




'''simple docstring'''






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    A						:




				'''simple docstring'''





				A       =							42

				# setable values
				A       =							42
				A       =							42
				A       =							42
				A       =							None

				# running values
				A       =							None
				@classmethod
				def 			a_			(cls					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase     )      ->			Any:
							return cls(common=_UpperCAmelCase					,    init_noise_sigma=_UpperCAmelCase					,    timesteps=_UpperCAmelCase					,    sigmas=_UpperCAmelCase     )







@dataclass
class    A						(					SCREAMING_SNAKE_CASE__						):




				'''simple docstring'''





				A       =							42







class    A						(					SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__						):




				'''simple docstring'''





				A       =							[e.name for e in FlaxKarrasDiffusionSchedulers]

				A       =							42
				@property
				def 			a_			(self     )      ->			str:
							return True
				@register_to_config
				def __init__(self					,    _UpperCAmelCase = 1_0_0_0					,    _UpperCAmelCase = 0.0_001					,    _UpperCAmelCase = 0.02					,    _UpperCAmelCase = "linear"					,    _UpperCAmelCase = None					,    _UpperCAmelCase = "epsilon"					,    _UpperCAmelCase = jnp.floataa					,    )      ->			Tuple:
							__UpperCamelCase :     Optional[int]										=  dtype
				def 			a_			(self					,    _UpperCAmelCase = None     )      ->			LMSDiscreteSchedulerState:
							if common is None:
										__UpperCamelCase :     str										=  CommonSchedulerState.create(self     )

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

							# standard deviation of the initial noise distribution
							__UpperCamelCase :     List[str]										=  sigmas.max()

							return LMSDiscreteSchedulerState.create(
							    common=_UpperCAmelCase					,    init_noise_sigma=_UpperCAmelCase					,    timesteps=_UpperCAmelCase					,    sigmas=_UpperCAmelCase					,    )
				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase     )      ->			jnp.ndarray:
							((__UpperCamelCase)						,   ) :     Tuple										=  jnp.where(state.timesteps == timestep					,    size=1     )
							__UpperCamelCase :     Any										=  step_index[0]

							__UpperCamelCase :     int										=  state.sigmas[step_index]
							__UpperCamelCase :     Tuple										=  sample / ((sigma**2 + 1) ** 0.5)
							return sample
				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase     )      ->			List[str]:

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

							__UpperCamelCase :     Union[str, Any]										=  integrate.quad(_UpperCAmelCase					,    state.sigmas[t]					,    state.sigmas[t + 1]					,    epsrel=1E-4     )[0]

							return integrated_coeff
				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase = ()     )      ->			LMSDiscreteSchedulerState:
							__UpperCamelCase :     int										=  jnp.linspace(self.config.num_train_timesteps - 1					,    0					,    _UpperCAmelCase					,    dtype=self.dtype     )

							__UpperCamelCase :     Any										=  jnp.floor(_UpperCAmelCase     ).astype(jnp.intaa     )
							__UpperCamelCase :     Optional[int]										=  jnp.ceil(_UpperCAmelCase     ).astype(jnp.intaa     )

							__UpperCamelCase :     int										=  jnp.mod(_UpperCAmelCase					,    1.0     )

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

							__UpperCamelCase :     str										=  timesteps.astype(jnp.intaa     )

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

							return state.replace(
							    timesteps=_UpperCAmelCase					,    sigmas=_UpperCAmelCase					,    num_inference_steps=_UpperCAmelCase					,    derivatives=_UpperCAmelCase					,    )
				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase = 4					,    _UpperCAmelCase = True					,    )      ->			Union[FlaxLMSSchedulerOutput, Tuple]:
							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 :     Optional[int]										=  state.sigmas[timestep]

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

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

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

							if not return_dict:
										return (prev_sample, state)

							return FlaxLMSSchedulerOutput(prev_sample=_UpperCAmelCase					,    state=_UpperCAmelCase     )
				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    )      ->			jnp.ndarray:
							__UpperCamelCase :     str										=  state.sigmas[timesteps].flatten()
							__UpperCamelCase :     Optional[int]										=  broadcast_to_shape_from_left(_UpperCAmelCase					,    noise.shape     )

							__UpperCamelCase :     List[str]										=  original_samples + noise * sigma

							return noisy_samples





				def __len__(self     )      ->			str:
							return self.config.num_train_timesteps









