


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 			SCREAMING_SNAKE_CASE__  :
      def __init__(	self    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	Dict					,							SCREAMING_SNAKE_CASE__    :	str=9_9					,							SCREAMING_SNAKE_CASE__    :	Tuple=1_3					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]=7					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=9					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=True					,							SCREAMING_SNAKE_CASE__    :	Tuple=True					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]=False					,							SCREAMING_SNAKE_CASE__    :	Dict=3_2					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]=5					,							SCREAMING_SNAKE_CASE__    :	str=4					,							SCREAMING_SNAKE_CASE__    :	Dict=3_7					,							SCREAMING_SNAKE_CASE__    :	List[str]=8					,							SCREAMING_SNAKE_CASE__    :	Tuple=0.1					,							SCREAMING_SNAKE_CASE__    :	Tuple=0.002					,							SCREAMING_SNAKE_CASE__    :	int=1					,							SCREAMING_SNAKE_CASE__    :	str=0					,							SCREAMING_SNAKE_CASE__    :	str=0					,							SCREAMING_SNAKE_CASE__    :	int=None					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]=None					,							)		->      List[Any]:
             a_ :		Optional[Any]							=  parent
             a_ :		Any							=  batch_size
             a_ :		Any							=  encoder_seq_length
             a_ :		Union[str, Any]							=  decoder_seq_length
             # For common tests
             a_ :		Optional[int]							=  self.decoder_seq_length
             a_ :		List[str]							=  is_training
             a_ :		List[str]							=  use_attention_mask
             a_ :		Tuple							=  use_labels
             a_ :		List[str]							=  vocab_size
             a_ :		int							=  hidden_size
             a_ :		int							=  num_hidden_layers
             a_ :		str							=  num_attention_heads
             a_ :		Union[str, Any]							=  d_ff
             a_ :		Any							=  relative_attention_num_buckets
             a_ :		List[str]							=  dropout_rate
             a_ :		List[str]							=  initializer_factor
             a_ :		Optional[Any]							=  eos_token_id
             a_ :		Any							=  pad_token_id
             a_ :		int							=  decoder_start_token_id
             a_ :		Union[str, Any]							=  None
             a_ :		List[Any]							=  decoder_layers
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      Tuple:
             return TaConfig.from_pretrained('google/umt5-base'					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	str					,							SCREAMING_SNAKE_CASE__    :	Any					,							SCREAMING_SNAKE_CASE__    :	Dict=None					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=None					,							SCREAMING_SNAKE_CASE__    :	Any=None					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=None					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=None					,							)		->      Any:
             if attention_mask is None:
                    a_ :		Optional[Any]							=  input_ids.ne(config.pad_token_id					)
             if decoder_attention_mask is None:
                    a_ :		Optional[Any]							=  decoder_input_ids.ne(config.pad_token_id					)
             if head_mask is None:
                    a_ :		Optional[Any]							=  torch.ones(config.num_hidden_layers					,							config.num_attention_heads					,							device=SCREAMING_SNAKE_CASE__					)
             if decoder_head_mask is None:
                    a_ :		Optional[int]							=  torch.ones(config.num_decoder_layers					,							config.num_attention_heads					,							device=SCREAMING_SNAKE_CASE__					)
             if cross_attn_head_mask is None:
                    a_ :		Optional[int]							=  torch.ones(
                        config.num_decoder_layers					,							config.num_attention_heads					,							device=SCREAMING_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    :	Optional[Any]					)		->      List[str]:
             a_ :		Dict							=  ids_tensor([self.batch_size, self.encoder_seq_length]					,							self.vocab_size					)
             a_ :		Any							=  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
             a_ :		Optional[int]							=  input_ids.clamp(self.pad_token_id + 1					)
             a_ :		List[Any]							=  decoder_input_ids.clamp(self.pad_token_id + 1					)

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

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

             a_							,		a_ :		List[str]							=  outputs.to_tuple()

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

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

             a_ :		Tuple							=  model(SCREAMING_SNAKE_CASE__					)['last_hidden_state']
             a_ :		Dict							=  model(SCREAMING_SNAKE_CASE__					,							past_key_values=SCREAMING_SNAKE_CASE__					)['last_hidden_state']

