


'''simple docstring'''







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 					lowercase_     (    unittest.TestCase   ):
    __UpperCAmelCase   			=	MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
    __UpperCAmelCase   			=	TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING


    def    __a							(     self  ,					a  ,					a  ,					a    ):
          UpperCamelCase__										=				TextaTextGenerationPipeline(model=a  ,					tokenizer=a    )
          return generator, ["Something to write", "Something else"]


    def    __a							(     self  ,					a  ,					a    ):
          UpperCamelCase__										=				generator("Something there"    )
          self.assertEqual(a  ,					[{"generated_text": ANY(a    )}]    )
          # These are encoder decoder, they don't just append to incoming string
          self.assertFalse(outputs[0]["generated_text"].startswith("Something there"    )    )

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

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

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


    @require_torch
    def    __a							(     self    ):
          UpperCamelCase__										=				pipeline("text2text-generation"  ,					model="patrickvonplaten/t5-tiny-random"  ,					framework="pt"    )
          # do_sample=False necessary for reproducibility
          UpperCamelCase__										=				generator("Something there"  ,					do_sample=a    )
          self.assertEqual(a  ,					[{"generated_text": ""}]    )

          UpperCamelCase__										=				3
          UpperCamelCase__										=				generator(
              "Something there"  ,					num_return_sequences=a  ,					num_beams=a  ,					)
          UpperCamelCase__										=				[
              {"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(a  ,					a    )

          UpperCamelCase__										=				generator("This is a test"  ,					do_sample=a  ,					num_return_sequences=2  ,					return_tensors=a    )
          self.assertEqual(
              a  ,					[
                  {"generated_token_ids": ANY(torch.Tensor    )},
                  {"generated_token_ids": ANY(torch.Tensor    )},
              ]  ,					)
          UpperCamelCase__										=				generator.model.config.eos_token_id
          UpperCamelCase__										=				"<pad>"
          UpperCamelCase__										=				generator(
              ["This is a test", "This is a second test"]  ,					do_sample=a  ,					num_return_sequences=2  ,					batch_size=2  ,					return_tensors=a  ,					)
          self.assertEqual(
              a  ,					[
                  [
                      {"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    __a							(     self    ):
          UpperCamelCase__										=				pipeline("text2text-generation"  ,					model="patrickvonplaten/t5-tiny-random"  ,					framework="tf"    )
          # do_sample=False necessary for reproducibility
          UpperCamelCase__										=				generator("Something there"  ,					do_sample=a    )
          self.assertEqual(a  ,					[{"generated_text": ""}]    )






def       _snake_case	(   lowerCAmelCase							:   list	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Dict				      =    len(lowerCAmelCase	)
		for i in range(1   , lowerCAmelCase	):
				SCREAMING_SNAKE_CASE_  :		int				      =    collection[i]
				SCREAMING_SNAKE_CASE_  :		Any				      =    0
				SCREAMING_SNAKE_CASE_  :		Tuple				      =    i - 1

				while low <= high:
						SCREAMING_SNAKE_CASE_  :		int				      =    (low + high) // 2
						if val < collection[mid]:
								SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    mid - 1
						else:
								SCREAMING_SNAKE_CASE_  :		Tuple				      =    mid + 1
				for j in range(lowerCAmelCase   , lowerCAmelCase   , -1	):
						SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    collection[j - 1]
				SCREAMING_SNAKE_CASE_  :		int				      =    val
		return collection


if __name__ == "__main__":
		__lowerCamelCase					:	Dict       =  input('''Enter numbers separated by a comma:\n''').strip()
		__lowerCamelCase					:	List[str]       =  [int(item) for item in user_input.split(''',''')]
		print(binary_insertion_sort(unsorted))
