






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 .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left




@flax.struct.dataclass
class  lowerCAmelCase__ :
   # setable values
   SCREAMING_SNAKE_CASE_         =None
   SCREAMING_SNAKE_CASE_         =None
   SCREAMING_SNAKE_CASE_         =None
   @classmethod
   def 			__a       (		cls			:  List[str]       ):


        '''simple docstring'''
        return cls()




@dataclass
class  lowerCAmelCase__ (						__magic_name__      ):
   SCREAMING_SNAKE_CASE_         =42
   SCREAMING_SNAKE_CASE_         =42
   SCREAMING_SNAKE_CASE_         =None




class  lowerCAmelCase__ (						__magic_name__       ,	__magic_name__      ):
   @property
   def 			__a       (		self			:  List[str]       ):


        '''simple docstring'''
        return True
   @register_to_config
   def __init__(		self			:  Tuple       , snake_case__			:  int = 2_0_0_0       , snake_case__			:  float = 0.15       , snake_case__			:  float = 0.01       , snake_case__			:  float = 1348.0       , snake_case__			:  float = 1e-5       , snake_case__			:  int = 1       , ):


        '''simple docstring'''
        pass
   def 			__a       (		self			:  Any       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Any       =       ScoreSdeVeSchedulerState.create()
        return self.set_sigmas(
            snake_case__       , self.config.num_train_timesteps       , self.config.sigma_min       , self.config.sigma_max       , self.config.sampling_eps       , )
   def 			__a       (		self			:  List[Any]       , snake_case__			:  ScoreSdeVeSchedulerState       , snake_case__			:  int       , snake_case__			:  Tuple = ()       , snake_case__			:  float = None       ):


        '''simple docstring'''
        UpperCAmelCase__	:						List[str]       =       sampling_eps if sampling_eps is not None else self.config.sampling_eps

        UpperCAmelCase__	:						Optional[int]       =       jnp.linspace(1       , snake_case__       , snake_case__       )
        return state.replace(timesteps=snake_case__       )
   def 			__a       (		self			:  int       , snake_case__			:  ScoreSdeVeSchedulerState       , snake_case__			:  int       , snake_case__			:  float = None       , snake_case__			:  float = None       , snake_case__			:  float = None       , ):


        '''simple docstring'''
        UpperCAmelCase__	:						Dict       =       sigma_min if sigma_min is not None else self.config.sigma_min
        UpperCAmelCase__	:						List[str]       =       sigma_max if sigma_max is not None else self.config.sigma_max
        UpperCAmelCase__	:						str       =       sampling_eps if sampling_eps is not None else self.config.sampling_eps
        if state.timesteps is None:
             UpperCAmelCase__	:						Union[str, Any]       =       self.set_timesteps(snake_case__       , snake_case__       , snake_case__       )

        UpperCAmelCase__	:						Union[str, Any]       =       jnp.exp(jnp.linspace(jnp.log(snake_case__       )       , jnp.log(snake_case__       )       , snake_case__       )       )
        UpperCAmelCase__	:						Any       =       jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]       )

        return state.replace(discrete_sigmas=snake_case__       , sigmas=snake_case__       )
   def 			__a       (		self			:  int       , snake_case__			:  Union[str, Any]       , snake_case__			:  List[Any]       , snake_case__			:  List[Any]       ):


        '''simple docstring'''
        return jnp.where(timesteps == 0       , jnp.zeros_like(snake_case__       )       , state.discrete_sigmas[timesteps - 1]       )
   def 			__a       (		self			:  Dict       , snake_case__			:  ScoreSdeVeSchedulerState       , snake_case__			:  jnp.ndarray       , snake_case__			:  int       , snake_case__			:  jnp.ndarray       , snake_case__			:  random.KeyArray       , snake_case__			:  bool = True       , ):


        '''simple docstring'''
        if state.timesteps is None:
             raise ValueError(
                 "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"       )

        UpperCAmelCase__	:						Tuple       =       timestep * jnp.ones(
            sample.shape[0]       , )
        UpperCAmelCase__	:						Any       =       (timestep * (len(state.timesteps       ) - 1)).long()

        UpperCAmelCase__	:						int       =       state.discrete_sigmas[timesteps]
        UpperCAmelCase__	:						Union[str, Any]       =       self.get_adjacent_sigma(snake_case__       , snake_case__       , snake_case__       )
        UpperCAmelCase__	:						Dict       =       jnp.zeros_like(snake_case__       )
        UpperCAmelCase__	:						Dict       =       (sigma**2 - adjacent_sigma**2) ** 0.5

        # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
        # also equation 47 shows the analog from SDE models to ancestral sampling methods
        UpperCAmelCase__	:						Dict       =       diffusion.flatten()
        UpperCAmelCase__	:						int       =       broadcast_to_shape_from_left(snake_case__       , sample.shape       )
        UpperCAmelCase__	:						str       =       drift - diffusion**2 * model_output

