



'''simple docstring'''
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 A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_=None  ,  UpperCAmelCase_=None  ,  UpperCAmelCase_=None  ,  UpperCAmelCase_=None  ,  UpperCAmelCase_=None  ,  ):
      if attention_mask is None:
            _UpperCamelCase :		int			    =       input_ids.ne(config.pad_token_id							)
      if decoder_attention_mask is None:
            _UpperCamelCase :		Tuple			    =       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=UpperCAmelCase_							)
      if decoder_head_mask is None:
            _UpperCamelCase :		Any			    =       torch.ones(config.decoder_layers  ,  config.decoder_attention_heads  ,  device=UpperCAmelCase_							)
      if cross_attn_head_mask is None:
            _UpperCamelCase :		Union[str, Any]			    =       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 :    Any			,lowerCamelCase__ :    Union[str, Any]			,lowerCamelCase__ :    Optional[int]=13			,lowerCamelCase__ :    Optional[int]=7			,lowerCamelCase__ :    List[str]=True			,lowerCamelCase__ :    Optional[int]=False			,lowerCamelCase__ :    Union[str, Any]=99			,lowerCamelCase__ :    int=16			,lowerCamelCase__ :    str=2			,lowerCamelCase__ :    Optional[int]=4			,lowerCamelCase__ :    int=4			,lowerCamelCase__ :    Union[str, Any]="relu"			,lowerCamelCase__ :    Tuple=0.1			,lowerCamelCase__ :    Tuple=0.1			,lowerCamelCase__ :    Optional[Any]=0.0			,lowerCamelCase__ :    Optional[int]=0.0			,lowerCamelCase__ :    List[Any]=20			,lowerCamelCase__ :    List[str]=2			,lowerCamelCase__ :    Any=1			,lowerCamelCase__ :    str=0			,):







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





 def      UpperCamelCase_  (			self :    Union[str, Any]			):







       '''simple docstring'''
       _UpperCamelCase :		Tuple			    =       ids_tensor([self.batch_size, self.seq_length]			,self.vocab_size			)
       _UpperCamelCase :		Optional[Any]			    =       self.eos_token_id  # Eos Token
       _UpperCamelCase :		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
       _UpperCamelCase :		Tuple			    =       input_ids.clamp(self.pad_token_id + 1			)
       _UpperCamelCase :		List[str]			    =       decoder_input_ids.clamp(self.pad_token_id + 1			)

       _UpperCamelCase :		int			    =       self.get_config()
       _UpperCamelCase :		Union[str, Any]			    =       prepare_mam_aaa_inputs_dict(lowerCamelCase__			,lowerCamelCase__			,lowerCamelCase__			)
       return config, inputs_dict





 def      UpperCamelCase_  (			self :    Union[str, Any]			):







       '''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 :    Dict			):







       '''simple docstring'''
       _UpperCamelCase :		List[str]			    =       self.prepare_config_and_inputs()
       return config, inputs_dict





 def      UpperCamelCase_  (			self :    int			,lowerCamelCase__ :    Optional[int]			,lowerCamelCase__ :    Optional[int]			):







       '''simple docstring'''
       _UpperCamelCase :		Union[str, Any]			    =       MaMaaaModel(config=lowerCamelCase__			).get_decoder().to(lowerCamelCase__			).eval()
       _UpperCamelCase :		Tuple			    =       inputs_dict['input_ids']
       _UpperCamelCase :		Tuple			    =       inputs_dict['attention_mask']
       _UpperCamelCase :		Optional[Any]			    =       inputs_dict['head_mask']

       # first forward pass
       _UpperCamelCase :		Any			    =       model(lowerCamelCase__			,attention_mask=lowerCamelCase__			,head_mask=lowerCamelCase__			,use_cache=lowerCamelCase__			)

       _UpperCamelCase :		Tuple			    =       outputs.to_tuple()

       # create hypothetical multiple next token and extent to next_input_ids
       _UpperCamelCase :		Tuple			    =       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 :		Tuple			    =       torch.cat([input_ids, next_tokens]			,dim=-1			)
       _UpperCamelCase :		Dict			    =       torch.cat([attention_mask, next_attn_mask]			,dim=-1			)

