import unittest

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


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import SwinaSRImageProcessor
		from transformers.image_transforms import get_image_size




class 					UpperCamelCase	(     unittest.TestCase      ):




					def __init__( self :					List[Any]      ,_lowerCAmelCase :					Optional[int]      ,_lowerCAmelCase :					List[Any]=7      ,_lowerCAmelCase :					Optional[Any]=3      ,_lowerCAmelCase :					Optional[Any]=18      ,_lowerCAmelCase :					str=30      ,_lowerCAmelCase :					str=400      ,_lowerCAmelCase :					Any=True      ,_lowerCAmelCase :					int=1 / 255      ,_lowerCAmelCase :					Dict=True      ,_lowerCAmelCase :					Tuple=8      ,):


							"""simple docstring"""




							__snake_case		       =     parent
							__snake_case		       =     batch_size
							__snake_case		       =     num_channels
							__snake_case		       =     image_size
							__snake_case		       =     min_resolution
							__snake_case		       =     max_resolution
							__snake_case		       =     do_rescale
							__snake_case		       =     rescale_factor
							__snake_case		       =     do_pad
							__snake_case		       =     pad_size




					def 							UpperCamelCase_						( self :					Tuple						):


							"""simple docstring"""




							return {
							    "do_rescale": self.do_rescale,
							    "rescale_factor": self.rescale_factor,
							    "do_pad": self.do_pad,
							    "pad_size": self.pad_size,
							}







					def 							UpperCamelCase_						( self :					str      ,_lowerCAmelCase :					List[Any]=False      ,_lowerCAmelCase :					Any=False      ,_lowerCAmelCase :					Tuple=False						):


							"""simple docstring"""




							assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

							if equal_resolution:
									__snake_case		       =     []
									for i in range(self.batch_size						):
											image_inputs.append(
											    np.random.randint(
											        255      ,size=(self.num_channels, self.max_resolution, self.max_resolution)      ,dtype=np.uinta						)						)
							else:
									__snake_case		       =     []
									for i in range(self.batch_size						):
											__snake_case       ,       __snake_case		       =     np.random.choice(np.arange(self.min_resolution      ,self.max_resolution						)      ,2						)
											image_inputs.append(np.random.randint(255      ,size=(self.num_channels, width, height)      ,dtype=np.uinta						)						)

							if not numpify and not torchify:
									# PIL expects the channel dimension as last dimension
									__snake_case		       =     [Image.fromarray(np.moveaxis(_lowerCAmelCase      ,0      ,-1						)						) for x in image_inputs]

							if torchify:
									__snake_case		       =     [torch.from_numpy(_lowerCAmelCase						) for x in image_inputs]

							return image_inputs




@require_torch
@require_vision
class 					UpperCamelCase	(     snake_case__			,      unittest.TestCase      ):
					__UpperCamelCase						=   SwinaSRImageProcessor if is_vision_available() else None




					def 							UpperCamelCase_						( self :					List[Any]						):


							"""simple docstring"""




							__snake_case		       =     SwinaSRImageProcessingTester(self						)




					@property
					def 							UpperCamelCase_						( self :					str						):


							"""simple docstring"""




							return self.image_processor_tester.prepare_image_processor_dict()




					def 							UpperCamelCase_						( self :					Dict						):


							"""simple docstring"""




							__snake_case		       =     self.image_processing_class(**self.image_processor_dict						)
							self.assertTrue(hasattr(_lowerCAmelCase      ,"do_rescale"						)						)
							self.assertTrue(hasattr(_lowerCAmelCase      ,"rescale_factor"						)						)
							self.assertTrue(hasattr(_lowerCAmelCase      ,"do_pad"						)						)
							self.assertTrue(hasattr(_lowerCAmelCase      ,"pad_size"						)						)




					def 							UpperCamelCase_						( self :					Dict						):


							"""simple docstring"""




							pass




					def 							UpperCamelCase_						( self :					Any      ,_lowerCAmelCase :					Optional[int]						):


							"""simple docstring"""




							__snake_case       ,       __snake_case		       =     get_image_size(_lowerCAmelCase						)
							__snake_case		       =     self.image_processor_tester.pad_size

							__snake_case		       =     (old_height // size + 1) * size - old_height
							__snake_case		       =     (old_width // size + 1) * size - old_width
							return old_height + pad_height, old_width + pad_width




					def 							UpperCamelCase_						( self :					Optional[Any]						):


							"""simple docstring"""




