

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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
       import PIL


snake_case_        =			logging.get_logger(__name__)


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	int														=					["""pixel_values"""]





    def __init__(				self						,       a = True						,       a = None						,       a = PILImageResampling.BICUBIC						,       a = True						,       a = 1 / 255						,       a = True						,       a = None						,       a = None						,       a = True						,       **a						,       ):
         super().__init__(**a)
         lowercase__   :       Any				=	size if size is not None else {'height': 384, 'width': 384}
         lowercase__   :       List[Any]				=	get_size_dict(a						,       default_to_square=a)

         lowercase__   :       Tuple				=	do_resize
         lowercase__   :       Optional[int]				=	size
         lowercase__   :       int				=	resample
         lowercase__   :       Optional[Any]				=	do_rescale
         lowercase__   :       Union[str, Any]				=	rescale_factor
         lowercase__   :       Dict				=	do_normalize
         lowercase__   :       Optional[Any]				=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         lowercase__   :       List[Any]				=	image_std if image_std is not None else OPENAI_CLIP_STD
         lowercase__   :       Union[str, Any]				=	do_convert_rgb





    def 				snake_case_    (				self						,       a						,       a						,       a = PILImageResampling.BICUBIC						,       a = None						,       **a						,       ):
         lowercase__   :       Optional[int]				=	get_size_dict(a						,       default_to_square=a)
         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()}""")
         lowercase__   :       Any				=	(size['height'], size['width'])
         return resize(a						,       size=a						,       resample=a						,       data_format=a						,       **a)





    def 				snake_case_    (				self						,       a						,       a						,       a = None						,       **a						,       ):
         return rescale(a						,       scale=a						,       data_format=a						,       **a)





    def 				snake_case_    (				self						,       a						,       a						,       a						,       a = None						,       **a						,       ):
         return normalize(a						,       mean=a						,       std=a						,       data_format=a						,       **a)





    def 				snake_case_    (				self						,       a						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = ChannelDimension.FIRST						,       **a						,       ):
         lowercase__   :       Dict				=	do_resize if do_resize is not None else self.do_resize
         lowercase__   :       Dict				=	resample if resample is not None else self.resample
         lowercase__   :       int				=	do_rescale if do_rescale is not None else self.do_rescale
         lowercase__   :       Optional[int]				=	rescale_factor if rescale_factor is not None else self.rescale_factor
         lowercase__   :       List[Any]				=	do_normalize if do_normalize is not None else self.do_normalize
         lowercase__   :       List[str]				=	image_mean if image_mean is not None else self.image_mean
         lowercase__   :       Optional[int]				=	image_std if image_std is not None else self.image_std
         lowercase__   :       Optional[int]				=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         lowercase__   :       Any				=	size if size is not None else self.size
         lowercase__   :       Any				=	get_size_dict(a						,       default_to_square=a)

         lowercase__   :       List[Any]				=	make_list_of_images(a)

         if not valid_images(a):
              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 or resample is None:
              raise ValueError('Size and resample 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.')

         if do_normalize and (image_mean is None or image_std is None):
              raise ValueError('Image mean and std must be specified if do_normalize is True.')

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
              lowercase__   :       Optional[int]				=	[convert_to_rgb(a) for image in images]

         # All transformations expect numpy arrays.
         lowercase__   :       int				=	[to_numpy_array(a) for image in images]

         if do_resize:
              lowercase__   :       str				=	[self.resize(image=a						,       size=a						,       resample=a) for image in images]

         if do_rescale:
              lowercase__   :       Union[str, Any]				=	[self.rescale(image=a						,       scale=a) for image in images]

         if do_normalize:
              lowercase__   :       Any				=	[self.normalize(image=a						,       mean=a						,       std=a) for image in images]

         lowercase__   :       List[str]				=	[to_channel_dimension_format(a						,       a) for image in images]

         lowercase__   :       str				=	BatchFeature(data={'pixel_values': images}						,       tensor_type=a)

         return encoded_outputs




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


snake_case_        =			logging.get_logger(__name__)

snake_case_        =			{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case_        =			{
    '''vocab_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/vocab.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/vocab.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/vocab.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/vocab.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/merges.txt''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/merges.txt''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/merges.txt''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/merges.txt''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/tokenizer.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/tokenizer.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/tokenizer.json''',
    },
}

snake_case_        =			{
    '''gpt2''': 1_024,
    '''gpt2-medium''': 1_024,
    '''gpt2-large''': 1_024,
    '''gpt2-xl''': 1_024,
    '''distilgpt2''': 1_024,
}


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	List[str]														=					VOCAB_FILES_NAMES
    __lowerCamelCase						:	Any														=					PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase						:	str														=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase						:	int														=					["""input_ids""", """attention_mask"""]
    __lowerCamelCase						:	str														=					GPTaTokenizer





    def __init__(				self						,       a=None						,       a=None						,       a=None						,       a="<|endoftext|>"						,       a="<|endoftext|>"						,       a="<|endoftext|>"						,       a=False						,       **a						,       ):
         super().__init__(
             a						,       a						,       tokenizer_file=a						,       unk_token=a						,       bos_token=a						,       eos_token=a						,       add_prefix_space=a						,       **a						,       )

         lowercase__   :       int				=	kwargs.pop('add_bos_token'						,       a)

         lowercase__   :       List[str]				=	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
         if pre_tok_state.get('add_prefix_space'						,       a) != add_prefix_space:
              lowercase__   :       Optional[Any]				=	getattr(a						,       pre_tok_state.pop('type'))
              lowercase__   :       List[Any]				=	add_prefix_space
              lowercase__   :       str				=	pre_tok_class(**a)

         lowercase__   :       Tuple				=	add_prefix_space





    def 				snake_case_    (				self						,       *a						,       **a):
         lowercase__   :       Tuple				=	kwargs.get('is_split_into_words'						,       a)
         assert self.add_prefix_space or not is_split_into_words, (
             f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
             "to use it with pretokenized inputs."
         )

         return super()._batch_encode_plus(*a						,       **a)





    def 				snake_case_    (				self						,       *a						,       **a):
         lowercase__   :       Optional[Any]				=	kwargs.get('is_split_into_words'						,       a)

         assert self.add_prefix_space or not is_split_into_words, (
             f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
             "to use it with pretokenized inputs."
         )

         return super()._encode_plus(*a						,       **a)





    def 				snake_case_    (				self						,       a						,       a = None):
         lowercase__   :       Any				=	self._tokenizer.model.save(a						,       name=a)
         return tuple(a)





    def 				snake_case_    (				self						,       a):
         lowercase__   :       Union[str, Any]				=	[]
         for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(a						,       add_special_tokens=a) + [self.eos_token_id])

         if len(a) > self.model_max_length:
              lowercase__   :       Union[str, Any]				=	input_ids[-self.model_max_length :]
         return input_ids

