





"""simple docstring"""
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       __snake_case    (   __A			,__A			,__A			,__A=None			,__A=None			,__A=None			,__A=None			,__A=None			,)		->					Tuple:


   if attention_mask is None:
      lowercase     :      Optional[int]	=							input_ids.ne(config.pad_token_id     )
   if decoder_attention_mask is None:
      lowercase     :      Optional[Any]	=							decoder_input_ids.ne(config.pad_token_id     )
   if head_mask is None:
      lowercase     :      Dict	=							torch.ones(config.encoder_layers			,config.encoder_attention_heads			,device=_UpperCamelCase     )
   if decoder_head_mask is None:
      lowercase     :      Any	=							torch.ones(config.decoder_layers			,config.decoder_attention_heads			,device=_UpperCamelCase     )
   if cross_attn_head_mask is None:
      lowercase     :      Union[str, Any]	=							torch.ones(config.decoder_layers			,config.decoder_attention_heads			,device=_UpperCamelCase     )
   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 lowerCamelCase__	:



   def __init__(							self   , snake_case   , snake_case=1_3   , snake_case=7   , snake_case=True   , snake_case=False   , snake_case=9_9   , snake_case=1_6   , snake_case=2   , snake_case=4   , snake_case=4   , snake_case="relu"   , snake_case=0.1   , snake_case=0.1   , snake_case=0.0   , snake_case=0.0   , snake_case=2_0   , snake_case=2   , snake_case=1   , snake_case=0   , )			->     Union[str, Any]:







      """simple docstring"""
      lowercase     :      int	=							parent
      lowercase     :      List[str]	=							batch_size
      lowercase     :      str	=							seq_length
      lowercase     :      List[Any]	=							is_training
      lowercase     :      int	=							use_labels
      lowercase     :      Tuple	=							vocab_size
      lowercase     :      List[Any]	=							hidden_size
      lowercase     :      int	=							num_hidden_layers
      lowercase     :      Optional[int]	=							num_attention_heads
      lowercase     :      List[str]	=							intermediate_size
      lowercase     :      Tuple	=							hidden_act
      lowercase     :      int	=							hidden_dropout_prob
      lowercase     :      Tuple	=							attention_probs_dropout_prob
      lowercase     :      Union[str, Any]	=							encoder_layerdrop
      lowercase     :      List[str]	=							decoder_layerdrop
      lowercase     :      List[Any]	=							max_position_embeddings
      lowercase     :      str	=							eos_token_id
      lowercase     :      str	=							pad_token_id
      lowercase     :      str	=							bos_token_id



   def  _UpperCAmelCase						(							self						)			->     str:







      """simple docstring"""
      lowercase     :      Dict	=							ids_tensor([self.batch_size, self.seq_length]   , self.vocab_size						)
      lowercase     :      Union[str, Any]	=							self.eos_token_id  # Eos Token
      lowercase     :      Optional[Any]	=							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
      lowercase     :      List[Any]	=							input_ids.clamp(self.pad_token_id + 1						)
      lowercase     :      Union[str, Any]	=							decoder_input_ids.clamp(self.pad_token_id + 1						)

      lowercase     :      str	=							self.get_config()
      lowercase     :      str	=							prepare_mam_aaa_inputs_dict(snake_case__   , snake_case__   , snake_case__						)
      return config, inputs_dict



   def  _UpperCAmelCase						(							self						)			->     Dict:







      """simple docstring"""
      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  _UpperCAmelCase						(							self						)			->     Dict:







      """simple docstring"""
      lowercase	,       lowercase     :      int	=							self.prepare_config_and_inputs()
      return config, inputs_dict



   def  _UpperCAmelCase						(							self   , snake_case   , snake_case						)			->     Tuple:







      """simple docstring"""
      lowercase     :      int	=							MaMaaaModel(config=snake_case__						).get_decoder().to(snake_case__						).eval()
      lowercase     :      int	=							inputs_dict["""input_ids"""]
      lowercase     :      List[str]	=							inputs_dict["""attention_mask"""]
      lowercase     :      List[str]	=							inputs_dict["""head_mask"""]

      # first forward pass
      lowercase     :      Dict	=							model(snake_case__   , attention_mask=snake_case__   , head_mask=snake_case__   , use_cache=snake_case__						)

      lowercase	,       lowercase     :      int	=							outputs.to_tuple()

      # create hypothetical multiple next token and extent to next_input_ids
      lowercase     :      Union[str, Any]	=							ids_tensor((self.batch_size, 3)   , config.vocab_size						)
      lowercase     :      Union[str, Any]	=							ids_tensor((self.batch_size, 3)   , 2						)

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

      lowercase     :      Dict	=							model(snake_case__   , attention_mask=snake_case__						)["""last_hidden_state"""]
      lowercase     :      List[Any]	=							model(snake_case__   , attention_mask=snake_case__   , past_key_values=snake_case__						)[
          """last_hidden_state"""
      ]

