
"""simple docstring"""






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'''

	UpperCamelCase				=  BlenderbotSmallConfig
	UpperCamelCase				=  {}
	UpperCamelCase				=  """gelu"""





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= parent
			SCREAMING_SNAKE_CASE       :     List[Any]				= batch_size
			SCREAMING_SNAKE_CASE       :     Optional[int]				= seq_length
			SCREAMING_SNAKE_CASE       :     Optional[int]				= is_training
			SCREAMING_SNAKE_CASE       :     Optional[int]				= use_labels
			SCREAMING_SNAKE_CASE       :     Optional[int]				= vocab_size
			SCREAMING_SNAKE_CASE       :     Optional[int]				= hidden_size
			SCREAMING_SNAKE_CASE       :     List[str]				= num_hidden_layers
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= num_attention_heads
			SCREAMING_SNAKE_CASE       :     Dict				= intermediate_size

			SCREAMING_SNAKE_CASE       :     Optional[int]				= hidden_dropout_prob
			SCREAMING_SNAKE_CASE       :     str				= attention_probs_dropout_prob
			SCREAMING_SNAKE_CASE       :     List[str]				= max_position_embeddings
			SCREAMING_SNAKE_CASE       :     List[str]				= eos_token_id
			SCREAMING_SNAKE_CASE       :     Any				= pad_token_id
			SCREAMING_SNAKE_CASE       :     Dict				= bos_token_id





	def   __lowerCAmelCase (						self    :Union[str, Any]					)							->			List[str]:







			'''simple docstring'''




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

			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= ids_tensor([self.batch_size, self.seq_length]		, self.vocab_size					)

			SCREAMING_SNAKE_CASE       :     Union[str, 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		, )
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= prepare_blenderbot_small_inputs_dict(snake_case__		, snake_case__		, snake_case__					)
			return config, inputs_dict







	def   __lowerCAmelCase (						self    :Dict		, lowerCamelCase_    :Optional[Any]		, lowerCamelCase_    :Tuple					)							->			int:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= TFBlenderbotSmallModel(config=snake_case__					).get_decoder()
			SCREAMING_SNAKE_CASE       :     List[str]				= inputs_dict["input_ids"]

			SCREAMING_SNAKE_CASE       :     Optional[int]				= input_ids[:1, :]
			SCREAMING_SNAKE_CASE       :     Tuple				= inputs_dict["attention_mask"][:1, :]
			SCREAMING_SNAKE_CASE       :     Optional[int]				= inputs_dict["head_mask"]
			SCREAMING_SNAKE_CASE       :     int				= 1

			# first forward pass
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= model(snake_case__		, attention_mask=snake_case__		, head_mask=snake_case__		, use_cache=snake_case__					)

			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= outputs.to_tuple()

			# create hypothetical next token and extent to next_input_ids
			SCREAMING_SNAKE_CASE       :     Optional[int]				= ids_tensor((self.batch_size, 3)		, config.vocab_size					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= tf.cast(ids_tensor((self.batch_size, 3)		, 2					)		, tf.inta					)

			# append to next input_ids and
			SCREAMING_SNAKE_CASE       :     Tuple				= tf.concat([input_ids, next_tokens]		, axis=-1					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= tf.concat([attention_mask, next_attn_mask]		, axis=-1					)

			SCREAMING_SNAKE_CASE       :     Optional[int]				= model(snake_case__		, attention_mask=snake_case__					)[0]
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= model(snake_case__		, attention_mask=snake_case__		, past_key_values=snake_case__					)[0]

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

			# select random slice
			SCREAMING_SNAKE_CASE       :     Optional[int]				= int(ids_tensor((1,)		, output_from_past.shape[-1]					)					)
			SCREAMING_SNAKE_CASE       :     List[str]				= output_from_no_past[:, -3:, random_slice_idx]
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= output_from_past[:, :, random_slice_idx]

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





def __A						(   a_ :							Tuple		,		a_ :							Tuple		,		a_ :							Optional[int]		,		a_ :							Optional[int]=None		,		a_ :							str=None		,		a_ :							List[str]=None		,		a_ :							str=None		,		a_ :							Tuple=None		,		)->							Union[str, Any]:




		'''simple docstring'''






		if attention_mask is None:
				SCREAMING_SNAKE_CASE       :     Optional[int]				= tf.cast(tf.math.not_equal(a_		,		config.pad_token_id     )		,		tf.inta     )
		if decoder_attention_mask is None:
				SCREAMING_SNAKE_CASE       :     List[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:
				SCREAMING_SNAKE_CASE       :     Tuple				= tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
		if decoder_head_mask is None:
				SCREAMING_SNAKE_CASE       :     List[str]				= tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
		if cross_attn_head_mask is None:
				SCREAMING_SNAKE_CASE       :     List[Any]				= 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__(				SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       ,						unittest.TestCase   ):
	'''simple docstring'''

	UpperCamelCase				=  (
	    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
	)
	UpperCamelCase				=  (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
	UpperCamelCase				=  (
	    {
	        """conversational""": TFBlenderbotSmallForConditionalGeneration,
	        """feature-extraction""": TFBlenderbotSmallModel,
	        """summarization""": TFBlenderbotSmallForConditionalGeneration,
	        """text2text-generation""": TFBlenderbotSmallForConditionalGeneration,
	        """translation""": TFBlenderbotSmallForConditionalGeneration,
	    }
	    if is_tf_available()
	    else {}
	)
	UpperCamelCase				=  True
	UpperCamelCase				=  False
	UpperCamelCase				=  False





	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			List[str]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[str]				= TFBlenderbotSmallModelTester(self					)
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= ConfigTester(self		, config_class=snake_case__					)





	def   __lowerCAmelCase (						self    :Any					)							->			str:







			'''simple docstring'''




			self.config_tester.run_common_tests()







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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= self.model_tester.prepare_config_and_inputs_for_common()
			self.model_tester.check_decoder_model_past_large_inputs(*snake_case__					)





@require_tokenizers
@require_tf
class 				lowercase__(				unittest.TestCase   ):
	'''simple docstring'''

	UpperCamelCase				=  [
	    """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?"""
	]
	UpperCamelCase				=  """facebook/blenderbot_small-90M"""





	@cached_property
	def   __lowerCAmelCase (						self    :List[Any]					)							->			Optional[int]:







			'''simple docstring'''




			return BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''					)





	@cached_property
	def   __lowerCAmelCase (						self    :List[Any]					)							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name					)
			return model







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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Any				= self.tokenizer(self.src_text		, return_tensors='''tf'''					)
			SCREAMING_SNAKE_CASE       :     Dict				= self.model.generate(
			    model_inputs.input_ids		, attention_mask=model_inputs.attention_mask		, num_beams=2		, use_cache=snake_case__		, )
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.tokenizer.batch_decode(generated_ids.numpy()		, skip_special_tokens=snake_case__					)[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.",
			)







"""simple docstring"""






import math




class 				lowercase__:
	'''simple docstring'''

	def __init__(						self    :Union[str, Any]		, lowerCamelCase_    :List[str]=0					)							->			List[Any]:  # a graph with Node 0,1,...,N-1







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= n
			SCREAMING_SNAKE_CASE       :     List[Any]				= [
			    [math.inf for j in range(0		, lowerCamelCase_					)] for i in range(0		, lowerCamelCase_					)
			]  # adjacency matrix for weight
			SCREAMING_SNAKE_CASE       :     Any				= [
			    [math.inf for j in range(0		, lowerCamelCase_					)] for i in range(0		, lowerCamelCase_					)
			]  # dp[i][j] stores minimum distance from i to j





	def   __lowerCAmelCase (						self    :Dict		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :Union[str, Any]		, lowerCamelCase_    :Union[str, Any]					)							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= w





	def   __lowerCAmelCase (						self    :str					)							->			Union[str, Any]:







			'''simple docstring'''




			for k in range(0		, self.n					):
					for i in range(0		, self.n					):
							for j in range(0		, self.n					):
									SCREAMING_SNAKE_CASE       :     List[str]				= min(self.dp[i][j]		, self.dp[i][k] + self.dp[k][j]					)







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







			'''simple docstring'''




			return self.dp[u][v]


if __name__ == "__main__":
					lowerCamelCase__ :       Dict						     =			Graph(5)
					graph.add_edge(0, 2, 9)
					graph.add_edge(0, 4, 10)
					graph.add_edge(1, 3, 5)
					graph.add_edge(2, 3, 7)
					graph.add_edge(3, 0, 10)
					graph.add_edge(3, 1, 2)
					graph.add_edge(3, 2, 1)
					graph.add_edge(3, 4, 6)
					graph.add_edge(4, 1, 3)
					graph.add_edge(4, 2, 4)
					graph.add_edge(4, 3, 9)
					graph.floyd_warshall()
					graph.show_min(1, 4)
					graph.show_min(0, 3)





