
'''simple docstring'''
import argparse
from collections import defaultdict

import yaml


lowercase__											="docs/source/en/_toctree.yml"






def      UpperCamelCase_				(      A__						):
	a_      				=						defaultdict(lowerCAmelCase__						)
	a_      				=						[]
	a_      				=						[]
	for doc in doc_list:
		if "local" in doc:
			counts[doc["local"]] += 1

		if doc["title"].lower() == "overview":
			overview_doc.append({"""local""": doc["""local"""], """title""": doc["""title"""]}						)
		else:
			new_doc_list.append(lowerCAmelCase__						)

	a_      				=						new_doc_list
	a_      				=						[key for key, value in counts.items() if value > 1]

	a_      				=						[]
	for duplicate_key in duplicates:
		a_      				=						list({doc["""title"""] for doc in doc_list if doc["""local"""] == duplicate_key}						)
		if len(lowerCAmelCase__						) > 1:
			raise ValueError(
			    F'''{duplicate_key} is present several times in the documentation table of content at '''
			    """`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
			    """others."""						)
		# Only add this once
		new_doc.append({"""local""": duplicate_key, """title""": titles[0]}						)

	# Add none duplicate-keys
	new_doc.extend([doc for doc in doc_list if """local""" not in counts or counts[doc["""local"""]] == 1]						)
	a_      				=						sorted(lowerCAmelCase__  ,      key=lambda A__						: s["title"].lower()						)

	# "overview" gets special treatment and is always first
	if len(lowerCAmelCase__						) > 1:
		raise ValueError("""{doc_list} has two \'overview\' docs which is not allowed."""						)

	overview_doc.extend(lowerCAmelCase__						)

	# Sort
	return overview_doc






def      UpperCamelCase_				(      A__=False						):
	with open(lowerCAmelCase__  ,      encoding="""utf-8"""						) as f:
		a_      				=						yaml.safe_load(f.read()						)

	# Get to the API doc
	a_      				=						0
	while content[api_idx]["title"] != "API":
		api_idx += 1
	a_      				=						content[api_idx]["""sections"""]

	# Then to the model doc
	a_      				=						0
	while api_doc[scheduler_idx]["title"] != "Schedulers":
		scheduler_idx += 1

	a_      				=						api_doc[scheduler_idx]["""sections"""]
	a_      				=						clean_doc_toc(lowerCAmelCase__						)

	a_      				=						False
	if new_scheduler_doc != scheduler_doc:
		a_      				=						True
		if overwrite:
			a_      				=						new_scheduler_doc

	if diff:
		if overwrite:
			a_      				=						api_doc
			with open(lowerCAmelCase__  ,      """w"""  ,      encoding="""utf-8"""						) as f:
				f.write(yaml.dump(lowerCAmelCase__  ,      allow_unicode=lowerCAmelCase__						)						)
		else:
			raise ValueError(
			    """The model doc part of the table of content is not properly sorted, run `make style` to fix this."""						)






def      UpperCamelCase_				(      A__=False						):
	with open(lowerCAmelCase__  ,      encoding="""utf-8"""						) as f:
		a_      				=						yaml.safe_load(f.read()						)

	# Get to the API doc
	a_      				=						0
	while content[api_idx]["title"] != "API":
		api_idx += 1
	a_      				=						content[api_idx]["""sections"""]

	# Then to the model doc
	a_      				=						0
	while api_doc[pipeline_idx]["title"] != "Pipelines":
		pipeline_idx += 1

	a_      				=						False
	a_      				=						api_doc[pipeline_idx]["""sections"""]
	a_      				=						[]

	# sort sub pipeline docs
	for pipeline_doc in pipeline_docs:
		if "section" in pipeline_doc:
			a_      				=						pipeline_doc["""section"""]
			a_      				=						clean_doc_toc(lowerCAmelCase__						)
			if overwrite:
				a_      				=						new_sub_pipeline_doc
		new_pipeline_docs.append(lowerCAmelCase__						)

