



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


a_								=   logging.get_logger(__name__)



class 	__lowerCAmelCase       (		lowerCAmelCase__				):
 lowerCAmelCase__       =					['pixel_values']




 def __init__(  self		,			__UpperCAmelCase = True		,			__UpperCAmelCase = None		,			__UpperCAmelCase = PILImageResampling.BILINEAR		,			__UpperCAmelCase = True		,			__UpperCAmelCase = 1 / 255		,			__UpperCAmelCase = True		,			__UpperCAmelCase = None		,			__UpperCAmelCase = None		,			**__UpperCAmelCase		,			):



    '''simple docstring'''
    super().__init__(**__UpperCAmelCase			)
    __lowerCamelCase  							=      size if size is not None else {"height": 224, "width": 224}
    __lowerCamelCase  							=      get_size_dict(__UpperCAmelCase			)
    __lowerCamelCase  							=      do_resize
    __lowerCamelCase  							=      do_rescale
    __lowerCamelCase  							=      do_normalize
    __lowerCamelCase  							=      size
    __lowerCamelCase  							=      resample
    __lowerCamelCase  							=      rescale_factor
    __lowerCamelCase  							=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    __lowerCamelCase  							=      image_std if image_std is not None else IMAGENET_STANDARD_STD




 def     lowerCamelCase (  self		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase = PILImageResampling.BILINEAR		,			__UpperCAmelCase = None		,			**__UpperCAmelCase		,			):



    '''simple docstring'''
    __lowerCamelCase  							=      get_size_dict(__UpperCAmelCase			)
    if "height" not in size or "width" not in size:
       raise ValueError(F"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""			)
    return resize(
        __UpperCAmelCase		,			size=(size['''height'''], size['''width'''])		,			resample=__UpperCAmelCase		,			data_format=__UpperCAmelCase		,			**__UpperCAmelCase			)




 def     lowerCamelCase (  self		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase = None		,			**__UpperCAmelCase			):



    '''simple docstring'''
    return rescale(__UpperCAmelCase		,			scale=__UpperCAmelCase		,			data_format=__UpperCAmelCase		,			**__UpperCAmelCase			)




 def     lowerCamelCase (  self		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase = None		,			**__UpperCAmelCase		,			):



    '''simple docstring'''
    return normalize(__UpperCAmelCase		,			mean=__UpperCAmelCase		,			std=__UpperCAmelCase		,			data_format=__UpperCAmelCase		,			**__UpperCAmelCase			)




 def     lowerCamelCase (  self		,			__UpperCAmelCase		,			__UpperCAmelCase = None		,			__UpperCAmelCase = None		,			__UpperCAmelCase = None		,			__UpperCAmelCase = None		,			__UpperCAmelCase = None		,			__UpperCAmelCase = None		,			__UpperCAmelCase = None		,			__UpperCAmelCase = None		,			__UpperCAmelCase = None		,			__UpperCAmelCase = ChannelDimension.FIRST		,			**__UpperCAmelCase		,			):



    '''simple docstring'''
    __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  							=      do_normalize if do_normalize is not None else self.do_normalize
    __lowerCamelCase  							=      resample if resample is not None else self.resample
    __lowerCamelCase  							=      rescale_factor if rescale_factor is not None else self.rescale_factor
    __lowerCamelCase  							=      image_mean if image_mean is not None else self.image_mean
    __lowerCamelCase  							=      image_std if image_std is not None else self.image_std

    __lowerCamelCase  							=      size if size is not None else self.size
    __lowerCamelCase  							=      get_size_dict(__UpperCAmelCase			)

    __lowerCamelCase  							=      make_list_of_images(__UpperCAmelCase			)

    if not valid_images(__UpperCAmelCase			):
       raise ValueError(
           '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
           '''torch.Tensor, tf.Tensor or jax.ndarray.'''			)

    if do_resize and size is None:
       raise ValueError('''Size must be specified if do_resize is True.'''			)

    if do_rescale and rescale_factor is None:
       raise ValueError('''Rescale factor must be specified if do_rescale is True.'''			)

    # All transformations expect numpy arrays.
    __lowerCamelCase  							=      [to_numpy_array(__UpperCAmelCase			) for image in images]

    if do_resize:
       __lowerCamelCase  							=      [self.resize(image=__UpperCAmelCase		,			size=__UpperCAmelCase		,			resample=__UpperCAmelCase			) for image in images]

    if do_rescale:
       __lowerCamelCase  							=      [self.rescale(image=__UpperCAmelCase		,			scale=__UpperCAmelCase			) for image in images]

    if do_normalize:
       __lowerCamelCase  							=      [self.normalize(image=__UpperCAmelCase		,			mean=__UpperCAmelCase		,			std=__UpperCAmelCase			) 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			)











from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_openai import OpenAIGPTTokenizer


a_								=   logging.get_logger(__name__)

a_								=   {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

a_								=   {
    """vocab_file""": {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/vocab.json"""},
    """merges_file""": {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/merges.txt"""},
    """tokenizer_file""": {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/tokenizer.json"""},
}

a_								=   {
    """openai-gpt""": 512,
}



class 	__lowerCAmelCase       (		lowerCAmelCase__				):
 lowerCAmelCase__       =					VOCAB_FILES_NAMES
 lowerCAmelCase__       =					PRETRAINED_VOCAB_FILES_MAP
 lowerCAmelCase__       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCAmelCase__       =					["""input_ids""", """attention_mask"""]
 lowerCAmelCase__       =					OpenAIGPTTokenizer




 def __init__(  self		,			__UpperCAmelCase=None		,			__UpperCAmelCase=None		,			__UpperCAmelCase=None		,			__UpperCAmelCase="<unk>"		,			**__UpperCAmelCase			):



    '''simple docstring'''
    super().__init__(__UpperCAmelCase		,			__UpperCAmelCase		,			tokenizer_file=__UpperCAmelCase		,			unk_token=__UpperCAmelCase		,			**__UpperCAmelCase			)




 @property
 def     lowerCamelCase (  self			):



    '''simple docstring'''
    return True




 def     lowerCamelCase (  self		,			__UpperCAmelCase		,			__UpperCAmelCase = None			):



    '''simple docstring'''
    __lowerCamelCase  							=      self._tokenizer.model.save(__UpperCAmelCase		,			name=__UpperCAmelCase			)
    return tuple(__UpperCAmelCase			)






