
'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase					:    str           = logging.get_logger(__name__)


lowerCAmelCase					:    Tuple           = {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

lowerCAmelCase					:    Dict           = {
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

lowerCAmelCase					:    Optional[int]           = {'facebook/blenderbot_small-90M': 5_12}







def 		A_(     A						:		int):
							UpperCamelCase   			=     set()
							UpperCamelCase   			=     word[0]
							for char in word[1:]:
														pairs.add((prev_char, char))
														UpperCamelCase   			=     char

							UpperCamelCase   			=     set(A)
							return pairs





class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							VOCAB_FILES_NAMES
				lowerCAmelCase_     			=							PRETRAINED_VOCAB_FILES_MAP
				lowerCAmelCase_     			=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCAmelCase_     			=							["""input_ids""", """attention_mask"""]







				def __init__(       self						,				A_						,				A_						,				A_="__start__"						,				A_="__end__"						,				A_="__unk__"						,				A_="__null__"						,				**A_						,				)->		Optional[int]:







											'''simple docstring'''






											super().__init__(unk_token=A_						,				bos_token=A_						,				eos_token=A_						,				pad_token=A_						,				**A_     )

											with open(A_						,				encoding='utf-8'     ) as vocab_handle:
																		UpperCamelCase   			=     json.load(A_     )
											UpperCamelCase   			=     {v: k for k, v in self.encoder.items()}
											with open(A_						,				encoding='utf-8'     ) as merges_handle:
																		UpperCamelCase   			=     merges_handle.read().split('\n'     )[1:-1]
											UpperCamelCase   			=     [tuple(merge.split()     ) for merge in merges]
											UpperCamelCase   			=     dict(zip(A_						,				range(len(A_     )     )     )     )
											UpperCamelCase   			=     {}







				@property
				def 						UpperCAmelCase_    (       self     )->		int:







											'''simple docstring'''






											return len(self.encoder     )







				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self						,				A_     )->		str:







											'''simple docstring'''






											if token in self.cache:
																		return self.cache[token]
											UpperCamelCase   			=     re.sub('([.,!?()])'						,				R' \1'						,				A_     )
											UpperCamelCase   			=     re.sub('(\')'						,				R' \1 '						,				A_     )
											UpperCamelCase   			=     re.sub(R'\s{2,}'						,				' '						,				A_     )
											if "\n" in token:
																		UpperCamelCase   			=     token.replace('\n'						,				' __newln__'     )

											UpperCamelCase   			=     token.split(' '     )
											UpperCamelCase   			=     []
											for token in tokens:
																		if not len(A_     ):
																									continue

																		UpperCamelCase   			=     token.lower()
																		UpperCamelCase   			=     tuple(A_     )
																		UpperCamelCase   			=     tuple(list(word[:-1]     ) + [word[-1] + '</w>']     )
																		UpperCamelCase   			=     get_pairs(A_     )

																		if not pairs:
																									words.append(A_     )
																									continue

																		while True:
																									UpperCamelCase   			=     min(A_						,				key=lambda A_     : self.bpe_ranks.get(A_						,				float('inf'     )     )     )
																									if bigram not in self.bpe_ranks:
																																break
																									UpperCamelCase			,	UpperCamelCase   			=     bigram
																									UpperCamelCase   			=     []
																									UpperCamelCase   			=     0

																									while i < len(A_     ):
																																try:
																																							UpperCamelCase   			=     word.index(A_						,				A_     )
																																							new_word.extend(word[i:j]     )
																																							UpperCamelCase   			=     j
																																except ValueError:
																																							new_word.extend(word[i:]     )
																																							break

																																if word[i] == first and i < len(A_     ) - 1 and word[i + 1] == second:
																																							new_word.append(first + second     )
																																							i += 2
																																else:
																																							new_word.append(word[i]     )
																																							i += 1
																									UpperCamelCase   			=     tuple(A_     )
																									UpperCamelCase   			=     new_word
																									if len(A_     ) == 1:
																																break
																									else:
																																UpperCamelCase   			=     get_pairs(A_     )
																		UpperCamelCase   			=     '@@ '.join(A_     )
																		UpperCamelCase   			=     word[:-4]

																		UpperCamelCase   			=     word
																		words.append(A_     )
											return " ".join(A_     )







				def 						UpperCAmelCase_    (       self						,				A_     )->		List[str]:







											'''simple docstring'''






											UpperCamelCase   			=     []

											UpperCamelCase   			=     re.findall(R'\S+\n?'						,				A_     )

											for token in words:
																		split_tokens.extend(list(self.bpe(A_     ).split(' '     )     )     )
											return split_tokens







				def 						UpperCAmelCase_    (       self						,				A_     )->		int:







											'''simple docstring'''






											UpperCamelCase   			=     token.lower()
											return self.encoder.get(A_						,				self.encoder.get(self.unk_token     )     )







				def 						UpperCAmelCase_    (       self						,				A_     )->		str:







											'''simple docstring'''






											return self.decoder.get(A_						,				self.unk_token     )







				def 						UpperCAmelCase_    (       self						,				A_     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     ' '.join(A_     ).replace('@@ '						,				''     ).strip()
											return out_string







				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = None     )->		Tuple[str]:







											'''simple docstring'''






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

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

											UpperCamelCase   			=     0
											with open(A_						,				'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 A_     : 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!'     )
																																UpperCamelCase   			=     token_index
																									writer.write(' '.join(A_     ) + '\n'     )
																									index += 1

											return vocab_file, merge_file






'''simple docstring'''
import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor


class 			SCREAMING_SNAKE_CASE__      (     unittest.TestCase):







				def __init__(       self						,				A_						,				A_=7						,				A_=3						,				A_=18						,				A_=30						,				A_=400						,				A_=True						,				A_=None						,				A_=True						,				A_=False						,				A_=True						,				A_=True						,				A_=[0.5, 0.5, 0.5]						,				A_=[0.5, 0.5, 0.5]						,				)->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     parent
											UpperCamelCase   			=     batch_size
											UpperCamelCase   			=     num_channels
											UpperCamelCase   			=     image_size
											UpperCamelCase   			=     min_resolution
											UpperCamelCase   			=     max_resolution
											UpperCamelCase   			=     do_resize
											UpperCamelCase   			=     size if size is not None else {'height': 18, 'width': 20}
											UpperCamelCase   			=     do_thumbnail
											UpperCamelCase   			=     do_align_axis
											UpperCamelCase   			=     do_pad
											UpperCamelCase   			=     do_normalize
											UpperCamelCase   			=     image_mean
											UpperCamelCase   			=     image_std







				def 						UpperCAmelCase_    (       self     )->		List[Any]:







											'''simple docstring'''






											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_thumbnail": self.do_thumbnail,
											    "do_align_long_axis": self.do_align_axis,
											    "do_pad": self.do_pad,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											}


@require_torch
@require_vision
class 			SCREAMING_SNAKE_CASE__      (     snake_case_							,       unittest.TestCase):
				lowerCAmelCase_     			=							DonutImageProcessor if is_vision_available() else None







				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     DonutImageProcessingTester(self     )







				@property
				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											return self.image_processor_tester.prepare_image_processor_dict()







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class(**self.image_processor_dict     )
											self.assertTrue(hasattr(A_						,				'do_resize'     )     )
											self.assertTrue(hasattr(A_						,				'size'     )     )
											self.assertTrue(hasattr(A_						,				'do_thumbnail'     )     )
											self.assertTrue(hasattr(A_						,				'do_align_long_axis'     )     )
											self.assertTrue(hasattr(A_						,				'do_pad'     )     )
											self.assertTrue(hasattr(A_						,				'do_normalize'     )     )
											self.assertTrue(hasattr(A_						,				'image_mean'     )     )
											self.assertTrue(hasattr(A_						,				'image_std'     )     )







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class.from_dict(self.image_processor_dict     )
											self.assertEqual(image_processor.size						,				{'height': 18, 'width': 20}     )

											UpperCamelCase   			=     self.image_processing_class.from_dict(self.image_processor_dict						,				size=42     )
											self.assertEqual(image_processor.size						,				{'height': 42, 'width': 42}     )

											# Previous config had dimensions in (width, height) order
											UpperCamelCase   			=     self.image_processing_class.from_dict(self.image_processor_dict						,				size=(42, 84)     )
											self.assertEqual(image_processor.size						,				{'height': 84, 'width': 42}     )







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






											pass







				@is_flaky()
				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class(**self.image_processor_dict     )
											# create random PIL images
											UpperCamelCase   			=     prepare_image_inputs(self.image_processor_tester						,				equal_resolution=A_     )
											for image in image_inputs:
																		self.assertIsInstance(A_						,				Image.Image     )

											# Test not batched input
											UpperCamelCase   			=     image_processing(image_inputs[0]						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)

											# Test batched
											UpperCamelCase   			=     image_processing(A_						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)







				@is_flaky()
				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






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

											# Test not batched input
											UpperCamelCase   			=     image_processing(image_inputs[0]						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)

											# Test batched
											UpperCamelCase   			=     image_processing(A_						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)







				@is_flaky()
				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






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

											# Test not batched input
											UpperCamelCase   			=     image_processing(image_inputs[0]						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)

											# Test batched
											UpperCamelCase   			=     image_processing(A_						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)




