

"""simple docstring"""




# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
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  lowerCamelCase    :
			lowerCamelCase__ :		CommonSchedulerState

			# setable values
			lowerCamelCase__ :		jnp.ndarray
			lowerCamelCase__ :		jnp.ndarray
			lowerCamelCase__ :		Optional[int]       		=				None

			@classmethod
			def        SCREAMING_SNAKE_CASE					(       cls					:    List[Any]     ,					__UpperCAmelCase					:    CommonSchedulerState     ,					__UpperCAmelCase					:    jnp.ndarray     ,					__UpperCAmelCase					:    jnp.ndarray							) ->     Tuple:
						return cls(common=__UpperCAmelCase     ,					init_noise_sigma=__UpperCAmelCase     ,					timesteps=__UpperCAmelCase							)





@dataclass
class  lowerCamelCase    (A__      ):
			lowerCamelCase__ :		DDPMSchedulerState





class  lowerCamelCase    (A__      ,A__      ):
			lowerCamelCase__ :		List[Any]       		=				[e.name for e in FlaxKarrasDiffusionSchedulers]

			lowerCamelCase__ :		jnp.dtype

			@property
			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     Dict:
						return True

			@register_to_config
			def __init__(       self					:    str     ,					__UpperCAmelCase					:    int = 1_0_0_0     ,					__UpperCAmelCase					:    float = 0.0_001     ,					__UpperCAmelCase					:    float = 0.02     ,					__UpperCAmelCase					:    str = "linear"     ,					__UpperCAmelCase					:    Optional[jnp.ndarray] = None     ,					__UpperCAmelCase					:    str = "fixed_small"     ,					__UpperCAmelCase					:    bool = True     ,					__UpperCAmelCase					:    str = "epsilon"     ,					__UpperCAmelCase					:    jnp.dtype = jnp.floataa     ,					) ->     Union[str, Any]:
						SCREAMING_SNAKE_CASE__										=   dtype

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]     ,					__UpperCAmelCase					:    Optional[CommonSchedulerState] = None							) ->     DDPMSchedulerState:
						if common is None:
									SCREAMING_SNAKE_CASE__										=   CommonSchedulerState.create(self							)

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

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

						return DDPMSchedulerState.create(
						    common=__UpperCAmelCase     ,					init_noise_sigma=__UpperCAmelCase     ,					timesteps=__UpperCAmelCase     ,					)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]     ,					__UpperCAmelCase					:    DDPMSchedulerState     ,					__UpperCAmelCase					:    jnp.ndarray     ,					__UpperCAmelCase					:    Optional[int] = None							) ->     jnp.ndarray:
						return sample

			def        SCREAMING_SNAKE_CASE					(       self					:    int     ,					__UpperCAmelCase					:    DDPMSchedulerState     ,					__UpperCAmelCase					:    int     ,					__UpperCAmelCase					:    Tuple = ()							) ->     DDPMSchedulerState:
						SCREAMING_SNAKE_CASE__										=   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
						SCREAMING_SNAKE_CASE__										=   (jnp.arange(0     ,					__UpperCAmelCase							) * step_ratio).round()[::-1]

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

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]     ,					__UpperCAmelCase					:    DDPMSchedulerState     ,					__UpperCAmelCase					:    int     ,					__UpperCAmelCase					:    Any=None     ,					__UpperCAmelCase					:    Optional[Any]=None							) ->     Any:
						SCREAMING_SNAKE_CASE__										=   state.common.alphas_cumprod[t]
						SCREAMING_SNAKE_CASE__										=   jnp.where(t > 0     ,					state.common.alphas_cumprod[t - 1]     ,					jnp.array(1.0     ,					dtype=self.dtype							)							)

						# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
						# and sample from it to get previous sample
						# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
						SCREAMING_SNAKE_CASE__										=   (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

						if variance_type is None:
									SCREAMING_SNAKE_CASE__										=   self.config.variance_type

						# hacks - were probably added for training stability
						if variance_type == "fixed_small":
									SCREAMING_SNAKE_CASE__										=   jnp.clip(__UpperCAmelCase     ,					a_min=1e-20							)
						# for rl-diffuser https://arxiv.org/abs/2205.09991
						elif variance_type == "fixed_small_log":
									SCREAMING_SNAKE_CASE__										=   jnp.log(jnp.clip(__UpperCAmelCase     ,					a_min=1e-20							)							)
						elif variance_type == "fixed_large":
									SCREAMING_SNAKE_CASE__										=   state.common.betas[t]
						elif variance_type == "fixed_large_log":
									# Glide max_log
									SCREAMING_SNAKE_CASE__										=   jnp.log(state.common.betas[t]							)
						elif variance_type == "learned":
									return predicted_variance
						elif variance_type == "learned_range":
									SCREAMING_SNAKE_CASE__										=   variance
									SCREAMING_SNAKE_CASE__										=   state.common.betas[t]
									SCREAMING_SNAKE_CASE__										=   (predicted_variance + 1) / 2
									SCREAMING_SNAKE_CASE__										=   frac * max_log + (1 - frac) * min_log

