


"""simple docstring"""






import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard


class 			SCREAMING_SNAKE_CASE_  (   unittest.TestCase):




							'''simple docstring'''





							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								snake_case__  :						str		=      {
								    "model_details": {
								        "Organization": "testing",
								        "Model date": "today",
								        "Model version": "v2.1, Developed by Test Corp in 2019.",
								        "Architecture": "Convolutional Neural Network.",
								    },
								    "metrics": "BLEU and ROUGE-1",
								    "evaluation_data": {
								        "Datasets": {"BLEU": "My-great-dataset-v1", "ROUGE-1": "My-short-dataset-v2.1"},
								        "Preprocessing": "See details on https://arxiv.org/pdf/1810.03993.pdf",
								    },
								    "training_data": {
								        "Dataset": "English Wikipedia dump dated 2018-12-01",
								        "Preprocessing": (
								            "Using SentencePiece vocabulary of size 52k tokens. See details on"
								            " https://arxiv.org/pdf/1810.03993.pdf"
								        ),
								    },
								    "quantitative_analyses": {"BLEU": 55.1, "ROUGE-1": 76},
								}




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







								'''simple docstring'''




								snake_case__  :						Optional[int]		=      ModelCard.from_dict(self.inputs_dict)
								self.assertTrue(hasattr(lowerCamelCase__	,					"model_details"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"intended_use"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"factors"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"metrics"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"evaluation_data"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"training_data"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"quantitative_analyses"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"ethical_considerations"))
								self.assertTrue(hasattr(lowerCamelCase__	,					"caveats_and_recommendations"))




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







								'''simple docstring'''




								snake_case__  :						List[str]		=      ModelCard.from_dict(self.inputs_dict)
								snake_case__  :						Any		=      json.loads(modelcard.to_json_string())
								for key, value in self.inputs_dict.items():
									self.assertEqual(obj[key]	,					lowerCamelCase__)




							def        UpperCAmelCase (						self)   ->	Dict:







								'''simple docstring'''




								snake_case__  :						List[str]		=      ModelCard.from_dict(self.inputs_dict)

								with tempfile.TemporaryDirectory() as tmpdirname:
									snake_case__  :						Optional[Any]		=      os.path.join(lowerCamelCase__	,					"modelcard.json")
									model_card_first.to_json_file(lowerCamelCase__)
									snake_case__  :						Dict		=      ModelCard.from_json_file(lowerCamelCase__)

								self.assertEqual(model_card_second.to_dict()	,					model_card_first.to_dict())







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







								'''simple docstring'''




								snake_case__  :						str		=      ModelCard.from_dict(self.inputs_dict)

								with tempfile.TemporaryDirectory() as tmpdirname:
									model_card_first.save_pretrained(lowerCamelCase__)
									snake_case__  :						Union[str, Any]		=      ModelCard.from_pretrained(lowerCamelCase__)

								self.assertEqual(model_card_second.to_dict()	,					model_card_first.to_dict())









"""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"""

  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 							lowercase_     (						cls  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					Tuple:
     return cls(common=__UpperCAmelCase  ,					init_noise_sigma=__UpperCAmelCase  ,					timesteps=__UpperCAmelCase  ,					sigmas=__UpperCAmelCase						)



@dataclass
class 	A_       (     _UpperCAmelCase   ):





  """simple docstring"""

  lowercase		:	LMSDiscreteSchedulerState



class 	A_       (     _UpperCAmelCase      ,  _UpperCAmelCase   ):





  """simple docstring"""

  lowercase		:	int   =						[e.name for e in FlaxKarrasDiffusionSchedulers]

  lowercase		:	jnp.dtype
  @property
  def 							lowercase_     (						self						)				->					Union[str, Any]:
     return True
  @register_to_config
  def __init__(						self  ,					__UpperCAmelCase = 10_00  ,					__UpperCAmelCase = 0.0001  ,					__UpperCAmelCase = 0.02  ,					__UpperCAmelCase = "linear"  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = "epsilon"  ,					__UpperCAmelCase = jnp.floataa  ,					)				->					Union[str, Any]:
     a   :	Dict						=    dtype
  def 							lowercase_     (						self  ,					__UpperCAmelCase = None						)				->					LMSDiscreteSchedulerState:
     if common is None:
        a   :	Dict						=    CommonSchedulerState.create(self						)

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

     # standard deviation of the initial noise distribution
     a   :	Union[str, Any]						=    sigmas.max()

     return LMSDiscreteSchedulerState.create(
         common=__UpperCAmelCase  ,					init_noise_sigma=__UpperCAmelCase  ,					timesteps=__UpperCAmelCase  ,					sigmas=__UpperCAmelCase  ,					)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					jnp.ndarray:
     ((a)	, )   :	Optional[int]						=    jnp.where(state.timesteps == timestep  ,					size=1						)
     a   :	Optional[Any]						=    step_index[0]

     a   :	Union[str, Any]						=    state.sigmas[step_index]
     a   :	Optional[int]						=    sample / ((sigma**2 + 1) ** 0.5)
     return sample
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					Optional[int]:

     def lms_derivative(__UpperCAmelCase						):
        a   :	Optional[Any]						=    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

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

     return integrated_coeff
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = ()						)				->					LMSDiscreteSchedulerState:
     a   :	Optional[int]						=    jnp.linspace(self.config.num_train_timesteps - 1  ,					0  ,					__UpperCAmelCase  ,					dtype=self.dtype						)

     a   :	Dict						=    jnp.floor(__UpperCAmelCase						).astype(jnp.intaa						)
     a   :	str						=    jnp.ceil(__UpperCAmelCase						).astype(jnp.intaa						)

     a   :	List[Any]						=    jnp.mod(__UpperCAmelCase  ,					1.0						)

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

     a   :	Any						=    timesteps.astype(jnp.intaa						)

     # initial running values
     a   :	Optional[int]						=    jnp.zeros((0,) + shape  ,					dtype=self.dtype						)

     return state.replace(
         timesteps=__UpperCAmelCase  ,					sigmas=__UpperCAmelCase  ,					num_inference_steps=__UpperCAmelCase  ,					derivatives=__UpperCAmelCase  ,					)
  def 							lowercase_     (						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'						)

     a   :	Dict						=    state.sigmas[timestep]

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

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

     # 4. Compute previous sample based on the derivatives path
     a   :	int						=    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 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					)				->					jnp.ndarray:
     a   :	Dict						=    state.sigmas[timesteps].flatten()
     a   :	Union[str, Any]						=    broadcast_to_shape_from_left(__UpperCAmelCase  ,					noise.shape						)

     a   :	List[Any]						=    original_samples + noise * sigma

     return noisy_samples






  def __len__(						self						)				->					List[Any]:
     return self.config.num_train_timesteps





