



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_      			=  "src/diffusers"
UpperCamelCase_      			=  "docs/source/en"
UpperCamelCase_      			=  "."







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )			->			Tuple:



						'''simple docstring'''






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

						UpperCAmelCase_					=	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_      			=  "Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
UpperCamelCase_      			=  re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
UpperCamelCase_      			=  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_      			=  re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


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







def 							A     (						__UpperCAmelCase      )			->			str:



						'''simple docstring'''






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







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase      )			->			Dict:



						'''simple docstring'''






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







def 							A     (						)			->			Optional[Any]:



						'''simple docstring'''






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

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

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

						# Build the table per se
						UpperCAmelCase_					=	'''|''' + '''|'''.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"

						UpperCAmelCase_					=	{True: '''✅''', False: '''❌'''}
						for name in model_names:
												UpperCAmelCase_					=	model_name_to_prefix[name]
												UpperCAmelCase_					=	[
												    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 							A     (						__UpperCAmelCase=False      )			->			Dict:



						'''simple docstring'''






						UpperCAmelCase_				, UpperCAmelCase_				, UpperCAmelCase_				, UpperCAmelCase_					=	_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-->'''				,   )
						UpperCAmelCase_					=	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_      			=  argparse.ArgumentParser()
							parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
							UpperCamelCase_      			=  parser.parse_args()

							check_model_table(args.fix_and_overwrite)










import random







class 				a_    :

						def __init__(       self      :Union[str, Any]  ,		_lowercase      :float  ,		_lowercase      :float)						->   None:
												UpperCAmelCase_					=	x
												UpperCAmelCase_					=	y

						def       __a			(       self      :str)						->   bool:
												return (self.x**2 + self.y**2) <= 1




						@classmethod
						def       __a			(       cls      :Optional[Any])						->   str:
												return cls(x=random.random()  ,		y=random.random())







def 							A     (						__UpperCAmelCase      )			->			float:



						'''simple docstring'''






						if number_of_simulations < 1:
												raise ValueError('''At least one simulation is necessary to estimate PI.'''      )

						UpperCAmelCase_					=	0
						for _ in range(__UpperCAmelCase      ):
												UpperCAmelCase_					=	Point.random_unit_square()

												if random_point.is_in_unit_circle():
																		number_in_unit_circle += 1

						return 4 * number_in_unit_circle / number_of_simulations


if __name__ == "__main__":
							# import doctest

							# doctest.testmod()
							from math import pi

							UpperCamelCase_      			=  "Please enter the desired number of Monte Carlo simulations: "
							UpperCamelCase_      			=  estimate_pi(int(input(prompt).strip()))
							print(f"An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}")





