



"""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
SCREAMING_SNAKE_CASE				:     Optional[Any]          =	'''src/diffusers'''
SCREAMING_SNAKE_CASE				:     Any          =	'''docs/source/en'''
SCREAMING_SNAKE_CASE				:     int          =	'''.'''






def 	__UpperCAmelCase					(       snake_case_  :  List[str]   ,	snake_case_  :  Dict   ,	snake_case_  :  Dict   )							->	Tuple:






   """simple docstring"""


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

   _lowerCAmelCase					     =		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 |
SCREAMING_SNAKE_CASE				:     str          =	'''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
SCREAMING_SNAKE_CASE				:     List[str]          =	re.compile(R'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
SCREAMING_SNAKE_CASE				:     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.
SCREAMING_SNAKE_CASE				:     int          =	re.compile(R'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


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






def 	__UpperCAmelCase					(       snake_case_  :  Dict   )							->	Optional[int]:






   """simple docstring"""


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






def 	__UpperCAmelCase					(       snake_case_  :  str   ,	snake_case_  :  Optional[Any]   )							->	List[str]:






   """simple docstring"""


   _lowerCAmelCase					     =		2 if text == """✅""" or text == """❌""" else len(snake_case_   )
   _lowerCAmelCase					     =		(width - text_length) // 2
   _lowerCAmelCase					     =		width - text_length - left_indent
   return " " * left_indent + text + " " * right_indent






def 	__UpperCAmelCase					(       )							->	Tuple:






   """simple docstring"""


   _lowerCAmelCase					     =		diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
   _lowerCAmelCase					     =		{
       name: config_mapping_names[code]
       for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
       if code in config_mapping_names
   }
   _lowerCAmelCase					     =		{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.
   _lowerCAmelCase					     =		collections.defaultdict(snake_case_   )
   _lowerCAmelCase					     =		collections.defaultdict(snake_case_   )
   _lowerCAmelCase					     =		collections.defaultdict(snake_case_   )
   _lowerCAmelCase					     =		collections.defaultdict(snake_case_   )
   _lowerCAmelCase					     =		collections.defaultdict(snake_case_   )

   # Let's lookup through all diffusers object (once).
   for attr_name in dir(snake_case_   ):
      _lowerCAmelCase					     =		None
      if attr_name.endswith("""Tokenizer"""   ):
         _lowerCAmelCase					     =		slow_tokenizers
         _lowerCAmelCase					     =		attr_name[:-9]
      elif attr_name.endswith("""TokenizerFast"""   ):
         _lowerCAmelCase					     =		fast_tokenizers
         _lowerCAmelCase					     =		attr_name[:-13]
      elif _re_tf_models.match(snake_case_   ) is not None:
         _lowerCAmelCase					     =		tf_models
         _lowerCAmelCase					     =		_re_tf_models.match(snake_case_   ).groups()[0]
      elif _re_flax_models.match(snake_case_   ) is not None:
         _lowerCAmelCase					     =		flax_models
         _lowerCAmelCase					     =		_re_flax_models.match(snake_case_   ).groups()[0]
      elif _re_pt_models.match(snake_case_   ) is not None:
         _lowerCAmelCase					     =		pt_models
         _lowerCAmelCase					     =		_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():
               _lowerCAmelCase					     =		True
               break
            # Try again after removing the last word in the name
            _lowerCAmelCase					     =		"""""".join(camel_case_split(snake_case_   )[:-1]   )

    # Let's build that table!
   _lowerCAmelCase					     =		list(model_name_to_config.keys()   )
   model_names.sort(key=str.lower   )
   _lowerCAmelCase					     =		["""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).
   _lowerCAmelCase					     =		[len(snake_case_   ) + 2 for c in columns]
   _lowerCAmelCase					     =		max([len(snake_case_   ) for name in model_names]   ) + 2

   # Build the table per se
   _lowerCAmelCase					     =		"""|""" + """|""".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"

   _lowerCAmelCase					     =		{True: """✅""", False: """❌"""}
   for name in model_names:
      _lowerCAmelCase					     =		model_name_to_prefix[name]
      _lowerCAmelCase					     =		[
          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 	__UpperCAmelCase					(       snake_case_  :  Dict=False   )							->	List[Any]:






   """simple docstring"""


   _lowerCAmelCase     ,						_lowerCAmelCase     ,						_lowerCAmelCase     ,						_lowerCAmelCase					     =		_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-->"""   ,	)
   _lowerCAmelCase					     =		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__":
  SCREAMING_SNAKE_CASE				:     str          =	argparse.ArgumentParser()
  parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
  SCREAMING_SNAKE_CASE				:     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       __lowerCamelCase	(   __lowercase    ):
   __UpperCamelCase									=   (HeunDiscreteScheduler,)
   __UpperCamelCase									=   10







   def 							A__						(self       ,	**lowerCamelCase				):




      '''simple docstring'''




      _lowerCAmelCase					     =		{
          """num_train_timesteps""": 1_100,
          """beta_start""": 0.0001,
          """beta_end""": 0.02,
          """beta_schedule""": """linear""",
      }

      config.update(**lowerCamelCase				)
      return config







   def 							A__						(self				):




