



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
_lowercase:				Union[str, Any]       =	"src/diffusers"
_lowercase:				Union[str, Any]       =	"docs/source/en"
_lowercase:				List[Any]       =	"."






def 					a(   A :					Union[str, Any]				,  A :					List[Any]				,  A :					Tuple )	-> Optional[Any]:
					"""simple docstring"""





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

					a        =     start_index
					while not lines[end_index].startswith(A ):
										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 |
_lowercase:				int       =	"Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
_lowercase:				Optional[Any]       =	re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
_lowercase:				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.
_lowercase:				Any       =	re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


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






def 					a(   A :					List[Any] )	-> Any:
					"""simple docstring"""





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






def 					a(   A :					Optional[int]				,  A :					str )	-> Dict:
					"""simple docstring"""





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






def 					a(   )	-> Tuple:
					"""simple docstring"""





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

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

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

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

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

					a        =     {True: "✅", False: "❌"}
					for name in model_names:
										a        =     model_name_to_prefix[name]
										a        =     [
										    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(A				,  A ) for l, w in zip(A				,  A )] ) + "|\n"
					return table






def 					a(   A :					Any=False )	-> Dict:
					"""simple docstring"""





					a  ,       a  ,       a  ,       a        =     _find_text_in_file(
					    filename=os.path.join(A				,  "index.mdx" )				,  start_prompt="<!--This table is updated automatically from the auto modules"				,  end_prompt="<!-- End table-->"				,  )
					a        =     get_model_table_from_auto_modules()

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

				check_model_table(args.fix_and_overwrite)





import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""
	__A					=							(EulerAncestralDiscreteScheduler,)
	__A					=							10




	def   UpperCamelCase_ (self	, **lowerCamelCase_	):
						"""simple docstring"""

						a        =     {
						    "num_train_timesteps": 1100,
						    "beta_start": 0.0001,
						    "beta_end": 0.02,
						    "beta_schedule": "linear",
						}

						config.update(**lowerCamelCase_	)
						return config




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						for timesteps in [10, 50, 100, 1000]:
											self.check_over_configs(num_train_timesteps=lowerCamelCase_	)




	def   UpperCamelCase_ (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   UpperCamelCase_ (self	):
						"""simple docstring"""

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




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

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




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.scheduler_classes[0]
						a        =     self.get_scheduler_config()
						a        =     scheduler_class(**lowerCamelCase_	)

						scheduler.set_timesteps(self.num_inference_steps	)

						a        =     torch.manual_seed(0	)

						a        =     self.dummy_model()
						a        =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						a        =     sample.to(lowerCamelCase_	)

						for i, t in enumerate(scheduler.timesteps	):
											a        =     scheduler.scale_model_input(lowerCamelCase_	, lowerCamelCase_	)

											a        =     model(lowerCamelCase_	, lowerCamelCase_	)

											a        =     scheduler.step(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, generator=lowerCamelCase_	)
											a        =     output.prev_sample

						a        =     torch.sum(torch.abs(lowerCamelCase_	)	)
						a        =     torch.mean(torch.abs(lowerCamelCase_	)	)

						assert abs(result_sum.item() - 152.3192	) < 1E-2
						assert abs(result_mean.item() - 0.1983	) < 1E-3




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.scheduler_classes[0]
						a        =     self.get_scheduler_config(prediction_type="v_prediction"	)
						a        =     scheduler_class(**lowerCamelCase_	)

						scheduler.set_timesteps(self.num_inference_steps	)

						a        =     torch.manual_seed(0	)

						a        =     self.dummy_model()
						a        =     self.dummy_sample_deter * scheduler.init_noise_sigma
						a        =     sample.to(lowerCamelCase_	)

						for i, t in enumerate(scheduler.timesteps	):
											a        =     scheduler.scale_model_input(lowerCamelCase_	, lowerCamelCase_	)

											a        =     model(lowerCamelCase_	, lowerCamelCase_	)

											a        =     scheduler.step(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, generator=lowerCamelCase_	)
											a        =     output.prev_sample

						a        =     torch.sum(torch.abs(lowerCamelCase_	)	)
						a        =     torch.mean(torch.abs(lowerCamelCase_	)	)

						assert abs(result_sum.item() - 108.4439	) < 1E-2
						assert abs(result_mean.item() - 0.1412	) < 1E-3




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.scheduler_classes[0]
						a        =     self.get_scheduler_config()
						a        =     scheduler_class(**lowerCamelCase_	)

						scheduler.set_timesteps(self.num_inference_steps	, device=lowerCamelCase_	)
						a        =     torch.manual_seed(0	)

						a        =     self.dummy_model()
						a        =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						a        =     sample.to(lowerCamelCase_	)

						for t in scheduler.timesteps:
											a        =     scheduler.scale_model_input(lowerCamelCase_	, lowerCamelCase_	)

											a        =     model(lowerCamelCase_	, lowerCamelCase_	)

											a        =     scheduler.step(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, generator=lowerCamelCase_	)
											a        =     output.prev_sample

						a        =     torch.sum(torch.abs(lowerCamelCase_	)	)
						a        =     torch.mean(torch.abs(lowerCamelCase_	)	)

						assert abs(result_sum.item() - 152.3192	) < 1E-2
						assert abs(result_mean.item() - 0.1983	) < 1E-3
