




'''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     _a						:
       __a       :  CommonSchedulerState

       # setable values
       __a       :  jnp.ndarray
       __a       :  jnp.ndarray
       __a       :  Optional[int]            =      None
       @classmethod
       def 					A   (			cls		:			Optional[int]     ,       lowercase		:			CommonSchedulerState     ,       lowercase		:			jnp.ndarray     ,       lowercase		:			jnp.ndarray       ):



           '''simple docstring'''

           return cls(common=lowercase     ,       init_noise_sigma=lowercase     ,       timesteps=lowercase       )






@dataclass
class     _a						(			__a				):
       __a       :  DDPMSchedulerState






class     _a						(			__a							,							__a				):
       __a       :  List[str]            =      [e.name for e in FlaxKarrasDiffusionSchedulers]

       __a       :  jnp.dtype
       @property
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           return True
       @register_to_config
       def __init__(			self		:			Union[str, Any]     ,       lowercase		:			int = 1_000     ,       lowercase		:			float = 0.0001     ,       lowercase		:			float = 0.02     ,       lowercase		:			str = "linear"     ,       lowercase		:			Optional[jnp.ndarray] = None     ,       lowercase		:			str = "fixed_small"     ,       lowercase		:			bool = True     ,       lowercase		:			str = "epsilon"     ,       lowercase		:			jnp.dtype = jnp.floataa     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	dtype
       def 					A   (			self		:			List[Any]     ,       lowercase		:			Optional[CommonSchedulerState] = None       ):



           '''simple docstring'''

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

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

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

           return DDPMSchedulerState.create(
               common=lowercase     ,       init_noise_sigma=lowercase     ,       timesteps=lowercase     ,       )
       def 					A   (			self		:			Any     ,       lowercase		:			DDPMSchedulerState     ,       lowercase		:			jnp.ndarray     ,       lowercase		:			Optional[int] = None       ):



           '''simple docstring'''

           return sample
       def 					A   (			self		:			Dict     ,       lowercase		:			DDPMSchedulerState     ,       lowercase		:			int     ,       lowercase		:			Tuple = ()       ):



           '''simple docstring'''

           UpperCAmelCase     =	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
           UpperCAmelCase     =	(jnp.arange(0     ,       lowercase       ) * step_ratio).round()[::-1]

           return state.replace(
               num_inference_steps=lowercase     ,       timesteps=lowercase     ,       )
       def 					A   (			self		:			Any     ,       lowercase		:			DDPMSchedulerState     ,       lowercase		:			Union[str, Any]     ,       lowercase		:			Dict=None     ,       lowercase		:			str=None       ):



           '''simple docstring'''

           UpperCAmelCase     =	state.common.alphas_cumprod[t]
           UpperCAmelCase     =	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
           UpperCAmelCase     =	(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

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

           return variance
       def 					A   (			self		:			Tuple     ,       lowercase		:			DDPMSchedulerState     ,       lowercase		:			jnp.ndarray     ,       lowercase		:			int     ,       lowercase		:			jnp.ndarray     ,       lowercase		:			Optional[jax.random.KeyArray] = None     ,       lowercase		:			bool = True     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	timestep

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

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

           # 1. compute alphas, betas
           UpperCAmelCase     =	state.common.alphas_cumprod[t]
           UpperCAmelCase     =	jnp.where(t > 0     ,       state.common.alphas_cumprod[t - 1]     ,       jnp.array(1.0     ,       dtype=self.dtype       )       )
           UpperCAmelCase     =	1 - alpha_prod_t
           UpperCAmelCase     =	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":
               UpperCAmelCase     =	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
           elif self.config.prediction_type == "sample":
               UpperCAmelCase     =	model_output
           elif self.config.prediction_type == "v_prediction":
               UpperCAmelCase     =	(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:
               UpperCAmelCase     =	jnp.clip(lowercase     ,       -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
           UpperCAmelCase     =	(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
           UpperCAmelCase     =	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
           UpperCAmelCase     =	pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

