
"""simple docstring"""







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 MobileNetVaImageProcessor




class 					A__			(      unittest.TestCase):

		def __init__( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE=7			,						_SCREAMING_SNAKE_CASE=3			,						_SCREAMING_SNAKE_CASE=18			,						_SCREAMING_SNAKE_CASE=30			,						_SCREAMING_SNAKE_CASE=4_00			,						_SCREAMING_SNAKE_CASE=True			,						_SCREAMING_SNAKE_CASE=None			,						_SCREAMING_SNAKE_CASE=True			,						_SCREAMING_SNAKE_CASE=None			,						):
			__lowerCAmelCase     :		Any		     = size if size is not None else {'shortest_edge': 20}
			__lowerCAmelCase     :		Dict		     = crop_size if crop_size is not None else {'height': 18, 'width': 18}
			__lowerCAmelCase     :		Dict		     = parent
			__lowerCAmelCase     :		Optional[Any]		     = batch_size
			__lowerCAmelCase     :		Tuple		     = num_channels
			__lowerCAmelCase     :		List[Any]		     = image_size
			__lowerCAmelCase     :		int		     = min_resolution
			__lowerCAmelCase     :		int		     = max_resolution
			__lowerCAmelCase     :		Any		     = do_resize
			__lowerCAmelCase     :		Union[str, Any]		     = size
			__lowerCAmelCase     :		Optional[int]		     = do_center_crop
			__lowerCAmelCase     :		str		     = crop_size







		def 				__lowerCamelCase				( self					):
			return {
			    "do_resize": self.do_resize,
			    "size": self.size,
			    "do_center_crop": self.do_center_crop,
			    "crop_size": self.crop_size,
			}




@require_torch
@require_vision
class 					A__			(      _lowerCamelCase			,	unittest.TestCase):
		A_		:			Tuple							  =      MobileNetVaImageProcessor if is_vision_available() else None

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Any		     = MobileNetVaImageProcessingTester(self					)

		@property
		def 				__lowerCamelCase				( self					):
			return self.image_processor_tester.prepare_image_processor_dict()

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		str		     = self.image_processing_class(**self.image_processor_dict					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'do_resize'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'size'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'do_center_crop'					)					)
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,						'center_crop'					)					)

		def 				__lowerCamelCase				( self					):
			__lowerCAmelCase     :		Tuple		     = self.image_processing_class.from_dict(self.image_processor_dict					)
			self.assertEqual(image_processor.size			,						{'shortest_edge': 20}					)
			self.assertEqual(image_processor.crop_size			,						{'height': 18, 'width': 18}					)

			__lowerCAmelCase     :		Any		     = self.image_processing_class.from_dict(self.image_processor_dict			,						size=42			,						crop_size=84					)
			self.assertEqual(image_processor.size			,						{'shortest_edge': 42}					)
			self.assertEqual(image_processor.crop_size			,						{'height': 84, 'width': 84}					)

		def 				__lowerCamelCase				( self					):
			pass

		def 				__lowerCamelCase				( self					):
			# Initialize image_processing
			__lowerCAmelCase     :		Optional[Any]		     = self.image_processing_class(**self.image_processor_dict					)
			# create random PIL images
			__lowerCAmelCase     :		Optional[int]		     = prepare_image_inputs(self.image_processor_tester			,						equal_resolution=_SCREAMING_SNAKE_CASE					)
			for image in image_inputs:
				self.assertIsInstance(_SCREAMING_SNAKE_CASE			,						Image.Image					)

			# Test not batched input
			__lowerCAmelCase     :		Optional[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
			__lowerCAmelCase     :		Tuple		     = image_processing(_SCREAMING_SNAKE_CASE			,						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 				__lowerCamelCase				( self					):
			# Initialize image_processing
			__lowerCAmelCase     :		str		     = self.image_processing_class(**self.image_processor_dict					)
			# create random numpy tensors
			__lowerCAmelCase     :		Optional[int]		     = prepare_image_inputs(self.image_processor_tester			,						equal_resolution=_SCREAMING_SNAKE_CASE			,						numpify=_SCREAMING_SNAKE_CASE					)
			for image in image_inputs:
				self.assertIsInstance(_SCREAMING_SNAKE_CASE			,						np.ndarray					)

