


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 ViltImageProcessor
class        a_				(		unittest.TestCase		):






 '''simple docstring'''






 def __init__(    self      :						int    ,					lowercase__      :						Optional[Any]    ,					lowercase__      :						Union[str, Any]=7    ,					lowercase__      :						Optional[Any]=3    ,					lowercase__      :						Any=18    ,					lowercase__      :						List[Any]=30    ,					lowercase__      :						Dict=400    ,					lowercase__      :						Any=True    ,					lowercase__      :						Dict=None    ,					lowercase__      :						List[str]=2    ,					lowercase__      :						Any=True    ,					lowercase__      :						str=[0.5, 0.5, 0.5]    ,					lowercase__      :						Optional[int]=[0.5, 0.5, 0.5]    ,					):







        '''simple docstring'''



        lowerCAmelCase__  			=      size if size is not None else {'''shortest_edge''': 30}
        lowerCAmelCase__  			=      parent
        lowerCAmelCase__  			=      batch_size
        lowerCAmelCase__  			=      num_channels
        lowerCAmelCase__  			=      image_size
        lowerCAmelCase__  			=      min_resolution
        lowerCAmelCase__  			=      max_resolution
        lowerCAmelCase__  			=      do_resize
        lowerCAmelCase__  			=      size
        lowerCAmelCase__  			=      size_divisor
        lowerCAmelCase__  			=      do_normalize
        lowerCAmelCase__  			=      image_mean
        lowerCAmelCase__  			=      image_std




 def 	__snake_case		(    self      :						Dict):







        '''simple docstring'''



        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "size_divisor": self.size_divisor,
        }




 def 	__snake_case		(    self      :						Optional[int]    ,					lowercase__      :						Any    ,					lowercase__      :						Optional[Any]=False):







        '''simple docstring'''



        if not batched:
               lowerCAmelCase__  			=      self.size['''shortest_edge''']
               lowerCAmelCase__  			=      image_inputs[0]
               if isinstance(lowercase__    ,					Image.Image):
                      lowerCAmelCase__  			=      image.size
               else:
                      lowerCAmelCase__  			=      image.shape[1], image.shape[2]
               lowerCAmelCase__  			=      size / min(lowercase__    ,					lowercase__)
               if h < w:
                      lowerCAmelCase__  			=      size, scale * w
               else:
                      lowerCAmelCase__  			=      scale * h, size

               lowerCAmelCase__  			=      int((1_333 / 800) * size)
               if max(lowercase__    ,					lowercase__) > max_size:
                      lowerCAmelCase__  			=      max_size / max(lowercase__    ,					lowercase__)
                      lowerCAmelCase__  			=      newh * scale
                      lowerCAmelCase__  			=      neww * scale

               lowerCAmelCase__  			=      int(newh + 0.5), int(neww + 0.5)
               lowerCAmelCase__  			=      (
                   newh // self.size_divisor * self.size_divisor,
                   neww // self.size_divisor * self.size_divisor,
               )

        else:
               lowerCAmelCase__  			=      []
               for image in image_inputs:
                      lowerCAmelCase__  			=      self.get_expected_values([image])
                      expected_values.append((expected_height, expected_width))
               lowerCAmelCase__  			=      max(lowercase__    ,					key=lambda lowercase__: item[0])[0]
               lowerCAmelCase__  			=      max(lowercase__    ,					key=lambda lowercase__: item[1])[1]

        return expected_height, expected_width






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






 '''simple docstring'''






 UpperCAmelCase_							=      ViltImageProcessor if is_vision_available() else None




 def 	__snake_case		(    self      :						Dict):







        '''simple docstring'''



        lowerCAmelCase__  			=      ViltImageProcessingTester(self)




 @property
 def 	__snake_case		(    self      :						str):







