





"""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


__UpperCAmelCase						=			logging.get_logger(__name__)


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

__UpperCAmelCase						=			{
    '''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'''
        )
    },
}

__UpperCAmelCase						=			{'''facebook/blenderbot_small-90M''': 512}






def      lowercase__ (					lowerCAmelCase__   :			List[Any]			)    ->			Optional[int]:



			'''simple docstring'''
			a__  :	List[str]		 =			set()
			a__  :	Optional[Any]		 =			word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)			)
						a__  :	Union[str, Any]		 =			char

			a__  :	Any		 =			set(lowerCAmelCase__			)
			return pairs





class 					__UpperCAmelCase							(						_UpperCamelCase  ):
				__lowerCamelCase				:	Union[str, Any]				     =				VOCAB_FILES_NAMES
				__lowerCamelCase				:	List[Any]				     =				PRETRAINED_VOCAB_FILES_MAP
				__lowerCamelCase				:	List[Any]				     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__lowerCamelCase				:	str				     =				["input_ids", "attention_mask"]
				def __init__(		self    :			int	, a_    :			Optional[Any]	, a_    :			Tuple	, a_    :			List[str]="__start__"	, a_    :			List[Any]="__end__"	, a_    :			int="__unk__"	, a_    :			Optional[int]="__null__"	, **a_    :			List[Any]	, )	->	Optional[Any]:



							'''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:
										a__  :	Union[str, Any]		 =			json.load(a_    )
							a__  :	Any		 =			{v: k for k, v in self.encoder.items()}
							with open(a_	, encoding="utf-8"    ) as merges_handle:
										a__  :	List[str]		 =			merges_handle.read().split("\n"    )[1:-1]
							a__  :	Optional[Any]		 =			[tuple(merge.split()    ) for merge in merges]
							a__  :	Tuple		 =			dict(zip(a_	, range(len(a_    )    )    )    )
							a__  :	List[Any]		 =			{}
				@property
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	int:



							'''simple docstring'''



							return len(self.encoder    )
				def       UpperCAmelCase      (		self    :			List[str]    )	->	Dict:



							'''simple docstring'''



							return dict(self.encoder	, **self.added_tokens_encoder    )
				def       UpperCAmelCase      (		self    :			List[str]	, a_    :			str    )	->	str:



							'''simple docstring'''



							if token in self.cache:
										return self.cache[token]
							a__  :	List[str]		 =			re.sub("([.,!?()])"	, R" \1"	, a_    )
							a__  :	Optional[Any]		 =			re.sub("(')"	, R" \1 "	, a_    )
							a__  :	Any		 =			re.sub(R"\s{2,}"	, " "	, a_    )
							if "\n" in token:
										a__  :	int		 =			token.replace("\n"	, " __newln__"    )

							a__  :	Tuple		 =			token.split(" "    )
							a__  :	Tuple		 =			[]
							for token in tokens:
										if not len(a_    ):
													continue

										a__  :	Optional[int]		 =			token.lower()
										a__  :	int		 =			tuple(a_    )
										a__  :	Tuple		 =			tuple(list(word[:-1]    ) + [word[-1] + "</w>"]    )
										a__  :	Optional[int]		 =			get_pairs(a_    )

										if not pairs:
													words.append(a_    )
													continue

										while True:
													a__  :	str		 =			min(a_	, key=lambda a_    : self.bpe_ranks.get(a_	, float("inf"    )    )    )
													if bigram not in self.bpe_ranks:
																break
													a__ ,	a__  :	Optional[int]		 =			bigram
													a__  :	Optional[int]		 =			[]
													a__  :	Union[str, Any]		 =			0

													while i < len(a_    ):
																try:
																			a__  :	int		 =			word.index(a_	, a_    )
																			new_word.extend(word[i:j]    )
																			a__  :	Optional[int]		 =			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
													a__  :	int		 =			tuple(a_    )
													a__  :	List[Any]		 =			new_word
													if len(a_    ) == 1:
																break
													else:
																a__  :	int		 =			get_pairs(a_    )
										a__  :	Optional[Any]		 =			"@@ ".join(a_    )
										a__  :	str		 =			word[:-4]

										a__  :	Union[str, Any]		 =			word
										words.append(a_    )
							return " ".join(a_    )
				def       UpperCAmelCase      (		self    :			List[str]	, a_    :			str    )	->	List[str]:



							'''simple docstring'''



							a__  :	int		 =			[]

							a__  :	Tuple		 =			re.findall(R"\S+\n?"	, a_    )

							for token in words:
										split_tokens.extend(list(self.bpe(a_    ).split(" "    )    )    )
							return split_tokens
				def       UpperCAmelCase      (		self    :			List[str]	, a_    :			str    )	->	int:



							'''simple docstring'''



							a__  :	int		 =			token.lower()
							return self.encoder.get(a_	, self.encoder.get(self.unk_token    )    )
				def       UpperCAmelCase      (		self    :			Tuple	, a_    :			int    )	->	str:



							'''simple docstring'''



							return self.decoder.get(a_	, self.unk_token    )
				def       UpperCAmelCase      (		self    :			str	, a_    :			List[str]    )	->	str:



							'''simple docstring'''



							a__  :	Any		 =			" ".join(a_    ).replace("@@ "	, ""    ).strip()
							return out_string






				def       UpperCAmelCase      (		self    :			Any	, a_    :			str	, a_    :			Optional[str] = None    )	->	Tuple[str]:



							'''simple docstring'''



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

							a__  :	List[str]		 =			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!"    )
																a__  :	Optional[Any]		 =			token_index
													writer.write(" ".join(a_    ) + "\n"    )
													index += 1

							return vocab_file, merge_file






"""simple docstring"""






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
		import torch


if is_vision_available():
		from PIL import Image

		from transformers import MgpstrProcessor, ViTImageProcessor



@require_torch
@require_vision
class 					__UpperCAmelCase							(						unittest.TestCase  ):
				__lowerCamelCase				:	int				     =				ViTImageProcessor if is_vision_available() else None
				@property
				def       UpperCAmelCase      (		self    :			List[str]    )	->	List[str]:



							'''simple docstring'''



							return self.image_processor_tester.prepare_image_processor_dict()
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	Optional[Any]:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			(3, 32, 1_28)
							a__  :	Any		 =			tempfile.mkdtemp()

							# fmt: off
							a__  :	str		 =			["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
							# fmt: on
							a__  :	List[Any]		 =			dict(zip(a_	, range(len(a_    )    )    )    )

							a__  :	int		 =			os.path.join(self.tmpdirname	, VOCAB_FILES_NAMES["vocab_file"]    )
							with open(self.vocab_file	, "w"	, encoding="utf-8"    ) as fp:
										fp.write(json.dumps(a_    ) + "\n"    )

							a__  :	Any		 =			{
							    "do_normalize": False,
							    "do_resize": True,
							    "image_processor_type": "ViTImageProcessor",
							    "resample": 3,
							    "size": {"height": 32, "width": 1_28},
							}
							a__  :	Tuple		 =			os.path.join(self.tmpdirname	, a_    )
							with open(self.image_processor_file	, "w"	, encoding="utf-8"    ) as fp:
										json.dump(a_	, a_    )
				def       UpperCAmelCase      (		self    :			List[str]	, **a_    :			List[str]    )	->	Union[str, Any]:



							'''simple docstring'''



							return MgpstrTokenizer.from_pretrained(self.tmpdirname	, **a_    )
				def       UpperCAmelCase      (		self    :			List[Any]	, **a_    :			Union[str, Any]    )	->	Dict:



							'''simple docstring'''



							return ViTImageProcessor.from_pretrained(self.tmpdirname	, **a_    )
				def       UpperCAmelCase      (		self    :			Dict    )	->	int:



							'''simple docstring'''



							shutil.rmtree(self.tmpdirname    )
				def       UpperCAmelCase      (		self    :			List[str]    )	->	Any:



							'''simple docstring'''



							a__  :	Any		 =			np.random.randint(2_55	, size=(3, 30, 4_00)	, dtype=np.uinta    )

							a__  :	List[str]		 =			Image.fromarray(np.moveaxis(a_	, 0	, -1    )    )

							return image_input
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	Optional[Any]:



							'''simple docstring'''



							a__  :	List[Any]		 =			self.get_tokenizer()
							a__  :	Any		 =			self.get_image_processor()

							a__  :	Optional[int]		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )
							processor.save_pretrained(self.tmpdirname    )
							a__  :	Optional[Any]		 =			MgpstrProcessor.from_pretrained(self.tmpdirname	, use_fast=a_    )

							self.assertEqual(processor.char_tokenizer.get_vocab()	, tokenizer.get_vocab()    )
							self.assertIsInstance(processor.char_tokenizer	, a_    )

							self.assertEqual(processor.image_processor.to_json_string()	, image_processor.to_json_string()    )
							self.assertIsInstance(processor.image_processor	, a_    )
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	str:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			self.get_tokenizer()
							a__  :	Tuple		 =			self.get_image_processor()