			# Test not batched input
			__lowerCAmelCase     :		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
			__lowerCAmelCase     :		List[str]		     = image_processing(_SCREAMING_SNAKE_CASE			,						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 				__lowerCamelCase				( self					):
			# Initialize image_processing
			__lowerCAmelCase     :		Optional[int]		     = self.image_processing_class(**self.image_processor_dict					)
			# create random PyTorch tensors
			__lowerCAmelCase     :		Optional[int]		     = prepare_image_inputs(self.image_processor_tester			,						equal_resolution=_SCREAMING_SNAKE_CASE			,						torchify=_SCREAMING_SNAKE_CASE					)
			for image in image_inputs:
				self.assertIsInstance(_SCREAMING_SNAKE_CASE			,						torch.Tensor					)

			# Test not batched input
			__lowerCAmelCase     :		Tuple		     = 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
			__lowerCAmelCase     :		List[Any]		     = image_processing(_SCREAMING_SNAKE_CASE			,						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'],
			    )			,						)

"""simple docstring"""







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

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


lowerCamelCase__     =						logging.get_logger(__name__)

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

lowerCamelCase__     =						{
    """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"""},
}

lowerCamelCase__     =						{
    """microsoft/biogpt""": 1_024,
}
def        __lowerCAmelCase							(_UpperCamelCase						):
	__lowerCAmelCase     :		Tuple		     = set()
	__lowerCAmelCase     :		str		     = word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)						)
		__lowerCAmelCase     :		Optional[Any]		     = char
	return pairs




class 					A__			(      _lowerCamelCase):
		A_		:			Union[str, Any]							  =      VOCAB_FILES_NAMES
		A_		:			List[Any]							  =      PRETRAINED_VOCAB_FILES_MAP
		A_		:			Optional[Any]							  =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		A_		:			int							  =      ['input_ids', 'attention_mask']

		def __init__( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE="<unk>"			,						_SCREAMING_SNAKE_CASE="<s>"			,						_SCREAMING_SNAKE_CASE="</s>"			,						_SCREAMING_SNAKE_CASE="</s>"			,						_SCREAMING_SNAKE_CASE="<pad>"			,						**_SCREAMING_SNAKE_CASE			,						):
			super().__init__(
			    bos_token=_SCREAMING_SNAKE_CASE			,						eos_token=_SCREAMING_SNAKE_CASE			,						sep_token=_SCREAMING_SNAKE_CASE			,						unk_token=_SCREAMING_SNAKE_CASE			,						pad_token=_SCREAMING_SNAKE_CASE			,						**_SCREAMING_SNAKE_CASE			,						)

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

			__lowerCAmelCase     :		int		     = 'en'
			__lowerCAmelCase     :		Optional[Any]		     = sacremoses
			# cache of sm.MosesTokenizer instance
			__lowerCAmelCase     :		Optional[int]		     = {}
			__lowerCAmelCase     :		Any		     = {}
			with open(_SCREAMING_SNAKE_CASE			,						encoding='utf-8'					) as vocab_handle:
				__lowerCAmelCase     :		int		     = json.load(_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		Any		     = {v: k for k, v in self.encoder.items()}
			with open(_SCREAMING_SNAKE_CASE			,						encoding='utf-8'					) as merges_handle:
				__lowerCAmelCase     :		Optional[int]		     = merges_handle.read().split('\n'					)[:-1]
			__lowerCAmelCase     :		Dict		     = [tuple(merge.split()[:2]					) for merge in merges]
			__lowerCAmelCase     :		Optional[Any]		     = dict(zip(_SCREAMING_SNAKE_CASE			,						range(len(_SCREAMING_SNAKE_CASE					)					)					)					)
			__lowerCAmelCase     :		Optional[int]		     = {}

		@property
		def 				__lowerCamelCase				( self					):
			return len(self.encoder					)

		def 				__lowerCamelCase				( self					):
			return dict(self.encoder			,						**self.added_tokens_encoder					)

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					):
			if lang not in self.cache_moses_tokenizer:
				__lowerCAmelCase     :		str		     = self.sm.MosesTokenizer(lang=_SCREAMING_SNAKE_CASE					)
				__lowerCAmelCase     :		Optional[int]		     = moses_tokenizer
			return self.cache_moses_tokenizer[lang].tokenize(
			    _SCREAMING_SNAKE_CASE			,						aggressive_dash_splits=_SCREAMING_SNAKE_CASE			,						return_str=_SCREAMING_SNAKE_CASE			,						escape=_SCREAMING_SNAKE_CASE					)

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					):
			if lang not in self.cache_moses_detokenizer:
				__lowerCAmelCase     :		Any		     = self.sm.MosesDetokenizer(lang=_SCREAMING_SNAKE_CASE					)
				__lowerCAmelCase     :		Any		     = moses_detokenizer
			return self.cache_moses_detokenizer[lang].detokenize(_SCREAMING_SNAKE_CASE					)

