






'''simple docstring'''







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      __SCREAMING_SNAKE_CASE					:


		"""simple docstring"""


		_a     :   CommonSchedulerState
		_a     :   jnp.ndarray

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







		@classmethod
		def       _a					( cls      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  ):






									"""simple docstring"""


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





@dataclass
class      __SCREAMING_SNAKE_CASE					(__A					):


		"""simple docstring"""


		_a     :   DDIMSchedulerState





class      __SCREAMING_SNAKE_CASE					(__A       ,					__A					):


		"""simple docstring"""


		_a     :   Any			 =    [e.name for e in FlaxKarrasDiffusionSchedulers]

		_a     :   jnp.dtype







		@property
		def       _a					( self  ):






									"""simple docstring"""


									return True







		@register_to_config
		def __init__( self      ,  UpperCamelCase__ = 1_000      ,  UpperCamelCase__ = 0.0_001      ,  UpperCamelCase__ = 0.02      ,  UpperCamelCase__ = "linear"      ,  UpperCamelCase__ = None      ,  UpperCamelCase__ = True      ,  UpperCamelCase__ = 0      ,  UpperCamelCase__ = "epsilon"      ,  UpperCamelCase__ = jnp.floataa      ,  ):






									"""simple docstring"""


									a_	    = dtype







		def       _a					( 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       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__ = None  ):






									"""simple docstring"""


									return sample







		def       _a					( 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       _a					( 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       _a					( 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       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  ):






									"""simple docstring"""


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







		def       _a					( 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












'''simple docstring'''







import unittest

import numpy as np

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask


if is_torch_available():
  import torch

  from transformers import BlipTextModel
  from transformers.models.blip.modeling_blip import BLIP_PRETRAINED_MODEL_ARCHIVE_LIST




class      __SCREAMING_SNAKE_CASE					:


  """simple docstring"""


  def __init__( self      ,  UpperCamelCase__      ,  UpperCamelCase__=12      ,  UpperCamelCase__=7      ,  UpperCamelCase__=True      ,  UpperCamelCase__=True      ,  UpperCamelCase__=True      ,  UpperCamelCase__=99      ,  UpperCamelCase__=32      ,  UpperCamelCase__=32      ,  UpperCamelCase__=5      ,  UpperCamelCase__=4      ,  UpperCamelCase__=37      ,  UpperCamelCase__=0.1      ,  UpperCamelCase__=0.1      ,  UpperCamelCase__=512      ,  UpperCamelCase__=0.02      ,  UpperCamelCase__=0      ,  UpperCamelCase__=None      ,  ):






         """simple docstring"""


         a_	    = parent
         a_	    = batch_size
         a_	    = seq_length
         a_	    = is_training
         a_	    = use_input_mask
         a_	    = use_labels
         a_	    = vocab_size
         a_	    = hidden_size
         a_	    = projection_dim
         a_	    = num_hidden_layers
         a_	    = num_attention_heads
         a_	    = intermediate_size
         a_	    = dropout
         a_	    = attention_dropout
         a_	    = max_position_embeddings
         a_	    = initializer_range
         a_	    = scope
         a_	    = bos_token_id







  def       _a					( self  ):






         """simple docstring"""


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

         a_	    = None
         if self.use_input_mask:
                a_	    = random_attention_mask([self.batch_size, self.seq_length]  )

         if input_mask is not None:
                a_    ,  a_	    = input_mask.shape
                a_	    = np.random.randint(1      ,  seq_length - 1      ,  size=(batch_size,)  )
                for batch_idx, start_index in enumerate(UpperCamelCase__  ):
                       a_	    = 1
                       a_	    = 0

         a_	    = self.get_config()

         return config, input_ids, input_mask







  def       _a					( self  ):






         """simple docstring"""


         return BlipTextConfig(
             vocab_size=self.vocab_size      ,  hidden_size=self.hidden_size      ,  projection_dim=self.projection_dim      ,  num_hidden_layers=self.num_hidden_layers      ,  num_attention_heads=self.num_attention_heads      ,  intermediate_size=self.intermediate_size      ,  dropout=self.dropout      ,  attention_dropout=self.attention_dropout      ,  max_position_embeddings=self.max_position_embeddings      ,  initializer_range=self.initializer_range      ,  bos_token_id=self.bos_token_id      ,  )







  def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__  ):






         """simple docstring"""


         a_	    = BlipTextModel(config=UpperCamelCase__  )
         model.to(UpperCamelCase__  )
         model.eval()
         with torch.no_grad():
                a_	    = model(UpperCamelCase__      ,  attention_mask=UpperCamelCase__  )
                a_	    = model(UpperCamelCase__  )
         self.parent.assertEqual(result.last_hidden_state.shape      ,  (self.batch_size, self.seq_length, self.hidden_size)  )
         self.parent.assertEqual(result.pooler_output.shape      ,  (self.batch_size, self.hidden_size)  )







  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.prepare_config_and_inputs()
         a_    ,  a_    ,  a_	    = config_and_inputs
         a_	    = {'input_ids': input_ids, 'attention_mask': input_mask}
         return config, inputs_dict





@require_torch
class      __SCREAMING_SNAKE_CASE					(__A       ,					unittest.TestCase					):


  """simple docstring"""


  _a     :   Dict			 =    (BlipTextModel,) if is_torch_available() else ()
  _a     :   str			 =    False
  _a     :   str			 =    False
  _a     :   str			 =    False







  def       _a					( self  ):






         """simple docstring"""


         a_	    = BlipTextModelTester(self  )
         a_	    = ConfigTester(self      ,  config_class=UpperCamelCase__      ,  hidden_size=37  )







  def       _a					( self  ):






         """simple docstring"""


         self.config_tester.run_common_tests()







  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*UpperCamelCase__  )







  def       _a					( self  ):






         """simple docstring"""


         pass







  def       _a					( self  ):






         """simple docstring"""


         pass







  @unittest.skip(reason='Blip does not use inputs_embeds'  )
  def       _a					( self  ):






         """simple docstring"""


         pass







  @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'  )
  def       _a					( self  ):






         """simple docstring"""


         pass







  @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'  )
  def       _a					( self  ):






         """simple docstring"""


         pass







  @slow
  def       _a					( self  ):






         """simple docstring"""


         for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                a_	    = BlipTextModel.from_pretrained(UpperCamelCase__  )
                self.assertIsNotNone(UpperCamelCase__  )







  def       _a					( self  ):






         """simple docstring"""


         super().test_pt_tf_model_equivalence(allow_missing_keys=UpperCamelCase__  )




