



'''simple docstring'''




import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
							import jax
							import jax.numpy as jnp

							from transformers.generation import (
							    FlaxForcedBOSTokenLogitsProcessor,
							    FlaxForcedEOSTokenLogitsProcessor,
							    FlaxLogitsProcessorList,
							    FlaxMinLengthLogitsProcessor,
							    FlaxTemperatureLogitsWarper,
							    FlaxTopKLogitsWarper,
							    FlaxTopPLogitsWarper,
							)

@require_flax
class 			UpperCamelCase_ (							unittest.TestCase			):





	def  _lowercase(  self		,		A		,		A    )			->			List[str]:
								UpperCAmelCase     :		Tuple							     =		jnp.ones((batch_size, length)    ) / length
								return scores





	def  _lowercase(  self    )			->			List[Any]:
								UpperCAmelCase     :		Dict							     =		None
								UpperCAmelCase     :		Dict							     =		20

								UpperCAmelCase     :		Optional[Any]							     =		self._get_uniform_logits(batch_size=2		,		length=A    )

								# tweak scores to not be uniform anymore
								UpperCAmelCase     :		Dict							     =		scores.at[1, 5].set((1 / length) + 0.1    )  # peak, 1st batch
								UpperCAmelCase     :		Union[str, Any]							     =		scores.at[1, 10].set((1 / length) - 0.4    )  # valley, 1st batch

								# compute softmax
								UpperCAmelCase     :		Any							     =		jax.nn.softmax(A		,		axis=-1    )

								UpperCAmelCase     :		int							     =		FlaxTemperatureLogitsWarper(temperature=0.5    )
								UpperCAmelCase     :		Optional[int]							     =		FlaxTemperatureLogitsWarper(temperature=1.3    )

								UpperCAmelCase     :		Union[str, Any]							     =		jax.nn.softmax(temp_dist_warper_sharper(A		,		scores.copy()		,		cur_len=A    )		,		axis=-1    )
								UpperCAmelCase     :		int							     =		jax.nn.softmax(temp_dist_warper_smoother(A		,		scores.copy()		,		cur_len=A    )		,		axis=-1    )

								# uniform distribution stays uniform
								self.assertTrue(jnp.allclose(probs[0, :]		,		warped_prob_sharp[0, :]		,		atol=1e-3    )    )
								self.assertTrue(jnp.allclose(probs[0, :]		,		warped_prob_smooth[0, :]		,		atol=1e-3    )    )

								# sharp peaks get higher, valleys get lower
								self.assertLess(probs[1, :].max()		,		warped_prob_sharp[1, :].max()    )
								self.assertGreater(probs[1, :].min()		,		warped_prob_sharp[1, :].min()    )

								# smooth peaks get lower, valleys get higher
								self.assertGreater(probs[1, :].max()		,		warped_prob_smooth[1, :].max()    )
								self.assertLess(probs[1, :].min()		,		warped_prob_smooth[1, :].min()    )





	def  _lowercase(  self    )			->			Union[str, Any]:
								UpperCAmelCase     :		Optional[int]							     =		None
								UpperCAmelCase     :		int							     =		10
								UpperCAmelCase     :		Optional[int]							     =		2

								# create ramp distribution
								UpperCAmelCase     :		Tuple							     =		np.broadcast_to(np.arange(A    )[None, :]		,		(batch_size, vocab_size)    ).copy()
								UpperCAmelCase     :		List[Any]							     =		ramp_logits[1:, : vocab_size // 2] + vocab_size

								UpperCAmelCase     :		List[Any]							     =		FlaxTopKLogitsWarper(3    )

								UpperCAmelCase     :		int							     =		top_k_warp(A		,		A		,		cur_len=A    )

								# check that correct tokens are filtered
								self.assertListEqual(jnp.isinf(scores[0]    ).tolist()		,		7 * [True] + 3 * [False]    )
								self.assertListEqual(jnp.isinf(scores[1]    ).tolist()		,		2 * [True] + 3 * [False] + 5 * [True]    )

								# check special case
								UpperCAmelCase     :		str							     =		5
								UpperCAmelCase     :		str							     =		FlaxTopKLogitsWarper(top_k=1		,		filter_value=0.0		,		min_tokens_to_keep=3    )

