
"""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 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase=None,		__lowerCamelCase=None,		__lowerCamelCase=None,		__lowerCamelCase=None,		__lowerCamelCase=None,		):
   if attention_mask is None:
      UpperCAmelCase_					:					List[str]			     =    input_ids.ne(config.pad_token_id  )
   if decoder_attention_mask is None:
      UpperCAmelCase_					:					List[Any]			     =    decoder_input_ids.ne(config.pad_token_id  )
   if head_mask is None:
      UpperCAmelCase_					:					Dict			     =    torch.ones(config.encoder_layers,		config.encoder_attention_heads,		device=__lowerCamelCase  )
   if decoder_head_mask is None:
      UpperCAmelCase_					:					Dict			     =    torch.ones(config.decoder_layers,		config.decoder_attention_heads,		device=__lowerCamelCase  )
   if cross_attn_head_mask is None:
      UpperCAmelCase_					:					Optional[Any]			     =    torch.ones(config.decoder_layers,		config.decoder_attention_heads,		device=__lowerCamelCase  )
   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 A_	:




  '''simple docstring'''







  def __init__(		self				,   lowercase_				,   lowercase_=13				,   lowercase_=7				,   lowercase_=True				,   lowercase_=False				,   lowercase_=99				,   lowercase_=16				,   lowercase_=2				,   lowercase_=4				,   lowercase_=4				,   lowercase_="relu"				,   lowercase_=0.1				,   lowercase_=0.1				,   lowercase_=0.0				,   lowercase_=0.0				,   lowercase_=20				,   lowercase_=2				,   lowercase_=1				,   lowercase_=0				,   ):

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







  def        UpperCamelCase__					(		self  ):

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

     UpperCAmelCase_					:					Any			     =    self.get_config()
     UpperCAmelCase_					:					List[Any]			     =    prepare_mam_aaa_inputs_dict(lowercase_				,   lowercase_				,   lowercase_  )
     return config, inputs_dict







  def        UpperCamelCase__					(		self  ):

     """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  ):

     """simple docstring"""
     UpperCAmelCase_     ,   UpperCAmelCase_					:					Optional[Any]			     =    self.prepare_config_and_inputs()
     return config, inputs_dict







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    MaMaaaModel(config=lowercase_  ).get_decoder().to(lowercase_  ).eval()
     UpperCAmelCase_					:					int			     =    inputs_dict["input_ids"]
     UpperCAmelCase_					:					Optional[Any]			     =    inputs_dict["attention_mask"]
     UpperCAmelCase_					:					Optional[int]			     =    inputs_dict["head_mask"]

     # first forward pass
     UpperCAmelCase_					:					Any			     =    model(lowercase_				,   attention_mask=lowercase_				,   head_mask=lowercase_				,   use_cache=lowercase_  )

     UpperCAmelCase_     ,   UpperCAmelCase_					:					Dict			     =    outputs.to_tuple()

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

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

     UpperCAmelCase_					:					Any			     =    model(lowercase_				,   attention_mask=lowercase_  )["last_hidden_state"]
     UpperCAmelCase_					:					List[str]			     =    model(lowercase_				,   attention_mask=lowercase_				,   past_key_values=lowercase_  )[
         "last_hidden_state"
     ]

     # select random slice
     UpperCAmelCase_					:					str			     =    ids_tensor((1,)				,   output_from_past.shape[-1]  ).item()
     UpperCAmelCase_					:					Any			     =    output_from_no_past[:, -3:, random_slice_idx].detach()
     UpperCAmelCase_					:					Union[str, 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(lowercase_				,   lowercase_				,   atol=1E-2  )  )







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    MaMaaaModel(config=lowercase_  ).to(lowercase_  ).eval()
     UpperCAmelCase_					:					Tuple			     =    model(**lowercase_  )

