






import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD



def  a__ (__lowercase							:Optional[Any]   )		-> int:
				def wrapper(*__lowercase							:Optional[int]					,  **__lowercase							:Optional[int]   ):
								_A	:				Union[str, Any]   =      timeit.default_timer()
								_A	:				int   =      func(*__lowercase					,  **__lowercase   )
								_A	:				Optional[int]   =      timeit.default_timer() - starttime
								return delta

				_A	:				Any   =      func.__name__

				return wrapper



def  a__ (__lowercase							:dict					,  __lowercase							:Tuple=100					,  __lowercase							:Optional[int]=None   )		-> List[str]:
				_A	:				int   =      []
				_A	:				Tuple   =      seq_shapes or {}
				for i in range(__lowercase   ):
								_A	:				Dict   =      {}
								for col_id, (k, v) in enumerate(features.items()   ):
												if isinstance(__lowercase					,  _ArrayXD   ):
																_A	:				Tuple   =      np.random.rand(*v.shape   ).astype(v.dtype   )
												elif isinstance(__lowercase					,  datasets.Value   ):
																if v.dtype == "string":
																				_A	:				str   =      '''The small grey turtle was surprisingly fast when challenged.'''
																else:
																				_A	:				Union[str, Any]   =      np.random.randint(10					,  size=1   ).astype(v.dtype   ).item()
												elif isinstance(__lowercase					,  datasets.Sequence   ):
																while isinstance(__lowercase					,  datasets.Sequence   ):
																				_A	:				List[str]   =      v.feature
																_A	:				List[Any]   =      seq_shapes[k]
																_A	:				List[Any]   =      np.random.rand(*__lowercase   ).astype(v.dtype   )
												_A	:				Optional[Any]   =      data

								dummy_data.append((i, example)   )

				return dummy_data






def  a__ (__lowercase							:List[Any]					,  __lowercase							:Optional[Any]					,  __lowercase							:List[Any]=100					,  __lowercase							:Dict=None   )		-> Tuple:
				_A	:				Dict   =      generate_examples(__lowercase					,  num_examples=__lowercase					,  seq_shapes=__lowercase   )

				with ArrowWriter(features=__lowercase					,  path=__lowercase   ) as writer:
								for key, record in dummy_data:
												_A	:				Any   =      features.encode_example(__lowercase   )
												writer.write(__lowercase   )

								_A  ,				_A	:				Any   =      writer.finalize()

				if not num_final_examples == num_examples:
								raise ValueError(
								    f"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""   )

				_A	:				Dict   =      datasets.Dataset.from_file(filename=__lowercase					,  info=datasets.DatasetInfo(features=__lowercase   )   )

				return dataset











from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Optional[Any]     =   ["image_processor", "tokenizer"]
		__snake_case      :       Tuple     =   "BridgeTowerImageProcessor"
		__snake_case      :       List[str]     =   ("RobertaTokenizer", "RobertaTokenizerFast")







		def __init__(					self       ,A__       ,A__		):
						super().__init__(A__       ,A__		)







		def __call__(					self       ,A__       ,A__ = None       ,A__ = True       ,A__ = False       ,A__ = None       ,A__ = None       ,A__ = 0       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = False       ,A__ = False       ,A__ = False       ,A__ = False       ,A__ = True       ,A__ = None       ,**A__       ,):
						_A	:				List[Any]   =      self.tokenizer(
						    text=A__       ,add_special_tokens=A__       ,padding=A__       ,truncation=A__       ,max_length=A__       ,stride=A__       ,pad_to_multiple_of=A__       ,return_token_type_ids=A__       ,return_attention_mask=A__       ,return_overflowing_tokens=A__       ,return_special_tokens_mask=A__       ,return_offsets_mapping=A__       ,return_length=A__       ,verbose=A__       ,return_tensors=A__       ,**A__       ,)
						# add pixel_values + pixel_mask
						_A	:				Optional[Any]   =      self.image_processor(
						    A__       ,return_tensors=A__       ,do_normalize=A__       ,do_center_crop=A__       ,**A__		)
						encoding.update(A__		)

						return encoding







		def  A__      (					self       ,*A__       ,**A__		):
						return self.tokenizer.batch_decode(*A__       ,**A__		)







		def  A__      (					self       ,*A__       ,**A__		):
						return self.tokenizer.decode(*A__       ,**A__		)







		@property
		def  A__      (					self		):
						_A	:				Union[str, Any]   =      self.tokenizer.model_input_names
						_A	:				str   =      self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)



