




'''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__		:
     def __init__(	self  :		int					,	__lowerCamelCase  :		List[Any]					,	__lowerCamelCase  :		Any=12					,	__lowerCamelCase  :		Any=7					,	__lowerCamelCase  :		Optional[Any]=True					,	__lowerCamelCase  :		Union[str, Any]=True					,	__lowerCamelCase  :		Dict=True					,	__lowerCamelCase  :		Any=99					,	__lowerCamelCase  :		Optional[Any]=32					,	__lowerCamelCase  :		Union[str, Any]=32					,	__lowerCamelCase  :		int=5					,	__lowerCamelCase  :		List[Any]=4					,	__lowerCamelCase  :		Tuple=37					,	__lowerCamelCase  :		str=0.1					,	__lowerCamelCase  :		str=0.1					,	__lowerCamelCase  :		int=5_12					,	__lowerCamelCase  :		str=0.02					,	__lowerCamelCase  :		List[Any]=0					,	__lowerCamelCase  :		List[Any]=None					,	):



       """simple docstring"""
       lowerCAmelCase__								=			parent
       lowerCAmelCase__								=			batch_size
       lowerCAmelCase__								=			seq_length
       lowerCAmelCase__								=			is_training
       lowerCAmelCase__								=			use_input_mask
       lowerCAmelCase__								=			use_labels
       lowerCAmelCase__								=			vocab_size
       lowerCAmelCase__								=			hidden_size
       lowerCAmelCase__								=			projection_dim
       lowerCAmelCase__								=			num_hidden_layers
       lowerCAmelCase__								=			num_attention_heads
       lowerCAmelCase__								=			intermediate_size
       lowerCAmelCase__								=			dropout
       lowerCAmelCase__								=			attention_dropout
       lowerCAmelCase__								=			max_position_embeddings
       lowerCAmelCase__								=			initializer_range
       lowerCAmelCase__								=			scope
       lowerCAmelCase__								=			bos_token_id
     def 					A__			(	self  :		List[Any]	):



       """simple docstring"""
       lowerCAmelCase__								=			ids_tensor([self.batch_size, self.seq_length]					,	self.vocab_size	)

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

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

       lowerCAmelCase__								=			self.get_config()

       return config, input_ids, input_mask
     def 					A__			(	self  :		int	):



       """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  :		Union[str, Any]					,	__lowerCamelCase  :		Tuple					,	__lowerCamelCase  :		Dict					,	__lowerCamelCase  :		int	):



       """simple docstring"""
       lowerCAmelCase__								=			BlipTextModel(config=__lowerCamelCase	)
       model.to(__lowerCamelCase	)
       model.eval()
       with torch.no_grad():
         lowerCAmelCase__								=			model(__lowerCamelCase					,	attention_mask=__lowerCamelCase	)
         lowerCAmelCase__								=			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 					A__			(	self  :		Tuple	):



       """simple docstring"""
       lowerCAmelCase__								=			self.prepare_config_and_inputs()
       lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__								=			config_and_inputs
       lowerCAmelCase__								=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
       return config, inputs_dict





@require_torch
class 						SCREAMING_SNAKE_CASE__		(_a						,				unittest.TestCase ):
     lowercase_   :			Any   =   (BlipTextModel,) if is_torch_available() else ()
     lowercase_   :			Any   =   False
     lowercase_   :			Optional[Any]   =   False
     lowercase_   :			List[Any]   =   False
     def 					A__			(	self  :		int	):



       """simple docstring"""
       lowerCAmelCase__								=			BlipTextModelTester(self	)
       lowerCAmelCase__								=			ConfigTester(self					,	config_class=__lowerCamelCase					,	hidden_size=37	)
     def 					A__			(	self  :		int	):



       """simple docstring"""
       self.config_tester.run_common_tests()
     def 					A__			(	self  :		List[Any]	):



       """simple docstring"""
       lowerCAmelCase__								=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__lowerCamelCase	)
     def 					A__			(	self  :		str	):



       """simple docstring"""
       pass
     def 					A__			(	self  :		Optional[int]	):



       """simple docstring"""
       pass
     @unittest.skip(reason='''Blip does not use inputs_embeds'''	)
     def 					A__			(	self  :		int	):



       """simple docstring"""
       pass
     @unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''	)
     def 					A__			(	self  :		Optional[Any]	):



       """simple docstring"""
       pass
     @unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''	)
     def 					A__			(	self  :		List[str]	):



       """simple docstring"""
       pass
     @slow
     def 					A__			(	self  :		Optional[int]	):



       """simple docstring"""
       for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowerCAmelCase__								=			BlipTextModel.from_pretrained(__lowerCamelCase	)
         self.assertIsNotNone(__lowerCamelCase	)



     def 					A__			(	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 						SCREAMING_SNAKE_CASE__		:
     lowercase_   :			CommonSchedulerState

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

     # running values
     lowercase_   :			Optional[jnp.ndarray]   =   None
     @classmethod
     def 					A__			(	cls  :		List[str]					,	__lowerCamelCase  :		CommonSchedulerState					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		jnp.ndarray	):



       """simple docstring"""
       return cls(common=__lowerCamelCase					,	init_noise_sigma=__lowerCamelCase					,	timesteps=__lowerCamelCase					,	sigmas=__lowerCamelCase	)





