



'''simple docstring'''






import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 				lowercase			(    _lowerCamelCase							):


 """simple docstring"""


 UpperCAmelCase		      =    ["""image_processor""", """tokenizer"""]
 UpperCAmelCase		      =    """LayoutLMv2ImageProcessor"""
 UpperCAmelCase		      =    ("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")







 def __init__(				self    ,a_=None    ,a_=None    ,**a_				)						->  Tuple:
        _UpperCAmelCase    :		List[Any]     	=					None
        if "feature_extractor" in kwargs:
               warnings.warn(
                   """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                   """ instead."""    ,a_    ,)
               _UpperCAmelCase    :		Tuple     	=					kwargs.pop("""feature_extractor"""				)

        _UpperCAmelCase    :		Any     	=					image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
               raise ValueError("""You need to specify an `image_processor`."""				)
        if tokenizer is None:
               raise ValueError("""You need to specify a `tokenizer`."""				)

        super().__init__(a_    ,a_				)







 def __call__(				self    ,a_    ,a_ = None    ,a_ = None    ,a_ = None    ,a_ = None    ,a_ = True    ,a_ = False    ,a_ = False    ,a_ = None    ,a_ = 0    ,a_ = None    ,a_ = None    ,a_ = None    ,a_ = False    ,a_ = False    ,a_ = False    ,a_ = False    ,a_ = True    ,a_ = None    ,**a_    ,)						->  BatchEncoding:
        # verify input
        if self.image_processor.apply_ocr and (boxes is not None):
               raise ValueError(
                   """You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."""				)

        if self.image_processor.apply_ocr and (word_labels is not None):
               raise ValueError(
                   """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""				)

        if return_overflowing_tokens is True and return_offsets_mapping is False:
               raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping."""				)

        # first, apply the image processor
        _UpperCAmelCase    :		Tuple     	=					self.image_processor(images=a_    ,return_tensors=a_				)

        # second, apply the tokenizer
        if text is not None and self.image_processor.apply_ocr and text_pair is None:
               if isinstance(a_    ,a_				):
                      _UpperCAmelCase    :		Union[str, Any]     	=					[text]  # add batch dimension (as the image processor always adds a batch dimension)
               _UpperCAmelCase    :		List[str]     	=					features["""words"""]

        _UpperCAmelCase    :		int     	=					self.tokenizer(
            text=text if text is not None else features["""words"""]    ,text_pair=text_pair if text_pair is not None else None    ,boxes=boxes if boxes is not None else features["""boxes"""]    ,word_labels=a_    ,add_special_tokens=a_    ,padding=a_    ,truncation=a_    ,max_length=a_    ,stride=a_    ,pad_to_multiple_of=a_    ,return_token_type_ids=a_    ,return_attention_mask=a_    ,return_overflowing_tokens=a_    ,return_special_tokens_mask=a_    ,return_offsets_mapping=a_    ,return_length=a_    ,verbose=a_    ,return_tensors=a_    ,**a_    ,)

        # add pixel values
        _UpperCAmelCase    :		int     	=					features.pop("""pixel_values"""				)
        if return_overflowing_tokens is True:
               _UpperCAmelCase    :		Any     	=					self.get_overflowing_images(a_    ,encoded_inputs["""overflow_to_sample_mapping"""]				)
        _UpperCAmelCase    :		List[str]     	=					images

        return encoded_inputs







 def _snake_case					(				self    ,a_    ,a_				)						->  str:
        # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
        _UpperCAmelCase    :		Optional[int]     	=					[]
        for sample_idx in overflow_to_sample_mapping:
               images_with_overflow.append(images[sample_idx]				)

        if len(a_				) != len(a_				):
               raise ValueError(
                   """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
                   f''' {len(a_				)} and {len(a_				)}'''				)

        return images_with_overflow







 def _snake_case					(				self    ,*a_    ,**a_				)						->  Union[str, Any]:
        return self.tokenizer.batch_decode(*a_    ,**a_				)







 def _snake_case					(				self    ,*a_    ,**a_				)						->  Dict:
        return self.tokenizer.decode(*a_    ,**a_				)







