
"""simple docstring"""






import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida

from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    is_jinja_available,
)
from packaging import version
from requests import HTTPError

from .. import __version__
from .constants import (
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
)
from .import_utils import (
    ENV_VARS_TRUE_VALUES,
    _flax_version,
    _jax_version,
    _onnxruntime_version,
    _torch_version,
    is_flax_available,
    is_onnx_available,
    is_torch_available,
)
from .logging import get_logger


lowerCamelCase__ :       Any						     =			get_logger(__name__)


lowerCamelCase__ :       Dict						     =			Path(__file__).parent / "model_card_template.md"
lowerCamelCase__ :       Optional[int]						     =			uuida().hex
lowerCamelCase__ :       Tuple						     =			os.getenv("HF_HUB_OFFLINE", "").upper() in ENV_VARS_TRUE_VALUES
lowerCamelCase__ :       Any						     =			os.getenv("DISABLE_TELEMETRY", "").upper() in ENV_VARS_TRUE_VALUES
lowerCamelCase__ :       Optional[Any]						     =			HUGGINGFACE_CO_RESOLVE_ENDPOINT + "/api/telemetry/"





def __A						(   a_ :							Union[Dict, str, None] = None     )->							str:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Any				= F"diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}"
		if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
				return ua + "; telemetry/off"
		if is_torch_available():
				ua += F"; torch/{_torch_version}"
		if is_flax_available():
				ua += F"; jax/{_jax_version}"
				ua += F"; flax/{_flax_version}"
		if is_onnx_available():
				ua += F"; onnxruntime/{_onnxruntime_version}"
		# CI will set this value to True
		if os.environ.get('''DIFFUSERS_IS_CI'''		,		''''''     ).upper() in ENV_VARS_TRUE_VALUES:
				ua += "; is_ci/true"
		if isinstance(a_		,		a_     ):
				ua += "; " + "; ".join(F"{k}/{v}" for k, v in user_agent.items()     )
		elif isinstance(a_		,		a_     ):
				ua += "; " + user_agent
		return ua





def __A						(   a_ :							str		,		a_ :							Optional[str] = None		,		a_ :							Optional[str] = None     )->							str:




		'''simple docstring'''






		if token is None:
				SCREAMING_SNAKE_CASE       :     Tuple				= HfFolder.get_token()
		if organization is None:
				SCREAMING_SNAKE_CASE       :     Any				= whoami(a_     )['''name''']
				return F"{username}/{model_id}"
		else:
				return F"{organization}/{model_id}"





def __A						(   a_ :							Union[str, Any]		,		a_ :							str     )->							List[Any]:




		'''simple docstring'''






		if not is_jinja_available():
				raise ValueError(
				    '''Modelcard rendering is based on Jinja templates.'''
				    ''' Please make sure to have `jinja` installed before using `create_model_card`.'''
				    ''' To install it, please run `pip install Jinja2`.'''     )

		if hasattr(a_		,		'''local_rank'''     ) and args.local_rank not in [-1, 0]:
				return

		SCREAMING_SNAKE_CASE       :     Any				= args.hub_token if hasattr(a_		,		'''hub_token'''     ) else None
		SCREAMING_SNAKE_CASE       :     Optional[int]				= get_full_repo_name(a_		,		token=a_     )

		SCREAMING_SNAKE_CASE       :     List[str]				= ModelCard.from_template(
		    card_data=ModelCardData(  # Card metadata object that will be converted to YAML block
		        language='''en'''		,		license='''apache-2.0'''		,		library_name='''diffusers'''		,		tags=[]		,		datasets=args.dataset_name		,		metrics=[]		,		)		,		template_path=a_		,		model_name=a_		,		repo_name=a_		,		dataset_name=args.dataset_name if hasattr(a_		,		'''dataset_name'''     ) else None		,		learning_rate=args.learning_rate		,		train_batch_size=args.train_batch_size		,		eval_batch_size=args.eval_batch_size		,		gradient_accumulation_steps=(
		        args.gradient_accumulation_steps if hasattr(a_		,		'''gradient_accumulation_steps'''     ) else None
		    )		,		adam_betaa=args.adam_betaa if hasattr(a_		,		'''adam_beta1'''     ) else None		,		adam_betaa=args.adam_betaa if hasattr(a_		,		'''adam_beta2'''     ) else None		,		adam_weight_decay=args.adam_weight_decay if hasattr(a_		,		'''adam_weight_decay'''     ) else None		,		adam_epsilon=args.adam_epsilon if hasattr(a_		,		'''adam_epsilon'''     ) else None		,		lr_scheduler=args.lr_scheduler if hasattr(a_		,		'''lr_scheduler'''     ) else None		,		lr_warmup_steps=args.lr_warmup_steps if hasattr(a_		,		'''lr_warmup_steps'''     ) else None		,		ema_inv_gamma=args.ema_inv_gamma if hasattr(a_		,		'''ema_inv_gamma'''     ) else None		,		ema_power=args.ema_power if hasattr(a_		,		'''ema_power'''     ) else None		,		ema_max_decay=args.ema_max_decay if hasattr(a_		,		'''ema_max_decay'''     ) else None		,		mixed_precision=args.mixed_precision		,		)

