'''simple docstring'''





import os
import re
import shutil
import sys
import tempfile
import unittest

import black


SCREAMING_SNAKE_CASE_:     int  =os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
SCREAMING_SNAKE_CASE_:     int  ='    \"""\n    Output class for the scheduler\'s step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"""\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n'




class        __A      ( unittest.TestCase	):



    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   tempfile.mkdtemp()
         os.makedirs(os.path.join(self.diffusers_dir   ,			"schedulers/" ) )
         UpperCAmelCase_           =   self.diffusers_dir
         shutil.copy(
             os.path.join(__a   ,			"src/diffusers/schedulers/scheduling_ddpm.py" )   ,			os.path.join(self.diffusers_dir   ,			"schedulers/scheduling_ddpm.py" )   ,			)



    def 					_lowercase						(self    :    str ):
         UpperCAmelCase_           =   "src/diffusers"
         shutil.rmtree(self.diffusers_dir )



    def 					_lowercase						(self    :    Any   ,			__a    :    List[str]   ,			__a    :    str   ,			__a    :    int   ,			__a    :    Any=None ):
         UpperCAmelCase_           =   comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
         if overwrite_result is not None:
              UpperCAmelCase_           =   comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
         UpperCAmelCase_           =   black.Mode(target_versions={black.TargetVersion.PYaa}   ,			line_length=119 )
         UpperCAmelCase_           =   black.format_str(__a   ,			mode=__a )
         UpperCAmelCase_           =   os.path.join(self.diffusers_dir   ,			"new_code.py" )
         with open(__a   ,			"w"   ,			newline="\n" ) as f:
              f.write(__a )
         if overwrite_result is None:
              self.assertTrue(len(check_copies.is_copy_consistent(__a ) ) == 0 )
         else:
              check_copies.is_copy_consistent(f.name   ,			overwrite=__a )
              with open(__a   ,			"r" ) as f:
                   self.assertTrue(f.read()   ,			__a )



    def 					_lowercase						(self    :    List[str] ):
         UpperCAmelCase_           =   check_copies.find_code_in_diffusers("schedulers.scheduling_ddpm.DDPMSchedulerOutput" )
         self.assertEqual(__a   ,			__a )






    def 					_lowercase						(self    :    int ):
         # Base copy consistency
         self.check_copy_consistency(
             "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput"   ,			"DDPMSchedulerOutput"   ,			REFERENCE_CODE + "\n"   ,			)

         # With no empty line at the end
         self.check_copy_consistency(
             "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput"   ,			"DDPMSchedulerOutput"   ,			__a   ,			)

         # Copy consistency with rename
         self.check_copy_consistency(
             "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test"   ,			"TestSchedulerOutput"   ,			re.sub("DDPM"   ,			"Test"   ,			__a )   ,			)

         # Copy consistency with a really long name
         UpperCAmelCase_           =   "TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
         self.check_copy_consistency(
             f"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}"""   ,			f"""{long_class_name}SchedulerOutput"""   ,			re.sub("Bert"   ,			__a   ,			__a )   ,			)

         # Copy consistency with overwrite
         self.check_copy_consistency(
             "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test"   ,			"TestSchedulerOutput"   ,			__a   ,			overwrite_result=re.sub("DDPM"   ,			"Test"   ,			__a )   ,			)











import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class 					__lowerCAmelCase       (			_a,     unittest.TestCase     ):
			lowerCamelCase_     :     Any       			=      BioGptTokenizer
			lowerCamelCase_     :     Optional[Any]       			=      False






			def 			lowerCamelCase  (self    )     ->   List[Any]:


							'''simple docstring'''



							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							snake_case_						:    Optional[Any]				=  [
							    '''l''',
							    '''o''',
							    '''w''',
							    '''e''',
							    '''r''',
							    '''s''',
							    '''t''',
							    '''i''',
							    '''d''',
							    '''n''',
							    '''w</w>''',
							    '''r</w>''',
							    '''t</w>''',
							    '''lo''',
							    '''low''',
							    '''er</w>''',
							    '''low</w>''',
							    '''lowest</w>''',
							    '''newer</w>''',
							    '''wider</w>''',
							    '''<unk>''',
							]
							snake_case_						:    Union[str, Any]				=  dict(zip(__magic_name__					,							range(len(__magic_name__    )    )    )    )
							snake_case_						:    Union[str, Any]				=  ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

							snake_case_						:    Any				=  os.path.join(self.tmpdirname					,							VOCAB_FILES_NAMES['''vocab_file''']    )
							snake_case_						:    Dict				=  os.path.join(self.tmpdirname					,							VOCAB_FILES_NAMES['''merges_file''']    )
							with open(self.vocab_file					,							'''w'''    ) as fp:
											fp.write(json.dumps(__magic_name__    )    )
							with open(self.merges_file					,							'''w'''    ) as fp:
											fp.write('''\n'''.join(__magic_name__    )    )






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   int:


							'''simple docstring'''



							snake_case_						:    str				=  '''lower newer'''
							snake_case_						:    Dict				=  '''lower newer'''
							return input_text, output_text






			def 			lowerCamelCase  (self    )     ->   Union[str, Any]:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  BioGptTokenizer(self.vocab_file					,							self.merges_file    )

							snake_case_						:    Union[str, Any]				=  '''lower'''
							snake_case_						:    Optional[int]				=  ['''low''', '''er</w>''']
							snake_case_						:    Any				=  tokenizer.tokenize(__magic_name__    )
							self.assertListEqual(__magic_name__					,							__magic_name__    )

							snake_case_						:    Optional[int]				=  tokens + ['''<unk>''']
							snake_case_						:    List[str]				=  [14, 15, 20]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__    )					,							__magic_name__    )






			@slow
			def 			lowerCamelCase  (self    )     ->   Optional[Any]:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  BioGptTokenizer.from_pretrained('''microsoft/biogpt'''    )

							snake_case_						:    List[Any]				=  tokenizer.encode('''sequence builders'''					,							add_special_tokens=__magic_name__    )
							snake_case_						:    str				=  tokenizer.encode('''multi-sequence build'''					,							add_special_tokens=__magic_name__    )

							snake_case_						:    str				=  tokenizer.build_inputs_with_special_tokens(__magic_name__    )
							snake_case_						:    List[str]				=  tokenizer.build_inputs_with_special_tokens(__magic_name__					,							__magic_name__    )

							self.assertTrue(encoded_sentence == [2] + text    )
							self.assertTrue(encoded_pair == [2] + text + [2] + text_a    )
