





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     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):
 __SCREAMING_SNAKE_CASE							       =	MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
 __SCREAMING_SNAKE_CASE							       =	TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
        A__											=						TextaTextGenerationPipeline(model=__lowerCamelCase,tokenizer=__lowerCamelCase )
        return generator, ["Something to write", "Something else"]



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        A__											=						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''' ) )

        A__											=						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 )}],
            ],)

        A__											=						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 				UpperCamelCase			(							self ):
        A__											=						pipeline('''text2text-generation''',model='''patrickvonplaten/t5-tiny-random''',framework='''pt''' )
        # do_sample=False necessary for reproducibility
        A__											=						generator('''Something there''',do_sample=__lowerCamelCase )
        self.assertEqual(__lowerCamelCase,[{'''generated_text''': ''''''}] )

        A__											=						3
        A__											=						generator(
            '''Something there''',num_return_sequences=__lowerCamelCase,num_beams=__lowerCamelCase,)
        A__											=						[
            {'''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 )

        A__											=						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 )},
            ],)
        A__											=						generator.model.config.eos_token_id
        A__											=						'''<pad>'''
        A__											=						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 				UpperCamelCase			(							self ):
        A__											=						pipeline('''text2text-generation''',model='''patrickvonplaten/t5-tiny-random''',framework='''tf''' )
        # do_sample=False necessary for reproducibility
        A__											=						generator('''Something there''',do_sample=__lowerCamelCase )
        self.assertEqual(__lowerCamelCase,[{'''generated_text''': ''''''}] )












import random


def 	UpperCamelCase__(       UpperCamelCase__			:       list ,     UpperCamelCase__			:       List[Any]  )->tuple:
       A__					,	A__					,	A__											=						[], [], []
       for element in data:
              if element < pivot:
                     less.append(UpperCamelCase__  )
              elif element > pivot:
                     greater.append(UpperCamelCase__  )
              else:
                     equal.append(UpperCamelCase__  )
       return less, equal, greater


def 	UpperCamelCase__(       UpperCamelCase__			:       list ,     UpperCamelCase__			:       int  )->Optional[int]:
       # index = len(items) // 2 when trying to find the median
       #   (value of index when items is sorted)

       # invalid input
       if index >= len(UpperCamelCase__  ) or index < 0:
              return None

       A__											=						items[random.randint(0 ,     len(UpperCamelCase__  ) - 1  )]
       A__											=						0
       A__					,	A__					,	A__											=						_partition(UpperCamelCase__ ,     UpperCamelCase__  )
       A__											=						len(UpperCamelCase__  )
       A__											=						len(UpperCamelCase__  )

       # index is the pivot
       if m <= index < m + count:
              return pivot
       # must be in smaller
       elif m > index:
              return quick_select(UpperCamelCase__ ,     UpperCamelCase__  )
       # must be in larger
       else:
              return quick_select(UpperCamelCase__ ,     index - (m + count)  )





