


'''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        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE=None				,  __SCREAMING_SNAKE_CASE=None				,  __SCREAMING_SNAKE_CASE=None				,  __SCREAMING_SNAKE_CASE=None				,  __SCREAMING_SNAKE_CASE=None				,  )				->					Optional[Any]:
     """simple docstring"""




     if attention_mask is None:
          __a       =   input_ids.ne(config.pad_token_id		)
     if decoder_attention_mask is None:
          __a       =   decoder_input_ids.ne(config.pad_token_id		)
     if head_mask is None:
          __a       =   torch.ones(config.encoder_layers				,  config.encoder_attention_heads				,  device=__SCREAMING_SNAKE_CASE		)
     if decoder_head_mask is None:
          __a       =   torch.ones(config.decoder_layers				,  config.decoder_attention_heads				,  device=__SCREAMING_SNAKE_CASE		)
     if cross_attn_head_mask is None:
          __a       =   torch.ones(config.decoder_layers				,  config.decoder_attention_heads				,  device=__SCREAMING_SNAKE_CASE		)
     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_						:





  """simple docstring"""



  def __init__(					self     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							str				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]=1_3				,							SCREAMING_SNAKE_CASE__     :							Dict=7				,							SCREAMING_SNAKE_CASE__     :							Tuple=True				,							SCREAMING_SNAKE_CASE__     :							Tuple=False				,							SCREAMING_SNAKE_CASE__     :							Tuple=9_9				,							SCREAMING_SNAKE_CASE__     :							Optional[int]=1_6				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]=2				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]=4				,							SCREAMING_SNAKE_CASE__     :							Dict=4				,							SCREAMING_SNAKE_CASE__     :							Dict="relu"				,							SCREAMING_SNAKE_CASE__     :							str=0.1				,							SCREAMING_SNAKE_CASE__     :							List[str]=0.1				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]=0.0				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]=0.0				,							SCREAMING_SNAKE_CASE__     :							Optional[int]=2_0				,							SCREAMING_SNAKE_CASE__     :							Dict=2				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]=1				,							SCREAMING_SNAKE_CASE__     :							Tuple=0				,							):





       '''simple docstring'''

       __a       =   parent
       __a       =   batch_size
       __a       =   seq_length
       __a       =   is_training
       __a       =   use_labels
       __a       =   vocab_size
       __a       =   hidden_size
       __a       =   num_hidden_layers
       __a       =   num_attention_heads
       __a       =   intermediate_size
       __a       =   hidden_act
       __a       =   hidden_dropout_prob
       __a       =   attention_probs_dropout_prob
       __a       =   encoder_layerdrop
       __a       =   decoder_layerdrop
       __a       =   max_position_embeddings
       __a       =   eos_token_id
       __a       =   pad_token_id
       __a       =   bos_token_id

  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       __a       =   ids_tensor([self.batch_size, self.seq_length]				,							self.vocab_size						)
       __a       =   self.eos_token_id  # Eos Token
       __a       =   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
       __a       =   input_ids.clamp(self.pad_token_id + 1						)
       __a       =   decoder_input_ids.clamp(self.pad_token_id + 1						)

       __a       =   self.get_config()
       __a       =   prepare_mam_aaa_inputs_dict(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						)
       return config, inputs_dict

  def __a						(					self     :							List[str]						):





       '''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 __a						(					self     :							str						):





       '''simple docstring'''

       __a ,						__a       =   self.prepare_config_and_inputs()
       return config, inputs_dict

  def __a						(					self     :							Optional[int]				,							SCREAMING_SNAKE_CASE__     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							Any						):





       '''simple docstring'''

       __a       =   MaMaaaModel(config=SCREAMING_SNAKE_CASE__						).get_decoder().to(SCREAMING_SNAKE_CASE__						).eval()
       __a       =   inputs_dict["""input_ids"""]
       __a       =   inputs_dict["""attention_mask"""]
       __a       =   inputs_dict["""head_mask"""]

       # first forward pass
       __a       =   model(SCREAMING_SNAKE_CASE__				,							attention_mask=SCREAMING_SNAKE_CASE__				,							head_mask=SCREAMING_SNAKE_CASE__				,							use_cache=SCREAMING_SNAKE_CASE__						)

       __a ,						__a       =   outputs.to_tuple()

       # create hypothetical multiple next token and extent to next_input_ids
       __a       =   ids_tensor((self.batch_size, 3)				,							config.vocab_size						)
       __a       =   ids_tensor((self.batch_size, 3)				,							2						)

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

       __a       =   model(SCREAMING_SNAKE_CASE__				,							attention_mask=SCREAMING_SNAKE_CASE__						)["""last_hidden_state"""]
       __a       =   model(SCREAMING_SNAKE_CASE__				,							attention_mask=SCREAMING_SNAKE_CASE__				,							past_key_values=SCREAMING_SNAKE_CASE__						)[
           """last_hidden_state"""
       ]