						return variance

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple     ,					__UpperCAmelCase					:    DDPMSchedulerState     ,					__UpperCAmelCase					:    jnp.ndarray     ,					__UpperCAmelCase					:    int     ,					__UpperCAmelCase					:    jnp.ndarray     ,					__UpperCAmelCase					:    Optional[jax.random.KeyArray] = None     ,					__UpperCAmelCase					:    bool = True     ,					) ->     Union[FlaxDDPMSchedulerOutput, Tuple]:
						SCREAMING_SNAKE_CASE__										=   timestep

						if key is None:
									SCREAMING_SNAKE_CASE__										=   jax.random.PRNGKey(0							)

						if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
									SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   jnp.split(__UpperCAmelCase     ,					sample.shape[1]     ,					axis=1							)
						else:
									SCREAMING_SNAKE_CASE__										=   None

						# 1. compute alphas, betas
						SCREAMING_SNAKE_CASE__										=   state.common.alphas_cumprod[t]
						SCREAMING_SNAKE_CASE__										=   jnp.where(t > 0     ,					state.common.alphas_cumprod[t - 1]     ,					jnp.array(1.0     ,					dtype=self.dtype							)							)
						SCREAMING_SNAKE_CASE__										=   1 - alpha_prod_t
						SCREAMING_SNAKE_CASE__										=   1 - alpha_prod_t_prev

						# 2. compute predicted original sample from predicted noise also called
						# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
						if self.config.prediction_type == "epsilon":
									SCREAMING_SNAKE_CASE__										=   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
						elif self.config.prediction_type == "sample":
									SCREAMING_SNAKE_CASE__										=   model_output
						elif self.config.prediction_type == "v_prediction":
									SCREAMING_SNAKE_CASE__										=   (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
						else:
									raise ValueError(
									    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
									    """ for the FlaxDDPMScheduler."""							)

						# 3. Clip "predicted x_0"
						if self.config.clip_sample:
									SCREAMING_SNAKE_CASE__										=   jnp.clip(__UpperCAmelCase     ,					-1     ,					1							)

						# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
						# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
						SCREAMING_SNAKE_CASE__										=   (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
						SCREAMING_SNAKE_CASE__										=   state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

						# 5. Compute predicted previous sample µ_t
						# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
						SCREAMING_SNAKE_CASE__										=   pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

						# 6. Add noise
						def random_variance():
									SCREAMING_SNAKE_CASE__										=   jax.random.split(__UpperCAmelCase     ,					num=1							)
									SCREAMING_SNAKE_CASE__										=   jax.random.normal(__UpperCAmelCase     ,					shape=model_output.shape     ,					dtype=self.dtype							)
									return (self._get_variance(__UpperCAmelCase     ,					__UpperCAmelCase     ,					predicted_variance=__UpperCAmelCase							) ** 0.5) * noise

						SCREAMING_SNAKE_CASE__										=   jnp.where(t > 0     ,					random_variance()     ,					jnp.zeros(model_output.shape     ,					dtype=self.dtype							)							)

						SCREAMING_SNAKE_CASE__										=   pred_prev_sample + variance

						if not return_dict:
									return (pred_prev_sample, state)

						return FlaxDDPMSchedulerOutput(prev_sample=__UpperCAmelCase     ,					state=__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]     ,					__UpperCAmelCase					:    DDPMSchedulerState     ,					__UpperCAmelCase					:    jnp.ndarray     ,					__UpperCAmelCase					:    jnp.ndarray     ,					__UpperCAmelCase					:    jnp.ndarray     ,					) ->     jnp.ndarray:
						return add_noise_common(state.common     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    int     ,					__UpperCAmelCase					:    DDPMSchedulerState     ,					__UpperCAmelCase					:    jnp.ndarray     ,					__UpperCAmelCase					:    jnp.ndarray     ,					__UpperCAmelCase					:    jnp.ndarray     ,					) ->     jnp.ndarray:
						return get_velocity_common(state.common     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase							)





			def __len__(       self					:    List[Any]							) ->     Optional[int]:
						return self.config.num_train_timesteps





"""simple docstring"""




import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
						import tensorflow as tf

if is_keras_nlp_available():
						from transformers.models.gpta import TFGPTaTokenizer


A_				:      Union[str, Any]           =			["gpt2"]
A_				:      Optional[int]           =			"gpt2"

if is_tf_available():





						class  lowerCamelCase    (tf.Module      ):

									def __init__(       self					:    Dict     ,					__UpperCAmelCase					:    str							) ->     Any:
												super().__init__()
												SCREAMING_SNAKE_CASE__										=   tokenizer
												SCREAMING_SNAKE_CASE__										=   AutoConfig.from_pretrained(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   TFGPTaLMHeadModel.from_config(__UpperCAmelCase							)





									@tf.function(input_signature=(tf.TensorSpec((None,)     ,					tf.string     ,					name="""text"""							),)							)
									def        SCREAMING_SNAKE_CASE					(       self					:    Tuple     ,					__UpperCAmelCase					:    Tuple							) ->     List[Any]:
												SCREAMING_SNAKE_CASE__										=   self.tokenizer(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   tokenized["""input_ids"""].to_tensor()

