'''simple docstring'''




import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin






class 							a__(   lowerCAmelCase__					,		unittest.TestCase      ):


			'''simple docstring'''






			UpperCAmelCase_     :	str						=			XLMTokenizer
			UpperCAmelCase_     :	Optional[int]						=			False



			def 				a_			(  self):




					"""simple docstring"""


					super().setUp()

					# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
					lowerCAmelCase     =    [
					    """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>""",
					]
					lowerCAmelCase     =    dict(zip(__lowerCAmelCase			,       range(len(__lowerCAmelCase))))
					lowerCAmelCase     =    ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

					lowerCAmelCase     =    os.path.join(self.tmpdirname			,       VOCAB_FILES_NAMES["""vocab_file"""])
					lowerCAmelCase     =    os.path.join(self.tmpdirname			,       VOCAB_FILES_NAMES["""merges_file"""])
					with open(self.vocab_file			,       """w""") as fp:
							fp.write(json.dumps(__lowerCAmelCase))
					with open(self.merges_file			,       """w""") as fp:
							fp.write("""\n""".join(__lowerCAmelCase))



			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    """lower newer"""
					lowerCAmelCase     =    """lower newer"""
					return input_text, output_text



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    XLMTokenizer(self.vocab_file			,       self.merges_file)

					lowerCAmelCase     =    """lower"""
					lowerCAmelCase     =    ["""low""", """er</w>"""]
					lowerCAmelCase     =    tokenizer.tokenize(__lowerCAmelCase)
					self.assertListEqual(__lowerCAmelCase			,       __lowerCAmelCase)

					lowerCAmelCase     =    tokens + ["""<unk>"""]
					lowerCAmelCase     =    [14, 15, 20]
					self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase)			,       __lowerCAmelCase)







			@slow
			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    XLMTokenizer.from_pretrained("""xlm-mlm-en-2048""")

					lowerCAmelCase     =    tokenizer.encode("""sequence builders"""			,       add_special_tokens=__lowerCAmelCase)
					lowerCAmelCase     =    tokenizer.encode("""multi-sequence build"""			,       add_special_tokens=__lowerCAmelCase)

					lowerCAmelCase     =    tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase)
					lowerCAmelCase     =    tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase			,       __lowerCAmelCase)

					assert encoded_sentence == [0] + text + [1]
					assert encoded_pair == [0] + text + [1] + text_a + [1]





'''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)




