



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 	lowerCamelCase      (						lowercase__,     unittest.TestCase  ):







					'''simple docstring'''







					lowerCAmelCase_				:    Tuple									=  LEDTokenizer
					lowerCAmelCase_				:    int									=  LEDTokenizerFast
					lowerCAmelCase_				:    Dict									=  True



					def    A__		(			self     ):
							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(lowerCAmelCase    ,	range(len(lowerCAmelCase     )     )     )     )
							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(lowerCAmelCase     ) + "\n"     )
							with open(self.merges_file    ,	"w"    ,	encoding="utf-8"     ) as fp:
									fp.write("\n".join(lowerCAmelCase     )     )



					def    A__		(			self    ,	**lowerCAmelCase     ):
							kwargs.update(self.special_tokens_map     )
							return self.tokenizer_class.from_pretrained(self.tmpdirname    ,	**lowerCAmelCase     )



					def    A__		(			self    ,	**lowerCAmelCase     ):
							kwargs.update(self.special_tokens_map     )
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname    ,	**lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase     ):
							return "lower newer", "lower newer"



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



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



					@require_torch
					def    A__		(			self     ):
							UpperCAmelCase_						=				["A long paragraph for summarization.", "Another paragraph for summarization."]
							UpperCAmelCase_						=				[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									UpperCAmelCase_						=				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     )
									UpperCAmelCase_						=				batch.input_ids.tolist()[0]
									self.assertListEqual(lowerCAmelCase    ,	lowerCAmelCase     )



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



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



					@require_torch
					def    A__		(			self     ):
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									UpperCAmelCase_						=				tokenizer(
									    ["I am a small frog" * 1024, "I am a small frog"]    ,	padding=lowerCAmelCase    ,	truncation=lowerCAmelCase    ,	return_tensors="pt"     )
									self.assertIsInstance(lowerCAmelCase    ,	lowerCAmelCase     )
									self.assertEqual(batch.input_ids.shape    ,	(2, 5122)     )



					@require_torch
					def    A__		(			self     ):
							UpperCAmelCase_						=				["A long paragraph for summarization."]
							UpperCAmelCase_						=				[
							    "Summary of the text.",
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									UpperCAmelCase_						=				tokenizer(lowerCAmelCase    ,	return_tensors="pt"     )
									UpperCAmelCase_						=				tokenizer(text_target=lowerCAmelCase    ,	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()     )



					@require_torch
					def    A__		(			self     ):
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									UpperCAmelCase_						=				["Summary of the text.", "Another summary."]
									UpperCAmelCase_						=				[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

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



					def    A__		(			self     ):
							pass



					def    A__		(			self     ):
							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(lowerCAmelCase    ,	**lowerCAmelCase     )
											UpperCAmelCase_						=				self.tokenizer_class.from_pretrained(lowerCAmelCase    ,	**lowerCAmelCase     )
											UpperCAmelCase_						=				"A, <mask> AllenNLP sentence."
											UpperCAmelCase_						=				tokenizer_r.encode_plus(lowerCAmelCase    ,	add_special_tokens=lowerCAmelCase    ,	return_token_type_ids=lowerCAmelCase     )
											UpperCAmelCase_						=				tokenizer_p.encode_plus(lowerCAmelCase    ,	add_special_tokens=lowerCAmelCase    ,	return_token_type_ids=lowerCAmelCase     )
											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"]     )    ,	)

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






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer


SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE     				=						{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE     				=						{
    "vocab_file": {
        "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt",
        "distilbert-base-uncased-distilled-squad": (
            "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt"
        ),
        "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt",
        "distilbert-base-cased-distilled-squad": (
            "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt"
        ),
        "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt",
        "distilbert-base-multilingual-cased": (
            "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json",
        "distilbert-base-uncased-distilled-squad": (
            "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json"
        ),
        "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json",
        "distilbert-base-cased-distilled-squad": (
            "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json"
        ),
        "distilbert-base-german-cased": (
            "https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json"
        ),
        "distilbert-base-multilingual-cased": (
            "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json"
        ),
    },
}

