





import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
       from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase=None      ,  UpperCAmelCase=None      ,  UpperCAmelCase=None      ,  UpperCAmelCase=None      ,  UpperCAmelCase=None      ,  )    ->    int:







   """simple docstring"""



   if attention_mask is None:
      lowerCamelCase__       :						Dict        =							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__       :						int        =							torch.ones(config.encoder_layers      ,  config.encoder_attention_heads      ,  device=a__					)
   if decoder_head_mask is None:
      lowerCamelCase__       :						Dict        =							torch.ones(config.decoder_layers      ,  config.decoder_attention_heads      ,  device=a__					)
   if cross_attn_head_mask is None:
      lowerCamelCase__       :						Optional[int]        =							torch.ones(config.decoder_layers      ,  config.decoder_attention_heads      ,  device=a__					)
   return {
       "input_ids": input_ids,
       "decoder_input_ids": decoder_input_ids,
       "attention_mask": attention_mask,
       "decoder_attention_mask": attention_mask,
       "head_mask": head_mask,
       "decoder_head_mask": decoder_head_mask,
       "cross_attn_head_mask": cross_attn_head_mask,
   }


class        __SCREAMING_SNAKE_CASE      :







       def __init__(				self					:     List[Any]		, A					:     List[Any]		, A					:     Tuple=1_3		, A					:     Optional[int]=7		, A					:     List[str]=True		, A					:     Union[str, Any]=False		, A					:     List[str]=9_9		, A					:     Optional[Any]=1_6		, A					:     int=2		, A					:     Tuple=4		, A					:     Dict=4		, A					:     Tuple="relu"		, A					:     Tuple=0.1		, A					:     Optional[int]=0.1		, A					:     str=0.0		, A					:     Optional[Any]=0.0		, A					:     Optional[Any]=2_0		, A					:     Optional[int]=2		, A					:     Any=1		, A					:     Union[str, Any]=0		, )       ->Optional[Any]:
          lowerCamelCase__       :						List[str]        =							parent
          lowerCamelCase__       :						Optional[int]        =							batch_size
          lowerCamelCase__       :						Optional[int]        =							seq_length
          lowerCamelCase__       :						Tuple        =							is_training
          lowerCamelCase__       :						List[Any]        =							use_labels
          lowerCamelCase__       :						Any        =							vocab_size
          lowerCamelCase__       :						int        =							hidden_size
          lowerCamelCase__       :						str        =							num_hidden_layers
          lowerCamelCase__       :						Optional[Any]        =							num_attention_heads
          lowerCamelCase__       :						str        =							intermediate_size
          lowerCamelCase__       :						int        =							hidden_act
          lowerCamelCase__       :						str        =							hidden_dropout_prob
          lowerCamelCase__       :						List[str]        =							attention_probs_dropout_prob
          lowerCamelCase__       :						str        =							encoder_layerdrop
          lowerCamelCase__       :						Any        =							decoder_layerdrop
          lowerCamelCase__       :						str        =							max_position_embeddings
          lowerCamelCase__       :						Any        =							eos_token_id
          lowerCamelCase__       :						int        =							pad_token_id
          lowerCamelCase__       :						str        =							bos_token_id







       def 						__lowerCamelCase   (				self					:     Tuple      )       ->List[Any]:
          lowerCamelCase__       :						Any        =							ids_tensor([self.batch_size, self.seq_length]		, self.vocab_size      )
          lowerCamelCase__       :						int        =							self.eos_token_id  # Eos Token
          lowerCamelCase__       :						Tuple        =							ids_tensor([self.batch_size, self.seq_length]		, self.vocab_size      )

          # we need to clamp the input ids here to avoid having pad token in between
          # this is because for M2M100 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__       :						List[str]        =							input_ids.clamp(self.pad_token_id + 1      )
          lowerCamelCase__       :						int        =							decoder_input_ids.clamp(self.pad_token_id + 1      )

          lowerCamelCase__       :						Tuple        =							self.get_config()
          lowerCamelCase__       :						str        =							prepare_mam_aaa_inputs_dict(_lowerCamelCase		, _lowerCamelCase		, _lowerCamelCase      )
          return config, inputs_dict







