






from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


_SCREAMING_SNAKE_CASE   =  logging.get_logger(__name__)






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       def __init__(				self	:Dict,      snake_case	:int,      snake_case	:int,      snake_case	:float,      **snake_case	:Tuple):



          """simple docstring"""

          _lowercase              =feature_size
          _lowercase              =sampling_rate
          _lowercase              =padding_value

          _lowercase              =kwargs.pop('padding_side',      'right')
          _lowercase              =kwargs.pop('return_attention_mask',      snake_case)

          super().__init__(**snake_case)





       def 				UpperCamelCase__						(				self	:Any,      snake_case	:Union[
               BatchFeature,
               List[BatchFeature],
               Dict[str, BatchFeature],
               Dict[str, List[BatchFeature]],
               List[Dict[str, BatchFeature]],
           ],      snake_case	:Union[bool, str, PaddingStrategy] = True,      snake_case	:Optional[int] = None,      snake_case	:bool = False,      snake_case	:Optional[int] = None,      snake_case	:Optional[bool] = None,      snake_case	:Optional[Union[str, TensorType]] = None,      ):



          """simple docstring"""

          if isinstance(snake_case,      (list, tuple)) and isinstance(processed_features[0],      (dict, BatchFeature)):
             _lowercase              ={
                 key: [example[key] for example in processed_features] for key in processed_features[0].keys()
             }

          # The model's main input name, usually `input_values`, has be passed for padding
          if self.model_input_names[0] not in processed_features:
             raise ValueError(
                 'You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'
                 f''' to this method that includes {self.model_input_names[0]}, but you provided'''
                 f''' {list(processed_features.keys())}''')

          _lowercase              =processed_features[self.model_input_names[0]]
          _lowercase              =(
              return_attention_mask if return_attention_mask is not None else self.return_attention_mask
          )

          if len(snake_case) == 0:
             if return_attention_mask:
                _lowercase              =[]
             return processed_features

          # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
          # and rebuild them afterwards if no return_tensors is specified
          # Note that we lose the specific device the tensor may be on for PyTorch

          _lowercase              =required_input[0]
          if isinstance(snake_case,      (list, tuple)):
             # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
             _lowercase              =0
             while len(required_input[index]) == 0:
                index += 1
             if index < len(snake_case):
                _lowercase              =required_input[index][0]

          if return_tensors is None:
             if is_tf_tensor(snake_case):
                _lowercase              ='tf'
             elif is_torch_tensor(snake_case):
                _lowercase              ='pt'
             elif isinstance(snake_case,      (int, float, list, tuple, np.ndarray)):
                _lowercase              ='np'
             else:
                raise ValueError(
                    f'''type of {first_element} unknown: {type(snake_case)}. '''
                    'Should be one of a python, numpy, pytorch or tensorflow object.')

          for key, value in processed_features.items():
             if isinstance(value[0],      (int, float)):
                _lowercase              =to_numpy(snake_case)
             else:
                _lowercase              =[to_numpy(snake_case) for v in value]

        # Convert padding_strategy in PaddingStrategy
          _lowercase              =self._get_padding_strategies(padding=snake_case,      max_length=snake_case)

          _lowercase              =processed_features[self.model_input_names[0]]

          _lowercase              =len(snake_case)
          if not all(len(snake_case) == batch_size for v in processed_features.values()):
             raise ValueError('Some items in the output dictionary have a different batch size than others.')

          _lowercase              =[]
          for i in range(snake_case):
             _lowercase              ={k: v[i] for k, v in processed_features.items()}
             # truncation
             _lowercase              =self._truncate(
                 snake_case,      max_length=snake_case,      pad_to_multiple_of=snake_case,      truncation=snake_case,      )
             truncated_inputs.append(snake_case)

          if padding_strategy == PaddingStrategy.LONGEST:
             # make sure that `max_length` cannot be longer than the longest truncated length
             _lowercase              =max(len(input_slice[self.model_input_names[0]]) for input_slice in truncated_inputs)
             _lowercase              =PaddingStrategy.MAX_LENGTH

          _lowercase              ={}
          for i in range(snake_case):
             # padding
             _lowercase              =self._pad(
                 truncated_inputs[i],      max_length=snake_case,      padding_strategy=snake_case,      pad_to_multiple_of=snake_case,      return_attention_mask=snake_case,      )

             for key, value in outputs.items():
                if key not in batch_outputs:
                   _lowercase              =[]
                if value.dtype is np.dtype(np.floataa):
                   _lowercase              =value.astype(np.floataa)
                batch_outputs[key].append(snake_case)

          return BatchFeature(snake_case,      tensor_type=snake_case)





       def 				UpperCamelCase__						(				self	:List[Any],      snake_case	:Union[Dict[str, np.ndarray], BatchFeature],      snake_case	:Optional[int] = None,      snake_case	:PaddingStrategy = PaddingStrategy.DO_NOT_PAD,      snake_case	:Optional[int] = None,      snake_case	:Optional[bool] = None,      ):



          """simple docstring"""

