






'''simple docstring'''





import json
import sys





def 	lowerCAmelCase_		(lowercase__						:       List[Any]     ,					lowercase__						:       int       )		->    Union[str, Any]:


				'''simple docstring'''




				with open(lowercase__     ,					encoding='''utf-8'''       ) as f:
								lowerCAmelCase__      			=						json.load(lowercase__       )

				lowerCAmelCase__      			=						['''<details>''', '''<summary>Show updated benchmarks!</summary>''', ''' ''']

				for benchmark_name in sorted(lowercase__       ):
								lowerCAmelCase__      			=						results[benchmark_name]

								lowerCAmelCase__      			=						benchmark_name.split('''/'''       )[-1]
								output_md.append(f'### Benchmark: {benchmark_file_name}'       )

								lowerCAmelCase__      			=						'''| metric |'''
								lowerCAmelCase__      			=						'''|--------|'''
								lowerCAmelCase__      			=						'''| new / old (diff) |'''
								for metric_name in sorted(lowercase__       ):
												lowerCAmelCase__      			=						benchmark_res[metric_name]
												lowerCAmelCase__      			=						metric_vals['''new''']
												lowerCAmelCase__      			=						metric_vals.get('''old'''     ,					lowercase__       )
												lowerCAmelCase__      			=						metric_vals.get('''diff'''     ,					lowercase__       )

												lowerCAmelCase__      			=						f' {new_val:f}' if isinstance(lowercase__     ,					(int, float)       ) else '''None'''

												if old_val is not None:
																val_str += f' / {old_val:f}' if isinstance(lowercase__     ,					(int, float)       ) else "None"
												if dif_val is not None:
																val_str += f' ({dif_val:f})' if isinstance(lowercase__     ,					(int, float)       ) else "None"

												title += " " + metric_name + " |"
												lines += "---|"
												value += val_str + " |"

								output_md += [title, lines, value, " "]

				output_md.append('''</details>'''       )

				with open(lowercase__     ,					'''w'''     ,					encoding='''utf-8'''       ) as f:
								f.writelines('''\n'''.join(lowercase__       )       )


if __name__ == "__main__":
			_UpperCAmelCase			:		Tuple   				=    sys.argv[1]
			_UpperCAmelCase			:		Dict   				=    sys.argv[2]

			format_json_to_md(input_json_file, output_md_file)













import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class  lowerCAmelCase_       (				snake_case__					):
							UpperCamelCase_		:Tuple		      =    ['image_processor', 'tokenizer']
							UpperCamelCase_		:Tuple		      =    'ViTImageProcessor'
							UpperCamelCase_		:Dict		      =    ('CLIPTokenizer', 'CLIPTokenizerFast')




							def __init__(   self     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   Dict=None ,							SCREAMING_SNAKE_CASE_     :   Tuple=None ,							**SCREAMING_SNAKE_CASE_     :   Union[str, Any]	):
											lowerCAmelCase__      			=						None
											if "feature_extractor" in kwargs:
															warnings.warn(
															    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
															    ''' instead.''' ,							SCREAMING_SNAKE_CASE_ ,							)
															lowerCAmelCase__      			=						kwargs.pop('''feature_extractor'''	)

											lowerCAmelCase__      			=						image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
															raise ValueError('''You need to specify an `image_processor`.'''	)
											if tokenizer is None:
															raise ValueError('''You need to specify a `tokenizer`.'''	)

											super().__init__(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)




							def __call__(   self     :   Dict ,							SCREAMING_SNAKE_CASE_     :   Any=None ,							SCREAMING_SNAKE_CASE_     :   Tuple=None ,							SCREAMING_SNAKE_CASE_     :   Dict=None ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=None ,							**SCREAMING_SNAKE_CASE_     :   Optional[int]	):
											if text is None and visual_prompt is None and images is None:
															raise ValueError('''You have to specify either text, visual prompt or images.'''	)

											if text is not None and visual_prompt is not None:
															raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''	)

											if text is not None:
															lowerCAmelCase__      			=						self.tokenizer(SCREAMING_SNAKE_CASE_ ,							return_tensors=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)

											if visual_prompt is not None:
															lowerCAmelCase__      			=						self.image_processor(SCREAMING_SNAKE_CASE_ ,							return_tensors=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)

											if images is not None:
															lowerCAmelCase__      			=						self.image_processor(SCREAMING_SNAKE_CASE_ ,							return_tensors=SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)

											if visual_prompt is not None and images is not None:
															lowerCAmelCase__      			=						{
															    '''pixel_values''': image_features.pixel_values,
															    '''conditional_pixel_values''': prompt_features.pixel_values,
															}
															return encoding
											elif text is not None and images is not None:
															lowerCAmelCase__      			=						image_features.pixel_values
															return encoding
											elif text is not None:
															return encoding
											elif visual_prompt is not None:
															lowerCAmelCase__      			=						{
															    '''conditional_pixel_values''': prompt_features.pixel_values,
															}
															return encoding
											else:
															return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_	) ,							tensor_type=SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Union[str, Any] ,							*SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							**SCREAMING_SNAKE_CASE_     :   str	):
											return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Optional[int] ,							*SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							**SCREAMING_SNAKE_CASE_     :   Tuple	):
											return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)




							@property
							def 						__snake_case					(   self     :   str	):
											warnings.warn(
											    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,							SCREAMING_SNAKE_CASE_ ,							)
											return self.image_processor_class




							@property
							def 						__snake_case					(   self     :   Tuple	):
											warnings.warn(
											    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,							SCREAMING_SNAKE_CASE_ ,							)
											return self.image_processor