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

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

      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	Optional[int]					,							)		->      Union[str, Any]:
             a_ :		Union[str, Any]							=  UMTaModel(config=SCREAMING_SNAKE_CASE__					).to(SCREAMING_SNAKE_CASE__					).half().eval()
             a_ :		Union[str, Any]							=  model(**SCREAMING_SNAKE_CASE__					)['last_hidden_state']
             self.parent.assertFalse(torch.isnan(SCREAMING_SNAKE_CASE__					).any().item()					)
@require_torch
class 			SCREAMING_SNAKE_CASE__  (    lowercase__						,   lowercase__						,   lowercase__						,   unittest.TestCase      ):
      snake_case__    :					Optional[int]					       =						(
          (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
      )
      snake_case__    :					Optional[Any]					       =						(UMTaForConditionalGeneration,) if is_torch_available() else ()
      snake_case__    :					Tuple					       =						(
          {
              '''conversational''': UMTaForConditionalGeneration,
              '''feature-extraction''': UMTaModel,
              '''summarization''': UMTaForConditionalGeneration,
              '''text2text-generation''': UMTaForConditionalGeneration,
              '''translation''': UMTaForConditionalGeneration,
              '''question-answering''': UMTaForQuestionAnswering,
          }
          if is_torch_available()
          else {}
      )
      snake_case__    :					Optional[int]					       =						True
      snake_case__    :					int					       =						False
      snake_case__    :					int					       =						False
      snake_case__    :					Optional[Any]					       =						True
      snake_case__    :					str					       =						True
      # The small UMT5 model needs higher percentages for CPU/MP tests
      snake_case__    :					List[str]					       =						[0.8, 0.9]
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      Dict:
             a_ :		int							=  UMTaModelTester(self					)
      @unittest.skip('Test has a segmentation fault on torch 1.8.0'					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	int					)		->      Optional[Any]:
             a_ :		List[str]							=  self.model_tester.prepare_config_and_inputs()
             a_ :		Any							=  UMTaModel(config_and_inputs[0]					).to(SCREAMING_SNAKE_CASE__					)
             with tempfile.TemporaryDirectory() as tmpdirname:
                    torch.onnx.export(
                        SCREAMING_SNAKE_CASE__					,							(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])					,							F"""{tmpdirname}/t5_test.onnx"""					,							export_params=SCREAMING_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    :	Tuple					)		->      Dict:
             a_ :		List[Any]							=  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model_fpaa_forward(*SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      Union[str, Any]:
             a_ :		Optional[Any]							=  ['encoder_attentions', 'decoder_attentions', 'cross_attentions']
             a_ :		Any							=  self.model_tester.prepare_config_and_inputs()
             a_ :		str							=  config_and_inputs[0]
             a_ :		Union[str, Any]							=  UMTaForConditionalGeneration(SCREAMING_SNAKE_CASE__					).eval()
             model.to(SCREAMING_SNAKE_CASE__					)

             a_ :		Optional[int]							=  {
                 'head_mask': torch.zeros(config.num_layers					,							config.num_heads					,							device=SCREAMING_SNAKE_CASE__					),
                 'decoder_head_mask': torch.zeros(config.num_decoder_layers					,							config.num_heads					,							device=SCREAMING_SNAKE_CASE__					),
                 'cross_attn_head_mask': torch.zeros(config.num_decoder_layers					,							config.num_heads					,							device=SCREAMING_SNAKE_CASE__					),
             }

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

                    a_ :		Union[str, Any]							=  model.generate(
                        config_and_inputs[1]['input_ids']					,							num_beams=1					,							max_length=3					,							output_attentions=SCREAMING_SNAKE_CASE__					,							return_dict_in_generate=SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					,							)
                    # We check the state of decoder_attentions and cross_attentions just from the last step
                    a_ :		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 	SCREAMING_SNAKE_CASE							(	self    :	int					)		->      List[str]:
             pass
@require_torch
@require_sentencepiece
@require_tokenizers
class 			SCREAMING_SNAKE_CASE__  (    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    :	List[str]					)		->      Dict:
             a_ :		str							=  UMTaForConditionalGeneration.from_pretrained('google/umt5-small'					,							return_dict=SCREAMING_SNAKE_CASE__					).to(SCREAMING_SNAKE_CASE__					)
             a_ :		Union[str, Any]							=  AutoTokenizer.from_pretrained('google/umt5-small'					,							use_fast=SCREAMING_SNAKE_CASE__					,							legacy=SCREAMING_SNAKE_CASE__					)
             a_ :		Dict							=  [
                 '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>.',
             ]
             a_ :		List[Any]							=  tokenizer(SCREAMING_SNAKE_CASE__					,							return_tensors='pt'					,							padding=SCREAMING_SNAKE_CASE__					).input_ids
             # fmt: off
             a_ :		Union[str, Any]							=  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(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)

             a_ :		Tuple							=  model.generate(input_ids.to(SCREAMING_SNAKE_CASE__					)					)
             a_ :		str							=  [
                 '<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>',
             ]
             a_ :		int							=  tokenizer.batch_decode(SCREAMING_SNAKE_CASE__					)
             self.assertEqual(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)






def 	SCREAMING_SNAKE_CASE_						(      __A			:      int    )     ->		int:

       """simple docstring"""

       if not isinstance(__A  ,   __A    ):
              raise ValueError('Input must be an integer'    )
       if input_num <= 0:
              raise ValueError('Input must be positive'    )
       return sum(
           divisor for divisor in range(1  ,   input_num // 2 + 1    ) if input_num % divisor == 0    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()


