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

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


_UpperCAmelCase   :					Optional[int]    =			logging.get_logger(__name__)

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

_UpperCAmelCase   :					Tuple    =			{
    'vocab_file': {
        'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json',
    },
    'merges_file': {'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt'},
}

_UpperCAmelCase   :					int    =			{
    'microsoft/biogpt': 1_024,
}


def  A			(   lowercase					)  ->    Tuple:
		'''simple docstring'''

		UpperCamelCase            =	set()
		UpperCamelCase            =	word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)					)
				UpperCamelCase            =	char
		return pairs


class 							lowercase   (						UpperCAmelCase__  ):
		__lowercase			:			Optional[Any]      	=							VOCAB_FILES_NAMES
		__lowercase			:			List[Any]      	=							PRETRAINED_VOCAB_FILES_MAP
		__lowercase			:			List[Any]      	=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowercase			:			Dict      	=							["""input_ids""", """attention_mask"""]



		def __init__(					self							, A_							, A_							, A_="<unk>"							, A_="<s>"							, A_="</s>"							, A_="</s>"							, A_="<pad>"							, **A_							, )    ->   Union[str, Any]:
				"""simple docstring"""
				super().__init__(
				    bos_token=A_							, eos_token=A_							, sep_token=A_							, unk_token=A_							, pad_token=A_							, **A_							, )

				try:
						import sacremoses
				except ImportError:
						raise ImportError(
						    'You need to install sacremoses to use BioGptTokenizer. '
						    'See https://pypi.org/project/sacremoses/ for installation.'			)

				UpperCamelCase            =	'en'
				UpperCamelCase            =	sacremoses
				# cache of sm.MosesTokenizer instance
				UpperCamelCase            =	{}
				UpperCamelCase            =	{}
				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]
				UpperCamelCase            =	[tuple(merge.split()[:2]			) for merge in merges]
				UpperCamelCase            =	dict(zip(A_							, range(len(A_			)			)			)			)
				UpperCamelCase            =	{}



		@property
		def __UpperCamelCase					(					self			)    ->   str:
				"""simple docstring"""
				return len(self.encoder			)



		def __UpperCamelCase					(					self			)    ->   Any:
				"""simple docstring"""
				return dict(self.encoder							, **self.added_tokens_encoder			)



		def __UpperCamelCase					(					self							, A_							, A_			)    ->   List[str]:
				"""simple docstring"""
				if lang not in self.cache_moses_tokenizer:
						UpperCamelCase            =	self.sm.MosesTokenizer(lang=A_			)
						UpperCamelCase            =	moses_tokenizer
				return self.cache_moses_tokenizer[lang].tokenize(
				    A_							, aggressive_dash_splits=A_							, return_str=A_							, escape=A_			)



		def __UpperCamelCase					(					self							, A_							, A_			)    ->   List[Any]:
				"""simple docstring"""
				if lang not in self.cache_moses_detokenizer:
						UpperCamelCase            =	self.sm.MosesDetokenizer(lang=A_			)
						UpperCamelCase            =	moses_detokenizer
				return self.cache_moses_detokenizer[lang].detokenize(A_			)



		def __UpperCamelCase					(					self							, A_			)    ->   Any:
				"""simple docstring"""
				UpperCamelCase            =	tuple(token[:-1]			) + (token[-1] + '</w>',)
				if token in self.cache:
						return self.cache[token]
				UpperCamelCase            =	get_pairs(A_			)

				if not pairs:
						return token + "</w>"

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

								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_			)
				if word == "\n  </w>":
						UpperCamelCase            =	'\n</w>'
				UpperCamelCase            =	word
				return word



		def __UpperCamelCase					(					self							, A_							, A_=False			)    ->   List[Any]:
				"""simple docstring"""
				if bypass_tokenizer:
						UpperCamelCase            =	text.split()
				else:
						UpperCamelCase            =	self.moses_tokenize(A_							, self.lang			)

				UpperCamelCase            =	[]
				for token in text:
						if token:
								split_tokens.extend(list(self.bpe(A_			).split(' '			)			)			)

				return split_tokens



		def __UpperCamelCase					(					self							, A_			)    ->   Optional[int]:
				"""simple docstring"""
				return self.encoder.get(A_							, self.encoder.get(self.unk_token			)			)



		def __UpperCamelCase					(					self							, A_			)    ->   List[Any]:
				"""simple docstring"""
				return self.decoder.get(A_							, self.unk_token			)



		def __UpperCamelCase					(					self							, A_			)    ->   List[str]:
				"""simple docstring"""
				UpperCamelCase            =	[t.replace(' '							, ''			).replace('</w>'							, ' '			) for t in tokens]
				UpperCamelCase            =	''.join(A_			).split()
				# detokenize
				UpperCamelCase            =	self.moses_detokenize(A_							, self.lang			)
				return text



		def __UpperCamelCase					(					self							, A_							, A_ = None			)    ->   List[int]:
				"""simple docstring"""
				if token_ids_a is None:
						return [self.sep_token_id] + token_ids_a
				UpperCamelCase            =	[self.sep_token_id]
				return sep + token_ids_a + sep + token_ids_a



		def __UpperCamelCase					(					self							, A_							, A_ = None							, A_ = False			)    ->   List[int]:
				"""simple docstring"""
				if already_has_special_tokens:
						return super().get_special_tokens_mask(
						    token_ids_a=A_							, token_ids_a=A_							, already_has_special_tokens=A_			)
				# no bos used in fairseq
				if token_ids_a is not None:
						return [1] + ([0] * len(A_			)) + [1] + ([0] * len(A_			))
				return [1] + ([0] * len(A_			))



		def __UpperCamelCase					(					self							, A_							, A_ = None			)    ->   List[int]:
				"""simple docstring"""
				UpperCamelCase            =	[self.sep_token_id]