'''simple docstring'''






import tempfile
import unittest

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

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


if is_torch_available():
				import torch

				from transformers import DPRContextEncoder, DPRQuestionEncoder, DPRReader, DPRReaderTokenizer
				from transformers.models.dpr.modeling_dpr import (
				    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
				)



class    A						:




				'''simple docstring'''





				def __init__(self					,    _UpperCAmelCase					,    _UpperCAmelCase=1_3					,    _UpperCAmelCase=7					,    _UpperCAmelCase=False					,    _UpperCAmelCase=True					,    _UpperCAmelCase=True					,    _UpperCAmelCase=True					,    _UpperCAmelCase=9_9					,    _UpperCAmelCase=3_2					,    _UpperCAmelCase=5					,    _UpperCAmelCase=4					,    _UpperCAmelCase=3_7					,    _UpperCAmelCase="gelu"					,    _UpperCAmelCase=0.1					,    _UpperCAmelCase=0.1					,    _UpperCAmelCase=5_1_2					,    _UpperCAmelCase=1_6					,    _UpperCAmelCase=2					,    _UpperCAmelCase=0.02					,    _UpperCAmelCase=3					,    _UpperCAmelCase=4					,    _UpperCAmelCase=None					,    _UpperCAmelCase=0					,    )      ->			List[Any]:
							__UpperCamelCase :     List[str]										=  parent
							__UpperCamelCase :     Optional[int]										=  batch_size
							__UpperCamelCase :     Union[str, Any]										=  seq_length
							__UpperCamelCase :     List[Any]										=  is_training
							__UpperCamelCase :     Union[str, Any]										=  use_input_mask
							__UpperCamelCase :     int										=  use_token_type_ids
							__UpperCamelCase :     Any										=  use_labels
							__UpperCamelCase :     Union[str, Any]										=  vocab_size
							__UpperCamelCase :     Any										=  hidden_size
							__UpperCamelCase :     Dict										=  num_hidden_layers
							__UpperCamelCase :     List[Any]										=  num_attention_heads
							__UpperCamelCase :     Union[str, Any]										=  intermediate_size
							__UpperCamelCase :     Tuple										=  hidden_act
							__UpperCamelCase :     int										=  hidden_dropout_prob
							__UpperCamelCase :     Optional[Any]										=  attention_probs_dropout_prob
							__UpperCamelCase :     List[str]										=  max_position_embeddings
							__UpperCamelCase :     str										=  type_vocab_size
							__UpperCamelCase :     str										=  type_sequence_label_size
							__UpperCamelCase :     List[str]										=  initializer_range
							__UpperCamelCase :     List[str]										=  num_labels
							__UpperCamelCase :     Any										=  num_choices
							__UpperCamelCase :     List[Any]										=  scope
							__UpperCamelCase :     Tuple										=  projection_dim
				def 			a_			(self     )      ->			Any:
							__UpperCamelCase :     str										=  ids_tensor([self.batch_size, self.seq_length]					,    self.vocab_size     )

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

							__UpperCamelCase :     Tuple										=  None
							if self.use_token_type_ids:
										__UpperCamelCase :     Optional[int]										=  ids_tensor([self.batch_size, self.seq_length]					,    self.type_vocab_size     )

							__UpperCamelCase :     List[Any]										=  None
							__UpperCamelCase :     Optional[Any]										=  None
							__UpperCamelCase :     List[str]										=  None
							if self.use_labels:
										__UpperCamelCase :     Dict										=  ids_tensor([self.batch_size]					,    self.type_sequence_label_size     )
										__UpperCamelCase :     Any										=  ids_tensor([self.batch_size, self.seq_length]					,    self.num_labels     )
										__UpperCamelCase :     str										=  ids_tensor([self.batch_size]					,    self.num_choices     )

