



"""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      A__  :




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      MBartConfig
	SCREAMING_SNAKE_CASE        =      {}
	SCREAMING_SNAKE_CASE        =      'gelu'

	def __init__(	self:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						Optional[int]=13			,     _SCREAMING_SNAKE_CASE:						Any=7			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=True			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=False			,     _SCREAMING_SNAKE_CASE:						int=99			,     _SCREAMING_SNAKE_CASE:						Any=32			,     _SCREAMING_SNAKE_CASE:						Dict=2			,     _SCREAMING_SNAKE_CASE:						Any=4			,     _SCREAMING_SNAKE_CASE:						Any=37			,     _SCREAMING_SNAKE_CASE:						List[Any]=0.1			,     _SCREAMING_SNAKE_CASE:						List[str]=0.1			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=20			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=2			,     _SCREAMING_SNAKE_CASE:						int=1			,     _SCREAMING_SNAKE_CASE:						str=0			,     )							->			str:

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			parent
				__lowerCAmelCase :    List[str]				=			batch_size
				__lowerCAmelCase :    Union[str, Any]				=			seq_length
				__lowerCAmelCase :    int				=			is_training
				__lowerCAmelCase :    Dict				=			use_labels
				__lowerCAmelCase :    Dict				=			vocab_size
				__lowerCAmelCase :    Dict				=			hidden_size
				__lowerCAmelCase :    List[str]				=			num_hidden_layers
				__lowerCAmelCase :    Optional[Any]				=			num_attention_heads
				__lowerCAmelCase :    List[Any]				=			intermediate_size
				__lowerCAmelCase :    List[str]				=			hidden_dropout_prob
				__lowerCAmelCase :    Any				=			attention_probs_dropout_prob
				__lowerCAmelCase :    Tuple				=			max_position_embeddings
				__lowerCAmelCase :    str				=			eos_token_id
				__lowerCAmelCase :    List[str]				=			pad_token_id
				__lowerCAmelCase :    int				=			bos_token_id

	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict)							->			Tuple:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[Any]				=			ids_tensor([self.batch_size, self.seq_length - 1]			,     self.vocab_size)
				__lowerCAmelCase :    Tuple				=			tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size)			,     1)
				__lowerCAmelCase :    Any				=			tf.concat([input_ids, eos_tensor]			,     axis=1)

				__lowerCAmelCase :    Tuple				=			ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size)

				__lowerCAmelCase :    List[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			,     )
				__lowerCAmelCase :    int				=			prepare_mbart_inputs_dict(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				return config, inputs_dict







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

				"""simple docstring"""

				__lowerCAmelCase :    int				=			TFMBartModel(config=_SCREAMING_SNAKE_CASE).get_decoder()
				__lowerCAmelCase :    Union[str, Any]				=			inputs_dict["input_ids"]

				__lowerCAmelCase :    str				=			input_ids[:1, :]
				__lowerCAmelCase :    str				=			inputs_dict["attention_mask"][:1, :]
				__lowerCAmelCase :    Union[str, Any]				=			inputs_dict["head_mask"]
				__lowerCAmelCase :    Optional[int]				=			1

				# first forward pass
				__lowerCAmelCase :    int				=			model(_SCREAMING_SNAKE_CASE			,     attention_mask=_SCREAMING_SNAKE_CASE			,     head_mask=_SCREAMING_SNAKE_CASE			,     use_cache=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase		,      __lowerCAmelCase :    Union[str, Any]				=			outputs.to_tuple()
				__lowerCAmelCase :    Optional[Any]				=			past_key_values[1]






def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case   ,__snake_case=None   ,__snake_case=None   ,__snake_case=None   ,__snake_case=None   ,__snake_case=None   ,)		->  str:
			if attention_mask is None:
						__lowerCAmelCase :    Optional[Any]				=			tf.cast(tf.math.not_equal(__snake_case   ,config.pad_token_id      )   ,tf.inta      )
			if decoder_attention_mask is None:
						__lowerCAmelCase :    List[Any]				=			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:
						__lowerCAmelCase :    int				=			tf.ones((config.encoder_layers, config.encoder_attention_heads)      )
			if decoder_head_mask is None:
						__lowerCAmelCase :    Optional[Any]				=			tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
			if cross_attn_head_mask is None:
						__lowerCAmelCase :    Tuple				=			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      A__  (    __SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ,						unittest.TestCase				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
	SCREAMING_SNAKE_CASE        =      (TFMBartForConditionalGeneration,) if is_tf_available() else ()
	SCREAMING_SNAKE_CASE        =      (
	    {
	        'conversational': TFMBartForConditionalGeneration,
	        'feature-extraction': TFMBartModel,
	        'summarization': TFMBartForConditionalGeneration,
	        'text2text-generation': TFMBartForConditionalGeneration,
	        'translation': TFMBartForConditionalGeneration,
	    }
	    if is_tf_available()
	    else {}
	)
	SCREAMING_SNAKE_CASE        =      True
	SCREAMING_SNAKE_CASE        =      False
	SCREAMING_SNAKE_CASE        =      False

