


import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
			import torch

@is_pipeline_test
class     a		(unittest.TestCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Any					  =  MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
							__UpperCAmelCase   :							Optional[Any]					  =  TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

							def    __snake_case (							self	:  List[str]   ,     lowerCamelCase	:  Optional[int]   ,     lowerCamelCase	:  Optional[Any]   ,     lowerCamelCase	:  Union[str, Any]    )     ->   Tuple:
														__snake_case		:      Dict										=       TextaTextGenerationPipeline(model=lowerCamelCase   ,     tokenizer=lowerCamelCase    )
														return generator, ["Something to write", "Something else"]

							def    __snake_case (							self	:  Union[str, Any]   ,     lowerCamelCase	:  List[str]   ,     lowerCamelCase	:  List[Any]    )     ->   str:
														__snake_case		:      List[str]										=       generator("Something there"    )
														self.assertEqual(lowerCamelCase   ,     [{"generated_text": ANY(lowerCamelCase    )}]    )
														# These are encoder decoder, they don't just append to incoming string
														self.assertFalse(outputs[0]["generated_text"].startswith("Something there"    )    )

														__snake_case		:      Optional[int]										=       generator(["This is great !", "Something else"]   ,     num_return_sequences=2   ,     do_sample=lowerCamelCase    )
														self.assertEqual(
														    lowerCamelCase   ,     [
														        [{"generated_text": ANY(lowerCamelCase    )}, {"generated_text": ANY(lowerCamelCase    )}],
														        [{"generated_text": ANY(lowerCamelCase    )}, {"generated_text": ANY(lowerCamelCase    )}],
														    ]   ,     )

														__snake_case		:      Dict										=       generator(
														    ["This is great !", "Something else"]   ,     num_return_sequences=2   ,     batch_size=2   ,     do_sample=lowerCamelCase    )
														self.assertEqual(
														    lowerCamelCase   ,     [
														        [{"generated_text": ANY(lowerCamelCase    )}, {"generated_text": ANY(lowerCamelCase    )}],
														        [{"generated_text": ANY(lowerCamelCase    )}, {"generated_text": ANY(lowerCamelCase    )}],
														    ]   ,     )

														with self.assertRaises(lowerCamelCase    ):
																					generator(4    )

							@require_torch
							def    __snake_case (							self	:  Dict    )     ->   Optional[int]:
														__snake_case		:      Dict										=       pipeline("text2text-generation"   ,     model="patrickvonplaten/t5-tiny-random"   ,     framework="pt"    )
														# do_sample=False necessary for reproducibility
														__snake_case		:      Optional[Any]										=       generator("Something there"   ,     do_sample=lowerCamelCase    )
														self.assertEqual(lowerCamelCase   ,     [{"generated_text": ""}]    )

														__snake_case		:      Optional[Any]										=       3
														__snake_case		:      Optional[Any]										=       generator(
														    "Something there"   ,     num_return_sequences=lowerCamelCase   ,     num_beams=lowerCamelCase   ,     )
														__snake_case		:      int										=       [
														    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide Beide"},
														    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide"},
														    {"generated_text": ""},
														]
														self.assertEqual(lowerCamelCase   ,     lowerCamelCase    )

														__snake_case		:      Optional[int]										=       generator("This is a test"   ,     do_sample=lowerCamelCase   ,     num_return_sequences=2   ,     return_tensors=lowerCamelCase    )
														self.assertEqual(
														    lowerCamelCase   ,     [
														        {"generated_token_ids": ANY(torch.Tensor    )},
														        {"generated_token_ids": ANY(torch.Tensor    )},
														    ]   ,     )
														__snake_case		:      Tuple										=       generator.model.config.eos_token_id
														__snake_case		:      List[str]										=       "<pad>"
														__snake_case		:      Union[str, Any]										=       generator(
														    ["This is a test", "This is a second test"]   ,     do_sample=lowerCamelCase   ,     num_return_sequences=2   ,     batch_size=2   ,     return_tensors=lowerCamelCase   ,     )
														self.assertEqual(
														    lowerCamelCase   ,     [
														        [
														            {"generated_token_ids": ANY(torch.Tensor    )},
														            {"generated_token_ids": ANY(torch.Tensor    )},
														        ],
														        [
														            {"generated_token_ids": ANY(torch.Tensor    )},
														            {"generated_token_ids": ANY(torch.Tensor    )},
														        ],
														    ]   ,     )






							@require_tf
							def    __snake_case (							self	:  str    )     ->   str:
														__snake_case		:      int										=       pipeline("text2text-generation"   ,     model="patrickvonplaten/t5-tiny-random"   ,     framework="tf"    )
														# do_sample=False necessary for reproducibility
														__snake_case		:      Optional[Any]										=       generator("Something there"   ,     do_sample=lowerCamelCase    )
														self.assertEqual(lowerCamelCase   ,     [{"generated_text": ""}]    )





from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

_snake_case					:		Union[str, Any]      =    datasets.load_iris()

_snake_case					:		Tuple      =    np.array(data["data"])
_snake_case					:		int      =    np.array(data["target"])
_snake_case					:		int      =    data["target_names"]

_snake_case ,				_snake_case ,				_snake_case ,				_snake_case					:		Any      =    train_test_split(X, y)







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							return np.linalg.norm(np.array(__lowerCamelCase     ) - np.array(__lowerCamelCase     )     )







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase=5     ):
							__snake_case		:      Optional[Any]										=       zip(__lowerCamelCase							,     __lowerCamelCase     )
							# List of distances of all points from the point to be classified
							__snake_case		:      Optional[int]										=       []
							for data_point in data:
														__snake_case		:      Union[str, Any]										=       euclidean_distance(data_point[0]							,     __lowerCamelCase     )
														distances.append((distance, data_point[1])     )
							# Choosing 'k' points with the least distances.
							__snake_case		:      Dict										=       [i[1] for i in sorted(__lowerCamelCase     )[:k]]
							# Most commonly occurring class among them
							# is the class into which the point is classified
							__snake_case		:      Any										=       Counter(__lowerCamelCase     ).most_common(1     )[0][0]
							return classes[result]


if __name__ == "__main__":
			print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))