							__snake_case		       =     self.image_processing_class(**self.image_processor_dict						)
							# create random PIL images
							__snake_case		       =     self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCAmelCase						)
							for image in image_inputs:
									self.assertIsInstance(_lowerCAmelCase      ,Image.Image						)

							# Test not batched input
							__snake_case		       =     image_processor(image_inputs[0]      ,return_tensors="pt"						).pixel_values
							__snake_case       ,       __snake_case		       =     self.calculate_expected_size(np.array(image_inputs[0]						)						)
							self.assertEqual(
							    encoded_images.shape      ,(
							        1,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )      ,)




					def 							UpperCamelCase_						( self :					Dict						):


							"""simple docstring"""




							__snake_case		       =     self.image_processing_class(**self.image_processor_dict						)
							# create random numpy tensors
							__snake_case		       =     self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCAmelCase      ,numpify=_lowerCAmelCase						)
							for image in image_inputs:
									self.assertIsInstance(_lowerCAmelCase      ,np.ndarray						)

							# Test not batched input
							__snake_case		       =     image_processor(image_inputs[0]      ,return_tensors="pt"						).pixel_values
							__snake_case       ,       __snake_case		       =     self.calculate_expected_size(image_inputs[0]						)
							self.assertEqual(
							    encoded_images.shape      ,(
							        1,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )      ,)







					def 							UpperCamelCase_						( self :					Any						):


							"""simple docstring"""




							__snake_case		       =     self.image_processing_class(**self.image_processor_dict						)
							# create random PyTorch tensors
							__snake_case		       =     self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCAmelCase      ,torchify=_lowerCAmelCase						)
							for image in image_inputs:
									self.assertIsInstance(_lowerCAmelCase      ,torch.Tensor						)

							# Test not batched input
							__snake_case		       =     image_processor(image_inputs[0]      ,return_tensors="pt"						).pixel_values
							__snake_case       ,       __snake_case		       =     self.calculate_expected_size(image_inputs[0]						)
							self.assertEqual(
							    encoded_images.shape      ,(
							        1,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )      ,)

import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase__           =				logging.get_logger(__name__)

lowerCamelCase__           =				{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

lowerCamelCase__           =				{
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
}

lowerCamelCase__           =				{
    'roberta-base': 5_12,
    'roberta-large': 5_12,
    'roberta-large-mnli': 5_12,
    'distilroberta-base': 5_12,
    'roberta-base-openai-detector': 5_12,
    'roberta-large-openai-detector': 5_12,
}



@lru_cache()
def 							_lowerCamelCase(				)  ->   List[str]:
		__snake_case		       =     (
		    list(range(ord("!"	)						,						ord("~"	) + 1	)	) + list(range(ord("¡"	)						,						ord("¬"	) + 1	)	) + list(range(ord("®"	)						,						ord("ÿ"	) + 1	)	)
		)
		__snake_case		       =     bs[:]
		__snake_case		       =     0
		for b in range(2**8	):
				if b not in bs:
						bs.append(__snake_case	)
						cs.append(2**8 + n	)
						n += 1
		__snake_case		       =     [chr(__snake_case	) for n in cs]
		return dict(zip(__snake_case						,						__snake_case	)	)



def 							_lowerCamelCase(				__snake_case	)  ->   Optional[Any]:
		__snake_case		       =     set()
		__snake_case		       =     word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)	)
				__snake_case		       =     char
		return pairs





class 					UpperCamelCase	(     snake_case__      ):
					__UpperCamelCase						=   VOCAB_FILES_NAMES
					__UpperCamelCase						=   PRETRAINED_VOCAB_FILES_MAP
					__UpperCamelCase						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__UpperCamelCase						=   ["""input_ids""", """attention_mask"""]




					def __init__( self :					str      ,_lowerCAmelCase :					Tuple      ,_lowerCAmelCase :					int      ,_lowerCAmelCase :					Union[str, Any]="replace"      ,_lowerCAmelCase :					List[Any]="<s>"      ,_lowerCAmelCase :					Union[str, Any]="</s>"      ,_lowerCAmelCase :					Any="</s>"      ,_lowerCAmelCase :					Optional[int]="<s>"      ,_lowerCAmelCase :					int="<unk>"      ,_lowerCAmelCase :					Tuple="<pad>"      ,_lowerCAmelCase :					Optional[Any]="<mask>"      ,_lowerCAmelCase :					List[str]=False      ,**_lowerCAmelCase :					Union[str, Any]      ,):


							"""simple docstring"""




