


"""simple docstring"""
import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


__SCREAMING_SNAKE_CASE							:      Any						=      logging.get_logger(__name__)







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE=False			)      ->			List[Any]:
							try:
														import torch  # noqa: F401
							except ImportError:
														logger.error(
														    """Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see"""
														    """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
														    """ instructions."""			)
														raise

							if not is_sharded:
														snake_case_          =						os.path.abspath(_a			)
														logger.info(f"""Loading PyTorch weights from {pt_path}"""			)

														snake_case_          =						torch.load(_a			,	map_location="""cpu"""			)
														logger.info(f"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()			):,} parameters."""			)

														snake_case_          =						convert_pytorch_state_dict_to_flax(_a			,	_a			)
							else:
														# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
														snake_case_          =						convert_pytorch_sharded_state_dict_to_flax(_a			,	_a			)
							return flax_state_dict







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	)      ->			Dict:

							def is_key_or_prefix_key_in_dict(_SCREAMING_SNAKE_CASE			) -> bool:
														return len(set(_a			) & {key, (model_prefix,) + key}			) > 0

							# layer norm
							snake_case_          =						pt_tuple_key[:-1] + ("scale",)
							if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(_a			):
														return renamed_pt_tuple_key, pt_tensor

							# batch norm layer mean
							snake_case_          =						pt_tuple_key[:-1] + ("mean",)
							if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(_a			):
														return renamed_pt_tuple_key, pt_tensor

							# batch norm layer var
							snake_case_          =						pt_tuple_key[:-1] + ("var",)
							if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(_a			):
														return renamed_pt_tuple_key, pt_tensor

							# embedding
							snake_case_          =						pt_tuple_key[:-1] + ("embedding",)
							if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(_a			):
														return renamed_pt_tuple_key, pt_tensor

							# conv layer
							snake_case_          =						pt_tuple_key[:-1] + ("kernel",)
							if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(_a			):
														snake_case_          =						pt_tensor.transpose(2			,	3			,	1			,	0			)
														return renamed_pt_tuple_key, pt_tensor

							# linear layer
							snake_case_          =						pt_tuple_key[:-1] + ("kernel",)
							if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(_a			):
														snake_case_          =						pt_tensor.T
														return renamed_pt_tuple_key, pt_tensor

							# old PyTorch layer norm weight
							snake_case_          =						pt_tuple_key[:-1] + ("weight",)
							if pt_tuple_key[-1] == "gamma":
														return renamed_pt_tuple_key, pt_tensor

							# old PyTorch layer norm bias
							snake_case_          =						pt_tuple_key[:-1] + ("bias",)
							if pt_tuple_key[-1] == "beta":
														return renamed_pt_tuple_key, pt_tensor

							# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
							snake_case_          =						None
							if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
														snake_case_          =						pt_tuple_key[-2] + "_g"
							elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
														snake_case_          =						pt_tuple_key[-2] + "_v"
							if name is not None:
														snake_case_          =						pt_tuple_key[:-3] + (name,)
														return renamed_pt_tuple_key, pt_tensor

							return pt_tuple_key, pt_tensor







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			List[Any]:
							# convert pytorch tensor to numpy
							snake_case_          =						{k: v.numpy() for k, v in pt_state_dict.items()}

							snake_case_          =						flax_model.base_model_prefix

							# use params dict if the model contains batch norm layers
							if "params" in flax_model.params:
														snake_case_          =						flax_model.params["params"]
							else:
														snake_case_          =						flax_model.params
							snake_case_          =						flatten_dict(_a			)

							# add batch_stats keys,values to dict
							if "batch_stats" in flax_model.params:
														snake_case_          =						flatten_dict(flax_model.params["""batch_stats"""]			)
														random_flax_state_dict.update(_a			)

							snake_case_          =						{}

							snake_case_          =						(model_prefix not in flax_model_params) and (
							    model_prefix in {k.split("""."""			)[0] for k in pt_state_dict.keys()}
							)
							snake_case_          =						(model_prefix in flax_model_params) and (
							    model_prefix not in {k.split("""."""			)[0] for k in pt_state_dict.keys()}
							)

							# Need to change some parameters name to match Flax names
							for pt_key, pt_tensor in pt_state_dict.items():
														snake_case_          =						tuple(pt_key.split("""."""			)			)

														# remove base model prefix if necessary
														snake_case_          =						pt_tuple_key[0] == model_prefix
														if load_model_with_head_into_base_model and has_base_model_prefix:
																					snake_case_          =						pt_tuple_key[1:]