       _UpperCamelCase :		int			    =       model(lowerCamelCase__			,attention_mask=lowerCamelCase__			)['last_hidden_state']
       _UpperCamelCase :		str			    =       model(lowerCamelCase__			,attention_mask=lowerCamelCase__			,past_key_values=lowerCamelCase__			)[
           'last_hidden_state'
       ]

       # select random slice
       _UpperCamelCase :		str			    =       ids_tensor((1,)			,output_from_past.shape[-1]			).item()
       _UpperCamelCase :		int			    =       output_from_no_past[:, -3:, random_slice_idx].detach()
       _UpperCamelCase :		Tuple			    =       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(lowerCamelCase__			,lowerCamelCase__			,atol=1E-2			)			)





 def      UpperCamelCase_  (			self :    Tuple			,lowerCamelCase__ :    List[Any]			,lowerCamelCase__ :    List[str]			):







       '''simple docstring'''
       _UpperCamelCase :		List[str]			    =       MaMaaaModel(config=lowerCamelCase__			).to(lowerCamelCase__			).eval()
       _UpperCamelCase :		Tuple			    =       model(**lowerCamelCase__			)

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

       with tempfile.TemporaryDirectory() as tmpdirname:
             _UpperCamelCase :		List[str]			    =       model.get_encoder()
             encoder.save_pretrained(lowerCamelCase__			)
             _UpperCamelCase :		int			    =       MaMaaaEncoder.from_pretrained(lowerCamelCase__			).to(lowerCamelCase__			)

       _UpperCamelCase :		Dict			    =       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 :		Optional[int]			    =       model.get_decoder()
             decoder.save_pretrained(lowerCamelCase__			)
             _UpperCamelCase :		List[str]			    =       MaMaaaDecoder.from_pretrained(lowerCamelCase__			).to(lowerCamelCase__			)

       _UpperCamelCase :		List[str]			    =       decoder(
           input_ids=inputs_dict['decoder_input_ids']			,attention_mask=inputs_dict['decoder_attention_mask']			,encoder_hidden_states=lowerCamelCase__			,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__     (							lowercase						, lowercase						, lowercase						, unittest.TestCase       ):
 lowercase__      =      (
     (
         MaMaaaModel,
         MaMaaaForConditionalGeneration,
     )
     if is_torch_available()
     else ()
 )
 lowercase__      =      (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
 lowercase__      =      (
     {
         """conversational""": MaMaaaForConditionalGeneration,
         """feature-extraction""": MaMaaaModel,
         """summarization""": MaMaaaForConditionalGeneration,
         """text2text-generation""": MaMaaaForConditionalGeneration,
         """translation""": MaMaaaForConditionalGeneration,
     }
     if is_torch_available()
     else {}
 )
 lowercase__      =      True
 lowercase__      =      True
 lowercase__      =      False
 lowercase__      =      False





 def      UpperCamelCase_  (			self :    List[Any]			,lowerCamelCase__ :    Optional[Any]			,lowerCamelCase__ :    Dict			,lowerCamelCase__ :    Dict			,lowerCamelCase__ :    str			,lowerCamelCase__ :    List[str]			):







       '''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 :    Tuple			):







       '''simple docstring'''
       _UpperCamelCase :		str			    =       MaMaaaModelTester(self			)
       _UpperCamelCase :		str			    =       ConfigTester(self			,config_class=lowerCamelCase__			)





 def      UpperCamelCase_  (			self :    int			):







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





 def      UpperCamelCase_  (			self :    Optional[int]			):







       '''simple docstring'''
       _UpperCamelCase :		List[str]			    =       self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
             _UpperCamelCase :		int			    =       model_class(lowerCamelCase__			)

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





 def      UpperCamelCase_  (			self :    Optional[Any]			):







       '''simple docstring'''
       _UpperCamelCase :		Dict			    =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowerCamelCase__			)





 def      UpperCamelCase_  (			self :    Any			):