        #  equation 6: sample noise for the diffusion term of
        UpperCAmelCase__	:						Dict       =       random.split(snake_case__       , num=1       )
        UpperCAmelCase__	:						str       =       random.normal(key=snake_case__       , shape=sample.shape       )
        UpperCAmelCase__	:						List[Any]       =       sample - drift  # subtract because `dt` is a small negative timestep
        # TODO is the variable diffusion the correct scaling term for the noise?
        UpperCAmelCase__	:						Optional[int]       =       prev_sample_mean + diffusion * noise  # add impact of diffusion field g

        if not return_dict:
             return (prev_sample, prev_sample_mean, state)

        return FlaxSdeVeOutput(prev_sample=snake_case__       , prev_sample_mean=snake_case__       , state=snake_case__       )
   def 			__a       (		self			:  Union[str, Any]       , snake_case__			:  ScoreSdeVeSchedulerState       , snake_case__			:  jnp.ndarray       , snake_case__			:  jnp.ndarray       , snake_case__			:  random.KeyArray       , snake_case__			:  bool = True       , ):


        '''simple docstring'''
        if state.timesteps is None:
             raise ValueError(
                 "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"       )

        # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
        # sample noise for correction
        UpperCAmelCase__	:						Dict       =       random.split(snake_case__       , num=1       )
        UpperCAmelCase__	:						List[Any]       =       random.normal(key=snake_case__       , shape=sample.shape       )

        # compute step size from the model_output, the noise, and the snr
        UpperCAmelCase__	:						Optional[int]       =       jnp.linalg.norm(snake_case__       )
        UpperCAmelCase__	:						Union[str, Any]       =       jnp.linalg.norm(snake_case__       )
        UpperCAmelCase__	:						int       =       (self.config.snr * noise_norm / grad_norm) ** 2 * 2
        UpperCAmelCase__	:						str       =       step_size * jnp.ones(sample.shape[0]       )

        # compute corrected sample: model_output term and noise term
        UpperCAmelCase__	:						Dict       =       step_size.flatten()
        UpperCAmelCase__	:						Union[str, Any]       =       broadcast_to_shape_from_left(snake_case__       , sample.shape       )
        UpperCAmelCase__	:						Optional[Any]       =       sample + step_size * model_output
        UpperCAmelCase__	:						Tuple       =       prev_sample_mean + ((step_size * 2) ** 0.5) * noise

        if not return_dict:
             return (prev_sample, state)

        return FlaxSdeVeOutput(prev_sample=snake_case__       , state=snake_case__       )




   def __len__(		self			:  str       ):


        '''simple docstring'''
        return self.config.num_train_timesteps










"""simple docstring"""



import argparse
import collections
import importlib.util
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
_lowerCAmelCase  :  List[str]   =			"""src/diffusers"""
_lowerCAmelCase  :  Union[str, Any]   =			"""docs/source/en"""
_lowerCAmelCase  :  List[str]   =			"""."""


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   List[Any] ,				snake_case					:   Optional[Any] ,				snake_case					:   Optional[Any]							)->		str:






     '''simple docstring'''



     with open(snake_case ,				"r" ,				encoding="utf-8" ,				newline="\n"							) as f:
          UpperCAmelCase__	:						Any       =       f.readlines()
     # Find the start prompt.
     UpperCAmelCase__	:						str       =       0
     while not lines[start_index].startswith(snake_case							):
          start_index += 1
     start_index += 1

     UpperCAmelCase__	:						Union[str, Any]       =       start_index
     while not lines[end_index].startswith(snake_case							):
          end_index += 1
     end_index -= 1

     while len(lines[start_index]							) <= 1:
          start_index += 1
     while len(lines[end_index]							) <= 1:
          end_index -= 1
     end_index += 1
     return "".join(lines[start_index:end_index]							), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
_lowerCAmelCase  :  str   =			"""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
_lowerCAmelCase  :  List[Any]   =			re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
_lowerCAmelCase  :  Optional[int]   =			re.compile(r"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
_lowerCAmelCase  :  str   =			re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the diffusers module imported is the one in the repo.
_lowerCAmelCase  :  Any   =			importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(TRANSFORMERS_PATH, """__init__.py"""),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
_lowerCAmelCase  :  int   =			spec.loader.load_module()


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   Optional[int]							)->		List[Any]:






     '''simple docstring'''



     UpperCAmelCase__	:						List[str]       =       re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)" ,				snake_case							)
     return [m.group(0							) for m in matches]


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   int ,				snake_case					:   Tuple							)->		Union[str, Any]:






     '''simple docstring'''



     UpperCAmelCase__	:						Optional[int]       =       2 if text == "✅" or text == "❌" else len(snake_case							)
     UpperCAmelCase__	:						int       =       (width - text_length) // 2
     UpperCAmelCase__	:						Any       =       width - text_length - left_indent
     return " " * left_indent + text + " " * right_indent


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






     '''simple docstring'''