 @property
 def _snake_case					(				self				)						->  Optional[int]:
        return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]







 @property
 def _snake_case					(				self				)						->  Union[str, Any]:
        warnings.warn(
            """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""    ,a_    ,)
        return self.image_processor_class







 @property
 def _snake_case					(				self				)						->  List[str]:
        warnings.warn(
            """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""    ,a_    ,)
        return self.image_processor











'''simple docstring'''







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 _lowercase      (							_lowercase							, unittest.TestCase						):
							a							 =			BartTokenizer
							a							 =			BartTokenizerFast
							a							 =			True
							a							 =			filter_roberta_detectors




							def 						lowerCamelCase_							(				self:							Union[str, Any]	):
														super().setUp()
														lowerCamelCase__   :      int			    =			[
														    """l""",
														    """o""",
														    """w""",
														    """e""",
														    """r""",
														    """s""",
														    """t""",
														    """i""",
														    """d""",
														    """n""",
														    """\u0120""",
														    """\u0120l""",
														    """\u0120n""",
														    """\u0120lo""",
														    """\u0120low""",
														    """er""",
														    """\u0120lowest""",
														    """\u0120newer""",
														    """\u0120wider""",
														    """<unk>""",
														]
														lowerCamelCase__   :      List[str]			    =			dict(zip(UpperCamelCase__     ,				range(len(UpperCamelCase__	)	)	)	)
														lowerCamelCase__   :      List[str]			    =			["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
														lowerCamelCase__   :      List[Any]			    =			{"""unk_token""": """<unk>"""}

														lowerCamelCase__   :      List[str]			    =			os.path.join(self.tmpdirname     ,				VOCAB_FILES_NAMES["""vocab_file"""]	)
														lowerCamelCase__   :      List[str]			    =			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(UpperCamelCase__	) + """\n"""	)
														with open(self.merges_file     ,				"""w"""     ,				encoding="""utf-8"""	) as fp:
																					fp.write("""\n""".join(UpperCamelCase__	)	)




							def 						lowerCamelCase_							(				self:							Tuple     ,				**UpperCamelCase__:							Dict	):
														kwargs.update(self.special_tokens_map	)
														return self.tokenizer_class.from_pretrained(self.tmpdirname     ,				**UpperCamelCase__	)




							def 						lowerCamelCase_							(				self:							Optional[int]     ,				**UpperCamelCase__:							Tuple	):
														kwargs.update(self.special_tokens_map	)
														return self.rust_tokenizer_class.from_pretrained(self.tmpdirname     ,				**UpperCamelCase__	)




							def 						lowerCamelCase_							(				self:							List[str]     ,				UpperCamelCase__:							List[Any]	):
														return "lower newer", "lower newer"




							@cached_property
							def 						lowerCamelCase_							(				self:							str	):
														return BartTokenizer.from_pretrained("""facebook/bart-large"""	)




							@cached_property
							def 						lowerCamelCase_							(				self:							Union[str, Any]	):
														return BartTokenizerFast.from_pretrained("""facebook/bart-large"""	)




							@require_torch
							def 						lowerCamelCase_							(				self:							Optional[int]	):
														lowerCamelCase__   :      Any			    =			["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
														lowerCamelCase__   :      List[Any]			    =			[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					lowerCamelCase__   :      Tuple			    =			tokenizer(UpperCamelCase__     ,				max_length=len(UpperCamelCase__	)     ,				padding=UpperCamelCase__     ,				return_tensors="""pt"""	)
																					self.assertIsInstance(UpperCamelCase__     ,				UpperCamelCase__	)

																					self.assertEqual((2, 9)     ,				batch.input_ids.shape	)
																					self.assertEqual((2, 9)     ,				batch.attention_mask.shape	)
																					lowerCamelCase__   :      Optional[Any]			    =			batch.input_ids.tolist()[0]
																					self.assertListEqual(UpperCamelCase__     ,				UpperCamelCase__	)
																					# Test that special tokens are reset