		SCREAMING_SNAKE_CASE       :     List[str]				= os.path.join(args.output_dir		,		'''README.md'''     )
		model_card.save(a_     )





def __A						(   a_ :							Optional[str]		,		a_ :							Optional[str] = None     )->							int:




		'''simple docstring'''






		if resolved_file is None or commit_hash is not None:
				return commit_hash
		SCREAMING_SNAKE_CASE       :     str				= str(Path(a_     ).as_posix()     )
		SCREAMING_SNAKE_CASE       :     Any				= re.search(r'''snapshots/([^/]+)/'''		,		a_     )
		if search is None:
				return None
		SCREAMING_SNAKE_CASE       :     List[Any]				= search.groups()[0]
		return commit_hash if REGEX_COMMIT_HASH.match(a_     ) else None


# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
lowerCamelCase__ :       Optional[Any]						     =			os.path.expanduser(
    os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
lowerCamelCase__ :       List[Any]						     =			os.path.join(hf_cache_home, "diffusers")





def __A						(   a_ :							Optional[str] = None		,		a_ :							Optional[str] = None     )->							None:




		'''simple docstring'''






		if new_cache_dir is None:
				SCREAMING_SNAKE_CASE       :     Union[str, Any]				= DIFFUSERS_CACHE
		if old_cache_dir is None:
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= old_diffusers_cache

		SCREAMING_SNAKE_CASE       :     Optional[Any]				= Path(a_     ).expanduser()
		SCREAMING_SNAKE_CASE       :     List[Any]				= Path(a_     ).expanduser()
		for old_blob_path in old_cache_dir.glob('''**/blobs/*'''     ):
				if old_blob_path.is_file() and not old_blob_path.is_symlink():
						SCREAMING_SNAKE_CASE       :     Dict				= new_cache_dir / old_blob_path.relative_to(a_     )
						new_blob_path.parent.mkdir(parents=a_		,		exist_ok=a_     )
						os.replace(a_		,		a_     )
						try:
								os.symlink(a_		,		a_     )
						except OSError:
								logger.warning(
								    '''Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded.'''     )
		# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).


lowerCamelCase__ :       int						     =			os.path.join(DIFFUSERS_CACHE, "version_diffusers_cache.txt")
if not os.path.isfile(cache_version_file):
					lowerCamelCase__ :       Tuple						     =			0
else:
					with open(cache_version_file) as f:
										try:
															lowerCamelCase__ :       str						     =			int(f.read())
										except ValueError:
															lowerCamelCase__ :       Any						     =			0

if cache_version < 1:
					lowerCamelCase__ :       Union[str, Any]						     =			os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
					if old_cache_is_not_empty:
										logger.warning(
										    "The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your "
										    "existing cached models. This is a one-time operation, you can interrupt it or run it "
										    "later by calling `diffusers.utils.hub_utils.move_cache()`."
										)
										try:
															move_cache()
										except Exception as e:
															lowerCamelCase__ :       List[Any]						     =			"\n".join(traceback.format_tb(e.__traceback__))
															logger.error(
															    f'''There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease '''
															    "file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole "
															    "message and we will do our best to help."
															)

if cache_version < 1:
					try:
										os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
										with open(cache_version_file, "w") as f:
															f.write("1")
					except Exception:
										logger.warning(
										    f'''There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure '''
										    "the directory exists and can be written to."
										)





def __A						(   a_ :							str		,		a_ :							Optional[str] = None     )->							str:




		'''simple docstring'''






		if variant is not None:
				SCREAMING_SNAKE_CASE       :     Optional[int]				= weights_name.split('''.'''     )
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= splits[:-1] + [variant] + splits[-1:]
				SCREAMING_SNAKE_CASE       :     List[str]				= '''.'''.join(a_     )

		return weights_name





def __A						(   a_ :							int		,		*,
    a_ :							List[str]		,		a_ :							List[str]		,		a_ :							Tuple		,		a_ :							Optional[int]		,		a_ :							int		,		a_ :							int		,		a_ :							Tuple		,		a_ :							Union[str, Any]		,		a_ :							Dict		,		a_ :							Dict		,		a_ :							int=None		,		)->							List[str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= str(a_     )
		if os.path.isfile(a_     ):
				return pretrained_model_name_or_path
		elif os.path.isdir(a_     ):
				if os.path.isfile(os.path.join(a_		,		a_     )     ):
						# Load from a PyTorch checkpoint
						SCREAMING_SNAKE_CASE       :     Optional[Any]				= os.path.join(a_		,		a_     )
						return model_file
				elif subfolder is not None and os.path.isfile(
				    os.path.join(a_		,		a_		,		a_     )     ):
						SCREAMING_SNAKE_CASE       :     List[str]				= os.path.join(a_		,		a_		,		a_     )
						return model_file
				else:
						raise EnvironmentError(
						    F"Error no file named {weights_name} found in directory {pretrained_model_name_or_path}."     )
		else:
				# 1. First check if deprecated way of loading from branches is used
				if (
				    revision in DEPRECATED_REVISION_ARGS
				    and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
				    and version.parse(version.parse(a_     ).base_version     ) >= version.parse('''0.20.0'''     )
				):
						try:
								SCREAMING_SNAKE_CASE       :     List[str]				= hf_hub_download(
								    a_		,		filename=_add_variant(a_		,		a_     )		,		cache_dir=a_		,		force_download=a_		,		proxies=a_		,		resume_download=a_		,		local_files_only=a_		,		use_auth_token=a_		,		user_agent=a_		,		subfolder=a_		,		revision=revision or commit_hash		,		)
								warnings.warn(
								    F"Loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` is deprecated. Loading instead from `revision='main'` with `variant={revision}`. Loading model variants via `revision='{revision}'` will be removed in diffusers v1. Please use `variant='{revision}'` instead."		,		a_		,		)
								return model_file
						except:  # noqa: E722
								warnings.warn(
								    F"You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(a_		,		a_     )} file in the 'main' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {_add_variant(a_		,		a_     )}' so that the correct variant file can be added."		,		a_		,		)
				try:
						# 2. Load model file as usual
						SCREAMING_SNAKE_CASE       :     Optional[Any]				= hf_hub_download(
						    a_		,		filename=a_		,		cache_dir=a_		,		force_download=a_		,		proxies=a_		,		resume_download=a_		,		local_files_only=a_		,		use_auth_token=a_		,		user_agent=a_		,		subfolder=a_		,		revision=revision or commit_hash		,		)
						return model_file

				except RepositoryNotFoundError:
						raise EnvironmentError(
						    F"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier "
						    '''listed on \'https://huggingface.co/models\'\nIf this is a private repository, make sure to pass a '''
						    '''token having permission to this repo with `use_auth_token` or log in with `huggingface-cli '''
						    '''login`.'''     )
				except RevisionNotFoundError:
						raise EnvironmentError(
						    F"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for "
						    '''this model name. Check the model page at '''
						    F"'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions."     )
				except EntryNotFoundError:
						raise EnvironmentError(
						    F"{pretrained_model_name_or_path} does not appear to have a file named {weights_name}."     )
				except HTTPError as err:
						raise EnvironmentError(
						    F"There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}"     )
				except ValueError:
						raise EnvironmentError(
						    F"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"
						    F" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"
						    F" directory containing a file named {weights_name} or"
						    ''' \nCheckout your internet connection or see how to run the library in'''
						    ''' offline mode at \'https://huggingface.co/docs/diffusers/installation#offline-mode\'.'''     )
				except EnvironmentError:
						raise EnvironmentError(
						    F"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from "
						    '''\'https://huggingface.co/models\', make sure you don\'t have a local directory with the same name. '''
						    F"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
						    F"containing a file named {weights_name}"     )







"""simple docstring"""






from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
lowerCamelCase__ :       Optional[Any]						     =			200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
lowerCamelCase__ :       Optional[int]						     =			50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
lowerCamelCase__ :       Optional[Any]						     =			0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))





def __A						(   a_ :							str		,		a_ :							str     )->							tuple[str, float]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Tuple				= len([g for position, g in enumerate(a_     ) if g == main_target[position]]     )
		return (item, float(a_     ))





def __A						(   a_ :							str		,		a_ :							str     )->							tuple[str, str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Tuple				= random.randint(0		,		len(a_     ) - 1     )
		SCREAMING_SNAKE_CASE       :     str				= parent_a[:random_slice] + parent_a[random_slice:]
		SCREAMING_SNAKE_CASE       :     Dict				= parent_a[:random_slice] + parent_a[random_slice:]
		return (child_a, child_a)





