
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 ,		):
      if attention_mask is None:
            UpperCamelCase__    :   List[Any]					=				input_ids.ne(config.pad_token_id		)
      if decoder_attention_mask is None:
            UpperCamelCase__    :   Union[str, Any]					=				decoder_input_ids.ne(config.pad_token_id		)
      if head_mask is None:
            UpperCamelCase__    :   Tuple					=				torch.ones(config.encoder_layers ,		config.encoder_attention_heads ,		device=UpperCamelCase__		)
      if decoder_head_mask is None:
            UpperCamelCase__    :   List[str]					=				torch.ones(config.decoder_layers ,		config.decoder_attention_heads ,		device=UpperCamelCase__		)
      if cross_attn_head_mask is None:
            UpperCamelCase__    :   str					=				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 :





     """simple docstring"""







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




           """simple docstring"""







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






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Tuple:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				ids_tensor([self.batch_size, self.seq_length]						,						self.vocab_size  )
           UpperCamelCase__    :   Optional[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__    :   List[Any]					=				input_ids.clamp(self.pad_token_id + 1  )
           UpperCamelCase__    :   Union[str, Any]					=				decoder_input_ids.clamp(self.pad_token_id + 1  )

           UpperCamelCase__    :   List[Any]					=				self.get_config()
           UpperCamelCase__    :   List[str]					=				prepare_mam_aaa_inputs_dict(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )
           return config, inputs_dict






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




           """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    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__				,UpperCamelCase__    :   str					=				self.prepare_config_and_inputs()
           return config, inputs_dict






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				MaMaaaModel(config=__SCREAMING_SNAKE_CASE  ).get_decoder().to(__SCREAMING_SNAKE_CASE  ).eval()
           UpperCamelCase__    :   Any					=				inputs_dict['''input_ids''']
           UpperCamelCase__    :   int					=				inputs_dict['''attention_mask''']
           UpperCamelCase__    :   Union[str, Any]					=				inputs_dict['''head_mask''']

           # first forward pass
           UpperCamelCase__    :   List[str]					=				model(__SCREAMING_SNAKE_CASE						,						attention_mask=__SCREAMING_SNAKE_CASE						,						head_mask=__SCREAMING_SNAKE_CASE						,						use_cache=__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__				,UpperCamelCase__    :   Dict					=				outputs.to_tuple()

           # create hypothetical multiple next token and extent to next_input_ids
           UpperCamelCase__    :   List[Any]					=				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__    :   List[str]					=				torch.cat([input_ids, next_tokens]						,						dim=-1  )
           UpperCamelCase__    :   Any					=				torch.cat([attention_mask, next_attn_mask]						,						dim=-1  )

           UpperCamelCase__    :   List[str]					=				model(__SCREAMING_SNAKE_CASE						,						attention_mask=__SCREAMING_SNAKE_CASE  )['''last_hidden_state''']
           UpperCamelCase__    :   str					=				model(__SCREAMING_SNAKE_CASE						,						attention_mask=__SCREAMING_SNAKE_CASE						,						past_key_values=__SCREAMING_SNAKE_CASE  )[
               '''last_hidden_state'''
           ]

           # select random slice
           UpperCamelCase__    :   int					=				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(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						atol=1e-2  )  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__    :   List[Any]					=				MaMaaaModel(config=__SCREAMING_SNAKE_CASE  ).to(__SCREAMING_SNAKE_CASE  ).eval()
           UpperCamelCase__    :   Tuple					=				model(**__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Optional[int]					=				outputs.encoder_last_hidden_state
           UpperCamelCase__    :   List[str]					=				outputs.last_hidden_state

           with tempfile.TemporaryDirectory() as tmpdirname:
                 UpperCamelCase__    :   Dict					=				model.get_encoder()
                 encoder.save_pretrained(__SCREAMING_SNAKE_CASE  )
                 UpperCamelCase__    :   Dict					=				MaMaaaEncoder.from_pretrained(__SCREAMING_SNAKE_CASE  ).to(__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   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__    :   str					=				model.get_decoder()
                 decoder.save_pretrained(__SCREAMING_SNAKE_CASE  )
                 UpperCamelCase__    :   Tuple					=				MaMaaaDecoder.from_pretrained(__SCREAMING_SNAKE_CASE  ).to(__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Dict					=				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 (      UpperCamelCase_	,							UpperCamelCase_	,							UpperCamelCase_	,							unittest.TestCase ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  (
         (
             MaMaaaModel,
             MaMaaaForConditionalGeneration,
         )
         if is_torch_available()
         else ()
     )
     SCREAMING_SNAKE_CASE_		   =  (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE_		   =  (
         {
             '''conversational''': MaMaaaForConditionalGeneration,
             '''feature-extraction''': MaMaaaModel,
             '''summarization''': MaMaaaForConditionalGeneration,
             '''text2text-generation''': MaMaaaForConditionalGeneration,
             '''translation''': MaMaaaForConditionalGeneration,
         }
         if is_torch_available()
         else {}
     )
     SCREAMING_SNAKE_CASE_		   =  True
     SCREAMING_SNAKE_CASE_		   =  True
     SCREAMING_SNAKE_CASE_		   =  False
     SCREAMING_SNAKE_CASE_		   =  False






