



"""simple docstring"""







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# 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 ast
import difflib
from collections.abc import Mapping
from typing import Any, Callable, Dict






class   UpperCAmelCase__		(   UpperCamelCase  ):
		pass



def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__=None					,   lowerCamelCase__=False      )			->				Optional[Any]:
							try:
														A    =       ast.parse(lowerCamelCase__      )
							except SyntaxError as e:
														print('The code generated by the agent is not valid.\n'					,   lowerCamelCase__      )
														return
							if state is None:
														A    =       {}
							A    =       None
							for idx, node in enumerate(expression.body      ):
														try:
																					A    =       evaluate_ast(lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )
														except InterpretorError as e:
																					A    =       f"""Evaluation of the code stopped at line {idx} before the end because of the following error"""
																					if chat_mode:
																												msg += (
																												    f""". Copy paste the following error message and send it back to the agent:\nI get an error: '{e}'"""
																												)
																					else:
																												msg += f""":\n{e}"""
																					print(lowerCamelCase__      )
																					break
														if line_result is not None:
																					A    =       line_result

							return result

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )			->				Any:
							if isinstance(lowerCamelCase__					,   ast.Assign      ):
														# Assignement -> we evaluate the assignement which should update the state
														# We return the variable assigned as it may be used to determine the final result.
														return evaluate_assign(lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )
							elif isinstance(lowerCamelCase__					,   ast.Call      ):
														# Function call -> we return the value of the function call
														return evaluate_call(lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )
							elif isinstance(lowerCamelCase__					,   ast.Constant      ):
														# Constant -> just return the value
														return expression.value
							elif isinstance(lowerCamelCase__					,   ast.Dict      ):
														# Dict -> evaluate all keys and values
														A    =       [evaluate_ast(lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      ) for k in expression.keys]
														A    =       [evaluate_ast(lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      ) for v in expression.values]
														return dict(zip(lowerCamelCase__					,   lowerCamelCase__      )      )
							elif isinstance(lowerCamelCase__					,   ast.Expr      ):
														# Expression -> evaluate the content
														return evaluate_ast(expression.value					,   lowerCamelCase__					,   lowerCamelCase__      )
							elif isinstance(lowerCamelCase__					,   ast.For      ):
														# For loop -> execute the loop
														return evaluate_for(lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )
							elif isinstance(lowerCamelCase__					,   ast.FormattedValue      ):
														# Formatted value (part of f-string) -> evaluate the content and return
														return evaluate_ast(expression.value					,   lowerCamelCase__					,   lowerCamelCase__      )
							elif isinstance(lowerCamelCase__					,   ast.If      ):
														# If -> execute the right branch
														return evaluate_if(lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )
							elif hasattr(lowerCamelCase__					,   'Index'      ) and isinstance(lowerCamelCase__					,   ast.Index      ):
														return evaluate_ast(expression.value					,   lowerCamelCase__					,   lowerCamelCase__      )
							elif isinstance(lowerCamelCase__					,   ast.JoinedStr      ):
														return "".join([str(evaluate_ast(lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )      ) for v in expression.values]      )
							elif isinstance(lowerCamelCase__					,   ast.List      ):
														# List -> evaluate all elements
														return [evaluate_ast(lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      ) for elt in expression.elts]
							elif isinstance(lowerCamelCase__					,   ast.Name      ):
														# Name -> pick up the value in the state
														return evaluate_name(lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )
							elif isinstance(lowerCamelCase__					,   ast.Subscript      ):
														# Subscript -> return the value of the indexing
														return evaluate_subscript(lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )
							else:
														# For now we refuse anything else. Let's add things as we need them.
														raise InterpretorError(f"""{expression.__class__.__name__} is not supported."""      )

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )			->				Dict:
							A    =       assign.targets
							A    =       evaluate_ast(assign.value					,   lowerCamelCase__					,   lowerCamelCase__      )

							if len(lowerCamelCase__      ) == 1:
														A    =       result
							else:
														if len(lowerCamelCase__      ) != len(lowerCamelCase__      ):
																					raise InterpretorError(f"""Expected {len(lowerCamelCase__      )} values but got {len(lowerCamelCase__      )}."""      )
														for var_name, r in zip(lowerCamelCase__					,   lowerCamelCase__      ):
																					A    =       r
							return result