        '''simple docstring'''



        return self.image_processor_tester.prepare_image_processor_dict()




 def 	__snake_case		(    self      :						Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__  			=      self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(lowercase__    ,					'image_mean'))
        self.assertTrue(hasattr(lowercase__    ,					'image_std'))
        self.assertTrue(hasattr(lowercase__    ,					'do_normalize'))
        self.assertTrue(hasattr(lowercase__    ,					'do_resize'))
        self.assertTrue(hasattr(lowercase__    ,					'size'))
        self.assertTrue(hasattr(lowercase__    ,					'size_divisor'))




 def 	__snake_case		(    self      :						Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      self.image_processing_class.from_dict(self.image_processor_dict)
        self.assertEqual(image_processor.size    ,					{'shortest_edge': 30})

        lowerCAmelCase__  			=      self.image_processing_class.from_dict(self.image_processor_dict    ,					size=42)
        self.assertEqual(image_processor.size    ,					{'shortest_edge': 42})




 def 	__snake_case		(    self      :						Dict):







        '''simple docstring'''



        pass




 def 	__snake_case		(    self      :						Dict):







        '''simple docstring'''



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

        # Test not batched input
        lowerCAmelCase__  			=      image_processing(image_inputs[0]    ,					return_tensors='pt').pixel_values

        lowerCAmelCase__  			=      self.image_processor_tester.get_expected_values(lowercase__)
        self.assertEqual(
            encoded_images.shape    ,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,					)

        # Test batched
        lowerCAmelCase__  			=      image_processing(lowercase__    ,					return_tensors='pt').pixel_values

        lowerCAmelCase__  			=      self.image_processor_tester.get_expected_values(lowercase__    ,					batched=lowercase__)
        self.assertEqual(
            encoded_images.shape    ,					(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )    ,					)




 def 	__snake_case		(    self      :						List[str]):







        '''simple docstring'''



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

        # Test not batched input
        lowerCAmelCase__  			=      image_processing(image_inputs[0]    ,					return_tensors='pt').pixel_values

        lowerCAmelCase__  			=      self.image_processor_tester.get_expected_values(lowercase__)
        self.assertEqual(
            encoded_images.shape    ,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,					)

        # Test batched
        lowerCAmelCase__  			=      image_processing(lowercase__    ,					return_tensors='pt').pixel_values

        lowerCAmelCase__  			=      self.image_processor_tester.get_expected_values(lowercase__    ,					batched=lowercase__)
        self.assertEqual(
            encoded_images.shape    ,					(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )    ,					)




 def 	__snake_case		(    self      :						Optional[int]):







        '''simple docstring'''



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

        # Test not batched input
        lowerCAmelCase__  			=      image_processing(image_inputs[0]    ,					return_tensors='pt').pixel_values

        lowerCAmelCase__  			=      self.image_processor_tester.get_expected_values(lowercase__)
        self.assertEqual(
            encoded_images.shape    ,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,					)

        # Test batched
        lowerCAmelCase__  			=      image_processing(lowercase__    ,					return_tensors='pt').pixel_values

        lowerCAmelCase__  			=      self.image_processor_tester.get_expected_values(lowercase__    ,					batched=lowercase__)
        self.assertEqual(
            encoded_images.shape    ,					(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )    ,					)

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': 1024,
}






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



class        a_				(		SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






 UpperCAmelCase_							=      VOCAB_FILES_NAMES
 UpperCAmelCase_							=      PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_							=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_							=      ['input_ids', 'attention_mask']




 def __init__(    self      :						List[str]    ,					lowercase__      :						Any    ,					lowercase__      :						int    ,					lowercase__      :						Union[str, Any]="<unk>"    ,					lowercase__      :						Optional[Any]="<s>"    ,					lowercase__      :						Dict="</s>"    ,					lowercase__      :						Tuple="</s>"    ,					lowercase__      :						Optional[Any]="<pad>"    ,					**lowercase__      :						Optional[int]    ,					):







        '''simple docstring'''



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




 @property
 def 	__snake_case		(    self      :						Optional[Any]):







        '''simple docstring'''



        return len(self.encoder)




 def 	__snake_case		(    self      :						List[str]):







        '''simple docstring'''



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




 def 	__snake_case		(    self      :						Optional[int]    ,					lowercase__      :						List[Any]    ,					lowercase__      :						int):







        '''simple docstring'''



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




 def 	__snake_case		(    self      :						Optional[int]    ,					lowercase__      :						Tuple    ,					lowercase__      :						Any):