												SCREAMING_SNAKE_CASE__										=   tf.cast(input_ids_dense > 0     ,					tf.intaa							)
												# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

												SCREAMING_SNAKE_CASE__										=   self.model(input_ids=__UpperCAmelCase     ,					attention_mask=__UpperCAmelCase							)["""logits"""]

												return outputs





@require_tf
@require_keras_nlp
class  lowerCamelCase    (unittest.TestCase      ):

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

						SCREAMING_SNAKE_CASE__										=   [GPTaTokenizer.from_pretrained(__UpperCAmelCase							) for checkpoint in (TOKENIZER_CHECKPOINTS)]
						SCREAMING_SNAKE_CASE__										=   [TFGPTaTokenizer.from_pretrained(__UpperCAmelCase							) for checkpoint in TOKENIZER_CHECKPOINTS]
						assert len(self.tokenizers							) == len(self.tf_tokenizers							)

						SCREAMING_SNAKE_CASE__										=   [
						    """This is a straightforward English test sentence.""",
						    """This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.""",
						    """Now we're going to add some Chinese: 一 二 三 一二三""",
						    """And some much more rare Chinese: 齉 堃 齉堃""",
						    """Je vais aussi écrire en français pour tester les accents""",
						    """Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
						]
						SCREAMING_SNAKE_CASE__										=   list(zip(self.test_sentences     ,					self.test_sentences[::-1]							)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     str:
						for tokenizer, tf_tokenizer in zip(self.tokenizers     ,					self.tf_tokenizers							):
									for test_inputs in self.test_sentences:
												SCREAMING_SNAKE_CASE__										=   tokenizer([test_inputs]     ,					return_tensors="""tf"""							)
												SCREAMING_SNAKE_CASE__										=   tf_tokenizer([test_inputs]							)

												for key in python_outputs.keys():
															# convert them to numpy to avoid messing with ragged tensors
															SCREAMING_SNAKE_CASE__										=   python_outputs[key].numpy()
															SCREAMING_SNAKE_CASE__										=   tf_outputs[key].numpy()

															self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape							)							)
															self.assertTrue(tf.reduce_all(tf.cast(__UpperCAmelCase     ,					tf.intaa							) == tf_outputs_values							)							)

			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]							) ->     str:
						for tf_tokenizer in self.tf_tokenizers:
									SCREAMING_SNAKE_CASE__										=   tf.function(__UpperCAmelCase							)
									for test_inputs in self.test_sentences:
												SCREAMING_SNAKE_CASE__										=   tf.constant(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   compiled_tokenizer(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   tf_tokenizer(__UpperCAmelCase							)

												for key in eager_outputs.keys():
															self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]							)							)

			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     List[str]:
						for tf_tokenizer in self.tf_tokenizers:
									SCREAMING_SNAKE_CASE__										=   ModelToSave(tokenizer=__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   tf.convert_to_tensor([self.test_sentences[0]]							)
									SCREAMING_SNAKE_CASE__										=   model.serving(__UpperCAmelCase							)  # Build model with some sample inputs
									with TemporaryDirectory() as tempdir:
												SCREAMING_SNAKE_CASE__										=   Path(__UpperCAmelCase							) / """saved.model"""
												tf.saved_model.save(__UpperCAmelCase     ,					__UpperCAmelCase     ,					signatures={"""serving_default""": model.serving}							)
												SCREAMING_SNAKE_CASE__										=   tf.saved_model.load(__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   loaded_model.signatures["""serving_default"""](__UpperCAmelCase							)["""output_0"""]
									# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
									self.assertTrue(tf.reduce_all(out == loaded_output							)							)

			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     List[Any]:
						for tf_tokenizer in self.tf_tokenizers:
									SCREAMING_SNAKE_CASE__										=   tf.convert_to_tensor([self.test_sentences[0]]							)
									SCREAMING_SNAKE_CASE__										=   tf_tokenizer(__UpperCAmelCase							)  # Build model with some sample inputs

									SCREAMING_SNAKE_CASE__										=   tf_tokenizer.get_config()
									SCREAMING_SNAKE_CASE__										=   TFGPTaTokenizer.from_config(__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   model_from_config(__UpperCAmelCase							)

									for key in from_config_output.keys():
												self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]							)							)





			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     Optional[int]:
						for tf_tokenizer in self.tf_tokenizers:
									# for the test to run
									SCREAMING_SNAKE_CASE__										=   1_2_3_1_2_3

									for max_length in [3, 5, 1_0_2_4]:
												SCREAMING_SNAKE_CASE__										=   tf.convert_to_tensor([self.test_sentences[0]]							)
												SCREAMING_SNAKE_CASE__										=   tf_tokenizer(__UpperCAmelCase     ,					max_length=__UpperCAmelCase							)

												SCREAMING_SNAKE_CASE__										=   out["""input_ids"""].numpy().shape[1]

												assert out_length == max_length


