



"""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
_UpperCamelCase:				Optional[int]    							=  'src/diffusers'
_UpperCamelCase:				Optional[int]    							=  'docs/source/en'
_UpperCamelCase:				str    							=  '.'


def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase				)     ->			Any:






      '''simple docstring'''


      with open(_UpperCAmelCase	,      'r'	,      encoding='utf-8'	,      newline='\n'				) as f:
            lowercase  :      List[Any]					=   f.readlines()
      # Find the start prompt.
      lowercase  :      int					=   0
      while not lines[start_index].startswith(_UpperCAmelCase				):
            start_index += 1
      start_index += 1

      lowercase  :      Optional[int]					=   start_index
      while not lines[end_index].startswith(_UpperCAmelCase				):
            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 |
_UpperCamelCase:				str    							=  'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
_UpperCamelCase:				Any    							=  re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
_UpperCamelCase:				Tuple    							=  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.
_UpperCamelCase:				int    							=  re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


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


def   lowercase__     (			_UpperCAmelCase				)     ->			Optional[int]:






      '''simple docstring'''


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


def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase				)     ->			int:






      '''simple docstring'''


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


def   lowercase__     (			)     ->			str:






      '''simple docstring'''


      lowercase  :      Union[str, Any]					=   diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
      lowercase  :      Any					=   {
          name: config_mapping_names[code]
          for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
          if code in config_mapping_names
      }
      lowercase  :      Optional[Any]					=   {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.
      lowercase  :      List[str]					=   collections.defaultdict(_UpperCAmelCase				)
      lowercase  :      Tuple					=   collections.defaultdict(_UpperCAmelCase				)
      lowercase  :      List[Any]					=   collections.defaultdict(_UpperCAmelCase				)
      lowercase  :      List[str]					=   collections.defaultdict(_UpperCAmelCase				)
      lowercase  :      Dict					=   collections.defaultdict(_UpperCAmelCase				)

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

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

    # Let's build that table!
      lowercase  :      List[Any]					=   list(model_name_to_config.keys()				)
      model_names.sort(key=str.lower				)
      lowercase  :      List[str]					=   ['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).
      lowercase  :      Any					=   [len(_UpperCAmelCase				) + 2 for c in columns]
      lowercase  :      Optional[int]					=   max([len(_UpperCAmelCase				) for name in model_names]				) + 2

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

      lowercase  :      List[Any]					=   {True: '✅', False: '❌'}
      for name in model_names:
            lowercase  :      str					=   model_name_to_prefix[name]
            lowercase  :      Any					=   [
                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(_UpperCAmelCase	,      _UpperCAmelCase				) for l, w in zip(_UpperCAmelCase	,      _UpperCAmelCase				)]				) + "|\n"
      return table


def   lowercase__     (			_UpperCAmelCase=False				)     ->			Tuple:






      '''simple docstring'''


      lowercase	,    lowercase	,    lowercase	,    lowercase  :      List[Any]					=   _find_text_in_file(
          filename=os.path.join(_UpperCAmelCase	,      'index.mdx'				)	,      start_prompt='<!--This table is updated automatically from the auto modules'	,      end_prompt='<!-- End table-->'	,      )
      lowercase  :      Optional[Any]					=   get_model_table_from_auto_modules()

      if current_table != new_table:
            if overwrite:
                  with open(os.path.join(_UpperCAmelCase	,      '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__":
     _UpperCamelCase:				str    							=  argparse.ArgumentParser()
     parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
     _UpperCamelCase:				Union[str, Any]    							=  parser.parse_args()

     check_model_table(args.fix_and_overwrite)





"""simple docstring"""




import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class   a__		(						SCREAMING_SNAKE_CASE__     ):
   _lowerCamelCase												=				(HeunDiscreteScheduler,)
   _lowerCamelCase												=				10


   def        lowercase				(							self      :							List[Any],     **lowerCAmelCase      :							Optional[int]			)    ->       Dict:
         lowercase  :      List[Any]					=   {
             'num_train_timesteps': 1100,
             'beta_start': 0.0001,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
         }

         config.update(**lowerCAmelCase			)
         return config


   def        lowercase				(							self      :							Any			)    ->       Union[str, Any]:
         for timesteps in [10, 50, 100, 1000]:
               self.check_over_configs(num_train_timesteps=lowerCAmelCase			)


   def        lowercase				(							self      :							Any			)    ->       Any:
         for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001],     [0.0002, 0.002, 0.02]			):
               self.check_over_configs(beta_start=lowerCAmelCase,     beta_end=lowerCAmelCase			)


   def        lowercase				(							self      :							Union[str, Any]			)    ->       Any:
         for schedule in ["linear", "scaled_linear", "exp"]:
               self.check_over_configs(beta_schedule=lowerCAmelCase			)


   def        lowercase				(							self      :							Optional[int]			)    ->       Dict:
         for clip_sample_range in [1.0, 2.0, 3.0]:
               self.check_over_configs(clip_sample_range=lowerCAmelCase,     clip_sample=lowerCAmelCase			)


   def        lowercase				(							self      :							Dict			)    ->       Union[str, Any]:
         for prediction_type in ["epsilon", "v_prediction", "sample"]:
               self.check_over_configs(prediction_type=lowerCAmelCase			)


   def        lowercase				(							self      :							str			)    ->       Tuple:
         lowercase  :      Tuple					=   self.scheduler_classes[0]
         lowercase  :      int					=   self.get_scheduler_config()
         lowercase  :      str					=   scheduler_class(**lowerCAmelCase			)