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

           UpperCAmelCase     =	pred_prev_sample + variance

           if not return_dict:
               return (pred_prev_sample, state)

           return FlaxDDPMSchedulerOutput(prev_sample=lowercase     ,       state=lowercase       )
       def 					A   (			self		:			Union[str, Any]     ,       lowercase		:			DDPMSchedulerState     ,       lowercase		:			jnp.ndarray     ,       lowercase		:			jnp.ndarray     ,       lowercase		:			jnp.ndarray     ,       ):



           '''simple docstring'''

           return add_noise_common(state.common     ,       lowercase     ,       lowercase     ,       lowercase       )
       def 					A   (			self		:			List[str]     ,       lowercase		:			DDPMSchedulerState     ,       lowercase		:			jnp.ndarray     ,       lowercase		:			jnp.ndarray     ,       lowercase		:			jnp.ndarray     ,       ):



           '''simple docstring'''

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







       def __len__(			self		:			str       ):



           '''simple docstring'''

           return self.config.num_train_timesteps










'''simple docstring'''







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
       OpenAIGPTConfig,
       OpenAIGPTDoubleHeadsModel,
       OpenAIGPTForSequenceClassification,
       OpenAIGPTLMHeadModel,
       OpenAIGPTModel,
   )


class     _a						:
       def __init__(			self		:			Optional[Any]     ,       lowercase		:			int     ,       lowercase		:			str=13     ,       lowercase		:			int=7     ,       lowercase		:			Optional[int]=True     ,       lowercase		:			Dict=True     ,       lowercase		:			Union[str, Any]=True     ,       lowercase		:			Union[str, Any]=99     ,       lowercase		:			Optional[Any]=32     ,       lowercase		:			List[Any]=5     ,       lowercase		:			List[Any]=4     ,       lowercase		:			int=37     ,       lowercase		:			int="gelu"     ,       lowercase		:			Tuple=0.1     ,       lowercase		:			Dict=0.1     ,       lowercase		:			Tuple=512     ,       lowercase		:			List[str]=16     ,       lowercase		:			Optional[int]=2     ,       lowercase		:			int=0.02     ,       lowercase		:			str=3     ,       lowercase		:			List[Any]=4     ,       lowercase		:			List[str]=None     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	parent
           UpperCAmelCase     =	batch_size
           UpperCAmelCase     =	seq_length
           UpperCAmelCase     =	is_training
           UpperCAmelCase     =	use_token_type_ids
           UpperCAmelCase     =	use_labels
           UpperCAmelCase     =	vocab_size
           UpperCAmelCase     =	hidden_size
           UpperCAmelCase     =	num_hidden_layers
           UpperCAmelCase     =	num_attention_heads
           UpperCAmelCase     =	intermediate_size
           UpperCAmelCase     =	hidden_act
           UpperCAmelCase     =	hidden_dropout_prob
           UpperCAmelCase     =	attention_probs_dropout_prob
           UpperCAmelCase     =	max_position_embeddings
           UpperCAmelCase     =	type_vocab_size
           UpperCAmelCase     =	type_sequence_label_size
           UpperCAmelCase     =	initializer_range
           UpperCAmelCase     =	num_labels
           UpperCAmelCase     =	num_choices
           UpperCAmelCase     =	scope
           UpperCAmelCase     =	self.vocab_size - 1
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	ids_tensor([self.batch_size, self.seq_length]     ,       self.vocab_size       )

           UpperCAmelCase     =	None
           if self.use_token_type_ids:
               UpperCAmelCase     =	ids_tensor([self.batch_size, self.seq_length]     ,       self.type_vocab_size       )