       # select random slice
       __a       =   ids_tensor((1,)				,							output_from_past.shape[-1]						).item()
       __a       =   output_from_no_past[:, -3:, random_slice_idx].detach()
       __a       =   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(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							atol=1E-2						)						)


  def __a						(					self     :							int				,							SCREAMING_SNAKE_CASE__     :							Any				,							SCREAMING_SNAKE_CASE__     :							Optional[int]						):





       '''simple docstring'''

       __a       =   MaMaaaModel(config=SCREAMING_SNAKE_CASE__						).to(SCREAMING_SNAKE_CASE__						).eval()
       __a       =   model(**SCREAMING_SNAKE_CASE__						)

       __a       =   outputs.encoder_last_hidden_state
       __a       =   outputs.last_hidden_state

       with tempfile.TemporaryDirectory() as tmpdirname:
            __a       =   model.get_encoder()
            encoder.save_pretrained(SCREAMING_SNAKE_CASE__						)
            __a       =   MaMaaaEncoder.from_pretrained(SCREAMING_SNAKE_CASE__						).to(SCREAMING_SNAKE_CASE__						)

       __a       =   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:
            __a       =   model.get_decoder()
            decoder.save_pretrained(SCREAMING_SNAKE_CASE__						)
            __a       =   MaMaaaDecoder.from_pretrained(SCREAMING_SNAKE_CASE__						).to(SCREAMING_SNAKE_CASE__						)

       __a       =   decoder(
           input_ids=inputs_dict["""decoder_input_ids"""]				,							attention_mask=inputs_dict["""decoder_attention_mask"""]				,							encoder_hidden_states=SCREAMING_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_						(       snake_case__		,    snake_case__		,    snake_case__		,    unittest.TestCase      ):





  """simple docstring"""



  a_       :str												=(
      (
          MaMaaaModel,
          MaMaaaForConditionalGeneration,
      )
      if is_torch_available()
      else ()
  )
  a_       :Optional[int]												=(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
  a_       :Union[str, Any]												=(
      {
          """conversational""": MaMaaaForConditionalGeneration,
          """feature-extraction""": MaMaaaModel,
          """summarization""": MaMaaaForConditionalGeneration,
          """text2text-generation""": MaMaaaForConditionalGeneration,
          """translation""": MaMaaaForConditionalGeneration,
      }
      if is_torch_available()
      else {}
  )
  a_       :int												=True
  a_       :Dict												=True
  a_       :str												=False
  a_       :Optional[Any]												=False

  def __a						(					self     :							Dict				,							SCREAMING_SNAKE_CASE__     :							str				,							SCREAMING_SNAKE_CASE__     :							Any				,							SCREAMING_SNAKE_CASE__     :							Dict				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							Dict						):





       '''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 __a						(					self     :							Optional[int]						):





       '''simple docstring'''

       __a       =   MaMaaaModelTester(self						)
       __a       =   ConfigTester(self				,							config_class=SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       self.config_tester.run_common_tests()

  def __a						(					self     :							List[str]						):





       '''simple docstring'''

       __a ,						__a       =   self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
            __a       =   model_class(SCREAMING_SNAKE_CASE__						)

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

  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       __a       =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       __a       =   self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_encoder_decoder_model_standalone(*SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							Union[str, Any]						):





       '''simple docstring'''

       __a ,						__a       =   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
            __a       =   model_class(SCREAMING_SNAKE_CASE__						)
            model.to(SCREAMING_SNAKE_CASE__						)
            model.eval()

            __a       =   copy.deepcopy(self._prepare_for_class(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						)						)

            if not self.is_encoder_decoder:
                 __a       =   inputs["""input_ids"""]
                 del inputs["input_ids"]
            else:
                 __a       =   inputs["""input_ids"""]
                 __a       =   inputs.get("""decoder_input_ids"""				,							SCREAMING_SNAKE_CASE__						)
                 del inputs["input_ids"]
                 inputs.pop("""decoder_input_ids"""				,							SCREAMING_SNAKE_CASE__						)

            __a       =   model.get_input_embeddings()
            if not self.is_encoder_decoder:
                 __a       =   wte(SCREAMING_SNAKE_CASE__						)
            else:
                 __a       =   wte(SCREAMING_SNAKE_CASE__						)
                 __a       =   wte(SCREAMING_SNAKE_CASE__						)

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


  def __a						(					self     :							List[Any]						):





       '''simple docstring'''

