
"""simple docstring"""
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left
@flax.struct.dataclass
class      lowerCAmelCase_						:



							'''simple docstring'''





							_snake_case		     =						None
							_snake_case		     =						None
							_snake_case		     =						None




							@classmethod
							def  A__		(				cls							)   ->       List[str]:
													return cls()
@dataclass
class      lowerCAmelCase_						(							_lowerCamelCase				):



							'''simple docstring'''





							_snake_case		     =						4_2
							_snake_case		     =						4_2
							_snake_case		     =						None
class      lowerCAmelCase_						(							_lowerCamelCase							,							_lowerCamelCase				):



							'''simple docstring'''





							@property
							def  A__		(				self							)   ->       Optional[Any]:
													return True




							@register_to_config
							def __init__(				self  ,							snake_case_ = 2_000  ,							snake_case_ = 0.15  ,							snake_case_ = 0.01  ,							snake_case_ = 1_348.0  ,							snake_case_ = 1e-5  ,							snake_case_ = 1  ,							)   ->       Optional[Any]:
													pass




							def  A__		(				self							)   ->       Any:
													__lowerCAmelCase        =       ScoreSdeVeSchedulerState.create()
													return self.set_sigmas(
													    __UpperCamelCase  ,							self.config.num_train_timesteps  ,							self.config.sigma_min  ,							self.config.sigma_max  ,							self.config.sampling_eps  ,							)




							def  A__		(				self  ,							snake_case_  ,							snake_case_  ,							snake_case_ = ()  ,							snake_case_ = None							)   ->       ScoreSdeVeSchedulerState:
													__lowerCAmelCase        =       sampling_eps if sampling_eps is not None else self.config.sampling_eps

													__lowerCAmelCase        =       jnp.linspace(1  ,							__UpperCamelCase  ,							__UpperCamelCase							)
													return state.replace(timesteps=__UpperCamelCase							)




							def  A__		(				self  ,							snake_case_  ,							snake_case_  ,							snake_case_ = None  ,							snake_case_ = None  ,							snake_case_ = None  ,							)   ->       ScoreSdeVeSchedulerState:
													__lowerCAmelCase        =       sigma_min if sigma_min is not None else self.config.sigma_min
													__lowerCAmelCase        =       sigma_max if sigma_max is not None else self.config.sigma_max
													__lowerCAmelCase        =       sampling_eps if sampling_eps is not None else self.config.sampling_eps
													if state.timesteps is None:
																			__lowerCAmelCase        =       self.set_timesteps(__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase							)

													__lowerCAmelCase        =       jnp.exp(jnp.linspace(jnp.log(__UpperCamelCase							)  ,							jnp.log(__UpperCamelCase							)  ,							__UpperCamelCase							)							)
													__lowerCAmelCase        =       jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]							)

													return state.replace(discrete_sigmas=__UpperCamelCase  ,							sigmas=__UpperCamelCase							)




							def  A__		(				self  ,							snake_case_  ,							snake_case_  ,							snake_case_							)   ->       Optional[Any]:
													return jnp.where(timesteps == 0  ,							jnp.zeros_like(__UpperCamelCase							)  ,							state.discrete_sigmas[timesteps - 1]							)




							def  A__		(				self  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_ = True  ,							)   ->       Union[FlaxSdeVeOutput, Tuple]:







													if state.timesteps is None:
																			raise ValueError(
																			    """`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler"""							)

													__lowerCAmelCase        =       timestep * jnp.ones(
													    sample.shape[0]  ,							)
													__lowerCAmelCase        =       (timestep * (len(state.timesteps							) - 1)).long()

													__lowerCAmelCase        =       state.discrete_sigmas[timesteps]
													__lowerCAmelCase        =       self.get_adjacent_sigma(__UpperCamelCase  ,							__UpperCamelCase  ,							__UpperCamelCase							)
													__lowerCAmelCase        =       jnp.zeros_like(__UpperCamelCase							)
													__lowerCAmelCase        =       (sigma**2 - adjacent_sigma**2) ** 0.5

													# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
													# also equation 47 shows the analog from SDE models to ancestral sampling methods
													__lowerCAmelCase        =       diffusion.flatten()
													__lowerCAmelCase        =       broadcast_to_shape_from_left(__UpperCamelCase  ,							sample.shape							)
													__lowerCAmelCase        =       drift - diffusion**2 * model_output