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE					):
			__lowerCAmelCase     :		List[Any]		     = tuple(token[:-1]					) + (token[-1] + '</w>',)
			if token in self.cache:
				return self.cache[token]
			__lowerCAmelCase     :		Union[str, Any]		     = get_pairs(_SCREAMING_SNAKE_CASE					)

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

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

					if word[i] == first and i < len(_SCREAMING_SNAKE_CASE					) - 1 and word[i + 1] == second:
						new_word.append(first + second					)
						i += 2
					else:
						new_word.append(word[i]					)
						i += 1
				__lowerCAmelCase     :		Optional[Any]		     = tuple(_SCREAMING_SNAKE_CASE					)
				__lowerCAmelCase     :		Optional[Any]		     = new_word
				if len(_SCREAMING_SNAKE_CASE					) == 1:
					break
				else:
					__lowerCAmelCase     :		Optional[Any]		     = get_pairs(_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		Optional[int]		     = ' '.join(_SCREAMING_SNAKE_CASE					)
			if word == "\n  </w>":
				__lowerCAmelCase     :		Dict		     = '\n</w>'
			__lowerCAmelCase     :		Optional[int]		     = word
			return word

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE=False					):
			if bypass_tokenizer:
				__lowerCAmelCase     :		Any		     = text.split()
			else:
				__lowerCAmelCase     :		Union[str, Any]		     = self.moses_tokenize(_SCREAMING_SNAKE_CASE			,						self.lang					)

			__lowerCAmelCase     :		List[str]		     = []
			for token in text:
				if token:
					split_tokens.extend(list(self.bpe(_SCREAMING_SNAKE_CASE					).split(' '					)					)					)

			return split_tokens

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE					):
			return self.encoder.get(_SCREAMING_SNAKE_CASE			,						self.encoder.get(self.unk_token					)					)

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE					):
			return self.decoder.get(_SCREAMING_SNAKE_CASE			,						self.unk_token					)

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE					):
			__lowerCAmelCase     :		List[str]		     = [t.replace(' '			,						''					).replace('</w>'			,						' '					) for t in tokens]
			__lowerCAmelCase     :		List[Any]		     = ''.join(_SCREAMING_SNAKE_CASE					).split()
			# detokenize
			__lowerCAmelCase     :		int		     = self.moses_detokenize(_SCREAMING_SNAKE_CASE			,						self.lang					)
			return text

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = None					):
			if token_ids_a is None:
				return [self.sep_token_id] + token_ids_a
			__lowerCAmelCase     :		Dict		     = [self.sep_token_id]
			return sep + token_ids_a + sep + token_ids_a

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = False					):
			if already_has_special_tokens:
				return super().get_special_tokens_mask(
				    token_ids_a=_SCREAMING_SNAKE_CASE			,						token_ids_a=_SCREAMING_SNAKE_CASE			,						already_has_special_tokens=_SCREAMING_SNAKE_CASE					)
			# no bos used in fairseq
			if token_ids_a is not None:
				return [1] + ([0] * len(_SCREAMING_SNAKE_CASE					)) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE					))
			return [1] + ([0] * len(_SCREAMING_SNAKE_CASE					))

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = None					):
			__lowerCAmelCase     :		int		     = [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 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = None					):
			if not os.path.isdir(_SCREAMING_SNAKE_CASE					):
				logger.error(f"Vocabulary path ({save_directory}) should be a directory"					)
				return
			__lowerCAmelCase     :		List[Any]		     = os.path.join(
			    _SCREAMING_SNAKE_CASE			,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)
			__lowerCAmelCase     :		List[str]		     = os.path.join(
			    _SCREAMING_SNAKE_CASE			,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']					)

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

			__lowerCAmelCase     :		str		     = 0
			with open(_SCREAMING_SNAKE_CASE			,						'w'			,						encoding='utf-8'					) as writer:
				for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,						key=lambda _SCREAMING_SNAKE_CASE					: 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!'					)
						__lowerCAmelCase     :		List[Any]		     = token_index
					writer.write(' '.join(_SCREAMING_SNAKE_CASE					) + '\n'					)
					index += 1

			return vocab_file, merge_file

		def __getstate__( self					):
			__lowerCAmelCase     :		Any		     = self.__dict__.copy()
			__lowerCAmelCase     :		List[str]		     = None
			return state







		def __setstate__( self			,						_SCREAMING_SNAKE_CASE					):
			__lowerCAmelCase     :		List[str]		     = d

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

			__lowerCAmelCase     :		int		     = sacremoses