
'''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  lowerCAmelCase__   (  unittest.TestCase     ):
					lowerCAmelCase_        =     MODEL_FOR_CAUSAL_LM_MAPPING
					lowerCAmelCase_        =     TF_MODEL_FOR_CAUSAL_LM_MAPPING






					@require_torch
					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Tuple													=       pipeline(task='''text-generation'''				,							model='''sshleifer/tiny-ctrl'''				,							framework='''pt'''		)
											# Using `do_sample=False` to force deterministic output
											lowercase_	:     List[Any]													=       text_generator('''This is a test'''				,							do_sample=__SCREAMING_SNAKE_CASE		)
											self.assertEqual(
											    __SCREAMING_SNAKE_CASE				,							[
											        {
											            '''generated_text''': (
											                '''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
											                ''' oscope. FiliFili@@'''
											            )
											        }
											    ]				,							)

											lowercase_	:     str													=       text_generator(['''This is a test''', '''This is a second test''']		)
											self.assertEqual(
											    __SCREAMING_SNAKE_CASE				,							[
											        [
											            {
											                '''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@@'''
											                )
											            }
											        ],
											    ]				,							)

											lowercase_	:     Optional[Any]													=       text_generator('''This is a test'''				,							do_sample=__SCREAMING_SNAKE_CASE				,							num_return_sequences=2				,							return_tensors=__SCREAMING_SNAKE_CASE		)
											self.assertEqual(
											    __SCREAMING_SNAKE_CASE				,							[
											        {'''generated_token_ids''': ANY(__SCREAMING_SNAKE_CASE		)},
											        {'''generated_token_ids''': ANY(__SCREAMING_SNAKE_CASE		)},
											    ]				,							)
											lowercase_	:     Union[str, Any]													=       text_generator.model.config.eos_token_id
											lowercase_	:     str													=       '<pad>'
											lowercase_	:     List[str]													=       text_generator(
											    ['''This is a test''', '''This is a second test''']				,							do_sample=__SCREAMING_SNAKE_CASE				,							num_return_sequences=2				,							batch_size=2				,							return_tensors=__SCREAMING_SNAKE_CASE				,							)
											self.assertEqual(
											    __SCREAMING_SNAKE_CASE				,							[
											        [
											            {'''generated_token_ids''': ANY(__SCREAMING_SNAKE_CASE		)},
											            {'''generated_token_ids''': ANY(__SCREAMING_SNAKE_CASE		)},
											        ],
											        [
											            {'''generated_token_ids''': ANY(__SCREAMING_SNAKE_CASE		)},
											            {'''generated_token_ids''': ANY(__SCREAMING_SNAKE_CASE		)},
											        ],
											    ]				,							)






					@require_tf
					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Optional[Any]													=       pipeline(task='''text-generation'''				,							model='''sshleifer/tiny-ctrl'''				,							framework='''tf'''		)

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

											lowercase_	:     List[str]													=       text_generator(['''This is a test''', '''This is a second test''']				,							do_sample=__SCREAMING_SNAKE_CASE		)
											self.assertEqual(
											    __SCREAMING_SNAKE_CASE				,							[
											        [
											            {
											                '''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 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Optional[Any]													=       TextGenerationPipeline(model=__SCREAMING_SNAKE_CASE				,							tokenizer=__SCREAMING_SNAKE_CASE		)
											return text_generator, ["This is a test", "Another test"]






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Union[str, Any]													=       'Hello I believe in'
											lowercase_	:     Optional[int]													=       pipeline('''text-generation'''				,							model='''hf-internal-testing/tiny-random-gpt2'''		)
											lowercase_	:     Optional[Any]													=       text_generator(__SCREAMING_SNAKE_CASE		)
											self.assertEqual(
											    __SCREAMING_SNAKE_CASE				,							[{'''generated_text''': '''Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'''}]				,							)

											lowercase_	:     List[Any]													=       text_generator(__SCREAMING_SNAKE_CASE				,							stop_sequence=''' fe'''		)
											self.assertEqual(__SCREAMING_SNAKE_CASE				,							[{'''generated_text''': '''Hello I believe in fe'''}]		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Any													=       text_generator.model
											lowercase_	:     Tuple													=       text_generator.tokenizer

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

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

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

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

											lowercase_	:     Union[str, Any]													=       text_generator(['''This is great !''', '''Something else''']				,							num_return_sequences=2				,							do_sample=__SCREAMING_SNAKE_CASE		)
											self.assertEqual(
											    __SCREAMING_SNAKE_CASE				,							[
											        [{'''generated_text''': ANY(__SCREAMING_SNAKE_CASE		)}, {'''generated_text''': ANY(__SCREAMING_SNAKE_CASE		)}],
											        [{'''generated_text''': ANY(__SCREAMING_SNAKE_CASE		)}, {'''generated_text''': ANY(__SCREAMING_SNAKE_CASE		)}],
											    ]				,							)

											if text_generator.tokenizer.pad_token is not None:
																	lowercase_	:     Optional[int]													=       text_generator(
																	    ['''This is great !''', '''Something else''']				,							num_return_sequences=2				,							batch_size=2				,							do_sample=__SCREAMING_SNAKE_CASE		)
																	self.assertEqual(
																	    __SCREAMING_SNAKE_CASE				,							[
																	        [{'''generated_text''': ANY(__SCREAMING_SNAKE_CASE		)}, {'''generated_text''': ANY(__SCREAMING_SNAKE_CASE		)}],
																	        [{'''generated_text''': ANY(__SCREAMING_SNAKE_CASE		)}, {'''generated_text''': ANY(__SCREAMING_SNAKE_CASE		)}],
																	    ]				,							)

