



import os
import unittest

from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__			,      unittest.TestCase					):
							__lowerCamelCase	:	List[Any]    =ProphetNetTokenizer
							__lowerCamelCase	:	Dict    =False





							def 						UpperCamelCase_					(   self		:		Any	):

											'''simple docstring'''
											super().setUp()

											__a       =		[
											    """[UNK]""",
											    """[CLS]""",
											    """[SEP]""",
											    """[PAD]""",
											    """[MASK]""",
											    """want""",
											    """##want""",
											    """##ed""",
											    """wa""",
											    """un""",
											    """runn""",
											    """##ing""",
											    """,""",
											    """low""",
											    """lowest""",
											]
											__a       =		os.path.join(self.tmpdirname      ,				VOCAB_FILES_NAMES["""vocab_file"""]	)
											with open(self.vocab_file      ,				"""w"""      ,				encoding="""utf-8"""	) as vocab_writer:
															vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]	)	)





							def 						UpperCamelCase_					(   self		:		List[str]      ,				__lowercase		:		List[str]	):

											'''simple docstring'''
											__a       =		"""UNwant\u00E9d,running"""
											__a       =		"""unwanted, running"""
											return input_text, output_text





							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											__a       =		self.tokenizer_class(self.vocab_file	)

											__a       =		tokenizer.tokenize("""UNwant\u00E9d,running"""	)
											self.assertListEqual(__lowercase      ,				["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]	)
											self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase	)      ,				[9, 6, 7, 12, 10, 11]	)





							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											__a       =		BasicTokenizer()

											self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz"""	)      ,				["""ah""", """\u535A""", """\u63A8""", """zz"""]	)





							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											__a       =		BasicTokenizer(do_lower_case=__lowercase	)

											self.assertListEqual(
											    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """	)      ,				["""hello""", """!""", """how""", """are""", """you""", """?"""]	)
											self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""	)      ,				["""hello"""]	)





							def 						UpperCamelCase_					(   self		:		List[str]	):

											'''simple docstring'''
											__a       =		BasicTokenizer(do_lower_case=__lowercase      ,				strip_accents=__lowercase	)

											self.assertListEqual(
											    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """	)      ,				["""hällo""", """!""", """how""", """are""", """you""", """?"""]	)
											self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""	)      ,				["""h\u00E9llo"""]	)





							def 						UpperCamelCase_					(   self		:		Any	):

											'''simple docstring'''
											__a       =		BasicTokenizer(do_lower_case=__lowercase      ,				strip_accents=__lowercase	)

											self.assertListEqual(
											    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """	)      ,				["""hallo""", """!""", """how""", """are""", """you""", """?"""]	)
											self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""	)      ,				["""hello"""]	)





							def 						UpperCamelCase_					(   self		:		Union[str, Any]	):

											'''simple docstring'''
											__a       =		BasicTokenizer(do_lower_case=__lowercase	)

											self.assertListEqual(
											    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """	)      ,				["""hallo""", """!""", """how""", """are""", """you""", """?"""]	)
											self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""	)      ,				["""hello"""]	)





							def 						UpperCamelCase_					(   self		:		Union[str, Any]	):

											'''simple docstring'''
											__a       =		BasicTokenizer(do_lower_case=__lowercase	)

											self.assertListEqual(
											    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """	)      ,				["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]	)





							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''simple docstring'''
											__a       =		BasicTokenizer(do_lower_case=__lowercase      ,				strip_accents=__lowercase	)

											self.assertListEqual(
											    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """	)      ,				["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""]	)





							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											__a       =		BasicTokenizer(do_lower_case=__lowercase      ,				strip_accents=__lowercase	)

											self.assertListEqual(
											    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """	)      ,				["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""]	)





							def 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		BasicTokenizer(do_lower_case=__lowercase      ,				never_split=["""[UNK]"""]	)

											self.assertListEqual(
											    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU? [UNK]"""	)      ,				["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""]	)





							def 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

											__a       =		{}
											for i, token in enumerate(__lowercase	):
															__a       =		i
											__a       =		WordpieceTokenizer(vocab=__lowercase      ,				unk_token="""[UNK]"""	)

											self.assertListEqual(tokenizer.tokenize(""""""	)      ,				[]	)

											self.assertListEqual(tokenizer.tokenize("""unwanted running"""	)      ,				["""un""", """##want""", """##ed""", """runn""", """##ing"""]	)

											self.assertListEqual(tokenizer.tokenize("""unwantedX running"""	)      ,				["""[UNK]""", """runn""", """##ing"""]	)