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Union[str, Any]:
          return MaMaaaConfig(
              vocab_size=self.vocab_size		, d_model=self.hidden_size		, encoder_layers=self.num_hidden_layers		, decoder_layers=self.num_hidden_layers		, encoder_attention_heads=self.num_attention_heads		, decoder_attention_heads=self.num_attention_heads		, encoder_ffn_dim=self.intermediate_size		, decoder_ffn_dim=self.intermediate_size		, dropout=self.hidden_dropout_prob		, attention_dropout=self.attention_probs_dropout_prob		, encoder_layerdrop=self.encoder_layerdrop		, decoder_layerdrop=self.decoder_layerdrop		, max_position_embeddings=self.max_position_embeddings		, eos_token_id=self.eos_token_id		, bos_token_id=self.bos_token_id		, pad_token_id=self.pad_token_id		, )







       def 						__lowerCamelCase   (				self					:     str      )       ->List[str]:
          lowerCamelCase__       :						int        =							self.prepare_config_and_inputs()
          return config, inputs_dict







       def 						__lowerCamelCase   (				self					:     Optional[Any]		, A					:     int		, A					:     Tuple      )       ->Dict:
          lowerCamelCase__       :						Optional[Any]        =							MaMaaaModel(config=_lowerCamelCase      ).get_decoder().to(_lowerCamelCase      ).eval()
          lowerCamelCase__       :						int        =							inputs_dict['''input_ids''']
          lowerCamelCase__       :						str        =							inputs_dict['''attention_mask''']
          lowerCamelCase__       :						int        =							inputs_dict['''head_mask''']

          # first forward pass
          lowerCamelCase__       :						Optional[Any]        =							model(_lowerCamelCase		, attention_mask=_lowerCamelCase		, head_mask=_lowerCamelCase		, use_cache=_lowerCamelCase      )

          lowerCamelCase__       :						int        =							outputs.to_tuple()

          # create hypothetical multiple next token and extent to next_input_ids
          lowerCamelCase__       :						Optional[int]        =							ids_tensor((self.batch_size, 3)		, config.vocab_size      )
          lowerCamelCase__       :						List[Any]        =							ids_tensor((self.batch_size, 3)		, 2      )

          # append to next input_ids and
          lowerCamelCase__       :						Union[str, Any]        =							torch.cat([input_ids, next_tokens]		, dim=-1      )
          lowerCamelCase__       :						Optional[int]        =							torch.cat([attention_mask, next_attn_mask]		, dim=-1      )

          lowerCamelCase__       :						str        =							model(_lowerCamelCase		, attention_mask=_lowerCamelCase      )['''last_hidden_state''']
          lowerCamelCase__       :						Dict        =							model(_lowerCamelCase		, attention_mask=_lowerCamelCase		, past_key_values=_lowerCamelCase      )[
              '''last_hidden_state'''
          ]

          # select random slice
          lowerCamelCase__       :						Tuple        =							ids_tensor((1,)		, output_from_past.shape[-1]      ).item()
          lowerCamelCase__       :						Optional[int]        =							output_from_no_past[:, -3:, random_slice_idx].detach()
          lowerCamelCase__       :						Optional[Any]        =							output_from_past[:, :, random_slice_idx].detach()

          self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]      )

