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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import List



def   UpperCamelCase(   lowercase_     ,   lowercase_				)      ->							List[Any]:





  '''simple docstring'''





  if name != "training_function" and name != "main":
    raise ValueError(f'''Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''''				)
  snake_case_		,							snake_case_					= [], False
  for line in lines:
    if not found_start and f'''def {name}''' in line:
      snake_case_					= True
      good_lines.append(lowercase_				)
      continue
    if found_start:
      if name == "training_function" and "def main" in line:
        return good_lines
      if name == "main" and "if __name__" in line:
        return good_lines
      good_lines.append(lowercase_				)



def   UpperCamelCase(   lowercase_				)      ->							Dict:





  '''simple docstring'''





  return [line for line in lines if not line.lstrip().startswith("""#"""				) and line != "\n"]



def   UpperCamelCase(   lowercase_     ,   lowercase_     ,   lowercase_     ,   lowercase_ = None				)      ->							List[Any]:





  '''simple docstring'''





  with open(lowercase_     ,   """r"""				) as f:
    snake_case_					= f.readlines()
  with open(os.path.abspath(os.path.join("""examples"""     ,   """nlp_example.py"""				)				)     ,   """r"""				) as f:
    snake_case_					= f.readlines()
  with open(lowercase_     ,   """r"""				) as f:
    snake_case_					= f.readlines()
  if secondary_filename is not None:
    with open(lowercase_     ,   """r"""				) as f:
      snake_case_					= f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
  if parser_only:
    snake_case_					= clean_lines(get_function_contents_by_name(lowercase_     ,   """main"""				)				)
    snake_case_					= clean_lines(get_function_contents_by_name(lowercase_     ,   """main"""				)				)
    snake_case_					= clean_lines(get_function_contents_by_name(lowercase_     ,   """main"""				)				)
    if secondary_filename is not None:
      snake_case_					= clean_lines(get_function_contents_by_name(lowercase_     ,   """main"""				)				)
  else:
    snake_case_					= clean_lines(get_function_contents_by_name(lowercase_     ,   """training_function"""				)				)
    snake_case_					= clean_lines(get_function_contents_by_name(lowercase_     ,   """training_function"""				)				)
    snake_case_					= clean_lines(get_function_contents_by_name(lowercase_     ,   """training_function"""				)				)
    if secondary_filename is not None:
      snake_case_					= clean_lines(
          get_function_contents_by_name(lowercase_     ,   """training_function"""				)				)

  snake_case_					= """train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n"""

  # Specific code in our script that differs from the full version, aka what is new
  snake_case_					= []
  snake_case_					= []  # We keep track of the idxs just in case it's a repeated statement
  snake_case_					= iter(lowercase_				)
  for i in range(len(lowercase_				) - 1				):
    if i not in passed_idxs:
      snake_case_					= next(lowercase_				)
      if (line not in full_file_func) and (line.lstrip() != _dl_line):
        if "TESTING_MOCKED_DATALOADERS" not in line:
          new_feature_code.append(lowercase_				)
          passed_idxs.append(lowercase_				)
        else:
          # Skip over the `config['num_epochs'] = 2` statement
          snake_case_					= next(lowercase_				)

    # Extract out just the new parts from the full_file_training_func
  snake_case_					= []
  snake_case_					= []  # We keep track of the idxs just in case it's a repeated statement
  for i, line in enumerate(lowercase_				):
    if i not in passed_idxs:
      if (line not in full_file_func) and (line.lstrip() != _dl_line):
        if "TESTING_MOCKED_DATALOADERS" not in line:
          new_full_example_parts.append(lowercase_				)
          passed_idxs.append(lowercase_				)

    # Finally, get the overall diff
  snake_case_					= [line for line in new_feature_code if line not in new_full_example_parts]
  if secondary_filename is not None:
    snake_case_					= [line for line in full_file_contents if line not in secondary_file_func]
    snake_case_					= [line for line in diff_from_example if line not in diff_from_two]

  return diff_from_example


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							:
      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       lowerCAmelCase_       (	cls				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )		->			Union[str, Any]:
        return cls(common=lowerCamelCase				,    init_noise_sigma=lowerCamelCase				,    timesteps=lowerCamelCase				,    sigmas=lowerCamelCase  )







@dataclass
class 					__lowerCamelCase							(      __snake_case      ):
      lowerCamelCase_      : LMSDiscreteSchedulerState







class 					__lowerCamelCase							(      __snake_case    ,				__snake_case      ):
      lowerCamelCase_      : str  			=   [e.name for e in FlaxKarrasDiffusionSchedulers]

      lowerCamelCase_      : jnp.dtype







      @property
      def       lowerCAmelCase_       (	self  )		->			List[str]:
        return True







      @register_to_config
      def __init__(	self				,    lowerCamelCase = 1000				,    lowerCamelCase = 0.0001				,    lowerCamelCase = 0.02				,    lowerCamelCase = "linear"				,    lowerCamelCase = None				,    lowerCamelCase = "epsilon"				,    lowerCamelCase = jnp.floataa				,    )		->			Optional[Any]:
        snake_case_					= dtype







      def       lowerCAmelCase_       (	self				,    lowerCamelCase = None  )		->			LMSDiscreteSchedulerState:
        if common is None:
          snake_case_					= CommonSchedulerState.create(self  )

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

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

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







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )		->			jnp.ndarray:
        ((snake_case_)		,							)					= jnp.where(state.timesteps == timestep				,    size=1  )
        snake_case_					= step_index[0]

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







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )		->			Any:

        def lms_derivative(lowerCamelCase  ):
          snake_case_					= 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

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

        return integrated_coeff







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase = ()  )		->			LMSDiscreteSchedulerState:
        snake_case_					= jnp.linspace(self.config.num_train_timesteps - 1				,    0				,    lowerCamelCase				,    dtype=self.dtype  )

        snake_case_					= jnp.floor(lowerCamelCase  ).astype(jnp.intaa  )
        snake_case_					= jnp.ceil(lowerCamelCase  ).astype(jnp.intaa  )

        snake_case_					= jnp.mod(lowerCamelCase				,    1.0  )

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

        snake_case_					= timesteps.astype(jnp.intaa  )

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

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







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase = 4				,    lowerCamelCase = 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"""  )

        snake_case_					= state.sigmas[timestep]

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

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

        # 4. Compute previous sample based on the derivatives path
        snake_case_					= 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       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    )		->			jnp.ndarray:
        snake_case_					= state.sigmas[timesteps].flatten()
        snake_case_					= broadcast_to_shape_from_left(lowerCamelCase				,    noise.shape  )

        snake_case_					= original_samples + noise * sigma

        return noisy_samples







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