





# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
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  __A:
     __A	  =       42

     # setable values
     __A	  =       42
     __A	  =       42
     __A	  =       None

     @classmethod
     def 	_UpperCamelCase     (      cls,       A,       A,       A      ):





            """simple docstring"""

            return cls(common=A,       init_noise_sigma=A,       timesteps=A      )






@dataclass
class  __A(						__UpperCAmelCase ):
     __A	  =       42






class  __A(						__UpperCAmelCase						,			__UpperCAmelCase ):
     __A	  =       [e.name for e in FlaxKarrasDiffusionSchedulers]

     __A	  =       42

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return True

     @register_to_config
     def __init__(      self,       A = 1000,       A = 0.0_001,       A = 0.02,       A = "linear",       A = None,       A = "fixed_small",       A = True,       A = "epsilon",       A = jnp.floataa,       ):





            """simple docstring"""

            _UpperCamelCase              =						dtype

     def 	_UpperCamelCase     (      self,       A = None      ):





            """simple docstring"""

            if common is None:
                   _UpperCamelCase              =						CommonSchedulerState.create(self      )

            # standard deviation of the initial noise distribution
            _UpperCamelCase              =						jnp.array(1.0,       dtype=self.dtype      )

            _UpperCamelCase              =						jnp.arange(0,       self.config.num_train_timesteps      ).round()[::-1]

            return DDPMSchedulerState.create(
                common=A,       init_noise_sigma=A,       timesteps=A,       )

     def 	_UpperCamelCase     (      self,       A,       A,       A = None      ):





            """simple docstring"""

            return sample

     def 	_UpperCamelCase     (      self,       A,       A,       A = ()      ):





            """simple docstring"""

            _UpperCamelCase              =						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
            _UpperCamelCase              =						(jnp.arange(0,       A      ) * step_ratio).round()[::-1]

            return state.replace(
                num_inference_steps=A,       timesteps=A,       )

     def 	_UpperCamelCase     (      self,       A,       A,       A=None,       A=None      ):





            """simple docstring"""

            _UpperCamelCase              =						state.common.alphas_cumprod[t]
            _UpperCamelCase              =						jnp.where(t > 0,       state.common.alphas_cumprod[t - 1],       jnp.array(1.0,       dtype=self.dtype      )      )

            # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
            # and sample from it to get previous sample
            # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
            _UpperCamelCase              =						(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

            if variance_type is None:
                   _UpperCamelCase              =						self.config.variance_type

            # hacks - were probably added for training stability
            if variance_type == "fixed_small":
                   _UpperCamelCase              =						jnp.clip(A,       a_min=1E-2_0      )
            # for rl-diffuser https://arxiv.org/abs/2205.09991
            elif variance_type == "fixed_small_log":
                   _UpperCamelCase              =						jnp.log(jnp.clip(A,       a_min=1E-2_0      )      )
            elif variance_type == "fixed_large":
                   _UpperCamelCase              =						state.common.betas[t]
            elif variance_type == "fixed_large_log":
                   # Glide max_log
                   _UpperCamelCase              =						jnp.log(state.common.betas[t]      )
            elif variance_type == "learned":
                   return predicted_variance
            elif variance_type == "learned_range":
                   _UpperCamelCase              =						variance
                   _UpperCamelCase              =						state.common.betas[t]
                   _UpperCamelCase              =						(predicted_variance + 1) / 2
                   _UpperCamelCase              =						frac * max_log + (1 - frac) * min_log

            return variance

     def 	_UpperCamelCase     (      self,       A,       A,       A,       A,       A = None,       A = True,       ):





            """simple docstring"""

            _UpperCamelCase              =						timestep

            if key is None:
                   _UpperCamelCase              =						jax.random.PRNGKey(0      )

            if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
                   _UpperCamelCase       ,		_UpperCamelCase              =						jnp.split(A,       sample.shape[1],       axis=1      )
            else:
                   _UpperCamelCase              =						None

            # 1. compute alphas, betas
            _UpperCamelCase              =						state.common.alphas_cumprod[t]
            _UpperCamelCase              =						jnp.where(t > 0,       state.common.alphas_cumprod[t - 1],       jnp.array(1.0,       dtype=self.dtype      )      )
            _UpperCamelCase              =						1 - alpha_prod_t
            _UpperCamelCase              =						1 - alpha_prod_t_prev

            # 2. compute predicted original sample from predicted noise also called
            # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
            if self.config.prediction_type == "epsilon":
                   _UpperCamelCase              =						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
            elif self.config.prediction_type == "sample":
                   _UpperCamelCase              =						model_output
            elif self.config.prediction_type == "v_prediction":
                   _UpperCamelCase              =						(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
            else:
                   raise ValueError(
                       F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
                       ''' for the FlaxDDPMScheduler.'''      )

