




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 			lowerCamelCase_				:


 '''simple docstring'''







 lowercase_							       =       MBartConfig
 lowercase_							       =       {}
 lowercase_							       =       "gelu"



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



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

       SCREAMING_SNAKE_CASE_      		=     ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size		)

       SCREAMING_SNAKE_CASE_      		=     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							,							)
       SCREAMING_SNAKE_CASE_      		=     prepare_mbart_inputs_dict(_lowerCAmelCase							,							_lowerCAmelCase							,							_lowerCAmelCase		)
       return config, inputs_dict




 def  lowerCAmelCase_    (				self    :						str							,							_lowerCAmelCase    :						Tuple							,							_lowerCAmelCase    :						int		):
       SCREAMING_SNAKE_CASE_      		=     TFMBartModel(config=_lowerCAmelCase		).get_decoder()
       SCREAMING_SNAKE_CASE_      		=     inputs_dict['input_ids']

       SCREAMING_SNAKE_CASE_      		=     input_ids[:1, :]
       SCREAMING_SNAKE_CASE_      		=     inputs_dict['attention_mask'][:1, :]
       SCREAMING_SNAKE_CASE_      		=     inputs_dict['head_mask']
       SCREAMING_SNAKE_CASE_      		=     1

       # first forward pass
       SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase							,							attention_mask=_lowerCAmelCase							,							head_mask=_lowerCAmelCase							,							use_cache=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     outputs.to_tuple()
       SCREAMING_SNAKE_CASE_      		=     past_key_values[1]


def    UpperCAmelCase_				( __UpperCAmelCase		:      Optional[Any]     ,     __UpperCAmelCase		:      Dict     ,     __UpperCAmelCase		:      Union[str, Any]     ,     __UpperCAmelCase		:      Dict=None     ,     __UpperCAmelCase		:      Union[str, Any]=None     ,     __UpperCAmelCase		:      Optional[int]=None     ,     __UpperCAmelCase		:      Any=None     ,     __UpperCAmelCase		:      Optional[Any]=None     ,     )   ->					List[str]:
      if attention_mask is None:
            SCREAMING_SNAKE_CASE_      		=     tf.cast(tf.math.not_equal(__UpperCAmelCase     ,     config.pad_token_id				)     ,     tf.inta				)
      if decoder_attention_mask is None:
            SCREAMING_SNAKE_CASE_      		=     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:
            SCREAMING_SNAKE_CASE_      		=     tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
      if decoder_head_mask is None:
            SCREAMING_SNAKE_CASE_      		=     tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
      if cross_attn_head_mask is None:
            SCREAMING_SNAKE_CASE_      		=     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 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE			,  _SCREAMING_SNAKE_CASE			,  unittest.TestCase   ):


 '''simple docstring'''







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



 def  lowerCAmelCase_    (				self    :						Optional[Any]							,							_lowerCAmelCase    :						Dict							,							_lowerCAmelCase    :						Any							,							_lowerCAmelCase    :						List[str]							,							_lowerCAmelCase    :						Optional[int]							,							_lowerCAmelCase    :						str		):
       if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
             # Exception encountered when calling layer '...'
             return True

       return False



 def  lowerCAmelCase_    (				self    :						List[Any]		):
       SCREAMING_SNAKE_CASE_      		=     TFMBartModelTester(self		)
       SCREAMING_SNAKE_CASE_      		=     ConfigTester(self							,							config_class=_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						Optional[Any]		):
       self.config_tester.run_common_tests()




 def  lowerCAmelCase_    (				self    :						Optional[Any]		):
       SCREAMING_SNAKE_CASE_      		=     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 			lowerCamelCase_				( unittest.TestCase   ):


 '''simple docstring'''







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



 @cached_property
 def  lowerCAmelCase_    (				self    :						Tuple		):
       return AutoTokenizer.from_pretrained(self.model_name		)



 @cached_property
 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       SCREAMING_SNAKE_CASE_      		=     TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
       return model



 def  lowerCAmelCase_    (				self    :						int							,							**_lowerCAmelCase    :						Optional[Any]		):
       SCREAMING_SNAKE_CASE_      		=     self.translate_src_text(**_lowerCAmelCase		)
       self.assertListEqual(self.expected_text							,							_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						str							,							**_lowerCAmelCase    :						Any		):
       SCREAMING_SNAKE_CASE_      		=     self.tokenizer(self.src_text							,							**_lowerCAmelCase							,							return_tensors='tf'		)
       SCREAMING_SNAKE_CASE_      		=     self.model.generate(
           model_inputs.input_ids							,							attention_mask=model_inputs.attention_mask							,							num_beams=2		)
       SCREAMING_SNAKE_CASE_      		=     self.tokenizer.batch_decode(_lowerCAmelCase							,							skip_special_tokens=_lowerCAmelCase		)
       return generated_words




 @slow
 def  lowerCAmelCase_    (				self    :						Any		):
       self._assert_generated_batch_equal_expected()


"""simple docstring"""




def 						A							(  snake_case__		,					snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   1  # To kept the Calculated Value
			# Since C(n, k) = C(n, n-k)
			if k > (n - k):
						SCREAMING_SNAKE_CASE__										=   n - k
			# Calculate C(n,k)
			for i in range(snake_case__     ):
						result *= n - i
						result //= i + 1
			return result

def 						A							(  snake_case__     ):




			'''simple docstring'''


			return binomial_coefficient(2 * node_count		,					snake_case__     ) // (node_count + 1)

def 						A							(  snake_case__     ):




			'''simple docstring'''


			if n < 0:
						raise ValueError("""factorial() not defined for negative values"""     )
			SCREAMING_SNAKE_CASE__										=   1
			for i in range(1		,					n + 1     ):
						result *= i
			return result

def 						A							(  snake_case__     ):




			'''simple docstring'''


			return catalan_number(snake_case__     ) * factorial(snake_case__     )


if __name__ == "__main__":
						A_				:      Optional[int]           =			int(input("Enter the number of nodes: ").strip() or 0)
						if node_count <= 0:
												raise ValueError("We need some nodes to work with.")
						print(
						    F'Given {node_count} nodes, there are {binary_tree_count(node_count)} '
						    F'binary trees and {catalan_number(node_count)} binary search trees.'
						)