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )			->				int:
							if not isinstance(call.func					,   ast.Name      ):
														raise InterpretorError(
														    f"""It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func} of """
														    f"""type {type(call.func      )}."""      )
							A    =       call.func.id
							if func_name not in tools:
														raise InterpretorError(
														    f"""It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func.id})."""      )

							A    =       tools[func_name]
							# Todo deal with args
							A    =       [evaluate_ast(lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      ) for arg in call.args]
							A    =       {keyword.arg: evaluate_ast(keyword.value					,   lowerCamelCase__					,   lowerCamelCase__      ) for keyword in call.keywords}
							return func(*lowerCamelCase__					,   **lowerCamelCase__      )

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )			->				Tuple:
							A    =       evaluate_ast(subscript.slice					,   lowerCamelCase__					,   lowerCamelCase__      )
							A    =       evaluate_ast(subscript.value					,   lowerCamelCase__					,   lowerCamelCase__      )
							if isinstance(lowerCamelCase__					,   (list, tuple)      ):
														return value[int(lowerCamelCase__      )]
							if index in value:
														return value[index]
							if isinstance(lowerCamelCase__					,   lowerCamelCase__      ) and isinstance(lowerCamelCase__					,   lowerCamelCase__      ):
														A    =       difflib.get_close_matches(lowerCamelCase__					,   list(value.keys()      )      )
														if len(lowerCamelCase__      ) > 0:
																					return value[close_matches[0]]

							raise InterpretorError(f"""Could not index {value} with '{index}'."""      )

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )			->				Optional[Any]:
							if name.id in state:
														return state[name.id]
							A    =       difflib.get_close_matches(name.id					,   list(state.keys()      )      )
							if len(lowerCamelCase__      ) > 0:
														return state[close_matches[0]]
							raise InterpretorError(f"""The variable `{name.id}` is not defined."""      )

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )			->				List[Any]:
							if len(condition.ops      ) > 1:
														raise InterpretorError('Cannot evaluate conditions with multiple operators'      )

							A    =       evaluate_ast(condition.left					,   lowerCamelCase__					,   lowerCamelCase__      )
							A    =       condition.ops[0]
							A    =       evaluate_ast(condition.comparators[0]					,   lowerCamelCase__					,   lowerCamelCase__      )

							if isinstance(lowerCamelCase__					,   ast.Eq      ):
														return left == right
							elif isinstance(lowerCamelCase__					,   ast.NotEq      ):
														return left != right
							elif isinstance(lowerCamelCase__					,   ast.Lt      ):
														return left < right
							elif isinstance(lowerCamelCase__					,   ast.LtE      ):
														return left <= right
							elif isinstance(lowerCamelCase__					,   ast.Gt      ):
														return left > right
							elif isinstance(lowerCamelCase__					,   ast.GtE      ):
														return left >= right
							elif isinstance(lowerCamelCase__					,   ast.Is      ):
														return left is right
							elif isinstance(lowerCamelCase__					,   ast.IsNot      ):
														return left is not right
							elif isinstance(lowerCamelCase__					,   ast.In      ):
														return left in right
							elif isinstance(lowerCamelCase__					,   ast.NotIn      ):
														return left not in right
							else:
														raise InterpretorError(f"""Operator not supported: {comparator}"""      )

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )			->				Union[str, Any]:
							A    =       None
							if evaluate_condition(if_statement.test					,   lowerCamelCase__					,   lowerCamelCase__      ):
														for line in if_statement.body:
																					A    =       evaluate_ast(lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )
																					if line_result is not None:
																												A    =       line_result
							else:
														for line in if_statement.orelse:
																					A    =       evaluate_ast(lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )
																					if line_result is not None:
																												A    =       line_result
							return result

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )			->				Any:
							A    =       None
							A    =       evaluate_ast(for_loop.iter					,   lowerCamelCase__					,   lowerCamelCase__      )
							for counter in iterator:
														A    =       counter
														for expression in for_loop.body:
																					A    =       evaluate_ast(lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )
																					if line_result is not None:
																												A    =       line_result
							return result