           UpperCAmelCase     =	None
           UpperCAmelCase     =	None
           UpperCAmelCase     =	None
           if self.use_labels:
               UpperCAmelCase     =	ids_tensor([self.batch_size]     ,       self.type_sequence_label_size       )
               UpperCAmelCase     =	ids_tensor([self.batch_size, self.seq_length]     ,       self.num_labels       )
               UpperCAmelCase     =	ids_tensor([self.batch_size]     ,       self.num_choices       )

           UpperCAmelCase     =	OpenAIGPTConfig(
               vocab_size=self.vocab_size     ,       n_embd=self.hidden_size     ,       n_layer=self.num_hidden_layers     ,       n_head=self.num_attention_heads     ,       n_positions=self.max_position_embeddings     ,       pad_token_id=self.pad_token_id     ,       )

           UpperCAmelCase     =	ids_tensor([self.num_hidden_layers, self.num_attention_heads]     ,       2       )

           return (
               config,
               input_ids,
               head_mask,
               token_type_ids,
               sequence_labels,
               token_labels,
               choice_labels,
           )
       def 					A   (			self		:			Any     ,       lowercase		:			Tuple     ,       lowercase		:			List[Any]     ,       lowercase		:			Optional[Any]     ,       lowercase		:			List[str]     ,       *lowercase		:			Optional[int]       ):



           '''simple docstring'''

           UpperCAmelCase     =	OpenAIGPTModel(config=lowercase       )
           model.to(lowercase       )
           model.eval()

           UpperCAmelCase     =	model(lowercase     ,       token_type_ids=lowercase     ,       head_mask=lowercase       )
           UpperCAmelCase     =	model(lowercase     ,       token_type_ids=lowercase       )
           UpperCAmelCase     =	model(lowercase       )

           self.parent.assertEqual(result.last_hidden_state.shape     ,       (self.batch_size, self.seq_length, self.hidden_size)       )
       def 					A   (			self		:			List[str]     ,       lowercase		:			Optional[int]     ,       lowercase		:			List[Any]     ,       lowercase		:			str     ,       lowercase		:			Optional[int]     ,       *lowercase		:			List[str]       ):



           '''simple docstring'''

           UpperCAmelCase     =	OpenAIGPTLMHeadModel(lowercase       )
           model.to(lowercase       )
           model.eval()

           UpperCAmelCase     =	model(lowercase     ,       token_type_ids=lowercase     ,       labels=lowercase       )
           self.parent.assertEqual(result.loss.shape     ,       ()       )
           self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.seq_length, self.vocab_size)       )
       def 					A   (			self		:			Optional[int]     ,       lowercase		:			Tuple     ,       lowercase		:			Tuple     ,       lowercase		:			Optional[int]     ,       lowercase		:			Optional[int]     ,       *lowercase		:			Optional[int]       ):



           '''simple docstring'''

           UpperCAmelCase     =	OpenAIGPTDoubleHeadsModel(lowercase       )
           model.to(lowercase       )
           model.eval()

           UpperCAmelCase     =	model(lowercase     ,       token_type_ids=lowercase     ,       labels=lowercase       )
           self.parent.assertEqual(result.loss.shape     ,       ()       )
           self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.seq_length, self.vocab_size)       )
       def 					A   (			self		:			Tuple     ,       lowercase		:			Dict     ,       lowercase		:			Any     ,       lowercase		:			Any     ,       lowercase		:			Optional[int]     ,       *lowercase		:			int       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.num_labels
           UpperCAmelCase     =	OpenAIGPTForSequenceClassification(lowercase       )
           model.to(lowercase       )
           model.eval()