							@require_torch
							def 						lowerCamelCase_							(				self:							Tuple	):
														lowerCamelCase__   :      str			    =			["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					lowerCamelCase__   :      Union[str, Any]			    =			tokenizer(UpperCamelCase__     ,				padding=UpperCamelCase__     ,				return_tensors="""pt"""	)
																					# check if input_ids are returned and no labels
																					self.assertIn("""input_ids"""     ,				UpperCamelCase__	)
																					self.assertIn("""attention_mask"""     ,				UpperCamelCase__	)
																					self.assertNotIn("""labels"""     ,				UpperCamelCase__	)
																					self.assertNotIn("""decoder_attention_mask"""     ,				UpperCamelCase__	)




							@require_torch
							def 						lowerCamelCase_							(				self:							Dict	):
														lowerCamelCase__   :      str			    =			[
														    """Summary of the text.""",
														    """Another summary.""",
														]
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					lowerCamelCase__   :      Any			    =			tokenizer(text_target=UpperCamelCase__     ,				max_length=32     ,				padding="""max_length"""     ,				return_tensors="""pt"""	)
																					self.assertEqual(32     ,				targets["""input_ids"""].shape[1]	)




							@require_torch
							def 						lowerCamelCase_							(				self:							str	):
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					lowerCamelCase__   :      Union[str, Any]			    =			tokenizer(
																					    ["""I am a small frog""" * 1_024, """I am a small frog"""]     ,				padding=UpperCamelCase__     ,				truncation=UpperCamelCase__     ,				return_tensors="""pt"""	)
																					self.assertIsInstance(UpperCamelCase__     ,				UpperCamelCase__	)
																					self.assertEqual(batch.input_ids.shape     ,				(2, 1_024)	)




							@require_torch
							def 						lowerCamelCase_							(				self:							Optional[Any]	):
														lowerCamelCase__   :      Optional[Any]			    =			["""A long paragraph for summarization."""]
														lowerCamelCase__   :      Any			    =			[
														    """Summary of the text.""",
														]
														for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																					lowerCamelCase__   :      Union[str, Any]			    =			tokenizer(UpperCamelCase__     ,				return_tensors="""pt"""	)
																					lowerCamelCase__   :      Union[str, Any]			    =			tokenizer(text_target=UpperCamelCase__     ,				return_tensors="""pt"""	)
																					lowerCamelCase__   :      List[str]			    =			inputs["""input_ids"""]
																					lowerCamelCase__   :      Optional[int]			    =			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 						lowerCamelCase_							(				self:							Optional[int]	):
														pass




							def 						lowerCamelCase_							(				self:							List[str]	):
														for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																					with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
																												lowerCamelCase__   :      int			    =			self.rust_tokenizer_class.from_pretrained(UpperCamelCase__     ,				**UpperCamelCase__	)
																												lowerCamelCase__   :      int			    =			self.tokenizer_class.from_pretrained(UpperCamelCase__     ,				**UpperCamelCase__	)
																												lowerCamelCase__   :      Dict			    =			"""A, <mask> AllenNLP sentence."""
																												lowerCamelCase__   :      Dict			    =			tokenizer_r.encode_plus(UpperCamelCase__     ,				add_special_tokens=UpperCamelCase__     ,				return_token_type_ids=UpperCamelCase__	)
																												lowerCamelCase__   :      Union[str, Any]			    =			tokenizer_p.encode_plus(UpperCamelCase__     ,				add_special_tokens=UpperCamelCase__     ,				return_token_type_ids=UpperCamelCase__	)

																												# 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"""]	)     ,				)

																												lowerCamelCase__   :      Any			    =			tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]	)
																												lowerCamelCase__   :      Any			    =			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, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]	)
																												self.assertSequenceEqual(tokens_r["""input_ids"""]     ,				[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]	)

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