"""simple docstring"""







import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__=0.9_99					,   lowerCamelCase__="cosine"					,   )			->				int:
							if alpha_transform_type == "cosine":

														def alpha_bar_fn(lowerCamelCase__      ):
																					return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2      ) ** 2

							elif alpha_transform_type == "exp":

														def alpha_bar_fn(lowerCamelCase__      ):
																					return math.exp(t * -12.0      )

							else:
														raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""      )

							A    =       []
							for i in range(lowerCamelCase__      ):
														A    =       i / num_diffusion_timesteps
														A    =       (i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar_fn(lowerCamelCase__      ) / alpha_bar_fn(lowerCamelCase__      )					,   lowerCamelCase__      )      )
							return torch.tensor(lowerCamelCase__					,   dtype=torch.floataa      )






class   UpperCAmelCase__		(   UpperCamelCase ,UpperCamelCase  ):
		lowerCAmelCase_							:						int							=  [e.name for e in KarrasDiffusionSchedulers]
		lowerCAmelCase_							:						Union[str, Any]							=  2



		@register_to_config
		def __init__(			self	:     Any   ,				snake_case	:     int = 1_000   ,				snake_case	:     float = 0.00085   ,				snake_case	:     float = 0.012   ,				snake_case	:     str = "linear"   ,				snake_case	:     Optional[Union[np.ndarray, List[float]]] = None   ,				snake_case	:     str = "epsilon"   ,				snake_case	:     str = "linspace"   ,				snake_case	:     int = 0   ,				)      ->    Dict:






									'''simple docstring'''

									if trained_betas is not None:
																A    =       torch.tensor(snake_case   ,				dtype=torch.floataa					)
									elif beta_schedule == "linear":
																A    =       torch.linspace(snake_case   ,				snake_case   ,				snake_case   ,				dtype=torch.floataa					)
									elif beta_schedule == "scaled_linear":
																# this schedule is very specific to the latent diffusion model.
																A    =       (
																    torch.linspace(beta_start**0.5   ,				beta_end**0.5   ,				snake_case   ,				dtype=torch.floataa					) ** 2
																)
									elif beta_schedule == "squaredcos_cap_v2":
																# Glide cosine schedule
																A    =       betas_for_alpha_bar(snake_case					)
									else:
																raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""					)

									A    =       1.0 - self.betas
									A    =       torch.cumprod(self.alphas   ,				dim=0					)

									#  set all values
									self.set_timesteps(snake_case   ,				snake_case   ,				snake_case					)



		def        A_		(			self	:     str   ,				snake_case	:     List[Any]   ,				snake_case	:     str=None					)      ->    Optional[Any]:






									'''simple docstring'''

									if schedule_timesteps is None:
																A    =       self.timesteps

									A    =       (schedule_timesteps == timestep).nonzero()

									# The sigma index that is taken for the **very** first `step`
									# is always the second index (or the last index if there is only 1)
									# This way we can ensure we don't accidentally skip a sigma in
									# case we start in the middle of the denoising schedule (e.g. for image-to-image)
									if len(self._index_counter					) == 0:
																A    =       1 if len(snake_case					) > 1 else 0
									else:
																A    =       timestep.cpu().item() if torch.is_tensor(snake_case					) else timestep
																A    =       self._index_counter[timestep_int]

									return indices[pos].item()



		@property
		def        A_		(			self	:     Optional[int]					)      ->    Union[str, Any]:






									'''simple docstring'''

									if self.config.timestep_spacing in ["linspace", "trailing"]:
																return self.sigmas.max()

									return (self.sigmas.max() ** 2 + 1) ** 0.5



		def        A_		(			self	:     Union[str, Any]   ,				snake_case	:     torch.FloatTensor   ,				snake_case	:     Union[float, torch.FloatTensor]   ,				)      ->    torch.FloatTensor:






									'''simple docstring'''

									A    =       self.index_for_timestep(snake_case					)

									if self.state_in_first_order:
																A    =       self.sigmas[step_index]
									else:
																A    =       self.sigmas_interpol[step_index - 1]

