





'''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__    (    lowercase :					Any, lowercase :					int, lowercase :					str, lowercase :					Tuple=None, lowercase :					Dict=None, lowercase :					Any=None, lowercase :					List[Any]=None, lowercase :					Optional[int]=None, )		->		List[str]:





     """simple docstring"""

     if attention_mask is None:
          _UpperCamelCase       =	input_ids.ne(config.pad_token_id				)
     if decoder_attention_mask is None:
          _UpperCamelCase       =	decoder_input_ids.ne(config.pad_token_id				)
     if head_mask is None:
          _UpperCamelCase       =	torch.ones(config.encoder_layers, config.encoder_attention_heads, device=lowercase				)
     if decoder_head_mask is None:
          _UpperCamelCase       =	torch.ones(config.decoder_layers, config.decoder_attention_heads, device=lowercase				)
     if cross_attn_head_mask is None:
          _UpperCamelCase       =	torch.ones(config.decoder_layers, config.decoder_attention_heads, device=lowercase				)
     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       :		Optional[int]	,			lowerCAmelCase__       :		Tuple	,			lowerCAmelCase__       :		Any=13	,			lowerCAmelCase__       :		Tuple=7	,			lowerCAmelCase__       :		Dict=True	,			lowerCAmelCase__       :		List[Any]=False	,			lowerCAmelCase__       :		Optional[int]=99	,			lowerCAmelCase__       :		Union[str, Any]=16	,			lowerCAmelCase__       :		Optional[int]=2	,			lowerCAmelCase__       :		Union[str, Any]=4	,			lowerCAmelCase__       :		int=4	,			lowerCAmelCase__       :		Dict="relu"	,			lowerCAmelCase__       :		Optional[int]=0.1	,			lowerCAmelCase__       :		int=0.1	,			lowerCAmelCase__       :		str=0.0	,			lowerCAmelCase__       :		Optional[Any]=0.0	,			lowerCAmelCase__       :		Dict=20	,			lowerCAmelCase__       :		List[Any]=2	,			lowerCAmelCase__       :		List[str]=1	,			lowerCAmelCase__       :		Optional[int]=0	,			)   ->					Union[str, Any]:

            '''simple docstring'''




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





       def snake_case__						(							self       :		Dict					)   ->					Union[str, Any]:

            '''simple docstring'''




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

            _UpperCamelCase       =	self.get_config()
            _UpperCamelCase       =	prepare_mam_aaa_inputs_dict(lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__					)
            return config, inputs_dict





       def snake_case__						(							self       :		Any					)   ->					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 snake_case__						(							self       :		Any					)   ->					Dict:

            '''simple docstring'''




            _UpperCamelCase   ,    _UpperCamelCase       =	self.prepare_config_and_inputs()
            return config, inputs_dict





       def snake_case__						(							self       :		Tuple	,			lowerCAmelCase__       :		Tuple	,			lowerCAmelCase__       :		Optional[int]					)   ->					List[str]:

            '''simple docstring'''




            _UpperCamelCase       =	MaMaaaModel(config=lowerCAmelCase__					).get_decoder().to(lowerCAmelCase__					).eval()
            _UpperCamelCase       =	inputs_dict['''input_ids''']
            _UpperCamelCase       =	inputs_dict['''attention_mask''']
            _UpperCamelCase       =	inputs_dict['''head_mask''']

            # first forward pass
            _UpperCamelCase       =	model(lowerCAmelCase__	,			attention_mask=lowerCAmelCase__	,			head_mask=lowerCAmelCase__	,			use_cache=lowerCAmelCase__					)

            _UpperCamelCase   ,    _UpperCamelCase       =	outputs.to_tuple()

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

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

            _UpperCamelCase       =	model(lowerCAmelCase__	,			attention_mask=lowerCAmelCase__					)['''last_hidden_state''']
            _UpperCamelCase       =	model(lowerCAmelCase__	,			attention_mask=lowerCAmelCase__	,			past_key_values=lowerCAmelCase__					)[
                '''last_hidden_state'''
            ]

