





"""simple docstring"""




from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel


@require_tf
class  __UpperCamelCase       :
							lowerCamelCase     :      Optional[Any]         =MBartConfig
							lowerCamelCase     :      Optional[Any]         ={}
							lowerCamelCase     :      List[str]         ="""gelu"""






							def __init__(     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__=13   ,      lowerCAmelCase__=7   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=False   ,      lowerCAmelCase__=99   ,      lowerCAmelCase__=32   ,      lowerCAmelCase__=2   ,      lowerCAmelCase__=4   ,      lowerCAmelCase__=37   ,      lowerCAmelCase__=0.1   ,      lowerCAmelCase__=0.1   ,      lowerCAmelCase__=20   ,      lowerCAmelCase__=2   ,      lowerCAmelCase__=1   ,      lowerCAmelCase__=0   ,      )			->				str:
								a   :	str			=       parent
								a   :	int			=       batch_size
								a   :	Any			=       seq_length
								a   :	Union[str, Any]			=       is_training
								a   :	List[Any]			=       use_labels
								a   :	int			=       vocab_size
								a   :	Union[str, Any]			=       hidden_size
								a   :	List[str]			=       num_hidden_layers
								a   :	Optional[Any]			=       num_attention_heads
								a   :	List[str]			=       intermediate_size
								a   :	Union[str, Any]			=       hidden_dropout_prob
								a   :	Any			=       attention_probs_dropout_prob
								a   :	List[str]			=       max_position_embeddings
								a   :	Tuple			=       eos_token_id
								a   :	Dict			=       pad_token_id
								a   :	List[str]			=       bos_token_id






							def 					__a     (     self   )			->				Optional[int]:
								a   :	str			=       ids_tensor([self.batch_size, self.seq_length - 1]   ,      self.vocab_size   )
								a   :	List[Any]			=       tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )   ,      1   )
								a   :	List[Any]			=       tf.concat([input_ids, eos_tensor]   ,      axis=1   )

								a   :	Any			=       ids_tensor([self.batch_size, self.seq_length]   ,      self.vocab_size   )

								a   :	Optional[Any]			=       self.config_cls(
								    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   ,      max_position_embeddings=self.max_position_embeddings   ,      eos_token_ids=[2]   ,      bos_token_id=self.bos_token_id   ,      pad_token_id=self.pad_token_id   ,      decoder_start_token_id=self.pad_token_id   ,      **self.config_updates   ,      )
								a   :	int			=       prepare_mbart_inputs_dict(lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )
								return config, inputs_dict






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Any:
								a   :	Tuple			=       TFMBartModel(config=lowerCAmelCase__   ).get_decoder()
								a   :	Tuple			=       inputs_dict["input_ids"]

								a   :	List[Any]			=       input_ids[:1, :]
								a   :	List[Any]			=       inputs_dict["attention_mask"][:1, :]
								a   :	Tuple			=       inputs_dict["head_mask"]
								a   :	Tuple			=       1

								# first forward pass
								a   :	Any			=       model(lowerCAmelCase__   ,      attention_mask=lowerCAmelCase__   ,      head_mask=lowerCAmelCase__   ,      use_cache=lowerCAmelCase__   )

								a, a   :	str			=       outputs.to_tuple()
								a   :	Dict			=       past_key_values[1]






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Dict					, _lowercase		:					Optional[int]					, _lowercase		:					Optional[Any]					, _lowercase		:					Optional[Any]=None					, _lowercase		:					Optional[Any]=None					, _lowercase		:					str=None					, _lowercase		:					Dict=None					, _lowercase		:					Tuple=None					, )				->Any:






	'''simple docstring'''






	if attention_mask is None:
		a   :	List[Any]			=       tf.cast(tf.math.not_equal(_lowercase					, config.pad_token_id      )					, tf.inta      )
	if decoder_attention_mask is None:
		a   :	str			=       tf.concat(
		    [
		        tf.ones(decoder_input_ids[:, :1].shape					, dtype=tf.inta      ),
		        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]					, config.pad_token_id      )					, tf.inta      ),
		    ]					, axis=-1					, )
	if head_mask is None:
		a   :	Union[str, Any]			=       tf.ones((config.encoder_layers, config.encoder_attention_heads)      )
	if decoder_head_mask is None:
		a   :	str			=       tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
	if cross_attn_head_mask is None:
		a   :	str			=       tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
	return {
	    "input_ids": input_ids,
	    "decoder_input_ids": decoder_input_ids,
	    "attention_mask": attention_mask,
	    "decoder_attention_mask": decoder_attention_mask,
	    "head_mask": head_mask,
	    "decoder_head_mask": decoder_head_mask,
	    "cross_attn_head_mask": cross_attn_head_mask,
	}


@require_tf
class  __UpperCamelCase       (		a__	, a__	, unittest.TestCase		):
							lowerCamelCase     :      Dict         =(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
							lowerCamelCase     :      List[str]         =(TFMBartForConditionalGeneration,) if is_tf_available() else ()
							lowerCamelCase     :      Dict         =(
							    {
							        """conversational""": TFMBartForConditionalGeneration,
							        """feature-extraction""": TFMBartModel,
							        """summarization""": TFMBartForConditionalGeneration,
							        """text2text-generation""": TFMBartForConditionalGeneration,
							        """translation""": TFMBartForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							lowerCamelCase     :      List[Any]         =True
							lowerCamelCase     :      Union[str, Any]         =False
							lowerCamelCase     :      Tuple         =False






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Dict:
								if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
									# Exception encountered when calling layer '...'
									return True

								return False






							def 					__a     (     self   )			->				List[Any]:
								a   :	List[Any]			=       TFMBartModelTester(self   )
								a   :	str			=       ConfigTester(self   ,      config_class=lowerCAmelCase__   )






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






							def 					__a     (     self   )			->				int:
								a   :	Any			=       self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase__   )


@require_sentencepiece
@require_tokenizers
@require_tf
class  __UpperCamelCase       (		unittest.TestCase		):
							lowerCamelCase     :      Optional[Any]         =[
							    """ UN Chief Says There Is No Military Solution in Syria""",
							]
							lowerCamelCase     :      Tuple         =[
							    """Şeful ONU declară că nu există o soluţie militară în Siria""",
							]
							lowerCamelCase     :      Dict         ="""facebook/mbart-large-en-ro"""






							@cached_property
							def 					__a     (     self   )			->				Tuple:
								return AutoTokenizer.from_pretrained(self.model_name   )






							@cached_property
							def 					__a     (     self   )			->				Any:
								a   :	Tuple			=       TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name   )
								return model






							def 					__a     (     self   ,      **lowerCAmelCase__   )			->				List[str]:
								a   :	Optional[int]			=       self.translate_src_text(**lowerCAmelCase__   )
								self.assertListEqual(self.expected_text   ,      lowerCAmelCase__   )






							def 					__a     (     self   ,      **lowerCAmelCase__   )			->				Dict:
								a   :	Union[str, Any]			=       self.tokenizer(self.src_text   ,      **lowerCAmelCase__   ,      return_tensors="tf"   )
								a   :	Optional[int]			=       self.model.generate(
								    model_inputs.input_ids   ,      attention_mask=model_inputs.attention_mask   ,      num_beams=2   )
								a   :	Optional[Any]			=       self.tokenizer.batch_decode(lowerCAmelCase__   ,      skip_special_tokens=lowerCAmelCase__   )
								return generated_words






							@slow
							def 					__a     (     self   )			->				int:
								self._assert_generated_batch_equal_expected()









"""simple docstring"""




a		:				str  			=    8.314_4598






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					float					, _lowercase		:					float      )				->float:






	'''simple docstring'''






	if temperature < 0:
		raise Exception("Temperature cannot be less than 0 K"      )
	if molar_mass <= 0:
		raise Exception("Molar mass cannot be less than or equal to 0 kg/mol"      )
	else:
		return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
		import doctest

		# run doctest
		doctest.testmod()

		# example
		a		:				Any  			=    300
		a		:				Dict  			=    28
		a		:				Dict  			=    rms_speed_of_molecule(temperature, molar_mass)
		print(F'''Vrms of Nitrogen gas at 300 K is {vrms} m/s''')


