


# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)





@flax.struct.dataclass
class        lowercase_				:






							"""simple docstring"""







							snake_case_        =		42
							snake_case_        =		42

							# setable values
							snake_case_        =		42
							snake_case_        =		42
							snake_case_        =		None


							@classmethod
							def 						lowercase__     (   cls			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    ):







													"""simple docstring"""


													return cls(
													    common=_UpperCAmelCase			,    final_alpha_cumprod=_UpperCAmelCase			,    init_noise_sigma=_UpperCAmelCase			,    timesteps=_UpperCAmelCase			,    )





@dataclass
class        lowercase_				( UpperCamelCase__):






							"""simple docstring"""







							snake_case_        =		42





class        lowercase_				( UpperCamelCase__			,UpperCamelCase__):






							"""simple docstring"""







							snake_case_        =		[e.name for e in FlaxKarrasDiffusionSchedulers]

							snake_case_        =		42


							@property
							def 						lowercase__     (   self		):







													"""simple docstring"""


													return True


							@register_to_config
							def __init__(   self			,    _UpperCAmelCase = 1_000			,    _UpperCAmelCase = 0.0_0_0_1			,    _UpperCAmelCase = 0.0_2			,    _UpperCAmelCase = "linear"			,    _UpperCAmelCase = None			,    _UpperCAmelCase = True			,    _UpperCAmelCase = 0			,    _UpperCAmelCase = "epsilon"			,    _UpperCAmelCase = jnp.floataa			,    ):







													"""simple docstring"""


													a_					=		dtype


							def 						lowercase__     (   self			,    _UpperCAmelCase = None		):







													"""simple docstring"""


													if common is None:
																			a_					=		CommonSchedulerState.create(self		)

													# At every step in ddim, we are looking into the previous alphas_cumprod
													# For the final step, there is no previous alphas_cumprod because we are already at 0
													# `set_alpha_to_one` decides whether we set this parameter simply to one or
													# whether we use the final alpha of the "non-previous" one.
													a_					=		(
													    jnp.array(1.0			,    dtype=self.dtype		) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
													)

													# standard deviation of the initial noise distribution
													a_					=		jnp.array(1.0			,    dtype=self.dtype		)

													a_					=		jnp.arange(0			,    self.config.num_train_timesteps		).round()[::-1]

													return DDIMSchedulerState.create(
													    common=_UpperCAmelCase			,    final_alpha_cumprod=_UpperCAmelCase			,    init_noise_sigma=_UpperCAmelCase			,    timesteps=_UpperCAmelCase			,    )


							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase = None		):







													"""simple docstring"""


													return sample


							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase = ()		):







													"""simple docstring"""


													a_					=		self.config.num_train_timesteps // num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# rounding to avoid issues when num_inference_step is power of 3
													a_					=		(jnp.arange(0			,    _UpperCAmelCase		) * step_ratio).round()[::-1] + self.config.steps_offset

													return state.replace(
													    num_inference_steps=_UpperCAmelCase			,    timesteps=_UpperCAmelCase			,    )


							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		):







													"""simple docstring"""


													a_					=		state.common.alphas_cumprod[timestep]
													a_					=		jnp.where(
													    prev_timestep >= 0			,    state.common.alphas_cumprod[prev_timestep]			,    state.final_alpha_cumprod		)
													a_					=		1 - alpha_prod_t
													a_					=		1 - alpha_prod_t_prev

													a_					=		(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

													return variance


							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase = 0.0			,    _UpperCAmelCase = True			,    ):







													"""simple docstring"""


													if state.num_inference_steps is None:
																			raise ValueError(
																			    """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""		)

													# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
													# Ideally, read DDIM paper in-detail understanding

													# Notation (<variable name> -> <name in paper>
													# - pred_noise_t -> e_theta(x_t, t)
													# - pred_original_sample -> f_theta(x_t, t) or x_0
													# - std_dev_t -> sigma_t
													# - eta -> η
													# - pred_sample_direction -> "direction pointing to x_t"
													# - pred_prev_sample -> "x_t-1"

													# 1. get previous step value (=t-1)
													a_					=		timestep - self.config.num_train_timesteps // state.num_inference_steps

													a_					=		state.common.alphas_cumprod
													a_					=		state.final_alpha_cumprod

													# 2. compute alphas, betas
													a_					=		alphas_cumprod[timestep]
													a_					=		jnp.where(prev_timestep >= 0			,    alphas_cumprod[prev_timestep]			,    _UpperCAmelCase		)

													a_					=		1 - alpha_prod_t

													# 3. compute predicted original sample from predicted noise also called
													# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
													if self.config.prediction_type == "epsilon":
																			a_					=		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																			a_					=		model_output
													elif self.config.prediction_type == "sample":
																			a_					=		model_output
																			a_					=		(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
													elif self.config.prediction_type == "v_prediction":
																			a_					=		(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																			a_					=		(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
													else:
																			raise ValueError(
																			    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
																			    """ `v_prediction`"""		)

