
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_	:     List[str]			,							UpperCAmelCase_	:     List[Any]			,							UpperCAmelCase_	:     List[Any]			,							UpperCAmelCase_	:     List[str]=None			,							UpperCAmelCase_	:     Dict=None			,							UpperCAmelCase_	:     List[str]=None			,							UpperCAmelCase_	:     Dict=None			,							UpperCAmelCase_	:     str=None			,							)			->						Tuple:
       if attention_mask is None:
              SCREAMING_SNAKE_CASE_							:				Tuple		=input_ids.ne(config.pad_token_id				)
       if decoder_attention_mask is None:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=decoder_input_ids.ne(config.pad_token_id				)
       if head_mask is None:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.ones(config.encoder_layers			,							config.encoder_attention_heads			,							device=UpperCAmelCase_				)
       if decoder_head_mask is None:
              SCREAMING_SNAKE_CASE_							:				Dict		=torch.ones(config.decoder_layers			,							config.decoder_attention_heads			,							device=UpperCAmelCase_				)
       if cross_attn_head_mask is None:
              SCREAMING_SNAKE_CASE_							:				List[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 			lowercase_		:


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


       def 	_snake_case				(    self				)						->    Any:
              SCREAMING_SNAKE_CASE_							:				Dict		=ids_tensor([self.batch_size, self.seq_length]						,       self.vocab_size				)
              SCREAMING_SNAKE_CASE_							:				str		=self.eos_token_id  # Eos Token
              SCREAMING_SNAKE_CASE_							:				Dict		=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
              SCREAMING_SNAKE_CASE_							:				int		=input_ids.clamp(self.pad_token_id + 1				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=decoder_input_ids.clamp(self.pad_token_id + 1				)

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


       def 	_snake_case				(    self				)						->    List[str]:
              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				)						->    Any:
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Any		=self.prepare_config_and_inputs()
              return config, inputs_dict


       def 	_snake_case				(    self						,       __A						,       __A				)						->    Optional[Any]:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=MaMaaaModel(config=__A				).get_decoder().to(__A				).eval()
              SCREAMING_SNAKE_CASE_							:				Any		=inputs_dict['''input_ids''']
              SCREAMING_SNAKE_CASE_							:				List[Any]		=inputs_dict['''attention_mask''']
              SCREAMING_SNAKE_CASE_							:				Tuple		=inputs_dict['''head_mask''']

              # first forward pass
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=model(__A						,       attention_mask=__A						,       head_mask=__A						,       use_cache=__A				)

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				int		=outputs.to_tuple()

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

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

              SCREAMING_SNAKE_CASE_							:				str		=model(__A						,       attention_mask=__A				)['''last_hidden_state''']
              SCREAMING_SNAKE_CASE_							:				int		=model(__A						,       attention_mask=__A						,       past_key_values=__A				)[
                  '''last_hidden_state'''
              ]

              # select random slice
              SCREAMING_SNAKE_CASE_							:				List[Any]		=ids_tensor((1,)						,       output_from_past.shape[-1]				).item()
              SCREAMING_SNAKE_CASE_							:				Dict		=output_from_no_past[:, -3:, random_slice_idx].detach()
              SCREAMING_SNAKE_CASE_							:				str		=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				)						->    int:
              SCREAMING_SNAKE_CASE_							:				Dict		=MaMaaaModel(config=__A				).to(__A				).eval()
              SCREAMING_SNAKE_CASE_							:				Any		=model(**__A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=outputs.encoder_last_hidden_state
              SCREAMING_SNAKE_CASE_							:				List[str]		=outputs.last_hidden_state

              with tempfile.TemporaryDirectory() as tmpdirname:
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=model.get_encoder()
                     encoder.save_pretrained(__A				)
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=MaMaaaEncoder.from_pretrained(__A				).to(__A				)

              SCREAMING_SNAKE_CASE_							:				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:
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=model.get_decoder()
                     decoder.save_pretrained(__A				)
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=MaMaaaDecoder.from_pretrained(__A				).to(__A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=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 			lowercase_		(      A  ,      A  ,      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						(
           (
               MaMaaaModel,
               MaMaaaForConditionalGeneration,
           )
           if is_torch_available()
           else ()
       )
       __lowerCamelCase				       =						(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
       __lowerCamelCase				       =						(
           {
               "conversational": MaMaaaForConditionalGeneration,
               "feature-extraction": MaMaaaModel,
               "summarization": MaMaaaForConditionalGeneration,
               "text2text-generation": MaMaaaForConditionalGeneration,
               "translation": MaMaaaForConditionalGeneration,
           }
           if is_torch_available()
           else {}
       )
       __lowerCamelCase				       =						True
       __lowerCamelCase				       =						True
       __lowerCamelCase				       =						False
       __lowerCamelCase				       =						False


       def 	_snake_case				(    self						,       __A						,       __A						,       __A						,       __A						,       __A				)						->    Any:
              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				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				int		=MaMaaaModelTester(self				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=ConfigTester(self						,       config_class=__A				)


       def 	_snake_case				(    self				)						->    List[str]:
              self.config_tester.run_common_tests()


       def 	_snake_case				(    self				)						->    str:
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Dict		=self.model_tester.prepare_config_and_inputs()
              for model_class in self.all_model_classes:
                     SCREAMING_SNAKE_CASE_							:				Dict		=model_class(__A				)

