
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  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase=None		, __UpperCamelCase=None		, __UpperCamelCase=None		, __UpperCamelCase=None		, __UpperCamelCase=None		, )      ->					str:
     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=_lowerCAmelCase)
     if decoder_head_mask is None:
          a     =				torch.ones(config.decoder_layers		, config.decoder_attention_heads		, device=_lowerCAmelCase)
     if cross_attn_head_mask is None:
          a     =				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__	:







     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  ,		)  ->   List[str]:



          '''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       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''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(snake_case_  ,		snake_case_  ,		snake_case_			)
          return config, inputs_dict







     def       lowerCAmelCase_    (     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       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          a     =				self.prepare_config_and_inputs()
          return config, inputs_dict







     def       lowerCAmelCase_    (     self  ,		A  ,		A			)  ->   int:



          '''simple docstring'''



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

          # first forward pass
          a     =				model(snake_case_  ,		attention_mask=snake_case_  ,		head_mask=snake_case_  ,		use_cache=snake_case_			)

          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(snake_case_  ,		attention_mask=snake_case_			)["""last_hidden_state"""]
          a     =				model(snake_case_  ,		attention_mask=snake_case_  ,		past_key_values=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(snake_case_  ,		snake_case_  ,		atol=1e-2			)			)







     def       lowerCAmelCase_    (     self  ,		A  ,		A			)  ->   List[Any]:



          '''simple docstring'''



          a     =				MaMaaaModel(config=snake_case_			).to(snake_case_			).eval()
          a     =				model(**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(snake_case_			)
               a     =				MaMaaaEncoder.from_pretrained(snake_case_			).to(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(snake_case_			)
               a     =				MaMaaaDecoder.from_pretrained(snake_case_			).to(snake_case_			)

          a     =				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       a__	( _a       , _a       , _a       , unittest.TestCase				):
     a		:      Union[str, Any]		 =     (
         (
             MaMaaaModel,
             MaMaaaForConditionalGeneration,
         )
         if is_torch_available()
         else ()
     )
     a		:      Tuple		 =     (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
     a		:      int		 =     (
         {
             """conversational""": MaMaaaForConditionalGeneration,
             """feature-extraction""": MaMaaaModel,
             """summarization""": MaMaaaForConditionalGeneration,
             """text2text-generation""": MaMaaaForConditionalGeneration,
             """translation""": MaMaaaForConditionalGeneration,
         }
         if is_torch_available()
         else {}
     )
     a		:      List[str]		 =     True
     a		:      str		 =     True
     a		:      List[str]		 =     False
     a		:      int		 =     False







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A  ,		A  ,		A			)  ->   Tuple:



          '''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       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          a     =				MaMaaaModelTester(self			)
          a     =				ConfigTester(self  ,		config_class=snake_case_			)







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



          '''simple docstring'''



          self.config_tester.run_common_tests()







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          a     =				self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
               a     =				model_class(snake_case_			)

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







     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



          a     =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_decoder_model_past_large_inputs(*snake_case_			)







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          a     =				self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.check_encoder_decoder_model_standalone(*snake_case_			)







     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          a     =				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
               a     =				model_class(snake_case_			)
               model.to(snake_case_			)
               model.eval()

               a     =				copy.deepcopy(self._prepare_for_class(snake_case_  ,		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"  ,		snake_case_			)
                    del inputs["input_ids"]
                    inputs.pop("decoder_input_ids"  ,		snake_case_			)

               a     =				model.get_input_embeddings()
               if not self.is_encoder_decoder:
                    a     =				wte(snake_case_			)
               else:
                    a     =				wte(snake_case_			)
                    a     =				wte(snake_case_			)

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







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          a     =				self.model_tester.prepare_config_and_inputs()
          a     =				input_dict["""input_ids"""]
          a     =				input_ids.ne(1			).to(snake_case_			)
          a     =				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  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Optional[Any]:
     return torch.tensor(_lowerCAmelCase		, dtype=torch.long		, device=_lowerCAmelCase)


lowercase__				:      Union[str, Any]	   = 1E-4





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







     @cached_property
     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          return MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M"			)







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



          '''simple docstring'''



          a     =				MaMaaaModel.from_pretrained("facebook/m2m100_418M"			).to(snake_case_			)
          a     =				_long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]]			)
          a     =				_long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]]			)
          a     =				prepare_mam_aaa_inputs_dict(model.config  ,		snake_case_  ,		snake_case_			)
          with torch.no_grad():
               a     =				model(**snake_case_			)[0]
          a     =				torch.Size((1, 11, 1024)			)
          self.assertEqual(output.shape  ,		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=snake_case_			)
          self.assertTrue(torch.allclose(output[:, :3, :3]  ,		snake_case_  ,		atol=snake_case_			)			)







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          a     =				MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M"			).to(snake_case_			)

          # change to intended input
          a     =				_long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]]			)
          a     =				_long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]]			)
          a     =				prepare_mam_aaa_inputs_dict(model.config  ,		snake_case_  ,		snake_case_			)
          with torch.no_grad():
               a     =				model(**snake_case_			)[0]
          a     =				torch.Size((1, 11, model.config.vocab_size)			)
          self.assertEqual(output.shape  ,		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=snake_case_			)
          self.assertTrue(torch.allclose(output[:, :3, :3]  ,		snake_case_  ,		atol=snake_case_			)			)







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



          '''simple docstring'''



          a     =				MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M"			).to(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(snake_case_  ,		padding=snake_case_  ,		return_tensors="pt"			)

          a     =				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"			)  ,		)

          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=snake_case_  ,		skip_special_tokens=snake_case_			)
          assert generated == expected_en







import math

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					bool:

     if 1 < number < 4:
          # 2 and 3 are primes
          return True
     elif number < 2 or number % 2 == 0 or number % 3 == 0:
          # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
          return False

     # All primes number are in format of 6k +/- 1
     for i in range(5		, int(math.sqrt(__UpperCamelCase) + 1)		, 6):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase = 0.1)      ->					int:
     a     =				3
     a     =				3

     while primes / (2 * j - 1) >= ratio:
          for i in range(j * j + j + 1		, (j + 2) * (j + 2)		, j + 1):
               primes += is_prime(__UpperCamelCase)
          j += 2
     return j


if __name__ == "__main__":
   import doctest

   doctest.testmod()