													# 4. compute variance: "sigma_t(η)" -> see formula (16)
													# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
													a_					=		self._get_variance(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		)
													a_					=		eta * variance ** 0.5

													# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
													a_					=		(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

													# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
													a_					=		alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

													if not return_dict:
																			return (prev_sample, state)

													return FlaxDDIMSchedulerOutput(prev_sample=_UpperCAmelCase			,    state=_UpperCAmelCase		)


							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    ):







													"""simple docstring"""


													return add_noise_common(state.common			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		)


							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    ):







													"""simple docstring"""


													return get_velocity_common(state.common			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		)






							def __len__(   self		):







													"""simple docstring"""


													return self.config.num_train_timesteps



import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta 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, filter_roberta_detectors





@require_tokenizers
class        lowercase_				( UpperCamelCase__			,unittest.TestCase):






							"""simple docstring"""







							snake_case_        =		BartTokenizer
							snake_case_        =		BartTokenizerFast
							snake_case_        =		True
							snake_case_        =		filter_roberta_detectors


							def 						lowercase__     (   self		):







													"""simple docstring"""


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

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


							def 						lowercase__     (   self			,    **_UpperCAmelCase		):







													"""simple docstring"""


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


							def 						lowercase__     (   self			,    **_UpperCAmelCase		):







													"""simple docstring"""


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


							def 						lowercase__     (   self			,    _UpperCAmelCase		):







													"""simple docstring"""


													return "lower newer", "lower newer"


							@cached_property
							def 						lowercase__     (   self		):







													"""simple docstring"""


													return BartTokenizer.from_pretrained("""facebook/bart-large"""		)


							@cached_property
							def 						lowercase__     (   self		):







													"""simple docstring"""


													return BartTokenizerFast.from_pretrained("""facebook/bart-large"""		)


							@require_torch
							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
													a_					=		[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

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

																			self.assertEqual((2, 9)			,    batch.input_ids.shape		)
																			self.assertEqual((2, 9)			,    batch.attention_mask.shape		)
																			a_					=		batch.input_ids.tolist()[0]
																			self.assertListEqual(_UpperCAmelCase			,    _UpperCAmelCase		)
																			# Test that special tokens are reset


							@require_torch
							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a_					=		tokenizer(_UpperCAmelCase			,    padding=_UpperCAmelCase			,    return_tensors="""pt"""		)
																			# check if input_ids are returned and no labels
																			self.assertIn("""input_ids"""			,    _UpperCAmelCase		)
																			self.assertIn("""attention_mask"""			,    _UpperCAmelCase		)
																			self.assertNotIn("""labels"""			,    _UpperCAmelCase		)
																			self.assertNotIn("""decoder_attention_mask"""			,    _UpperCAmelCase		)


							@require_torch
							def 						lowercase__     (   self		):







													"""simple docstring"""


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


							@require_torch
							def 						lowercase__     (   self		):







													"""simple docstring"""


													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a_					=		tokenizer(
																			    ["""I am a small frog""" * 1_024, """I am a small frog"""]			,    padding=_UpperCAmelCase			,    truncation=_UpperCAmelCase			,    return_tensors="""pt"""		)
																			self.assertIsInstance(_UpperCAmelCase			,    _UpperCAmelCase		)
																			self.assertEqual(batch.input_ids.shape			,    (2, 1_024)		)


							@require_torch
							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		["""A long paragraph for summarization."""]
													a_					=		[
													    """Summary of the text.""",
													]
													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a_					=		tokenizer(_UpperCAmelCase			,    return_tensors="""pt"""		)
																			a_					=		tokenizer(text_target=_UpperCAmelCase			,    return_tensors="""pt"""		)
																			a_					=		inputs["""input_ids"""]
																			a_					=		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()		)


							def 						lowercase__     (   self		):







													"""simple docstring"""


													pass






							def 						lowercase__     (   self		):







													"""simple docstring"""


													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"		):
																									a_					=		self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase			,    **_UpperCAmelCase		)
																									a_					=		self.tokenizer_class.from_pretrained(_UpperCAmelCase			,    **_UpperCAmelCase		)
																									a_					=		"""A, <mask> AllenNLP sentence."""
																									a_					=		tokenizer_r.encode_plus(_UpperCAmelCase			,    add_special_tokens=_UpperCAmelCase			,    return_token_type_ids=_UpperCAmelCase		)
																									a_					=		tokenizer_p.encode_plus(_UpperCAmelCase			,    add_special_tokens=_UpperCAmelCase			,    return_token_type_ids=_UpperCAmelCase		)

																									# 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"""]		)			,    )

																									a_					=		tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]		)
																									a_					=		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, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]		)
																									self.assertSequenceEqual(tokens_r["""input_ids"""]			,    [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]		)

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