													#  equation 6: sample noise for the diffusion term of
													__lowerCAmelCase        =       random.split(__UpperCamelCase  ,							num=1							)
													__lowerCAmelCase        =       random.normal(key=__UpperCamelCase  ,							shape=sample.shape							)
													__lowerCAmelCase        =       sample - drift  # subtract because `dt` is a small negative timestep
													# TODO is the variable diffusion the correct scaling term for the noise?
													__lowerCAmelCase        =       prev_sample_mean + diffusion * noise  # add impact of diffusion field g

													if not return_dict:
																			return (prev_sample, prev_sample_mean, state)

													return FlaxSdeVeOutput(prev_sample=__UpperCamelCase  ,							prev_sample_mean=__UpperCamelCase  ,							state=__UpperCamelCase							)




							def  A__		(				self  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_ = True  ,							)   ->       Union[FlaxSdeVeOutput, Tuple]:







													if state.timesteps is None:
																			raise ValueError(
																			    """`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler"""							)

													# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
													# sample noise for correction
													__lowerCAmelCase        =       random.split(__UpperCamelCase  ,							num=1							)
													__lowerCAmelCase        =       random.normal(key=__UpperCamelCase  ,							shape=sample.shape							)

													# compute step size from the model_output, the noise, and the snr
													__lowerCAmelCase        =       jnp.linalg.norm(__UpperCamelCase							)
													__lowerCAmelCase        =       jnp.linalg.norm(__UpperCamelCase							)
													__lowerCAmelCase        =       (self.config.snr * noise_norm / grad_norm) ** 2 * 2
													__lowerCAmelCase        =       step_size * jnp.ones(sample.shape[0]							)

													# compute corrected sample: model_output term and noise term
													__lowerCAmelCase        =       step_size.flatten()
													__lowerCAmelCase        =       broadcast_to_shape_from_left(__UpperCamelCase  ,							sample.shape							)
													__lowerCAmelCase        =       sample + step_size * model_output
													__lowerCAmelCase        =       prev_sample_mean + ((step_size * 2) ** 0.5) * noise

													if not return_dict:
																			return (prev_sample, state)

													return FlaxSdeVeOutput(prev_sample=__UpperCamelCase  ,							state=__UpperCamelCase							)




							def __len__(				self							)   ->       Dict:
													return self.config.num_train_timesteps


"""simple docstring"""
import os
import unittest

from transformers import ElectraTokenizerFast
from transformers.models.electra.tokenization_electra import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    ElectraTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class      lowerCAmelCase_						(							A__							,							unittest.TestCase				):



							'''simple docstring'''





							_snake_case		     =						ElectraTokenizer
							_snake_case		     =						ElectraTokenizerFast
							_snake_case		     =						True
							_snake_case		     =						True
							_snake_case		     =						filter_non_english




							def  A__		(				self							)   ->       Union[str, Any]:
													super().setUp()

													__lowerCAmelCase        =       [
													    """[UNK]""",
													    """[CLS]""",
													    """[SEP]""",
													    """[PAD]""",
													    """[MASK]""",
													    """want""",
													    """##want""",
													    """##ed""",
													    """wa""",
													    """un""",
													    """runn""",
													    """##ing""",
													    """,""",
													    """low""",
													    """lowest""",
													]
													__lowerCAmelCase        =       os.path.join(self.tmpdirname  ,							VOCAB_FILES_NAMES["""vocab_file"""]							)
													with open(self.vocab_file  ,							"""w"""  ,							encoding="""utf-8"""							) as vocab_writer:
																			vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]							)							)




							def  A__		(				self  ,							snake_case_							)   ->       str:
													__lowerCAmelCase        =       """UNwant\u00E9d,running"""
													__lowerCAmelCase        =       """unwanted, running"""
													return input_text, output_text




							def  A__		(				self							)   ->       List[Any]:
													__lowerCAmelCase        =       self.tokenizer_class(self.vocab_file							)

													__lowerCAmelCase        =       tokenizer.tokenize("""UNwant\u00E9d,running"""							)
													self.assertListEqual(snake_case_  ,							["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]							)
													self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_							)  ,							[9, 6, 7, 12, 10, 11]							)




							def  A__		(				self							)   ->       Any:
													if not self.test_rust_tokenizer:
																			return

