


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 							__lowerCamelCase		(					UpperCAmelCase_		:						Optional[Any]					,    UpperCAmelCase_		:						Any					,    UpperCAmelCase_		:						Union[str, Any]					,    UpperCAmelCase_		:						List[Any]=None					,    UpperCAmelCase_		:						Optional[int]=None					,    UpperCAmelCase_		:						List[str]=None					,    UpperCAmelCase_		:						int=None					,    UpperCAmelCase_		:						Dict=None					,    ):




      """simple docstring"""





      if attention_mask is None:
            a		:str									=			input_ids.ne(config.pad_token_id		)
      if decoder_attention_mask is None:
            a		:Optional[int]									=			decoder_input_ids.ne(config.pad_token_id		)
      if head_mask is None:
            a		:Optional[Any]									=			torch.ones(config.encoder_layers					,    config.encoder_attention_heads					,    device=__A		)
      if decoder_head_mask is None:
            a		:List[Any]									=			torch.ones(config.decoder_layers					,    config.decoder_attention_heads					,    device=__A		)
      if cross_attn_head_mask is None:
            a		:Dict									=			torch.ones(config.decoder_layers					,    config.decoder_attention_heads					,    device=__A		)
      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 						_snake_case    :




 def __init__(						self   ,    _lowerCamelCase   ,    _lowerCamelCase=13   ,    _lowerCamelCase=7   ,    _lowerCamelCase=True   ,    _lowerCamelCase=False   ,    _lowerCamelCase=99   ,    _lowerCamelCase=16   ,    _lowerCamelCase=2   ,    _lowerCamelCase=4   ,    _lowerCamelCase=4   ,    _lowerCamelCase="relu"   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.0   ,    _lowerCamelCase=0.0   ,    _lowerCamelCase=20   ,    _lowerCamelCase=2   ,    _lowerCamelCase=1   ,    _lowerCamelCase=0   ,    ):
       a		:Union[str, Any]									=			parent
       a		:Tuple									=			batch_size
       a		:Union[str, Any]									=			seq_length
       a		:Dict									=			is_training
       a		:Tuple									=			use_labels
       a		:int									=			vocab_size
       a		:List[Any]									=			hidden_size
       a		:Optional[Any]									=			num_hidden_layers
       a		:List[Any]									=			num_attention_heads
       a		:List[str]									=			intermediate_size
       a		:List[str]									=			hidden_act
       a		:Any									=			hidden_dropout_prob
       a		:Union[str, Any]									=			attention_probs_dropout_prob
       a		:Dict									=			encoder_layerdrop
       a		:int									=			decoder_layerdrop
       a		:int									=			max_position_embeddings
       a		:int									=			eos_token_id
       a		:List[Any]									=			pad_token_id
       a		:Tuple									=			bos_token_id




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

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




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       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       ):
       a				,							a		:str									=			self.prepare_config_and_inputs()
       return config, inputs_dict




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase       ):
       a		:List[Any]									=			MaMaaaModel(config=_UpperCamelCase       ).get_decoder().to(_UpperCamelCase       ).eval()
       a		:Optional[Any]									=			inputs_dict['''input_ids''']
       a		:str									=			inputs_dict['''attention_mask''']
       a		:Dict									=			inputs_dict['''head_mask''']

       # first forward pass
       a		:str									=			model(_UpperCamelCase   ,    attention_mask=_UpperCamelCase   ,    head_mask=_UpperCamelCase   ,    use_cache=_UpperCamelCase       )

       a				,							a		:Optional[Any]									=			outputs.to_tuple()

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

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

       a		:Any									=			model(_UpperCamelCase   ,    attention_mask=_UpperCamelCase       )['''last_hidden_state''']
       a		:int									=			model(_UpperCamelCase   ,    attention_mask=_UpperCamelCase   ,    past_key_values=_UpperCamelCase       )[
           '''last_hidden_state'''
       ]

       # select random slice
       a		:Union[str, Any]									=			ids_tensor((1,)   ,    output_from_past.shape[-1]       ).item()
       a		:Optional[Any]									=			output_from_no_past[:, -3:, random_slice_idx].detach()
       a		:List[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(_UpperCamelCase   ,    _UpperCamelCase   ,    atol=1e-2       )       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase       ):
       a		:Tuple									=			MaMaaaModel(config=_UpperCamelCase       ).to(_UpperCamelCase       ).eval()
       a		:Any									=			model(**_UpperCamelCase       )

       a		:Optional[Any]									=			outputs.encoder_last_hidden_state
       a		:Union[str, Any]									=			outputs.last_hidden_state

       with tempfile.TemporaryDirectory() as tmpdirname:
             a		:Any									=			model.get_encoder()
             encoder.save_pretrained(_UpperCamelCase       )
             a		:Union[str, Any]									=			MaMaaaEncoder.from_pretrained(_UpperCamelCase       ).to(_UpperCamelCase       )

       a		:List[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:
             a		:List[str]									=			model.get_decoder()
             decoder.save_pretrained(_UpperCamelCase       )
             a		:str									=			MaMaaaDecoder.from_pretrained(_UpperCamelCase       ).to(_UpperCamelCase       )

       a		:int									=			decoder(
           input_ids=inputs_dict['''decoder_input_ids''']   ,    attention_mask=inputs_dict['''decoder_attention_mask''']   ,    encoder_hidden_states=_UpperCamelCase   ,    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 						_snake_case    ( snake_case__		,       snake_case__		,       snake_case__		,       unittest.TestCase				):
 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   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase       ):