	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						Tuple			,     _SCREAMING_SNAKE_CASE:						List[str]			,     _SCREAMING_SNAKE_CASE:						Any			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						str)							->			Tuple:

				"""simple docstring"""

				if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
							# Exception encountered when calling layer '...'
							return True

				return False

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

				"""simple docstring"""

				__lowerCAmelCase :    Union[str, Any]				=			TFMBartModelTester(self)
				__lowerCAmelCase :    List[str]				=			ConfigTester(self			,     config_class=_SCREAMING_SNAKE_CASE)

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

				"""simple docstring"""

				self.config_tester.run_common_tests()







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

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.check_decoder_model_past_large_inputs(*_SCREAMING_SNAKE_CASE)






@require_sentencepiece
@require_tokenizers
@require_tf
class      A__  (    unittest.TestCase				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      [
	    ' UN Chief Says There Is No Military Solution in Syria',
	]
	SCREAMING_SNAKE_CASE        =      [
	    'Şeful ONU declară că nu există o soluţie militară în Siria',
	]
	SCREAMING_SNAKE_CASE        =      'facebook/mbart-large-en-ro'

	@cached_property
	def 				_SCREAMING_SNAKE_CASE  (	self:						int)							->			Optional[int]:

				"""simple docstring"""

				return AutoTokenizer.from_pretrained(self.model_name)

	@cached_property
	def 				_SCREAMING_SNAKE_CASE  (	self:						Tuple)							->			int:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[int]				=			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
				return model

	def 				_SCREAMING_SNAKE_CASE  (	self:						Tuple			,     **_SCREAMING_SNAKE_CASE:						List[Any])							->			Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase :    str				=			self.translate_src_text(**_SCREAMING_SNAKE_CASE)
				self.assertListEqual(self.expected_text			,     _SCREAMING_SNAKE_CASE)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[int]			,     **_SCREAMING_SNAKE_CASE:						Dict)							->			str:

				"""simple docstring"""

				__lowerCAmelCase :    int				=			self.tokenizer(self.src_text			,     **_SCREAMING_SNAKE_CASE			,     return_tensors="tf")
				__lowerCAmelCase :    Optional[Any]				=			self.model.generate(
				    model_inputs.input_ids			,     attention_mask=model_inputs.attention_mask			,     num_beams=2)
				__lowerCAmelCase :    List[str]				=			self.tokenizer.batch_decode(_SCREAMING_SNAKE_CASE			,     skip_special_tokens=_SCREAMING_SNAKE_CASE)
				return generated_words







	@slow
	def 				_SCREAMING_SNAKE_CASE  (	self:						str)							->			Optional[Any]:

				"""simple docstring"""

				self._assert_generated_batch_equal_expected()




"""simple docstring"""






def      _lowercase							(    __snake_case   ,__snake_case      )		->  Tuple:
			# "extended trapezoidal rule"
			# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
			__lowerCAmelCase :    Optional[Any]				=			(boundary[1] - boundary[0]) / steps
			__lowerCAmelCase :    List[Any]				=			boundary[0]
			__lowerCAmelCase :    Any				=			boundary[1]
			__lowerCAmelCase :    Dict				=			make_points(__snake_case   ,__snake_case   ,__snake_case      )
			__lowerCAmelCase :    List[str]				=			0.0
			y += (h / 2.0) * f(__snake_case      )
			for i in x_i:
						# print(i)
						y += h * f(__snake_case      )
			y += (h / 2.0) * f(__snake_case      )
			return y


def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case      )		->  Union[str, Any]:
			__lowerCAmelCase :    int				=			a + h
			while x < (b - h):
						yield x
						__lowerCAmelCase :    List[str]				=			x + h


def      _lowercase							(    __snake_case      )		->  Tuple:  # enter your function here
			__lowerCAmelCase :    int				=			(x - 0) * (x - 0)
			return y


def      _lowercase							(    )		->  int:
			__lowerCAmelCase :    Tuple				=			0.0  # Lower bound of integration
			__lowerCAmelCase :    Union[str, Any]				=			1.0  # Upper bound of integration
			__lowerCAmelCase :    Dict				=			10.0  # define number of steps or resolution
			__lowerCAmelCase :    Optional[Any]				=			[a, b]  # define boundary of integration
			__lowerCAmelCase :    Optional[int]				=			method_a(__snake_case   ,__snake_case      )
			print(F"""y = {y}"""      )


if __name__ == "__main__":
	main()