





'''simple docstring'''




import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 					SCREAMING_SNAKE_CASE       (a__   ,						unittest.TestCase				):
     lowerCAmelCase						     =			OpenAIGPTTokenizer
     lowerCAmelCase						     =			OpenAIGPTTokenizerFast
     lowerCAmelCase						     =			True
     lowerCAmelCase						     =			False


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          super().setUp()

          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          __A		:					Optional[Any]						=			[
              'l',
              'o',
              'w',
              'e',
              'r',
              's',
              't',
              'i',
              'd',
              'n',
              'w</w>',
              'r</w>',
              't</w>',
              'lo',
              'low',
              'er</w>',
              'low</w>',
              'lowest</w>',
              'newer</w>',
              'wider</w>',
              '<unk>',
          ]
          __A		:					Dict						=			dict(zip(_UpperCAmelCase		,      range(len(_UpperCAmelCase))))
          __A		:					List[Any]						=			['#version: 0.2', 'l o', 'lo w', 'e r</w>', '']

          __A		:					List[str]						=			os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES['vocab_file'])
          __A		:					Optional[Any]						=			os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES['merges_file'])
          with open(self.vocab_file		,      'w') as fp:
               fp.write(json.dumps(_UpperCAmelCase))
          with open(self.merges_file		,      'w') as fp:
               fp.write('\n'.join(_UpperCAmelCase))


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          return "lower newer", "lower newer"


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Tuple						=			OpenAIGPTTokenizer(self.vocab_file		,      self.merges_file)

          __A		:					int						=			'lower'
          __A		:					Dict						=			['low', 'er</w>']
          __A		:					Union[str, Any]						=			tokenizer.tokenize(_UpperCAmelCase)
          self.assertListEqual(_UpperCAmelCase		,      _UpperCAmelCase)

          __A		:					Dict						=			tokens + ['<unk>']
          __A		:					str						=			[14, 15, 20]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase)		,      _UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase=15):

          '''simple docstring'''






          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
                    __A		:					int						=			self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase		,      **_UpperCAmelCase)

                    # Simple input
                    __A		:					Tuple						=			'This is a simple input'
                    __A		:					Dict						=			['This is a simple input 1', 'This is a simple input 2']
                    __A		:					int						=			('This is a simple input', 'This is a pair')
                    __A		:					List[Any]						=			[
                        ('This is a simple input 1', 'This is a simple input 2'),
                        ('This is a simple pair 1', 'This is a simple pair 2'),
                    ]

                    # Simple input tests
                    self.assertRaises(_UpperCAmelCase		,      tokenizer_r.encode		,      _UpperCAmelCase		,      max_length=_UpperCAmelCase		,      padding='max_length')

                    # Simple input
                    self.assertRaises(_UpperCAmelCase		,      tokenizer_r.encode_plus		,      _UpperCAmelCase		,      max_length=_UpperCAmelCase		,      padding='max_length')

                    # Simple input
                    self.assertRaises(
                        _UpperCAmelCase		,      tokenizer_r.batch_encode_plus		,      _UpperCAmelCase		,      max_length=_UpperCAmelCase		,      padding='max_length'		,      )

                    # Pair input
                    self.assertRaises(_UpperCAmelCase		,      tokenizer_r.encode		,      _UpperCAmelCase		,      max_length=_UpperCAmelCase		,      padding='max_length')

                    # Pair input
                    self.assertRaises(_UpperCAmelCase		,      tokenizer_r.encode_plus		,      _UpperCAmelCase		,      max_length=_UpperCAmelCase		,      padding='max_length')

                    # Pair input
                    self.assertRaises(
                        _UpperCAmelCase		,      tokenizer_r.batch_encode_plus		,      _UpperCAmelCase		,      max_length=_UpperCAmelCase		,      padding='max_length'		,      )


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          pass




@require_ftfy
@require_spacy
@require_tokenizers
class 					SCREAMING_SNAKE_CASE       (a__				):
     pass
'''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


__lowercase        =    logging.get_logger(__name__)






def 							snake_case__				(     _A:   Dict ,  _A:   Any					)       ->      Union[str, Any]:

		'''simple docstring'''


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






def 							snake_case__				(     _A:   Dict ,  _A:   Dict					)       ->      Optional[Any]:

		'''simple docstring'''


		lowerCAmelCase     =    x.reshape(-1 ,  3					)
		lowerCAmelCase     =    squared_euclidean_distance(_A ,  _A					)
		return np.argmin(_A ,  axis=1					)






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[Any]						=			['''pixel_values''']



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




					"""simple docstring"""


					super().__init__(**__lowerCAmelCase)
					lowerCAmelCase     =    size if size is not None else {"""height""": 256, """width""": 256}
					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase)
					lowerCAmelCase     =    np.array(__lowerCAmelCase) if clusters is not None else None
					lowerCAmelCase     =    do_resize
					lowerCAmelCase     =    size
					lowerCAmelCase     =    resample
					lowerCAmelCase     =    do_normalize
					lowerCAmelCase     =    do_color_quantize



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase = PILImageResampling.BILINEAR			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase)
					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(
					    __lowerCAmelCase			,       size=(size["""height"""], size["""width"""])			,       resample=__lowerCAmelCase			,       data_format=__lowerCAmelCase			,       **__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    rescale(image=__lowerCAmelCase			,       scale=1 / 127.5			,       data_format=__lowerCAmelCase)
					lowerCAmelCase     =    image - 1
					return image







			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = ChannelDimension.FIRST			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    do_resize if do_resize is not None else self.do_resize
					lowerCAmelCase     =    size if size is not None else self.size
					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase)
					lowerCAmelCase     =    resample if resample is not None else self.resample
					lowerCAmelCase     =    do_normalize if do_normalize is not None else self.do_normalize
					lowerCAmelCase     =    do_color_quantize if do_color_quantize is not None else self.do_color_quantize
					lowerCAmelCase     =    clusters if clusters is not None else self.clusters
					lowerCAmelCase     =    np.array(__lowerCAmelCase)

					lowerCAmelCase     =    make_list_of_images(__lowerCAmelCase)

					if not valid_images(__lowerCAmelCase):
							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.
					lowerCAmelCase     =    [to_numpy_array(__lowerCAmelCase) for image in images]

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

					if do_normalize:
							lowerCAmelCase     =    [self.normalize(image=__lowerCAmelCase) for image in images]

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

							# flatten to (batch_size, height*width)
							lowerCAmelCase     =    images.shape[0]
							lowerCAmelCase     =    images.reshape(__lowerCAmelCase			,       -1)

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

					lowerCAmelCase     =    {"""input_ids""": images}
					return BatchFeature(data=__lowerCAmelCase			,       tensor_type=__lowerCAmelCase)