           UpperCAmelCase     =	ids_tensor([self.batch_size]     ,       self.type_sequence_label_size       )
           UpperCAmelCase     =	model(lowercase     ,       token_type_ids=lowercase     ,       labels=lowercase       )
           self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.num_labels)       )







       def 					A   (			self		:			List[str]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.prepare_config_and_inputs()
           (
               (
               UpperCAmelCase
           ) ,  (
               UpperCAmelCase
           ) ,  (
               UpperCAmelCase
           ) ,  (
               UpperCAmelCase
           ) ,  (
               UpperCAmelCase
           ) ,  (
               UpperCAmelCase
           ) ,  (
               UpperCAmelCase
           ) ,  
           )     =	config_and_inputs
           UpperCAmelCase     =	{
               '''input_ids''': input_ids,
               '''token_type_ids''': token_type_ids,
               '''head_mask''': head_mask,
           }

           return config, inputs_dict






@require_torch
class     _a						(			__a							,							__a							,							__a							,							unittest.TestCase				):
       __a       :  List[str]            =      (
           (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
           if is_torch_available()
           else ()
       )
       __a       :  List[Any]            =      (
           (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
       )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
       __a       :  Dict            =      (
           {
               """feature-extraction""": OpenAIGPTModel,
               """text-classification""": OpenAIGPTForSequenceClassification,
               """text-generation""": OpenAIGPTLMHeadModel,
               """zero-shot""": OpenAIGPTForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       def 					A   (			self		:			Optional[Any]     ,       lowercase		:			Union[str, Any]     ,       lowercase		:			Tuple     ,       lowercase		:			int     ,       lowercase		:			List[Any]     ,       lowercase		:			Optional[Any]       ):



           '''simple docstring'''

           if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
               # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
               # `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
               # tiny config could not be created.
               return True

           return False
       def 					A   (			self		:			List[Any]     ,       lowercase		:			int     ,       lowercase		:			List[Any]     ,       lowercase		:			Optional[Any]=False       ):



           '''simple docstring'''

           UpperCAmelCase     =	super()._prepare_for_class(lowercase     ,       lowercase     ,       return_labels=lowercase       )

           if return_labels:
               if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
                   UpperCAmelCase     =	torch.zeros(
                       (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)     ,       dtype=torch.long     ,       device=lowercase     ,       )
                   UpperCAmelCase     =	inputs_dict['''labels''']
                   UpperCAmelCase     =	inputs_dict['''labels''']
                   UpperCAmelCase     =	torch.zeros(
                       (self.model_tester.batch_size, self.model_tester.num_choices)     ,       dtype=torch.long     ,       device=lowercase     ,       )
                   UpperCAmelCase     =	torch.zeros(
                       self.model_tester.batch_size     ,       dtype=torch.long     ,       device=lowercase       )
           return inputs_dict
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	OpenAIGPTModelTester(self       )
           UpperCAmelCase     =	ConfigTester(self     ,       config_class=lowercase     ,       n_embd=37       )
       def 					A   (			self		:			str       ):



           '''simple docstring'''

           self.config_tester.run_common_tests()
       def 					A   (			self		:			Tuple       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_openai_gpt_model(*lowercase       )
       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_lm_head_model(*lowercase       )
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_double_lm_head_model(*lowercase       )
       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowercase       )







       @slow
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               UpperCAmelCase     =	OpenAIGPTModel.from_pretrained(lowercase       )
               self.assertIsNotNone(lowercase       )






@require_torch
class     _a						(			unittest.TestCase				):
       @slow
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           UpperCAmelCase     =	OpenAIGPTLMHeadModel.from_pretrained('''openai-gpt'''       )
           model.to(lowercase       )
           UpperCAmelCase     =	torch.tensor([[481, 4_735, 544]]     ,       dtype=torch.long     ,       device=lowercase       )  # the president is
           UpperCAmelCase     =	[
               481,
               4_735,
               544,
               246,
               963,
               870,
               762,
               239,
               244,
               40_477,
               244,
               249,
               719,
               881,
               487,
               544,
               240,
               244,
               603,
               481,
           ]  # the president is a very good man. " \n " i\'m sure he is, " said the

           UpperCAmelCase     =	model.generate(lowercase     ,       do_sample=lowercase       )
           self.assertListEqual(output_ids[0].tolist()     ,       lowercase       )