     UpperCAmelCase__	:						Tuple       =       diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
     UpperCAmelCase__	:						Any       =       {
         name: config_mapping_names[code]
         for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
         if code in config_mapping_names
     }
     UpperCAmelCase__	:						Optional[int]       =       {name: config.replace("ConfigMixin" ,				""							) for name, config in model_name_to_config.items()}

     # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
     UpperCAmelCase__	:						Optional[Any]       =       collections.defaultdict(snake_case							)
     UpperCAmelCase__	:						Dict       =       collections.defaultdict(snake_case							)
     UpperCAmelCase__	:						List[str]       =       collections.defaultdict(snake_case							)
     UpperCAmelCase__	:						Tuple       =       collections.defaultdict(snake_case							)
     UpperCAmelCase__	:						Any       =       collections.defaultdict(snake_case							)

     # Let's lookup through all diffusers object (once).
     for attr_name in dir(snake_case							):
          UpperCAmelCase__	:						Optional[int]       =       None
          if attr_name.endswith("Tokenizer"							):
               UpperCAmelCase__	:						Optional[Any]       =       slow_tokenizers
               UpperCAmelCase__	:						Optional[Any]       =       attr_name[:-9]
          elif attr_name.endswith("TokenizerFast"							):
               UpperCAmelCase__	:						Union[str, Any]       =       fast_tokenizers
               UpperCAmelCase__	:						str       =       attr_name[:-13]
          elif _re_tf_models.match(snake_case							) is not None:
               UpperCAmelCase__	:						List[str]       =       tf_models
               UpperCAmelCase__	:						List[str]       =       _re_tf_models.match(snake_case							).groups()[0]
          elif _re_flax_models.match(snake_case							) is not None:
               UpperCAmelCase__	:						str       =       flax_models
               UpperCAmelCase__	:						Union[str, Any]       =       _re_flax_models.match(snake_case							).groups()[0]
          elif _re_pt_models.match(snake_case							) is not None:
               UpperCAmelCase__	:						int       =       pt_models
               UpperCAmelCase__	:						Optional[Any]       =       _re_pt_models.match(snake_case							).groups()[0]

          if lookup_dict is not None:
               while len(snake_case							) > 0:
                    if attr_name in model_name_to_prefix.values():
                         UpperCAmelCase__	:						int       =       True
                         break
                    # Try again after removing the last word in the name
                    UpperCAmelCase__	:						List[str]       =       "".join(camel_case_split(snake_case							)[:-1]							)

    # Let's build that table!
     UpperCAmelCase__	:						int       =       list(model_name_to_config.keys()							)
     model_names.sort(key=str.lower							)
     UpperCAmelCase__	:						Dict       =       ["Model", "Tokenizer slow", "Tokenizer fast", "PyTorch support", "TensorFlow support", "Flax Support"]
     # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
     UpperCAmelCase__	:						List[str]       =       [len(snake_case							) + 2 for c in columns]
     UpperCAmelCase__	:						Tuple       =       max([len(snake_case							) for name in model_names]							) + 2

     # Build the table per se
     UpperCAmelCase__	:						Any       =       "|" + "|".join([_center_text(snake_case ,				snake_case							) for c, w in zip(snake_case ,				snake_case							)]							) + "|\n"
     # Use ":-----:" format to center-aligned table cell texts
     table += "|" + "|".join([":" + "-" * (w - 2) + ":" for w in widths]							) + "|\n"

     UpperCAmelCase__	:						str       =       {True: "✅", False: "❌"}
     for name in model_names:
          UpperCAmelCase__	:						str       =       model_name_to_prefix[name]
          UpperCAmelCase__	:						Dict       =       [
              name,
              check[slow_tokenizers[prefix]],
              check[fast_tokenizers[prefix]],
              check[pt_models[prefix]],
              check[tf_models[prefix]],
              check[flax_models[prefix]],
          ]
          table += "|" + "|".join([_center_text(snake_case ,				snake_case							) for l, w in zip(snake_case ,				snake_case							)]							) + "|\n"
     return table


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   Union[str, Any]=False							)->		List[str]:






     '''simple docstring'''



     UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__	:						str       =       _find_text_in_file(
         filename=os.path.join(snake_case ,				"index.mdx"							) ,				start_prompt="<!--This table is updated automatically from the auto modules" ,				end_prompt="<!-- End table-->" ,				)
     UpperCAmelCase__	:						Union[str, Any]       =       get_model_table_from_auto_modules()

     if current_table != new_table:
          if overwrite:
               with open(os.path.join(snake_case ,				"index.mdx"							) ,				"w" ,				encoding="utf-8" ,				newline="\n"							) as f:
                    f.writelines(lines[:start_index] + [new_table] + lines[end_index:]							)
          else:
               raise ValueError(
                   "The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this."							)


if __name__ == "__main__":
 _lowerCAmelCase  :  Union[str, Any]   =			argparse.ArgumentParser()
 parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
 _lowerCAmelCase  :  Dict   =			parser.parse_args()

 check_model_table(args.fix_and_overwrite)