def __A						(   a_ :							str		,		a_ :							list[str]     )->							str:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Any				= list(a_     )
		if random.uniform(0		,		1     ) < MUTATION_PROBABILITY:
				SCREAMING_SNAKE_CASE       :     Any				= random.choice(a_     )
		return "".join(a_     )





def __A						(   a_ :							tuple[str, float]		,		a_ :							list[tuple[str, float]]		,		a_ :							list[str]		,		)->							list[str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[int]				= []
		# Generate more children proportionally to the fitness score.
		SCREAMING_SNAKE_CASE       :     List[str]				= int(parent_a[1] * 1_00     ) + 1
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= 10 if child_n >= 10 else child_n
		for _ in range(a_     ):
				SCREAMING_SNAKE_CASE       :     List[str]				= population_score[random.randint(0		,		a_     )][0]

				SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Tuple				= crossover(parent_a[0]		,		a_     )
				# Append new string to the population list.
				pop.append(mutate(a_		,		a_     )     )
				pop.append(mutate(a_		,		a_     )     )
		return pop





def __A						(   a_ :							str		,		a_ :							list[str]		,		a_ :							bool = True     )->							tuple[int, int, str]:




		'''simple docstring'''






		if N_POPULATION < N_SELECTED:
				SCREAMING_SNAKE_CASE       :     List[Any]				= F"{N_POPULATION} must be bigger than {N_SELECTED}"
				raise ValueError(a_     )
		# Verify that the target contains no genes besides the ones inside genes variable.
		SCREAMING_SNAKE_CASE       :     List[str]				= sorted({c for c in target if c not in genes}     )
		if not_in_genes_list:
				SCREAMING_SNAKE_CASE       :     str				= F"{not_in_genes_list} is not in genes list, evolution cannot converge"
				raise ValueError(a_     )

		# Generate random starting population.
		SCREAMING_SNAKE_CASE       :     Tuple				= []
		for _ in range(a_     ):
				population.append(''''''.join([random.choice(a_     ) for i in range(len(a_     )     )]     )     )

		# Just some logs to know what the algorithms is doing.
		SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     List[str]				= 0, 0

		# This loop will end when we find a perfect match for our target.
		while True:
				generation += 1
				total_population += len(a_     )

				# Random population created. Now it's time to evaluate.

				# Adding a bit of concurrency can make everything faster,
				#
				# import concurrent.futures
				# population_score: list[tuple[str, float]] = []
				# with concurrent.futures.ThreadPoolExecutor(
				#                                   max_workers=NUM_WORKERS) as executor:
				#     futures = {executor.submit(evaluate, item) for item in population}
				#     concurrent.futures.wait(futures)
				#     population_score = [item.result() for item in futures]
				#
				# but with a simple algorithm like this, it will probably be slower.
				# We just need to call evaluate for every item inside the population.
				SCREAMING_SNAKE_CASE       :     int				= [evaluate(a_		,		a_     ) for item in population]

				# Check if there is a matching evolution.
				SCREAMING_SNAKE_CASE       :     List[Any]				= sorted(a_		,		key=lambda a_     : x[1]		,		reverse=a_     )
				if population_score[0][0] == target:
						return (generation, total_population, population_score[0][0])

				# Print the best result every 10 generation.
				# Just to know that the algorithm is working.
				if debug and generation % 10 == 0:
						print(
						    F"\nGeneration: {generation}"
						    F"\nTotal Population:{total_population}"
						    F"\nBest score: {population_score[0][1]}"
						    F"\nBest string: {population_score[0][0]}"     )

				# Flush the old population, keeping some of the best evolutions.
				# Keeping this avoid regression of evolution.
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= population[: int(N_POPULATION / 3     )]
				population.clear()
				population.extend(a_     )
				# Normalize population score to be between 0 and 1.
				SCREAMING_SNAKE_CASE       :     Optional[int]				= [
				    (item, score / len(a_     )) for item, score in population_score
				]

				# This is selection
				for i in range(a_     ):
						population.extend(select(population_score[int(a_     )]		,		a_		,		a_     )     )
						# Check if the population has already reached the maximum value and if so,
						# break the cycle.  If this check is disabled, the algorithm will take
						# forever to compute large strings, but will also calculate small strings in
						# a far fewer generations.
						if len(a_     ) > N_POPULATION:
								break


if __name__ == "__main__":
					lowerCamelCase__ :       Dict						     =			(
					    "This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
					)
					lowerCamelCase__ :       int						     =			list(
					    " ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
					    "nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
					)
					lowerCamelCase__     , lowerCamelCase__     , lowerCamelCase__ :       Dict						     =			basic(target_str, genes_list)
					print(
					    f'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
					)





