



"""simple docstring"""







import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
UpperCamelCase     =							{"""LayoutLMv2Config""", """LayoutLMv3Config"""}






@is_pipeline_test
class       UpperCamelCase__			(   unittest.TestCase      ):

			"""simple docstring"""


			A__	:					Union[str, Any]							  =     MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
			A__	:					List[str]							  =     TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

			if model_mapping is not None:
						A__	:					List[str]							  =     {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
			if tf_model_mapping is not None:
						A__	:					Union[str, Any]							  =     {
						    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
						}


			@require_torch
			def   snake_case__	(      self		)      ->	Any:
				A__											=					pipeline(
				    task="text-classification"						,	model="hf-internal-testing/tiny-random-distilbert"						,	framework="pt"		)

				A__											=					text_classifier("This is great !"		)
				self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__		)						,	[{"label": "LABEL_0", "score": 0.5_0_4}]		)

				A__											=					text_classifier("This is great !"						,	top_k=2		)
				self.assertEqual(
				    nested_simplify(SCREAMING_SNAKE_CASE__		)						,	[{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}]		)

				A__											=					text_classifier(["This is great !", "This is bad"]						,	top_k=2		)
				self.assertEqual(
				    nested_simplify(SCREAMING_SNAKE_CASE__		)						,	[
				        [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}],
				        [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}],
				    ]						,	)

				A__											=					text_classifier("This is great !"						,	top_k=1		)
				self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__		)						,	[{"label": "LABEL_0", "score": 0.5_0_4}]		)

				# Legacy behavior
				A__											=					text_classifier("This is great !"						,	return_all_scores=SCREAMING_SNAKE_CASE__		)
				self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__		)						,	[{"label": "LABEL_0", "score": 0.5_0_4}]		)

				A__											=					text_classifier("This is great !"						,	return_all_scores=SCREAMING_SNAKE_CASE__		)
				self.assertEqual(
				    nested_simplify(SCREAMING_SNAKE_CASE__		)						,	[[{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}]]		)

				A__											=					text_classifier(["This is great !", "Something else"]						,	return_all_scores=SCREAMING_SNAKE_CASE__		)
				self.assertEqual(
				    nested_simplify(SCREAMING_SNAKE_CASE__		)						,	[
				        [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}],
				        [{"label": "LABEL_0", "score": 0.5_0_4}, {"label": "LABEL_1", "score": 0.4_9_6}],
				    ]						,	)

				A__											=					text_classifier(["This is great !", "Something else"]						,	return_all_scores=SCREAMING_SNAKE_CASE__		)
				self.assertEqual(
				    nested_simplify(SCREAMING_SNAKE_CASE__		)						,	[
				        {"label": "LABEL_0", "score": 0.5_0_4},
				        {"label": "LABEL_0", "score": 0.5_0_4},
				    ]						,	)


			@require_torch
			def   snake_case__	(      self		)      ->	List[Any]:
				import torch

				A__											=					pipeline(
				    task="text-classification"						,	model="hf-internal-testing/tiny-random-distilbert"						,	framework="pt"						,	device=torch.device("cpu"		)						,	)

				A__											=					text_classifier("This is great !"		)
				self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__		)						,	[{"label": "LABEL_0", "score": 0.5_0_4}]		)


			@require_tf
			def   snake_case__	(      self		)      ->	Tuple:
				A__											=					pipeline(
				    task="text-classification"						,	model="hf-internal-testing/tiny-random-distilbert"						,	framework="tf"		)

				A__											=					text_classifier("This is great !"		)
				self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__		)						,	[{"label": "LABEL_0", "score": 0.5_0_4}]		)


			@slow
			@require_torch
			def   snake_case__	(      self		)      ->	Union[str, Any]:
				A__											=					pipeline("text-classification"		)

				A__											=					text_classifier("This is great !"		)
				self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__		)						,	[{"label": "POSITIVE", "score": 1.0}]		)
				A__											=					text_classifier("This is bad !"		)
				self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__		)						,	[{"label": "NEGATIVE", "score": 1.0}]		)
				A__											=					text_classifier("Birds are a type of animal"		)
				self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__		)						,	[{"label": "POSITIVE", "score": 0.9_8_8}]		)


			@slow
			@require_tf
			def   snake_case__	(      self		)      ->	List[str]:
				A__											=					pipeline("text-classification"						,	framework="tf"		)

				A__											=					text_classifier("This is great !"		)
				self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__		)						,	[{"label": "POSITIVE", "score": 1.0}]		)
				A__											=					text_classifier("This is bad !"		)
				self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__		)						,	[{"label": "NEGATIVE", "score": 1.0}]		)
				A__											=					text_classifier("Birds are a type of animal"		)
				self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__		)						,	[{"label": "POSITIVE", "score": 0.9_8_8}]		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Optional[int]:
				A__											=					TextClassificationPipeline(model=SCREAMING_SNAKE_CASE__						,	tokenizer=SCREAMING_SNAKE_CASE__		)
				return text_classifier, ["HuggingFace is in", "This is another test"]







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Union[str, Any]:
				A__											=					text_classifier.model
				# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
				A__											=					"HuggingFace is in"
				A__											=					text_classifier(SCREAMING_SNAKE_CASE__		)

				self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__		)						,	[{"label": ANY(SCREAMING_SNAKE_CASE__		), "score": ANY(SCREAMING_SNAKE_CASE__		)}]		)
				self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()		)