            # select random slice
            _UpperCamelCase       =	ids_tensor((1,)	,			output_from_past.shape[-1]					).item()
            _UpperCamelCase       =	output_from_no_past[:, -3:, random_slice_idx].detach()
            _UpperCamelCase       =	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 snake_case__						(							self       :		int	,			lowerCAmelCase__       :		List[Any]	,			lowerCAmelCase__       :		Tuple					)   ->					Tuple:

            '''simple docstring'''




            _UpperCamelCase       =	MaMaaaModel(config=lowerCAmelCase__					).to(lowerCAmelCase__					).eval()
            _UpperCamelCase       =	model(**lowerCAmelCase__					)

            _UpperCamelCase       =	outputs.encoder_last_hidden_state
            _UpperCamelCase       =	outputs.last_hidden_state

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

            _UpperCamelCase       =	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       =	model.get_decoder()
                 decoder.save_pretrained(lowerCAmelCase__					)
                 _UpperCamelCase       =	MaMaaaDecoder.from_pretrained(lowerCAmelCase__					).to(lowerCAmelCase__					)

            _UpperCamelCase       =	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     __lowerCAmelCase					(   __magic_name__						,						__magic_name__						,						__magic_name__						,						unittest.TestCase			):






       """simple docstring"""
       _snake_case     :					Optional[int]						    =   (
           (
               MaMaaaModel,
               MaMaaaForConditionalGeneration,
           )
           if is_torch_available()
           else ()
       )
       _snake_case     :					int						    =   (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
       _snake_case     :					str						    =   (
           {
               'conversational': MaMaaaForConditionalGeneration,
               'feature-extraction': MaMaaaModel,
               'summarization': MaMaaaForConditionalGeneration,
               'text2text-generation': MaMaaaForConditionalGeneration,
               'translation': MaMaaaForConditionalGeneration,
           }
           if is_torch_available()
           else {}
       )
       _snake_case     :					List[Any]						    =   True
       _snake_case     :					List[Any]						    =   True
       _snake_case     :					Any						    =   False
       _snake_case     :					int						    =   False





       def snake_case__						(							self       :		Optional[Any]	,			lowerCAmelCase__       :		Optional[Any]	,			lowerCAmelCase__       :		Any	,			lowerCAmelCase__       :		Tuple	,			lowerCAmelCase__       :		Tuple	,			lowerCAmelCase__       :		Any					)   ->					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 snake_case__						(							self       :		Optional[Any]					)   ->					Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase       =	MaMaaaModelTester(self					)
            _UpperCamelCase       =	ConfigTester(self	,			config_class=lowerCAmelCase__					)





       def snake_case__						(							self       :		str					)   ->					Tuple:

            '''simple docstring'''




            self.config_tester.run_common_tests()





       def snake_case__						(							self       :		List[str]					)   ->					Dict:

            '''simple docstring'''




            _UpperCamelCase   ,    _UpperCamelCase       =	self.model_tester.prepare_config_and_inputs()
            for model_class in self.all_model_classes:
                 _UpperCamelCase       =	model_class(lowerCAmelCase__					)

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





       def snake_case__						(							self       :		Optional[Any]					)   ->					Optional[int]:

            '''simple docstring'''




            _UpperCamelCase       =	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowerCAmelCase__					)





       def snake_case__						(							self       :		Tuple					)   ->					Optional[int]:

            '''simple docstring'''




            _UpperCamelCase       =	self.model_tester.prepare_config_and_inputs_for_common()
            self.model_tester.check_encoder_decoder_model_standalone(*lowerCAmelCase__					)





       def snake_case__						(							self       :		str					)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase   ,    _UpperCamelCase       =	self.model_tester.prepare_config_and_inputs_for_common()

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

                 _UpperCamelCase       =	copy.deepcopy(self._prepare_for_class(lowerCAmelCase__	,			lowerCAmelCase__					)					)

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

                 _UpperCamelCase       =	model.get_input_embeddings()
                 if not self.is_encoder_decoder:
                      _UpperCamelCase       =	wte(lowerCAmelCase__					)
                 else:
                      _UpperCamelCase       =	wte(lowerCAmelCase__					)
                      _UpperCamelCase       =	wte(lowerCAmelCase__					)

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







       def snake_case__						(							self       :		Optional[Any]					)   ->					Dict:

            '''simple docstring'''