											with self.assertRaises(__SCREAMING_SNAKE_CASE		):
																	lowercase_	:     int													=       text_generator('''test'''				,							return_full_text=__SCREAMING_SNAKE_CASE				,							return_text=__SCREAMING_SNAKE_CASE		)
											with self.assertRaises(__SCREAMING_SNAKE_CASE		):
																	lowercase_	:     Any													=       text_generator('''test'''				,							return_full_text=__SCREAMING_SNAKE_CASE				,							return_tensors=__SCREAMING_SNAKE_CASE		)
											with self.assertRaises(__SCREAMING_SNAKE_CASE		):
																	lowercase_	:     Union[str, Any]													=       text_generator('''test'''				,							return_text=__SCREAMING_SNAKE_CASE				,							return_tensors=__SCREAMING_SNAKE_CASE		)

											# 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__
											):
																	lowercase_	:     Optional[int]													=       text_generator(''''''		)
																	self.assertEqual(__SCREAMING_SNAKE_CASE				,							[{'''generated_text''': ANY(__SCREAMING_SNAKE_CASE		)}]		)
											else:
																	with self.assertRaises((ValueError, AssertionError)		):
																							lowercase_	:     Optional[int]													=       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.
											lowercase_	:     Optional[int]													=       ['RwkvForCausalLM', 'XGLMForCausalLM', 'GPTNeoXForCausalLM']
											if (
											    tokenizer.model_max_length < 1_00_00
											    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''' * 5_00				,							max_new_tokens=20		)

																	lowercase_	:     Tuple													=       text_generator('''This is a test''' * 5_00				,							handle_long_generation='''hole'''				,							max_new_tokens=20		)
																	# Hole strategy cannot work
																	with self.assertRaises(__SCREAMING_SNAKE_CASE		):
																							text_generator(
																							    '''This is a test''' * 5_00				,							handle_long_generation='''hole'''				,							max_new_tokens=tokenizer.model_max_length + 10				,							)






					@require_torch
					@require_accelerate
					@require_torch_gpu
					def 	_snake_case    (   self		):


											"""simple docstring"""





											import torch

											# Classic `model_kwargs`
											lowercase_	:     Tuple													=       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		)
											lowercase_	:     Any													=       pipe('''This is a test'''		)
											self.assertEqual(
											    __SCREAMING_SNAKE_CASE				,							[
											        {
											            '''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.)
											lowercase_	:     int													=       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		)
											lowercase_	:     str													=       pipe('''This is a test'''		)
											self.assertEqual(
											    __SCREAMING_SNAKE_CASE				,							[
											        {
											            '''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
											lowercase_	:     List[Any]													=       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		)
											lowercase_	:     Optional[int]													=       pipe('''This is a test'''		)
											self.assertEqual(
											    __SCREAMING_SNAKE_CASE				,							[
											        {
											            '''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 	_snake_case    (   self		):


											"""simple docstring"""





											import torch

											lowercase_	:     Optional[Any]													=       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 	_snake_case    (   self		):


											"""simple docstring"""





											import torch

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






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     List[str]													=       'Hello world'
											lowercase_	:     Optional[int]													=       pipeline('''text-generation'''				,							model='''hf-internal-testing/tiny-random-gpt2'''		)
											if text_generator.model.framework == "tf":
																	lowercase_	:     Any													=       logging.get_logger('''transformers.generation.tf_utils'''		)
											else:
																	lowercase_	:     str													=       logging.get_logger('''transformers.generation.utils'''		)
											lowercase_	:     Tuple													=       '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(__SCREAMING_SNAKE_CASE		) as cl:
																	lowercase_	:     Dict													=       text_generator(__SCREAMING_SNAKE_CASE				,							max_length=10				,							max_new_tokens=1		)
											self.assertIn(__SCREAMING_SNAKE_CASE				,							cl.out		)

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

											with CaptureLogger(__SCREAMING_SNAKE_CASE		) as cl:
																	lowercase_	:     int													=       text_generator(__SCREAMING_SNAKE_CASE				,							max_length=10		)
											self.assertNotIn(__SCREAMING_SNAKE_CASE				,							cl.out		)










def A_	(		A__				)  ->		int:
       stooge(A__		,      0		,      len(A__				) - 1				)
       return arr



def A_	(		A__		,      A__		,      A__				)  ->		List[Any]:
       if i >= h:
              return

       # If first element is smaller than the last then swap them
       if arr[i] > arr[h]:
              a__ ,							a__						:	List[str] =				arr[h], arr[i]

       # If there are more than 2 elements in the array
       if h - i + 1 > 2:
              a__						:	Dict =				(int)((h - i + 1) / 3				)

              # Recursively sort first 2/3 elements
              stooge(A__		,      A__		,      (h - t)				)

              # Recursively sort last 2/3 elements
              stooge(A__		,      i + t		,      (A__)				)

              # Recursively sort first 2/3 elements
              stooge(A__		,      A__		,      (h - t)				)


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


