


"""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	,					lowerCamelCase__	,					lowerCamelCase__=12	,					lowerCamelCase__=7	,					lowerCamelCase__=True	,					lowerCamelCase__=True	,					lowerCamelCase__=True	,					lowerCamelCase__=99	,					lowerCamelCase__=32	,					lowerCamelCase__=32	,					lowerCamelCase__=5	,					lowerCamelCase__=4	,					lowerCamelCase__=37	,					lowerCamelCase__=0.1	,					lowerCamelCase__=0.1	,					lowerCamelCase__=512	,					lowerCamelCase__=0.02	,					lowerCamelCase__=0	,					lowerCamelCase__=None	,					)   ->	Any:







								'''simple docstring'''




								snake_case__  :						Optional[int]		=      parent
								snake_case__  :						str		=      batch_size
								snake_case__  :						Optional[int]		=      seq_length
								snake_case__  :						Optional[int]		=      is_training
								snake_case__  :						str		=      use_input_mask
								snake_case__  :						Any		=      use_labels
								snake_case__  :						Tuple		=      vocab_size
								snake_case__  :						Optional[Any]		=      hidden_size
								snake_case__  :						List[str]		=      projection_dim
								snake_case__  :						List[Any]		=      num_hidden_layers
								snake_case__  :						List[str]		=      num_attention_heads
								snake_case__  :						Optional[Any]		=      intermediate_size
								snake_case__  :						Any		=      dropout
								snake_case__  :						Optional[int]		=      attention_dropout
								snake_case__  :						List[Any]		=      max_position_embeddings
								snake_case__  :						Optional[Any]		=      initializer_range
								snake_case__  :						Tuple		=      scope
								snake_case__  :						int		=      bos_token_id




							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								snake_case__  :						Optional[int]		=      ids_tensor([self.batch_size, self.seq_length]	,					self.vocab_size)

								snake_case__  :						List[str]		=      None
								if self.use_input_mask:
									snake_case__  :						List[Any]		=      random_attention_mask([self.batch_size, self.seq_length])

								if input_mask is not None:
									snake_case__,					snake_case__  :						str		=      input_mask.shape
									snake_case__  :						List[Any]		=      np.random.randint(1	,					seq_length - 1	,					size=(batch_size,))
									for batch_idx, start_index in enumerate(lowerCamelCase__):
										snake_case__  :						Tuple		=      1
										snake_case__  :						List[str]		=      0

								snake_case__  :						str		=      self.get_config()

								return config, input_ids, input_mask




							def        UpperCAmelCase (						self)   ->	Dict:







								'''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        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__)   ->	Optional[int]:







								'''simple docstring'''




								snake_case__  :						Union[str, Any]		=      BlipTextModel(config=lowerCamelCase__)
								model.to(lowerCamelCase__)
								model.eval()
								with torch.no_grad():
									snake_case__  :						Union[str, Any]		=      model(lowerCamelCase__	,					attention_mask=lowerCamelCase__)
									snake_case__  :						Optional[int]		=      model(lowerCamelCase__)
								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        UpperCAmelCase (						self)   ->	Union[str, Any]:







								'''simple docstring'''




								snake_case__  :						Any		=      self.prepare_config_and_inputs()
								snake_case__,					snake_case__,					snake_case__  :						Tuple		=      config_and_inputs
								snake_case__  :						Dict		=      {"input_ids": input_ids, "attention_mask": input_mask}
								return config, inputs_dict


@require_torch
class 			SCREAMING_SNAKE_CASE_  (   _lowercase	,      unittest.TestCase):




							'''simple docstring'''





							__magic_name__					:     Tuple				  =  (BlipTextModel,) if is_torch_available() else ()
							__magic_name__					:     Tuple				  =  False
							__magic_name__					:     Tuple				  =  False
							__magic_name__					:     int				  =  False




