
"""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_       :    Optional[Any]						      =			{'''LayoutLMv2Config''', '''LayoutLMv3Config'''}



@is_pipeline_test
class __lowerCAmelCase					(       unittest.TestCase   ):




			"""simple docstring"""







			snake_case						   =					MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
			snake_case						   =					TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

			if model_mapping is not None:
						snake_case						   =					{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
			if tf_model_mapping is not None:
						snake_case						   =					{
						    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
						}




			@require_torch
			def   lowerCamelCase__  (					self    :       Any						)   ->			str:







								"""simple docstring"""
								A_								=    pipeline(
								    task="text-classification"							, model="hf-internal-testing/tiny-random-distilbert"							, framework="pt"						)

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

								A_								=    text_classifier("This is great !"							, top_k=2						)
								self.assertEqual(
								    nested_simplify(_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(_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(_snake_case						)							, [{"label": "LABEL_0", "score": 0.5_0_4}]						)

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

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




			@require_torch
			def   lowerCamelCase__  (					self    :       int						)   ->			Union[str, Any]:







								"""simple docstring"""
								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(_snake_case						)							, [{"label": "LABEL_0", "score": 0.5_0_4}]						)




			@require_tf
			def   lowerCamelCase__  (					self    :       int						)   ->			str:







								"""simple docstring"""
								A_								=    pipeline(
								    task="text-classification"							, model="hf-internal-testing/tiny-random-distilbert"							, framework="tf"						)

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




			@slow
			@require_torch
			def   lowerCamelCase__  (					self    :       Optional[int]						)   ->			Union[str, Any]:







								"""simple docstring"""
								A_								=    pipeline("text-classification"						)

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




			@slow
			@require_tf
			def   lowerCamelCase__  (					self    :       Optional[int]						)   ->			int:







								"""simple docstring"""
								A_								=    pipeline("text-classification"							, framework="tf"						)

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




			def   lowerCamelCase__  (					self    :       int							, _snake_case    :       str							, _snake_case    :       List[str]							, _snake_case    :       Optional[Any]						)   ->			Any:







								"""simple docstring"""
								A_								=    TextClassificationPipeline(model=_snake_case							, tokenizer=_snake_case						)
								return text_classifier, ["HuggingFace is in", "This is another test"]






			def   lowerCamelCase__  (					self    :       Dict							, _snake_case    :       Optional[int]							, _snake_case    :       Tuple						)   ->			List[str]:







								"""simple docstring"""
								A_								=    text_classifier.model
								# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
								A_								=    "HuggingFace is in"
								A_								=    text_classifier(_snake_case						)

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

								A_								=    ["HuggingFace is in ", "Paris is in France"]
								A_								=    text_classifier(_snake_case						)
								self.assertEqual(
								    nested_simplify(_snake_case						)							, [{"label": ANY(_snake_case						), "score": ANY(_snake_case						)}, {"label": ANY(_snake_case						), "score": ANY(_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(_snake_case							, top_k=_snake_case						)
								A_								=    len(model.config.idalabel.values()						)
								self.assertEqual(
								    nested_simplify(_snake_case						)							, [[{"label": ANY(_snake_case						), "score": ANY(_snake_case						)}] * N, [{"label": ANY(_snake_case						), "score": ANY(_snake_case						)}] * N]							, )

								A_								=    {"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
								A_								=    text_classifier(_snake_case						)
								self.assertEqual(
								    nested_simplify(_snake_case						)							, {"label": ANY(_snake_case						), "score": ANY(_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(_snake_case						):
													text_classifier(_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(_snake_case						)							, [{"label": ANY(_snake_case						), "score": ANY(_snake_case						)}]							, )
								self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()						)








'''simple docstring'''
from __future__ import annotations

from typing import Any







def       a_				(	UpperCamelCase_ ):
							create_state_space_tree(UpperCamelCase_						,  []						,  0 )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):

							if index == len(UpperCamelCase_ ):
														print(UpperCamelCase_ )
														return

							create_state_space_tree(UpperCamelCase_						,  UpperCamelCase_						,  index + 1 )
							current_subsequence.append(sequence[index] )
							create_state_space_tree(UpperCamelCase_						,  UpperCamelCase_						,  index + 1 )
							current_subsequence.pop()


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE					:				list[Any]									=		[3, 1, 2, 4]
							generate_all_subsequences(seq)

							seq.clear()
							seq.extend(['''A''', '''B''', '''C'''])
							generate_all_subsequences(seq)