													__lowerCAmelCase        =       self.get_tokenizer()
													__lowerCAmelCase        =       self.get_rust_tokenizer()

													__lowerCAmelCase        =       """UNwant\u00E9d,running"""

													__lowerCAmelCase        =       tokenizer.tokenize(snake_case_							)
													__lowerCAmelCase        =       rust_tokenizer.tokenize(snake_case_							)
													self.assertListEqual(snake_case_  ,							snake_case_							)

													__lowerCAmelCase        =       tokenizer.encode(snake_case_  ,							add_special_tokens=snake_case_							)
													__lowerCAmelCase        =       rust_tokenizer.encode(snake_case_  ,							add_special_tokens=snake_case_							)
													self.assertListEqual(snake_case_  ,							snake_case_							)

													__lowerCAmelCase        =       self.get_rust_tokenizer()
													__lowerCAmelCase        =       tokenizer.encode(snake_case_							)
													__lowerCAmelCase        =       rust_tokenizer.encode(snake_case_							)
													self.assertListEqual(snake_case_  ,							snake_case_							)

													# With lower casing
													__lowerCAmelCase        =       self.get_tokenizer(do_lower_case=snake_case_							)
													__lowerCAmelCase        =       self.get_rust_tokenizer(do_lower_case=snake_case_							)

													__lowerCAmelCase        =       """UNwant\u00E9d,running"""

													__lowerCAmelCase        =       tokenizer.tokenize(snake_case_							)
													__lowerCAmelCase        =       rust_tokenizer.tokenize(snake_case_							)
													self.assertListEqual(snake_case_  ,							snake_case_							)

													__lowerCAmelCase        =       tokenizer.encode(snake_case_  ,							add_special_tokens=snake_case_							)
													__lowerCAmelCase        =       rust_tokenizer.encode(snake_case_  ,							add_special_tokens=snake_case_							)
													self.assertListEqual(snake_case_  ,							snake_case_							)

													__lowerCAmelCase        =       self.get_rust_tokenizer()
													__lowerCAmelCase        =       tokenizer.encode(snake_case_							)
													__lowerCAmelCase        =       rust_tokenizer.encode(snake_case_							)
													self.assertListEqual(snake_case_  ,							snake_case_							)




							def  A__		(				self							)   ->       Union[str, Any]:
													__lowerCAmelCase        =       BasicTokenizer()

													self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz"""							)  ,							["""ah""", """\u535A""", """\u63A8""", """zz"""]							)




							def  A__		(				self							)   ->       str:
													__lowerCAmelCase        =       BasicTokenizer(do_lower_case=snake_case_							)

													self.assertListEqual(
													    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """							)  ,							["""hello""", """!""", """how""", """are""", """you""", """?"""]							)
													self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""							)  ,							["""hello"""]							)




							def  A__		(				self							)   ->       str:
													__lowerCAmelCase        =       BasicTokenizer(do_lower_case=snake_case_  ,							strip_accents=snake_case_							)

													self.assertListEqual(
													    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """							)  ,							["""hällo""", """!""", """how""", """are""", """you""", """?"""]							)
													self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""							)  ,							["""h\u00E9llo"""]							)




							def  A__		(				self							)   ->       int:
													__lowerCAmelCase        =       BasicTokenizer(do_lower_case=snake_case_  ,							strip_accents=snake_case_							)

													self.assertListEqual(
													    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """							)  ,							["""hallo""", """!""", """how""", """are""", """you""", """?"""]							)
													self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""							)  ,							["""hello"""]							)




							def  A__		(				self							)   ->       Dict:
													__lowerCAmelCase        =       BasicTokenizer(do_lower_case=snake_case_							)

													self.assertListEqual(
													    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """							)  ,							["""hallo""", """!""", """how""", """are""", """you""", """?"""]							)
													self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""							)  ,							["""hello"""]							)




							def  A__		(				self							)   ->       List[str]:
													__lowerCAmelCase        =       BasicTokenizer(do_lower_case=snake_case_							)

													self.assertListEqual(
													    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """							)  ,							["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]							)




							def  A__		(				self							)   ->       Any:
													__lowerCAmelCase        =       BasicTokenizer(do_lower_case=snake_case_  ,							strip_accents=snake_case_							)

