



'''simple docstring'''


import warnings
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  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  ["""image_processor""", """tokenizer"""]
    _SCREAMING_SNAKE_CASE	  =  """ViltImageProcessor"""
    _SCREAMING_SNAKE_CASE	  =  ("""BertTokenizer""", """BertTokenizerFast""")






    def __init__(  self      :					Any  ,						UpperCamelCase__      :					int=None  ,						UpperCamelCase__      :					List[Any]=None  ,						**UpperCamelCase__      :					str ):


       """simple docstring"""







       UpperCamelCase					=  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__  ,						)
          UpperCamelCase					=  kwargs.pop('feature_extractor' )

       UpperCamelCase					=  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__ )
       UpperCamelCase					=  self.image_processor






    def __call__(  self      :					Tuple  ,						UpperCamelCase__      :					Optional[int]  ,						UpperCamelCase__      :					Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None  ,						UpperCamelCase__      :					bool = True  ,						UpperCamelCase__      :					Union[bool, str, PaddingStrategy] = False  ,						UpperCamelCase__      :					Union[bool, str, TruncationStrategy] = None  ,						UpperCamelCase__      :					Optional[int] = None  ,						UpperCamelCase__      :					int = 0  ,						UpperCamelCase__      :					Optional[int] = 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__      :					Optional[Any]  ,						):


       """simple docstring"""







       UpperCamelCase					=  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__  ,						)
       # add pixel_values + pixel_mask
       UpperCamelCase					=  self.image_processor(UpperCamelCase__  ,						return_tensors=UpperCamelCase__ )
       encoding.update(UpperCamelCase__ )

       return encoding






    def  A       (  self      :					int  ,						*UpperCamelCase__      :					List[Any]  ,						**UpperCamelCase__      :					str ):


       """simple docstring"""







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






    def  A       (  self      :					Tuple  ,						*UpperCamelCase__      :					Optional[int]  ,						**UpperCamelCase__      :					Optional[int] ):


       """simple docstring"""







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






    @property
    def  A       (  self      :					str ):


       """simple docstring"""







       UpperCamelCase					=  self.tokenizer.model_input_names
       UpperCamelCase					=  self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )






    @property
    def  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







       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      :					Optional[Any] ):


       """simple docstring"""







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







"""simple docstring"""




import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''





				def __init__(       self		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					dataset
							lowerCamelCase			      =					process
							lowerCamelCase			      =					params





				def __len__(       self ):





							"""simple docstring"""
							return len(self.dataset )





				def __getitem__(       self		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					self.dataset[i]
							lowerCamelCase			      =					self.process(_a		, **self.params )
							return processed






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''





				def __init__(       self		, _a		, _a		, _a		, _a=None ):





							"""simple docstring"""
							lowerCamelCase			      =					loader
							lowerCamelCase			      =					infer
							lowerCamelCase			      =					params
							if loader_batch_size == 1:
										# Let's spare some time by deactivating altogether
										lowerCamelCase			      =					None
							lowerCamelCase			      =					loader_batch_size

							# Internal bookkeeping
							lowerCamelCase			      =					None
							lowerCamelCase			      =					None





				def __len__(       self ):





							"""simple docstring"""
							return len(self.loader )





				def __iter__(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					iter(self.loader )
							return self





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							if isinstance(self._loader_batch_data		, torch.Tensor ):
										# Batch data is simple tensor, just fetch the slice
										lowerCamelCase			      =					self._loader_batch_data[self._loader_batch_index]
							else:
										# Batch data is assumed to be BaseModelOutput (or dict)
										lowerCamelCase			      =					{}
										for k, element in self._loader_batch_data.items():
													if isinstance(_a		, _a ):
																# Convert ModelOutput to tuple first
																lowerCamelCase			      =					element.to_tuple()
																if isinstance(element[0]		, torch.Tensor ):
																			lowerCamelCase			      =					tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
																elif isinstance(element[0]		, np.ndarray ):
																			lowerCamelCase			      =					tuple(np.expand_dims(el[self._loader_batch_index]		, 0 ) for el in element )
																continue
													if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(_a		, _a ):
																# Those are stored as lists of tensors so need specific unbatching.
																if isinstance(element[0]		, torch.Tensor ):
																			lowerCamelCase			      =					tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
																elif isinstance(element[0]		, np.ndarray ):
																			lowerCamelCase			      =					tuple(np.expand_dims(el[self._loader_batch_index]		, 0 ) for el in element )
																continue
													if element is None:
																# This can happen for optional data that get passed around
																lowerCamelCase			      =					None
													elif isinstance(element[self._loader_batch_index]		, torch.Tensor ):
																# Take correct batch data, but make it looked like batch_size=1
																# For compatibility with other methods within transformers

																lowerCamelCase			      =					element[self._loader_batch_index].unsqueeze(0 )
													elif isinstance(element[self._loader_batch_index]		, np.ndarray ):
																# Take correct batch data, but make it looked like batch_size=1
																# For compatibility with other methods within transformers
																lowerCamelCase			      =					np.expand_dims(element[self._loader_batch_index]		, 0 )
													else:
																# This is typically a list, so no need to `unsqueeze`.
																lowerCamelCase			      =					element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
										lowerCamelCase			      =					self._loader_batch_data.__class__(_a )
							self._loader_batch_index += 1
							return result