							a__  :	Optional[int]		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )
							processor.save_pretrained(self.tmpdirname    )

							a__  :	Union[str, Any]		 =			self.get_tokenizer(bos_token="(BOS)"	, eos_token="(EOS)"    )
							a__  :	Any		 =			self.get_image_processor(do_normalize=a_	, padding_value=1.0    )

							a__  :	Dict		 =			MgpstrProcessor.from_pretrained(
							    self.tmpdirname	, bos_token="(BOS)"	, eos_token="(EOS)"	, do_normalize=a_	, padding_value=1.0    )

							self.assertEqual(processor.char_tokenizer.get_vocab()	, tokenizer_add_kwargs.get_vocab()    )
							self.assertIsInstance(processor.char_tokenizer	, a_    )

							self.assertEqual(processor.image_processor.to_json_string()	, image_processor_add_kwargs.to_json_string()    )
							self.assertIsInstance(processor.image_processor	, a_    )
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	Dict:



							'''simple docstring'''



							a__  :	str		 =			self.get_image_processor()
							a__  :	Union[str, Any]		 =			self.get_tokenizer()

							a__  :	Any		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	List[str]		 =			self.prepare_image_inputs()

							a__  :	List[Any]		 =			image_processor(a_	, return_tensors="np"    )
							a__  :	Optional[Any]		 =			processor(images=a_	, return_tensors="np"    )

							for key in input_image_proc.keys():
										self.assertAlmostEqual(input_image_proc[key].sum()	, input_processor[key].sum()	, delta=1E-2    )
				def       UpperCAmelCase      (		self    :			Tuple    )	->	int:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			self.get_image_processor()
							a__  :	List[Any]		 =			self.get_tokenizer()

							a__  :	int		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	List[str]		 =			"test"

							a__  :	Any		 =			processor(text=a_    )

							a__  :	Tuple		 =			tokenizer(a_    )
							for key in encoded_tok.keys():
										self.assertListEqual(encoded_tok[key]	, encoded_processor[key]    )
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	Optional[int]:



							'''simple docstring'''



							a__  :	Tuple		 =			self.get_image_processor()
							a__  :	List[str]		 =			self.get_tokenizer()

							a__  :	List[Any]		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	str		 =			"test"
							a__  :	str		 =			self.prepare_image_inputs()

							a__  :	Any		 =			processor(text=a_	, images=a_    )

							self.assertListEqual(list(inputs.keys()    )	, ["pixel_values", "labels"]    )

							# test if it raises when no input is passed
							with pytest.raises(a_    ):
										processor()
				def       UpperCAmelCase      (		self    :			List[str]    )	->	int:



							'''simple docstring'''



							a__  :	Any		 =			self.get_image_processor()
							a__  :	Tuple		 =			self.get_tokenizer()

							a__  :	Dict		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	Optional[Any]		 =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

							a__  :	Any		 =			processor.char_decode(a_    )
							a__  :	str		 =			tokenizer.batch_decode(a_    )
							a__  :	Union[str, Any]		 =			[seq.replace(" "	, ""    ) for seq in decoded_tok]

							self.assertListEqual(a_	, a_    )
				def       UpperCAmelCase      (		self    :			List[Any]    )	->	Dict:



							'''simple docstring'''



							a__  :	List[str]		 =			self.get_image_processor()
							a__  :	Any		 =			self.get_tokenizer()

							a__  :	Any		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	str		 =			None
							a__  :	Optional[Any]		 =			self.prepare_image_inputs()

							a__  :	Optional[int]		 =			processor(text=a_	, images=a_    )

							self.assertListEqual(list(inputs.keys()    )	, processor.model_input_names    )






				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	List[Any]:



							'''simple docstring'''



							a__  :	List[Any]		 =			self.get_image_processor()
							a__  :	List[str]		 =			self.get_tokenizer()

							a__  :	Optional[Any]		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	List[str]		 =			torch.randn(1	, 27	, 38    )
							a__  :	Tuple		 =			torch.randn(1	, 27	, 5_02_57    )
							a__  :	List[Any]		 =			torch.randn(1	, 27	, 3_05_22    )

							a__  :	Optional[Any]		 =			processor.batch_decode([char_input, bpe_input, wp_input]    )

							self.assertListEqual(list(results.keys()    )	, ["generated_text", "scores", "char_preds", "bpe_preds", "wp_preds"]    )