	# sort overall pipeline doc
	a_      				=						clean_doc_toc(lowerCAmelCase__						)

	if new_pipeline_docs != pipeline_docs:
		a_      				=						True
		if overwrite:
			a_      				=						new_pipeline_docs

	if diff:
		if overwrite:
			a_      				=						api_doc
			with open(lowerCAmelCase__  ,      """w"""  ,      encoding="""utf-8"""						) as f:
				f.write(yaml.dump(lowerCAmelCase__  ,      allow_unicode=lowerCAmelCase__						)						)
		else:
			raise ValueError(
			    """The model doc part of the table of content is not properly sorted, run `make style` to fix this."""						)


if __name__ == "__main__":
		lowercase__											=argparse.ArgumentParser()
		parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
		lowercase__											=parser.parse_args()

		check_scheduler_doc(args.fix_and_overwrite)
		check_pipeline_doc(args.fix_and_overwrite)



'''simple docstring'''

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin






@flax.struct.dataclass
class    snake_case     :






				"""simple docstring"""
				# setable values
				_a    	=     None
				_a    	=     None
				_a    	=     None  # sigma(t_i)



				@classmethod
				def   a__					(    cls       )     ->							Dict:
											return cls()






@dataclass
class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     42
				_a    	=     42
				_a    	=     42






class    snake_case     (      lowercase_,						lowercase_		):






				"""simple docstring"""



				@property
				def   a__					(    self       )     ->							int:
											return True



				@register_to_config
				def __init__(    self,     _lowercase = 0.02,     _lowercase = 100,     _lowercase = 1.007,     _lowercase = 80,     _lowercase = 0.05,     _lowercase = 50,     )     ->							List[Any]:
											pass



				def   a__					(    self       )     ->							List[Any]:
											return KarrasVeSchedulerState.create()



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase = ()       )     ->							KarrasVeSchedulerState:
											SCREAMING_SNAKE_CASE_				=				jnp.arange(0,     _lowercase       )[::-1].copy()
											SCREAMING_SNAKE_CASE_				=				[
											    (
											        self.config.sigma_max**2
											        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
											    )
											    for i in timesteps
											]

											return state.replace(
											    num_inference_steps=_lowercase,     schedule=jnp.array(_lowercase,     dtype=jnp.floataa       ),     timesteps=_lowercase,     )



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     )     ->							Tuple[jnp.ndarray, float]:
											if self.config.s_min <= sigma <= self.config.s_max:
																		SCREAMING_SNAKE_CASE_				=				min(self.config.s_churn / state.num_inference_steps,     2**0.5 - 1       )
											else:
																		SCREAMING_SNAKE_CASE_				=				0

											# sample eps ~ N(0, S_noise^2 * I)
											SCREAMING_SNAKE_CASE_				=				random.split(_lowercase,     num=1       )
											SCREAMING_SNAKE_CASE_				=				self.config.s_noise * random.normal(key=_lowercase,     shape=sample.shape       )
											SCREAMING_SNAKE_CASE_				=				sigma + gamma * sigma
											SCREAMING_SNAKE_CASE_				=				sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

											return sample_hat, sigma_hat



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase = True,     )     ->							Union[FlaxKarrasVeOutput, Tuple]:
											SCREAMING_SNAKE_CASE_				=				sample_hat + sigma_hat * model_output
											SCREAMING_SNAKE_CASE_				=				(sample_hat - pred_original_sample) / sigma_hat
											SCREAMING_SNAKE_CASE_				=				sample_hat + (sigma_prev - sigma_hat) * derivative

											if not return_dict:
																		return (sample_prev, derivative, state)

											return FlaxKarrasVeOutput(prev_sample=_lowercase,     derivative=_lowercase,     state=_lowercase       )



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase = True,     )     ->							Union[FlaxKarrasVeOutput, Tuple]:
											SCREAMING_SNAKE_CASE_				=				sample_prev + sigma_prev * model_output
											SCREAMING_SNAKE_CASE_				=				(sample_prev - pred_original_sample) / sigma_prev
											SCREAMING_SNAKE_CASE_				=				sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

											if not return_dict:
																		return (sample_prev, derivative, state)

											return FlaxKarrasVeOutput(prev_sample=_lowercase,     derivative=_lowercase,     state=_lowercase       )





				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase       )     ->							List[Any]:
											raise NotImplementedError()