				A__											=					["HuggingFace is in ", "Paris is in France"]
				A__											=					text_classifier(SCREAMING_SNAKE_CASE__		)
				self.assertEqual(
				    nested_simplify(SCREAMING_SNAKE_CASE__		)						,	[{"label": ANY(SCREAMING_SNAKE_CASE__		), "score": ANY(SCREAMING_SNAKE_CASE__		)}, {"label": ANY(SCREAMING_SNAKE_CASE__		), "score": ANY(SCREAMING_SNAKE_CASE__		)}]						,	)
				self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()		)
				self.assertTrue(outputs[1]["label"] in model.config.idalabel.values()		)

				# Forcing to get all results with `top_k=None`
				# This is NOT the legacy format
				A__											=					text_classifier(SCREAMING_SNAKE_CASE__						,	top_k=SCREAMING_SNAKE_CASE__		)
				A__											=					len(model.config.idalabel.values()		)
				self.assertEqual(
				    nested_simplify(SCREAMING_SNAKE_CASE__		)						,	[[{"label": ANY(SCREAMING_SNAKE_CASE__		), "score": ANY(SCREAMING_SNAKE_CASE__		)}] * N, [{"label": ANY(SCREAMING_SNAKE_CASE__		), "score": ANY(SCREAMING_SNAKE_CASE__		)}] * N]						,	)

				A__											=					{"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
				A__											=					text_classifier(SCREAMING_SNAKE_CASE__		)
				self.assertEqual(
				    nested_simplify(SCREAMING_SNAKE_CASE__		)						,	{"label": ANY(SCREAMING_SNAKE_CASE__		), "score": ANY(SCREAMING_SNAKE_CASE__		)}						,	)
				self.assertTrue(outputs["label"] in model.config.idalabel.values()		)

				# This might be used a text pair, but tokenizer + pipe interaction
				# makes it hard to understand that it's not using the pair properly
				# https://github.com/huggingface/transformers/issues/17305
				# We disabled this usage instead as it was outputting wrong outputs.
				A__											=					[["HuggingFace is in ", "Paris is in France"]]
				with self.assertRaises(SCREAMING_SNAKE_CASE__		):
					text_classifier(SCREAMING_SNAKE_CASE__		)

				# This used to be valid for doing text pairs
				# We're keeping it working because of backward compatibility
				A__											=					text_classifier([[["HuggingFace is in ", "Paris is in France"]]]		)
				self.assertEqual(
				    nested_simplify(SCREAMING_SNAKE_CASE__		)						,	[{"label": ANY(SCREAMING_SNAKE_CASE__		), "score": ANY(SCREAMING_SNAKE_CASE__		)}]						,	)
				self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()		)









"""simple docstring"""







class       UpperCamelCase__			:

			"""simple docstring"""


			def __init__(      self						,	SCREAMING_SNAKE_CASE__ = ""						,	SCREAMING_SNAKE_CASE__ = False		)      ->	None:
				# Mapping from the first character of the prefix of the node
				A__											=					{}

				# A node will be a leaf if the tree contains its word
				A__											=					is_leaf

				A__											=					prefix


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	tuple[str, str, str]:
				A__											=					0
				for q, w in zip(self.prefix						,	SCREAMING_SNAKE_CASE__		):
					if q != w:
						break

					x += 1

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


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	None:
				for word in words:
					self.insert(SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	None:
				# Case 1: If the word is the prefix of the node
				# Solution: We set the current node as leaf
				if self.prefix == word:
					A__											=					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:
					A__											=					RadixNode(prefix=SCREAMING_SNAKE_CASE__						,	is_leaf=SCREAMING_SNAKE_CASE__		)

				else:
					A__											=					self.nodes[word[0]]
					A__							,							A__							,							A__											=					incoming_node.match(
					    SCREAMING_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(SCREAMING_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:
						A__											=					remaining_prefix

						A__											=					self.nodes[matching_string[0]]
						A__											=					RadixNode(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
						A__											=					aux_node

						if remaining_word == "":
							A__											=					True
						else:
							self.nodes[matching_string[0]].insert(SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	bool:
				A__											=					self.nodes.get(word[0]						,	SCREAMING_SNAKE_CASE__		)
				if not incoming_node:
					return False
				else:
					A__							,							A__							,							A__											=					incoming_node.match(
					    SCREAMING_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(SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	bool:
				A__											=					self.nodes.get(word[0]						,	SCREAMING_SNAKE_CASE__		)
				if not incoming_node:
					return False
				else:
					A__							,							A__							,							A__											=					incoming_node.match(
					    SCREAMING_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(SCREAMING_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:
									A__											=					list(self.nodes.values()		)[0]
									A__											=					merging_node.is_leaf
									self.prefix += merging_node.prefix
									A__											=					merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
							elif len(incoming_node.nodes		) > 1:
								A__											=					False
							# If there is 1 edge, we merge it with its child
							else:
								A__											=					list(incoming_node.nodes.values()		)[0]
								A__											=					merging_node.is_leaf
								incoming_node.prefix += merging_node.prefix
								A__											=					merging_node.nodes

							return True







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__ = 0		)      ->	None:
				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       _lowerCamelCase				(							)	-> bool:






	"""simple docstring"""





	A__											=					"banana bananas bandana band apple all beast".split()
	A__											=					RadixNode()
	root.insert_many(UpperCAmelCase_     )

	assert all(root.find(UpperCAmelCase_     ) 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       _lowerCamelCase				(							)	-> None:






	"""simple docstring"""





	assert test_trie()

def       _lowerCamelCase				(							)	-> None:






	"""simple docstring"""





	A__											=					RadixNode()
	A__											=					"banana bananas bandanas bandana band apple all beast".split()
	root.insert_many(UpperCAmelCase_     )

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


if __name__ == "__main__":
			main()