				# no bos used in fairseq
				if token_ids_a is None:
						return len(token_ids_a + sep			) * [0]
				return len(token_ids_a + sep			) * [0] + len(token_ids_a + sep			) * [1]



		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:
						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



		def __getstate__(					self			)    ->   Optional[int]:
				"""simple docstring"""
				UpperCamelCase            =	self.__dict__.copy()
				UpperCamelCase            =	None
				return state



		def __setstate__(					self							, A_			)    ->   Dict:
				"""simple docstring"""
				UpperCamelCase            =	d

				try:
						import sacremoses
				except ImportError:
						raise ImportError(
						    'You need to install sacremoses to use XLMTokenizer. '
						    'See https://pypi.org/project/sacremoses/ for installation.'			)

				UpperCamelCase            =	sacremoses


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, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import DeiTImageProcessor


class 	UpperCamelCase__       (  unittest.TestCase):

						'''simple docstring'''



						def __init__(  self  , A  , A=7  , A=3  , A=18  , A=30  , A=4_00  , A=True  , A=None  , A=True  , A=None  , A=True  , A=[0.5, 0.5, 0.5]  , A=[0.5, 0.5, 0.5]  , )  ->Any:
									UpperCAmelCase__		:Optional[int] 		=    size if size is not None else {'height': 20, 'width': 20}
									UpperCAmelCase__		:str 		=    crop_size if crop_size is not None else {'height': 18, 'width': 18}

									UpperCAmelCase__		:Dict 		=    parent
									UpperCAmelCase__		:Optional[Any] 		=    batch_size
									UpperCAmelCase__		:Optional[Any] 		=    num_channels
									UpperCAmelCase__		:str 		=    image_size
									UpperCAmelCase__		:Optional[Any] 		=    min_resolution
									UpperCAmelCase__		:Optional[int] 		=    max_resolution
									UpperCAmelCase__		:Dict 		=    do_resize
									UpperCAmelCase__		:Any 		=    size
									UpperCAmelCase__		:Any 		=    do_center_crop
									UpperCAmelCase__		:Optional[int] 		=    crop_size
									UpperCAmelCase__		:Dict 		=    do_normalize
									UpperCAmelCase__		:List[Any] 		=    image_mean
									UpperCAmelCase__		:Tuple 		=    image_std


						def    A__       (  self	)  ->str:
									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_center_crop": self.do_center_crop,
									    "crop_size": self.crop_size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									}


@require_torch
@require_vision
class 	UpperCamelCase__       (  UpperCAmelCase__						,			unittest.TestCase):

						'''simple docstring'''



						__a     :    Tuple												=			DeiTImageProcessor if is_vision_available() else None
						__a     :    Tuple												=			True


						def    A__       (  self	)  ->Optional[int]:
									UpperCAmelCase__		:int 		=    DeiTImageProcessingTester(self	)


						@property
						def    A__       (  self	)  ->List[Any]:
									return self.image_processor_tester.prepare_image_processor_dict()


						def    A__       (  self	)  ->Optional[Any]:
									UpperCAmelCase__		:Tuple 		=    self.image_processing_class(**self.image_processor_dict	)
									self.assertTrue(hasattr(A  , 'do_resize'	)	)
									self.assertTrue(hasattr(A  , 'size'	)	)
									self.assertTrue(hasattr(A  , 'do_center_crop'	)	)
									self.assertTrue(hasattr(A  , 'center_crop'	)	)
									self.assertTrue(hasattr(A  , 'do_normalize'	)	)
									self.assertTrue(hasattr(A  , 'image_mean'	)	)
									self.assertTrue(hasattr(A  , 'image_std'	)	)


						def    A__       (  self	)  ->Tuple:
									UpperCAmelCase__		:Union[str, Any] 		=    self.image_processing_class.from_dict(self.image_processor_dict	)
									self.assertEqual(image_processor.size  , {'height': 20, 'width': 20}	)
									self.assertEqual(image_processor.crop_size  , {'height': 18, 'width': 18}	)

									UpperCAmelCase__		:List[Any] 		=    self.image_processing_class.from_dict(self.image_processor_dict  , size=42  , crop_size=84	)
									self.assertEqual(image_processor.size  , {'height': 42, 'width': 42}	)
									self.assertEqual(image_processor.crop_size  , {'height': 84, 'width': 84}	)


						def    A__       (  self	)  ->List[str]:
									pass


						def    A__       (  self	)  ->List[str]:
									# Initialize image_processing
									UpperCAmelCase__		:int 		=    self.image_processing_class(**self.image_processor_dict	)
									# create random PIL images
									UpperCAmelCase__		:Tuple 		=    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__		:int 		=    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.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )  , )

									# Test batched
									UpperCAmelCase__		:List[str] 		=    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.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )  , )


						def    A__       (  self	)  ->str:
									# Initialize image_processing
									UpperCAmelCase__		:Tuple 		=    self.image_processing_class(**self.image_processor_dict	)
									# create random numpy tensors
									UpperCAmelCase__		:Dict 		=    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__		:List[str] 		=    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.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )  , )

									# Test batched
									UpperCAmelCase__		:Optional[Any] 		=    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.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )  , )


						def    A__       (  self	)  ->Tuple:
									# Initialize image_processing
									UpperCAmelCase__		:List[Any] 		=    self.image_processing_class(**self.image_processor_dict	)
									# create random PyTorch tensors
									UpperCAmelCase__		:str 		=    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__		:List[str] 		=    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.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )  , )

									# Test batched
									UpperCAmelCase__		:Optional[int] 		=    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.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )  , )