							__UpperCamelCase :     Optional[int]										=  self.get_config()

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
				def 			a_			(self     )      ->			Optional[Any]:
							return DPRConfig(
							    projection_dim=self.projection_dim					,    vocab_size=self.vocab_size					,    hidden_size=self.hidden_size					,    num_hidden_layers=self.num_hidden_layers					,    num_attention_heads=self.num_attention_heads					,    intermediate_size=self.intermediate_size					,    hidden_act=self.hidden_act					,    hidden_dropout_prob=self.hidden_dropout_prob					,    attention_probs_dropout_prob=self.attention_probs_dropout_prob					,    max_position_embeddings=self.max_position_embeddings					,    type_vocab_size=self.type_vocab_size					,    initializer_range=self.initializer_range					,    )
				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase     )      ->			str:
							__UpperCamelCase :     Optional[int]										=  DPRContextEncoder(config=_UpperCAmelCase     )
							model.to(_UpperCAmelCase     )
							model.eval()
							__UpperCamelCase :     Optional[int]										=  model(_UpperCAmelCase					,    attention_mask=_UpperCAmelCase					,    token_type_ids=_UpperCAmelCase     )
							__UpperCamelCase :     Optional[int]										=  model(_UpperCAmelCase					,    token_type_ids=_UpperCAmelCase     )
							__UpperCamelCase :     int										=  model(_UpperCAmelCase     )
							self.parent.assertEqual(result.pooler_output.shape					,    (self.batch_size, self.projection_dim or self.hidden_size)     )
				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase     )      ->			Optional[int]:
							__UpperCamelCase :     int										=  DPRQuestionEncoder(config=_UpperCAmelCase     )
							model.to(_UpperCAmelCase     )
							model.eval()
							__UpperCamelCase :     Optional[int]										=  model(_UpperCAmelCase					,    attention_mask=_UpperCAmelCase					,    token_type_ids=_UpperCAmelCase     )
							__UpperCamelCase :     List[str]										=  model(_UpperCAmelCase					,    token_type_ids=_UpperCAmelCase     )
							__UpperCamelCase :     Optional[Any]										=  model(_UpperCAmelCase     )
							self.parent.assertEqual(result.pooler_output.shape					,    (self.batch_size, self.projection_dim or self.hidden_size)     )
				def 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase     )      ->			str:
							__UpperCamelCase :     Optional[int]										=  DPRReader(config=_UpperCAmelCase     )
							model.to(_UpperCAmelCase     )
							model.eval()
							__UpperCamelCase :     Optional[Any]										=  model(
							    _UpperCAmelCase					,    attention_mask=_UpperCAmelCase					,    )

							self.parent.assertEqual(result.start_logits.shape					,    (self.batch_size, self.seq_length)     )
							self.parent.assertEqual(result.end_logits.shape					,    (self.batch_size, self.seq_length)     )
							self.parent.assertEqual(result.relevance_logits.shape					,    (self.batch_size,)     )





				def 			a_			(self     )      ->			List[Any]:
							__UpperCamelCase :     Tuple										=  self.prepare_config_and_inputs()
							(
							    (
							    __UpperCamelCase
							)						,   (
							    __UpperCamelCase
							)						,   (
							    __UpperCamelCase
							)						,   (
							    __UpperCamelCase
							)						,   (
							    __UpperCamelCase
							)						,   (
							    __UpperCamelCase
							)						,   (
							    __UpperCamelCase
							)						,   
							) :     Optional[int]										=  config_and_inputs
							__UpperCamelCase :     Dict										=  {"input_ids": input_ids}
							return config, inputs_dict







@require_torch
class    A						(					SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__     ,				unittest.TestCase						):




				'''simple docstring'''





				A       =							(
				    (
				        DPRContextEncoder,
				        DPRQuestionEncoder,
				        DPRReader,
				    )
				    if is_torch_available()
				    else ()
				)
				A       =							{"feature-extraction": DPRQuestionEncoder} if is_torch_available() else {}

				A       =							False
				A       =							False  # why?
				A       =							False
				A       =							False
				def 			a_			(self     )      ->			Union[str, Any]:
							__UpperCamelCase :     Dict										=  DPRModelTester(self     )
							__UpperCamelCase :     Tuple										=  ConfigTester(self					,    config_class=_UpperCAmelCase					,    hidden_size=3_7     )
				def 			a_			(self     )      ->			List[str]:
							self.config_tester.run_common_tests()
				def 			a_			(self     )      ->			Optional[int]:
							__UpperCamelCase :     List[Any]										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_context_encoder(*_UpperCAmelCase     )
				def 			a_			(self     )      ->			str:
							__UpperCamelCase :     int										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_question_encoder(*_UpperCAmelCase     )
				def 			a_			(self     )      ->			Any:
							__UpperCamelCase :     List[Any]										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_reader(*_UpperCAmelCase     )
				def 			a_			(self     )      ->			Optional[Any]:
							__UpperCamelCase :     List[Any]										=  self.model_tester.prepare_config_and_inputs()[0]

