



import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_lowercase:				List[str]       =	get_tests_dir("fixtures/spiece.model")



@require_sentencepiece
@require_tokenizers
class 			_lowercase							(     snake_case_,   unittest.TestCase			):





	"""simple docstring"""
	__A					=							ErnieMTokenizer
	__A					=							False
	__A					=							True
	__A					=							False
	__A					=							False




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						super().setUp()

						# We have a SentencePiece fixture for testing
						a        =     ErnieMTokenizer(lowerCamelCase_	, unk_token="<unk>"	, pad_token="<pad>"	)
						tokenizer.save_pretrained(self.tmpdirname	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     "this is a test"
						a        =     "this is a test"
						return input_text, output_text




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     "<pad>"
						a        =     0

						self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase_	)	, lowerCamelCase_	)
						self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase_	)	, lowerCamelCase_	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     list(self.get_tokenizer().get_vocab().keys()	)

						self.assertEqual(vocab_keys[0]	, "<pad>"	)
						self.assertEqual(vocab_keys[1]	, "<unk>"	)
						self.assertEqual(vocab_keys[-1]	, "▁eloquent"	)
						self.assertEqual(len(lowerCamelCase_	)	, 30000	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						self.assertEqual(self.get_tokenizer().vocab_size	, 30000	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						if not self.test_rust_tokenizer:
											return

						a        =     self.get_tokenizer()
						a        =     self.get_rust_tokenizer()

						a        =     "I was born in 92000, and this is falsé."

						a        =     tokenizer.tokenize(lowerCamelCase_	)
						a        =     rust_tokenizer.tokenize(lowerCamelCase_	)
						self.assertListEqual(lowerCamelCase_	, lowerCamelCase_	)

						a        =     tokenizer.encode(lowerCamelCase_	, add_special_tokens=lowerCamelCase_	)
						a        =     rust_tokenizer.encode(lowerCamelCase_	, add_special_tokens=lowerCamelCase_	)
						self.assertListEqual(lowerCamelCase_	, lowerCamelCase_	)

						a        =     self.get_rust_tokenizer()
						a        =     tokenizer.encode(lowerCamelCase_	)
						a        =     rust_tokenizer.encode(lowerCamelCase_	)
						self.assertListEqual(lowerCamelCase_	, lowerCamelCase_	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     ErnieMTokenizer(lowerCamelCase_	, do_lower_case=lowerCamelCase_	, unk_token="<unk>"	, pad_token="<pad>"	)

						a        =     tokenizer.tokenize("This is a test"	)
						self.assertListEqual(lowerCamelCase_	, ["▁this", "▁is", "▁a", "▁test"]	)

						self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_	)	, [48, 25, 21, 1289]	)

						a        =     tokenizer.tokenize("I was born in 92000, and this is falsé."	)
						# ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
						self.assertListEqual(
						    lowerCamelCase_	, ["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."]	)
						a        =     tokenizer.convert_tokens_to_ids(lowerCamelCase_	)
						self.assertListEqual(lowerCamelCase_	, [31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]	)

						a        =     tokenizer.convert_ids_to_tokens(lowerCamelCase_	)
						self.assertListEqual(
						    lowerCamelCase_	, ["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."]	, )




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     ErnieMTokenizer(lowerCamelCase_	, unk_token="<unk>"	, pad_token="<pad>"	)

						a        =     tokenizer.encode("sequence builders"	)
						a        =     tokenizer.encode("multi-sequence build"	)

						a        =     tokenizer.build_inputs_with_special_tokens(lowerCamelCase_	)
						a        =     tokenizer.build_inputs_with_special_tokens(lowerCamelCase_	, lowerCamelCase_	)

						assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
						assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
						    tokenizer.sep_token_id
						] + text_a + [tokenizer.sep_token_id]




	@slow
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     {"input_ids": [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 9, 304, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 5, 5, 5, 16, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 122009, 115774, 23, 3559, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
						# fmt: on

						self.tokenizer_integration_test_util(
						    expected_encoding=lowerCamelCase_	, model_name="susnato/ernie-m-base_pytorch"	, sequences=[
						        "Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
						        "general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
						        "Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained "
						        "models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.",
						        "BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
						        "conditioning on both left and right context in all layers.",
						        "The quick brown fox jumps over the lazy dog.",
						    ]	, )





# 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 			_lowercase							:





	"""simple docstring"""
	# setable values
	__A					=							None
	__A					=							None
	__A					=							None




	@classmethod
	def   UpperCamelCase_ (cls	):
						"""simple docstring"""

						return cls()




@dataclass
class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""
	__A					=							42
	__A					=							42
	__A					=							None




class 			_lowercase							(     lowerCAmelCase,   lowerCAmelCase			):





	"""simple docstring"""




	@property
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						return True




	@register_to_config
	def __init__(self	, lowerCamelCase_ = 2000	, lowerCamelCase_ = 0.15	, lowerCamelCase_ = 0.01	, lowerCamelCase_ = 1348.0	, lowerCamelCase_ = 1E-5	, lowerCamelCase_ = 1	, ):
						"""simple docstring"""

						pass




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     ScoreSdeVeSchedulerState.create()
						return self.set_sigmas(
						    lowerCamelCase_	, self.config.num_train_timesteps	, self.config.sigma_min	, self.config.sigma_max	, self.config.sampling_eps	, )




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = ()	, lowerCamelCase_ = None	):
						"""simple docstring"""

						a        =     sampling_eps if sampling_eps is not None else self.config.sampling_eps

						a        =     jnp.linspace(1	, lowerCamelCase_	, lowerCamelCase_	)
						return state.replace(timesteps=lowerCamelCase_	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = None	, lowerCamelCase_ = None	, lowerCamelCase_ = None	, ):
						"""simple docstring"""

						a        =     sigma_min if sigma_min is not None else self.config.sigma_min
						a        =     sigma_max if sigma_max is not None else self.config.sigma_max
						a        =     sampling_eps if sampling_eps is not None else self.config.sampling_eps
						if state.timesteps is None:
											a        =     self.set_timesteps(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	)

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

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




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						return jnp.where(timesteps == 0	, jnp.zeros_like(lowerCamelCase_	)	, state.discrete_sigmas[timesteps - 1]	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = True	, ):
						"""simple docstring"""

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

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

						a        =     state.discrete_sigmas[timesteps]
						a        =     self.get_adjacent_sigma(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	)
						a        =     jnp.zeros_like(lowerCamelCase_	)
						a        =     (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
						a        =     diffusion.flatten()
						a        =     broadcast_to_shape_from_left(lowerCamelCase_	, sample.shape	)
						a        =     drift - diffusion**2 * model_output

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




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = True	, ):
						"""simple docstring"""

						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
						a        =     random.split(lowerCamelCase_	, num=1	)
						a        =     random.normal(key=lowerCamelCase_	, shape=sample.shape	)

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

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

						if not return_dict:
											return (prev_sample, state)

						return FlaxSdeVeOutput(prev_sample=lowerCamelCase_	, state=lowerCamelCase_	)




	def __len__(self	):
						"""simple docstring"""

						return self.config.num_train_timesteps
