



import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class     UpperCAmelCase_       (			a_  ,   unittest.TestCase):
							lowerCamelCase__       =						DebertaTokenizer
							lowerCamelCase__       =						True
							lowerCamelCase__       =						DebertaTokenizerFast







							def   snake_case__ (  self):

														'''simple docstring'''

														super().setUp()

														# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
														_lowerCAmelCase	:				int					  =       [
														    "l",
														    "o",
														    "w",
														    "e",
														    "r",
														    "s",
														    "t",
														    "i",
														    "d",
														    "n",
														    "\u0120",
														    "\u0120l",
														    "\u0120n",
														    "\u0120lo",
														    "\u0120low",
														    "er",
														    "\u0120lowest",
														    "\u0120newer",
														    "\u0120wider",
														    "[UNK]",
														]
														_lowerCAmelCase	:				Dict					  =       dict(zip(lowerCAmelCase_,     range(len(lowerCAmelCase_))))
														_lowerCAmelCase	:				Optional[Any]					  =       ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
														_lowerCAmelCase	:				int					  =       {"unk_token": "[UNK]"}

														_lowerCAmelCase	:				Tuple					  =       os.path.join(self.tmpdirname,     VOCAB_FILES_NAMES["vocab_file"])
														_lowerCAmelCase	:				Any					  =       os.path.join(self.tmpdirname,     VOCAB_FILES_NAMES["merges_file"])
														with open(self.vocab_file,     "w",     encoding="utf-8") as fp:
																					fp.write(json.dumps(lowerCAmelCase_) + "\n")
														with open(self.merges_file,     "w",     encoding="utf-8") as fp:
																					fp.write("\n".join(lowerCAmelCase_))







							def   snake_case__ (  self,     **__a):

														'''simple docstring'''

														kwargs.update(self.special_tokens_map)
														return self.tokenizer_class.from_pretrained(self.tmpdirname,     **lowerCAmelCase_)







							def   snake_case__ (  self,     __a):

														'''simple docstring'''

														_lowerCAmelCase	:				int					  =       "lower newer"
														_lowerCAmelCase	:				Union[str, Any]					  =       "lower newer"
														return input_text, output_text







							def   snake_case__ (  self):

														'''simple docstring'''

														_lowerCAmelCase	:				int					  =       self.get_tokenizer()
														_lowerCAmelCase	:				int					  =       "lower newer"
														_lowerCAmelCase	:				List[str]					  =       ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
														_lowerCAmelCase	:				Dict					  =       tokenizer.tokenize(lowerCAmelCase_)
														self.assertListEqual(lowerCAmelCase_,     lowerCAmelCase_)

														_lowerCAmelCase	:				Union[str, Any]					  =       tokens + [tokenizer.unk_token]
														_lowerCAmelCase	:				List[str]					  =       [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
														self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_),     lowerCAmelCase_)







							def   snake_case__ (  self):

														'''simple docstring'''

														_lowerCAmelCase	:				int					  =       self.get_tokenizer()
														_lowerCAmelCase	:				List[Any]					  =       tokenizer("Hello",     "World")
														_lowerCAmelCase	:				Tuple					  =       [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
														self.assertListEqual(tokd["token_type_ids"],     lowerCAmelCase_)







							@slow
							def   snake_case__ (  self):

														'''simple docstring'''

														_lowerCAmelCase	:				Dict					  =       self.tokenizer_class.from_pretrained("microsoft/deberta-base")

														_lowerCAmelCase	:				Union[str, Any]					  =       tokenizer.encode("sequence builders",     add_special_tokens=lowerCAmelCase_)
														_lowerCAmelCase	:				Tuple					  =       tokenizer.encode("multi-sequence build",     add_special_tokens=lowerCAmelCase_)

														_lowerCAmelCase	:				Dict					  =       tokenizer.encode(
														    "sequence builders",     add_special_tokens=lowerCAmelCase_,     add_prefix_space=lowerCAmelCase_)
														_lowerCAmelCase	:				Tuple					  =       tokenizer.encode(
														    "sequence builders",     "multi-sequence build",     add_special_tokens=lowerCAmelCase_,     add_prefix_space=lowerCAmelCase_)

