


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





def 				a_							(    lowercase				,    lowercase				,    lowercase     )      ->List[str]:






				'''simple docstring'''

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

				__lowerCamelCase		:   List[str]           =       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 |
__lowercase	:   Tuple   		=					"""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
__lowercase	:   Optional[Any]   		=					re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
__lowercase	:   Dict   		=					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	:   Optional[Any]   		=					re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


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





def 				a_							(    lowercase     )      ->Tuple:






				'''simple docstring'''

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





def 				a_							(    lowercase				,    lowercase     )      ->str:






				'''simple docstring'''

				__lowerCamelCase		:   List[Any]           =       2 if text == "✅" or text == "❌" else len(lowercase     )
				__lowerCamelCase		:   str           =       (width - text_length) // 2
				__lowerCamelCase		:   Union[str, Any]           =       width - text_length - left_indent
				return " " * left_indent + text + " " * right_indent





def 				a_							(    )      ->Tuple:






				'''simple docstring'''

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

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

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

				# Build the table per se
				__lowerCamelCase		:   Union[str, Any]           =       "|" + "|".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"

				__lowerCamelCase		:   List[str]           =       {True: "✅", False: "❌"}
				for name in model_names:
								__lowerCamelCase		:   Union[str, Any]           =       model_name_to_prefix[name]
								__lowerCamelCase		:   int           =       [
								    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 				a_							(    lowercase=False     )      ->Union[str, Any]:






				'''simple docstring'''

				__lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase		:   int           =       _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-->"				,    )
				__lowerCamelCase		:   Union[str, Any]           =       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__":
				__lowercase	:   Union[str, Any]   		=					argparse.ArgumentParser()
				parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
				__lowercase	:   Tuple   		=					parser.parse_args()

				check_model_table(args.fix_and_overwrite)









'''simple docstring'''




import requests

try:
				from django.contrib.auth import authenticate, login
				from django.shortcuts import redirect, render
except ImportError:
				__lowercase	:   Dict   		=					print





def 				a_							(    lowercase     )      ->Any:






				'''simple docstring'''

				__lowerCamelCase		:   str           =       "secretKey"
				__lowerCamelCase		:   Optional[Any]           =       "https://www.google.com/recaptcha/api/siteverify"

				# when method is not POST, direct user to login page
				if request.method != "POST":
								return render(lowercase				,    "login.html"     )

				# from the frontend, get username, password, and client_key
				__lowerCamelCase		:   str           =       request.POST.get("username"     )
				__lowerCamelCase		:   Dict           =       request.POST.get("password"     )
				__lowerCamelCase		:   List[str]           =       request.POST.get("g-recaptcha-response"     )

				# post recaptcha response to Google's recaptcha api
				__lowerCamelCase		:   str           =       requests.post(lowercase				,    data={"secret": secret_key, "response": client_key}     )
				# if the recaptcha api verified our keys
				if response.json().get("success"				,    lowercase     ):
								# authenticate the user
								__lowerCamelCase		:   List[Any]           =       authenticate(lowercase				,    username=lowercase				,    password=lowercase     )
								if user_in_database:
												login(lowercase				,    lowercase     )
												return redirect("/your-webpage"     )
				return render(lowercase				,    "login.html"     )