      '''simple docstring'''




      for timesteps in [10, 50, 100, 1_000]:
         self.check_over_configs(num_train_timesteps=lowerCamelCase				)







   def 							A__						(self				):




      '''simple docstring'''




      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 							A__						(self				):




      '''simple docstring'''




      for schedule in ["linear", "scaled_linear", "exp"]:
         self.check_over_configs(beta_schedule=lowerCamelCase				)







   def 							A__						(self				):




      '''simple docstring'''




      for clip_sample_range in [1.0, 2.0, 3.0]:
         self.check_over_configs(clip_sample_range=lowerCamelCase       ,	clip_sample=lowerCamelCase				)







   def 							A__						(self				):




      '''simple docstring'''




      for prediction_type in ["epsilon", "v_prediction", "sample"]:
         self.check_over_configs(prediction_type=lowerCamelCase				)







   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		self.scheduler_classes[0]
      _lowerCAmelCase					     =		self.get_scheduler_config()
      _lowerCAmelCase					     =		scheduler_class(**lowerCamelCase				)

      scheduler.set_timesteps(self.num_inference_steps				)

      _lowerCAmelCase					     =		self.dummy_model()
      _lowerCAmelCase					     =		self.dummy_sample_deter * scheduler.init_noise_sigma
      _lowerCAmelCase					     =		sample.to(lowerCamelCase				)

      for i, t in enumerate(scheduler.timesteps				):
         _lowerCAmelCase					     =		scheduler.scale_model_input(lowerCamelCase       ,	lowerCamelCase				)

         _lowerCAmelCase					     =		model(lowerCamelCase       ,	lowerCamelCase				)

         _lowerCAmelCase					     =		scheduler.step(lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase				)
         _lowerCAmelCase					     =		output.prev_sample

      _lowerCAmelCase					     =		torch.sum(torch.abs(lowerCamelCase				)				)
      _lowerCAmelCase					     =		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 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		self.scheduler_classes[0]
      _lowerCAmelCase					     =		self.get_scheduler_config(prediction_type="""v_prediction"""				)
      _lowerCAmelCase					     =		scheduler_class(**lowerCamelCase				)

      scheduler.set_timesteps(self.num_inference_steps				)

      _lowerCAmelCase					     =		self.dummy_model()
      _lowerCAmelCase					     =		self.dummy_sample_deter * scheduler.init_noise_sigma
      _lowerCAmelCase					     =		sample.to(lowerCamelCase				)

      for i, t in enumerate(scheduler.timesteps				):
         _lowerCAmelCase					     =		scheduler.scale_model_input(lowerCamelCase       ,	lowerCamelCase				)

         _lowerCAmelCase					     =		model(lowerCamelCase       ,	lowerCamelCase				)

         _lowerCAmelCase					     =		scheduler.step(lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase				)
         _lowerCAmelCase					     =		output.prev_sample

      _lowerCAmelCase					     =		torch.sum(torch.abs(lowerCamelCase				)				)
      _lowerCAmelCase					     =		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() - 4.693428650170972e-07				) < 1e-2
         assert abs(result_mean.item() - 0.0002				) < 1e-3







   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		self.scheduler_classes[0]
      _lowerCAmelCase					     =		self.get_scheduler_config()
      _lowerCAmelCase					     =		scheduler_class(**lowerCamelCase				)

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

      _lowerCAmelCase					     =		self.dummy_model()
      _lowerCAmelCase					     =		self.dummy_sample_deter.to(lowerCamelCase				) * scheduler.init_noise_sigma

      for t in scheduler.timesteps:
         _lowerCAmelCase					     =		scheduler.scale_model_input(lowerCamelCase       ,	lowerCamelCase				)

         _lowerCAmelCase					     =		model(lowerCamelCase       ,	lowerCamelCase				)

         _lowerCAmelCase					     =		scheduler.step(lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase				)
         _lowerCAmelCase					     =		output.prev_sample

      _lowerCAmelCase					     =		torch.sum(torch.abs(lowerCamelCase				)				)
      _lowerCAmelCase					     =		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 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		self.scheduler_classes[0]
      _lowerCAmelCase					     =		self.get_scheduler_config()
      _lowerCAmelCase					     =		scheduler_class(**lowerCamelCase       ,	use_karras_sigmas=lowerCamelCase				)

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

      _lowerCAmelCase					     =		self.dummy_model()
      _lowerCAmelCase					     =		self.dummy_sample_deter.to(lowerCamelCase				) * scheduler.init_noise_sigma
      _lowerCAmelCase					     =		sample.to(lowerCamelCase				)

      for t in scheduler.timesteps:
         _lowerCAmelCase					     =		scheduler.scale_model_input(lowerCamelCase       ,	lowerCamelCase				)

         _lowerCAmelCase					     =		model(lowerCamelCase       ,	lowerCamelCase				)

         _lowerCAmelCase					     =		scheduler.step(lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase				)
         _lowerCAmelCase					     =		output.prev_sample

      _lowerCAmelCase					     =		torch.sum(torch.abs(lowerCamelCase				)				)
      _lowerCAmelCase					     =		torch.mean(torch.abs(lowerCamelCase				)				)

      assert abs(result_sum.item() - 0.0_0015				) < 1e-2
      assert abs(result_mean.item() - 1.9869554535034695e-07				) < 1e-2