            _UpperCamelCase   ,    _UpperCamelCase       =	self.model_tester.prepare_config_and_inputs()
            _UpperCamelCase       =	input_dict['''input_ids''']
            _UpperCamelCase       =	input_ids.ne(1					).to(lowerCAmelCase__					)
            _UpperCamelCase       =	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__    (    lowercase :					Optional[int]				)		->		int:





     """simple docstring"""

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


lowercase__						:      List[str]	=    1E-4




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






       """simple docstring"""





       @cached_property
       def snake_case__						(							self       :		List[Any]					)   ->					List[str]:

            '''simple docstring'''




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





       def snake_case__						(							self       :		List[Any]					)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	MaMaaaModel.from_pretrained('''facebook/m2m100_418M'''					).to(lowerCAmelCase__					)
            _UpperCamelCase       =	_long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]]					)
            _UpperCamelCase       =	_long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]]					)
            _UpperCamelCase       =	prepare_mam_aaa_inputs_dict(model.config	,			lowerCAmelCase__	,			lowerCAmelCase__					)
            with torch.no_grad():
                 _UpperCamelCase       =	model(**lowerCAmelCase__					)[0]
            _UpperCamelCase       =	torch.Size((1, 11, 1024)					)
            self.assertEqual(output.shape	,			lowerCAmelCase__					)
            # change to expected output here
            _UpperCamelCase       =	torch.tensor(
                [[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]]	,			device=lowerCAmelCase__					)
            self.assertTrue(torch.allclose(output[:, :3, :3]	,			lowerCAmelCase__	,			atol=lowerCAmelCase__					)					)





       def snake_case__						(							self       :		List[str]					)   ->					Tuple:

            '''simple docstring'''




            _UpperCamelCase       =	MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M'''					).to(lowerCAmelCase__					)

            # change to intended input
            _UpperCamelCase       =	_long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]]					)
            _UpperCamelCase       =	_long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]]					)
            _UpperCamelCase       =	prepare_mam_aaa_inputs_dict(model.config	,			lowerCAmelCase__	,			lowerCAmelCase__					)
            with torch.no_grad():
                 _UpperCamelCase       =	model(**lowerCAmelCase__					)[0]
            _UpperCamelCase       =	torch.Size((1, 11, model.config.vocab_size)					)
            self.assertEqual(output.shape	,			lowerCAmelCase__					)
            # change to expected output here
            _UpperCamelCase       =	torch.tensor(
                [[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]]	,			device=lowerCAmelCase__					)
            self.assertTrue(torch.allclose(output[:, :3, :3]	,			lowerCAmelCase__	,			atol=lowerCAmelCase__					)					)







       def snake_case__						(							self       :		Union[str, Any]					)   ->					int:

            '''simple docstring'''




            _UpperCamelCase       =	MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M'''					).to(lowerCAmelCase__					)
            _UpperCamelCase       =	MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M'''	,			src_lang='''fr'''	,			tgt_lang='''en'''					)

            _UpperCamelCase       =	[
                '''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       =	tokenizer(lowerCAmelCase__	,			padding=lowerCAmelCase__	,			return_tensors='''pt'''					)

            _UpperCamelCase       =	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       =	[
                '''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       =	tokenizer.batch_decode(
                hypotheses_batch.tolist()	,			clean_up_tokenization_spaces=lowerCAmelCase__	,			skip_special_tokens=lowerCAmelCase__					)
            assert generated == expected_en










'''simple docstring'''

from __future__ import annotations







def  a__    (    lowercase :					list[int], lowercase :					list[int], lowercase :					int				)		->		tuple[float, list[float]]:





     """simple docstring"""

     _UpperCamelCase       =	list(range(len(lowercase				)				)				)
     _UpperCamelCase       =	[v / w for v, w in zip(lowercase, lowercase				)]
     index.sort(key=lambda lowercase				: ratio[i], reverse=lowercase				)

     _UpperCamelCase       =	0
     _UpperCamelCase       =	[0] * len(lowercase				)
     for i in index:
          if weight[i] <= capacity:
               _UpperCamelCase       =	1
               max_value += value[i]
               capacity -= weight[i]
          else:
               _UpperCamelCase       =	capacity / weight[i]
               max_value += value[i] * capacity / weight[i]
               break

     return max_value, fractions


if __name__ == "__main__":
   import doctest

   doctest.testmod()