													self.assertListEqual(
													    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """							)  ,							["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""]							)




							def  A__		(				self							)   ->       Tuple:
													__lowerCAmelCase        =       BasicTokenizer(do_lower_case=snake_case_  ,							strip_accents=snake_case_							)

													self.assertListEqual(
													    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """							)  ,							["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""]							)




							def  A__		(				self							)   ->       Dict:
													__lowerCAmelCase        =       BasicTokenizer(do_lower_case=snake_case_  ,							never_split=["""[UNK]"""]							)

													self.assertListEqual(
													    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU? [UNK]"""							)  ,							["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""]							)




							def  A__		(				self							)   ->       List[str]:
													__lowerCAmelCase        =       ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

													__lowerCAmelCase        =       {}
													for i, token in enumerate(snake_case_							):
																			__lowerCAmelCase        =       i
													__lowerCAmelCase        =       WordpieceTokenizer(vocab=snake_case_  ,							unk_token="""[UNK]"""							)

													self.assertListEqual(tokenizer.tokenize(""""""							)  ,							[]							)

													self.assertListEqual(tokenizer.tokenize("""unwanted running"""							)  ,							["""un""", """##want""", """##ed""", """runn""", """##ing"""]							)

													self.assertListEqual(tokenizer.tokenize("""unwantedX running"""							)  ,							["""[UNK]""", """runn""", """##ing"""]							)




							def  A__		(				self							)   ->       str:
													self.assertTrue(_is_whitespace(""" """							)							)
													self.assertTrue(_is_whitespace("""\t"""							)							)
													self.assertTrue(_is_whitespace("""\r"""							)							)
													self.assertTrue(_is_whitespace("""\n"""							)							)
													self.assertTrue(_is_whitespace("""\u00A0"""							)							)

													self.assertFalse(_is_whitespace("""A"""							)							)
													self.assertFalse(_is_whitespace("""-"""							)							)




							def  A__		(				self							)   ->       Optional[int]:
													self.assertTrue(_is_control("""\u0005"""							)							)

													self.assertFalse(_is_control("""A"""							)							)
													self.assertFalse(_is_control(""" """							)							)
													self.assertFalse(_is_control("""\t"""							)							)
													self.assertFalse(_is_control("""\r"""							)							)




							def  A__		(				self							)   ->       int:
													self.assertTrue(_is_punctuation("""-"""							)							)
													self.assertTrue(_is_punctuation("""$"""							)							)
													self.assertTrue(_is_punctuation("""`"""							)							)
													self.assertTrue(_is_punctuation("""."""							)							)

													self.assertFalse(_is_punctuation("""A"""							)							)
													self.assertFalse(_is_punctuation(""" """							)							)




							def  A__		(				self							)   ->       str:
													__lowerCAmelCase        =       self.get_tokenizer()
													__lowerCAmelCase        =       self.get_rust_tokenizer()

													# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
													self.assertListEqual([tokenizer.tokenize(snake_case_							) for t in ["""Test""", """\xad""", """test"""]]  ,							[["""[UNK]"""], [], ["""[UNK]"""]]							)

													self.assertListEqual(
													    [rust_tokenizer.tokenize(snake_case_							) for t in ["""Test""", """\xad""", """test"""]]  ,							[["""[UNK]"""], [], ["""[UNK]"""]]							)




							@slow
							def  A__		(				self							)   ->       List[str]:
													__lowerCAmelCase        =       self.tokenizer_class.from_pretrained("""google/electra-base-discriminator"""							)

													__lowerCAmelCase        =       tokenizer.encode("""sequence builders"""  ,							add_special_tokens=snake_case_							)
													__lowerCAmelCase        =       tokenizer.encode("""multi-sequence build"""  ,							add_special_tokens=snake_case_							)

													__lowerCAmelCase        =       tokenizer.build_inputs_with_special_tokens(snake_case_							)
													__lowerCAmelCase        =       tokenizer.build_inputs_with_special_tokens(snake_case_  ,							snake_case_							)

													assert encoded_sentence == [101] + text + [102]
													assert encoded_pair == [101] + text + [102] + text_a + [102]




							def  A__		(				self							)   ->       int:
													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(snake_case_  ,							**snake_case_							)