SCREAMING_SNAKE_CASE     				=						{
    "distilbert-base-uncased": 512,
    "distilbert-base-uncased-distilled-squad": 512,
    "distilbert-base-cased": 512,
    "distilbert-base-cased-distilled-squad": 512,
    "distilbert-base-german-cased": 512,
    "distilbert-base-multilingual-cased": 512,
}


SCREAMING_SNAKE_CASE     				=						{
    "distilbert-base-uncased": {"do_lower_case": True},
    "distilbert-base-uncased-distilled-squad": {"do_lower_case": True},
    "distilbert-base-cased": {"do_lower_case": False},
    "distilbert-base-cased-distilled-squad": {"do_lower_case": False},
    "distilbert-base-german-cased": {"do_lower_case": False},
    "distilbert-base-multilingual-cased": {"do_lower_case": False},
}



class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					lowerCAmelCase_				:    Any									=  VOCAB_FILES_NAMES
					lowerCAmelCase_				:    List[str]									=  PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase_				:    str									=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase_				:    Union[str, Any]									=  PRETRAINED_INIT_CONFIGURATION
					lowerCAmelCase_				:    int									=  ['input_ids', 'attention_mask']
					lowerCAmelCase_				:    str									=  DistilBertTokenizer



					def __init__(			self    ,	lowerCAmelCase=None    ,	lowerCAmelCase=None    ,	lowerCAmelCase=True    ,	lowerCAmelCase="[UNK]"    ,	lowerCAmelCase="[SEP]"    ,	lowerCAmelCase="[PAD]"    ,	lowerCAmelCase="[CLS]"    ,	lowerCAmelCase="[MASK]"    ,	lowerCAmelCase=True    ,	lowerCAmelCase=None    ,	**lowerCAmelCase    ,	):
							super().__init__(
							    lowerCAmelCase    ,	tokenizer_file=lowerCAmelCase    ,	do_lower_case=lowerCAmelCase    ,	unk_token=lowerCAmelCase    ,	sep_token=lowerCAmelCase    ,	pad_token=lowerCAmelCase    ,	cls_token=lowerCAmelCase    ,	mask_token=lowerCAmelCase    ,	tokenize_chinese_chars=lowerCAmelCase    ,	strip_accents=lowerCAmelCase    ,	**lowerCAmelCase    ,	)

							UpperCAmelCase_						=				json.loads(self.backend_tokenizer.normalizer.__getstate__()     )
							if (
							    normalizer_state.get("lowercase"    ,	lowerCAmelCase     ) != do_lower_case
							    or normalizer_state.get("strip_accents"    ,	lowerCAmelCase     ) != strip_accents
							    or normalizer_state.get("handle_chinese_chars"    ,	lowerCAmelCase     ) != tokenize_chinese_chars
							):
									UpperCAmelCase_						=				getattr(lowerCAmelCase    ,	normalizer_state.pop("type"     )     )
									UpperCAmelCase_						=				do_lower_case
									UpperCAmelCase_						=				strip_accents
									UpperCAmelCase_						=				tokenize_chinese_chars
									UpperCAmelCase_						=				normalizer_class(**lowerCAmelCase     )

							UpperCAmelCase_						=				do_lower_case



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase=None     ):
							UpperCAmelCase_						=				[self.cls_token_id] + token_ids_a + [self.sep_token_id]

							if token_ids_a:
									output += token_ids_a + [self.sep_token_id]

							return output



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None     ):
							UpperCAmelCase_						=				[self.sep_token_id]
							UpperCAmelCase_						=				[self.cls_token_id]
							if token_ids_a is None:
									return len(cls + token_ids_a + sep     ) * [0]
							return len(cls + token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1]



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None     ):
							UpperCAmelCase_						=				self._tokenizer.model.save(lowerCAmelCase    ,	name=lowerCAmelCase     )
							return tuple(lowerCAmelCase     )

