






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      _lowerCamelCase	(						__A    :							str		,		__A    :							Dict		,		__A    :							List[Any]		,		__A    :							Dict=None		,		__A    :							Tuple=None		,		__A    :							List[str]=None		,		__A    :							List[str]=None		,		__A    :							List[str]=None		,		)      ->					Dict:
    if attention_mask is None:
        _UpperCAmelCase				:      Any			  =				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				:      str			  =				torch.ones(config.encoder_layers		,		config.encoder_attention_heads		,		device=snake_case_ )
    if decoder_head_mask is None:
        _UpperCAmelCase				:      Optional[int]			  =				torch.ones(config.decoder_layers		,		config.decoder_attention_heads		,		device=snake_case_ )
    if cross_attn_head_mask is None:
        _UpperCAmelCase				:      Union[str, Any]			  =				torch.ones(config.decoder_layers		,		config.decoder_attention_heads		,		device=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 							A_    :






 '''simple docstring'''




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

     """simple docstring"""



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


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

     """simple docstring"""



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

     _UpperCAmelCase				:      Union[str, Any]			  =				self.get_config()
     _UpperCAmelCase				:      Union[str, Any]			  =				prepare_mam_aaa_inputs_dict(__A							,  __A							,  __A)
     return config, inputs_dict


 def  snake_case__				(	self) ->		Tuple:

     """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  snake_case__				(	self) ->		Optional[Any]:

     """simple docstring"""



     _UpperCAmelCase				:      Any			  =				self.prepare_config_and_inputs()
     return config, inputs_dict


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

     """simple docstring"""



     _UpperCAmelCase				:      Union[str, Any]			  =				MaMaaaModel(config=__A).get_decoder().to(__A).eval()
     _UpperCAmelCase				:      List[Any]			  =				inputs_dict["input_ids"]
     _UpperCAmelCase				:      Optional[Any]			  =				inputs_dict["attention_mask"]
     _UpperCAmelCase				:      Union[str, Any]			  =				inputs_dict["head_mask"]

     # first forward pass
     _UpperCAmelCase				:      Dict			  =				model(__A							,  attention_mask=__A							,  head_mask=__A							,  use_cache=__A)

     _UpperCAmelCase				:      Dict			  =				outputs.to_tuple()

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

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

     _UpperCAmelCase				:      Tuple			  =				model(__A							,  attention_mask=__A)["last_hidden_state"]
     _UpperCAmelCase				:      Tuple			  =				model(__A							,  attention_mask=__A							,  past_key_values=__A)[
         "last_hidden_state"
     ]

     # select random slice
     _UpperCAmelCase				:      Optional[Any]			  =				ids_tensor((1,)							,  output_from_past.shape[-1]).item()
     _UpperCAmelCase				:      Optional[Any]			  =				output_from_no_past[:, -3:, random_slice_idx].detach()
     _UpperCAmelCase				:      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(__A							,  __A							,  atol=1e-2))


 def  snake_case__				(	self							,  _A							,  _A) ->		str:

     """simple docstring"""



     _UpperCAmelCase				:      Union[str, Any]			  =				MaMaaaModel(config=__A).to(__A).eval()
     _UpperCAmelCase				:      Union[str, Any]			  =				model(**__A)

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

     with tempfile.TemporaryDirectory() as tmpdirname:
         _UpperCAmelCase				:      Dict			  =				model.get_encoder()
         encoder.save_pretrained(__A)
         _UpperCAmelCase				:      Any			  =				MaMaaaEncoder.from_pretrained(__A).to(__A)

     _UpperCAmelCase				:      List[str]			  =				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				:      Dict			  =				model.get_decoder()
         decoder.save_pretrained(__A)
         _UpperCAmelCase				:      Optional[Any]			  =				MaMaaaDecoder.from_pretrained(__A).to(__A)

     _UpperCAmelCase				:      List[str]			  =				decoder(
         input_ids=inputs_dict['''decoder_input_ids''']							,  attention_mask=inputs_dict['''decoder_attention_mask''']							,  encoder_hidden_states=__A							,  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_    (						UpperCamelCase_ ,   UpperCamelCase_ ,   UpperCamelCase_ ,   unittest.TestCase			):






 '''simple docstring'''




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


 def  snake_case__				(	self							,  _A							,  _A							,  _A							,  _A							,  _A) ->		List[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  snake_case__				(	self) ->		Dict:

     """simple docstring"""