							@require_torch
							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											__a       =		self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased"""	)

											__a       =		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
											__a       =		[1037, 2146, 20423, 2005, 7680, 7849, 3989, 1012, 102]
											__a       =		tokenizer(__lowercase      ,				padding=__lowercase      ,				return_tensors="""pt"""	)
											self.assertIsInstance(__lowercase      ,				__lowercase	)
											__a       =		list(batch.input_ids.numpy()[0]	)
											self.assertListEqual(__lowercase      ,				__lowercase	)

											self.assertEqual((2, 9)      ,				batch.input_ids.shape	)
											self.assertEqual((2, 9)      ,				batch.attention_mask.shape	)





							def 						UpperCamelCase_					(   self		:		Any	):

											'''simple docstring'''
											self.assertTrue(_is_whitespace(""" """	)	)
											self.assertTrue(_is_whitespace("""\t"""	)	)
											self.assertTrue(_is_whitespace("""\r"""	)	)
											self.assertTrue(_is_whitespace("""\n"""	)	)
											self.assertTrue(_is_whitespace("""\u00A0"""	)	)

											self.assertFalse(_is_whitespace("""A"""	)	)
											self.assertFalse(_is_whitespace("""-"""	)	)





							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''simple docstring'''
											self.assertTrue(_is_control("""\u0005"""	)	)

											self.assertFalse(_is_control("""A"""	)	)
											self.assertFalse(_is_control(""" """	)	)
											self.assertFalse(_is_control("""\t"""	)	)
											self.assertFalse(_is_control("""\r"""	)	)





							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											self.assertTrue(_is_punctuation("""-"""	)	)
											self.assertTrue(_is_punctuation("""$"""	)	)
											self.assertTrue(_is_punctuation("""`"""	)	)
											self.assertTrue(_is_punctuation("""."""	)	)

											self.assertFalse(_is_punctuation("""A"""	)	)
											self.assertFalse(_is_punctuation(""" """	)	)







							@slow
							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											__a       =		self.tokenizer_class.from_pretrained("""microsoft/prophetnet-large-uncased"""	)

											__a       =		tokenizer.encode("""sequence builders"""      ,				add_special_tokens=__lowercase	)
											__a       =		tokenizer.encode("""multi-sequence build"""      ,				add_special_tokens=__lowercase	)

											__a       =		tokenizer.build_inputs_with_special_tokens(__lowercase	)
											__a       =		tokenizer.build_inputs_with_special_tokens(__lowercase      ,				__lowercase	)

											assert encoded_sentence == text + [102]
											assert encoded_pair == text + [102] + text_a + [102]




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 center_crop, 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, is_vision_available, logging


if is_vision_available():
						import PIL


lowerCamelCase__			= logging.get_logger(__name__)






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							__lowerCamelCase	:	str    =['pixel_values']





							def __init__(   self		:		str      ,				__lowercase		:		bool = True      ,				__lowercase		:		Dict[str, int] = None      ,				__lowercase		:		PILImageResampling = PIL.Image.BICUBIC      ,				__lowercase		:		bool = True      ,				__lowercase		:		Dict[str, int] = None      ,				__lowercase		:		Union[int, float] = 1 / 255      ,				__lowercase		:		bool = True      ,				__lowercase		:		bool = True      ,				__lowercase		:		Optional[Union[float, List[float]]] = None      ,				__lowercase		:		Optional[Union[float, List[float]]] = None      ,				**__lowercase		:		Any      ,				):

											'''simple docstring'''
											super().__init__(**__lowercase	)
											__a       =		size if size is not None else {"""height""": 256, """width""": 256}
											__a       =		get_size_dict(__lowercase	)
											__a       =		crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
											__a       =		get_size_dict(__lowercase      ,				param_name="""crop_size"""	)

