




import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors

@require_tokenizers
class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE							,	unittest.TestCase      ):

      """simple docstring"""

      lowerCAmelCase__	:       str			=    BartTokenizer
      lowerCAmelCase__	:       List[Any]			=    BartTokenizerFast
      lowerCAmelCase__	:       str			=    True
      lowerCAmelCase__	:       List[str]			=    filter_roberta_detectors







      def 					snake_case		(   self    :       Optional[int] ):
          super().setUp()
          __UpperCamelCase     						=  [
              '''l''',
              '''o''',
              '''w''',
              '''e''',
              '''r''',
              '''s''',
              '''t''',
              '''i''',
              '''d''',
              '''n''',
              '''\u0120''',
              '''\u0120l''',
              '''\u0120n''',
              '''\u0120lo''',
              '''\u0120low''',
              '''er''',
              '''\u0120lowest''',
              '''\u0120newer''',
              '''\u0120wider''',
              '''<unk>''',
          ]
          __UpperCamelCase     						=  dict(zip(snake_case       ,				range(len(snake_case ) ) ) )
          __UpperCamelCase     						=  ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
          __UpperCamelCase     						=  {'''unk_token''': '''<unk>'''}

          __UpperCamelCase     						=  os.path.join(self.tmpdirname       ,				VOCAB_FILES_NAMES['''vocab_file'''] )
          __UpperCamelCase     						=  os.path.join(self.tmpdirname       ,				VOCAB_FILES_NAMES['''merges_file'''] )
          with open(self.vocab_file       ,				'''w'''       ,				encoding='''utf-8''' ) as fp:
              fp.write(json.dumps(snake_case ) + '''\n''' )
          with open(self.merges_file       ,				'''w'''       ,				encoding='''utf-8''' ) as fp:
              fp.write('''\n'''.join(snake_case ) )







      def 					snake_case		(   self    :       Tuple       ,				**snake_case    :       Optional[Any] ):
          kwargs.update(self.special_tokens_map )
          return self.tokenizer_class.from_pretrained(self.tmpdirname       ,				**snake_case )







      def 					snake_case		(   self    :       str       ,				**snake_case    :       Optional[Any] ):
          kwargs.update(self.special_tokens_map )
          return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,				**snake_case )







      def 					snake_case		(   self    :       Any       ,				snake_case    :       Dict ):
          return "lower newer", "lower newer"







      @cached_property
      def 					snake_case		(   self    :       Optional[Any] ):
          return BartTokenizer.from_pretrained('''facebook/bart-large''' )







      @cached_property
      def 					snake_case		(   self    :       Tuple ):
          return BartTokenizerFast.from_pretrained('''facebook/bart-large''' )







      @require_torch
      def 					snake_case		(   self    :       str ):
          __UpperCamelCase     						=  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
          __UpperCamelCase     						=  [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              __UpperCamelCase     						=  tokenizer(snake_case       ,				max_length=len(snake_case )       ,				padding=snake_case       ,				return_tensors='''pt''' )
              self.assertIsInstance(snake_case       ,				snake_case )

              self.assertEqual((2, 9)       ,				batch.input_ids.shape )
              self.assertEqual((2, 9)       ,				batch.attention_mask.shape )
              __UpperCamelCase     						=  batch.input_ids.tolist()[0]
              self.assertListEqual(snake_case       ,				snake_case )
              # Test that special tokens are reset







      @require_torch
      def 					snake_case		(   self    :       List[str] ):
          __UpperCamelCase     						=  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              __UpperCamelCase     						=  tokenizer(snake_case       ,				padding=snake_case       ,				return_tensors='''pt''' )
              # check if input_ids are returned and no labels
              self.assertIn('''input_ids'''       ,				snake_case )
              self.assertIn('''attention_mask'''       ,				snake_case )
              self.assertNotIn('''labels'''       ,				snake_case )
              self.assertNotIn('''decoder_attention_mask'''       ,				snake_case )







      @require_torch
      def 					snake_case		(   self    :       Optional[Any] ):
          __UpperCamelCase     						=  [
              '''Summary of the text.''',
              '''Another summary.''',
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              __UpperCamelCase     						=  tokenizer(text_target=snake_case       ,				max_length=32       ,				padding='''max_length'''       ,				return_tensors='''pt''' )
              self.assertEqual(32       ,				targets['''input_ids'''].shape[1] )