							__snake_case		       =     AddedToken(_lowerCAmelCase      ,lstrip=_lowerCAmelCase      ,rstrip=_lowerCAmelCase						) if isinstance(_lowerCAmelCase      ,_lowerCAmelCase						) else bos_token
							__snake_case		       =     AddedToken(_lowerCAmelCase      ,lstrip=_lowerCAmelCase      ,rstrip=_lowerCAmelCase						) if isinstance(_lowerCAmelCase      ,_lowerCAmelCase						) else eos_token
							__snake_case		       =     AddedToken(_lowerCAmelCase      ,lstrip=_lowerCAmelCase      ,rstrip=_lowerCAmelCase						) if isinstance(_lowerCAmelCase      ,_lowerCAmelCase						) else sep_token
							__snake_case		       =     AddedToken(_lowerCAmelCase      ,lstrip=_lowerCAmelCase      ,rstrip=_lowerCAmelCase						) if isinstance(_lowerCAmelCase      ,_lowerCAmelCase						) else cls_token
							__snake_case		       =     AddedToken(_lowerCAmelCase      ,lstrip=_lowerCAmelCase      ,rstrip=_lowerCAmelCase						) if isinstance(_lowerCAmelCase      ,_lowerCAmelCase						) else unk_token
							__snake_case		       =     AddedToken(_lowerCAmelCase      ,lstrip=_lowerCAmelCase      ,rstrip=_lowerCAmelCase						) if isinstance(_lowerCAmelCase      ,_lowerCAmelCase						) else pad_token

							# Mask token behave like a normal word, i.e. include the space before it
							__snake_case		       =     AddedToken(_lowerCAmelCase      ,lstrip=_lowerCAmelCase      ,rstrip=_lowerCAmelCase						) if isinstance(_lowerCAmelCase      ,_lowerCAmelCase						) else mask_token

							super().__init__(
							    errors=_lowerCAmelCase      ,bos_token=_lowerCAmelCase      ,eos_token=_lowerCAmelCase      ,unk_token=_lowerCAmelCase      ,sep_token=_lowerCAmelCase      ,cls_token=_lowerCAmelCase      ,pad_token=_lowerCAmelCase      ,mask_token=_lowerCAmelCase      ,add_prefix_space=_lowerCAmelCase      ,**_lowerCAmelCase      ,)

							with open(_lowerCAmelCase      ,encoding="utf-8"						) as vocab_handle:
									__snake_case		       =     json.load(_lowerCAmelCase						)
							__snake_case		       =     {v: k for k, v in self.encoder.items()}
							__snake_case		       =     errors  # how to handle errors in decoding
							__snake_case		       =     bytes_to_unicode()
							__snake_case		       =     {v: k for k, v in self.byte_encoder.items()}
							with open(_lowerCAmelCase      ,encoding="utf-8"						) as merges_handle:
									__snake_case		       =     merges_handle.read().split("\n"						)[1:-1]
							__snake_case		       =     [tuple(merge.split()						) for merge in bpe_merges]
							__snake_case		       =     dict(zip(_lowerCAmelCase      ,range(len(_lowerCAmelCase						)						)						)						)
							__snake_case		       =     {}
							__snake_case		       =     add_prefix_space

							# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
							__snake_case		       =     re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"						)




					@property
					def 							UpperCamelCase_						( self :					int						):


							"""simple docstring"""




							return len(self.encoder						)




					def 							UpperCamelCase_						( self :					List[Any]						):


							"""simple docstring"""




							return dict(self.encoder      ,**self.added_tokens_encoder						)




					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					Tuple						):


							"""simple docstring"""




							if token in self.cache:
									return self.cache[token]
							__snake_case		       =     tuple(_lowerCAmelCase						)
							__snake_case		       =     get_pairs(_lowerCAmelCase						)

							if not pairs:
									return token

							while True:
									__snake_case		       =     min(_lowerCAmelCase      ,key=lambda _lowerCAmelCase						: self.bpe_ranks.get(_lowerCAmelCase      ,float("inf"						)						)						)
									if bigram not in self.bpe_ranks:
											break
									__snake_case       ,       __snake_case		       =     bigram
									__snake_case		       =     []
									__snake_case		       =     0
									while i < len(_lowerCAmelCase						):
											try:
													__snake_case		       =     word.index(_lowerCAmelCase      ,_lowerCAmelCase						)
											except ValueError:
													new_word.extend(word[i:]						)
													break
											else:
													new_word.extend(word[i:j]						)
													__snake_case		       =     j

											if word[i] == first and i < len(_lowerCAmelCase						) - 1 and word[i + 1] == second:
													new_word.append(first + second						)
													i += 2
											else:
													new_word.append(word[i]						)
													i += 1
									__snake_case		       =     tuple(_lowerCAmelCase						)
									__snake_case		       =     new_word
									if len(_lowerCAmelCase						) == 1:
											break
									else:
											__snake_case		       =     get_pairs(_lowerCAmelCase						)
							__snake_case		       =     " ".join(_lowerCAmelCase						)
							__snake_case		       =     word
							return word




