"""simple docstring"""



import unittest

from transformers import (
    MODEL_FOR_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
    TextGenerationPipeline,
    logging,
    pipeline,
)
from transformers.testing_utils import (
    CaptureLogger,
    is_pipeline_test,
    require_accelerate,
    require_tf,
    require_torch,
    require_torch_gpu,
    require_torch_or_tf,
)

from .test_pipelines_common import ANY



@is_pipeline_test
@require_torch_or_tf
class      _A	(						unittest.TestCase					):




    """simple docstring"""




    UpperCamelCase_	:					int							=					MODEL_FOR_CAUSAL_LM_MAPPING
    UpperCamelCase_	:					Optional[int]							=					TF_MODEL_FOR_CAUSAL_LM_MAPPING
    @require_torch
    def  lowercase					(		self							:     int    )    ->       List[str]:
         __snake_case				=							pipeline(task='''text-generation'''	,      model='''sshleifer/tiny-ctrl'''	,      framework='''pt'''    )
         # Using `do_sample=False` to force deterministic output
         __snake_case				=							text_generator('''This is a test'''	,      do_sample=A_    )
         self.assertEqual(
             A_	,      [
                 {
                     '''generated_text''': (
                         '''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
                         ''' oscope. FiliFili@@'''
                     )
                 }
             ]	,      )

         __snake_case				=							text_generator(['''This is a test''', '''This is a second test''']    )
         self.assertEqual(
             A_	,      [
                 [
                     {
                         '''generated_text''': (
                             '''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
                             ''' oscope. FiliFili@@'''
                         )
                     }
                 ],
                 [
                     {
                         '''generated_text''': (
                             '''This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy'''
                             ''' oscope. oscope. FiliFili@@'''
                         )
                     }
                 ],
             ]	,      )

         __snake_case				=							text_generator('''This is a test'''	,      do_sample=A_	,      num_return_sequences=2	,      return_tensors=A_    )
         self.assertEqual(
             A_	,      [
                 {'''generated_token_ids''': ANY(A_    )},
                 {'''generated_token_ids''': ANY(A_    )},
             ]	,      )
         __snake_case				=							text_generator.model.config.eos_token_id
         __snake_case				=							'''<pad>'''
         __snake_case				=							text_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(A_    )},
                     {'''generated_token_ids''': ANY(A_    )},
                 ],
                 [
                     {'''generated_token_ids''': ANY(A_    )},
                     {'''generated_token_ids''': ANY(A_    )},
                 ],
             ]	,      )
    @require_tf
    def  lowercase					(		self							:     Optional[int]    )    ->       List[str]:
         __snake_case				=							pipeline(task='''text-generation'''	,      model='''sshleifer/tiny-ctrl'''	,      framework='''tf'''    )

         # Using `do_sample=False` to force deterministic output
         __snake_case				=							text_generator('''This is a test'''	,      do_sample=A_    )
         self.assertEqual(
             A_	,      [
                 {
                     '''generated_text''': (
                         '''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
                         ''' please,'''
                     )
                 }
             ]	,      )

         __snake_case				=							text_generator(['''This is a test''', '''This is a second test''']	,      do_sample=A_    )
         self.assertEqual(
             A_	,      [
                 [
                     {
                         '''generated_text''': (
                             '''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
                             ''' please,'''
                         )
                     }
                 ],
                 [
                     {
                         '''generated_text''': (
                             '''This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes'''
                             ''' Cannes 閲閲Cannes Cannes Cannes 攵 please,'''
                         )
                     }
                 ],
             ]	,      )
    def  lowercase					(		self							:     Tuple	,      A_							:     str	,      A_							:     str	,      A_							:     Union[str, Any]    )    ->       str:
         __snake_case				=							TextGenerationPipeline(model=A_	,      tokenizer=A_    )
         return text_generator, ["This is a test", "Another test"]
    def  lowercase					(		self							:     Optional[Any]    )    ->       Dict:
         __snake_case				=							'''Hello I believe in'''
         __snake_case				=							pipeline('''text-generation'''	,      model='''hf-internal-testing/tiny-random-gpt2'''    )
         __snake_case				=							text_generator(A_    )
         self.assertEqual(
             A_	,      [{'''generated_text''': '''Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'''}]	,      )