       '''simple docstring'''
       _UpperCamelCase :		Dict			    =       self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_encoder_decoder_model_standalone(*lowerCamelCase__			)





 def      UpperCamelCase_  (			self :    List[str]			):







       '''simple docstring'''
       _UpperCamelCase :		int			    =       self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
             _UpperCamelCase :		str			    =       model_class(lowerCamelCase__			)
             model.to(lowerCamelCase__			)
             model.eval()

             _UpperCamelCase :		List[Any]			    =       copy.deepcopy(self._prepare_for_class(lowerCamelCase__			,lowerCamelCase__			)			)

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

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

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





 def      UpperCamelCase_  (			self :    Dict			):







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






def A__   (					UpperCAmelCase_							):
      return torch.tensor(UpperCAmelCase_  ,  dtype=torch.long  ,  device=UpperCAmelCase_							)


snake_case_			:     Union[str, Any]  	=  1e-4






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





 @cached_property
 def      UpperCamelCase_  (			self :    List[str]			):







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





 def      UpperCamelCase_  (			self :    List[Any]			):







       '''simple docstring'''
       _UpperCamelCase :		Dict			    =       MaMaaaModel.from_pretrained('facebook/m2m100_418M'			).to(lowerCamelCase__			)
       _UpperCamelCase :		List[Any]			    =       _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]]			)
       _UpperCamelCase :		Any			    =       _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]]			)
       _UpperCamelCase :		str			    =       prepare_mam_aaa_inputs_dict(model.config			,lowerCamelCase__			,lowerCamelCase__			)
       with torch.no_grad():
             _UpperCamelCase :		Any			    =       model(**lowerCamelCase__			)[0]
       _UpperCamelCase :		List[str]			    =       torch.Size((1, 11, 1024)			)
       self.assertEqual(output.shape			,lowerCamelCase__			)
       # change to expected output here
       _UpperCamelCase :		int			    =       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=lowerCamelCase__			)
       self.assertTrue(torch.allclose(output[:, :3, :3]			,lowerCamelCase__			,atol=lowerCamelCase__			)			)





 def      UpperCamelCase_  (			self :    Optional[int]			):







       '''simple docstring'''
       _UpperCamelCase :		Any			    =       MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'			).to(lowerCamelCase__			)

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





 def      UpperCamelCase_  (			self :    Any			):







       '''simple docstring'''
       _UpperCamelCase :		Optional[int]			    =       MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'			).to(lowerCamelCase__			)
       _UpperCamelCase :		Optional[int]			    =       MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M'			,src_lang='fr'			,tgt_lang='en'			)

       _UpperCamelCase :		Optional[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 :		List[str]			    =       tokenizer(lowerCamelCase__			,padding=lowerCamelCase__			,return_tensors='pt'			)

       _UpperCamelCase :		List[Any]			    =       model.generate(
           input_ids=dct['input_ids'].to(lowerCamelCase__			)			,attention_mask=dct['attention_mask'].to(lowerCamelCase__			)			,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 :		int			    =       tokenizer.batch_decode(
           hypotheses_batch.tolist()			,clean_up_tokenization_spaces=lowerCamelCase__			,skip_special_tokens=lowerCamelCase__			)
       assert generated == expected_en







'''simple docstring'''
from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


snake_case_			:     Any  	=  {
    'configuration_gpt_neox_japanese': ['GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXJapaneseConfig'],
    'tokenization_gpt_neox_japanese': ['GPTNeoXJapaneseTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     Optional[int]  	=  [
					    'GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'GPTNeoXJapaneseForCausalLM',
					    'GPTNeoXJapaneseLayer',
					    'GPTNeoXJapaneseModel',
					    'GPTNeoXJapanesePreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
					from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_gpt_neox_japanese import (
										    GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GPTNeoXJapaneseForCausalLM,
										    GPTNeoXJapaneseLayer,
										    GPTNeoXJapaneseModel,
										    GPTNeoXJapanesePreTrainedModel,
										)


else:
					import sys

					snake_case_			:     Any  	=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


