






import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led 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




@require_tokenizers
class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					,  unittest.TestCase					):


      '''simple docstring'''




      A     :   Union[str, Any]						=					LEDTokenizer
      A     :   Optional[Any]						=					LEDTokenizerFast
      A     :   Union[str, Any]						=					True






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

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






      def       _lowerCAmelCase       (	self		,				**_SCREAMING_SNAKE_CASE				) ->				List[str]:
        kwargs.update(self.special_tokens_map				)
        return self.tokenizer_class.from_pretrained(self.tmpdirname		,				**_SCREAMING_SNAKE_CASE				)






      def       _lowerCAmelCase       (	self		,				**_SCREAMING_SNAKE_CASE				) ->				Optional[Any]:
        kwargs.update(self.special_tokens_map				)
        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,				**_SCREAMING_SNAKE_CASE				)






      def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				Any:
        return "lower newer", "lower newer"






      @cached_property
      def       _lowerCAmelCase       (	self				) ->				Optional[Any]:
        return LEDTokenizer.from_pretrained("allenai/led-base-16384"				)






      @cached_property
      def       _lowerCAmelCase       (	self				) ->				Optional[int]:
        return LEDTokenizerFast.from_pretrained("allenai/led-base-16384"				)






      @require_torch
      def       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
        snake_case_				:							Any						 =       ["A long paragraph for summarization.", "Another paragraph for summarization."]
        snake_case_				:							Tuple						 =       [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

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

          self.assertEqual((2, 9)		,				batch.input_ids.shape				)
          self.assertEqual((2, 9)		,				batch.attention_mask.shape				)
          snake_case_				:							Dict						 =       batch.input_ids.tolist()[0]
          self.assertListEqual(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)






      @require_torch
      def       _lowerCAmelCase       (	self				) ->				List[str]:
        snake_case_				:							int						 =       ["A long paragraph for summarization.", "Another paragraph for summarization."]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          snake_case_				:							Union[str, Any]						 =       tokenizer(_SCREAMING_SNAKE_CASE		,				padding=_SCREAMING_SNAKE_CASE		,				return_tensors="pt"				)
          self.assertIn("input_ids"		,				_SCREAMING_SNAKE_CASE				)
          self.assertIn("attention_mask"		,				_SCREAMING_SNAKE_CASE				)
          self.assertNotIn("labels"		,				_SCREAMING_SNAKE_CASE				)
          self.assertNotIn("decoder_attention_mask"		,				_SCREAMING_SNAKE_CASE				)






      @require_torch
      def       _lowerCAmelCase       (	self				) ->				Optional[int]:
        snake_case_				:							Any						 =       [
            "Summary of the text.",
            "Another summary.",
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          snake_case_				:							str						 =       tokenizer(text_target=_SCREAMING_SNAKE_CASE		,				max_length=32		,				padding="max_length"		,				return_tensors="pt"				)
          self.assertEqual(32		,				targets["input_ids"].shape[1]				)






      @require_torch
      def       _lowerCAmelCase       (	self				) ->				int:
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          snake_case_				:							Tuple						 =       tokenizer(
              ["I am a small frog" * 1024, "I am a small frog"]		,				padding=_SCREAMING_SNAKE_CASE		,				truncation=_SCREAMING_SNAKE_CASE		,				return_tensors="pt"				)
          self.assertIsInstance(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
          self.assertEqual(batch.input_ids.shape		,				(2, 5122)				)






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






      @require_torch
      def       _lowerCAmelCase       (	self				) ->				Tuple:
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          snake_case_				:							int						 =       ["Summary of the text.", "Another summary."]
          snake_case_				:							Dict						 =       [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

          snake_case_				:							str						 =       tokenizer(_SCREAMING_SNAKE_CASE		,				padding=_SCREAMING_SNAKE_CASE				)
          snake_case_				:							Union[str, Any]						 =       [[0] * len(_SCREAMING_SNAKE_CASE				) for x in encoded_output["input_ids"]]
          snake_case_				:							Optional[int]						 =       tokenizer.pad(_SCREAMING_SNAKE_CASE				)
          self.assertSequenceEqual(outputs["global_attention_mask"]		,				_SCREAMING_SNAKE_CASE				)






