






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
a_      = 'src/diffusers'
a_      = 'docs/source/en'
a_      = '.'



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				int:

						'''simple docstring'''


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

						a_				     =   start_index
						while not lines[end_index].startswith(lowercase__				):
												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 |
a_      = 'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
a_      = re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
a_      = 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.
a_      = re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


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



def 			__UpperCAmelCase				(lowercase__				)		->				str:

						'''simple docstring'''


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



def 			__UpperCAmelCase				(lowercase__	,lowercase__				)		->				Optional[Any]:

						'''simple docstring'''


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



def 			__UpperCAmelCase				()		->				int:

						'''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(lowercase__				)
						a_				     =   collections.defaultdict(lowercase__				)
						a_				     =   collections.defaultdict(lowercase__				)
						a_				     =   collections.defaultdict(lowercase__				)
						a_				     =   collections.defaultdict(lowercase__				)

						# Let's lookup through all diffusers object (once).
						for attr_name in dir(lowercase__				):
												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(lowercase__				) is not None:
																		a_				     =   tf_models
																		a_				     =   _re_tf_models.match(lowercase__				).groups()[0]
												elif _re_flax_models.match(lowercase__				) is not None:
																		a_				     =   flax_models
																		a_				     =   _re_flax_models.match(lowercase__				).groups()[0]
												elif _re_pt_models.match(lowercase__				) is not None:
																		a_				     =   pt_models
																		a_				     =   _re_pt_models.match(lowercase__				).groups()[0]

												if lookup_dict is not None:
																		while len(lowercase__				) > 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(lowercase__				)[:-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(lowercase__				) + 2 for c in columns]
						a_				     =   max([len(lowercase__				) for name in model_names]				) + 2

						# Build the table per se
						a_				     =   "|" + "|".join([_center_text(lowercase__	,lowercase__				) for c, w in zip(lowercase__	,lowercase__				)]				) + "|\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(lowercase__	,lowercase__				) for l, w in zip(lowercase__	,lowercase__				)]				) + "|\n"
						return table



def 			__UpperCAmelCase				(lowercase__=False				)		->				str:

						'''simple docstring'''


						a_     ,       a_     ,       a_     ,       a_				     =   _find_text_in_file(
						    filename=os.path.join(lowercase__	,"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(lowercase__	,"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__":
							a_      = argparse.ArgumentParser()
							parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
							a_      = parser.parse_args()

							check_model_table(args.fix_and_overwrite)











'''simple docstring'''





import requests
from bsa import BeautifulSoup



def 			__UpperCAmelCase				(lowercase__	,lowercase__				)		->				str:

						'''simple docstring'''


						a_				     =   (
						    "https://www.horoscope.com/us/horoscopes/general/"
						    F"""horoscope-general-daily-{day}.aspx?sign={zodiac_sign}"""
						)
						a_				     =   BeautifulSoup(requests.get(lowercase__				).content	,"html.parser"				)
						return soup.find("div"	,class_="main-horoscope"				).p.text


if __name__ == "__main__":
							print('Daily Horoscope. \n')
							print(
							    'enter your Zodiac sign number:\n',
							    '1. Aries\n',
							    '2. Taurus\n',
							    '3. Gemini\n',
							    '4. Cancer\n',
							    '5. Leo\n',
							    '6. Virgo\n',
							    '7. Libra\n',
							    '8. Scorpio\n',
							    '9. Sagittarius\n',
							    '10. Capricorn\n',
							    '11. Aquarius\n',
							    '12. Pisces\n',
							)
							a_      = int(input('number> ').strip())
							print('choose some day:\n', 'yesterday\n', 'today\n', 'tomorrow\n')
							a_      = input('enter the day> ')
							a_      = horoscope(zodiac_sign, day)
							print(horoscope_text)