     UpperCAmelCase_					:					int			     =    outputs.encoder_last_hidden_state
     UpperCAmelCase_					:					Any			     =    outputs.last_hidden_state

     with tempfile.TemporaryDirectory() as tmpdirname:
        UpperCAmelCase_					:					List[Any]			     =    model.get_encoder()
        encoder.save_pretrained(lowercase_  )
        UpperCAmelCase_					:					Tuple			     =    MaMaaaEncoder.from_pretrained(lowercase_  ).to(lowercase_  )

     UpperCAmelCase_					:					List[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:
        UpperCAmelCase_					:					Optional[Any]			     =    model.get_decoder()
        decoder.save_pretrained(lowercase_  )
        UpperCAmelCase_					:					List[Any]			     =    MaMaaaDecoder.from_pretrained(lowercase_  ).to(lowercase_  )

     UpperCAmelCase_					:					str			     =    decoder(
         input_ids=inputs_dict["decoder_input_ids"]				,   attention_mask=inputs_dict["decoder_attention_mask"]				,   encoder_hidden_states=lowercase_				,   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 A_	(lowercase__				,lowercase__				,lowercase__				,unittest.TestCase      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						List[Any]       =		(
      (
          MaMaaaModel,
          MaMaaaForConditionalGeneration,
      )
      if is_torch_available()
      else ()
  )
  SCREAMING_SNAKE_CASE__			:						Union[str, Any]       =		(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE__			:						Tuple       =		(
      {
          """conversational""": MaMaaaForConditionalGeneration,
          """feature-extraction""": MaMaaaModel,
          """summarization""": MaMaaaForConditionalGeneration,
          """text2text-generation""": MaMaaaForConditionalGeneration,
          """translation""": MaMaaaForConditionalGeneration,
      }
      if is_torch_available()
      else {}
  )
  SCREAMING_SNAKE_CASE__			:						Optional[Any]       =		True
  SCREAMING_SNAKE_CASE__			:						int       =		True
  SCREAMING_SNAKE_CASE__			:						Any       =		False
  SCREAMING_SNAKE_CASE__			:						Tuple       =		False







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_  ):

     """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  ):

     """simple docstring"""
     UpperCAmelCase_					:					str			     =    MaMaaaModelTester(self  )
     UpperCAmelCase_					:					Any			     =    ConfigTester(self				,   config_class=lowercase_  )







  def        UpperCamelCase__					(		self  ):

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







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_     ,   UpperCAmelCase_					:					List[str]			     =    self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
        UpperCAmelCase_					:					Optional[Any]			     =    model_class(lowercase_  )

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







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_encoder_decoder_model_standalone(*lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_     ,   UpperCAmelCase_					:					str			     =    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
        UpperCAmelCase_					:					Any			     =    model_class(lowercase_  )
        model.to(lowercase_  )
        model.eval()

        UpperCAmelCase_					:					Union[str, Any]			     =    copy.deepcopy(self._prepare_for_class(lowercase_				,   lowercase_  )  )

        if not self.is_encoder_decoder:
           UpperCAmelCase_					:					Tuple			     =    inputs["input_ids"]
           del inputs["input_ids"]
        else:
           UpperCAmelCase_					:					Optional[Any]			     =    inputs["input_ids"]
           UpperCAmelCase_					:					List[str]			     =    inputs.get("decoder_input_ids"				,   lowercase_  )
           del inputs["input_ids"]
           inputs.pop("decoder_input_ids"				,   lowercase_  )

        UpperCAmelCase_					:					List[str]			     =    model.get_input_embeddings()
        if not self.is_encoder_decoder:
           UpperCAmelCase_					:					Dict			     =    wte(lowercase_  )
        else:
           UpperCAmelCase_					:					Dict			     =    wte(lowercase_  )
           UpperCAmelCase_					:					Optional[int]			     =    wte(lowercase_  )

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







  def        UpperCamelCase__					(		self  ):

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







def 	__a     (		__lowerCamelCase  ):
   return torch.tensor(__lowerCamelCase,		dtype=torch.long,		device=__lowerCamelCase  )