          # test that outputs are equal for slice
          self.parent.assertTrue(torch.allclose(_lowerCamelCase		, _lowerCamelCase		, atol=1e-2      )      )







       def 						__lowerCamelCase   (				self					:     List[str]		, A					:     Any		, A					:     str      )       ->Optional[int]:
          lowerCamelCase__       :						List[Any]        =							MaMaaaModel(config=_lowerCamelCase      ).to(_lowerCamelCase      ).eval()
          lowerCamelCase__       :						Tuple        =							model(**_lowerCamelCase      )

          lowerCamelCase__       :						Any        =							outputs.encoder_last_hidden_state
          lowerCamelCase__       :						Optional[int]        =							outputs.last_hidden_state

          with tempfile.TemporaryDirectory() as tmpdirname:
             lowerCamelCase__       :						str        =							model.get_encoder()
             encoder.save_pretrained(_lowerCamelCase      )
             lowerCamelCase__       :						List[str]        =							MaMaaaEncoder.from_pretrained(_lowerCamelCase      ).to(_lowerCamelCase      )

          lowerCamelCase__       :						int        =							encoder(inputs_dict['''input_ids''']		, attention_mask=inputs_dict['''attention_mask''']      )[
              0
          ]

          self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3      )

          with tempfile.TemporaryDirectory() as tmpdirname:
             lowerCamelCase__       :						List[Any]        =							model.get_decoder()
             decoder.save_pretrained(_lowerCamelCase      )
             lowerCamelCase__       :						int        =							MaMaaaDecoder.from_pretrained(_lowerCamelCase      ).to(_lowerCamelCase      )

          lowerCamelCase__       :						List[Any]        =							decoder(
              input_ids=inputs_dict['''decoder_input_ids''']		, attention_mask=inputs_dict['''decoder_attention_mask''']		, encoder_hidden_states=_lowerCamelCase		, encoder_attention_mask=inputs_dict['''attention_mask''']		, )[0]

          self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3      )


@require_torch
class        __SCREAMING_SNAKE_CASE      (			a__					,a__					,a__					,unittest.TestCase ):
       snake_case				:  Union[str, Any]			   =   (
           (
               MaMaaaModel,
               MaMaaaForConditionalGeneration,
           )
           if is_torch_available()
           else ()
       )
       snake_case				:  Any			   =   (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
       snake_case				:  Optional[int]			   =   (
           {
               'conversational': MaMaaaForConditionalGeneration,
               'feature-extraction': MaMaaaModel,
               'summarization': MaMaaaForConditionalGeneration,
               'text2text-generation': MaMaaaForConditionalGeneration,
               'translation': MaMaaaForConditionalGeneration,
           }
           if is_torch_available()
           else {}
       )
       snake_case				:  str			   =   True
       snake_case				:  int			   =   True
       snake_case				:  Any			   =   False
       snake_case				:  int			   =   False







       def 						__lowerCamelCase   (				self					:     Optional[int]		, A					:     Optional[int]		, A					:     Optional[Any]		, A					:     Union[str, Any]		, A					:     Dict		, A					:     Optional[Any]      )       ->str:
          if pipeline_test_casse_name == "TranslationPipelineTests":
             # Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
             # `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
             return True

          return False







       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->Any:
          lowerCamelCase__       :						Dict        =							MaMaaaModelTester(self      )
          lowerCamelCase__       :						Dict        =							ConfigTester(self		, config_class=_lowerCamelCase      )







       def 						__lowerCamelCase   (				self					:     int      )       ->Tuple:
          self.config_tester.run_common_tests()







       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->str:
          lowerCamelCase__       :						Union[str, Any]        =							self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
             lowerCamelCase__       :						Dict        =							model_class(_lowerCamelCase      )

             with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(_lowerCamelCase      )
                lowerCamelCase__       :						str        =							model_class.from_pretrained(_lowerCamelCase		, output_loading_info=_lowerCamelCase      )
             self.assertEqual(info['''missing_keys''']		, []      )







       def 						__lowerCamelCase   (				self					:     str      )       ->Optional[int]:
          lowerCamelCase__       :						Any        =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_decoder_model_past_large_inputs(*_lowerCamelCase      )







       def 						__lowerCamelCase   (				self					:     int      )       ->Optional[Any]:
          lowerCamelCase__       :						List[str]        =							self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.check_encoder_decoder_model_standalone(*_lowerCamelCase      )







