

"""simple docstring"""





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

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


__UpperCamelCase						:   str							   =	logging.get_logger(__name__)

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

__UpperCamelCase						:   Union[str, Any]							   =	{
    '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						:   Dict							   =	{
    'microsoft/biogpt': 1_0_2_4,
}

def 						__SCREAMING_SNAKE_CASE						(   A_				):
 lowerCAmelCase__			:		str						       =			set()
 lowerCAmelCase__			:		Dict						       =			word[0]
 for char in word[1:]:
  pairs.add((prev_char, char)				)
  lowerCAmelCase__			:		Optional[int]						       =			char
 return pairs







class        SCREAMING_SNAKE_CASE      (	lowercase_ ):






     """simple docstring"""



     lowercase__       				= VOCAB_FILES_NAMES
     lowercase__       				= PRETRAINED_VOCAB_FILES_MAP
     lowercase__       				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase__       				= ["""input_ids""", """attention_mask"""]







     def __init__( self    :       int     ,lowercase_    :       List[Any]     ,lowercase_    :       List[Any]     ,lowercase_    :       Optional[int]="<unk>"     ,lowercase_    :       Dict="<s>"     ,lowercase_    :       str="</s>"     ,lowercase_    :       str="</s>"     ,lowercase_    :       Union[str, Any]="<pad>"     ,**lowercase_    :       Dict     ,):
      super().__init__(
          bos_token=lowercase_     ,eos_token=lowercase_     ,sep_token=lowercase_     ,unk_token=lowercase_     ,pad_token=lowercase_     ,**lowercase_     ,)

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

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







     @property
     def  __lowerCAmelCase				( self    :       Optional[int]	):
      return len(self.encoder	)







     def  __lowerCAmelCase				( self    :       List[Any]	):
      return dict(self.encoder     ,**self.added_tokens_encoder	)







     def  __lowerCAmelCase				( self    :       int     ,lowercase_    :       List[Any]     ,lowercase_    :       Optional[Any]	):







      if lang not in self.cache_moses_tokenizer:
       lowerCAmelCase__			:		List[Any]						       =			self.sm.MosesTokenizer(lang=lowercase_	)
       lowerCAmelCase__			:		List[str]						       =			moses_tokenizer
      return self.cache_moses_tokenizer[lang].tokenize(
          lowercase_     ,aggressive_dash_splits=lowercase_     ,return_str=lowercase_     ,escape=lowercase_	)







     def  __lowerCAmelCase				( self    :       List[str]     ,lowercase_    :       List[Any]     ,lowercase_    :       Union[str, Any]	):







      if lang not in self.cache_moses_detokenizer:
       lowerCAmelCase__			:		int						       =			self.sm.MosesDetokenizer(lang=lowercase_	)
       lowerCAmelCase__			:		List[str]						       =			moses_detokenizer
      return self.cache_moses_detokenizer[lang].detokenize(lowercase_	)







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

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

      while True:
       lowerCAmelCase__			:		Tuple						       =			min(lowercase_     ,key=lambda lowercase_	: self.bpe_ranks.get(lowercase_     ,float('''inf'''	)	)	)
       if bigram not in self.bpe_ranks:
        break
       lowerCAmelCase__       ,lowerCAmelCase__			:		Optional[int]						       =			bigram
       lowerCAmelCase__			:		List[Any]						       =			[]
       lowerCAmelCase__			:		List[str]						       =			0
       while i < len(lowercase_	):
        try:
         lowerCAmelCase__			:		List[Any]						       =			word.index(lowercase_     ,lowercase_	)
        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(lowercase_	) - 1 and word[i + 1] == second:
         new_word.append(first + second	)
         i += 2
        else:
         new_word.append(word[i]	)
         i += 1
       lowerCAmelCase__			:		List[str]						       =			tuple(lowercase_	)
       lowerCAmelCase__			:		Union[str, Any]						       =			new_word
       if len(lowercase_	) == 1:
        break
       else:
        lowerCAmelCase__			:		int						       =			get_pairs(lowercase_	)
      lowerCAmelCase__			:		Optional[int]						       =			''' '''.join(lowercase_	)
      if word == "\n  </w>":
       lowerCAmelCase__			:		Any						       =			'''\n</w>'''
      lowerCAmelCase__			:		int						       =			word
      return word







