


"""simple docstring"""


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  (   lowerCAmelCase__       ,unittest.TestCase		):
							'''simple docstring'''







							__UpperCAmelCase						:    Optional[Any]				      =LEDTokenizer
							__UpperCAmelCase						:    Optional[Any]				      =LEDTokenizerFast
							__UpperCAmelCase						:    List[Any]				      =True

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

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

							def 			snake_case				(     self    ,   **__a	):
										kwargs.update(self.special_tokens_map	)
										return self.tokenizer_class.from_pretrained(self.tmpdirname    ,   **__a	)

							def 			snake_case				(     self    ,   **__a	):
										kwargs.update(self.special_tokens_map	)
										return self.rust_tokenizer_class.from_pretrained(self.tmpdirname    ,   **__a	)

							def 			snake_case				(     self    ,   __a	):
										return "lower newer", "lower newer"

							@cached_property
							def 			snake_case				(     self	):
										return LEDTokenizer.from_pretrained("allenai/led-base-16384"	)

							@cached_property
							def 			snake_case				(     self	):
										return LEDTokenizerFast.from_pretrained("allenai/led-base-16384"	)

							@require_torch
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			["A long paragraph for summarization.", "Another paragraph for summarization."]
										__lowerCAmelCase				    =			[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]:
													__lowerCAmelCase				    =			tokenizer(__a    ,   max_length=len(__a	)    ,   padding=__a    ,   return_tensors="pt"	)
													self.assertIsInstance(__a    ,   __a	)

													self.assertEqual((2, 9)    ,   batch.input_ids.shape	)
													self.assertEqual((2, 9)    ,   batch.attention_mask.shape	)
													__lowerCAmelCase				    =			batch.input_ids.tolist()[0]
													self.assertListEqual(__a    ,   __a	)

							@require_torch
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			["A long paragraph for summarization.", "Another paragraph for summarization."]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													__lowerCAmelCase				    =			tokenizer(__a    ,   padding=__a    ,   return_tensors="pt"	)
													self.assertIn("input_ids"    ,   __a	)
													self.assertIn("attention_mask"    ,   __a	)
													self.assertNotIn("labels"    ,   __a	)
													self.assertNotIn("decoder_attention_mask"    ,   __a	)

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

							@require_torch
							def 			snake_case				(     self	):
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													__lowerCAmelCase				    =			tokenizer(
													    ["I am a small frog" * 10_24, "I am a small frog"]    ,   padding=__a    ,   truncation=__a    ,   return_tensors="pt"	)
													self.assertIsInstance(__a    ,   __a	)
													self.assertEqual(batch.input_ids.shape    ,   (2, 51_22)	)

							@require_torch
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			["A long paragraph for summarization."]
										__lowerCAmelCase				    =			[
										    "Summary of the text.",
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													__lowerCAmelCase				    =			tokenizer(__a    ,   return_tensors="pt"	)
													__lowerCAmelCase				    =			tokenizer(text_target=__a    ,   return_tensors="pt"	)
													__lowerCAmelCase				    =			inputs["input_ids"]
													__lowerCAmelCase				    =			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 			snake_case				(     self	):
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													__lowerCAmelCase				    =			["Summary of the text.", "Another summary."]
													__lowerCAmelCase				    =			[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

													__lowerCAmelCase				    =			tokenizer(__a    ,   padding=__a	)
													__lowerCAmelCase				    =			[[0] * len(__a	) for x in encoded_output["input_ids"]]
													__lowerCAmelCase				    =			tokenizer.pad(__a	)
													self.assertSequenceEqual(outputs["global_attention_mask"]    ,   __a	)

							def 			snake_case				(     self	):
										pass



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

																__lowerCAmelCase				    =			tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]	)
																__lowerCAmelCase				    =			tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]	)
																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(
																    __a    ,   ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]	)
																self.assertSequenceEqual(
																    __a    ,   ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]	)












"""simple docstring"""






import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


UpperCAmelCase_   : Optional[Any]   			= logging.get_logger(__name__)




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      def __init__(						self     :  List[str]						,	*lowercase_     :  Dict						,	**lowercase_     :  Union[str, Any]):




          '''simple docstring'''


          warnings.warn(
              '''The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
              ''' use DeiTImageProcessor instead.'''						,	lowercase_						,	)
          super().__init__(*lowercase_						,	**lowercase_)