       def 						__lowerCamelCase   (				self					:     str      )       ->Any:
          lowerCamelCase__       :						Optional[int]        =							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
             lowerCamelCase__       :						Any        =							model_class(_lowerCamelCase      )
             model.to(_lowerCamelCase      )
             model.eval()

             lowerCamelCase__       :						Optional[int]        =							copy.deepcopy(self._prepare_for_class(_lowerCamelCase		, _lowerCamelCase      )      )

             if not self.is_encoder_decoder:
                lowerCamelCase__       :						int        =							inputs['''input_ids''']
                del inputs["input_ids"]
             else:
                lowerCamelCase__       :						int        =							inputs['''input_ids''']
                lowerCamelCase__       :						int        =							inputs.get('''decoder_input_ids'''		, _lowerCamelCase      )
                del inputs["input_ids"]
                inputs.pop('''decoder_input_ids'''		, _lowerCamelCase      )

             lowerCamelCase__       :						List[Any]        =							model.get_input_embeddings()
             if not self.is_encoder_decoder:
                lowerCamelCase__       :						Optional[Any]        =							wte(_lowerCamelCase      )
             else:
                lowerCamelCase__       :						List[str]        =							wte(_lowerCamelCase      )
                lowerCamelCase__       :						Optional[int]        =							wte(_lowerCamelCase      )

             with torch.no_grad():
                model(**_lowerCamelCase      )[0]







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Optional[int]:
          lowerCamelCase__       :						Any        =							self.model_tester.prepare_config_and_inputs()
          lowerCamelCase__       :						Optional[int]        =							input_dict['''input_ids''']
          lowerCamelCase__       :						Tuple        =							input_ids.ne(1      ).to(_lowerCamelCase      )
          lowerCamelCase__       :						Optional[int]        =							MaMaaaForConditionalGeneration(_lowerCamelCase      ).eval().to(_lowerCamelCase      )
          if torch_device == "cuda":
             model.half()
          model.generate(_lowerCamelCase		, attention_mask=_lowerCamelCase      )
          model.generate(num_beams=4		, do_sample=_lowerCamelCase		, early_stopping=_lowerCamelCase		, num_return_sequences=3      )



def 	_a						(   UpperCAmelCase					)    ->    int:







   """simple docstring"""
   return torch.tensor(a__      ,  dtype=torch.long      ,  device=a__					)


_A :   Optional[int]  			=  1e-4




@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class        __SCREAMING_SNAKE_CASE      (			unittest.TestCase ):







       @cached_property
       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->Any:
          return MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M'''      )







       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->Dict:
          lowerCamelCase__       :						List[str]        =							MaMaaaModel.from_pretrained('''facebook/m2m100_418M'''      ).to(_lowerCamelCase      )
          lowerCamelCase__       :						Dict        =							_long_tensor([[1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8, 2]]      )
          lowerCamelCase__       :						Any        =							_long_tensor([[2, 1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8]]      )
          lowerCamelCase__       :						int        =							prepare_mam_aaa_inputs_dict(model.config		, _lowerCamelCase		, _lowerCamelCase      )
          with torch.no_grad():
             lowerCamelCase__       :						str        =							model(**_lowerCamelCase      )[0]
          lowerCamelCase__       :						Tuple        =							torch.Size((1, 1_1, 1_0_2_4)      )
          self.assertEqual(output.shape		, _lowerCamelCase      )
          # change to expected output here
          lowerCamelCase__       :						List[str]        =							torch.tensor(
              [[-0.77_80, -0.16_76, 0.10_38], [-6.75_56, -1.39_92, 0.05_67], [-7.53_83, -0.59_20, -0.27_79]]		, device=_lowerCamelCase      )
          self.assertTrue(torch.allclose(output[:, :3, :3]		, _lowerCamelCase		, atol=_lowerCamelCase      )      )







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Optional[int]:
          lowerCamelCase__       :						Tuple        =							MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M'''      ).to(_lowerCamelCase      )