     def  __lowerCAmelCase				( self    :       int     ,lowercase_    :       Dict     ,lowercase_    :       str=False	):







      if bypass_tokenizer:
       lowerCAmelCase__			:		Union[str, Any]						       =			text.split()
      else:
       lowerCAmelCase__			:		Optional[Any]						       =			self.moses_tokenize(lowercase_     ,self.lang	)

      lowerCAmelCase__			:		Optional[Any]						       =			[]
      for token in text:
       if token:
        split_tokens.extend(list(self.bpe(lowercase_	).split(''' '''	)	)	)

      return split_tokens







     def  __lowerCAmelCase				( self    :       Any     ,lowercase_    :       Optional[int]	):
      return self.encoder.get(lowercase_     ,self.encoder.get(self.unk_token	)	)







     def  __lowerCAmelCase				( self    :       Optional[Any]     ,lowercase_    :       str	):
      return self.decoder.get(lowercase_     ,self.unk_token	)







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







     def  __lowerCAmelCase				( self    :       int     ,lowercase_    :       List[int]     ,lowercase_    :       Optional[List[int]] = None	):







      if token_ids_a is None:
       return [self.sep_token_id] + token_ids_a
      lowerCAmelCase__			:		Any						       =			[self.sep_token_id]
      return sep + token_ids_a + sep + token_ids_a







     def  __lowerCAmelCase				( self    :       Dict     ,lowercase_    :       List[int]     ,lowercase_    :       Optional[List[int]] = None     ,lowercase_    :       bool = False	):







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







     def  __lowerCAmelCase				( self    :       Any     ,lowercase_    :       List[int]     ,lowercase_    :       Optional[List[int]] = None	):
      lowerCAmelCase__			:		Union[str, Any]						       =			[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    :       int     ,lowercase_    :       str     ,lowercase_    :       Optional[str] = None	):







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

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

      lowerCAmelCase__			:		Tuple						       =			0
      with open(lowercase_     ,'''w'''     ,encoding='''utf-8'''	) as writer:
       for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,key=lambda lowercase_	: 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(lowercase_	) + '''\n'''	)
        index += 1

      return vocab_file, merge_file







     def __getstate__( self    :       Union[str, Any]	):
      lowerCAmelCase__			:		Tuple						       =			self.__dict__.copy()
      lowerCAmelCase__			:		List[Any]						       =			None
      return state







     def __setstate__( self    :       List[str]     ,lowercase_    :       int	):
      lowerCAmelCase__			:		Optional[Any]						       =			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__			:		Union[str, Any]						       =			sacremoses







"""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


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        SCREAMING_SNAKE_CASE      (	unittest.TestCase ):






					"""simple docstring"""



					def __init__( self    :       Dict     ,lowercase_    :       List[str]     ,lowercase_    :       List[str]=7     ,lowercase_    :       List[Any]=3     ,lowercase_    :       str=1_8     ,lowercase_    :       Any=3_0     ,lowercase_    :       int=4_0_0     ,lowercase_    :       Tuple=True     ,lowercase_    :       Optional[int]=1 / 2_5_5     ,lowercase_    :       int=True     ,lowercase_    :       Union[str, Any]=8     ,):
						lowerCAmelCase__			:		Tuple						       =			parent
						lowerCAmelCase__			:		Optional[Any]						       =			batch_size
						lowerCAmelCase__			:		int						       =			num_channels
						lowerCAmelCase__			:		Union[str, Any]						       =			image_size
						lowerCAmelCase__			:		Optional[Any]						       =			min_resolution
						lowerCAmelCase__			:		Union[str, Any]						       =			max_resolution
						lowerCAmelCase__			:		int						       =			do_rescale
						lowerCAmelCase__			:		Tuple						       =			rescale_factor
						lowerCAmelCase__			:		Dict						       =			do_pad
						lowerCAmelCase__			:		str						       =			pad_size







					def  __lowerCAmelCase				( self    :       int	):
						return {
						    "do_rescale": self.do_rescale,
						    "rescale_factor": self.rescale_factor,
						    "do_pad": self.do_pad,
						    "pad_size": self.pad_size,
						}







					def  __lowerCAmelCase				( self    :       Union[str, Any]     ,lowercase_    :       List[str]=False     ,lowercase_    :       Any=False     ,lowercase_    :       Dict=False	):
						assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

						if equal_resolution:
							lowerCAmelCase__			:		int						       =			[]
							for i in range(self.batch_size	):
								image_inputs.append(
								    np.random.randint(
								        2_5_5     ,size=(self.num_channels, self.max_resolution, self.max_resolution)     ,dtype=np.uinta	)	)
						else:
							lowerCAmelCase__			:		str						       =			[]
							for i in range(self.batch_size	):
								lowerCAmelCase__       ,lowerCAmelCase__			:		Any						       =			np.random.choice(np.arange(self.min_resolution     ,self.max_resolution	)     ,2	)
								image_inputs.append(np.random.randint(2_5_5     ,size=(self.num_channels, width, height)     ,dtype=np.uinta	)	)