								UpperCAmelCase     :		Dict							     =		np.broadcast_to(np.arange(A    )[None, :]		,		(batch_size, length)    ).copy()
								UpperCAmelCase     :		Tuple							     =		top_k_warp_safety_check(A		,		A		,		cur_len=A    )

								# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
								self.assertListEqual((scores == 0.0).sum(axis=-1    ).tolist()		,		[2, 2]    )





	def  _lowercase(  self    )			->			List[str]:
								UpperCAmelCase     :		Optional[Any]							     =		None
								UpperCAmelCase     :		Tuple							     =		10
								UpperCAmelCase     :		Dict							     =		2

								# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
								UpperCAmelCase     :		Dict							     =		np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.1_5, 0.3, 0.3, 0.2_5]]    )    )

								UpperCAmelCase     :		Union[str, Any]							     =		FlaxTopPLogitsWarper(0.8    )
								UpperCAmelCase     :		Optional[Any]							     =		np.exp(top_p_warp(A		,		A		,		cur_len=A    )    )

								# dist should be filtered to keep min num values so that sum is >= top_p
								# exp (-inf) => 0
								UpperCAmelCase     :		Union[str, Any]							     =		np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.2_5]]    )
								self.assertTrue(np.allclose(A		,		A		,		atol=1e-3    )    )

								# check edge cases with negative and extreme logits
								UpperCAmelCase     :		List[str]							     =		np.broadcast_to(np.arange(A    )[None, :]		,		(batch_size, vocab_size)    ).copy() - (
								    vocab_size // 2
								)

								# make ramp_logits more extreme
								UpperCAmelCase     :		Dict							     =		ramp_logits[1] * 1_0_0.0

								# make sure at least 2 tokens are kept
								UpperCAmelCase     :		str							     =		FlaxTopPLogitsWarper(0.9		,		min_tokens_to_keep=2		,		filter_value=0.0    )
								UpperCAmelCase     :		int							     =		top_p_warp(A		,		A		,		cur_len=A    )

								# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
								self.assertListEqual((filtered_dist != 0.0).sum(axis=-1    ).tolist()		,		[3, 2]    )





	def  _lowercase(  self    )			->			Union[str, Any]:
								UpperCAmelCase     :		List[str]							     =		20
								UpperCAmelCase     :		str							     =		4
								UpperCAmelCase     :		Any							     =		0

								UpperCAmelCase     :		List[str]							     =		FlaxMinLengthLogitsProcessor(min_length=10		,		eos_token_id=A    )

								# check that min length is applied at length 5
								UpperCAmelCase     :		Any							     =		ids_tensor((batch_size, 20)		,		vocab_size=20    )
								UpperCAmelCase     :		List[str]							     =		5
								UpperCAmelCase     :		Dict							     =		self._get_uniform_logits(A		,		A    )
								UpperCAmelCase     :		Dict							     =		min_dist_processor(A		,		A		,		cur_len=A    )
								self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist()		,		4 * [-float("""inf"""    )]    )

								# check that min length is not applied anymore at length 15
								UpperCAmelCase     :		Optional[Any]							     =		self._get_uniform_logits(A		,		A    )
								UpperCAmelCase     :		List[Any]							     =		15
								UpperCAmelCase     :		int							     =		min_dist_processor(A		,		A		,		cur_len=A    )
								self.assertFalse(jnp.isinf(A    ).any()    )





	def  _lowercase(  self    )			->			List[str]:
								UpperCAmelCase     :		Union[str, Any]							     =		20
								UpperCAmelCase     :		Tuple							     =		4
								UpperCAmelCase     :		Optional[Any]							     =		0

								UpperCAmelCase     :		int							     =		FlaxForcedBOSTokenLogitsProcessor(bos_token_id=A    )

								# check that all scores are -inf except the bos_token_id score
								UpperCAmelCase     :		Dict							     =		ids_tensor((batch_size, 1)		,		vocab_size=20    )
								UpperCAmelCase     :		Union[str, Any]							     =		1
								UpperCAmelCase     :		Union[str, Any]							     =		self._get_uniform_logits(A		,		A    )
								UpperCAmelCase     :		Union[str, Any]							     =		logits_processor(A		,		A		,		cur_len=A    )
								self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]    ).all()    )
								self.assertListEqual(scores[:, bos_token_id].tolist()		,		4 * [0]    )  # score for bos_token_id shold be zero

