


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	,   _UpperCAmelCase	,   _UpperCAmelCase=99	,   _UpperCAmelCase=13	,   _UpperCAmelCase=7	,   _UpperCAmelCase=9	,   _UpperCAmelCase=True	,   _UpperCAmelCase=True	,   _UpperCAmelCase=False	,   _UpperCAmelCase=32	,   _UpperCAmelCase=5	,   _UpperCAmelCase=4	,   _UpperCAmelCase=37	,   _UpperCAmelCase=8	,   _UpperCAmelCase=0.1	,   _UpperCAmelCase=0.002	,   _UpperCAmelCase=1	,   _UpperCAmelCase=0	,   _UpperCAmelCase=0	,   _UpperCAmelCase=None	,   _UpperCAmelCase=None	,   ):
       snake_case_ =   parent
       snake_case_ =   batch_size
       snake_case_ =   encoder_seq_length
       snake_case_ =   decoder_seq_length
       # For common tests
       snake_case_ =   self.decoder_seq_length
       snake_case_ =   is_training
       snake_case_ =   use_attention_mask
       snake_case_ =   use_labels
       snake_case_ =   vocab_size
       snake_case_ =   hidden_size
       snake_case_ =   num_hidden_layers
       snake_case_ =   num_attention_heads
       snake_case_ =   d_ff
       snake_case_ =   relative_attention_num_buckets
       snake_case_ =   dropout_rate
       snake_case_ =   initializer_factor
       snake_case_ =   eos_token_id
       snake_case_ =   pad_token_id
       snake_case_ =   decoder_start_token_id
       snake_case_ =   None
       snake_case_ =   decoder_layers



 def 		UpperCamelCase__  (			self      ):
       return TaConfig.from_pretrained('''google/umt5-base'''      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase=None	,   _UpperCAmelCase=None	,   _UpperCAmelCase=None	,   _UpperCAmelCase=None	,   _UpperCAmelCase=None	,   ):
       if attention_mask is None:
             snake_case_ =   input_ids.ne(config.pad_token_id      )
       if decoder_attention_mask is None:
             snake_case_ =   decoder_input_ids.ne(config.pad_token_id      )
       if head_mask is None:
             snake_case_ =   torch.ones(config.num_hidden_layers	,   config.num_attention_heads	,   device=_UpperCAmelCase      )
       if decoder_head_mask is None:
             snake_case_ =   torch.ones(config.num_decoder_layers	,   config.num_attention_heads	,   device=_UpperCAmelCase      )
       if cross_attn_head_mask is None:
             snake_case_ =   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 		UpperCamelCase__  (			self      ):
       snake_case_ =   ids_tensor([self.batch_size, self.encoder_seq_length]	,   self.vocab_size      )
       snake_case_ =   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
       snake_case_ =   input_ids.clamp(self.pad_token_id + 1      )
       snake_case_ =   decoder_input_ids.clamp(self.pad_token_id + 1      )

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



 def 		UpperCamelCase__  (			self      ):
       snake_case_ ,					snake_case_ =   self.prepare_config_and_inputs()
       return config, inputs_dict



 def 		UpperCamelCase__  (			self      ):
       return TaConfig(
           vocab_size=1_66	,   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 		UpperCamelCase__  (			self      ):
       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 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   ):
       snake_case_ =   UMTaModel(config=_UpperCAmelCase      )
       model.to(_UpperCAmelCase      )
       model.eval()
       snake_case_ =   model(
           input_ids=_UpperCAmelCase	,   decoder_input_ids=_UpperCAmelCase	,   attention_mask=_UpperCAmelCase	,   decoder_attention_mask=_UpperCAmelCase	,   )
       snake_case_ =   model(input_ids=_UpperCAmelCase	,   decoder_input_ids=_UpperCAmelCase      )
       snake_case_ =   result.last_hidden_state
       snake_case_ =   result.past_key_values
       snake_case_ =   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 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   ):
       snake_case_ =   UMTaModel(config=_UpperCAmelCase      ).get_decoder().to(_UpperCAmelCase      ).eval()
       # first forward pass
       snake_case_ =   model(_UpperCAmelCase	,   use_cache=_UpperCAmelCase      )
       snake_case_ =   model(_UpperCAmelCase      )
       snake_case_ =   model(_UpperCAmelCase	,   use_cache=_UpperCAmelCase      )

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

       snake_case_ ,					snake_case_ =   outputs.to_tuple()

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

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

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

       # select random slice
       snake_case_ =   ids_tensor((1,)	,   output_from_past.shape[-1]      ).item()
       snake_case_ =   output_from_no_past[:, -1, random_slice_idx].detach()
       snake_case_ =   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 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   ):
       snake_case_ =   UMTaModel(config=_UpperCAmelCase      ).to(_UpperCAmelCase      ).half().eval()
       snake_case_ =   model(**_UpperCAmelCase      )['''last_hidden_state''']
       self.parent.assertFalse(torch.isnan(_UpperCAmelCase      ).any().item()      )







@require_torch
class lowerCAmelCase_		(				lowerCamelCase__      ,		lowerCamelCase__      ,		lowerCamelCase__      ,		unittest.TestCase ):

 '''simple docstring'''

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



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   UMTaModelTester(self      )



 @unittest.skip('''Test has a segmentation fault on torch 1.8.0'''      )
 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.model_tester.prepare_config_and_inputs()
       snake_case_ =   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 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model_fpaa_forward(*_UpperCAmelCase      )



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

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

             snake_case_ =   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
             snake_case_ =   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 		UpperCamelCase__  (			self      ):
       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 		UpperCamelCase__  (			self      ):
       snake_case_ =   UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''	,   return_dict=_UpperCAmelCase      ).to(_UpperCAmelCase      )
       snake_case_ =   AutoTokenizer.from_pretrained('''google/umt5-small'''	,   use_fast=_UpperCAmelCase	,   legacy=_UpperCAmelCase      )
       snake_case_ =   [
           '''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>.''',
       ]
       snake_case_ =   tokenizer(_UpperCAmelCase	,   return_tensors='''pt'''	,   padding=_UpperCAmelCase      ).input_ids
       # fmt: off
       snake_case_ =   torch.tensor(
           [
               [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
               [   8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
               [  14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
               [   5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
               [   3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
           ]      )
       # fmt: on
       torch.testing.assert_allclose(_UpperCAmelCase	,   _UpperCAmelCase      )

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



def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE = 6008_5147_5143      )->						int:




      """simple docstring"""

      try:
            snake_case_ =   int(SCREAMING_SNAKE_CASE      )
      except (TypeError, ValueError):
            raise TypeError('''Parameter n must be int or castable to int.'''      )
      if n <= 0:
            raise ValueError('''Parameter n must be greater than or equal to one.'''      )
      snake_case_ =   1
      snake_case_ =   2
      while i * i <= n:
            while n % i == 0:
                  snake_case_ =   i
                  n //= i
            i += 1
      if n > 1:
            snake_case_ =   n
      return int(SCREAMING_SNAKE_CASE      )


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