														_lowerCAmelCase	:				Optional[int]					  =       tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_)
														_lowerCAmelCase	:				Tuple					  =       tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_,     lowerCAmelCase_)

														assert encoded_sentence == encoded_text_from_decode
														assert encoded_pair == encoded_pair_from_decode







							@slow
							def   snake_case__ (  self):

														'''simple docstring'''

														_lowerCAmelCase	:				Tuple					  =       [self.tokenizer_class]
														if self.test_rust_tokenizer:
																					tokenizer_classes.append(self.rust_tokenizer_class)

														for tokenizer_class in tokenizer_classes:
																					_lowerCAmelCase	:				Optional[Any]					  =       tokenizer_class.from_pretrained("microsoft/deberta-base")

																					_lowerCAmelCase	:				Any					  =       [
																					    "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
																					    "ALBERT incorporates two parameter reduction techniques",
																					    "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
																					    " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
																					    " vocabulary embedding.",
																					]

																					_lowerCAmelCase	:				List[Any]					  =       tokenizer(lowerCAmelCase_,     padding=lowerCAmelCase_)
																					_lowerCAmelCase	:				int					  =       [tokenizer.decode(lowerCAmelCase_,     skip_special_tokens=lowerCAmelCase_) for seq in encoding["input_ids"]]

																					# fmt: off
																					_lowerCAmelCase	:				Optional[int]					  =       {
																					    "input_ids": [
																					        [1, 2118, 1_1126, 565, 35, 83, 2_5191, 163, 1_8854, 13, 1_2156, 12, 1_6101, 2_5376, 1_3807, 9, 2_2205, 2_7893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																					        [1, 2118, 1_1126, 565, 2_4536, 80, 4_3797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																					        [1, 133, 78, 65, 16, 10, 3724, 1538, 3_3183, 1_1303, 4_3797, 1938, 4, 870, 2_4165, 2_9105, 5, 739, 3_2644, 3_3183, 1_1303, 3_6173, 88, 80, 650, 7821, 4_5940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 1_3171, 31, 5, 1836, 9, 3_2644, 3_3183, 1_1303, 4, 2]
																					    ],
																					    "token_type_ids": [
																					        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																					        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																					        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
																					    ],
																					    "attention_mask": [
																					        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																					        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																					        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
																					    ]
																					}
																					# fmt: on

																					_lowerCAmelCase	:				Optional[Any]					  =       [
																					    "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
																					    "ALBERT incorporates two parameter reduction techniques",
																					    "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
																					    " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
																					    " vocabulary embedding.",
																					]

																					self.assertDictEqual(encoding.data,     lowerCAmelCase_)

																					for expected, decoded in zip(lowerCAmelCase_,     lowerCAmelCase_):
																												self.assertEqual(lowerCAmelCase_,     lowerCAmelCase_)




'''simple docstring'''





import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import BridgeTowerImageProcessor
class 						a__					(					unittest.TestCase   ):





			'''simple docstring'''

			def __init__(  self							:			str			,    lowerCAmelCase_							:			str			,    lowerCAmelCase_							:			bool = True			,    lowerCAmelCase_							:			Dict[str, int] = None			,    lowerCAmelCase_							:			int = 32			,    lowerCAmelCase_							:			bool = True			,    lowerCAmelCase_							:			Union[int, float] = 1 / 255			,    lowerCAmelCase_							:			bool = True			,    lowerCAmelCase_							:			bool = True			,    lowerCAmelCase_							:			Optional[Union[float, List[float]]] = [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]			,    lowerCAmelCase_							:			Optional[Union[float, List[float]]] = [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]			,    lowerCAmelCase_							:			bool = True			,    lowerCAmelCase_							:			str=7			,    lowerCAmelCase_							:			Union[str, Any]=30			,    lowerCAmelCase_							:			str=400			,    lowerCAmelCase_							:			int=3			,    )      ->		str:
						__A=	parent
						__A=	do_resize
						__A=	size if size is not None else {'shortest_edge': 288}
						__A=	size_divisor
						__A=	do_rescale
						__A=	rescale_factor
						__A=	do_normalize
						__A=	do_center_crop
						__A=	image_mean
						__A=	image_std
						__A=	do_pad
						__A=	batch_size
						__A=	num_channels
						__A=	min_resolution
						__A=	max_resolution
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		int:
						return {
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_normalize": self.do_normalize,
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "size_divisor": self.size_divisor,
						}