     _UpperCAmelCase				:      Any			  =				MaMaaaModelTester(self)
     _UpperCAmelCase				:      Dict			  =				ConfigTester(self							,  config_class=__A)


 def  snake_case__				(	self) ->		Dict:

     """simple docstring"""



     self.config_tester.run_common_tests()


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

     """simple docstring"""



     _UpperCAmelCase				:      int			  =				self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
         _UpperCAmelCase				:      int			  =				model_class(__A)

         with tempfile.TemporaryDirectory() as tmpdirname:
             model.save_pretrained(__A)
             _UpperCAmelCase				:      Optional[int]			  =				model_class.from_pretrained(__A							,  output_loading_info=__A)
         self.assertEqual(info['''missing_keys''']							,  [])


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

     """simple docstring"""



     _UpperCAmelCase				:      Dict			  =				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_decoder_model_past_large_inputs(*__A)


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

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_encoder_decoder_model_standalone(*__A)


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

     """simple docstring"""



     _UpperCAmelCase				:      Dict			  =				self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
         _UpperCAmelCase				:      str			  =				model_class(__A)
         model.to(__A)
         model.eval()

         _UpperCAmelCase				:      str			  =				copy.deepcopy(self._prepare_for_class(__A							,  __A))

         if not self.is_encoder_decoder:
             _UpperCAmelCase				:      Optional[Any]			  =				inputs["input_ids"]
             del inputs["input_ids"]
         else:
             _UpperCAmelCase				:      Union[str, Any]			  =				inputs["input_ids"]
             _UpperCAmelCase				:      List[str]			  =				inputs.get('''decoder_input_ids'''							,  __A)
             del inputs["input_ids"]
             inputs.pop('''decoder_input_ids'''							,  __A)

         _UpperCAmelCase				:      Tuple			  =				model.get_input_embeddings()
         if not self.is_encoder_decoder:
             _UpperCAmelCase				:      List[Any]			  =				wte(__A)
         else:
             _UpperCAmelCase				:      Any			  =				wte(__A)
             _UpperCAmelCase				:      Optional[int]			  =				wte(__A)

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


 def  snake_case__				(	self) ->		Tuple:

     """simple docstring"""



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



def      _lowerCamelCase	(						__A    :							int )      ->					Union[str, Any]:
    return torch.tensor(snake_case_		,		dtype=torch.long		,		device=snake_case_ )


SCREAMING_SNAKE_CASE 							=						1e-4


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






 '''simple docstring'''




 @cached_property
 def  snake_case__				(	self) ->		List[str]:

     """simple docstring"""



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


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

     """simple docstring"""



     _UpperCAmelCase				:      List[str]			  =				MaMaaaModel.from_pretrained('''facebook/m2m100_418M''').to(__A)
     _UpperCAmelCase				:      Optional[Any]			  =				_long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]])
     _UpperCAmelCase				:      str			  =				_long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]])
     _UpperCAmelCase				:      int			  =				prepare_mam_aaa_inputs_dict(model.config							,  __A							,  __A)
     with torch.no_grad():
         _UpperCAmelCase				:      str			  =				model(**__A)[0]
     _UpperCAmelCase				:      Tuple			  =				torch.Size((1, 11, 1024))
     self.assertEqual(output.shape							,  __A)
     # change to expected output here
     _UpperCAmelCase				:      Optional[Any]			  =				torch.tensor(
         [[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]]							,  device=__A)
     self.assertTrue(torch.allclose(output[:, :3, :3]							,  __A							,  atol=__A))


 def  snake_case__				(	self) ->		Tuple:

     """simple docstring"""



     _UpperCAmelCase				:      Union[str, Any]			  =				MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M''').to(__A)

     # change to intended input
     _UpperCAmelCase				:      Union[str, Any]			  =				_long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]])
     _UpperCAmelCase				:      List[str]			  =				_long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]])
     _UpperCAmelCase				:      int			  =				prepare_mam_aaa_inputs_dict(model.config							,  __A							,  __A)
     with torch.no_grad():
         _UpperCAmelCase				:      Union[str, Any]			  =				model(**__A)[0]
     _UpperCAmelCase				:      Tuple			  =				torch.Size((1, 11, model.config.vocab_size))
     self.assertEqual(output.shape							,  __A)
     # change to expected output here
     _UpperCAmelCase				:      List[str]			  =				torch.tensor(
         [[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]]							,  device=__A)
     self.assertTrue(torch.allclose(output[:, :3, :3]							,  __A							,  atol=__A))


 def  snake_case__				(	self) ->		str:

     """simple docstring"""



     _UpperCAmelCase				:      List[Any]			  =				MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M''').to(__A)
     _UpperCAmelCase				:      List[str]			  =				MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M'''							,  src_lang='''fr'''							,  tgt_lang='''en''')

     _UpperCAmelCase				:      List[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
     _UpperCAmelCase				:      str			  =				tokenizer(__A							,  padding=__A							,  return_tensors='''pt''')

     _UpperCAmelCase				:      Tuple			  =				model.generate(
         input_ids=dct['''input_ids'''].to(__A)							,  attention_mask=dct['''attention_mask'''].to(__A)							,  num_beams=5							,  forced_bos_token_id=tokenizer.get_lang_id('''en''')							,  )

     _UpperCAmelCase				:      List[str]			  =				[
         "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				:      Dict			  =				tokenizer.batch_decode(
         hypotheses_batch.tolist()							,  clean_up_tokenization_spaces=__A							,  skip_special_tokens=__A)
     assert generated == expected_en












import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
  # Create universe of discourse in Python using linspace ()
  SCREAMING_SNAKE_CASE 							=						np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

  # Create two fuzzy sets by defining any membership function
  # (trapmf(), gbellmf(), gaussmf(), etc).
  SCREAMING_SNAKE_CASE 							=						[0, 25, 50]
  SCREAMING_SNAKE_CASE 							=						[25, 50, 75]
  SCREAMING_SNAKE_CASE 							=						fuzz.membership.trimf(X, abca)
  SCREAMING_SNAKE_CASE 							=						fuzz.membership.trimf(X, abca)

  # Compute the different operations using inbuilt functions.
  SCREAMING_SNAKE_CASE 							=						np.ones(75)
  SCREAMING_SNAKE_CASE 							=						np.zeros((75,))
  # 1. Union = max(µA(x), µB(x))
  SCREAMING_SNAKE_CASE 							=						fuzz.fuzzy_or(X, young, X, middle_aged)[1]
  # 2. Intersection = min(µA(x), µB(x))
  SCREAMING_SNAKE_CASE 							=						fuzz.fuzzy_and(X, young, X, middle_aged)[1]
  # 3. Complement (A) = (1- min(µA(x))
  SCREAMING_SNAKE_CASE 							=						fuzz.fuzzy_not(young)
  # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
  SCREAMING_SNAKE_CASE 							=						fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
  # 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
  SCREAMING_SNAKE_CASE 							=						young + middle_aged - (young * middle_aged)
  # 6. Algebraic Product = (µA(x) * µB(x))
  SCREAMING_SNAKE_CASE 							=						young * middle_aged
  # 7. Bounded Sum = min[1,(µA(x), µB(x))]
  SCREAMING_SNAKE_CASE 							=						fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
  # 8. Bounded difference = min[0,(µA(x), µB(x))]
  SCREAMING_SNAKE_CASE 							=						fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

  # max-min composition
  # max-product composition

  # Plot each set A, set B and each operation result using plot() and subplot().
  from matplotlib import pyplot as plt

  plt.figure()

  plt.subplot(4, 3, 1)
  plt.plot(X, young)
  plt.title('Young')
  plt.grid(True)

  plt.subplot(4, 3, 2)
  plt.plot(X, middle_aged)
  plt.title('Middle aged')
  plt.grid(True)

  plt.subplot(4, 3, 3)
  plt.plot(X, union)
  plt.title('union')
  plt.grid(True)

  plt.subplot(4, 3, 4)
  plt.plot(X, intersection)
  plt.title('intersection')
  plt.grid(True)

  plt.subplot(4, 3, 5)
  plt.plot(X, complement_a)
  plt.title('complement_a')
  plt.grid(True)

  plt.subplot(4, 3, 6)
  plt.plot(X, difference)
  plt.title('difference a/b')
  plt.grid(True)

  plt.subplot(4, 3, 7)
  plt.plot(X, alg_sum)
  plt.title('alg_sum')
  plt.grid(True)

  plt.subplot(4, 3, 8)
  plt.plot(X, alg_product)
  plt.title('alg_product')
  plt.grid(True)

  plt.subplot(4, 3, 9)
  plt.plot(X, bdd_sum)
  plt.title('bdd_sum')
  plt.grid(True)

  plt.subplot(4, 3, 10)
  plt.plot(X, bdd_difference)
  plt.title('bdd_difference')
  plt.grid(True)

  plt.subplots_adjust(hspace=0.5)
  plt.show()