							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								snake_case__  :						List[Any]		=      BlipTextModelTester(self)
								snake_case__  :						Any		=      ConfigTester(self	,					config_class=lowerCamelCase__	,					hidden_size=37)




							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								self.config_tester.run_common_tests()




							def        UpperCAmelCase (						self)   ->	Dict:







								'''simple docstring'''




								snake_case__  :						Optional[Any]		=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase__)




							def        UpperCAmelCase (						self)   ->	Union[str, Any]:







								'''simple docstring'''




								pass




							def        UpperCAmelCase (						self)   ->	Optional[Any]:







								'''simple docstring'''




								pass




							@unittest.skip(reason="Blip does not use inputs_embeds")
							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								pass




							@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING")
							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




								pass




							@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING")
							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




								pass




							@slow
							def        UpperCAmelCase (						self)   ->	List[str]:







								'''simple docstring'''




								for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									snake_case__  :						Optional[int]		=      BlipTextModel.from_pretrained(lowerCamelCase__)
									self.assertIsNotNone(lowerCamelCase__)







							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								super().test_pt_tf_model_equivalence(allow_missing_keys=lowerCamelCase__)











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

import flax
import jax.numpy as jnp
from scipy import integrate

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

@flax.struct.dataclass
class 			_lowerCamelCase    :

      """simple docstring"""

      lowerCAmelCase__      :			CommonSchedulerState

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

      # running values
      lowerCAmelCase__      :			Optional[jnp.ndarray]     =       None







      @classmethod
      def    snake_case						(  cls       :			str			,							snake_case       :			CommonSchedulerState			,							snake_case       :			jnp.ndarray			,							snake_case       :			jnp.ndarray			,							snake_case       :			jnp.ndarray					):
          return cls(common=snake_case			,							init_noise_sigma=snake_case			,							timesteps=snake_case			,							sigmas=snake_case					)







@dataclass
class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      lowerCAmelCase__      :			LMSDiscreteSchedulerState







class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE				,      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      lowerCAmelCase__      :			List[Any]     =       [e.name for e in FlaxKarrasDiffusionSchedulers]

      lowerCAmelCase__      :			jnp.dtype







      @property
      def    snake_case						(  self       :			Any					):
          return True







      @register_to_config
      def __init__(  self       :			Union[str, Any]			,							snake_case       :			int = 1000			,							snake_case       :			float = 0.00_01			,							snake_case       :			float = 0.02			,							snake_case       :			str = "linear"			,							snake_case       :			Optional[jnp.ndarray] = None			,							snake_case       :			str = "epsilon"			,							snake_case       :			jnp.dtype = jnp.floataa			,							):
          __UpperCamelCase			=    dtype







      def    snake_case						(  self       :			int			,							snake_case       :			Optional[CommonSchedulerState] = None					):
          if common is None:
              __UpperCamelCase			=    CommonSchedulerState.create(self					)

          __UpperCamelCase			=    jnp.arange(0			,							self.config.num_train_timesteps					).round()[::-1]
          __UpperCamelCase			=    ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

          # standard deviation of the initial noise distribution
          __UpperCamelCase			=    sigmas.max()

          return LMSDiscreteSchedulerState.create(
              common=snake_case			,							init_noise_sigma=snake_case			,							timesteps=snake_case			,							sigmas=snake_case			,							)







      def    snake_case						(  self       :			str			,							snake_case       :			LMSDiscreteSchedulerState			,							snake_case       :			jnp.ndarray			,							snake_case       :			int					):
          ((__UpperCamelCase)		,   )			=    jnp.where(state.timesteps == timestep			,							size=1					)
          __UpperCamelCase			=    step_index[0]

          __UpperCamelCase			=    state.sigmas[step_index]
          __UpperCamelCase			=    sample / ((sigma**2 + 1) ** 0.5)
          return sample







      def    snake_case						(  self       :			Optional[Any]			,							snake_case       :			LMSDiscreteSchedulerState			,							snake_case       :			str			,							snake_case       :			Tuple			,							snake_case       :			int					):

          def lms_derivative(snake_case       :			int					):
              __UpperCamelCase			=    1.0
              for k in range(snake_case					):
                  if current_order == k:
                      continue
                  prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
              return prod