								# check that bos_token_id is not forced if current length is greater than 1
								UpperCAmelCase     :		List[Any]							     =		3
								UpperCAmelCase     :		List[Any]							     =		self._get_uniform_logits(A		,		A    )
								UpperCAmelCase     :		Optional[Any]							     =		logits_processor(A		,		A		,		cur_len=A    )
								self.assertFalse(jnp.isinf(A    ).any()    )





	def  _lowercase(  self    )			->			int:
								UpperCAmelCase     :		Optional[Any]							     =		20
								UpperCAmelCase     :		int							     =		4
								UpperCAmelCase     :		Union[str, Any]							     =		0
								UpperCAmelCase     :		str							     =		5

								UpperCAmelCase     :		List[str]							     =		FlaxForcedEOSTokenLogitsProcessor(max_length=A		,		eos_token_id=A    )

								# check that all scores are -inf except the eos_token_id when max_length is reached
								UpperCAmelCase     :		str							     =		ids_tensor((batch_size, 4)		,		vocab_size=20    )
								UpperCAmelCase     :		str							     =		4
								UpperCAmelCase     :		Optional[Any]							     =		self._get_uniform_logits(A		,		A    )
								UpperCAmelCase     :		Dict							     =		logits_processor(A		,		A		,		cur_len=A    )
								self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]    ).all()    )
								self.assertListEqual(scores[:, eos_token_id].tolist()		,		4 * [0]    )  # score for eos_token_id should be zero

								# check that eos_token_id is not forced if max_length is not reached
								UpperCAmelCase     :		Tuple							     =		3
								UpperCAmelCase     :		Union[str, Any]							     =		self._get_uniform_logits(A		,		A    )
								UpperCAmelCase     :		str							     =		logits_processor(A		,		A		,		cur_len=A    )
								self.assertFalse(jnp.isinf(A    ).any()    )





	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		List[str]							     =		4
								UpperCAmelCase     :		Optional[int]							     =		10
								UpperCAmelCase     :		Union[str, Any]							     =		15
								UpperCAmelCase     :		Union[str, Any]							     =		2
								UpperCAmelCase     :		Optional[Any]							     =		1
								UpperCAmelCase     :		Any							     =		15

								# dummy input_ids and scores
								UpperCAmelCase     :		Optional[int]							     =		ids_tensor((batch_size, sequence_length)		,		A    )
								UpperCAmelCase     :		Tuple							     =		input_ids.copy()

								UpperCAmelCase     :		Union[str, Any]							     =		self._get_uniform_logits(A		,		A    )
								UpperCAmelCase     :		Tuple							     =		scores.copy()

								# instantiate all dist processors
								UpperCAmelCase     :		Any							     =		FlaxTemperatureLogitsWarper(temperature=0.5    )
								UpperCAmelCase     :		Union[str, Any]							     =		FlaxTopKLogitsWarper(3    )
								UpperCAmelCase     :		Optional[Any]							     =		FlaxTopPLogitsWarper(0.8    )

								# instantiate all logits processors
								UpperCAmelCase     :		List[str]							     =		FlaxMinLengthLogitsProcessor(min_length=10		,		eos_token_id=A    )
								UpperCAmelCase     :		Any							     =		FlaxForcedBOSTokenLogitsProcessor(bos_token_id=A    )
								UpperCAmelCase     :		List[Any]							     =		FlaxForcedEOSTokenLogitsProcessor(max_length=A		,		eos_token_id=A    )

								UpperCAmelCase     :		Tuple							     =		10

								# no processor list
								UpperCAmelCase     :		Dict							     =		temp_dist_warp(A		,		A		,		cur_len=A    )
								UpperCAmelCase     :		int							     =		top_k_warp(A		,		A		,		cur_len=A    )
								UpperCAmelCase     :		Union[str, Any]							     =		top_p_warp(A		,		A		,		cur_len=A    )
								UpperCAmelCase     :		Optional[Any]							     =		min_dist_proc(A		,		A		,		cur_len=A    )
								UpperCAmelCase     :		Optional[int]							     =		bos_dist_proc(A		,		A		,		cur_len=A    )
								UpperCAmelCase     :		Union[str, Any]							     =		eos_dist_proc(A		,		A		,		cur_len=A    )

