

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     a__  :
			"""simple docstring"""







			__lowerCamelCase       				=				BlenderbotSmallConfig
			__lowerCamelCase       				=				{}
			__lowerCamelCase       				=				'gelu'


			def __init__(			self					,					lowercase					,					lowercase=13					,					lowercase=7					,					lowercase=True					,					lowercase=False					,					lowercase=99					,					lowercase=32					,					lowercase=2					,					lowercase=4					,					lowercase=37					,					lowercase=0.1					,					lowercase=0.1					,					lowercase=20					,					lowercase=2					,					lowercase=1					,					lowercase=0					,					)     ->  Any:
							'''simple docstring'''






							A__						=	parent
							A__						=	batch_size
							A__						=	seq_length
							A__						=	is_training
							A__						=	use_labels
							A__						=	vocab_size
							A__						=	hidden_size
							A__						=	num_hidden_layers
							A__						=	num_attention_heads
							A__						=	intermediate_size

							A__						=	hidden_dropout_prob
							A__						=	attention_probs_dropout_prob
							A__						=	max_position_embeddings
							A__						=	eos_token_id
							A__						=	pad_token_id
							A__						=	bos_token_id


			def    UpperCamelCase						(			self				)     ->  Tuple:
							'''simple docstring'''






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

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

							A__						=	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					,					)
							A__						=	prepare_blenderbot_small_inputs_dict(lowercase					,					lowercase					,					lowercase				)
							return config, inputs_dict


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase				)     ->  str:
							'''simple docstring'''






							A__						=	TFBlenderbotSmallModel(config=lowercase				).get_decoder()
							A__						=	inputs_dict["input_ids"]

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

							# first forward pass
							A__						=	model(lowercase					,					attention_mask=lowercase					,					head_mask=lowercase					,					use_cache=lowercase				)

							A__			, A__						=	outputs.to_tuple()

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

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

							A__						=	model(lowercase					,					attention_mask=lowercase				)[0]
							A__						=	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
							A__						=	int(ids_tensor((1,)					,					output_from_past.shape[-1]				)				)
							A__						=	output_from_no_past[:, -3:, random_slice_idx]
							A__						=	output_from_past[:, :, random_slice_idx]

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



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							int		,				SCREAMING_SNAKE_CASE_:							List[Any]		,				SCREAMING_SNAKE_CASE_:							Optional[Any]		,				SCREAMING_SNAKE_CASE_:							Optional[Any]=None		,				SCREAMING_SNAKE_CASE_:							Optional[int]=None		,				SCREAMING_SNAKE_CASE_:							Optional[int]=None		,				SCREAMING_SNAKE_CASE_:							Dict=None		,				SCREAMING_SNAKE_CASE_:							List[str]=None		,				)	->					List[Any]:




				'''simple docstring'''





				if attention_mask is None:
								A__						=	tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE_		,				config.pad_token_id					)		,				tf.inta					)
				if decoder_attention_mask is None:
								A__						=	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:
								A__						=	tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
				if decoder_head_mask is None:
								A__						=	tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
				if cross_attn_head_mask is None:
								A__						=	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__  (    snake_case							,		snake_case							,		unittest.TestCase	):
			"""simple docstring"""







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


			def    UpperCamelCase						(			self				)     ->  Tuple:
							'''simple docstring'''






							A__						=	TFBlenderbotSmallModelTester(self				)
							A__						=	ConfigTester(self					,					config_class=lowercase				)


			def    UpperCamelCase						(			self				)     ->  Tuple:
							'''simple docstring'''






							self.config_tester.run_common_tests()


			def    UpperCamelCase						(			self				)     ->  Tuple:
							'''simple docstring'''






							A__						=	self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*lowercase				)

@require_tokenizers
@require_tf
class     a__  (    unittest.TestCase	):
			"""simple docstring"""







			__lowerCamelCase       				=				[
			    '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?'
			]
			__lowerCamelCase       				=				'facebook/blenderbot_small-90M'


			@cached_property
			def    UpperCamelCase						(			self				)     ->  Tuple:
							'''simple docstring'''






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


			@cached_property
			def    UpperCamelCase						(			self				)     ->  Any:
							'''simple docstring'''






							A__						=	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name				)
							return model


			@slow
			def    UpperCamelCase						(			self				)     ->  int:
							'''simple docstring'''






							A__						=	self.tokenizer(self.src_text					,					return_tensors="tf"				)
							A__						=	self.model.generate(
							    model_inputs.input_ids					,					attention_mask=model_inputs.attention_mask					,					num_beams=2					,					use_cache=lowercase					,					)
							A__						=	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.",
							)





'''simple docstring'''


class     lowercase__  :





							'''simple docstring'''


							def __init__(    self    ,					__snake_case = ""    ,					__snake_case = False  ):
											# Mapping from the first character of the prefix of the node
											_SCREAMING_SNAKE_CASE :       dict[str, RadixNode]     		=      {}

