCase			).input_ids
     # fmt: off
     lowerCamelCase__					:   List[Any]      				=	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(UpperCAmelCase    ,  UpperCAmelCase			)

     lowerCamelCase__					:   List[str]      				=	model.generate(input_ids.to(UpperCAmelCase			)			)
     lowerCamelCase__					:   int      				=	[
         '<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__					:   str      				=	tokenizer.batch_decode(UpperCAmelCase			)
     self.assertEqual(UpperCAmelCase    ,  UpperCAmelCase			)












from collections import defaultdict
from math import ceil, sqrt


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase = 100_0000		,			_UpperCAmelCase = 10				)    ->  int:
    lowerCamelCase__					:   defaultdict      				=	defaultdict(_UpperCAmelCase				)

    for outer_width in range(3		,			(t_limit // 4) + 2				):
        if outer_width * outer_width > t_limit:
            lowerCamelCase__					:   List[str]      				=	max(
                ceil(sqrt(outer_width * outer_width - t_limit				)				)		,			1				)
        else:
            lowerCamelCase__					:   int      				=	1

        hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

        for hole_width in range(_UpperCAmelCase		,			outer_width - 1		,			2				):
            count[outer_width * outer_width - hole_width * hole_width] += 1

    return sum(1 for n in count.values() if 1 <= n <= 10				)


if __name__ == "__main__":
    print(F"""{solution() = }""")





