


'''simple docstring'''




from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__snake_case    						=logging.get_logger(__name__)

class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	Tuple		=     ['''pixel_values''']





     def __init__( self       :      List[Any]    ,      UpperCAmelCase__       :      bool = True    ,      UpperCAmelCase__       :      int = 3_2    ,      UpperCAmelCase__       :      str=PILImageResampling.BILINEAR    ,      UpperCAmelCase__       :      bool = True    ,      **UpperCAmelCase__       :      int    ,      )				->  None:
        lowerCAmelCase     =			do_resize
        lowerCAmelCase     =			do_rescale
        lowerCAmelCase     =			size_divisor
        lowerCAmelCase     =			resample
        super().__init__(**UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      int    ,      UpperCAmelCase__       :      np.ndarray    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      Optional[Any]    ,      UpperCAmelCase__       :      Optional[ChannelDimension] = None    ,      **UpperCAmelCase__       :      int    )				->  np.ndarray:
        lowerCAmelCase     ,  lowerCAmelCase     =			get_image_size(UpperCAmelCase__    )
        # Rounds the height and width down to the closest multiple of size_divisor
        lowerCAmelCase     =			height // size_divisor * size_divisor
        lowerCAmelCase     =			width // size_divisor * size_divisor
        lowerCAmelCase     =			resize(UpperCAmelCase__    ,      (new_h, new_w)    ,      resample=UpperCAmelCase__    ,      data_format=UpperCAmelCase__    ,      **UpperCAmelCase__    )
        return image





     def 							__UpperCAmelCase      ( self       :      Optional[int]    ,      UpperCAmelCase__       :      np.ndarray    ,      UpperCAmelCase__       :      float    ,      UpperCAmelCase__       :      Optional[ChannelDimension] = None    ,      **UpperCAmelCase__       :      Optional[int]    )				->  np.ndarray:
        return rescale(image=UpperCAmelCase__    ,      scale=UpperCAmelCase__    ,      data_format=UpperCAmelCase__    ,      **UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    ,      UpperCAmelCase__       :      Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]    ,      UpperCAmelCase__       :      Optional[bool] = None    ,      UpperCAmelCase__       :      Optional[int] = None    ,      UpperCAmelCase__       :      Optional[Any]=None    ,      UpperCAmelCase__       :      Optional[bool] = None    ,      UpperCAmelCase__       :      Optional[Union[TensorType, str]] = None    ,      UpperCAmelCase__       :      ChannelDimension = ChannelDimension.FIRST    ,      **UpperCAmelCase__       :      List[Any]    ,      )				->  BatchFeature:
        lowerCAmelCase     =			do_resize if do_resize is not None else self.do_resize
        lowerCAmelCase     =			do_rescale if do_rescale is not None else self.do_rescale
        lowerCAmelCase     =			size_divisor if size_divisor is not None else self.size_divisor
        lowerCAmelCase     =			resample if resample is not None else self.resample

        if do_resize and size_divisor is None:
           raise ValueError('size_divisor is required for resizing'    )

        lowerCAmelCase     =			make_list_of_images(UpperCAmelCase__    )

        if not valid_images(UpperCAmelCase__    ):
           raise ValueError('Invalid image(s)'    )

        # All transformations expect numpy arrays.
        lowerCAmelCase     =			[to_numpy_array(UpperCAmelCase__    ) for img in images]

        if do_resize:
           lowerCAmelCase     =			[self.resize(UpperCAmelCase__    ,      size_divisor=UpperCAmelCase__    ,      resample=UpperCAmelCase__    ) for image in images]

        if do_rescale:
           lowerCAmelCase     =			[self.rescale(UpperCAmelCase__    ,      scale=1 / 2_5_5    ) for image in images]

        lowerCAmelCase     =			[to_channel_dimension_format(UpperCAmelCase__    ,      UpperCAmelCase__    ) for image in images]

        lowerCAmelCase     =			{'pixel_values': images}
        return BatchFeature(data=UpperCAmelCase__    ,      tensor_type=UpperCAmelCase__    )









'''simple docstring'''




from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


__snake_case    						=logging.get_logger(__name__)

__snake_case    						={
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

__snake_case    						={
    """vocab_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
    },
    """merges_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
    },
    """tokenizer_config_file""": {
        """facebook/blenderbot_small-90M""": (
            """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
        )
    },
}

__snake_case    						={
    """facebook/blenderbot_small-90M""": 512,
}

class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	Tuple		=     VOCAB_FILES_NAMES
     lowerCamelCase				:	Optional[int]		=     PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase				:	Union[str, Any]		=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase				:	Optional[Any]		=     BlenderbotSmallTokenizer





     def __init__( self       :      Any    ,      UpperCAmelCase__       :      Union[str, Any]=None    ,      UpperCAmelCase__       :      Dict=None    ,      UpperCAmelCase__       :      int="<|endoftext|>"    ,      UpperCAmelCase__       :      Dict="<|endoftext|>"    ,      UpperCAmelCase__       :      str="<|endoftext|>"    ,      UpperCAmelCase__       :      str=False    ,      UpperCAmelCase__       :      Tuple=True    ,      **UpperCAmelCase__       :      Optional[Any]    ,      )				->  Any:
        super().__init__(
            ByteLevelBPETokenizer(
                vocab=UpperCAmelCase__    ,      merges=UpperCAmelCase__    ,      add_prefix_space=UpperCAmelCase__    ,      trim_offsets=UpperCAmelCase__    ,      )    ,      bos_token=UpperCAmelCase__    ,      eos_token=UpperCAmelCase__    ,      unk_token=UpperCAmelCase__    ,      **UpperCAmelCase__    ,      )
        lowerCAmelCase     =			add_prefix_space





     def 							__UpperCAmelCase      ( self       :      List[Any]    ,      UpperCAmelCase__       :      Union[str, Any]    ,      UpperCAmelCase__       :      Dict=None    )				->  Any:
        lowerCAmelCase     =			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
           return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





     def 							__UpperCAmelCase      ( self       :      Any    ,      UpperCAmelCase__       :      List[int]    ,      UpperCAmelCase__       :      Optional[List[int]] = None    )				->  List[int]:
        lowerCAmelCase     =			[self.sep_token_id]
        lowerCAmelCase     =			[self.cls_token_id]

        if token_ids_a is None:
           return len(cls + token_ids_a + sep    ) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep    ) * [0]