          _lowercase              =processed_features[self.model_input_names[0]]

          if padding_strategy == PaddingStrategy.LONGEST:
             _lowercase              =len(snake_case)

          if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
             _lowercase              =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

          _lowercase              =padding_strategy != PaddingStrategy.DO_NOT_PAD and len(snake_case) < max_length

          if return_attention_mask and "attention_mask" not in processed_features:
             _lowercase              =np.ones(len(snake_case),      dtype=np.intaa)

          if needs_to_be_padded:
             _lowercase              =max_length - len(snake_case)
             if self.padding_side == "right":
                if return_attention_mask:
                   _lowercase              =np.pad(
                       processed_features['attention_mask'],      (0, difference))
                _lowercase              =((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
                _lowercase              =np.pad(
                    snake_case,      snake_case,      'constant',      constant_values=self.padding_value)
             elif self.padding_side == "left":
                if return_attention_mask:
                   _lowercase              =np.pad(
                       processed_features['attention_mask'],      (difference, 0))
                _lowercase              =((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
                _lowercase              =np.pad(
                    snake_case,      snake_case,      'constant',      constant_values=self.padding_value)
             else:
                raise ValueError('Invalid padding strategy:' + str(self.padding_side))

          return processed_features





       def 				UpperCamelCase__						(				self	:Any,      snake_case	:Union[Dict[str, np.ndarray], BatchFeature],      snake_case	:Optional[int] = None,      snake_case	:Optional[int] = None,      snake_case	:Optional[bool] = None,      ):



          """simple docstring"""

          if not truncation:
             return processed_features
          elif truncation and max_length is None:
             raise ValueError('When setting ``truncation=True``, make sure that ``max_length`` is defined.')

          _lowercase              =processed_features[self.model_input_names[0]]

          # find `max_length` that fits `pad_to_multiple_of`
          if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
             _lowercase              =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

          _lowercase              =len(snake_case) > max_length

          if needs_to_be_truncated:
             _lowercase              =processed_features[self.model_input_names[0]][:max_length]
             if "attention_mask" in processed_features:
                _lowercase              =processed_features['attention_mask'][:max_length]

          return processed_features





       def 				UpperCamelCase__						(				self	:List[str],      snake_case	:Dict=False,      snake_case	:Tuple=None):



          """simple docstring"""

          if padding is not False:
             if padding is True:
                _lowercase              =PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
             elif not isinstance(snake_case,      snake_case):
                _lowercase              =PaddingStrategy(snake_case)
             elif isinstance(snake_case,      snake_case):
                _lowercase              =padding
          else:
             _lowercase              =PaddingStrategy.DO_NOT_PAD

          # Set max length if needed
          if max_length is None:
             if padding_strategy == PaddingStrategy.MAX_LENGTH:
                raise ValueError(
                    f'''When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined''')

        # Test if we have a padding value
          if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
             raise ValueError(
                 'Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'
                 ' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.')

          return padding_strategy










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_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		Dict					=['''image_processor''', '''tokenizer''']
       __lowerCAmelCase    :		List[Any]					='''BlipImageProcessor'''
       __lowerCAmelCase    :		Union[str, Any]					=('''BertTokenizer''', '''BertTokenizerFast''')





       def __init__(				self	:Optional[int],      snake_case	:int,      snake_case	:Optional[int]):



          """simple docstring"""

          _lowercase              =False
          super().__init__(snake_case,      snake_case)
          _lowercase              =self.image_processor





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



          """simple docstring"""

          if images is None and text is None:
             raise ValueError('You have to specify either images or text.')

          # Get only text
          if images is None:
             _lowercase              =self.tokenizer
             _lowercase              =self.tokenizer(
                 text=snake_case,      add_special_tokens=snake_case,      padding=snake_case,      truncation=snake_case,      max_length=snake_case,      stride=snake_case,      pad_to_multiple_of=snake_case,      return_attention_mask=snake_case,      return_overflowing_tokens=snake_case,      return_special_tokens_mask=snake_case,      return_offsets_mapping=snake_case,      return_token_type_ids=snake_case,      return_length=snake_case,      verbose=snake_case,      return_tensors=snake_case,      **snake_case,      )
             return text_encoding

          # add pixel_values
          _lowercase              =self.image_processor(snake_case,      return_tensors=snake_case)

          if text is not None:
             _lowercase              =self.tokenizer(
                 text=snake_case,      add_special_tokens=snake_case,      padding=snake_case,      truncation=snake_case,      max_length=snake_case,      stride=snake_case,      pad_to_multiple_of=snake_case,      return_attention_mask=snake_case,      return_overflowing_tokens=snake_case,      return_special_tokens_mask=snake_case,      return_offsets_mapping=snake_case,      return_token_type_ids=snake_case,      return_length=snake_case,      verbose=snake_case,      return_tensors=snake_case,      **snake_case,      )
          else:
             _lowercase              =None

          if text_encoding is not None:
             encoding_image_processor.update(snake_case)

          return encoding_image_processor





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



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:Tuple,      *snake_case	:List[str],      **snake_case	:str):



          """simple docstring"""

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





       @property
       def 				UpperCamelCase__						(				self	:List[Any]):



          """simple docstring"""

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


