


'''simple docstring'''



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class 				__UpperCamelCase							(						__A   ):
	A_           =       ["image_processor", "tokenizer"]
	A_           =       "ViTImageProcessor"
	A_           =       ("CLIPTokenizer", "CLIPTokenizerFast")




	def __init__(  self		,							__a=None		,							__a=None		,							**__a				):

						'''simple docstring'''





						__a     :			Optional[int]          =    None
						if "feature_extractor" in kwargs:
											warnings.warn(
											    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
											    ' instead.'		,							lowerCAmelCase_		,							)
											__a     :			Dict          =    kwargs.pop('feature_extractor'				)

						__a     :			Optional[Any]          =    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__(lowerCAmelCase_		,							lowerCAmelCase_				)




	def __call__(  self		,							__a=None		,							__a=None		,							__a=None		,							__a=None		,							**__a				):

						'''simple docstring'''





						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:
											__a     :			Optional[Any]          =    self.tokenizer(lowerCAmelCase_		,							return_tensors=lowerCAmelCase_		,							**lowerCAmelCase_				)

						if visual_prompt is not None:
											__a     :			Optional[int]          =    self.image_processor(lowerCAmelCase_		,							return_tensors=lowerCAmelCase_		,							**lowerCAmelCase_				)

						if images is not None:
											__a     :			Optional[int]          =    self.image_processor(lowerCAmelCase_		,							return_tensors=lowerCAmelCase_		,							**lowerCAmelCase_				)

						if visual_prompt is not None and images is not None:
											__a     :			Optional[Any]          =    {
											    "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:
											__a     :			str          =    image_features.pixel_values
											return encoding
						elif text is not None:
											return encoding
						elif visual_prompt is not None:
											__a     :			Union[str, Any]          =    {
											    "conditional_pixel_values": prompt_features.pixel_values,
											}
											return encoding
						else:
											return BatchEncoding(data=dict(**lowerCAmelCase_				)		,							tensor_type=lowerCAmelCase_				)




	def  __UpperCAmelCase				(  self		,							*__a		,							**__a				):

						'''simple docstring'''





						return self.tokenizer.batch_decode(*lowerCAmelCase_		,							**lowerCAmelCase_				)




	def  __UpperCAmelCase				(  self		,							*__a		,							**__a				):

						'''simple docstring'''





						return self.tokenizer.decode(*lowerCAmelCase_		,							**lowerCAmelCase_				)




	@property
	def  __UpperCAmelCase				(  self				):

						'''simple docstring'''





						warnings.warn(
						    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'		,							lowerCAmelCase_		,							)
						return self.image_processor_class





	@property
	def  __UpperCAmelCase				(  self				):

						'''simple docstring'''





						warnings.warn(
						    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'		,							lowerCAmelCase_		,							)
						return self.image_processor











"""simple docstring"""
import timeit

import numpy as np

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

def 							snake_case (  A__		):
			def wrapper(*A__      ,**A__		):
						UpperCAmelCase_	:					Union[str, Any]     	=   timeit.default_timer()
						UpperCAmelCase_	:					Union[str, Any]     	=   func(*A__      ,**A__		)
						UpperCAmelCase_	:					Dict     	=   timeit.default_timer() - starttime
						return delta

			UpperCAmelCase_	:					Optional[Any]     	=   func.__name__

			return wrapper

def 							snake_case (  A__      ,A__=1_00      ,A__=None		):
			UpperCAmelCase_	:					str     	=   []
			UpperCAmelCase_	:					List[str]     	=   seq_shapes or {}
			for i in range(A__		):
						UpperCAmelCase_	:					int     	=   {}
						for col_id, (k, v) in enumerate(features.items()		):
									if isinstance(A__      ,_ArrayXD		):
												UpperCAmelCase_	:					Optional[int]     	=   np.random.rand(*v.shape		).astype(v.dtype		)
									elif isinstance(A__      ,datasets.Value		):
												if v.dtype == "string":
															UpperCAmelCase_	:					List[str]     	=   "The small grey turtle was surprisingly fast when challenged."
												else:
															UpperCAmelCase_	:					List[str]     	=   np.random.randint(10      ,size=1		).astype(v.dtype		).item()
									elif isinstance(A__      ,datasets.Sequence		):
												while isinstance(A__      ,datasets.Sequence		):
															UpperCAmelCase_	:					List[str]     	=   v.feature
												UpperCAmelCase_	:					List[Any]     	=   seq_shapes[k]
												UpperCAmelCase_	:					Dict     	=   np.random.rand(*A__		).astype(v.dtype		)
									UpperCAmelCase_	:					Dict     	=   data

						dummy_data.append((i, example)		)

			return dummy_data




def 							snake_case (  A__      ,A__      ,A__=1_00      ,A__=None		):
			UpperCAmelCase_	:					Optional[Any]     	=   generate_examples(A__      ,num_examples=A__      ,seq_shapes=A__		)

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

						UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[int]     	=   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}."""		)

			UpperCAmelCase_	:					List[Any]     	=   datasets.Dataset.from_file(filename=A__      ,info=datasets.DatasetInfo(features=A__		)		)

			return dataset