      def       _lowerCAmelCase       (	self				) ->				Optional[Any]:
        pass






      def       _lowerCAmelCase       (	self				) ->				Any:
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
          with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''				):
            snake_case_				:							int						 =       self.rust_tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)
            snake_case_				:							Union[str, Any]						 =       self.tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)
            snake_case_				:							Optional[int]						 =       "A, <mask> AllenNLP sentence."
            snake_case_				:							int						 =       tokenizer_r.encode_plus(_SCREAMING_SNAKE_CASE		,				add_special_tokens=_SCREAMING_SNAKE_CASE		,				return_token_type_ids=_SCREAMING_SNAKE_CASE				)
            snake_case_				:							Union[str, Any]						 =       tokenizer_p.encode_plus(_SCREAMING_SNAKE_CASE		,				add_special_tokens=_SCREAMING_SNAKE_CASE		,				return_token_type_ids=_SCREAMING_SNAKE_CASE				)
            self.assertEqual(sum(tokens_r["token_type_ids"]				)		,				sum(tokens_p["token_type_ids"]				)				)
            self.assertEqual(
                sum(tokens_r["attention_mask"]				) / len(tokens_r["attention_mask"]				)		,				sum(tokens_p["attention_mask"]				) / len(tokens_p["attention_mask"]				)		,				)

            snake_case_				:							Union[str, Any]						 =       tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]				)
            snake_case_				:							Optional[int]						 =       tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]				)
            self.assertSequenceEqual(tokens_p["input_ids"]		,				[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]				)
            self.assertSequenceEqual(tokens_r["input_ids"]		,				[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]				)

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








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 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						A     :   Tuple						=					['image_processor', 'tokenizer']
						A     :   List[Any]						=					'ViltImageProcessor'
						A     :   Optional[Any]						=					('BertTokenizer', 'BertTokenizerFast')






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

								snake_case_				:							Tuple						 =       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__(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
								snake_case_				:							str						 =       self.image_processor






						def __call__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = True		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = 0		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = True		,				_SCREAMING_SNAKE_CASE = None		,				**_SCREAMING_SNAKE_CASE		,				) ->				BatchEncoding:
								snake_case_				:							List[Any]						 =       self.tokenizer(
								    text=_SCREAMING_SNAKE_CASE		,				add_special_tokens=_SCREAMING_SNAKE_CASE		,				padding=_SCREAMING_SNAKE_CASE		,				truncation=_SCREAMING_SNAKE_CASE		,				max_length=_SCREAMING_SNAKE_CASE		,				stride=_SCREAMING_SNAKE_CASE		,				pad_to_multiple_of=_SCREAMING_SNAKE_CASE		,				return_token_type_ids=_SCREAMING_SNAKE_CASE		,				return_attention_mask=_SCREAMING_SNAKE_CASE		,				return_overflowing_tokens=_SCREAMING_SNAKE_CASE		,				return_special_tokens_mask=_SCREAMING_SNAKE_CASE		,				return_offsets_mapping=_SCREAMING_SNAKE_CASE		,				return_length=_SCREAMING_SNAKE_CASE		,				verbose=_SCREAMING_SNAKE_CASE		,				return_tensors=_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE		,				)
								# add pixel_values + pixel_mask
								snake_case_				:							str						 =       self.image_processor(_SCREAMING_SNAKE_CASE		,				return_tensors=_SCREAMING_SNAKE_CASE				)
								encoding.update(_SCREAMING_SNAKE_CASE				)

								return encoding






						def       _lowerCAmelCase       (	self		,				*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				) ->				List[str]:
								return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self		,				*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				) ->				Dict:
								return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)






						@property
						def       _lowerCAmelCase       (	self				) ->				Tuple:
								snake_case_				:							List[Any]						 =       self.tokenizer.model_input_names
								snake_case_				:							Tuple						 =       self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)






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






						@property
						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								warnings.warn(
								    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,				_SCREAMING_SNAKE_CASE		,				)
								return self.image_processor
