






import warnings
from typing import List, Optional, Union

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


class 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase       ):
     '''simple docstring'''


     UpperCamelCase_				:		List[str]								=						['''image_processor''', '''tokenizer''']
     UpperCamelCase_				:		str								=						'''FlavaImageProcessor'''
     UpperCamelCase_				:		Union[str, Any]								=						('''BertTokenizer''', '''BertTokenizerFast''')


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

       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     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__(UpperCAmelCase_       ,			UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Dict						    =     self.image_processor


     def __call__(					self							:						str       ,			UpperCAmelCase_							:						Optional[ImageInput] = None       ,			UpperCAmelCase_							:						Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None       ,			UpperCAmelCase_							:						bool = True       ,			UpperCAmelCase_							:						Union[bool, str, PaddingStrategy] = False       ,			UpperCAmelCase_							:						Union[bool, str, TruncationStrategy] = False       ,			UpperCAmelCase_							:						Optional[int] = None       ,			UpperCAmelCase_							:						int = 0       ,			UpperCAmelCase_							:						Optional[int] = None       ,			UpperCAmelCase_							:						Optional[bool] = None       ,			UpperCAmelCase_							:						Optional[bool] = None       ,			UpperCAmelCase_							:						Optional[bool] = None       ,			UpperCAmelCase_							:						Optional[bool] = None       ,			UpperCAmelCase_							:						bool = False       ,			UpperCAmelCase_							:						bool = False       ,			UpperCAmelCase_							:						bool = False       ,			UpperCAmelCase_							:						bool = False       ,			UpperCAmelCase_							:						bool = True       ,			UpperCAmelCase_							:						Optional[Union[str, TensorType]] = None       ,			**UpperCAmelCase_							:						str       ,			):

       if text is None and images is None:
         raise ValueError("You have to specify either text or images. Both cannot be none."   )

       if text is not None:
         SCREAMING_SNAKE_CASE							:   str						    =     self.tokenizer(
             text=UpperCAmelCase_       ,			add_special_tokens=UpperCAmelCase_       ,			padding=UpperCAmelCase_       ,			truncation=UpperCAmelCase_       ,			max_length=UpperCAmelCase_       ,			stride=UpperCAmelCase_       ,			pad_to_multiple_of=UpperCAmelCase_       ,			return_token_type_ids=UpperCAmelCase_       ,			return_attention_mask=UpperCAmelCase_       ,			return_overflowing_tokens=UpperCAmelCase_       ,			return_special_tokens_mask=UpperCAmelCase_       ,			return_offsets_mapping=UpperCAmelCase_       ,			return_length=UpperCAmelCase_       ,			verbose=UpperCAmelCase_       ,			return_tensors=UpperCAmelCase_       ,			**UpperCAmelCase_       ,			)
       if images is not None:
         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     self.image_processor(
             UpperCAmelCase_       ,			return_image_mask=UpperCAmelCase_       ,			return_codebook_pixels=UpperCAmelCase_       ,			return_tensors=UpperCAmelCase_       ,			**UpperCAmelCase_       ,			)

       if text is not None and images is not None:
         encoding.update(UpperCAmelCase_   )
         return encoding
       elif text is not None:
         return encoding
       else:
         return BatchEncoding(data=dict(**UpperCAmelCase_   )       ,			tensor_type=UpperCAmelCase_   )


     def 					_A							(					self							:						Union[str, Any]       ,			*UpperCAmelCase_							:						Optional[Any]       ,			**UpperCAmelCase_							:						List[str]   ):
       return self.tokenizer.batch_decode(*UpperCAmelCase_       ,			**UpperCAmelCase_   )


     def 					_A							(					self							:						List[Any]       ,			*UpperCAmelCase_							:						int       ,			**UpperCAmelCase_							:						Union[str, Any]   ):
       return self.tokenizer.decode(*UpperCAmelCase_       ,			**UpperCAmelCase_   )


     @property
     def 					_A							(					self							:						Any   ):
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     self.tokenizer.model_input_names
       SCREAMING_SNAKE_CASE							:   Any						    =     self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )


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






     @property
     def 					_A							(					self							:						Union[str, Any]   ):
       warnings.warn(
           "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."       ,			UpperCAmelCase_       ,			)
       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