       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       ):
       a		:List[Any]									=			MaMaaaModelTester(self       )
       a		:Tuple									=			ConfigTester(self   ,    config_class=_UpperCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       self.config_tester.run_common_tests()




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a				,							a		:str									=			self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
             a		:Tuple									=			model_class(_UpperCamelCase       )

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




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:int									=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_decoder_model_past_large_inputs(*_UpperCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Any									=			self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_encoder_decoder_model_standalone(*_UpperCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a				,							a		:Optional[int]									=			self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
             a		:Optional[Any]									=			model_class(_UpperCamelCase       )
             model.to(_UpperCamelCase       )
             model.eval()

             a		:Optional[Any]									=			copy.deepcopy(self._prepare_for_class(_UpperCamelCase   ,    _UpperCamelCase       )       )

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

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

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




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







def 							__lowerCamelCase		(					UpperCAmelCase_		:						Any		):




      """simple docstring"""





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


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







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




 @cached_property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M'''       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Any									=			MaMaaaModel.from_pretrained('''facebook/m2m100_418M'''       ).to(_UpperCamelCase       )
       a		:Union[str, Any]									=			_long_tensor([[12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38, 2]]       )
       a		:Union[str, Any]									=			_long_tensor([[2, 12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38]]       )
       a		:int									=			prepare_mam_aaa_inputs_dict(model.config   ,    _UpperCamelCase   ,    _UpperCamelCase       )
       with torch.no_grad():
             a		:str									=			model(**_UpperCamelCase       )[0]
       a		:Any									=			torch.Size((1, 11, 1024)       )
       self.assertEqual(output.shape   ,    _UpperCamelCase       )
       # change to expected output here
       a		:Optional[Any]									=			torch.tensor(
           [[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]]   ,    device=_UpperCamelCase       )
       self.assertTrue(torch.allclose(output[:, :3, :3]   ,    _UpperCamelCase   ,    atol=_UpperCamelCase       )       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[Any]									=			MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M'''       ).to(_UpperCamelCase       )

       # change to intended input
       a		:Dict									=			_long_tensor([[12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38, 2]]       )
       a		:Any									=			_long_tensor([[2, 12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38]]       )
       a		:Union[str, Any]									=			prepare_mam_aaa_inputs_dict(model.config   ,    _UpperCamelCase   ,    _UpperCamelCase       )
       with torch.no_grad():
             a		:Dict									=			model(**_UpperCamelCase       )[0]
       a		:List[Any]									=			torch.Size((1, 11, model.config.vocab_size)       )
       self.assertEqual(output.shape   ,    _UpperCamelCase       )
       # change to expected output here
       a		:Optional[int]									=			torch.tensor(
           [[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]]   ,    device=_UpperCamelCase       )
       self.assertTrue(torch.allclose(output[:, :3, :3]   ,    _UpperCamelCase   ,    atol=_UpperCamelCase       )       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[Any]									=			MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M'''       ).to(_UpperCamelCase       )
       a		:Tuple									=			MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M'''   ,    src_lang='''fr'''   ,    tgt_lang='''en'''       )

       a		: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
       a		:Tuple									=			tokenizer(_UpperCamelCase   ,    padding=_UpperCamelCase   ,    return_tensors='''pt'''       )

       a		:Union[str, Any]									=			model.generate(
           input_ids=dct['''input_ids'''].to(_UpperCamelCase       )   ,    attention_mask=dct['''attention_mask'''].to(_UpperCamelCase       )   ,    num_beams=5   ,    forced_bos_token_id=tokenizer.get_lang_id('''en'''       )   ,    )

       a		:List[str]									=			[
           '''The NSA case highlights the total absence of intelligence debate''',
           '''I think there are two levels of response from the French government.''',
           '''When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'''
           ''' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'''
           ''' communications in France.''',
       ]

       a		:str									=			tokenizer.batch_decode(
           hypotheses_batch.tolist()   ,    clean_up_tokenization_spaces=_UpperCamelCase   ,    skip_special_tokens=_UpperCamelCase       )
       assert generated == expected_en








import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


UpperCamelCase__		 =     logging.get_logger(__name__)

class     __snake_case    (				snake_case__  ):
       """simple docstring"""







       def __init__(		self      , *_UpperCamelCase      , **_UpperCamelCase   )      ->							None:






              """simple docstring"""


              warnings.warn(
                  """The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
                  """ use YolosImageProcessor instead."""      , _UpperCamelCase      , )
              super().__init__(*_UpperCamelCase      , **_UpperCamelCase   )