								# with processor list
								UpperCAmelCase     :		Dict							     =		FlaxLogitsProcessorList(
								    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]    )
								UpperCAmelCase     :		Tuple							     =		processor(A		,		A		,		cur_len=A    )

								# scores should be equal
								self.assertTrue(jnp.allclose(A		,		A		,		atol=1e-3    )    )

								# input_ids should never be changed
								self.assertListEqual(input_ids.tolist()		,		input_ids_comp.tolist()    )






	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		Tuple							     =		4
								UpperCAmelCase     :		Any							     =		10
								UpperCAmelCase     :		int							     =		15
								UpperCAmelCase     :		Union[str, Any]							     =		2
								UpperCAmelCase     :		Optional[int]							     =		1
								UpperCAmelCase     :		Union[str, Any]							     =		15

								# dummy input_ids and scores
								UpperCAmelCase     :		Dict							     =		ids_tensor((batch_size, sequence_length)		,		A    )
								UpperCAmelCase     :		Tuple							     =		input_ids.copy()

								UpperCAmelCase     :		Tuple							     =		self._get_uniform_logits(A		,		A    )
								UpperCAmelCase     :		str							     =		scores.copy()

								# instantiate all dist processors
								UpperCAmelCase     :		Optional[Any]							     =		FlaxTemperatureLogitsWarper(temperature=0.5    )
								UpperCAmelCase     :		Tuple							     =		FlaxTopKLogitsWarper(3    )
								UpperCAmelCase     :		List[str]							     =		FlaxTopPLogitsWarper(0.8    )

								# instantiate all logits processors
								UpperCAmelCase     :		Optional[int]							     =		FlaxMinLengthLogitsProcessor(min_length=10		,		eos_token_id=A    )
								UpperCAmelCase     :		Tuple							     =		FlaxForcedBOSTokenLogitsProcessor(bos_token_id=A    )
								UpperCAmelCase     :		Optional[int]							     =		FlaxForcedEOSTokenLogitsProcessor(max_length=A		,		eos_token_id=A    )

								UpperCAmelCase     :		Optional[Any]							     =		10

								# no processor list
								def run_no_processor_list(A		,		A		,		A    ):
															UpperCAmelCase     :		Union[str, Any]							     =		temp_dist_warp(A		,		A		,		cur_len=A    )
															UpperCAmelCase     :		str							     =		top_k_warp(A		,		A		,		cur_len=A    )
															UpperCAmelCase     :		int							     =		top_p_warp(A		,		A		,		cur_len=A    )
															UpperCAmelCase     :		Dict							     =		min_dist_proc(A		,		A		,		cur_len=A    )
															UpperCAmelCase     :		Optional[Any]							     =		bos_dist_proc(A		,		A		,		cur_len=A    )
															UpperCAmelCase     :		Tuple							     =		eos_dist_proc(A		,		A		,		cur_len=A    )
															return scores

								# with processor list
								def run_processor_list(A		,		A		,		A    ):
															UpperCAmelCase     :		Optional[int]							     =		FlaxLogitsProcessorList(
															    [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]    )
															UpperCAmelCase     :		List[str]							     =		processor(A		,		A		,		cur_len=A    )
															return scores

								UpperCAmelCase     :		str							     =		jax.jit(A    )
								UpperCAmelCase     :		Dict							     =		jax.jit(A    )

								UpperCAmelCase     :		List[Any]							     =		jitted_run_no_processor_list(A		,		A		,		A    )
								UpperCAmelCase     :		int							     =		jitted_run_processor_list(A		,		A		,		A    )