            # 3. Clip "predicted x_0"
            if self.config.clip_sample:
                   _UpperCamelCase              =						jnp.clip(A,       -1,       1      )

            # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
            # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
            _UpperCamelCase              =						(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
            _UpperCamelCase              =						state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

            # 5. Compute predicted previous sample µ_t
            # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
            _UpperCamelCase              =						pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

            # 6. Add noise
            def random_variance():
                   _UpperCamelCase              =						jax.random.split(A,       num=1      )
                   _UpperCamelCase              =						jax.random.normal(A,       shape=model_output.shape,       dtype=self.dtype      )
                   return (self._get_variance(A,       A,       predicted_variance=A      ) ** 0.5) * noise

            _UpperCamelCase              =						jnp.where(t > 0,       random_variance(),       jnp.zeros(model_output.shape,       dtype=self.dtype      )      )

            _UpperCamelCase              =						pred_prev_sample + variance

            if not return_dict:
                   return (pred_prev_sample, state)

            return FlaxDDPMSchedulerOutput(prev_sample=A,       state=A      )

     def 	_UpperCamelCase     (      self,       A,       A,       A,       A,       ):





            """simple docstring"""

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

     def 	_UpperCamelCase     (      self,       A,       A,       A,       A,       ):





            """simple docstring"""

            return get_velocity_common(state.common,       A,       A,       A      )



     def __len__(      self      ):





            """simple docstring"""

            return self.config.num_train_timesteps













import os
import sys
import unittest


lowercase    : Any    	=  os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
lowercase    : int    	=  os.path.join(git_repo_path, """src""", """transformers""")

lowercase    : str    	=  """
{0} = None
"""

lowercase    : Union[str, Any]    	=  """
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
"""


lowercase    : Optional[Any]    	=  """
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""

class  __A(						unittest.TestCase ):

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						find_backend('''    _import_structure["models.albert"].append("AlbertTokenizerFast")'''      )
            self.assertIsNone(A      )

            _UpperCamelCase              =						find_backend('''    if not is_tokenizers_available():'''      )
            self.assertEqual(A,       '''tokenizers'''      )

            _UpperCamelCase              =						find_backend('''    if not is_tensorflow_text_available():'''      )
            self.assertEqual(A,       '''tensorflow_text'''      )

            _UpperCamelCase              =						find_backend('''    if not (is_sentencepiece_available() and is_tokenizers_available()):'''      )
            self.assertEqual(A,       '''sentencepiece_and_tokenizers'''      )

            _UpperCamelCase              =						find_backend(
                '''    if not (is_sentencepiece_available() and is_tensorflow_text_available()):'''      )
            self.assertEqual(A,       '''sentencepiece_and_tensorflow_text'''      )

            _UpperCamelCase              =						find_backend(
                '''    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'''      )
            self.assertEqual(A,       '''sentencepiece_and_tokenizers_and_vision'''      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						read_init()
            # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
            self.assertIn('''torch''',       A      )
            self.assertIn('''tensorflow_text''',       A      )
            self.assertIn('''sentencepiece_and_tokenizers''',       A      )

            # Likewise, we can't assert on the exact content of a key
            self.assertIn('''BertModel''',       objects['''torch''']      )
            self.assertIn('''TFBertModel''',       objects['''tf''']      )
            self.assertIn('''FlaxBertModel''',       objects['''flax''']      )
            self.assertIn('''BertModel''',       objects['''torch''']      )
            self.assertIn('''TFBertTokenizer''',       objects['''tensorflow_text''']      )
            self.assertIn('''convert_slow_tokenizer''',       objects['''sentencepiece_and_tokenizers''']      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						create_dummy_object('''CONSTANT''',       '''\'torch\''''      )
            self.assertEqual(A,       '''\nCONSTANT = None\n'''      )

            _UpperCamelCase              =						create_dummy_object('''function''',       '''\'torch\''''      )
            self.assertEqual(
                A,       '''\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'''      )

            _UpperCamelCase              =						'''
class FakeClass(metaclass=DummyObject):
    _backends = \'torch\'

    def __init__(self, *args, **kwargs):
        requires_backends(self, \'torch\')
'''
            _UpperCamelCase              =						create_dummy_object('''FakeClass''',       '''\'torch\''''      )
            self.assertEqual(A,       A      )



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						'''# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, ["torch"])


class FakeClass(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])
'''
            _UpperCamelCase              =						create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']}      )
            self.assertEqual(dummy_files['''torch'''],       A      )






