




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        lowercase      (    __snake_case     ,	unittest.TestCase):
				__lowerCAmelCase    :    Any         =   LEDTokenizer
				__lowerCAmelCase    :    List[str]         =   LEDTokenizerFast
				__lowerCAmelCase    :    Tuple         =   True







				def 					a_  (							self   :					int       ):



					"""simple docstring"""

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

					A_  :				Union[str, Any]	   =		os.path.join(self.tmpdirname      ,     VOCAB_FILES_NAMES['''vocab_file''']       )
					A_  :				Union[str, 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 					a_  (							self   :					int      ,     **_lowerCamelCase   :					Any       ):



					"""simple docstring"""

					kwargs.update(self.special_tokens_map       )
					return self.tokenizer_class.from_pretrained(self.tmpdirname      ,     **_lowercase       )







				def 					a_  (							self   :					int      ,     **_lowerCamelCase   :					Optional[int]       ):



					"""simple docstring"""

					kwargs.update(self.special_tokens_map       )
					return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,     **_lowercase       )







				def 					a_  (							self   :					Tuple      ,     _lowerCamelCase   :					int       ):



					"""simple docstring"""

					return "lower newer", "lower newer"







				@cached_property
				def 					a_  (							self   :					Optional[Any]       ):



					"""simple docstring"""

					return LEDTokenizer.from_pretrained('''allenai/led-base-16384'''       )







				@cached_property
				def 					a_  (							self   :					Union[str, Any]       ):



					"""simple docstring"""

					return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384'''       )







				@require_torch
				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					A_  :				Any	   =		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
					A_  :				List[str]	   =		[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_  :				Optional[int]	   =		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       )
						A_  :				Dict	   =		batch.input_ids.tolist()[0]
						self.assertListEqual(_lowercase      ,     _lowercase       )







				@require_torch
				def 					a_  (							self   :					List[str]       ):



					"""simple docstring"""

					A_  :				Union[str, Any]	   =		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						A_  :				Tuple	   =		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 					a_  (							self   :					Union[str, Any]       ):



					"""simple docstring"""

					A_  :				Optional[Any]	   =		[
					    """Summary of the text.""",
					    """Another summary.""",
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						A_  :				Dict	   =		tokenizer(text_target=_lowercase      ,     max_length=32      ,     padding='''max_length'''      ,     return_tensors='''pt'''       )
						self.assertEqual(32      ,     targets['''input_ids'''].shape[1]       )







				@require_torch
				def 					a_  (							self   :					Union[str, Any]       ):



					"""simple docstring"""

					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						A_  :				Optional[int]	   =		tokenizer(
						    ['''I am a small frog''' * 10_24, '''I am a small frog''']      ,     padding=_lowercase      ,     truncation=_lowercase      ,     return_tensors='''pt'''       )
						self.assertIsInstance(_lowercase      ,     _lowercase       )
						self.assertEqual(batch.input_ids.shape      ,     (2, 51_22)       )







				@require_torch
				def 					a_  (							self   :					str       ):



					"""simple docstring"""

					A_  :				Tuple	   =		["""A long paragraph for summarization."""]
					A_  :				Dict	   =		[
					    """Summary of the text.""",
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						A_  :				int	   =		tokenizer(_lowercase      ,     return_tensors='''pt'''       )
						A_  :				Dict	   =		tokenizer(text_target=_lowercase      ,     return_tensors='''pt'''       )
						A_  :				List[str]	   =		inputs["""input_ids"""]
						A_  :				List[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 					a_  (							self   :					List[str]       ):



					"""simple docstring"""

					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
						A_  :				Optional[Any]	   =		["""Summary of the text.""", """Another summary."""]
						A_  :				List[Any]	   =		[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

						A_  :				Union[str, Any]	   =		tokenizer(_lowercase      ,     padding=_lowercase       )
						A_  :				Tuple	   =		[[0] * len(_lowercase       ) for x in encoded_output["""input_ids"""]]
						A_  :				Union[str, Any]	   =		tokenizer.pad(_lowercase       )
						self.assertSequenceEqual(outputs['''global_attention_mask''']      ,     _lowercase       )







				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					pass







				def 					a_  (							self   :					Union[str, Any]       ):



					"""simple docstring"""

					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
						with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""       ):
							A_  :				Dict	   =		self.rust_tokenizer_class.from_pretrained(_lowercase      ,     **_lowercase       )
							A_  :				Union[str, Any]	   =		self.tokenizer_class.from_pretrained(_lowercase      ,     **_lowercase       )
							A_  :				Union[str, Any]	   =		"""A, <mask> AllenNLP sentence."""
							A_  :				Dict	   =		tokenizer_r.encode_plus(_lowercase      ,     add_special_tokens=_lowercase      ,     return_token_type_ids=_lowercase       )
							A_  :				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''']       )      ,     )

							A_  :				Tuple	   =		tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']       )
							A_  :				Any	   =		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(
							    _lowercase      ,     ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']       )
							self.assertSequenceEqual(
							    _lowercase      ,     ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']       )











"""simple docstring"""

from collections.abc import Generator
from math import sin

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	if len(_UpperCAmelCase	) != 32:
		raise ValueError('''Input must be of length 32'''	)

	A_  :				int	   =		b''''''
	for i in [3, 2, 1, 0]:
		little_endian += string_aa[8 * i : 8 * i + 8]
	return little_endian

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	if i < 0:
		raise ValueError('''Input must be non-negative'''	)