          __UpperCamelCase			=    integrate.quad(snake_case			,							state.sigmas[t]			,							state.sigmas[t + 1]			,							epsrel=1E-4					)[0]

          return integrated_coeff







      def    snake_case						(  self       :			int			,							snake_case       :			LMSDiscreteSchedulerState			,							snake_case       :			int			,							snake_case       :			Tuple = ()					):
          __UpperCamelCase			=    jnp.linspace(self.config.num_train_timesteps - 1			,							0			,							snake_case			,							dtype=self.dtype					)

          __UpperCamelCase			=    jnp.floor(snake_case					).astype(jnp.intaa					)
          __UpperCamelCase			=    jnp.ceil(snake_case					).astype(jnp.intaa					)

          __UpperCamelCase			=    jnp.mod(snake_case			,							1.0					)

          __UpperCamelCase			=    ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
          __UpperCamelCase			=    (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
          __UpperCamelCase			=    jnp.concatenate([sigmas, jnp.array([0.0]			,							dtype=self.dtype					)]					)

          __UpperCamelCase			=    timesteps.astype(jnp.intaa					)

          # initial running values
          __UpperCamelCase			=    jnp.zeros((0,) + shape			,							dtype=self.dtype					)

          return state.replace(
              timesteps=snake_case			,							sigmas=snake_case			,							num_inference_steps=snake_case			,							derivatives=snake_case			,							)







      def    snake_case						(  self       :			Tuple			,							snake_case       :			LMSDiscreteSchedulerState			,							snake_case       :			jnp.ndarray			,							snake_case       :			int			,							snake_case       :			jnp.ndarray			,							snake_case       :			int = 4			,							snake_case       :			bool = True			,							):
          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'''					)

          __UpperCamelCase			=    state.sigmas[timestep]

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
              __UpperCamelCase			=    sample - sigma * model_output
          elif self.config.prediction_type == "v_prediction":
              # * c_out + input * c_skip
              __UpperCamelCase			=    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
          else:
              raise ValueError(
                  F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"					)

          # 2. Convert to an ODE derivative
          __UpperCamelCase			=    (sample - pred_original_sample) / sigma
          __UpperCamelCase			=    state.replace(derivatives=jnp.append(state.derivatives			,							snake_case					)					)
          if len(state.derivatives					) > order:
              __UpperCamelCase			=    state.replace(derivatives=jnp.delete(state.derivatives			,							0					)					)

          # 3. Compute linear multistep coefficients
          __UpperCamelCase			=    min(timestep + 1			,							snake_case					)
          __UpperCamelCase			=    [self.get_lms_coefficient(snake_case			,							snake_case			,							snake_case			,							snake_case					) for curr_order in range(snake_case					)]

          # 4. Compute previous sample based on the derivatives path
          __UpperCamelCase			=    sample + sum(
              coeff * derivative for coeff, derivative in zip(snake_case			,							reversed(state.derivatives					)					)					)

          if not return_dict:
              return (prev_sample, state)

          return FlaxLMSSchedulerOutput(prev_sample=snake_case			,							state=snake_case					)







      def    snake_case						(  self       :			Dict			,							snake_case       :			LMSDiscreteSchedulerState			,							snake_case       :			jnp.ndarray			,							snake_case       :			jnp.ndarray			,							snake_case       :			jnp.ndarray			,							):
          __UpperCamelCase			=    state.sigmas[timesteps].flatten()
          __UpperCamelCase			=    broadcast_to_shape_from_left(snake_case			,							noise.shape					)

          __UpperCamelCase			=    original_samples + noise * sigma

          return noisy_samples







      def __len__(  self       :			Optional[Any]					):
          return self.config.num_train_timesteps