								# scores should be equal
								self.assertTrue(jnp.allclose(A		,		A		,		atol=1e-3    )    )

								# input_ids should never be changed
								self.assertListEqual(input_ids.tolist()		,		input_ids_comp.tolist()    )






'''simple docstring'''




import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 			UpperCamelCase_ (							__magic_name__      ,     unittest.TestCase			):
	lowercase          = LongformerTokenizer
	lowercase          = True
	lowercase          = LongformerTokenizerFast
	lowercase          = True





	def  _lowercase(  self    )			->			List[Any]:
								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								UpperCAmelCase     :		List[str]							     =		[
								    """l""",
								    """o""",
								    """w""",
								    """e""",
								    """r""",
								    """s""",
								    """t""",
								    """i""",
								    """d""",
								    """n""",
								    """\u0120""",
								    """\u0120l""",
								    """\u0120n""",
								    """\u0120lo""",
								    """\u0120low""",
								    """er""",
								    """\u0120lowest""",
								    """\u0120newer""",
								    """\u0120wider""",
								    """<unk>""",
								]
								UpperCAmelCase     :		int							     =		dict(zip(A		,		range(len(A    )    )    )    )
								UpperCAmelCase     :		Any							     =		["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
								UpperCAmelCase     :		Dict							     =		{"""unk_token""": """<unk>"""}

								UpperCAmelCase     :		int							     =		os.path.join(self.tmpdirname		,		VOCAB_FILES_NAMES["""vocab_file"""]    )
								UpperCAmelCase     :		Optional[int]							     =		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  _lowercase(  self		,		**A    )			->			Optional[Any]:
								kwargs.update(self.special_tokens_map    )
								return self.tokenizer_class.from_pretrained(self.tmpdirname		,		**A    )





	def  _lowercase(  self		,		**A    )			->			int:
								kwargs.update(self.special_tokens_map    )
								return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,		**A    )





	def  _lowercase(  self		,		A    )			->			Optional[int]:
								UpperCAmelCase     :		Optional[Any]							     =		"""lower newer"""
								UpperCAmelCase     :		Optional[int]							     =		"""lower newer"""
								return input_text, output_text





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		Tuple							     =		self.tokenizer_class(self.vocab_file		,		self.merges_file		,		**self.special_tokens_map    )
								UpperCAmelCase     :		Dict							     =		"""lower newer"""
								UpperCAmelCase     :		int							     =		["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
								UpperCAmelCase     :		Tuple							     =		tokenizer.tokenize(A    )  # , add_prefix_space=True)
								self.assertListEqual(A		,		A    )

								UpperCAmelCase     :		Any							     =		tokens + [tokenizer.unk_token]
								UpperCAmelCase     :		Tuple							     =		[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(A    )		,		A    )





	def  _lowercase(  self    )			->			Union[str, Any]:
								UpperCAmelCase     :		str							     =		self.get_tokenizer()

								self.assertListEqual(tokenizer.encode("""Hello world!"""		,		add_special_tokens=A    )		,		[0, 31414, 232, 328, 2]    )
								self.assertListEqual(
								    tokenizer.encode("""Hello world! cécé herlolip 418"""		,		add_special_tokens=A    )		,		[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]		,		)





	@slow
	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		Any							     =		self.tokenizer_class.from_pretrained("""allenai/longformer-base-4096"""    )

								UpperCAmelCase     :		List[Any]							     =		tokenizer.encode("""sequence builders"""		,		add_special_tokens=A    )
								UpperCAmelCase     :		Optional[Any]							     =		tokenizer.encode("""multi-sequence build"""		,		add_special_tokens=A    )

								UpperCAmelCase     :		List[str]							     =		tokenizer.encode(
								    """sequence builders"""		,		add_special_tokens=A		,		add_prefix_space=A    )
								UpperCAmelCase     :		List[str]							     =		tokenizer.encode(
								    """sequence builders"""		,		"""multi-sequence build"""		,		add_special_tokens=A		,		add_prefix_space=A    )

								UpperCAmelCase     :		Union[str, Any]							     =		tokenizer.build_inputs_with_special_tokens(A    )
								UpperCAmelCase     :		Any							     =		tokenizer.build_inputs_with_special_tokens(A		,		A    )

								assert encoded_sentence == encoded_text_from_decode
								assert encoded_pair == encoded_pair_from_decode





	def  _lowercase(  self    )			->			List[Any]:
								UpperCAmelCase     :		str							     =		self.get_tokenizer()

								UpperCAmelCase     :		List[Any]							     =		"""Encode this sequence."""
								UpperCAmelCase     :		List[str]							     =		tokenizer.byte_encoder[""" """.encode("""utf-8"""    )[0]]

								# Testing encoder arguments
								UpperCAmelCase     :		Union[str, Any]							     =		tokenizer.encode(A		,		add_special_tokens=A		,		add_prefix_space=A    )
								UpperCAmelCase     :		Dict							     =		tokenizer.convert_ids_to_tokens(encoded[0]    )[0]
								self.assertNotEqual(A		,		A    )