         scheduler.set_timesteps(self.num_inference_steps			)

         lowercase  :      Tuple					=   self.dummy_model()
         lowercase  :      Dict					=   self.dummy_sample_deter * scheduler.init_noise_sigma
         lowercase  :      Optional[Any]					=   sample.to(lowerCAmelCase			)

         for i, t in enumerate(scheduler.timesteps			):
               lowercase  :      Any					=   scheduler.scale_model_input(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      Union[str, Any]					=   model(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      List[str]					=   scheduler.step(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)
               lowercase  :      Tuple					=   output.prev_sample

         lowercase  :      Tuple					=   torch.sum(torch.abs(lowerCAmelCase			)			)
         lowercase  :      Dict					=   torch.mean(torch.abs(lowerCAmelCase			)			)

         if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 0.1233			) < 1e-2
               assert abs(result_mean.item() - 0.0002			) < 1e-3
         else:
               # CUDA
               assert abs(result_sum.item() - 0.1233			) < 1e-2
               assert abs(result_mean.item() - 0.0002			) < 1e-3


   def        lowercase				(							self      :							Tuple			)    ->       List[str]:
         lowercase  :      str					=   self.scheduler_classes[0]
         lowercase  :      Any					=   self.get_scheduler_config(prediction_type='v_prediction'			)
         lowercase  :      List[str]					=   scheduler_class(**lowerCAmelCase			)

         scheduler.set_timesteps(self.num_inference_steps			)

         lowercase  :      List[Any]					=   self.dummy_model()
         lowercase  :      List[Any]					=   self.dummy_sample_deter * scheduler.init_noise_sigma
         lowercase  :      str					=   sample.to(lowerCAmelCase			)

         for i, t in enumerate(scheduler.timesteps			):
               lowercase  :      List[str]					=   scheduler.scale_model_input(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      List[Any]					=   model(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      List[str]					=   scheduler.step(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)
               lowercase  :      Any					=   output.prev_sample

         lowercase  :      Dict					=   torch.sum(torch.abs(lowerCAmelCase			)			)
         lowercase  :      List[str]					=   torch.mean(torch.abs(lowerCAmelCase			)			)

         if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 4.6934e-07			) < 1e-2
               assert abs(result_mean.item() - 6.1112e-10			) < 1e-3
         else:
               # CUDA
               assert abs(result_sum.item() - 4.693_4286_5017_0972e-07			) < 1e-2
               assert abs(result_mean.item() - 0.0002			) < 1e-3


   def        lowercase				(							self      :							Optional[Any]			)    ->       Tuple:
         lowercase  :      Optional[int]					=   self.scheduler_classes[0]
         lowercase  :      Optional[int]					=   self.get_scheduler_config()
         lowercase  :      Dict					=   scheduler_class(**lowerCAmelCase			)

         scheduler.set_timesteps(self.num_inference_steps,     device=lowerCAmelCase			)

         lowercase  :      int					=   self.dummy_model()
         lowercase  :      Tuple					=   self.dummy_sample_deter.to(lowerCAmelCase			) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
               lowercase  :      Optional[Any]					=   scheduler.scale_model_input(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      Optional[Any]					=   model(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      List[str]					=   scheduler.step(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)
               lowercase  :      Optional[int]					=   output.prev_sample

         lowercase  :      str					=   torch.sum(torch.abs(lowerCAmelCase			)			)
         lowercase  :      Dict					=   torch.mean(torch.abs(lowerCAmelCase			)			)

         if str(lowerCAmelCase			).startswith('cpu'			):
               # The following sum varies between 148 and 156 on mps. Why?
               assert abs(result_sum.item() - 0.1233			) < 1e-2
               assert abs(result_mean.item() - 0.0002			) < 1e-3
         elif str(lowerCAmelCase			).startswith('mps'			):
               # Larger tolerance on mps
               assert abs(result_mean.item() - 0.0002			) < 1e-2
         else:
               # CUDA
               assert abs(result_sum.item() - 0.1233			) < 1e-2
               assert abs(result_mean.item() - 0.0002			) < 1e-3






   def        lowercase				(							self      :							Optional[Any]			)    ->       List[Any]:
         lowercase  :      str					=   self.scheduler_classes[0]
         lowercase  :      str					=   self.get_scheduler_config()
         lowercase  :      Tuple					=   scheduler_class(**lowerCAmelCase,     use_karras_sigmas=lowerCAmelCase			)

         scheduler.set_timesteps(self.num_inference_steps,     device=lowerCAmelCase			)

         lowercase  :      Dict					=   self.dummy_model()
         lowercase  :      Any					=   self.dummy_sample_deter.to(lowerCAmelCase			) * scheduler.init_noise_sigma
         lowercase  :      str					=   sample.to(lowerCAmelCase			)

         for t in scheduler.timesteps:
               lowercase  :      int					=   scheduler.scale_model_input(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      Optional[Any]					=   model(lowerCAmelCase,     lowerCAmelCase			)

               lowercase  :      int					=   scheduler.step(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)
               lowercase  :      str					=   output.prev_sample

         lowercase  :      Dict					=   torch.sum(torch.abs(lowerCAmelCase			)			)
         lowercase  :      Union[str, Any]					=   torch.mean(torch.abs(lowerCAmelCase			)			)

         assert abs(result_sum.item() - 0.0_0015			) < 1e-2
         assert abs(result_mean.item() - 1.9869_5545_3503_4695e-07			) < 1e-2