	A_  :				int	   =		format(_UpperCAmelCase  ,      '''08x'''	)[-8:]
	A_  :				Optional[Any]	   =		b''''''
	for i in [3, 2, 1, 0]:
		little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode('''utf-8'''	)
	return little_endian_hex

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	A_  :				int	   =		b''''''
	for char in message:
		bit_string += format(_UpperCAmelCase  ,      '''08b'''	).encode('''utf-8'''	)
	A_  :				Optional[Any]	   =		format(len(_UpperCAmelCase	)  ,      '''064b'''	).encode('''utf-8'''	)

	# Pad bit_string to a multiple of 512 chars
	bit_string += b"1"
	while len(_UpperCAmelCase	) % 512 != 448:
		bit_string += b"0"
	bit_string += to_little_endian(start_len[32:]	) + to_little_endian(start_len[:32]	)

	return bit_string

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	if len(_UpperCAmelCase	) % 512 != 0:
		raise ValueError('''Input must have length that\'s a multiple of 512'''	)

	for pos in range(0  ,      len(_UpperCAmelCase	)  ,      512	):
		A_  :				Union[str, Any]	   =		bit_string[pos : pos + 512]
		A_  :				Optional[int]	   =		[]
		for i in range(0  ,      512  ,      32	):
			block_words.append(int(to_little_endian(block[i : i + 32]	)  ,      2	)	)
		yield block_words

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	if i < 0:
		raise ValueError('''Input must be non-negative'''	)

	A_  :				str	   =		format(_UpperCAmelCase  ,      '''032b'''	)
	A_  :				List[str]	   =		''''''
	for c in i_str:
		new_str += "1" if c == "0" else "0"
	return int(_UpperCAmelCase  ,      2	)

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	return (a + b) % 2**32

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	if i < 0:
		raise ValueError('''Input must be non-negative'''	)
	if shift < 0:
		raise ValueError('''Shift must be non-negative'''	)
	return ((i << shift) ^ (i >> (32 - shift))) % 2**32

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Any	   =		preprocess(_UpperCAmelCase	)

	A_  :				Any	   =		[int(2**32 * abs(sin(i + 1	)	)	) for i in range(64	)]

	# Starting states
	A_  :				List[str]	   =		0x6_7_4_5_2_3_0_1
	A_  :				str	   =		0xe_f_c_d_a_b_8_9
	A_  :				Union[str, Any]	   =		0x9_8_b_a_d_c_f_e
	A_  :				Dict	   =		0x1_0_3_2_5_4_7_6

	A_  :				int	   =		[
	    7,
	    12,
	    17,
	    22,
	    7,
	    12,
	    17,
	    22,
	    7,
	    12,
	    17,
	    22,
	    7,
	    12,
	    17,
	    22,
	    5,
	    9,
	    14,
	    20,
	    5,
	    9,
	    14,
	    20,
	    5,
	    9,
	    14,
	    20,
	    5,
	    9,
	    14,
	    20,
	    4,
	    11,
	    16,
	    23,
	    4,
	    11,
	    16,
	    23,
	    4,
	    11,
	    16,
	    23,
	    4,
	    11,
	    16,
	    23,
	    6,
	    10,
	    15,
	    21,
	    6,
	    10,
	    15,
	    21,
	    6,
	    10,
	    15,
	    21,
	    6,
	    10,
	    15,
	    21,
	]

	# Process bit string in chunks, each with 16 32-char words
	for block_words in get_block_words(_UpperCAmelCase	):
		A_  :				str	   =		aa
		A_  :				Tuple	   =		ba
		A_  :				Tuple	   =		ca
		A_  :				int	   =		da

		# Hash current chunk
		for i in range(64	):
			if i <= 15:
				# f = (b & c) | (not_32(b) & d)     # Alternate definition for f
				A_  :				str	   =		d ^ (b & (c ^ d))
				A_  :				int	   =		i
			elif i <= 31:
				# f = (d & b) | (not_32(d) & c)     # Alternate definition for f
				A_  :				List[Any]	   =		c ^ (d & (b ^ c))
				A_  :				Optional[int]	   =		(5 * i + 1) % 16
			elif i <= 47:
				A_  :				Optional[Any]	   =		b ^ c ^ d
				A_  :				List[Any]	   =		(3 * i + 5) % 16
			else:
				A_  :				Dict	   =		c ^ (b | not_aa(_UpperCAmelCase	))
				A_  :				Dict	   =		(7 * i) % 16
			A_  :				Optional[int]	   =		(f + a + added_consts[i] + block_words[g]) % 2**32
			A_  :				List[str]	   =		d
			A_  :				str	   =		c
			A_  :				Tuple	   =		b
			A_  :				Tuple	   =		sum_aa(_UpperCAmelCase  ,      left_rotate_aa(_UpperCAmelCase  ,      shift_amounts[i]	)	)

		# Add hashed chunk to running total
		A_  :				str	   =		sum_aa(_UpperCAmelCase  ,      _UpperCAmelCase	)
		A_  :				int	   =		sum_aa(_UpperCAmelCase  ,      _UpperCAmelCase	)
		A_  :				int	   =		sum_aa(_UpperCAmelCase  ,      _UpperCAmelCase	)
		A_  :				Optional[Any]	   =		sum_aa(_UpperCAmelCase  ,      _UpperCAmelCase	)

	A_  :				Dict	   =		reformat_hex(_UpperCAmelCase	) + reformat_hex(_UpperCAmelCase	) + reformat_hex(_UpperCAmelCase	) + reformat_hex(_UpperCAmelCase	)
	return digest


if __name__ == "__main__":
		import doctest

		doctest.testmod()





