


import copy
import tempfile
import unittest

from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError

from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test



class 	__lowerCamelCase			(unittest.TestCase						):







       @parameterized.expand([(None,), ('foo.json',)]					)
       def 		snake_case_	(			self: int,A_: int					):






            '''simple docstring'''




            __UpperCamelCase =			GenerationConfig(
                do_sample=A_,temperature=0.7,length_penalty=1.0,bad_words_ids=[[1, 2, 3], [4, 5]],)
            with tempfile.TemporaryDirectory() as tmp_dir:
                 config.save_pretrained(A_,config_name=A_					)
                 __UpperCamelCase =			GenerationConfig.from_pretrained(A_,config_name=A_					)

            # Checks parameters that were specified
            self.assertEqual(loaded_config.do_sample,A_					)
            self.assertEqual(loaded_config.temperature,0.7					)
            self.assertEqual(loaded_config.length_penalty,1.0					)
            self.assertEqual(loaded_config.bad_words_ids,[[1, 2, 3], [4, 5]]					)

            # Checks parameters that were not specified (defaults)
            self.assertEqual(loaded_config.top_k,50					)
            self.assertEqual(loaded_config.max_length,20					)
            self.assertEqual(loaded_config.max_time,A_					)







       def 		snake_case_	(			self: Tuple					):






            '''simple docstring'''




            __UpperCamelCase =			AutoConfig.from_pretrained('gpt2'					)
            __UpperCamelCase =			GenerationConfig.from_model_config(A_					)
            __UpperCamelCase =			GenerationConfig()

            # The generation config has loaded a few non-default parameters from the model config
            self.assertNotEqual(A_,A_					)

            # One of those parameters is eos_token_id -- check if it matches
            self.assertNotEqual(generation_config_from_model.eos_token_id,default_generation_config.eos_token_id					)
            self.assertEqual(generation_config_from_model.eos_token_id,model_config.eos_token_id					)







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            __UpperCamelCase =			GenerationConfig()
            __UpperCamelCase =			{
                'max_new_tokens': 1024,
                'foo': 'bar',
            }
            __UpperCamelCase =			copy.deepcopy(A_					)
            __UpperCamelCase =			generation_config.update(**A_					)

            # update_kwargs was not modified (no side effects)
            self.assertEqual(A_,A_					)

            # update_kwargs was used to update the config on valid attributes
            self.assertEqual(generation_config.max_new_tokens,1024					)

            # `.update()` returns a dictionary of unused kwargs
            self.assertEqual(A_,{'foo': 'bar'}					)







       def 		snake_case_	(			self: List[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			GenerationConfig()
            __UpperCamelCase =			'bar'

            with tempfile.TemporaryDirectory('test-generation-config'					) as tmp_dir:
                 generation_config.save_pretrained(A_					)

                 __UpperCamelCase =			GenerationConfig.from_pretrained(A_					)
            # update_kwargs was used to update the config on valid attributes
            self.assertEqual(new_config.foo,'bar'					)

            __UpperCamelCase =			GenerationConfig.from_model_config(A_					)
            assert not hasattr(A_,'foo'					)  # no new kwargs should be initialized if from config







       def 		snake_case_	(			self: List[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			GenerationConfig()
            self.assertEqual(default_config.temperature,1.0					)
            self.assertEqual(default_config.do_sample,A_					)
            self.assertEqual(default_config.num_beams,1					)

            __UpperCamelCase =			GenerationConfig(
                do_sample=A_,temperature=0.7,length_penalty=1.0,bad_words_ids=[[1, 2, 3], [4, 5]],)
            self.assertEqual(config.temperature,0.7					)
            self.assertEqual(config.do_sample,A_					)
            self.assertEqual(config.num_beams,1					)

            with tempfile.TemporaryDirectory() as tmp_dir:
                 config.save_pretrained(A_					)
                 __UpperCamelCase =			GenerationConfig.from_pretrained(A_,temperature=1.0					)

            self.assertEqual(loaded_config.temperature,1.0					)
            self.assertEqual(loaded_config.do_sample,A_					)
            self.assertEqual(loaded_config.num_beams,1					)  # default value



@is_staging_test
class 	__lowerCamelCase			(unittest.TestCase						):







       @classmethod
       def 		snake_case_	(			cls: int					):






            '''simple docstring'''




            __UpperCamelCase =			TOKEN
            HfFolder.save_token(A_					)







       @classmethod
       def 		snake_case_	(			cls: int					):






            '''simple docstring'''




            try:
                 delete_repo(token=cls._token,repo_id='test-generation-config'					)
            except HTTPError:
                 pass

            try:
                 delete_repo(token=cls._token,repo_id='valid_org/test-generation-config-org'					)
            except HTTPError:
                 pass







