





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__						=      MODEL_FOR_CAUSAL_LM_MAPPING
      UpperCAmelCase__						=      TF_MODEL_FOR_CAUSAL_LM_MAPPING


      @require_torch
      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[int]	=		pipeline(task='''text-generation'''      ,							model='''sshleifer/tiny-ctrl'''      ,							framework='''pt''')
             # Using `do_sample=False` to force deterministic output
             _lowerCamelCase							:     Tuple	=		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@@'''
                         )
                     }
                 ]      ,							)

             _lowerCamelCase							:     Optional[int]	=		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@@'''
                             )
                         }
                     ],
                 ]      ,							)

             _lowerCamelCase							:     Optional[Any]	=		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__)},
                 ]      ,							)
             _lowerCamelCase							:     int	=		text_generator.model.config.eos_token_id
             _lowerCamelCase							:     List[str]	=		'''<pad>'''
             _lowerCamelCase							:     str	=		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 				__snake_case			(		self):



             """simple docstring"""





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

             # Using `do_sample=False` to force deterministic output
             _lowerCamelCase							:     Optional[Any]	=		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,'''
                         )
                     }
                 ]      ,							)

             _lowerCamelCase							:     Optional[Any]	=		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 				__snake_case			(		self      ,							a__      ,							a__      ,							a__):



             """simple docstring"""





             _lowerCamelCase							:     List[Any]	=		TextGenerationPipeline(model=a__      ,							tokenizer=a__)
             return text_generator, ["This is a test", "Another test"]


      def 				__snake_case			(		self):



             """simple docstring"""





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

             _lowerCamelCase							:     int	=		text_generator(a__      ,							stop_sequence=''' fe''')
             self.assertEqual(a__      ,							[{'''generated_text''': '''Hello I believe in fe'''}])


      def 				__snake_case			(		self      ,							a__      ,							a__):



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		text_generator.model
             _lowerCamelCase							:     Optional[int]	=		text_generator.tokenizer

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

             _lowerCamelCase							:     List[Any]	=		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'''])

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

             _lowerCamelCase							:     Optional[int]	=		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'''))

             _lowerCamelCase							:     List[str]	=		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:
                    _lowerCamelCase							:     Optional[Any]	=		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__):
                    _lowerCamelCase							:     int	=		text_generator('''test'''      ,							return_full_text=a__      ,							return_text=a__)
             with self.assertRaises(a__):
                    _lowerCamelCase							:     Optional[int]	=		text_generator('''test'''      ,							return_full_text=a__      ,							return_tensors=a__)
             with self.assertRaises(a__):
                    _lowerCamelCase							:     str	=		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__
             ):
                    _lowerCamelCase							:     str	=		text_generator('''''')
                    self.assertEqual(a__      ,							[{'''generated_text''': ANY(a__)}])
             else:
                    with self.assertRaises((ValueError, AssertionError)):
                           _lowerCamelCase							:     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.
             _lowerCamelCase							:     str	=		['''RwkvForCausalLM''', '''XGLMForCausalLM''', '''GPTNeoXForCausalLM''']
             if (
                 tokenizer.model_max_length < 1_0000
                 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)

                    _lowerCamelCase							:     Optional[int]	=		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 				__snake_case			(		self):



             """simple docstring"""





             import torch

             # Classic `model_kwargs`
             _lowerCamelCase							:     List[Any]	=		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)
             _lowerCamelCase							:     int	=		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.)
             _lowerCamelCase							:     List[Any]	=		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)
             _lowerCamelCase							:     Optional[Any]	=		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
             _lowerCamelCase							:     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)
             _lowerCamelCase							:     List[str]	=		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 				__snake_case			(		self):



             """simple docstring"""





             import torch

             _lowerCamelCase							:     Union[str, 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

             _lowerCamelCase							:     str	=		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 				__snake_case			(		self):



             """simple docstring"""





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

             with CaptureLogger(a__) as cl:
                    _lowerCamelCase							:     Dict	=		text_generator(a__      ,							max_length=10)
             self.assertNotIn(a__      ,							cl.out)








def    __UpperCAmelCase(      lowercase_      ):
       # if the collection is empty, returns empty
       if collection == []:
              return []

       # get some information about the collection
       _lowerCamelCase							:     Tuple	=		len(lowercase_      )
       _lowerCamelCase							:     List[str]	=		max(lowercase_      )
       _lowerCamelCase							:     Any	=		min(lowercase_      )

       # create the counting array
       _lowerCamelCase							:     str	=		coll_max + 1 - coll_min
       _lowerCamelCase							:     Union[str, Any]	=		[0] * counting_arr_length

       # count how much a number appears in the collection
       for number in collection:
              counting_arr[number - coll_min] += 1

       # sum each position with it's predecessors. now, counting_arr[i] tells
       # us how many elements <= i has in the collection
       for i in range(1 ,    lowercase_      ):
              _lowerCamelCase							:     Union[str, Any]	=		counting_arr[i] + counting_arr[i - 1]

       # create the output collection
       _lowerCamelCase							:     Dict	=		[0] * coll_len

       # place the elements in the output, respecting the original order (stable
       # sort) from end to begin, updating counting_arr
       for i in reversed(range(0 ,    lowercase_      )      ):
              _lowerCamelCase							:     Union[str, Any]	=		collection[i]
              counting_arr[collection[i] - coll_min] -= 1

       return ordered




def    __UpperCAmelCase(      lowercase_      ):
       return "".join([chr(lowercase_      ) for i in counting_sort([ord(lowercase_      ) for c in string]      )]      )


if __name__ == "__main__":
       # Test string sort
       assert counting_sort_string('thisisthestring') == "eghhiiinrsssttt"

       _lowerCamelCase			       =      input('Enter numbers separated by a comma:\n').strip()
       _lowerCamelCase			       =      [int(item) for item in user_input.split(',')]
       print(counting_sort(unsorted))

