


lowerCAmelCase__  :Tuple             =							8.314_462  # Unit - J mol-1 K-1

def 	lowerCAmelCase__			(      a__:		float      ,  a__:		float      ,  a__:		float	)  ->		float:


							'''simple docstring'''





							if moles < 0 or kelvin < 0 or volume < 0:
														raise ValueError('Invalid inputs. Enter positive value.'	)
							return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume

def 	lowerCAmelCase__			(      a__:		float      ,  a__:		float      ,  a__:		float	)  ->		float:


							'''simple docstring'''





							if moles < 0 or kelvin < 0 or pressure < 0:
														raise ValueError('Invalid inputs. Enter positive value.'	)
							return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


if __name__ == "__main__":
		from doctest import testmod

		testmod()




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


							'''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=a__	)
							if decoder_head_mask is None:
														_UpperCAmelCase					=      torch.ones(config.decoder_layers      ,  config.decoder_attention_heads      ,  device=a__	)
							if cross_attn_head_mask is None:
														_UpperCAmelCase					=      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 		__a				:
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=13      ,   _SCREAMING_SNAKE_CASE=7      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=False      ,   _SCREAMING_SNAKE_CASE=99      ,   _SCREAMING_SNAKE_CASE=16      ,   _SCREAMING_SNAKE_CASE=2      ,   _SCREAMING_SNAKE_CASE=4      ,   _SCREAMING_SNAKE_CASE=4      ,   _SCREAMING_SNAKE_CASE="relu"      ,   _SCREAMING_SNAKE_CASE=0.1      ,   _SCREAMING_SNAKE_CASE=0.1      ,   _SCREAMING_SNAKE_CASE=0.0      ,   _SCREAMING_SNAKE_CASE=0.0      ,   _SCREAMING_SNAKE_CASE=20      ,   _SCREAMING_SNAKE_CASE=2      ,   _SCREAMING_SNAKE_CASE=1      ,   _SCREAMING_SNAKE_CASE=0      ,   )			->	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      UpperCAmelCase__    (      self						)			->	str:




									"""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(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
									return config, inputs_dict
		def      UpperCAmelCase__    (      self						)			->	Optional[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						)			->	str:




									"""simple docstring"""





									_UpperCAmelCase		,	_UpperCAmelCase					=      self.prepare_config_and_inputs()
									return config, inputs_dict
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[int]:




									"""simple docstring"""





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

									# first forward pass
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE      ,   attention_mask=_SCREAMING_SNAKE_CASE      ,   head_mask=_SCREAMING_SNAKE_CASE      ,   use_cache=_SCREAMING_SNAKE_CASE						)

									_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(_SCREAMING_SNAKE_CASE      ,   attention_mask=_SCREAMING_SNAKE_CASE						)['last_hidden_state']
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE      ,   attention_mask=_SCREAMING_SNAKE_CASE      ,   past_key_values=_SCREAMING_SNAKE_CASE						)[
									    '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(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   atol=1e-2						)						)






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=      MaMaaaModel(config=_SCREAMING_SNAKE_CASE						).to(_SCREAMING_SNAKE_CASE						).eval()
									_UpperCAmelCase					=      model(**_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      outputs.encoder_last_hidden_state
									_UpperCAmelCase					=      outputs.last_hidden_state

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

									_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(_SCREAMING_SNAKE_CASE						)
																_UpperCAmelCase					=      MaMaaaDecoder.from_pretrained(_SCREAMING_SNAKE_CASE						).to(_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      decoder(
									    input_ids=inputs_dict['decoder_input_ids']      ,   attention_mask=inputs_dict['decoder_attention_mask']      ,   encoder_hidden_states=_SCREAMING_SNAKE_CASE      ,   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 		__a				(    UpperCAmelCase   ,				UpperCAmelCase   ,				UpperCAmelCase   ,				unittest.TestCase      ):
		_a     :  List[Any]           =     (
		    (
		        MaMaaaModel,
		        MaMaaaForConditionalGeneration,
		    )
		    if is_torch_available()
		    else ()
		)
		_a     :  List[str]           =     (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
		_a     :  int           =     (
		    {
		        'conversational': MaMaaaForConditionalGeneration,
		        'feature-extraction': MaMaaaModel,
		        'summarization': MaMaaaForConditionalGeneration,
		        'text2text-generation': MaMaaaForConditionalGeneration,
		        'translation': MaMaaaForConditionalGeneration,
		    }
		    if is_torch_available()
		    else {}
		)
		_a     :  str           =     True
		_a     :  Union[str, Any]           =     True
		_a     :  Optional[int]           =     False
		_a     :  Union[str, Any]           =     False
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	int:




									"""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"""





									_UpperCAmelCase					=      MaMaaaModelTester(self						)
									_UpperCAmelCase					=      ConfigTester(self      ,   config_class=_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	List[Any]:




									"""simple docstring"""





									self.config_tester.run_common_tests()
		def      UpperCAmelCase__    (      self						)			->	Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase		,	_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
																_UpperCAmelCase					=      model_class(_SCREAMING_SNAKE_CASE						)

																with tempfile.TemporaryDirectory() as tmpdirname:
																							model.save_pretrained(_SCREAMING_SNAKE_CASE						)
																							_UpperCAmelCase		,	_UpperCAmelCase					=      model_class.from_pretrained(_SCREAMING_SNAKE_CASE      ,   output_loading_info=_SCREAMING_SNAKE_CASE						)
																self.assertEqual(info['missing_keys']      ,   []						)
		def      UpperCAmelCase__    (      self						)			->	Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_decoder_model_past_large_inputs(*_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_encoder_decoder_model_standalone(*_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase		,	_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
																_UpperCAmelCase					=      model_class(_SCREAMING_SNAKE_CASE						)
																model.to(_SCREAMING_SNAKE_CASE						)
																model.eval()

																_UpperCAmelCase					=      copy.deepcopy(self._prepare_for_class(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)						)

																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'      ,   _SCREAMING_SNAKE_CASE						)
																							del inputs["input_ids"]
																							inputs.pop('decoder_input_ids'      ,   _SCREAMING_SNAKE_CASE						)

																_UpperCAmelCase					=      model.get_input_embeddings()
																if not self.is_encoder_decoder:
																							_UpperCAmelCase					=      wte(_SCREAMING_SNAKE_CASE						)
																else:
																							_UpperCAmelCase					=      wte(_SCREAMING_SNAKE_CASE						)
																							_UpperCAmelCase					=      wte(_SCREAMING_SNAKE_CASE						)

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






		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									_UpperCAmelCase		,	_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs()
									_UpperCAmelCase					=      input_dict['input_ids']
									_UpperCAmelCase					=      input_ids.ne(1						).to(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      MaMaaaForConditionalGeneration(_SCREAMING_SNAKE_CASE						).eval().to(_SCREAMING_SNAKE_CASE						)
									if torch_device == "cuda":
																model.half()
									model.generate(_SCREAMING_SNAKE_CASE      ,   attention_mask=_SCREAMING_SNAKE_CASE						)
									model.generate(num_beams=4      ,   do_sample=_SCREAMING_SNAKE_CASE      ,   early_stopping=_SCREAMING_SNAKE_CASE      ,   num_return_sequences=3						)



def 	lowerCAmelCase__			(      a__:		Tuple	)  ->		Optional[int]:


							'''simple docstring'''





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


lowerCAmelCase__  :str             =							1e-4







@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class 		__a				(    unittest.TestCase      ):
		@cached_property
		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





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




									"""simple docstring"""





									_UpperCAmelCase					=      MaMaaaModel.from_pretrained('facebook/m2m100_418M'						).to(_SCREAMING_SNAKE_CASE						)
									_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      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
									with torch.no_grad():
																_UpperCAmelCase					=      model(**_SCREAMING_SNAKE_CASE						)[0]
									_UpperCAmelCase					=      torch.Size((1, 11, 1024)						)
									self.assertEqual(output.shape      ,   _SCREAMING_SNAKE_CASE						)
									# 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=_SCREAMING_SNAKE_CASE						)
									self.assertTrue(torch.allclose(output[:, :3, :3]      ,   _SCREAMING_SNAKE_CASE      ,   atol=_SCREAMING_SNAKE_CASE						)						)
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase					=      MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'						).to(_SCREAMING_SNAKE_CASE						)

									# 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      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
									with torch.no_grad():
																_UpperCAmelCase					=      model(**_SCREAMING_SNAKE_CASE						)[0]
									_UpperCAmelCase					=      torch.Size((1, 11, model.config.vocab_size)						)
									self.assertEqual(output.shape      ,   _SCREAMING_SNAKE_CASE						)
									# 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=_SCREAMING_SNAKE_CASE						)
									self.assertTrue(torch.allclose(output[:, :3, :3]      ,   _SCREAMING_SNAKE_CASE      ,   atol=_SCREAMING_SNAKE_CASE						)						)






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




									"""simple docstring"""





									_UpperCAmelCase					=      MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'						).to(_SCREAMING_SNAKE_CASE						)
									_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(_SCREAMING_SNAKE_CASE      ,   padding=_SCREAMING_SNAKE_CASE      ,   return_tensors='pt'						)

									_UpperCAmelCase					=      model.generate(
									    input_ids=dct['input_ids'].to(_SCREAMING_SNAKE_CASE						)      ,   attention_mask=dct['attention_mask'].to(_SCREAMING_SNAKE_CASE						)      ,   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=_SCREAMING_SNAKE_CASE      ,   skip_special_tokens=_SCREAMING_SNAKE_CASE						)
									assert generated == expected_en