         __snake_case				=							text_generator(A_	,      stop_sequence=''' fe'''    )
         self.assertEqual(A_	,      [{'''generated_text''': '''Hello I believe in fe'''}]    )
    def  lowercase					(		self							:     Optional[Any]	,      A_							:     str	,      A_							:     Union[str, Any]    )    ->       Optional[Any]:
         __snake_case				=							text_generator.model
         __snake_case				=							text_generator.tokenizer

         __snake_case				=							text_generator('''This is a test'''    )
         self.assertEqual(A_	,      [{'''generated_text''': ANY(A_    )}]    )
         self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test'''    )    )

         __snake_case				=							text_generator('''This is a test'''	,      return_full_text=A_    )
         self.assertEqual(A_	,      [{'''generated_text''': ANY(A_    )}]    )
         self.assertNotIn('''This is a test'''	,      outputs[0]['''generated_text''']    )

         __snake_case				=							pipeline(task='''text-generation'''	,      model=A_	,      tokenizer=A_	,      return_full_text=A_    )
         __snake_case				=							text_generator('''This is a test'''    )
         self.assertEqual(A_	,      [{'''generated_text''': ANY(A_    )}]    )
         self.assertNotIn('''This is a test'''	,      outputs[0]['''generated_text''']    )

         __snake_case				=							text_generator('''This is a test'''	,      return_full_text=A_    )
         self.assertEqual(A_	,      [{'''generated_text''': ANY(A_    )}]    )
         self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test'''    )    )

         __snake_case				=							text_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_    )}],
             ]	,      )

         if text_generator.tokenizer.pad_token is not None:
              __snake_case				=							text_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_    ):
              __snake_case				=							text_generator('''test'''	,      return_full_text=A_	,      return_text=A_    )
         with self.assertRaises(A_    ):
              __snake_case				=							text_generator('''test'''	,      return_full_text=A_	,      return_tensors=A_    )
         with self.assertRaises(A_    ):
              __snake_case				=							text_generator('''test'''	,      return_text=A_	,      return_tensors=A_    )

         # Empty prompt is slighly special
         # it requires BOS token to exist.
         # Special case for Pegasus which will always append EOS so will
         # work even without BOS.
         if (
             text_generator.tokenizer.bos_token_id is not None
             or "Pegasus" in tokenizer.__class__.__name__
             or "Git" in model.__class__.__name__
         ):
              __snake_case				=							text_generator(''''''    )
              self.assertEqual(A_	,      [{'''generated_text''': ANY(A_    )}]    )
         else:
              with self.assertRaises((ValueError, AssertionError)    ):
                   __snake_case				=							text_generator(''''''    )

         if text_generator.framework == "tf":
              # TF generation does not support max_new_tokens, and it's impossible
              # to control long generation with only max_length without
              # fancy calculation, dismissing tests for now.
              return
         # We don't care about infinite range models.
         # They already work.
         # Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
         __snake_case				=							['''RwkvForCausalLM''', '''XGLMForCausalLM''', '''GPTNeoXForCausalLM''']
         if (
             tokenizer.model_max_length < 10_000
             and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
         ):
              # Handling of large generations
              with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError)    ):
                   text_generator('''This is a test''' * 500	,      max_new_tokens=20    )

              __snake_case				=							text_generator('''This is a test''' * 500	,      handle_long_generation='''hole'''	,      max_new_tokens=20    )
              # Hole strategy cannot work
              with self.assertRaises(A_    ):
                   text_generator(
                       '''This is a test''' * 500	,      handle_long_generation='''hole'''	,      max_new_tokens=tokenizer.model_max_length + 10	,      )
    @require_torch
    @require_accelerate
    @require_torch_gpu
    def  lowercase					(		self							:     str    )    ->       Tuple:
         import torch