      @require_torch
      def 					snake_case		(   self    :       Union[str, Any] ):
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              __UpperCamelCase     						=  tokenizer(
                  ['''I am a small frog''' * 1024, '''I am a small frog''']       ,				padding=snake_case       ,				truncation=snake_case       ,				return_tensors='''pt''' )
              self.assertIsInstance(snake_case       ,				snake_case )
              self.assertEqual(batch.input_ids.shape       ,				(2, 1024) )







      @require_torch
      def 					snake_case		(   self    :       Dict ):
          __UpperCamelCase     						=  ['''A long paragraph for summarization.''']
          __UpperCamelCase     						=  [
              '''Summary of the text.''',
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              __UpperCamelCase     						=  tokenizer(snake_case       ,				return_tensors='''pt''' )
              __UpperCamelCase     						=  tokenizer(text_target=snake_case       ,				return_tensors='''pt''' )
              __UpperCamelCase     						=  inputs['''input_ids''']
              __UpperCamelCase     						=  targets['''input_ids''']
              self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
              self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
              self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
              self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )







      def 					snake_case		(   self    :       str ):
          pass







      def 					snake_case		(   self    :       Dict ):
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
                  __UpperCamelCase     						=  self.rust_tokenizer_class.from_pretrained(snake_case       ,				**snake_case )
                  __UpperCamelCase     						=  self.tokenizer_class.from_pretrained(snake_case       ,				**snake_case )
                  __UpperCamelCase     						=  '''A, <mask> AllenNLP sentence.'''
                  __UpperCamelCase     						=  tokenizer_r.encode_plus(snake_case       ,				add_special_tokens=snake_case       ,				return_token_type_ids=snake_case )
                  __UpperCamelCase     						=  tokenizer_p.encode_plus(snake_case       ,				add_special_tokens=snake_case       ,				return_token_type_ids=snake_case )

                  # token_type_ids should put 0 everywhere
                  self.assertEqual(sum(tokens_r['''token_type_ids'''] )       ,				sum(tokens_p['''token_type_ids'''] ) )

                  # attention_mask should put 1 everywhere, so sum over length should be 1
                  self.assertEqual(
                      sum(tokens_r['''attention_mask'''] ) / len(tokens_r['''attention_mask'''] )       ,				sum(tokens_p['''attention_mask'''] ) / len(tokens_p['''attention_mask'''] )       ,				)

                  __UpperCamelCase     						=  tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids'''] )
                  __UpperCamelCase     						=  tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids'''] )

                  # Rust correctly handles the space before the mask while python doesnt
                  self.assertSequenceEqual(tokens_p['''input_ids''']       ,				[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2] )
                  self.assertSequenceEqual(tokens_r['''input_ids''']       ,				[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2] )

                  self.assertSequenceEqual(
                      snake_case       ,				['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )
                  self.assertSequenceEqual(
                      snake_case       ,				['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )













from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCAmelCase__  :	Dict     =logging.get_logger(__name__)


if is_vision_available():
							import PIL

class 					__A   (  a			):
				__A            = ["""pixel_values"""]


				def __init__(						self      ,       UpperCAmelCase_ = True      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = PILImageResampling.BICUBIC      ,       UpperCAmelCase_ = True      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = True      ,       UpperCAmelCase_ = 1 / 255      ,       UpperCAmelCase_ = True      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = True      ,       **UpperCAmelCase_      ,       ):
								super().__init__(**UpperCAmelCase_							)
								lowerCamelCase		       =size if size is not None else {"""shortest_edge""": 224}
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_      ,       default_to_square=UpperCAmelCase_							)
								lowerCamelCase		       =crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_							)

								lowerCamelCase		       =do_resize
								lowerCamelCase		       =size
								lowerCamelCase		       =resample
								lowerCamelCase		       =do_center_crop
								lowerCamelCase		       =crop_size
								lowerCamelCase		       =do_rescale
								lowerCamelCase		       =rescale_factor
								lowerCamelCase		       =do_normalize
								lowerCamelCase		       =image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								lowerCamelCase		       =image_std if image_std is not None else OPENAI_CLIP_STD
								lowerCamelCase		       =do_convert_rgb