											__a       =		do_resize
											__a       =		size
											__a       =		resample
											__a       =		do_center_crop
											__a       =		crop_size
											__a       =		do_rescale
											__a       =		rescale_factor
											__a       =		do_normalize
											__a       =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											__a       =		image_std if image_std is not None else IMAGENET_STANDARD_STD





							def 						UpperCamelCase_					(   self		:		List[Any]      ,				__lowercase		:		np.ndarray      ,				__lowercase		:		Dict[str, int]      ,				__lowercase		:		PILImageResampling = PIL.Image.BICUBIC      ,				__lowercase		:		Optional[Union[str, ChannelDimension]] = None      ,				**__lowercase		:		Union[str, Any]      ,				):

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





							def 						UpperCamelCase_					(   self		:		Dict      ,				__lowercase		:		np.ndarray      ,				__lowercase		:		Dict[str, int]      ,				__lowercase		:		Optional[Union[str, ChannelDimension]] = None      ,				**__lowercase		:		str      ,				):

											'''simple docstring'''
											__a       =		get_size_dict(__lowercase	)
											if "height" not in size or "width" not in size:
															raise ValueError(F"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"	)
											return center_crop(__lowercase      ,				size=(size["""height"""], size["""width"""])      ,				data_format=__lowercase      ,				**__lowercase	)





							def 						UpperCamelCase_					(   self		:		int      ,				__lowercase		:		np.ndarray      ,				__lowercase		:		Union[int, float]      ,				__lowercase		:		Optional[Union[str, ChannelDimension]] = None      ,				**__lowercase		:		List[str]      ,				):

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





							def 						UpperCamelCase_					(   self		:		Tuple      ,				__lowercase		:		np.ndarray      ,				__lowercase		:		Union[float, List[float]]      ,				__lowercase		:		Union[float, List[float]]      ,				__lowercase		:		Optional[Union[str, ChannelDimension]] = None      ,				**__lowercase		:		str      ,				):

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







							def 						UpperCamelCase_					(   self		:		Optional[Any]      ,				__lowercase		:		ImageInput      ,				__lowercase		:		bool = None      ,				__lowercase		:		Dict[str, int] = None      ,				__lowercase		:		Dict=None      ,				__lowercase		:		bool = None      ,				__lowercase		:		Dict[str, int] = None      ,				__lowercase		:		bool = None      ,				__lowercase		:		float = None      ,				__lowercase		:		bool = None      ,				__lowercase		:		Optional[Union[float, List[float]]] = None      ,				__lowercase		:		Optional[Union[float, List[float]]] = None      ,				__lowercase		:		Optional[Union[str, TensorType]] = None      ,				__lowercase		:		ChannelDimension = ChannelDimension.FIRST      ,				**__lowercase		:		Optional[Any]      ,				):

											'''simple docstring'''
											__a       =		do_resize if do_resize is not None else self.do_resize
											__a       =		resample if resample is not None else self.resample
											__a       =		do_center_crop if do_center_crop is not None else self.do_center_crop
											__a       =		do_rescale if do_rescale is not None else self.do_rescale
											__a       =		rescale_factor if rescale_factor is not None else self.rescale_factor
											__a       =		do_normalize if do_normalize is not None else self.do_normalize
											__a       =		image_mean if image_mean is not None else self.image_mean
											__a       =		image_std if image_std is not None else self.image_std

											__a       =		size if size is not None else self.size
											__a       =		get_size_dict(__lowercase	)
											__a       =		crop_size if crop_size is not None else self.crop_size
											__a       =		get_size_dict(__lowercase      ,				param_name="""crop_size"""	)

											__a       =		make_list_of_images(__lowercase	)

											if not valid_images(__lowercase	):
															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_center_crop and crop_size is None:
															raise ValueError("""Crop size must be specified if do_center_crop 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."""	)

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

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

											if do_center_crop:
															__a       =		[self.center_crop(image=__lowercase      ,				size=__lowercase	) for image in images]

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

											if do_normalize:
															__a       =		[self.normalize(image=__lowercase      ,				mean=__lowercase      ,				std=__lowercase	) for image in images]

											__a       =		[to_channel_dimension_format(__lowercase      ,				__lowercase	) for image in images]

											__a       =		{"""pixel_values""": images}
											return BatchFeature(data=__lowercase      ,				tensor_type=__lowercase	)