											# A node will be a leaf if the tree contains its word
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      is_leaf

											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      prefix






							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      0
											for q, w in zip(self.prefix    ,					__snake_case  ):
															if q != w:
																			break

															x += 1

											return self.prefix[:x], self.prefix[x:], word[x:]






							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											for word in words:
															self.insert(__snake_case  )






							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											# Case 1: If the word is the prefix of the node
											# Solution: We set the current node as leaf
											if self.prefix == word:
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      True

											# Case 2: The node has no edges that have a prefix to the word
											# Solution: We create an edge from the current node to a new one
											# containing the word
											elif word[0] not in self.nodes:
															_SCREAMING_SNAKE_CASE :       List[str]     		=      RadixNode(prefix=__snake_case    ,					is_leaf=__snake_case  )

											else:
															_SCREAMING_SNAKE_CASE :       int     		=      self.nodes[word[0]]
															_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Optional[Any]     		=      incoming_node.match(
															    __snake_case  )

															# Case 3: The node prefix is equal to the matching
															# Solution: We insert remaining word on the next node
															if remaining_prefix == "":
																			self.nodes[matching_string[0]].insert(__snake_case  )

															# Case 4: The word is greater equal to the matching
															# Solution: Create a node in between both nodes, change
															# prefixes and add the new node for the remaining word
															else:
																			_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      remaining_prefix

																			_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.nodes[matching_string[0]]
																			_SCREAMING_SNAKE_CASE :       List[Any]     		=      RadixNode(__snake_case    ,					__snake_case  )
																			_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      aux_node

																			if remaining_word == "":
																							_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      True
																			else:
																							self.nodes[matching_string[0]].insert(__snake_case  )






							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.nodes.get(word[0]    ,					__snake_case  )
											if not incoming_node:
															return False
											else:
															_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Tuple     		=      incoming_node.match(
															    __snake_case  )
															# If there is remaining prefix, the word can't be on the tree
															if remaining_prefix != "":
																			return False
															# This applies when the word and the prefix are equal
															elif remaining_word == "":
																			return incoming_node.is_leaf
															# We have word remaining so we check the next node
															else:
																			return incoming_node.find(__snake_case  )






							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Tuple     		=      self.nodes.get(word[0]    ,					__snake_case  )
											if not incoming_node:
															return False
											else:
															_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       List[Any]     		=      incoming_node.match(
															    __snake_case  )
															# If there is remaining prefix, the word can't be on the tree
															if remaining_prefix != "":
																			return False
															# We have word remaining so we check the next node
															elif remaining_word != "":
																			return incoming_node.delete(__snake_case  )
															else:
																			# If it is not a leaf, we don't have to delete
																			if not incoming_node.is_leaf:
																							return False
																			else:
																							# We delete the nodes if no edges go from it
																							if len(incoming_node.nodes  ) == 0:
																											del self.nodes[word[0]]
																											# We merge the current node with its only child
																											if len(self.nodes  ) == 1 and not self.is_leaf:
																															_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      list(self.nodes.values()  )[0]
																															_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      merging_node.is_leaf
																															self.prefix += merging_node.prefix
																															_SCREAMING_SNAKE_CASE :       Tuple     		=      merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
																							elif len(incoming_node.nodes  ) > 1:
																											_SCREAMING_SNAKE_CASE :       List[str]     		=      False
																							# If there is 1 edge, we merge it with its child
																							else:
																											_SCREAMING_SNAKE_CASE :       int     		=      list(incoming_node.nodes.values()  )[0]
																											_SCREAMING_SNAKE_CASE :       Tuple     		=      merging_node.is_leaf
																											incoming_node.prefix += merging_node.prefix
																											_SCREAMING_SNAKE_CASE :       str     		=      merging_node.nodes

																							return True






							def UpperCAmelCase_	(    self    ,					__snake_case = 0  ):
											if self.prefix != "":
															print("""-""" * height    ,					self.prefix    ,					"""  (leaf)""" if self.is_leaf else """"""  )

											for value in self.nodes.values():
															value.print_tree(height + 1  )







def 			snake_case_   (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      """banana bananas bandana band apple all beast""".split()
				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      RadixNode()
				root.insert_many(SCREAMING_SNAKE_CASE__     )

				assert all(root.find(SCREAMING_SNAKE_CASE__     ) for word in words     )
				assert not root.find("""bandanas"""     )
				assert not root.find("""apps"""     )
				root.delete("""all"""     )
				assert not root.find("""all"""     )
				root.delete("""banana"""     )
				assert not root.find("""banana"""     )
				assert root.find("""bananas"""     )

				return True







def 			snake_case_   (  ):







				"""simple docstring"""





				assert test_trie()







def 			snake_case_   (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Tuple     		=      RadixNode()
				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      """banana bananas bandanas bandana band apple all beast""".split()
				root.insert_many(SCREAMING_SNAKE_CASE__     )

				print("""Words:"""							,      SCREAMING_SNAKE_CASE__     )
				print("""Tree:"""     )
				root.print_tree()


if __name__ == "__main__":
				main()




