'''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 					__magic_name__		(					lowercase__,  unittest.TestCase):
			UpperCamelCase__       = LEDTokenizer
			UpperCamelCase__       = LEDTokenizerFast
			UpperCamelCase__       = True







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

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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]       ,    **lowercase_       :						Optional[int]					):
										kwargs.update(self.special_tokens_map					)
										return self.tokenizer_class.from_pretrained(self.tmpdirname       ,    **lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						int       ,    **lowercase_       :						Tuple					):
										kwargs.update(self.special_tokens_map					)
										return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,    **lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]       ,    lowercase_       :						Tuple					):
										return "lower newer", "lower newer"







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







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







			@require_torch
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict					):
										lowercase_      :    Tuple       		= ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
										lowercase_      :    Any       		= [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

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

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







			@require_torch
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
										lowercase_      :    str       		= ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	lowercase_      :    Dict       		= tokenizer(lowercase_       ,    padding=lowercase_       ,    return_tensors="""pt"""					)
																	self.assertIn("""input_ids"""       ,    lowercase_					)
																	self.assertIn("""attention_mask"""       ,    lowercase_					)
																	self.assertNotIn("""labels"""       ,    lowercase_					)
																	self.assertNotIn("""decoder_attention_mask"""       ,    lowercase_					)







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







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







			@require_torch
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]					):
										lowercase_      :    str       		= ["""A long paragraph for summarization."""]
										lowercase_      :    Optional[int]       		= [
										    """Summary of the text.""",
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	lowercase_      :    int       		= tokenizer(lowercase_       ,    return_tensors="""pt"""					)
																	lowercase_      :    Tuple       		= tokenizer(text_target=lowercase_       ,    return_tensors="""pt"""					)
																	lowercase_      :    List[Any]       		= inputs["""input_ids"""]
																	lowercase_      :    Optional[Any]       		= 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 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]					):
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																	lowercase_      :    str       		= ["""Summary of the text.""", """Another summary."""]
																	lowercase_      :    Dict       		= [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]					):
										pass







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
																								lowercase_      :    Optional[Any]       		= self.rust_tokenizer_class.from_pretrained(lowercase_       ,    **lowercase_					)
																								lowercase_      :    Tuple       		= self.tokenizer_class.from_pretrained(lowercase_       ,    **lowercase_					)
																								lowercase_      :    Optional[Any]       		= """A, <mask> AllenNLP sentence."""
																								lowercase_      :    Dict       		= tokenizer_r.encode_plus(lowercase_       ,    add_special_tokens=lowercase_       ,    return_token_type_ids=lowercase_					)
																								lowercase_      :    Tuple       		= tokenizer_p.encode_plus(lowercase_       ,    add_special_tokens=lowercase_       ,    return_token_type_ids=lowercase_					)
																								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"""]					)       ,    )

																								lowercase_      :    List[str]       		= tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]					)
																								lowercase_      :    Tuple       		= tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]					)
																								self.assertSequenceEqual(tokens_p["""input_ids"""]       ,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]					)
																								self.assertSequenceEqual(tokens_r["""input_ids"""]       ,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]					)

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






'''simple docstring'''




import argparse


_lowercase		:				Optional[int]	       =		"docs/source/_static/js/custom.js"




def 							lowerCamelCase			(   UpperCAmelCase__ :      Tuple				)    ->   Dict:
							with open(UpperCAmelCase__   ,   encoding="""utf-8"""   ,   newline="""\n"""				) as f:
														lowercase_      :    Optional[int]       		= f.readlines()
							lowercase_      :    Tuple       		= 0

							# First let's put the right version
							while not lines[index].startswith("""const stableVersion ="""				):
														index += 1
							lowercase_      :    Optional[Any]       		= F'''const stableVersion = "v{version}"\n'''

							# Then update the dictionary
							while not lines[index].startswith("""const versionMapping = {"""				):
														index += 1

							# We go until the end
							while not lines[index].startswith("""}"""				):
														index += 1
							# We add the new version at the end
							lines[index - 1] += F'''    "v{version}": "v{version}",\n'''

							with open(UpperCAmelCase__   ,   """w"""   ,   encoding="""utf-8"""   ,   newline="""\n"""				) as f:
														f.writelines(UpperCAmelCase__				)


if __name__ == "__main__":
						_lowercase		:				Union[str, Any]	       =		argparse.ArgumentParser()
						parser.add_argument("--version", help="Release version.")
						_lowercase		:				Dict	       =		parser.parse_args()
						update_custom_js(args.version)