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = PILImageResampling.BICUBIC      ,       UpperCAmelCase_ = None      ,       **UpperCAmelCase_      ,       ):
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_      ,       default_to_square=UpperCAmelCase_							)
								lowerCamelCase		       =get_resize_output_image_size(
								    UpperCAmelCase_      ,       size=(size["""height"""], size["""width"""])      ,       default_to_square=UpperCAmelCase_							)
								return resize(UpperCAmelCase_      ,       size=UpperCAmelCase_      ,       resample=UpperCAmelCase_      ,       data_format=UpperCAmelCase_      ,       **UpperCAmelCase_							)


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None      ,       **UpperCAmelCase_      ,       ):
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_							)
								return center_crop(UpperCAmelCase_      ,       size=(size["""height"""], size["""width"""])      ,       data_format=UpperCAmelCase_      ,       **UpperCAmelCase_							)


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None      ,       **UpperCAmelCase_      ,       ):
								return rescale(UpperCAmelCase_      ,       scale=UpperCAmelCase_      ,       data_format=UpperCAmelCase_      ,       **UpperCAmelCase_							)


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None      ,       **UpperCAmelCase_      ,       ):
								return normalize(UpperCAmelCase_      ,       mean=UpperCAmelCase_      ,       std=UpperCAmelCase_      ,       data_format=UpperCAmelCase_      ,       **UpperCAmelCase_							)


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = ChannelDimension.FIRST      ,       **UpperCAmelCase_      ,       ):
								lowerCamelCase		       =do_resize if do_resize is not None else self.do_resize
								lowerCamelCase		       =size if size is not None else self.size
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_      ,       default_to_square=UpperCAmelCase_							)
								lowerCamelCase		       =resample if resample is not None else self.resample
								lowerCamelCase		       =do_center_crop if do_center_crop is not None else self.do_center_crop
								lowerCamelCase		       =crop_size if crop_size is not None else self.crop_size
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_							)
								lowerCamelCase		       =do_rescale if do_rescale is not None else self.do_rescale
								lowerCamelCase		       =rescale_factor if rescale_factor is not None else self.rescale_factor
								lowerCamelCase		       =do_normalize if do_normalize is not None else self.do_normalize
								lowerCamelCase		       =image_mean if image_mean is not None else self.image_mean
								lowerCamelCase		       =image_std if image_std is not None else self.image_std
								lowerCamelCase		       =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								lowerCamelCase		       =make_list_of_images(UpperCAmelCase_							)

								if not valid_images(UpperCAmelCase_							):
												raise ValueError(
												    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
												    """torch.Tensor, tf.Tensor or jax.ndarray."""							)

								if do_resize and size is None:
												raise ValueError("""Size must be specified if do_resize is True."""							)

								if do_center_crop and crop_size is None:
												raise ValueError("""Crop size must be specified if do_center_crop is True."""							)

								if do_rescale and rescale_factor is None:
												raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

								if do_normalize and (image_mean is None or image_std is None):
												raise ValueError("""Image mean and std must be specified if do_normalize is True."""							)

								# PIL RGBA images are converted to RGB
								if do_convert_rgb:
												lowerCamelCase		       =[convert_to_rgb(UpperCAmelCase_							) for image in images]

								# All transformations expect numpy arrays.
								lowerCamelCase		       =[to_numpy_array(UpperCAmelCase_							) for image in images]

								if do_resize:
												lowerCamelCase		       =[self.resize(image=UpperCAmelCase_      ,       size=UpperCAmelCase_      ,       resample=UpperCAmelCase_							) for image in images]

								if do_center_crop:
												lowerCamelCase		       =[self.center_crop(image=UpperCAmelCase_      ,       size=UpperCAmelCase_							) for image in images]

								if do_rescale:
												lowerCamelCase		       =[self.rescale(image=UpperCAmelCase_      ,       scale=UpperCAmelCase_							) for image in images]

								if do_normalize:
												lowerCamelCase		       =[self.normalize(image=UpperCAmelCase_      ,       mean=UpperCAmelCase_      ,       std=UpperCAmelCase_							) for image in images]

								lowerCamelCase		       =[to_channel_dimension_format(UpperCAmelCase_      ,       UpperCAmelCase_							) for image in images]

								lowerCamelCase		       ={"""pixel_values""": images}
								return BatchFeature(data=UpperCAmelCase_      ,       tensor_type=UpperCAmelCase_							)