@dataclass
class 						SCREAMING_SNAKE_CASE__		(_a ):
     lowercase_   :			LMSDiscreteSchedulerState





class 						SCREAMING_SNAKE_CASE__		(_a						,				_a ):
     lowercase_   :			int   =   [e.name for e in FlaxKarrasDiffusionSchedulers]

     lowercase_   :			jnp.dtype
     @property
     def 					A__			(	self  :		Optional[Any]	):



       """simple docstring"""
       return True
     @register_to_config
     def __init__(	self  :		int					,	__lowerCamelCase  :		int = 10_00					,	__lowerCamelCase  :		float = 0.0001					,	__lowerCamelCase  :		float = 0.02					,	__lowerCamelCase  :		str = "linear"					,	__lowerCamelCase  :		Optional[jnp.ndarray] = None					,	__lowerCamelCase  :		str = "epsilon"					,	__lowerCamelCase  :		jnp.dtype = jnp.floataa					,	):



       """simple docstring"""
       lowerCAmelCase__								=			dtype
     def 					A__			(	self  :		List[str]					,	__lowerCamelCase  :		Optional[CommonSchedulerState] = None	):



       """simple docstring"""
       if common is None:
         lowerCAmelCase__								=			CommonSchedulerState.create(self	)

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

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

       return LMSDiscreteSchedulerState.create(
           common=__lowerCamelCase					,	init_noise_sigma=__lowerCamelCase					,	timesteps=__lowerCamelCase					,	sigmas=__lowerCamelCase					,	)
     def 					A__			(	self  :		str					,	__lowerCamelCase  :		LMSDiscreteSchedulerState					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		int	):



       """simple docstring"""
       ((lowerCAmelCase__)							,			)								=			jnp.where(state.timesteps == timestep					,	size=1	)
       lowerCAmelCase__								=			step_index[0]

       lowerCAmelCase__								=			state.sigmas[step_index]
       lowerCAmelCase__								=			sample / ((sigma**2 + 1) ** 0.5)
       return sample
     def 					A__			(	self  :		List[Any]					,	__lowerCamelCase  :		LMSDiscreteSchedulerState					,	__lowerCamelCase  :		Optional[int]					,	__lowerCamelCase  :		str					,	__lowerCamelCase  :		Any	):



       """simple docstring"""

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

       lowerCAmelCase__								=			integrate.quad(__lowerCamelCase					,	state.sigmas[t]					,	state.sigmas[t + 1]					,	epsrel=1e-4	)[0]

       return integrated_coeff
     def 					A__			(	self  :		str					,	__lowerCamelCase  :		LMSDiscreteSchedulerState					,	__lowerCamelCase  :		int					,	__lowerCamelCase  :		Tuple = ()	):



       """simple docstring"""
       lowerCAmelCase__								=			jnp.linspace(self.config.num_train_timesteps - 1					,	0					,	__lowerCamelCase					,	dtype=self.dtype	)

       lowerCAmelCase__								=			jnp.floor(__lowerCamelCase	).astype(jnp.intaa	)
       lowerCAmelCase__								=			jnp.ceil(__lowerCamelCase	).astype(jnp.intaa	)

       lowerCAmelCase__								=			jnp.mod(__lowerCamelCase					,	1.0	)

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

       lowerCAmelCase__								=			timesteps.astype(jnp.intaa	)

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

       return state.replace(
           timesteps=__lowerCamelCase					,	sigmas=__lowerCamelCase					,	num_inference_steps=__lowerCamelCase					,	derivatives=__lowerCamelCase					,	)
     def 					A__			(	self  :		Union[str, Any]					,	__lowerCamelCase  :		LMSDiscreteSchedulerState					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		int					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		int = 4					,	__lowerCamelCase  :		bool = 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'''	)

       lowerCAmelCase__								=			state.sigmas[timestep]

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       if self.config.prediction_type == "epsilon":
         lowerCAmelCase__								=			sample - sigma * model_output
       elif self.config.prediction_type == "v_prediction":
         # * c_out + input * c_skip
         lowerCAmelCase__								=			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
       lowerCAmelCase__								=			(sample - pred_original_sample) / sigma
       lowerCAmelCase__								=			state.replace(derivatives=jnp.append(state.derivatives					,	__lowerCamelCase	)	)
       if len(state.derivatives	) > order:
         lowerCAmelCase__								=			state.replace(derivatives=jnp.delete(state.derivatives					,	0	)	)

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

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

       if not return_dict:
         return (prev_sample, state)

       return FlaxLMSSchedulerOutput(prev_sample=__lowerCamelCase					,	state=__lowerCamelCase	)
     def 					A__			(	self  :		Dict					,	__lowerCamelCase  :		LMSDiscreteSchedulerState					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		jnp.ndarray					,	):



       """simple docstring"""
       lowerCAmelCase__								=			state.sigmas[timesteps].flatten()
       lowerCAmelCase__								=			broadcast_to_shape_from_left(__lowerCamelCase					,	noise.shape	)

       lowerCAmelCase__								=			original_samples + noise * sigma

       return noisy_samples



     def __len__(	self  :		str	):



       """simple docstring"""
       return self.config.num_train_timesteps