      # select random slice
      lowercase     :      Tuple	=							ids_tensor((1,)   , output_from_past.shape[-1]						).item()
      lowercase     :      Optional[Any]	=							output_from_no_past[:, -3:, random_slice_idx].detach()
      lowercase     :      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(snake_case__   , snake_case__   , atol=1E-2						)						)





   def  _UpperCAmelCase						(							self   , snake_case   , snake_case						)			->     Tuple:







      """simple docstring"""
      lowercase     :      List[Any]	=							MaMaaaModel(config=snake_case__						).to(snake_case__						).eval()
      lowercase     :      Optional[Any]	=							model(**snake_case__						)

      lowercase     :      Union[str, Any]	=							outputs.encoder_last_hidden_state
      lowercase     :      str	=							outputs.last_hidden_state

      with tempfile.TemporaryDirectory() as tmpdirname:
         lowercase     :      str	=							model.get_encoder()
         encoder.save_pretrained(snake_case__						)
         lowercase     :      str	=							MaMaaaEncoder.from_pretrained(snake_case__						).to(snake_case__						)

      lowercase     :      Union[str, Any]	=							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:
         lowercase     :      Tuple	=							model.get_decoder()
         decoder.save_pretrained(snake_case__						)
         lowercase     :      str	=							MaMaaaDecoder.from_pretrained(snake_case__						).to(snake_case__						)

      lowercase     :      Union[str, Any]	=							decoder(
          input_ids=inputs_dict["""decoder_input_ids"""]   , attention_mask=inputs_dict["""decoder_attention_mask"""]   , encoder_hidden_states=snake_case__   , 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 lowerCamelCase__	(     __UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					unittest.TestCase					):
   __UpperCAmelCase										=       (
       (
           MaMaaaModel,
           MaMaaaForConditionalGeneration,
       )
       if is_torch_available()
       else ()
   )
   __UpperCAmelCase										=       (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
   __UpperCAmelCase										=       (
       {
           """conversational""": MaMaaaForConditionalGeneration,
           """feature-extraction""": MaMaaaModel,
           """summarization""": MaMaaaForConditionalGeneration,
           """text2text-generation""": MaMaaaForConditionalGeneration,
           """translation""": MaMaaaForConditionalGeneration,
       }
       if is_torch_available()
       else {}
   )
   __UpperCAmelCase										=       True
   __UpperCAmelCase										=       True
   __UpperCAmelCase										=       False
   __UpperCAmelCase										=       False



   def  _UpperCAmelCase						(							self   , snake_case   , snake_case   , snake_case   , snake_case   , snake_case						)			->     Optional[Any]:







      """simple docstring"""






      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  _UpperCAmelCase						(							self						)			->     Tuple:







      """simple docstring"""
      lowercase     :      Dict	=							MaMaaaModelTester(self						)
      lowercase     :      Union[str, Any]	=							ConfigTester(self   , config_class=snake_case__						)



   def  _UpperCAmelCase						(							self						)			->     Dict:







      """simple docstring"""
      self.config_tester.run_common_tests()



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







      """simple docstring"""
      lowercase	,       lowercase     :      Tuple	=							self.model_tester.prepare_config_and_inputs()
      for model_class in self.all_model_classes:
         lowercase     :      Any	=							model_class(snake_case__						)

         with tempfile.TemporaryDirectory() as tmpdirname:
            model.save_pretrained(snake_case__						)
            lowercase	,       lowercase     :      Tuple	=							model_class.from_pretrained(snake_case__   , output_loading_info=snake_case__						)
         self.assertEqual(info["""missing_keys"""]   , []						)



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







      """simple docstring"""
      lowercase     :      List[Any]	=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_decoder_model_past_large_inputs(*snake_case__						)



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







      """simple docstring"""
      lowercase     :      List[str]	=							self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_encoder_decoder_model_standalone(*snake_case__						)



   def  _UpperCAmelCase						(							self						)			->     Optional[int]:







      """simple docstring"""
      lowercase	,       lowercase     :      int	=							self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
         lowercase     :      str	=							model_class(snake_case__						)
         model.to(snake_case__						)
         model.eval()

         lowercase     :      Optional[Any]	=							copy.deepcopy(self._prepare_for_class(snake_case__   , snake_case__						)						)

         if not self.is_encoder_decoder:
            lowercase     :      Dict	=							inputs["""input_ids"""]
            del inputs["input_ids"]
         else:
            lowercase     :      str	=							inputs["""input_ids"""]
            lowercase     :      Tuple	=							inputs.get("""decoder_input_ids"""   , snake_case__						)
            del inputs["input_ids"]
            inputs.pop("""decoder_input_ids"""   , snake_case__						)

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

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





   def  _UpperCAmelCase						(							self						)			->     Tuple:







      """simple docstring"""
      lowercase	,       lowercase     :      Any	=							self.model_tester.prepare_config_and_inputs()
      lowercase     :      Optional[Any]	=							input_dict["""input_ids"""]
      lowercase     :      str	=							input_ids.ne(1						).to(snake_case__						)
      lowercase     :      Union[str, Any]	=							MaMaaaForConditionalGeneration(snake_case__						).eval().to(snake_case__						)
      if torch_device == "cuda":
         model.half()
      model.generate(snake_case__   , attention_mask=snake_case__						)
      model.generate(num_beams=4   , do_sample=snake_case__   , early_stopping=snake_case__   , num_return_sequences=3						)




def       __snake_case    (   __A     )		->					Dict:
   return torch.tensor(_UpperCamelCase			,dtype=torch.long			,device=_UpperCamelCase     )


lowerCAmelCase:						Optional[int]				   =1e-4







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



   @cached_property
   def  _UpperCAmelCase						(							self						)			->     Tuple:







      """simple docstring"""
      return MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M"""						)



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







      """simple docstring"""
      lowercase     :      List[str]	=							MaMaaaModel.from_pretrained("""facebook/m2m100_418M"""						).to(snake_case__						)
      lowercase     :      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]]						)
      lowercase     :      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]]						)
      lowercase     :      Dict	=							prepare_mam_aaa_inputs_dict(model.config   , snake_case__   , snake_case__						)
      with torch.no_grad():
         lowercase     :      Tuple	=							model(**snake_case__						)[0]
      lowercase     :      List[str]	=							torch.Size((1, 1_1, 1_0_2_4)						)
      self.assertEqual(output.shape   , snake_case__						)
      # change to expected output here
      lowercase     :      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=snake_case__						)
      self.assertTrue(torch.allclose(output[:, :3, :3]   , snake_case__   , atol=snake_case__						)						)



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







      """simple docstring"""
      lowercase     :      List[str]	=							MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M"""						).to(snake_case__						)

      # change to intended input
      lowercase     :      Tuple	=							_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]]						)
      lowercase     :      List[str]	=							_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]]						)
      lowercase     :      str	=							prepare_mam_aaa_inputs_dict(model.config   , snake_case__   , snake_case__						)
      with torch.no_grad():
         lowercase     :      Union[str, Any]	=							model(**snake_case__						)[0]
      lowercase     :      Tuple	=							torch.Size((1, 1_1, model.config.vocab_size)						)
      self.assertEqual(output.shape   , snake_case__						)
      # change to expected output here
      lowercase     :      Any	=							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=snake_case__						)
      self.assertTrue(torch.allclose(output[:, :3, :3]   , snake_case__   , atol=snake_case__						)						)





   def  _UpperCAmelCase						(							self						)			->     str:







      """simple docstring"""
      lowercase     :      List[str]	=							MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M"""						).to(snake_case__						)
      lowercase     :      Dict	=							MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M"""   , src_lang="""fr"""   , tgt_lang="""en"""						)

      lowercase     :      Union[str, Any]	=							[
          """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
      lowercase     :      Dict	=							tokenizer(snake_case__   , padding=snake_case__   , return_tensors="""pt"""						)

      lowercase     :      Tuple	=							model.generate(
          input_ids=dct["""input_ids"""].to(snake_case__						)   , attention_mask=dct["""attention_mask"""].to(snake_case__						)   , num_beams=5   , forced_bos_token_id=tokenizer.get_lang_id("""en"""						)   , )

      lowercase     :      Any	=							[
          """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.""",
      ]

      lowercase     :      Dict	=							tokenizer.batch_decode(
          hypotheses_batch.tolist()   , clean_up_tokenization_spaces=snake_case__   , skip_special_tokens=snake_case__						)
      assert generated == expected_en





import string


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str						)		->				str:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      ''
 for i in sequence:
  SCREAMING_SNAKE_CASE		    =      ord(_UpperCamelCase						)
  if 65 <= extract <= 90:
   output += chr(1_55 - extract						)
  elif 97 <= extract <= 1_22:
   output += chr(2_19 - extract						)
  else:
   output += i
 return output


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str						)		->				str:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      string.ascii_letters
 SCREAMING_SNAKE_CASE		    =      string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
 return "".join(
     letters_reversed[letters.index(_UpperCamelCase						)] if c in letters else c for c in sequence						)


def 							__lowerCAmelCase     (							)		->				None:
 '''simple docstring'''


 from timeit import timeit

 print('Running performance benchmarks...'						)
 SCREAMING_SNAKE_CASE		    =      'from string import printable ; from __main__ import atbash, atbash_slow'
 print(f"""> atbash_slow(): {timeit('atbash_slow(printable)'		,				setup=_UpperCamelCase						)} seconds"""						)
 print(f""">      atbash(): {timeit('atbash(printable)'		,				setup=_UpperCamelCase						)} seconds"""						)


if __name__ == "__main__":
      for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
            print(F"""{example} encrypted in atbash: {atbash(example)}""")
      benchmark()

