



from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import 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, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel






@require_tf
class 		lowercase						:






	"""simple docstring"""


	a__				: List[str]		       =							BlenderbotSmallConfig
	a__				: Any		       =							{}
	a__				: Dict		       =							"gelu"




	def __init__(     self     :						Dict						,  __UpperCAmelCase     :						int						,  __UpperCAmelCase     :						str=13						,  __UpperCAmelCase     :						List[str]=7						,  __UpperCAmelCase     :						str=True						,  __UpperCAmelCase     :						Union[str, Any]=False						,  __UpperCAmelCase     :						str=99						,  __UpperCAmelCase     :						Dict=32						,  __UpperCAmelCase     :						Dict=2						,  __UpperCAmelCase     :						Union[str, Any]=4						,  __UpperCAmelCase     :						Dict=37						,  __UpperCAmelCase     :						Optional[Any]=0.1						,  __UpperCAmelCase     :						Tuple=0.1						,  __UpperCAmelCase     :						Optional[Any]=20						,  __UpperCAmelCase     :						Any=2						,  __UpperCAmelCase     :						str=1						,  __UpperCAmelCase     :						Union[str, Any]=0						,  )      ->			Optional[Any]:
							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_dropout_prob
							UpperCAmelCase_=       attention_probs_dropout_prob
							UpperCAmelCase_=       max_position_embeddings
							UpperCAmelCase_=       eos_token_id
							UpperCAmelCase_=       pad_token_id
							UpperCAmelCase_=       bos_token_id




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

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

							UpperCAmelCase_=       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						,  )
							UpperCAmelCase_=       prepare_blenderbot_small_inputs_dict(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase					)
							return config, inputs_dict




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple						,  __UpperCAmelCase     :						str						,  __UpperCAmelCase     :						int					)      ->			Optional[int]:
							UpperCAmelCase_=       TFBlenderbotSmallModel(config=__UpperCAmelCase					).get_decoder()
							UpperCAmelCase_=       inputs_dict["""input_ids"""]

							UpperCAmelCase_=       input_ids[:1, :]
							UpperCAmelCase_=       inputs_dict["""attention_mask"""][:1, :]
							UpperCAmelCase_=       inputs_dict["""head_mask"""]
							UpperCAmelCase_=       1

							# first forward pass
							UpperCAmelCase_=       model(__UpperCAmelCase						,  attention_mask=__UpperCAmelCase						,  head_mask=__UpperCAmelCase						,  use_cache=__UpperCAmelCase					)

							UpperCAmelCase_,       UpperCAmelCase_=       outputs.to_tuple()

							# create hypothetical next token and extent to next_input_ids
							UpperCAmelCase_=       ids_tensor((self.batch_size, 3)						,  config.vocab_size					)
							UpperCAmelCase_=       tf.cast(ids_tensor((self.batch_size, 3)						,  2					)						,  tf.inta					)

							# append to next input_ids and
							UpperCAmelCase_=       tf.concat([input_ids, next_tokens]						,  axis=-1					)
							UpperCAmelCase_=       tf.concat([attention_mask, next_attn_mask]						,  axis=-1					)

							UpperCAmelCase_=       model(__UpperCAmelCase						,  attention_mask=__UpperCAmelCase					)[0]
							UpperCAmelCase_=       model(__UpperCAmelCase						,  attention_mask=__UpperCAmelCase						,  past_key_values=__UpperCAmelCase					)[0]

							self.parent.assertEqual(next_tokens.shape[1]						,  output_from_past.shape[1]					)

							# select random slice
							UpperCAmelCase_=       int(ids_tensor((1,)						,  output_from_past.shape[-1]					)					)
							UpperCAmelCase_=       output_from_no_past[:, -3:, random_slice_idx]
							UpperCAmelCase_=       output_from_past[:, :, random_slice_idx]

							# test that outputs are equal for slice
							tf.debugging.assert_near(__UpperCAmelCase						,  __UpperCAmelCase						,  rtol=1E-3					)



def 	__a					(   lowerCAmelCase_       : Optional[Any]      ,lowerCAmelCase_       : Union[str, Any]      ,lowerCAmelCase_       : Any      ,lowerCAmelCase_       : List[Any]=None      ,lowerCAmelCase_       : str=None      ,lowerCAmelCase_       : Dict=None      ,lowerCAmelCase_       : Dict=None      ,lowerCAmelCase_       : Any=None      ,)				->			Any:






						'''simple docstring'''



						if attention_mask is None:
												UpperCAmelCase_=       tf.cast(tf.math.not_equal(lowerCAmelCase_      ,config.pad_token_id    )      ,tf.inta    )
						if decoder_attention_mask is None:
												UpperCAmelCase_=       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:
												UpperCAmelCase_=       tf.ones((config.encoder_layers, config.encoder_attention_heads)    )
						if decoder_head_mask is None:
												UpperCAmelCase_=       tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
						if cross_attn_head_mask is None:
												UpperCAmelCase_=       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 		lowercase						(			snake_case__			,   snake_case__			,   unittest.TestCase):






	"""simple docstring"""


	a__				: Optional[Any]		       =							(
	    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
	)
	a__				: Any		       =							(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
	a__				: Optional[Any]		       =							(
	    {
	        "conversational": TFBlenderbotSmallForConditionalGeneration,
	        "feature-extraction": TFBlenderbotSmallModel,
	        "summarization": TFBlenderbotSmallForConditionalGeneration,
	        "text2text-generation": TFBlenderbotSmallForConditionalGeneration,
	        "translation": TFBlenderbotSmallForConditionalGeneration,
	    }
	    if is_tf_available()
	    else {}
	)
	a__				: List[Any]		       =							True
	a__				: List[Any]		       =							False
	a__				: Optional[Any]		       =							False




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			int:
							UpperCAmelCase_=       TFBlenderbotSmallModelTester(self					)
							UpperCAmelCase_=       ConfigTester(self						,  config_class=__UpperCAmelCase					)




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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			Optional[Any]:
							UpperCAmelCase_=       self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*__UpperCAmelCase					)






@require_tokenizers
@require_tf
class 		lowercase						(			unittest.TestCase):






	"""simple docstring"""


	a__				: List[str]		       =							[
	    "Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like "
	    "  i'm going to throw up.\nand why is that?"
	]
	a__				: List[Any]		       =							"facebook/blenderbot_small-90M"




	@cached_property
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			Dict:
							# use "old" tokenizer here because of bug when downloading new tokenizer
							return BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""					)




	@cached_property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			int:
							UpperCAmelCase_=       TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name					)
							return model




	@slow
	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			List[str]:
							UpperCAmelCase_=       self.tokenizer(self.src_text						,  return_tensors="""tf"""					)
							UpperCAmelCase_=       self.model.generate(
							    model_inputs.input_ids						,  attention_mask=model_inputs.attention_mask						,  num_beams=2						,  use_cache=__UpperCAmelCase						,  )
							UpperCAmelCase_=       self.tokenizer.batch_decode(generated_ids.numpy()						,  skip_special_tokens=__UpperCAmelCase					)[0]
							assert generated_words in (
							    "i don't know. i just feel like i'm going to throw up. it's not fun.",
							    "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
							    "i'm not sure. i just feel like i've been in a bad situation.",
							)









from __future__ import annotations



def 	__a					(   lowerCAmelCase_       : list[int]      ,lowerCAmelCase_       : int    )				->			list[list[int]]:






						'''simple docstring'''



						UpperCAmelCase_=       []
						UpperCAmelCase_=       []
						UpperCAmelCase_=       0
						UpperCAmelCase_=       sum(lowerCAmelCase_    )
						create_state_space_tree(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )
						return result



def 	__a					(   lowerCAmelCase_       : list[int]      ,lowerCAmelCase_       : int      ,lowerCAmelCase_       : int      ,lowerCAmelCase_       : list[int]      ,lowerCAmelCase_       : list[list[int]]      ,lowerCAmelCase_       : int      ,)				->			None:






						'''simple docstring'''



						if sum(lowerCAmelCase_    ) > max_sum or (remaining_nums_sum + sum(lowerCAmelCase_    )) < max_sum:
												return
						if sum(lowerCAmelCase_    ) == max_sum:
												result.append(lowerCAmelCase_    )
												return
						for index in range(lowerCAmelCase_      ,len(lowerCAmelCase_    )    ):
												create_state_space_tree(
												    lowerCAmelCase_      ,lowerCAmelCase_      ,index + 1      ,[*path, nums[index]]      ,lowerCAmelCase_      ,remaining_nums_sum - nums[index]      ,)
__A       =						[3, 34, 4, 12, 5, 2]
__A       =						9
__A       =						generate_sum_of_subsets_soln(nums, max_sum)



print(*result)