								UpperCAmelCase     :		Tuple							     =		tokenizer.encode(A		,		add_special_tokens=A		,		add_prefix_space=A    )
								UpperCAmelCase     :		int							     =		tokenizer.convert_ids_to_tokens(encoded[0]    )[0]
								self.assertEqual(A		,		A    )

								tokenizer.add_special_tokens({"""bos_token""": """<s>"""}    )
								UpperCAmelCase     :		int							     =		tokenizer.encode(A		,		add_special_tokens=A    )
								UpperCAmelCase     :		List[Any]							     =		tokenizer.convert_ids_to_tokens(encoded[1]    )[0]
								self.assertNotEqual(A		,		A    )

								# Testing spaces after special tokens
								UpperCAmelCase     :		Union[str, Any]							     =		"""<mask>"""
								tokenizer.add_special_tokens(
								    {"""mask_token""": AddedToken(A		,		lstrip=A		,		rstrip=A    )}    )  # mask token has a left space
								UpperCAmelCase     :		str							     =		tokenizer.convert_tokens_to_ids(A    )

								UpperCAmelCase     :		Union[str, Any]							     =		"""Encode <mask> sequence"""
								UpperCAmelCase     :		Union[str, Any]							     =		"""Encode <mask>sequence"""

								UpperCAmelCase     :		Union[str, Any]							     =		tokenizer.encode(A    )
								UpperCAmelCase     :		Union[str, Any]							     =		encoded.index(A    )
								UpperCAmelCase     :		List[str]							     =		tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]    )[0]
								self.assertEqual(A		,		A    )

								UpperCAmelCase     :		Tuple							     =		tokenizer.encode(A    )
								UpperCAmelCase     :		Optional[int]							     =		encoded.index(A    )
								UpperCAmelCase     :		Optional[int]							     =		tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]    )[0]
								self.assertNotEqual(A		,		A    )





	def  _lowercase(  self    )			->			Optional[int]:
								pass





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

																						# token_type_ids should put 0 everywhere
																						self.assertEqual(sum(tokens_r["""token_type_ids"""]    )		,		sum(tokens_p["""token_type_ids"""]    )    )

																						# attention_mask should put 1 everywhere, so sum over length should be 1
																						self.assertEqual(
																						    sum(tokens_r["""attention_mask"""]    ) / len(tokens_r["""attention_mask"""]    )		,		sum(tokens_p["""attention_mask"""]    ) / len(tokens_p["""attention_mask"""]    )		,		)

																						UpperCAmelCase     :		List[Any]							     =		tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]    )
																						UpperCAmelCase     :		int							     =		tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]    )

																						# Rust correctly handles the space before the mask while python doesnt
																						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(
																						    A		,		["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]    )
																						self.assertSequenceEqual(
																						    A		,		["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]    )





	def  _lowercase(  self    )			->			List[Any]:
								for trim_offsets, add_prefix_space in itertools.product([True, False]		,		repeat=2    ):
															UpperCAmelCase     :		Optional[int]							     =		self.rust_tokenizer_class.from_pretrained(
															    self.tmpdirname		,		use_fast=A		,		add_prefix_space=A		,		trim_offsets=A    )

															UpperCAmelCase     :		Optional[int]							     =		json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()    )
															UpperCAmelCase     :		Optional[int]							     =		json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()    )

															self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]		,		A    )

															self.assertEqual(post_processor_state["""add_prefix_space"""]		,		A    )
															self.assertEqual(post_processor_state["""trim_offsets"""]		,		A    )






	def  _lowercase(  self    )			->			Optional[Any]:
								# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
								# `trim_offsets`
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
																						UpperCAmelCase     :		Union[str, Any]							     =		"""hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
																						UpperCAmelCase     :		int							     =		f'''{text_of_1_token} {text_of_1_token}'''

																						UpperCAmelCase     :		List[Any]							     =		self.rust_tokenizer_class.from_pretrained(
																						    A		,		use_fast=A		,		add_prefix_space=A		,		trim_offsets=A    )
																						UpperCAmelCase     :		str							     =		tokenizer_r(A		,		return_offsets_mapping=A		,		add_special_tokens=A    )
																						self.assertEqual(encoding.offset_mapping[0]		,		(0, len(A    ))    )
																						self.assertEqual(
																						    encoding.offset_mapping[1]		,		(len(A    ) + 1, len(A    ) + 1 + len(A    ))		,		)

