






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 							_UpperCamelCase			(unittest.TestCase			):
      snake_case_  =							MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
      snake_case_  =							TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)->	str:
           __lowerCAmelCase								=					TextaTextGenerationPipeline(model=__UpperCamelCase					,						tokenizer=__UpperCamelCase	)
           return generator, ["Something to write", "Something else"]


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

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

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

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


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

           __lowerCAmelCase								=					3
           __lowerCAmelCase								=					generator(
               "Something there"					,						num_return_sequences=__UpperCamelCase					,						num_beams=__UpperCamelCase					,						)
           __lowerCAmelCase								=					[
               {"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(__UpperCamelCase					,						__UpperCamelCase	)

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














def       __lowerCAmelCase      (						__snake_case       ):
     __lowerCAmelCase								=					len(__snake_case       )
     while cur > 1:
          # Find the maximum number in arr
          __lowerCAmelCase								=					arr.index(max(arr[0:cur]       )       )
          # Reverse from 0 to mi
          __lowerCAmelCase								=					arr[mi::-1] + arr[mi + 1 : len(__snake_case       )]
          # Reverse whole list
          __lowerCAmelCase								=					arr[cur - 1 :: -1] + arr[cur : len(__snake_case       )]
          cur -= 1
     return arr


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