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


       def 	_snake_case				(    self				)						->    List[str]:
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_decoder_model_past_large_inputs(*__A				)


       def 	_snake_case				(    self				)						->    List[str]:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.model_tester.prepare_config_and_inputs_for_common()
              self.model_tester.check_encoder_decoder_model_standalone(*__A				)


       def 	_snake_case				(    self				)						->    int:
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[Any]		=self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
                     SCREAMING_SNAKE_CASE_							:				Any		=model_class(__A				)
                     model.to(__A				)
                     model.eval()

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

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

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

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





       def 	_snake_case				(    self				)						->    List[str]:
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Tuple		=self.model_tester.prepare_config_and_inputs()
              SCREAMING_SNAKE_CASE_							:				Dict		=input_dict['''input_ids''']
              SCREAMING_SNAKE_CASE_							:				List[str]		=input_ids.ne(1				).to(__A				)
              SCREAMING_SNAKE_CASE_							:				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 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int				)			->						Any:
       return torch.tensor(UpperCAmelCase_			,							dtype=torch.long			,							device=UpperCAmelCase_				)


_lowercase											=    1e-4


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


       @cached_property
       def 	_snake_case				(    self				)						->    Optional[Any]:
              return MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M'''				)


       def 	_snake_case				(    self				)						->    str:
              SCREAMING_SNAKE_CASE_							:				List[str]		=MaMaaaModel.from_pretrained('''facebook/m2m100_418M'''				).to(__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=_long_tensor([[128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38, 2]]				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=_long_tensor([[2, 128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38]]				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=prepare_mam_aaa_inputs_dict(model.config						,       __A						,       __A				)
              with torch.no_grad():
                     SCREAMING_SNAKE_CASE_							:				List[str]		=model(**__A				)[0]
              SCREAMING_SNAKE_CASE_							:				Any		=torch.Size((1, 11, 1_024)				)
              self.assertEqual(output.shape						,       __A				)
              # change to expected output here
              SCREAMING_SNAKE_CASE_							:				List[str]		=torch.tensor(
                  [[-0.7_780, -0.1_676, 0.1_038], [-6.7_556, -1.3_992, 0.0_567], [-7.5_383, -0.5_920, -0.2_779]]						,       device=__A				)
              self.assertTrue(torch.allclose(output[:, :3, :3]						,       __A						,       atol=__A				)				)


       def 	_snake_case				(    self				)						->    Dict:
              SCREAMING_SNAKE_CASE_							:				Tuple		=MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M'''				).to(__A				)

              # change to intended input
              SCREAMING_SNAKE_CASE_							:				List[str]		=_long_tensor([[128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38, 2]]				)
              SCREAMING_SNAKE_CASE_							:				Dict		=_long_tensor([[2, 128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38]]				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=prepare_mam_aaa_inputs_dict(model.config						,       __A						,       __A				)
              with torch.no_grad():
                     SCREAMING_SNAKE_CASE_							:				str		=model(**__A				)[0]
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=torch.Size((1, 11, model.config.vocab_size)				)
              self.assertEqual(output.shape						,       __A				)
              # change to expected output here
              SCREAMING_SNAKE_CASE_							:				str		=torch.tensor(
                  [[-1.0_448, -1.0_411, 3.7_992], [-3.2_191, -3.2_386, -1.3_451], [-3.6_210, -3.5_993, 0.4_925]]						,       device=__A				)
              self.assertTrue(torch.allclose(output[:, :3, :3]						,       __A						,       atol=__A				)				)





       def 	_snake_case				(    self				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M'''				).to(__A				)
              SCREAMING_SNAKE_CASE_							:				Any		=MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M'''						,       src_lang='''fr'''						,       tgt_lang='''en'''				)

              SCREAMING_SNAKE_CASE_							:				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
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=tokenizer(__A						,       padding=__A						,       return_tensors='''pt'''				)

              SCREAMING_SNAKE_CASE_							:				List[str]		=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'''				)						,       )

              SCREAMING_SNAKE_CASE_							:				Dict		=[
                  '''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.''',
              ]

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=tokenizer.batch_decode(
                  hypotheses_batch.tolist()						,       clean_up_tokenization_spaces=__A						,       skip_special_tokens=__A				)
              assert generated == expected_en


import os
def 							SCREAMING_SNAKE_CASE_	(					)			->						List[str]:
       with open(os.path.dirname(UpperCAmelCase_				) + '''/p022_names.txt'''				) as file:
              SCREAMING_SNAKE_CASE_							:				Any		=str(file.readlines()[0]				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=names.replace('''"'''			,							''''''				).split(''','''				)

       names.sort()

       SCREAMING_SNAKE_CASE_							:				Optional[int]		=0
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=0

       for i, name in enumerate(UpperCAmelCase_				):
              for letter in name:
                     name_score += ord(UpperCAmelCase_				) - 6_4

              total_score += (i + 1) * name_score
              SCREAMING_SNAKE_CASE_							:				Any		=0
       return total_score


if __name__ == "__main__":
 print(solution())