									A    =       sample / ((sigma**2 + 1) ** 0.5)
									return sample



		def        A_		(			self	:     Union[str, Any]   ,				snake_case	:     int   ,				snake_case	:     Union[str, torch.device] = None   ,				snake_case	:     Optional[int] = None   ,				)      ->    Any:






									'''simple docstring'''

									A    =       num_inference_steps

									A    =       num_train_timesteps or self.config.num_train_timesteps

									# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
									if self.config.timestep_spacing == "linspace":
																A    =       np.linspace(0   ,				num_train_timesteps - 1   ,				snake_case   ,				dtype=snake_case					)[::-1].copy()
									elif self.config.timestep_spacing == "leading":
																A    =       num_train_timesteps // self.num_inference_steps
																# creates integer timesteps by multiplying by ratio
																# casting to int to avoid issues when num_inference_step is power of 3
																A    =       (np.arange(0   ,				snake_case					) * step_ratio).round()[::-1].copy().astype(snake_case					)
																timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
																A    =       num_train_timesteps / self.num_inference_steps
																# creates integer timesteps by multiplying by ratio
																# casting to int to avoid issues when num_inference_step is power of 3
																A    =       (np.arange(snake_case   ,				0   ,				-step_ratio					)).round().copy().astype(snake_case					)
																timesteps -= 1
									else:
																raise ValueError(
																    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""					)

									A    =       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
									A    =       torch.from_numpy(np.log(snake_case					)					).to(snake_case					)

									A    =       np.interp(snake_case   ,				np.arange(0   ,				len(snake_case					)					)   ,				snake_case					)
									A    =       np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
									A    =       torch.from_numpy(snake_case					).to(device=snake_case					)

									# compute up and down sigmas
									A    =       sigmas.roll(-1					)
									A    =       0.0
									A    =       (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
									A    =       (sigmas_next**2 - sigmas_up**2) ** 0.5
									A    =       0.0

									# compute interpolated sigmas
									A    =       sigmas.log().lerp(sigmas_down.log()   ,				0.5					).exp()
									A    =       0.0

									# set sigmas
									A    =       torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2					), sigmas[-1:]]					)
									A    =       torch.cat(
									    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2					), sigmas_interpol[-1:]]					)
									A    =       torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2					), sigmas_up[-1:]]					)
									A    =       torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2					), sigmas_down[-1:]]					)

									if str(snake_case					).startswith('mps'					):
																# mps does not support float64
																A    =       torch.from_numpy(snake_case					).to(snake_case   ,				dtype=torch.floataa					)
									else:
																A    =       torch.from_numpy(snake_case					).to(snake_case					)

									A    =       self.sigma_to_t(snake_case					).to(snake_case   ,				dtype=timesteps.dtype					)
									A    =       torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])   ,				dim=-1					).flatten()

									A    =       torch.cat([timesteps[:1], interleaved_timesteps]					)

									A    =       None

									# for exp beta schedules, such as the one for `pipeline_shap_e.py`
									# we need an index counter
									A    =       defaultdict(snake_case					)



		def        A_		(			self	:     Dict   ,				snake_case	:     Dict					)      ->    Tuple:






									'''simple docstring'''

									A    =       sigma.log()

									# get distribution
									A    =       log_sigma - self.log_sigmas[:, None]

									# get sigmas range
									A    =       dists.ge(0					).cumsum(dim=0					).argmax(dim=0					).clamp(max=self.log_sigmas.shape[0] - 2					)
									A    =       low_idx + 1

									A    =       self.log_sigmas[low_idx]
									A    =       self.log_sigmas[high_idx]

									# interpolate sigmas
									A    =       (low - log_sigma) / (low - high)
									A    =       w.clamp(0   ,				1					)

									# transform interpolation to time range
									A    =       (1 - w) * low_idx + w * high_idx
									A    =       t.view(sigma.shape					)
									return t



		@property
		def        A_		(			self	:     Any					)      ->    int:






									'''simple docstring'''

									return self.sample is None



		def        A_		(			self	:     Tuple   ,				snake_case	:     Union[torch.FloatTensor, np.ndarray]   ,				snake_case	:     Union[float, torch.FloatTensor]   ,				snake_case	:     Union[torch.FloatTensor, np.ndarray]   ,				snake_case	:     Optional[torch.Generator] = None   ,				snake_case	:     bool = True   ,				)      ->    Union[SchedulerOutput, Tuple]:






									'''simple docstring'''

									A    =       self.index_for_timestep(snake_case					)

									# advance index counter by 1
									A    =       timestep.cpu().item() if torch.is_tensor(snake_case					) else timestep
									self._index_counter[timestep_int] += 1

									if self.state_in_first_order:
																A    =       self.sigmas[step_index]
																A    =       self.sigmas_interpol[step_index]
																A    =       self.sigmas_up[step_index]
																A    =       self.sigmas_down[step_index - 1]
									else:
																# 2nd order / KPDM2's method
																A    =       self.sigmas[step_index - 1]
																A    =       self.sigmas_interpol[step_index - 1]
																A    =       self.sigmas_up[step_index - 1]
																A    =       self.sigmas_down[step_index - 1]

									# currently only gamma=0 is supported. This usually works best anyways.
									# We can support gamma in the future but then need to scale the timestep before
									# passing it to the model which requires a change in API
									A    =       0
									A    =       sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

									A    =       model_output.device
									A    =       randn_tensor(model_output.shape   ,				dtype=model_output.dtype   ,				device=snake_case   ,				generator=snake_case					)

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									if self.config.prediction_type == "epsilon":
																A    =       sigma_hat if self.state_in_first_order else sigma_interpol
																A    =       sample - sigma_input * model_output
									elif self.config.prediction_type == "v_prediction":
																A    =       sigma_hat if self.state_in_first_order else sigma_interpol
																A    =       model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
																    sample / (sigma_input**2 + 1)
																)
									elif self.config.prediction_type == "sample":
																raise NotImplementedError('prediction_type not implemented yet: sample'					)
									else:
																raise ValueError(
																    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""					)

									if self.state_in_first_order:
																# 2. Convert to an ODE derivative for 1st order
																A    =       (sample - pred_original_sample) / sigma_hat
																# 3. delta timestep
																A    =       sigma_interpol - sigma_hat

																# store for 2nd order step
																A    =       sample
																A    =       dt
																A    =       sample + derivative * dt
									else:
																# DPM-Solver-2
																# 2. Convert to an ODE derivative for 2nd order
																A    =       (sample - pred_original_sample) / sigma_interpol
																# 3. delta timestep
																A    =       sigma_down - sigma_hat

																A    =       self.sample
																A    =       None

																A    =       sample + derivative * dt
																A    =       prev_sample + noise * sigma_up

									if not return_dict:
																return (prev_sample,)

									return SchedulerOutput(prev_sample=snake_case					)



		def        A_		(			self	:     Dict   ,				snake_case	:     torch.FloatTensor   ,				snake_case	:     torch.FloatTensor   ,				snake_case	:     torch.FloatTensor   ,				)      ->    torch.FloatTensor:






									'''simple docstring'''

									A    =       self.sigmas.to(device=original_samples.device   ,				dtype=original_samples.dtype					)
									if original_samples.device.type == "mps" and torch.is_floating_point(snake_case					):
																# mps does not support float64
																A    =       self.timesteps.to(original_samples.device   ,				dtype=torch.floataa					)
																A    =       timesteps.to(original_samples.device   ,				dtype=torch.floataa					)
									else:
																A    =       self.timesteps.to(original_samples.device					)
																A    =       timesteps.to(original_samples.device					)

									A    =       [self.index_for_timestep(snake_case   ,				snake_case					) for t in timesteps]

									A    =       sigmas[step_indices].flatten()
									while len(sigma.shape					) < len(original_samples.shape					):
																A    =       sigma.unsqueeze(-1					)

									A    =       original_samples + noise * sigma
									return noisy_samples



		def __len__(			self	:     List[Any]					)      ->    int:






									'''simple docstring'''

									return self.config.num_train_timesteps