			def 					lowerCAmelCase     (  self							:			Dict			,    lowerCAmelCase_							:			str			,    lowerCAmelCase_							:			str=False	)      ->		str:
						if not batched:
									__A=	self.size['shortest_edge']
									__A=	image_inputs[0]
									if isinstance(lowerCAmelCase_			,    Image.Image	):
												__A,	__A=	image.size
									else:
												__A,	__A=	image.shape[1], image.shape[2]
									__A=	size / min(lowerCAmelCase_			,    lowerCAmelCase_	)
									if h < w:
												__A,	__A=	size, scale * w
									else:
												__A,	__A=	scale * h, size

									__A=	int((1_333 / 800) * size	)
									if max(lowerCAmelCase_			,    lowerCAmelCase_	) > max_size:
												__A=	max_size / max(lowerCAmelCase_			,    lowerCAmelCase_	)
												__A=	newh * scale
												__A=	neww * scale

									__A,	__A=	int(newh + 0.5	), int(neww + 0.5	)
									__A,	__A=	(
									    newh // self.size_divisor * self.size_divisor,
									    neww // self.size_divisor * self.size_divisor,
									)

						else:
									__A=	[]
									for image in image_inputs:
												__A,	__A=	self.get_expected_values([image]	)
												expected_values.append((expected_height, expected_width)	)
									__A=	max(lowerCAmelCase_			,    key=lambda lowerCAmelCase_	: item[0]	)[0]
									__A=	max(lowerCAmelCase_			,    key=lambda lowerCAmelCase_	: item[1]	)[1]

						return expected_height, expected_width


@require_torch
@require_vision
class 						a__					(					a_      ,  unittest.TestCase   ):





			'''simple docstring'''

			A						:				int					      =			BridgeTowerImageProcessor if is_vision_available() else None
			def 					lowerCAmelCase     (  self							:			Dict	)      ->		Optional[int]:
						__A=	BridgeTowerImageProcessingTester(self	)
			@property
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Union[str, Any]:
						return self.image_processor_tester.prepare_image_processor_dict()
			def 					lowerCAmelCase     (  self							:			Tuple	)      ->		List[Any]:
						__A=	self.image_processing_class(**self.image_processor_dict	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'image_mean'	)	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'image_std'	)	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'do_normalize'	)	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'do_resize'	)	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'size'	)	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'size_divisor'	)	)
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		str:
						pass
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Any:
						# Initialize image processor
						__A=	self.image_processing_class(**self.image_processor_dict	)
						# create random PIL images
						__A=	prepare_image_inputs(self.image_processor_tester			,    equal_resolution=lowerCAmelCase_	)
						for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_			,    Image.Image	)

						# Test not batched input
						__A=	image_processing(image_inputs[0]			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,    )

						# Test batched
						__A=	image_processing(lowerCAmelCase_			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_			,    batched=lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )			,    )
			def 					lowerCAmelCase     (  self							:			Any	)      ->		Any:
						# Initialize image processor
						__A=	self.image_processing_class(**self.image_processor_dict	)
						# create random numpy tensors
						__A=	prepare_image_inputs(self.image_processor_tester			,    equal_resolution=lowerCAmelCase_			,    numpify=lowerCAmelCase_	)
						for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_			,    np.ndarray	)

						# Test not batched input
						__A=	image_processing(image_inputs[0]			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,    )

						# Test batched
						__A=	image_processing(lowerCAmelCase_			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_			,    batched=lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )			,    )

			def 					lowerCAmelCase     (  self							:			List[Any]	)      ->		Tuple:
						# Initialize image processor
						__A=	self.image_processing_class(**self.image_processor_dict	)
						# create random PyTorch tensors
						__A=	prepare_image_inputs(self.image_processor_tester			,    equal_resolution=lowerCAmelCase_			,    torchify=lowerCAmelCase_	)
						for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_			,    torch.Tensor	)

						# Test not batched input
						__A=	image_processing(image_inputs[0]			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,    )

						# Test batched
						__A=	image_processing(lowerCAmelCase_			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_			,    batched=lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )			,    )