          # change to intended input
          lowerCamelCase__       :						List[Any]        =							_long_tensor([[1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8, 2]]      )
          lowerCamelCase__       :						Optional[int]        =							_long_tensor([[2, 1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8]]      )
          lowerCamelCase__       :						Any        =							prepare_mam_aaa_inputs_dict(model.config		, _lowerCamelCase		, _lowerCamelCase      )
          with torch.no_grad():
             lowerCamelCase__       :						int        =							model(**_lowerCamelCase      )[0]
          lowerCamelCase__       :						Tuple        =							torch.Size((1, 1_1, model.config.vocab_size)      )
          self.assertEqual(output.shape		, _lowerCamelCase      )
          # change to expected output here
          lowerCamelCase__       :						List[str]        =							torch.tensor(
              [[-1.04_48, -1.04_11, 3.79_92], [-3.21_91, -3.23_86, -1.34_51], [-3.62_10, -3.59_93, 0.49_25]]		, device=_lowerCamelCase      )
          self.assertTrue(torch.allclose(output[:, :3, :3]		, _lowerCamelCase		, atol=_lowerCamelCase      )      )







       def 						__lowerCamelCase   (				self					:     Any      )       ->List[Any]:
          lowerCamelCase__       :						List[str]        =							MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M'''      ).to(_lowerCamelCase      )
          lowerCamelCase__       :						Optional[Any]        =							MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M'''		, src_lang='''fr'''		, tgt_lang='''en'''      )

          lowerCamelCase__       :						str        =							[
              '''L\'affaire NSA souligne l\'absence totale de débat sur le renseignement''',
              '''Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.''',
              '''Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent'''
              ''' Fabius convoque l\'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de'''
              ''' l\'ampleur de la surveillance américaine sur l\'ensemble des communications en France.''',
          ]

          # The below article tests that we don't add any hypotheses outside of the top n_beams
          lowerCamelCase__       :						List[str]        =							tokenizer(_lowerCamelCase		, padding=_lowerCamelCase		, return_tensors='''pt'''      )

          lowerCamelCase__       :						str        =							model.generate(
              input_ids=dct['''input_ids'''].to(_lowerCamelCase      )		, attention_mask=dct['''attention_mask'''].to(_lowerCamelCase      )		, num_beams=5		, forced_bos_token_id=tokenizer.get_lang_id('''en'''      )		, )

          lowerCamelCase__       :						Dict        =							[
              '''The NSA case highlights the total absence of intelligence debate''',
              '''I think there are two levels of response from the French government.''',
              '''When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'''
              ''' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'''
              ''' communications in France.''',
          ]

          lowerCamelCase__       :						int        =							tokenizer.batch_decode(
              hypotheses_batch.tolist()		, clean_up_tokenization_spaces=_lowerCamelCase		, skip_special_tokens=_lowerCamelCase      )
          assert generated == expected_en










def 	_a						(   UpperCAmelCase					)    ->    bool:







   """simple docstring"""
   return str(UpperCAmelCase					) == str(UpperCAmelCase					)[::-1]



def 	_a						(   UpperCAmelCase					)    ->    int:







   """simple docstring"""
   return int(UpperCAmelCase					) + int(str(UpperCAmelCase					)[::-1]					)



def 	_a						(   UpperCAmelCase = 10000					)    ->    int:







   """simple docstring"""
   lowerCamelCase__       :						Tuple        =							[]
   for num in range(1      ,  UpperCAmelCase					):
      lowerCamelCase__       :						List[str]        =							0
      lowerCamelCase__       :						Union[str, Any]        =							num
      while iterations < 50:
         lowerCamelCase__       :						Dict        =							sum_reverse(UpperCAmelCase					)
         iterations += 1
         if is_palindrome(UpperCAmelCase					):
            break
      else:
         lychrel_nums.append(UpperCAmelCase					)
   return len(UpperCAmelCase					)


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



