




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    snake_case__							(_UpperCamelCase   ,	unittest.TestCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							Dict										= BartTokenizer
				SCREAMING_SNAKE_CASE_	:							List[Any]										= BartTokenizerFast
				SCREAMING_SNAKE_CASE_	:							Any										= True
				SCREAMING_SNAKE_CASE_	:							Tuple										= filter_roberta_detectors





				def 							__UpperCAmelCase							(				self    :					Tuple							) ->					int:
							super().setUp()
							a   =      [
							    "l",
							    "o",
							    "w",
							    "e",
							    "r",
							    "s",
							    "t",
							    "i",
							    "d",
							    "n",
							    "\u0120",
							    "\u0120l",
							    "\u0120n",
							    "\u0120lo",
							    "\u0120low",
							    "er",
							    "\u0120lowest",
							    "\u0120newer",
							    "\u0120wider",
							    "<unk>",
							]
							a   =      dict(zip(__lowerCamelCase ,				range(len(__lowerCamelCase							)							)							)							)
							a   =      ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
							a   =      {"unk_token": "<unk>"}

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





				def 							__UpperCAmelCase							(				self    :					Optional[int] ,				**__lowerCamelCase    :					List[Any]							) ->					List[Any]:
							kwargs.update(self.special_tokens_map							)
							return self.tokenizer_class.from_pretrained(self.tmpdirname ,				**__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					int ,				**__lowerCamelCase    :					Dict							) ->					Union[str, Any]:
							kwargs.update(self.special_tokens_map							)
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,				**__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					str ,				__lowerCamelCase    :					Dict							) ->					Dict:
							return "lower newer", "lower newer"





				@cached_property
				def 							__UpperCAmelCase							(				self    :					Tuple							) ->					Any:
							return BartTokenizer.from_pretrained("facebook/bart-large"							)





				@cached_property
				def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					List[Any]:
							return BartTokenizerFast.from_pretrained("facebook/bart-large"							)





				@require_torch
				def 							__UpperCAmelCase							(				self    :					List[str]							) ->					int:
							a   =      ["A long paragraph for summarization.", "Another paragraph for summarization."]
							a   =      [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

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

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





				@require_torch
				def 							__UpperCAmelCase							(				self    :					Dict							) ->					int:
							a   =      ["A long paragraph for summarization.", "Another paragraph for summarization."]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										a   =      tokenizer(__lowerCamelCase ,				padding=__lowerCamelCase ,				return_tensors="pt"							)
										# check if input_ids are returned and no labels
										self.assertIn("input_ids" ,				__lowerCamelCase							)
										self.assertIn("attention_mask" ,				__lowerCamelCase							)
										self.assertNotIn("labels" ,				__lowerCamelCase							)
										self.assertNotIn("decoder_attention_mask" ,				__lowerCamelCase							)





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





				@require_torch
				def 							__UpperCAmelCase							(				self    :					Dict							) ->					Union[str, Any]:
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										a   =      tokenizer(
										    ["I am a small frog" * 10_24, "I am a small frog"] ,				padding=__lowerCamelCase ,				truncation=__lowerCamelCase ,				return_tensors="pt"							)
										self.assertIsInstance(__lowerCamelCase ,				__lowerCamelCase							)
										self.assertEqual(batch.input_ids.shape ,				(2, 10_24)							)





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





				def 							__UpperCAmelCase							(				self    :					Union[str, Any]							) ->					Dict:
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
													a   =      self.rust_tokenizer_class.from_pretrained(__lowerCamelCase ,				**__lowerCamelCase							)
													a   =      self.tokenizer_class.from_pretrained(__lowerCamelCase ,				**__lowerCamelCase							)
													a   =      "A, <mask> AllenNLP sentence."
													a   =      tokenizer_r.encode_plus(__lowerCamelCase ,				add_special_tokens=__lowerCamelCase ,				return_token_type_ids=__lowerCamelCase							)
													a   =      tokenizer_p.encode_plus(__lowerCamelCase ,				add_special_tokens=__lowerCamelCase ,				return_token_type_ids=__lowerCamelCase							)

													# 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"]							) ,				)

													a   =      tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]							)
													a   =      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, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]							)
													self.assertSequenceEqual(tokens_r["input_ids"] ,				[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]							)

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












import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class    snake_case__							(_UpperCamelCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							List[Any]										= ["""image_processor""", """tokenizer"""]
				SCREAMING_SNAKE_CASE_	:							List[Any]										= """AutoImageProcessor"""
				SCREAMING_SNAKE_CASE_	:							Dict										= """AutoTokenizer"""





				def __init__(				self    :					int ,				__lowerCamelCase    :					Dict=None ,				__lowerCamelCase    :					int=None ,				**__lowerCamelCase    :					Optional[Any]							) ->					str:
							a   =      None
							if "feature_extractor" in kwargs:
										warnings.warn(
										    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
										    " instead." ,				__lowerCamelCase ,				)
										a   =      kwargs.pop("feature_extractor"							)

							a   =      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__(__lowerCamelCase ,				__lowerCamelCase							)
							a   =      self.image_processor
							a   =      False





				def __call__(				self    :					Any ,				*__lowerCamelCase    :					int ,				**__lowerCamelCase    :					Optional[int]							) ->					int:
							# For backward compatibility
							if self._in_target_context_manager:
										return self.current_processor(*__lowerCamelCase ,				**__lowerCamelCase							)

							a   =      kwargs.pop("images" ,				__lowerCamelCase							)
							a   =      kwargs.pop("text" ,				__lowerCamelCase							)
							if len(__lowerCamelCase							) > 0:
										a   =      args[0]
										a   =      args[1:]

							if images is None and text is None:
										raise ValueError("You need to specify either an `images` or `text` input to process."							)

							if images is not None:
										a   =      self.image_processor(__lowerCamelCase ,				*__lowerCamelCase ,				**__lowerCamelCase							)
							if text is not None:
										a   =      self.tokenizer(__lowerCamelCase ,				**__lowerCamelCase							)

							if text is None:
										return inputs
							elif images is None:
										return encodings
							else:
										a   =      encodings["input_ids"]
										return inputs





				def 							__UpperCAmelCase							(				self    :					Union[str, Any] ,				*__lowerCamelCase    :					str ,				**__lowerCamelCase    :					str							) ->					Optional[Any]:
							return self.tokenizer.batch_decode(*__lowerCamelCase ,				**__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Optional[Any] ,				*__lowerCamelCase    :					Union[str, Any] ,				**__lowerCamelCase    :					Any							) ->					str:
							return self.tokenizer.decode(*__lowerCamelCase ,				**__lowerCamelCase							)





				@contextmanager
				def 							__UpperCAmelCase							(				self    :					Any							) ->					Optional[int]:
							warnings.warn(
							    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
							    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
							    "your images inputs, or in a separate call."							)
							a   =      True
							a   =      self.tokenizer
							yield
							a   =      self.image_processor
							a   =      False





				@property
				def 							__UpperCAmelCase							(				self    :					Dict							) ->					Optional[Any]:
							warnings.warn(
							    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." ,				__lowerCamelCase ,				)
							return self.image_processor_class





				@property
				def 							__UpperCAmelCase							(				self    :					int							) ->					Union[str, Any]:
							warnings.warn(
							    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." ,				__lowerCamelCase ,				)
							return self.image_processor