         # Classic `model_kwargs`
         __snake_case				=							pipeline(
             model='''hf-internal-testing/tiny-random-bloom'''	,      model_kwargs={'''device_map''': '''auto''', '''torch_dtype''': torch.bfloataa}	,      )
         self.assertEqual(pipe.model.device	,      torch.device(0    )    )
         self.assertEqual(pipe.model.lm_head.weight.dtype	,      torch.bfloataa    )
         __snake_case				=							pipe('''This is a test'''    )
         self.assertEqual(
             A_	,      [
                 {
                     '''generated_text''': (
                         '''This is a test test test test test test test test test test test test test test test test'''
                         ''' test'''
                     )
                 }
             ]	,      )

         # Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
         __snake_case				=							pipeline(model='''hf-internal-testing/tiny-random-bloom'''	,      device_map='''auto'''	,      torch_dtype=torch.bfloataa    )
         self.assertEqual(pipe.model.device	,      torch.device(0    )    )
         self.assertEqual(pipe.model.lm_head.weight.dtype	,      torch.bfloataa    )
         __snake_case				=							pipe('''This is a test'''    )
         self.assertEqual(
             A_	,      [
                 {
                     '''generated_text''': (
                         '''This is a test test test test test test test test test test test test test test test test'''
                         ''' test'''
                     )
                 }
             ]	,      )

         # torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
         __snake_case				=							pipeline(model='''hf-internal-testing/tiny-random-bloom'''	,      device_map='''auto'''    )
         self.assertEqual(pipe.model.device	,      torch.device(0    )    )
         self.assertEqual(pipe.model.lm_head.weight.dtype	,      torch.floataa    )
         __snake_case				=							pipe('''This is a test'''    )
         self.assertEqual(
             A_	,      [
                 {
                     '''generated_text''': (
                         '''This is a test test test test test test test test test test test test test test test test'''
                         ''' test'''
                     )
                 }
             ]	,      )
    @require_torch
    @require_torch_gpu
    def  lowercase					(		self							:     Union[str, Any]    )    ->       Union[str, Any]:
         import torch

         __snake_case				=							pipeline(model='''hf-internal-testing/tiny-random-bloom'''	,      device=0	,      torch_dtype=torch.floataa    )
         pipe('''This is a test'''    )
    @require_torch
    @require_accelerate
    @require_torch_gpu
    def  lowercase					(		self							:     str    )    ->       Optional[Any]:
         import torch

         __snake_case				=							pipeline(model='''hf-internal-testing/tiny-random-bloom'''	,      device_map='''auto'''	,      torch_dtype=torch.floataa    )
         pipe('''This is a test'''	,      do_sample=A_	,      top_p=0.5    )







    def  lowercase					(		self							:     int    )    ->       Any:
         __snake_case				=							'''Hello world'''
         __snake_case				=							pipeline('''text-generation'''	,      model='''hf-internal-testing/tiny-random-gpt2'''    )
         if text_generator.model.framework == "tf":
              __snake_case				=							logging.get_logger('''transformers.generation.tf_utils'''    )
         else:
              __snake_case				=							logging.get_logger('''transformers.generation.utils'''    )
         __snake_case				=							'''Both `max_new_tokens`'''  # The beggining of the message to be checked in this test

         # Both are set by the user -> log warning
         with CaptureLogger(A_    ) as cl:
              __snake_case				=							text_generator(A_	,      max_length=10	,      max_new_tokens=1    )
         self.assertIn(A_	,      cl.out    )

         # The user only sets one -> no warning
         with CaptureLogger(A_    ) as cl:
              __snake_case				=							text_generator(A_	,      max_new_tokens=1    )
         self.assertNotIn(A_	,      cl.out    )

         with CaptureLogger(A_    ) as cl:
              __snake_case				=							text_generator(A_	,      max_length=10    )
         self.assertNotIn(A_	,      cl.out    )
"""simple docstring"""



def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							len(snake_case)
     for i in range(length - 1):
          __snake_case				=							i
          for k in range(i + 1,      snake_case):
               if collection[k] < collection[least]:
                    __snake_case				=							k
          if least != i:
               __snake_case		,	__snake_case				=							(collection[i], collection[least])
     return collection


if __name__ == "__main__":
       __lowercase		:		Optional[Any]														=							input("Enter numbers separated by a comma:\n").strip()
       __lowercase		:		Tuple														=							[int(item) for item in user_input.split(",")]
       print(selection_sort(unsorted))