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

						if torchify:
							lowerCAmelCase__			:		int						       =			[torch.from_numpy(lowercase_	) for x in image_inputs]

						return image_inputs







@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE      (	a_					,							unittest.TestCase ):






					"""simple docstring"""



					lowercase__       				= SwinaSRImageProcessor if is_vision_available() else None







					def  __lowerCAmelCase				( self    :       Dict	):
						lowerCAmelCase__			:		Dict						       =			SwinaSRImageProcessingTester(self	)







					@property
					def  __lowerCAmelCase				( self    :       str	):
						return self.image_processor_tester.prepare_image_processor_dict()







					def  __lowerCAmelCase				( self    :       List[str]	):
						lowerCAmelCase__			:		Any						       =			self.image_processing_class(**self.image_processor_dict	)
						self.assertTrue(hasattr(lowercase_     ,'''do_rescale'''	)	)
						self.assertTrue(hasattr(lowercase_     ,'''rescale_factor'''	)	)
						self.assertTrue(hasattr(lowercase_     ,'''do_pad'''	)	)
						self.assertTrue(hasattr(lowercase_     ,'''pad_size'''	)	)







					def  __lowerCAmelCase				( self    :       Dict	):
						pass







					def  __lowerCAmelCase				( self    :       List[str]     ,lowercase_    :       Optional[Any]	):
						lowerCAmelCase__       ,lowerCAmelCase__			:		Tuple						       =			get_image_size(lowercase_	)
						lowerCAmelCase__			:		Dict						       =			self.image_processor_tester.pad_size

						lowerCAmelCase__			:		List[Any]						       =			(old_height // size + 1) * size - old_height
						lowerCAmelCase__			:		Union[str, Any]						       =			(old_width // size + 1) * size - old_width
						return old_height + pad_height, old_width + pad_width







					def  __lowerCAmelCase				( self    :       List[Any]	):
						# Initialize image_processor
						lowerCAmelCase__			:		str						       =			self.image_processing_class(**self.image_processor_dict	)
						# create random PIL images
						lowerCAmelCase__			:		int						       =			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_	)
						for image in image_inputs:
							self.assertIsInstance(lowercase_     ,Image.Image	)

						# Test not batched input
						lowerCAmelCase__			:		List[str]						       =			image_processor(image_inputs[0]     ,return_tensors='''pt'''	).pixel_values
						lowerCAmelCase__       ,lowerCAmelCase__			:		List[Any]						       =			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  __lowerCAmelCase				( self    :       int	):
						# Initialize image_processor
						lowerCAmelCase__			:		str						       =			self.image_processing_class(**self.image_processor_dict	)
						# create random numpy tensors
						lowerCAmelCase__			:		Tuple						       =			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_     ,numpify=lowercase_	)
						for image in image_inputs:
							self.assertIsInstance(lowercase_     ,np.ndarray	)

						# Test not batched input
						lowerCAmelCase__			:		Optional[Any]						       =			image_processor(image_inputs[0]     ,return_tensors='''pt'''	).pixel_values
						lowerCAmelCase__       ,lowerCAmelCase__			:		List[Any]						       =			self.calculate_expected_size(image_inputs[0]	)
						self.assertEqual(
						    encoded_images.shape     ,(
						        1,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )     ,)







					def  __lowerCAmelCase				( self    :       Dict	):
						# Initialize image_processor
						lowerCAmelCase__			:		Tuple						       =			self.image_processing_class(**self.image_processor_dict	)
						# create random PyTorch tensors
						lowerCAmelCase__			:		Union[str, Any]						       =			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_     ,torchify=lowercase_	)
						for image in image_inputs:
							self.assertIsInstance(lowercase_     ,torch.Tensor	)

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