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__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    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           UpperCamelCase__    :   List[Any]					=				MaMaaaModelTester(self  )
           UpperCamelCase__    :   List[str]					=				ConfigTester(self						,						config_class=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           self.config_tester.run_common_tests()






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




           """simple docstring"""







           UpperCamelCase__				,UpperCamelCase__    :   Tuple					=				self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                 UpperCamelCase__    :   Optional[int]					=				model_class(__SCREAMING_SNAKE_CASE  )

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       model.save_pretrained(__SCREAMING_SNAKE_CASE  )
                       UpperCamelCase__				,UpperCamelCase__    :   Dict					=				model_class.from_pretrained(__SCREAMING_SNAKE_CASE						,						output_loading_info=__SCREAMING_SNAKE_CASE  )
                 self.assertEqual(info['''missing_keys''']						,						[]  )






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




           """simple docstring"""







           UpperCamelCase__    :   Any					=				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_decoder_model_past_large_inputs(*__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           UpperCamelCase__    :   Any					=				self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.check_encoder_decoder_model_standalone(*__SCREAMING_SNAKE_CASE  )






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




           """simple docstring"""







           UpperCamelCase__				,UpperCamelCase__    :   List[Any]					=				self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
                 UpperCamelCase__    :   str					=				model_class(__SCREAMING_SNAKE_CASE  )
                 model.to(__SCREAMING_SNAKE_CASE  )
                 model.eval()

                 UpperCamelCase__    :   Optional[int]					=				copy.deepcopy(self._prepare_for_class(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  )

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

                 UpperCamelCase__    :   str					=				model.get_input_embeddings()
                 if not self.is_encoder_decoder:
                       UpperCamelCase__    :   str					=				wte(__SCREAMING_SNAKE_CASE  )
                 else:
                       UpperCamelCase__    :   Union[str, Any]					=				wte(__SCREAMING_SNAKE_CASE  )
                       UpperCamelCase__    :   Dict					=				wte(__SCREAMING_SNAKE_CASE  )

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






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




           """simple docstring"""







           UpperCamelCase__				,UpperCamelCase__    :   Any					=				self.model_tester.prepare_config_and_inputs()
           UpperCamelCase__    :   Dict					=				input_dict['''input_ids''']
           UpperCamelCase__    :   Optional[Any]					=				input_ids.ne(1  ).to(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   List[str]					=				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 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      return torch.tensor(UpperCamelCase__ ,		dtype=torch.long ,		device=UpperCamelCase__		)


lowerCamelCase      =1e-4




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





     """simple docstring"""







     @cached_property
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







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






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				MaMaaaModel.from_pretrained('''facebook/m2m100_418M'''  ).to(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Union[str, 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]]  )
           UpperCamelCase__    :   Any					=				_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]]  )
           UpperCamelCase__    :   Dict					=				prepare_mam_aaa_inputs_dict(model.config						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )
           with torch.no_grad():
                 UpperCamelCase__    :   str					=				model(**__SCREAMING_SNAKE_CASE  )[0]
           UpperCamelCase__    :   List[Any]					=				torch.Size((1, 1_1, 1_0_2_4)  )
           self.assertEqual(output.shape						,						__SCREAMING_SNAKE_CASE  )
           # change to expected output here
           UpperCamelCase__    :   Union[str, Any]					=				torch.tensor(
               [[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]]						,						device=__SCREAMING_SNAKE_CASE  )
           self.assertTrue(torch.allclose(output[:, :3, :3]						,						__SCREAMING_SNAKE_CASE						,						atol=__SCREAMING_SNAKE_CASE  )  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M'''  ).to(__SCREAMING_SNAKE_CASE  )