       __a ,						__a       =   self.model_tester.prepare_config_and_inputs()
       __a       =   input_dict["""input_ids"""]
       __a       =   input_ids.ne(1						).to(SCREAMING_SNAKE_CASE__						)
       __a       =   MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE__						).eval().to(SCREAMING_SNAKE_CASE__						)
       if torch_device == "cuda":
            model.half()
       model.generate(SCREAMING_SNAKE_CASE__				,							attention_mask=SCREAMING_SNAKE_CASE__						)
       model.generate(num_beams=4				,							do_sample=SCREAMING_SNAKE_CASE__				,							early_stopping=SCREAMING_SNAKE_CASE__				,							num_return_sequences=3						)




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					List[str]:
     """simple docstring"""




     return torch.tensor(__SCREAMING_SNAKE_CASE				,  dtype=torch.long				,  device=__SCREAMING_SNAKE_CASE		)


SCREAMING_SNAKE_CASE_      =			1E-4



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





  """simple docstring"""



  @cached_property
  def __a						(					self     :							List[Any]						):





       '''simple docstring'''

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

  def __a						(					self     :							int						):





       '''simple docstring'''

       __a       =   MaMaaaModel.from_pretrained("""facebook/m2m100_418M"""						).to(SCREAMING_SNAKE_CASE__						)
       __a       =   _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]]						)
       __a       =   _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]]						)
       __a       =   prepare_mam_aaa_inputs_dict(model.config				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						)
       with torch.no_grad():
            __a       =   model(**SCREAMING_SNAKE_CASE__						)[0]
       __a       =   torch.Size((1, 1_1, 1_0_2_4)						)
       self.assertEqual(output.shape				,							SCREAMING_SNAKE_CASE__						)
       # change to expected output here
       __a       =   torch.tensor(
           [[-0.7_7_8_0, -0.1_6_7_6, 0.1_0_3_8], [-6.7_5_5_6, -1.3_9_9_2, 0.0_5_6_7], [-7.5_3_8_3, -0.5_9_2_0, -0.2_7_7_9]]				,							device=SCREAMING_SNAKE_CASE__						)
       self.assertTrue(torch.allclose(output[:, :3, :3]				,							SCREAMING_SNAKE_CASE__				,							atol=SCREAMING_SNAKE_CASE__						)						)

  def __a						(					self     :							int						):





       '''simple docstring'''

       __a       =   MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M"""						).to(SCREAMING_SNAKE_CASE__						)

       # change to intended input
       __a       =   _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]]						)
       __a       =   _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]]						)
       __a       =   prepare_mam_aaa_inputs_dict(model.config				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						)
       with torch.no_grad():
            __a       =   model(**SCREAMING_SNAKE_CASE__						)[0]
       __a       =   torch.Size((1, 1_1, model.config.vocab_size)						)
       self.assertEqual(output.shape				,							SCREAMING_SNAKE_CASE__						)
       # change to expected output here
       __a       =   torch.tensor(
           [[-1.0_4_4_8, -1.0_4_1_1, 3.7_9_9_2], [-3.2_1_9_1, -3.2_3_8_6, -1.3_4_5_1], [-3.6_2_1_0, -3.5_9_9_3, 0.4_9_2_5]]				,							device=SCREAMING_SNAKE_CASE__						)
       self.assertTrue(torch.allclose(output[:, :3, :3]				,							SCREAMING_SNAKE_CASE__				,							atol=SCREAMING_SNAKE_CASE__						)						)


  def __a						(					self     :							List[str]						):





       '''simple docstring'''

       __a       =   MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M"""						).to(SCREAMING_SNAKE_CASE__						)
       __a       =   MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M"""				,							src_lang="""fr"""				,							tgt_lang="""en"""						)

       __a       =   [
           """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
       __a       =   tokenizer(SCREAMING_SNAKE_CASE__				,							padding=SCREAMING_SNAKE_CASE__				,							return_tensors="""pt"""						)

       __a       =   model.generate(
           input_ids=dct["""input_ids"""].to(SCREAMING_SNAKE_CASE__						)				,							attention_mask=dct["""attention_mask"""].to(SCREAMING_SNAKE_CASE__						)				,							num_beams=5				,							forced_bos_token_id=tokenizer.get_lang_id("""en"""						)				,							)

       __a       =   [
           """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.""",
       ]

       __a       =   tokenizer.batch_decode(
           hypotheses_batch.tolist()				,							clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__				,							skip_special_tokens=SCREAMING_SNAKE_CASE__						)
       assert generated == expected_en










'''simple docstring'''







def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					str:
     """simple docstring"""




     __a       =   """"""
     for word_or_phrase in separated:
          if not isinstance(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		):
               raise Exception("""join() accepts only strings to be joined"""		)
          joined += word_or_phrase + separator
     return joined.strip(__SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
 from doctest import testmod

 testmod()