							__UpperCamelCase :     Union[str, Any]										=  DPRQuestionEncoder(config=_UpperCAmelCase     )
							model.to(_UpperCAmelCase     )
							model.eval()

							with tempfile.TemporaryDirectory() as tmp_dirname:
										model.save_pretrained(_UpperCAmelCase     )
										__UpperCamelCase :     Tuple										=  DPRQuestionEncoder.from_pretrained(_UpperCAmelCase					,    projection_dim=5_1_2     )

							self.assertIsNotNone(_UpperCAmelCase     )





				@slow
				def 			a_			(self     )      ->			Optional[Any]:
							for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										__UpperCamelCase :     Union[str, Any]										=  DPRContextEncoder.from_pretrained(_UpperCAmelCase     )
										self.assertIsNotNone(_UpperCAmelCase     )

							for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										__UpperCamelCase :     Tuple										=  DPRContextEncoder.from_pretrained(_UpperCAmelCase     )
										self.assertIsNotNone(_UpperCAmelCase     )

							for model_name in DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										__UpperCamelCase :     Union[str, Any]										=  DPRQuestionEncoder.from_pretrained(_UpperCAmelCase     )
										self.assertIsNotNone(_UpperCAmelCase     )

							for model_name in DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										__UpperCamelCase :     Optional[int]										=  DPRReader.from_pretrained(_UpperCAmelCase     )
										self.assertIsNotNone(_UpperCAmelCase     )







@require_torch
class    A						(					unittest.TestCase						):




				'''simple docstring'''





				@slow
				def 			a_			(self     )      ->			Dict:
							__UpperCamelCase :     int										=  DPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-single-nq-base"					,    return_dict=_UpperCAmelCase     )
							model.to(_UpperCAmelCase     )

							__UpperCamelCase :     Optional[int]										=  torch.tensor(
							    [[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_0_3, 2_0_2_6, 3_8_9_9, 1_0_1_4_0, 1_0_2_9, 1_0_2]]					,    dtype=torch.long					,    device=_UpperCAmelCase     )  # [CLS] hello, is my dog cute? [SEP]
							__UpperCamelCase :     Dict										=  model(_UpperCAmelCase     )[0]  # embedding shape = (1, 768)
							# compare the actual values for a slice.
							__UpperCamelCase :     Union[str, Any]										=  torch.tensor(
							    [
							        [
							            0.03_236_253,
							            0.12_753_335,
							            0.16_818_509,
							            0.00_279_786,
							            0.3_896_933,
							            0.24_264_945,
							            0.2_178_971,
							            -0.02_335_227,
							            -0.08_481_959,
							            -0.14_324_117,
							        ]
							    ]					,    dtype=torch.float					,    device=_UpperCAmelCase					,    )
							self.assertTrue(torch.allclose(output[:, :1_0]					,    _UpperCAmelCase					,    atol=1E-4     )     )





				@slow
				def 			a_			(self     )      ->			List[str]:
							__UpperCamelCase :     Any										=  DPRReaderTokenizer.from_pretrained("facebook/dpr-reader-single-nq-base"     )
							__UpperCamelCase :     str										=  DPRReader.from_pretrained("facebook/dpr-reader-single-nq-base"     )
							model.to(_UpperCAmelCase     )

							__UpperCamelCase :     Optional[int]										=  tokenizer(
							    questions="What is love ?"					,    titles="Haddaway"					,    texts="What Is Love is a song recorded by the artist Haddaway"					,    padding=_UpperCAmelCase					,    return_tensors="pt"					,    )
							encoded_inputs.to(_UpperCAmelCase     )

							__UpperCamelCase :     int										=  model(**_UpperCAmelCase     )

							# compare the actual values for a slice.
							__UpperCamelCase :     Dict										=  torch.tensor(
							    [[-10.3_005, -10.7_765, -11.4_872, -11.6_841, -11.9_312, -10.3_002, -9.8_544, -11.7_378, -12.0_821, -10.2_975]]					,    dtype=torch.float					,    device=_UpperCAmelCase					,    )

							__UpperCamelCase :     Optional[Any]										=  torch.tensor(
							    [[-11.0_684, -11.7_041, -11.5_397, -10.3_465, -10.8_791, -6.8_443, -11.9_959, -11.0_364, -10.0_096, -6.8_405]]					,    dtype=torch.float					,    device=_UpperCAmelCase					,    )
							self.assertTrue(torch.allclose(outputs.start_logits[:, :1_0]					,    _UpperCAmelCase					,    atol=1E-4     )     )
							self.assertTrue(torch.allclose(outputs.end_logits[:, :1_0]					,    _UpperCAmelCase					,    atol=1E-4     )     )