           # change to intended input
           UpperCamelCase__    :   Union[str, 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]]  )
           UpperCamelCase__    :   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]]  )
           UpperCamelCase__    :   List[str]					=				prepare_mam_aaa_inputs_dict(model.config						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )
           with torch.no_grad():
                 UpperCamelCase__    :   Optional[Any]					=				model(**__SCREAMING_SNAKE_CASE  )[0]
           UpperCamelCase__    :   Dict					=				torch.Size((1, 1_1, model.config.vocab_size)  )
           self.assertEqual(output.shape						,						__SCREAMING_SNAKE_CASE  )
           # change to expected output here
           UpperCamelCase__    :   Union[str, Any]					=				torch.tensor(
               [[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]]						,						device=__SCREAMING_SNAKE_CASE  )
           self.assertTrue(torch.allclose(output[:, :3, :3]						,						__SCREAMING_SNAKE_CASE						,						atol=__SCREAMING_SNAKE_CASE  )  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           UpperCamelCase__    :   int					=				MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M'''  ).to(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Optional[Any]					=				MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M'''						,						src_lang='''fr'''						,						tgt_lang='''en'''  )

           UpperCamelCase__    :   Tuple					=				[
               '''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__    :   Optional[int]					=				tokenizer(__SCREAMING_SNAKE_CASE						,						padding=__SCREAMING_SNAKE_CASE						,						return_tensors='''pt'''  )

           UpperCamelCase__    :   List[Any]					=				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'''  )						,						)

           UpperCamelCase__    :   Tuple					=				[
               '''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__    :   Any					=				tokenizer.batch_decode(
               hypotheses_batch.tolist()						,						clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE						,						skip_special_tokens=__SCREAMING_SNAKE_CASE  )
           assert generated == expected_en




import torch

from transformers import AutoModel




class 				_lowerCamelCase (      torch.nn.Module ):





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE="sayef/fsner-bert-base-uncased"  )  ->   Optional[Any]:




           """simple docstring"""







           super(__SCREAMING_SNAKE_CASE						,						self  ).__init__()

           UpperCamelCase__    :   Tuple					=				AutoModel.from_pretrained(__SCREAMING_SNAKE_CASE						,						return_dict=__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Tuple					=				torch.nn.CosineSimilarity(3						,						1e-08  )
           UpperCamelCase__    :   List[Any]					=				torch.nn.Softmax(dim=1  )






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




           """simple docstring"""







           return self.bert(**__SCREAMING_SNAKE_CASE  ).last_hidden_state






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   Dict:




           """simple docstring"""







           return token_embeddings.sum(2						,						keepdim=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=1  )  ->   str:




           """simple docstring"""







           return self.softmax(T * self.cos(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   List[str]:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				W_supports['''sizes'''].tolist()
           UpperCamelCase__    :   Tuple					=				W_supports['''start_token_id'''].item()
           UpperCamelCase__    :   int					=				W_supports['''end_token_id'''].item()

           del W_supports["sizes"]
           del W_supports["start_token_id"]
           del W_supports["end_token_id"]

           UpperCamelCase__    :   Optional[int]					=				self.BERT(**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Dict					=				self.BERT(**__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   int					=				None
           UpperCamelCase__    :   Dict					=				None

           UpperCamelCase__    :   Any					=				W_supports['''input_ids'''] == start_token_id
           UpperCamelCase__    :   Optional[int]					=				W_supports['''input_ids'''] == end_token_id

           for i, size in enumerate(__SCREAMING_SNAKE_CASE  ):
                 if i == 0:
                       UpperCamelCase__    :   int					=				0
                 else:
                       UpperCamelCase__    :   Optional[int]					=				support_sizes[i - 1]

                 UpperCamelCase__    :   List[Any]					=				S[s : s + size][start_token_masks[s : s + size]]
                 UpperCamelCase__    :   Optional[Any]					=				S[s : s + size][end_token_masks[s : s + size]]

                 UpperCamelCase__    :   Tuple					=				torch.matmul(q[i]						,						s_start.T  ).sum(1  ).softmax(0  )
                 UpperCamelCase__    :   Optional[Any]					=				torch.matmul(q[i]						,						s_end.T  ).sum(1  ).softmax(0  )

                 if p_starts is not None:
                       UpperCamelCase__    :   Union[str, Any]					=				torch.vstack((p_starts, p_start)  )
                       UpperCamelCase__    :   str					=				torch.vstack((p_ends, p_end)  )
                 else:
                       UpperCamelCase__    :   List[Any]					=				p_start
                       UpperCamelCase__    :   List[str]					=				p_end

           return p_starts, p_ends