        '''simple docstring'''



        if lang not in self.cache_moses_detokenizer:
               lowerCAmelCase__  			=      self.sm.MosesDetokenizer(lang=lowercase__)
               lowerCAmelCase__  			=      moses_detokenizer
        return self.cache_moses_detokenizer[lang].detokenize(lowercase__)




 def 	__snake_case		(    self      :						Dict    ,					lowercase__      :						Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      tuple(token[:-1]) + (token[-1] + '</w>',)
        if token in self.cache:
               return self.cache[token]
        lowerCAmelCase__  			=      get_pairs(lowercase__)

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

        while True:
               lowerCAmelCase__  			=      min(lowercase__    ,					key=lambda lowercase__: self.bpe_ranks.get(lowercase__    ,					float('inf')))
               if bigram not in self.bpe_ranks:
                      break
               lowerCAmelCase__			,      lowerCAmelCase__  			=      bigram
               lowerCAmelCase__  			=      []
               lowerCAmelCase__  			=      0
               while i < len(lowercase__):
                      try:
                             lowerCAmelCase__  			=      word.index(lowercase__    ,					lowercase__)
                      except ValueError:
                             new_word.extend(word[i:])
                             break
                      else:
                             new_word.extend(word[i:j])
                             lowerCAmelCase__  			=      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__  			=      tuple(lowercase__)
               lowerCAmelCase__  			=      new_word
               if len(lowercase__) == 1:
                      break
               else:
                      lowerCAmelCase__  			=      get_pairs(lowercase__)
        lowerCAmelCase__  			=      ' '.join(lowercase__)
        if word == "\n  </w>":
               lowerCAmelCase__  			=      '\n</w>'
        lowerCAmelCase__  			=      word
        return word




 def 	__snake_case		(    self      :						Optional[Any]    ,					lowercase__      :						Any    ,					lowercase__      :						Dict=False):







        '''simple docstring'''



        if bypass_tokenizer:
               lowerCAmelCase__  			=      text.split()
        else:
               lowerCAmelCase__  			=      self.moses_tokenize(lowercase__    ,					self.lang)

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

        return split_tokens




 def 	__snake_case		(    self      :						List[str]    ,					lowercase__      :						List[Any]):







        '''simple docstring'''



        return self.encoder.get(lowercase__    ,					self.encoder.get(self.unk_token))




 def 	__snake_case		(    self      :						Optional[int]    ,					lowercase__      :						Optional[Any]):







        '''simple docstring'''



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




 def 	__snake_case		(    self      :						Any    ,					lowercase__      :						int):







        '''simple docstring'''



        lowerCAmelCase__  			=      [t.replace(' '    ,					'').replace('</w>'    ,					' ') for t in tokens]
        lowerCAmelCase__  			=      ''.join(lowercase__).split()
        # detokenize
        lowerCAmelCase__  			=      self.moses_detokenize(lowercase__    ,					self.lang)
        return text




 def 	__snake_case		(    self      :						Optional[int]    ,					lowercase__      :						List[int]    ,					lowercase__      :						Optional[List[int]] = None):







        '''simple docstring'''



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




 def 	__snake_case		(    self      :						Union[str, Any]    ,					lowercase__      :						List[int]    ,					lowercase__      :						Optional[List[int]] = None    ,					lowercase__      :						bool = False):







        '''simple docstring'''



        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 	__snake_case		(    self      :						Union[str, Any]    ,					lowercase__      :						List[int]    ,					lowercase__      :						Optional[List[int]] = None):







        '''simple docstring'''



        lowerCAmelCase__  			=      [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 	__snake_case		(    self      :						Tuple    ,					lowercase__      :						str    ,					lowercase__      :						Optional[str] = None):







        '''simple docstring'''



        if not os.path.isdir(lowercase__):
               logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
               return
        lowerCAmelCase__  			=      os.path.join(
            lowercase__    ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
        lowerCAmelCase__  			=      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__  			=      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__  			=      token_index
                      writer.write(' '.join(lowercase__) + '\n')
                      index += 1

        return vocab_file, merge_file




 def __getstate__(    self      :						List[str]):







        '''simple docstring'''



        lowerCAmelCase__  			=      self.__dict__.copy()
        lowerCAmelCase__  			=      None
        return state




 def __setstate__(    self      :						List[str]    ,					lowercase__      :						Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      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__  			=      sacremoses