_a		       =							1e-4


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




  '''simple docstring'''







  @cached_property
  def        UpperCamelCase__					(		self  ):

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







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[Any]			     =    MaMaaaModel.from_pretrained("facebook/m2m100_418M"  ).to(lowercase_  )
     UpperCAmelCase_					:					str			     =    _long_tensor([[12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38, 2]]  )
     UpperCAmelCase_					:					str			     =    _long_tensor([[2, 12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38]]  )
     UpperCAmelCase_					:					str			     =    prepare_mam_aaa_inputs_dict(model.config				,   lowercase_				,   lowercase_  )
     with torch.no_grad():
        UpperCAmelCase_					:					Optional[int]			     =    model(**lowercase_  )[0]
     UpperCAmelCase_					:					Optional[int]			     =    torch.Size((1, 11, 1024)  )
     self.assertEqual(output.shape				,   lowercase_  )
     # change to expected output here
     UpperCAmelCase_					:					List[Any]			     =    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=lowercase_  )
     self.assertTrue(torch.allclose(output[:, :3, :3]				,   lowercase_				,   atol=lowercase_  )  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[Any]			     =    MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M"  ).to(lowercase_  )

     # change to intended input
     UpperCAmelCase_					:					str			     =    _long_tensor([[12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38, 2]]  )
     UpperCAmelCase_					:					Optional[int]			     =    _long_tensor([[2, 12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38]]  )
     UpperCAmelCase_					:					str			     =    prepare_mam_aaa_inputs_dict(model.config				,   lowercase_				,   lowercase_  )
     with torch.no_grad():
        UpperCAmelCase_					:					Dict			     =    model(**lowercase_  )[0]
     UpperCAmelCase_					:					str			     =    torch.Size((1, 11, model.config.vocab_size)  )
     self.assertEqual(output.shape				,   lowercase_  )
     # change to expected output here
     UpperCAmelCase_					:					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=lowercase_  )
     self.assertTrue(torch.allclose(output[:, :3, :3]				,   lowercase_				,   atol=lowercase_  )  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Dict			     =    MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M"  ).to(lowercase_  )
     UpperCAmelCase_					:					Tuple			     =    MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M"				,   src_lang="fr"				,   tgt_lang="en"  )

     UpperCAmelCase_					:					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
     UpperCAmelCase_					:					List[Any]			     =    tokenizer(lowercase_				,   padding=lowercase_				,   return_tensors="pt"  )

     UpperCAmelCase_					:					Optional[int]			     =    model.generate(
         input_ids=dct["input_ids"].to(lowercase_  )				,   attention_mask=dct["attention_mask"].to(lowercase_  )				,   num_beams=5				,   forced_bos_token_id=tokenizer.get_lang_id("en"  )				,   )

     UpperCAmelCase_					:					List[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.",
     ]

     UpperCAmelCase_					:					List[str]			     =    tokenizer.batch_decode(
         hypotheses_batch.tolist()				,   clean_up_tokenization_spaces=lowercase_				,   skip_special_tokens=lowercase_  )
     assert generated == expected_en




"""simple docstring"""





from __future__ import annotations







def 	__a     (		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_     ,   UpperCAmelCase_					:					str			     =    set(__lowerCamelCase  ), [start]

   while stack:
      UpperCAmelCase_					:					Any			     =    stack.pop()
      explored.add(__lowerCamelCase  )
      # Differences from BFS:
      # 1) pop last element instead of first one
      # 2) add adjacent elements to stack without exploring them
      for adj in reversed(graph[v]  ):
         if adj not in explored:
            stack.append(__lowerCamelCase  )
   return explored


_a		       =							{
    'A': ['B', 'C', 'D'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B', 'D'],
    'E': ['B', 'F'],
    'F': ['C', 'E', 'G'],
    'G': ['F'],
}

if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(depth_first_search(G, 'A'))