																						UpperCAmelCase     :		Optional[Any]							     =		self.rust_tokenizer_class.from_pretrained(
																						    A		,		use_fast=A		,		add_prefix_space=A		,		trim_offsets=A    )
																						UpperCAmelCase     :		Dict							     =		tokenizer_r(A		,		return_offsets_mapping=A		,		add_special_tokens=A    )
																						self.assertEqual(encoding.offset_mapping[0]		,		(0, len(A    ))    )
																						self.assertEqual(
																						    encoding.offset_mapping[1]		,		(len(A    ) + 1, len(A    ) + 1 + len(A    ))		,		)

																						UpperCAmelCase     :		int							     =		self.rust_tokenizer_class.from_pretrained(
																						    A		,		use_fast=A		,		add_prefix_space=A		,		trim_offsets=A    )
																						UpperCAmelCase     :		List[Any]							     =		tokenizer_r(A		,		return_offsets_mapping=A		,		add_special_tokens=A    )
																						self.assertEqual(encoding.offset_mapping[0]		,		(0, len(A    ))    )
																						self.assertEqual(
																						    encoding.offset_mapping[1]		,		(len(A    ), len(A    ) + 1 + len(A    ))		,		)

																						UpperCAmelCase     :		Any							     =		self.rust_tokenizer_class.from_pretrained(
																						    A		,		use_fast=A		,		add_prefix_space=A		,		trim_offsets=A    )
																						UpperCAmelCase     :		str							     =		tokenizer_r(A		,		return_offsets_mapping=A		,		add_special_tokens=A    )
																						self.assertEqual(encoding.offset_mapping[0]		,		(0, len(A    ))    )
																						self.assertEqual(
																						    encoding.offset_mapping[1]		,		(len(A    ), len(A    ) + 1 + len(A    ))		,		)

																						UpperCAmelCase     :		Optional[Any]							     =		f''' {text}'''

																						# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
																						#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
																						# )
																						# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
																						# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
																						# self.assertEqual(
																						#     encoding.offset_mapping[1],
																						#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
																						# )

																						UpperCAmelCase     :		Any							     =		self.rust_tokenizer_class.from_pretrained(
																						    A		,		use_fast=A		,		add_prefix_space=A		,		trim_offsets=A    )
																						UpperCAmelCase     :		str							     =		tokenizer_r(A		,		return_offsets_mapping=A		,		add_special_tokens=A    )
																						self.assertEqual(encoding.offset_mapping[0]		,		(1, 1 + len(A    ))    )
																						self.assertEqual(
																						    encoding.offset_mapping[1]		,		(1 + len(A    ) + 1, 1 + len(A    ) + 1 + len(A    ))		,		)

																						UpperCAmelCase     :		Optional[int]							     =		self.rust_tokenizer_class.from_pretrained(
																						    A		,		use_fast=A		,		add_prefix_space=A		,		trim_offsets=A    )
																						UpperCAmelCase     :		Union[str, Any]							     =		tokenizer_r(A		,		return_offsets_mapping=A		,		add_special_tokens=A    )
																						self.assertEqual(encoding.offset_mapping[0]		,		(0, 1 + len(A    ))    )
																						self.assertEqual(
																						    encoding.offset_mapping[1]		,		(1 + len(A    ), 1 + len(A    ) + 1 + len(A    ))		,		)

																						UpperCAmelCase     :		Union[str, Any]							     =		self.rust_tokenizer_class.from_pretrained(
																						    A		,		use_fast=A		,		add_prefix_space=A		,		trim_offsets=A    )
																						UpperCAmelCase     :		Optional[int]							     =		tokenizer_r(A		,		return_offsets_mapping=A		,		add_special_tokens=A    )
																						self.assertEqual(encoding.offset_mapping[0]		,		(0, 1 + len(A    ))    )
																						self.assertEqual(
																						    encoding.offset_mapping[1]		,		(1 + len(A    ), 1 + len(A    ) + 1 + len(A    ))		,		)

