"""simple docstring"""



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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    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


_UpperCamelCase								=		logging.get_logger(__name__)

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Optional[Any]								=			b.T
							A__       :     List[str]								=			np.sum(np.square(_A							)			,  axis=1							)
							A__       :     Any								=			np.sum(np.square(_A							)			,  axis=0							)
							A__       :     int								=			np.matmul(_A			,  _A							)
							A__       :     Any								=			aa[:, None] - 2 * ab + ba[None, :]
							return d

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Optional[Any]								=			x.reshape(-1			,  3							)
							A__       :     int								=			squared_euclidean_distance(_A			,  _A							)
							return np.argmin(_A			,  axis=1							)



class 						__UpperCAmelCase    (SCREAMING_SNAKE_CASE_     ):







			'''simple docstring'''
			_UpperCamelCase  :						Tuple					     =				['pixel_values']





			def __init__(			self   ,      snake_case_ = None   ,      snake_case_ = True   ,      snake_case_ = None   ,      snake_case_ = PILImageResampling.BILINEAR   ,      snake_case_ = True   ,      snake_case_ = True   ,      **snake_case_   ,      ):







										'''simple docstring'''


										super().__init__(**snake_case__							)
										A__       :     List[str]								=			size if size is not None else {'height': 256, 'width': 256}
										A__       :     List[Any]								=			get_size_dict(snake_case__							)
										A__       :     str								=			np.array(snake_case__							) if clusters is not None else None
										A__       :     Dict								=			do_resize
										A__       :     int								=			size
										A__       :     int								=			resample
										A__       :     List[str]								=			do_normalize
										A__       :     Optional[int]								=			do_color_quantize





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







										'''simple docstring'''


										A__       :     int								=			get_size_dict(snake_case__							)
										if "height" not in size or "width" not in size:
																	raise ValueError(F'''Size dictionary must contain both height and width keys. Got {size.keys()}'''							)
										return resize(
										    snake_case__   ,      size=(size["""height"""], size["""width"""])   ,      resample=snake_case__   ,      data_format=snake_case__   ,      **snake_case__							)





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







										'''simple docstring'''


										A__       :     Union[str, Any]								=			rescale(image=snake_case__   ,      scale=1 / 1_27.5   ,      data_format=snake_case__							)
										A__       :     Union[str, Any]								=			image - 1
										return image





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







										'''simple docstring'''


										A__       :     List[Any]								=			do_resize if do_resize is not None else self.do_resize
										A__       :     int								=			size if size is not None else self.size
										A__       :     Union[str, Any]								=			get_size_dict(snake_case__							)
										A__       :     List[str]								=			resample if resample is not None else self.resample
										A__       :     Optional[int]								=			do_normalize if do_normalize is not None else self.do_normalize
										A__       :     Any								=			do_color_quantize if do_color_quantize is not None else self.do_color_quantize
										A__       :     Optional[int]								=			clusters if clusters is not None else self.clusters
										A__       :     Union[str, Any]								=			np.array(snake_case__							)

										A__       :     Tuple								=			make_list_of_images(snake_case__							)

										if not valid_images(snake_case__							):
																	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_color_quantize and clusters is None:
																	raise ValueError("""Clusters must be specified if do_color_quantize is True."""							)

										# All transformations expect numpy arrays.
										A__       :     Union[str, Any]								=			[to_numpy_array(snake_case__							) for image in images]

										if do_resize:
																	A__       :     int								=			[self.resize(image=snake_case__   ,      size=snake_case__   ,      resample=snake_case__							) for image in images]

										if do_normalize:
																	A__       :     Tuple								=			[self.normalize(image=snake_case__							) for image in images]

										if do_color_quantize:
																	A__       :     List[Any]								=			[to_channel_dimension_format(snake_case__   ,      ChannelDimension.LAST							) for image in images]
																	# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
																	A__       :     Any								=			np.array(snake_case__							)
																	A__       :     Optional[Any]								=			color_quantize(snake_case__   ,      snake_case__							).reshape(images.shape[:-1]							)

																	# flatten to (batch_size, height*width)
																	A__       :     Optional[Any]								=			images.shape[0]
																	A__       :     Tuple								=			images.reshape(snake_case__   ,      -1							)

																	# We need to convert back to a list of images to keep consistent behaviour across processors.
																	A__       :     Union[str, Any]								=			list(snake_case__							)
										else:
																	A__       :     Any								=			[to_channel_dimension_format(snake_case__   ,      snake_case__							) for image in images]

										A__       :     List[str]								=			{'input_ids': images}
										return BatchFeature(data=snake_case__   ,      tensor_type=snake_case__							)









'''simple docstring'''







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


lowerCAmelCase     :  Tuple											=    logging.get_logger(__name__)

lowerCAmelCase     :  Union[str, Any]											=    {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

lowerCAmelCase     :  str											=    {
    """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""",
    },
}

lowerCAmelCase     :  str											=    {
    """gpt2""": 10_24,
    """gpt2-medium""": 10_24,
    """gpt2-large""": 10_24,
    """gpt2-xl""": 10_24,
    """distilgpt2""": 10_24,
}
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     __magic_name__           =     VOCAB_FILES_NAMES
     __magic_name__           =     PRETRAINED_VOCAB_FILES_MAP
     __magic_name__           =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__           =     ["input_ids", "attention_mask"]
     __magic_name__           =     GPTaTokenizer







     def __init__(			self   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__="<|endoftext|>"   ,						snake_case__="<|endoftext|>"   ,						snake_case__="<|endoftext|>"   ,						snake_case__=False   ,						**snake_case__   ,						):

            '''simple docstring'''

            super().__init__(
                snake_case__   ,						snake_case__   ,						tokenizer_file=snake_case__   ,						unk_token=snake_case__   ,						bos_token=snake_case__   ,						eos_token=snake_case__   ,						add_prefix_space=snake_case__   ,						**snake_case__   ,						)

            _lowerCAmelCase     :				str       =							kwargs.pop('add_bos_token'   ,						snake_case__ )

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

            _lowerCAmelCase     :				Any       =							add_prefix_space







     def a						(			self   ,						*snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							kwargs.get('is_split_into_words'   ,						snake_case__ )
            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(*snake_case__   ,						**snake_case__ )







     def a						(			self   ,						*snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							kwargs.get('is_split_into_words'   ,						snake_case__ )

            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(*snake_case__   ,						**snake_case__ )







     def a						(			self   ,						snake_case__   ,						snake_case__ = None ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							self._tokenizer.model.save(snake_case__   ,						name=snake_case__ )
            return tuple(snake_case__ )







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Any       =							[]
            for is_user, text in conversation.iter_texts():
                   input_ids.extend(self.encode(snake_case__   ,						add_special_tokens=snake_case__ ) + [self.eos_token_id] )

            if len(snake_case__ ) > self.model_max_length:
                   _lowerCAmelCase     :				str       =							input_ids[-self.model_max_length :]
            return input_ids