					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					Dict						):


							"""simple docstring"""




							__snake_case		       =     []
							for token in re.findall(self.pat      ,_lowerCAmelCase						):
									__snake_case		       =     "".join(
									    self.byte_encoder[b] for b in token.encode("utf-8"						)						)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
									bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_lowerCAmelCase						).split(" "						)						)
							return bpe_tokens




					def 							UpperCamelCase_						( self :					List[str]      ,_lowerCAmelCase :					Dict						):


							"""simple docstring"""




							return self.encoder.get(_lowerCAmelCase      ,self.encoder.get(self.unk_token						)						)




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


							"""simple docstring"""




							return self.decoder.get(_lowerCAmelCase						)




					def 							UpperCamelCase_						( self :					Dict      ,_lowerCAmelCase :					List[Any]						):


							"""simple docstring"""




							__snake_case		       =     "".join(_lowerCAmelCase						)
							__snake_case		       =     bytearray([self.byte_decoder[c] for c in text]						).decode("utf-8"      ,errors=self.errors						)
							return text




					def 							UpperCamelCase_						( self :					str      ,_lowerCAmelCase :					str      ,_lowerCAmelCase :					Optional[str] = None						):


							"""simple docstring"""




							if not os.path.isdir(_lowerCAmelCase						):
									logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""						)
									return
							__snake_case		       =     os.path.join(
							    _lowerCAmelCase      ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)
							__snake_case		       =     os.path.join(
							    _lowerCAmelCase      ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]						)

							with open(_lowerCAmelCase      ,"w"      ,encoding="utf-8"						) as f:
									f.write(json.dumps(self.encoder      ,indent=2      ,sort_keys=_lowerCAmelCase      ,ensure_ascii=_lowerCAmelCase						) + "\n"						)

							__snake_case		       =     0
							with open(_lowerCAmelCase      ,"w"      ,encoding="utf-8"						) as writer:
									writer.write("#version: 0.2\n"						)
									for bpe_tokens, token_index in sorted(self.bpe_ranks.items()      ,key=lambda _lowerCAmelCase						: kv[1]						):
											if index != token_index:
													logger.warning(
													    F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
													    " Please check that the tokenizer is not corrupted!"						)
													__snake_case		       =     token_index
											writer.write(" ".join(_lowerCAmelCase						) + "\n"						)
											index += 1

							return vocab_file, merge_file




					def 							UpperCamelCase_						( self :					Dict      ,_lowerCAmelCase :					List[int]      ,_lowerCAmelCase :					Optional[List[int]] = None						):


							"""simple docstring"""




							if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							__snake_case		       =     [self.cls_token_id]
							__snake_case		       =     [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep




					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					List[int]      ,_lowerCAmelCase :					Optional[List[int]] = None      ,_lowerCAmelCase :					bool = False						):


							"""simple docstring"""




							if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=_lowerCAmelCase      ,token_ids_a=_lowerCAmelCase      ,already_has_special_tokens=_lowerCAmelCase						)

							if token_ids_a is None:
									return [1] + ([0] * len(_lowerCAmelCase						)) + [1]
							return [1] + ([0] * len(_lowerCAmelCase						)) + [1, 1] + ([0] * len(_lowerCAmelCase						)) + [1]




					def 							UpperCamelCase_						( self :					Tuple      ,_lowerCAmelCase :					List[int]      ,_lowerCAmelCase :					Optional[List[int]] = None						):


							"""simple docstring"""




							__snake_case		       =     [self.sep_token_id]
							__snake_case		       =     [self.cls_token_id]

							if token_ids_a is None:
									return len(cls + token_ids_a + sep						) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]







					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					List[Any]      ,_lowerCAmelCase :					Optional[Any]=False      ,**_lowerCAmelCase :					List[str]						):


							"""simple docstring"""




							__snake_case		       =     kwargs.pop("add_prefix_space"      ,self.add_prefix_space						)
							if (is_split_into_words or add_prefix_space) and (len(_lowerCAmelCase						) > 0 and not text[0].isspace()):
									__snake_case		       =     " " + text
							return (text, kwargs)