       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            __UpperCamelCase =			GenerationConfig(
                do_sample=A_,temperature=0.7,length_penalty=1.0,)
            config.push_to_hub('test-generation-config',use_auth_token=self._token					)

            __UpperCamelCase =			GenerationConfig.from_pretrained(F'''{USER}/test-generation-config'''					)
            for k, v in config.to_dict().items():
                 if k != "transformers_version":
                      self.assertEqual(A_,getattr(A_,A_					)					)

        # Reset repo
            delete_repo(token=self._token,repo_id='test-generation-config'					)

            # Push to hub via save_pretrained
            with tempfile.TemporaryDirectory() as tmp_dir:
                 config.save_pretrained(
                     A_,repo_id='test-generation-config',push_to_hub=A_,use_auth_token=self._token					)

            __UpperCamelCase =			GenerationConfig.from_pretrained(F'''{USER}/test-generation-config'''					)
            for k, v in config.to_dict().items():
                 if k != "transformers_version":
                      self.assertEqual(A_,getattr(A_,A_					)					)







       def 		snake_case_	(			self: Dict					):






            '''simple docstring'''




            __UpperCamelCase =			GenerationConfig(
                do_sample=A_,temperature=0.7,length_penalty=1.0,)
            config.push_to_hub('valid_org/test-generation-config-org',use_auth_token=self._token					)

            __UpperCamelCase =			GenerationConfig.from_pretrained('valid_org/test-generation-config-org'					)
            for k, v in config.to_dict().items():
                 if k != "transformers_version":
                      self.assertEqual(A_,getattr(A_,A_					)					)

        # Reset repo
            delete_repo(token=self._token,repo_id='valid_org/test-generation-config-org'					)

            # Push to hub via save_pretrained
            with tempfile.TemporaryDirectory() as tmp_dir:
                 config.save_pretrained(
                     A_,repo_id='valid_org/test-generation-config-org',push_to_hub=A_,use_auth_token=self._token					)

            __UpperCamelCase =			GenerationConfig.from_pretrained('valid_org/test-generation-config-org'					)
            for k, v in config.to_dict().items():
                 if k != "transformers_version":
                      self.assertEqual(A_,getattr(A_,A_					)					)




"""simple docstring"""

from __future__ import annotations

SCREAMING_SNAKE_CASE_            =    [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
SCREAMING_SNAKE_CASE_            =    [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]




def      lowercase__    (       lowerCAmelCase					:       list[float]			)			->   list[float]:






    """simple docstring"""




    UpperCAmelCase          =       []
    UpperCAmelCase          =       len(lowerCAmelCase			)

    for i in range(lowerCAmelCase			):
        UpperCAmelCase          =       -1
        for j in range(i + 1			,					lowerCAmelCase			):
            if arr[i] < arr[j]:
                UpperCAmelCase          =       arr[j]
                break
        result.append(lowerCAmelCase			)
    return result




def      lowercase__    (       lowerCAmelCase					:       list[float]			)			->   list[float]:






    """simple docstring"""




    UpperCAmelCase          =       []
    for i, outer in enumerate(lowerCAmelCase			):
        UpperCAmelCase          =       -1
        for inner in arr[i + 1 :]:
            if outer < inner:
                UpperCAmelCase          =       inner
                break
        result.append(lowerCAmelCase			)
    return result




def      lowercase__    (       lowerCAmelCase					:       list[float]			)			->   list[float]:






    """simple docstring"""




    UpperCAmelCase          =       len(lowerCAmelCase			)
    UpperCAmelCase          =       []
    UpperCAmelCase          =       [-1] * arr_size

    for index in reversed(range(lowerCAmelCase			)			):
        if stack:
            while stack[-1] <= arr[index]:
                stack.pop()
                if not stack:
                    break
        if stack:
            UpperCAmelCase          =       stack[-1]
        stack.append(arr[index]			)
    return result


if __name__ == "__main__":
   from doctest import testmod
   from timeit import timeit

   testmod()
   print(next_greatest_element_slow(arr))
   print(next_greatest_element_fast(arr))
   print(next_greatest_element(arr))

   SCREAMING_SNAKE_CASE_            =    (
       '''from __main__ import arr, next_greatest_element_slow, '''
       '''next_greatest_element_fast, next_greatest_element'''
   )
   print(
       '''next_greatest_element_slow():''',
       timeit('''next_greatest_element_slow(arr)''', setup=setup),
   )
   print(
       '''next_greatest_element_fast():''',
       timeit('''next_greatest_element_fast(arr)''', setup=setup),
   )
   print(
       '''     next_greatest_element():''',
       timeit('''next_greatest_element(arr)''', setup=setup),
   )