																									__lowerCAmelCase        =       f"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
																									__lowerCAmelCase        =       tokenizer_r.encode_plus(
																									    snake_case_  ,							return_attention_mask=snake_case_  ,							return_token_type_ids=snake_case_  ,							return_offsets_mapping=snake_case_  ,							add_special_tokens=snake_case_  ,							)

																									__lowerCAmelCase        =       tokenizer_r.do_lower_case if hasattr(snake_case_  ,							"""do_lower_case"""							) else False
																									__lowerCAmelCase        =       (
																									    [
																									        ((0, 0), tokenizer_r.cls_token),
																									        ((0, 1), """A"""),
																									        ((1, 2), ""","""),
																									        ((3, 5), """na"""),
																									        ((5, 6), """##ï"""),
																									        ((6, 8), """##ve"""),
																									        ((9, 15), tokenizer_r.mask_token),
																									        ((16, 21), """Allen"""),
																									        ((21, 23), """##NL"""),
																									        ((23, 24), """##P"""),
																									        ((25, 33), """sentence"""),
																									        ((33, 34), """."""),
																									        ((0, 0), tokenizer_r.sep_token),
																									    ]
																									    if not do_lower_case
																									    else [
																									        ((0, 0), tokenizer_r.cls_token),
																									        ((0, 1), """a"""),
																									        ((1, 2), ""","""),
																									        ((3, 8), """naive"""),
																									        ((9, 15), tokenizer_r.mask_token),
																									        ((16, 21), """allen"""),
																									        ((21, 23), """##nl"""),
																									        ((23, 24), """##p"""),
																									        ((25, 33), """sentence"""),
																									        ((33, 34), """."""),
																									        ((0, 0), tokenizer_r.sep_token),
																									    ]
																									)

																									self.assertEqual(
																									    [e[1] for e in expected_results]  ,							tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""]							)							)
																									self.assertEqual([e[0] for e in expected_results]  ,							tokens["""offset_mapping"""]							)




							def  A__		(				self							)   ->       List[Any]:
													__lowerCAmelCase        =       ["""的""", """人""", """有"""]
													__lowerCAmelCase        =       """""".join(snake_case_							)
													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
																									__lowerCAmelCase        =       True
																									__lowerCAmelCase        =       self.tokenizer_class.from_pretrained(snake_case_  ,							**snake_case_							)
																									__lowerCAmelCase        =       self.rust_tokenizer_class.from_pretrained(snake_case_  ,							**snake_case_							)

																									__lowerCAmelCase        =       tokenizer_p.encode(snake_case_  ,							add_special_tokens=snake_case_							)
																									__lowerCAmelCase        =       tokenizer_r.encode(snake_case_  ,							add_special_tokens=snake_case_							)

																									__lowerCAmelCase        =       tokenizer_r.convert_ids_to_tokens(snake_case_							)
																									__lowerCAmelCase        =       tokenizer_p.convert_ids_to_tokens(snake_case_							)

																									# it is expected that each Chinese character is not preceded by "##"
																									self.assertListEqual(snake_case_  ,							snake_case_							)
																									self.assertListEqual(snake_case_  ,							snake_case_							)

																									__lowerCAmelCase        =       False
																									__lowerCAmelCase        =       self.rust_tokenizer_class.from_pretrained(snake_case_  ,							**snake_case_							)
																									__lowerCAmelCase        =       self.tokenizer_class.from_pretrained(snake_case_  ,							**snake_case_							)

																									__lowerCAmelCase        =       tokenizer_r.encode(snake_case_  ,							add_special_tokens=snake_case_							)
																									__lowerCAmelCase        =       tokenizer_p.encode(snake_case_  ,							add_special_tokens=snake_case_							)

																									__lowerCAmelCase        =       tokenizer_r.convert_ids_to_tokens(snake_case_							)
																									__lowerCAmelCase        =       tokenizer_p.convert_ids_to_tokens(snake_case_							)

																									# it is expected that only the first Chinese character is not preceded by "##".
																									__lowerCAmelCase        =       [
																									    f"""##{token}""" if idx != 0 else token for idx, token in enumerate(snake_case_							)
																									]
																									self.assertListEqual(snake_case_  ,							snake_case_							)
																									self.assertListEqual(snake_case_  ,							snake_case_							)