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
										# We are currently unrolling a batch so we just need to return
										# the current item within a batch
										return self.loader_batch_item()

							# We're out of items within a batch
							lowerCamelCase			      =					next(self.iterator )
							lowerCamelCase			      =					self.infer(_a		, **self.params )
							# We now have a batch of "inferred things".
							if self.loader_batch_size is not None:
										# Try to infer the size of the batch
										if isinstance(_a		, torch.Tensor ):
													lowerCamelCase			      =					processed
										else:
													lowerCamelCase			      =					list(processed.keys() )[0]
													lowerCamelCase			      =					processed[key]
										if isinstance(_a		, _a ):
													lowerCamelCase			      =					len(_a )
										else:
													lowerCamelCase			      =					first_tensor.shape[0]
										if 0 < observed_batch_size < self.loader_batch_size:
													# could be last batch so we can't unroll as many
													# elements.
													lowerCamelCase			      =					observed_batch_size
										# Setting internal index to unwrap the batch
										lowerCamelCase			      =					processed
										lowerCamelCase			      =					0
										return self.loader_batch_item()
							else:
										# We're not unrolling batches
										return processed






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''





				def __init__(       self		, _a		, _a		, _a		, _a=None ):





							"""simple docstring"""
							super().__init__(_a		, _a		, _a )





				def __iter__(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					iter(self.loader )
							lowerCamelCase			      =					None
							return self





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							if self.subiterator is None:
										lowerCamelCase			      =					self.infer(next(self.iterator )		, **self.params )
							try:
										# Try to return next item
										lowerCamelCase			      =					next(self.subiterator )
							except StopIteration:
										# When a preprocess iterator ends, we can start lookig at the next item
										# ChunkIterator will keep feeding until ALL elements of iterator
										# all have created their subiterator and have been iterating against.
										#
										# Another way to look at it, is we're basically flattening lists of lists
										# into a single list, but with generators
										lowerCamelCase			      =					self.infer(next(self.iterator )		, **self.params )
										lowerCamelCase			      =					next(self.subiterator )
							return processed






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''





				def __iter__(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					iter(self.loader )
							return self





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							# Extremely similar to PipelineIterator in its unpacking mechanism
							# BUT, we have an extra required item which is the presence of `is_last`
							# That is because everything is flattened by `PipelineChunkIterator` we
							# need to keep track of how to regroup here in the original `process`
							# boundaries so that `process` and `postprocess` see the same data.

							# This iterator accumulates items (possibly while unbatching) until it
							# its a `is_last` and then just passes it on to the caller.
							lowerCamelCase			      =					False
							lowerCamelCase			      =					[]
							if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
										while self._loader_batch_index < self.loader_batch_size:
													lowerCamelCase			      =					self.loader_batch_item()
													lowerCamelCase			      =					item.pop("""is_last""" )
													accumulator.append(_a )
													if is_last:
																return accumulator

							while not is_last:
										lowerCamelCase			      =					self.infer(next(self.iterator )		, **self.params )
										if self.loader_batch_size is not None:
													if isinstance(_a		, torch.Tensor ):
																lowerCamelCase			      =					processed
													else:
																lowerCamelCase			      =					list(processed.keys() )[0]
																lowerCamelCase			      =					processed[key]
													if isinstance(_a		, _a ):
																lowerCamelCase			      =					len(_a )
													else:
																lowerCamelCase			      =					first_tensor.shape[0]
													if 0 < observed_batch_size < self.loader_batch_size:
																# could be last batch so we can't unroll as many
																# elements.
																lowerCamelCase			      =					observed_batch_size
													lowerCamelCase			      =					processed
													lowerCamelCase			      =					0
													while self._loader_batch_index < self.loader_batch_size:
																lowerCamelCase			      =					self.loader_batch_item()
																lowerCamelCase			      =					item.pop("""is_last""" )
																accumulator.append(_a )
																if is_last:
																			return accumulator
										else:
													lowerCamelCase			      =					processed
													lowerCamelCase			      =					item.pop("""is_last""" )
													accumulator.append(_a )
							return accumulator






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''





				def __init__(       self		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					dataset
							lowerCamelCase			      =					key





				def __len__(       self ):





							"""simple docstring"""
							return len(self.dataset )





				def __getitem__(       self		, _a ):





							"""simple docstring"""
							return self.dataset[i][self.key]






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''





				def __init__(       self		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					dataset
							lowerCamelCase			      =					keya
							lowerCamelCase			      =					keya





				def __len__(       self ):





							"""simple docstring"""
							return len(self.dataset )





				def __getitem__(       self		, _a ):





							"""simple docstring"""
							return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}



