




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 							__UpperCamelCase			:





							'''simple docstring'''



							__a							: int										=BlenderbotSmallConfig
							__a							: int										={}
							__a							: List[str]										="""gelu"""



							def __init__( self       , UpperCAmelCase_       , UpperCAmelCase_=13       , UpperCAmelCase_=7       , UpperCAmelCase_=True       , UpperCAmelCase_=False       , UpperCAmelCase_=99       , UpperCAmelCase_=32       , UpperCAmelCase_=2       , UpperCAmelCase_=4       , UpperCAmelCase_=37       , UpperCAmelCase_=0.1       , UpperCAmelCase_=0.1       , UpperCAmelCase_=20       , UpperCAmelCase_=2       , UpperCAmelCase_=1       , UpperCAmelCase_=0       , ):
														lowerCAmelCase   =		parent
														lowerCAmelCase   =		batch_size
														lowerCAmelCase   =		seq_length
														lowerCAmelCase   =		is_training
														lowerCAmelCase   =		use_labels
														lowerCAmelCase   =		vocab_size
														lowerCAmelCase   =		hidden_size
														lowerCAmelCase   =		num_hidden_layers
														lowerCAmelCase   =		num_attention_heads
														lowerCAmelCase   =		intermediate_size

														lowerCAmelCase   =		hidden_dropout_prob
														lowerCAmelCase   =		attention_probs_dropout_prob
														lowerCAmelCase   =		max_position_embeddings
														lowerCAmelCase   =		eos_token_id
														lowerCAmelCase   =		pad_token_id
														lowerCAmelCase   =		bos_token_id



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

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

														lowerCAmelCase   =		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   =		prepare_blenderbot_small_inputs_dict(lowercase__       , lowercase__       , lowercase__						)
														return config, inputs_dict






							def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_						):
														lowerCAmelCase   =		TFBlenderbotSmallModel(config=lowercase__						).get_decoder()
														lowerCAmelCase   =		inputs_dict["""input_ids"""]

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

														# first forward pass
														lowerCAmelCase   =		model(lowercase__       , attention_mask=lowercase__       , head_mask=lowercase__       , use_cache=lowercase__						)

														lowerCAmelCase   =		outputs.to_tuple()

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

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

														lowerCAmelCase   =		model(lowercase__       , attention_mask=lowercase__						)[0]
														lowerCAmelCase   =		model(lowercase__       , attention_mask=lowercase__       , past_key_values=lowercase__						)[0]

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

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

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




def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case						, _snake_case=None						, _snake_case=None						, _snake_case=None						, _snake_case=None						, _snake_case=None						, ):





							if attention_mask is None:
														lowerCAmelCase   =		tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE__						, config.pad_token_id							)						, tf.inta							)
							if decoder_attention_mask is None:
														lowerCAmelCase   =		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   =		tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
							if decoder_head_mask is None:
														lowerCAmelCase   =		tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
							if cross_attn_head_mask is None:
														lowerCAmelCase   =		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			( _UpperCAmelCase     ,					_UpperCAmelCase     ,					unittest.TestCase  ):





							'''simple docstring'''



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



							def       __snake_case		( self						):
														lowerCAmelCase   =		TFBlenderbotSmallModelTester(self						)
														lowerCAmelCase   =		ConfigTester(self       , config_class=lowercase__						)



							def       __snake_case		( self						):
														self.config_tester.run_common_tests()






							def       __snake_case		( self						):
														lowerCAmelCase   =		self.model_tester.prepare_config_and_inputs_for_common()
														self.model_tester.check_decoder_model_past_large_inputs(*lowercase__						)




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





							'''simple docstring'''



							__a							: Optional[Any]										=[
							    """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							: Optional[Any]										="""facebook/blenderbot_small-90M"""



							@cached_property
							def       __snake_case		( self						):
														# use "old" tokenizer here because of bug when downloading new tokenizer
														return BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''						)



							@cached_property
							def       __snake_case		( self						):
														lowerCAmelCase   =		TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
														return model






							@slow
							def       __snake_case		( self						):
														lowerCAmelCase   =		self.tokenizer(self.src_text       , return_tensors='''tf'''						)
														lowerCAmelCase   =		self.model.generate(
														    model_inputs.input_ids       , attention_mask=model_inputs.attention_mask       , num_beams=2       , use_cache=lowercase__       , )
														lowerCAmelCase   =		self.tokenizer.batch_decode(generated_ids.numpy()       , skip_special_tokens=lowercase__						)[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.",
														)









def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case							):

							def count_of_possible_combinations(_snake_case							) -> int:
														if target < 0:
																					return 0
														if target == 0:
																					return 1
														return sum(count_of_possible_combinations(target - item							) for item in array							)

							return count_of_possible_combinations(_snake_case							)



def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case							):

							def count_of_possible_combinations_with_dp_array(
							    _snake_case						, _snake_case							) -> int:
														if target < 0:
																					return 0
														if target == 0:
																					return 1
														if dp_array[target] != -1:
																					return dp_array[target]
														lowerCAmelCase   =		sum(
														    count_of_possible_combinations_with_dp_array(target - item						, _snake_case							)
														    for item in array							)
														lowerCAmelCase   =		answer
														return answer

							lowerCAmelCase   =		[-1] * (target + 1)
							return count_of_possible_combinations_with_dp_array(_snake_case						, _snake_case							)



def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case							):
							lowerCAmelCase   =		[0] * (target + 1)
							lowerCAmelCase   =		1

							for i in range(1						, target + 1							):
														for j in range(_snake_case							):
																					if i - array[j] >= 0:
																												dp_array[i] += dp_array[i - array[j]]

							return dp_array[target]


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		UpperCAmelCase_						=3
		UpperCAmelCase_						=5
		UpperCAmelCase_						=[1, 2, 5]
		print(combination_sum_iv(n, array, target))



