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       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase=None ,						__UpperCAmelCase=None ,						__UpperCAmelCase=None ,						__UpperCAmelCase=None ,						__UpperCAmelCase=None ,						)						->    str:



      '''simple docstring'''

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

     '''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				,				) ->				Union[str, Any]:






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



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






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

           _lowerCamelCase			    =				self.get_config()
           _lowerCamelCase			    =				prepare_mam_aaa_inputs_dict(A_				,				A_				,				A_						)
           return config, inputs_dict



     def 		UpperCamelCase_						(					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 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           _lowerCamelCase    ,					_lowerCamelCase			    =				self.prepare_config_and_inputs()
           return config, inputs_dict



     def 		UpperCamelCase_						(					self				,				A_				,				A_						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase			    =				MaMaaaModel(config=A_						).get_decoder().to(A_						).eval()
           _lowerCamelCase			    =				inputs_dict['''input_ids''']
           _lowerCamelCase			    =				inputs_dict['''attention_mask''']
           _lowerCamelCase			    =				inputs_dict['''head_mask''']

           # first forward pass
           _lowerCamelCase			    =				model(A_				,				attention_mask=A_				,				head_mask=A_				,				use_cache=A_						)

           _lowerCamelCase    ,					_lowerCamelCase			    =				outputs.to_tuple()

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

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

           _lowerCamelCase			    =				model(A_				,				attention_mask=A_						)['''last_hidden_state''']
           _lowerCamelCase			    =				model(A_				,				attention_mask=A_				,				past_key_values=A_						)[
               '''last_hidden_state'''
           ]

           # select random slice
           _lowerCamelCase			    =				ids_tensor((1,)				,				output_from_past.shape[-1]						).item()
           _lowerCamelCase			    =				output_from_no_past[:, -3:, random_slice_idx].detach()
           _lowerCamelCase			    =				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 		UpperCamelCase_						(					self				,				A_				,				A_						) ->				List[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				MaMaaaModel(config=A_						).to(A_						).eval()
           _lowerCamelCase			    =				model(**A_						)

           _lowerCamelCase			    =				outputs.encoder_last_hidden_state
           _lowerCamelCase			    =				outputs.last_hidden_state

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

           _lowerCamelCase			    =				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:
                 _lowerCamelCase			    =				model.get_decoder()
                 decoder.save_pretrained(A_						)
                 _lowerCamelCase			    =				MaMaaaDecoder.from_pretrained(A_						).to(A_						)

           _lowerCamelCase			    =				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  UpperCamelCase				( __lowercase   ,  __lowercase   ,  __lowercase   ,  unittest.TestCase ):

     '''simple docstring'''

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



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






           """simple docstring"""
           _lowerCamelCase			    =				MaMaaaModelTester(self						)
           _lowerCamelCase			    =				ConfigTester(self				,				config_class=A_						)



     def 		UpperCamelCase_						(					self						) ->				Tuple:






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



     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           _lowerCamelCase    ,					_lowerCamelCase			    =				self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                 _lowerCamelCase			    =				model_class(A_						)

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



     def 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_decoder_model_past_large_inputs(*A_						)



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






           """simple docstring"""
           _lowerCamelCase			    =				self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.check_encoder_decoder_model_standalone(*A_						)



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






           """simple docstring"""
           _lowerCamelCase    ,					_lowerCamelCase			    =				self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
                 _lowerCamelCase			    =				model_class(A_						)
                 model.to(A_						)
                 model.eval()

                 _lowerCamelCase			    =				copy.deepcopy(self._prepare_for_class(A_				,				A_						)						)

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

                 _lowerCamelCase			    =				model.get_input_embeddings()
                 if not self.is_encoder_decoder:
                       _lowerCamelCase			    =				wte(A_						)
                 else:
                       _lowerCamelCase			    =				wte(A_						)
                       _lowerCamelCase			    =				wte(A_						)

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





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






           """simple docstring"""
           _lowerCamelCase    ,					_lowerCamelCase			    =				self.model_tester.prepare_config_and_inputs()
           _lowerCamelCase			    =				input_dict['''input_ids''']
           _lowerCamelCase			    =				input_ids.ne(1						).to(A_						)
           _lowerCamelCase			    =				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       __magic_name__(  __UpperCAmelCase		)						->    Dict:



      '''simple docstring'''

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


snake_case__				= 1E-4




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

     '''simple docstring'''

     @cached_property
     def 		UpperCamelCase_						(					self						) ->				Union[str, Any]:






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



     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				MaMaaaModel.from_pretrained('''facebook/m2m100_418M'''						).to(A_						)
           _lowerCamelCase			    =				_long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]]						)
           _lowerCamelCase			    =				_long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]]						)
           _lowerCamelCase			    =				prepare_mam_aaa_inputs_dict(model.config				,				A_				,				A_						)
           with torch.no_grad():
                 _lowerCamelCase			    =				model(**A_						)[0]
           _lowerCamelCase			    =				torch.Size((1, 11, 10_24)						)
           self.assertEqual(output.shape				,				A_						)
           # change to expected output here
           _lowerCamelCase			    =				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 		UpperCamelCase_						(					self						) ->				List[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M'''						).to(A_						)

           # change to intended input
           _lowerCamelCase			    =				_long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]]						)
           _lowerCamelCase			    =				_long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]]						)
           _lowerCamelCase			    =				prepare_mam_aaa_inputs_dict(model.config				,				A_				,				A_						)
           with torch.no_grad():
                 _lowerCamelCase			    =				model(**A_						)[0]
           _lowerCamelCase			    =				torch.Size((1, 11, model.config.vocab_size)						)
           self.assertEqual(output.shape				,				A_						)
           # change to expected output here
           _lowerCamelCase			    =				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 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M'''						).to(A_						)
           _lowerCamelCase			    =				MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M'''				,				src_lang='''fr'''				,				tgt_lang='''en'''						)

           _lowerCamelCase			    =				[
               '''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
           _lowerCamelCase			    =				tokenizer(A_				,				padding=A_				,				return_tensors='''pt'''						)

           _lowerCamelCase			    =				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'''						)				,				)

           _lowerCamelCase			    =				[
               '''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.''',
           ]

           _lowerCamelCase			    =				tokenizer.batch_decode(
               hypotheses_batch.tolist()				,				clean_up_tokenization_spaces=A_				,				skip_special_tokens=A_						)
           assert generated == expected_en
import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property



@unittest.skipUnless(os.path.exists(__lowercase )   ,  'Tatoeba directory does not exist.' )
class  UpperCamelCase				( unittest.TestCase ):

     '''simple docstring'''

     @cached_property
     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				tempfile.mkdtemp()
           return TatoebaConverter(save_dir=A_						)



     @slow
     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           self.resolver.convert_models(['''heb-eng''']						)





     @slow
     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase    ,					_lowerCamelCase			    =				self.resolver.write_model_card('''opus-mt-he-en'''				,				dry_run=A_						)
           assert mmeta["long_pair"] == "heb-eng"