														# Correctly rename weight parameters
														snake_case_          =						rename_key_and_reshape_tensor(
														    _a			,	_a			,	_a			,	_a			)

														# add model prefix if necessary
														snake_case_          =						(model_prefix,) + flax_key in random_flax_state_dict
														if load_base_model_into_model_with_head and require_base_model_prefix:
																					snake_case_          =						(model_prefix,) + flax_key

														if flax_key in random_flax_state_dict:
																					if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																												raise ValueError(
																												    f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
																												    f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""			)

        # add batch stats if the model contains batchnorm layers
														if "batch_stats" in flax_model.params:
																					if "mean" in flax_key[-1] or "var" in flax_key[-1]:
																												snake_case_          =						jnp.asarray(_a			)
																												continue
																					# remove num_batches_tracked key
																					if "num_batches_tracked" in flax_key[-1]:
																												flax_state_dict.pop(_a			,	_a			)
																												continue

																					# also add unexpected weight so that warning is thrown
																					snake_case_          =						jnp.asarray(_a			)

														else:
																					# also add unexpected weight so that warning is thrown
																					snake_case_          =						jnp.asarray(_a			)

							return unflatten_dict(_a			)







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			Any:
							import torch

							# Load the index
							snake_case_          =						{}
							for shard_file in shard_filenames:
														# load using msgpack utils
														snake_case_          =						torch.load(_a			)
														snake_case_          =						{k: v.numpy() for k, v in pt_state_dict.items()}

														snake_case_          =						flax_model.base_model_prefix

														# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
														if "batch_stats" in flax_model.params:
																					snake_case_          =						flax_model.params["params"]

																					snake_case_          =						flatten_dict(_a			)
																					random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""]			)			)
														else:
																					snake_case_          =						flax_model.params
																					snake_case_          =						flatten_dict(_a			)

														snake_case_          =						(model_prefix not in flax_model_params) and (
														    model_prefix in {k.split("""."""			)[0] for k in pt_state_dict.keys()}
														)
														snake_case_          =						(model_prefix in flax_model_params) and (
														    model_prefix not in {k.split("""."""			)[0] for k in pt_state_dict.keys()}
														)
														# Need to change some parameters name to match Flax names
														for pt_key, pt_tensor in pt_state_dict.items():
																					snake_case_          =						tuple(pt_key.split("""."""			)			)

																					# remove base model prefix if necessary
																					snake_case_          =						pt_tuple_key[0] == model_prefix
																					if load_model_with_head_into_base_model and has_base_model_prefix:
																												snake_case_          =						pt_tuple_key[1:]

																					# Correctly rename weight parameters
																					snake_case_          =						rename_key_and_reshape_tensor(
																					    _a			,	_a			,	_a			,	_a			)
																					# add model prefix if necessary
																					snake_case_          =						(model_prefix,) + flax_key in random_flax_state_dict
																					if load_base_model_into_model_with_head and require_base_model_prefix:
																												snake_case_          =						(model_prefix,) + flax_key

																					if flax_key in random_flax_state_dict:
																												if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																																			raise ValueError(
																																			    f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
																																			    f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""			)

            # add batch stats if the model contains batchnorm layers
																					if "batch_stats" in flax_model.params:
																												if "mean" in flax_key[-1]:
																																			snake_case_          =						jnp.asarray(_a			)
																																			continue
																												if "var" in flax_key[-1]:
																																			snake_case_          =						jnp.asarray(_a			)
																																			continue
																												# remove num_batches_tracked key
																												if "num_batches_tracked" in flax_key[-1]:
																																			flax_state_dict.pop(_a			,	_a			)
																																			continue

																												# also add unexpected weight so that warning is thrown
																												snake_case_          =						jnp.asarray(_a			)

																					else:
																												# also add unexpected weight so that warning is thrown
																												snake_case_          =						jnp.asarray(_a			)
							return unflatten_dict(_a			)







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			Dict:
							snake_case_          =						os.path.abspath(_a			)
							logger.info(f"""Loading Flax weights from {flax_checkpoint_path}"""			)

							# import correct flax class
							snake_case_          =						getattr(_a			,	"""Flax""" + model.__class__.__name__			)

							# load flax weight dict
							with open(_a			,	"""rb"""			) as state_f:
														try:
																					snake_case_          =						from_bytes(_a			,	state_f.read()			)
														except UnpicklingError:
																					raise EnvironmentError(f"""Unable to convert {flax_checkpoint_path} to Flax deserializable object. """			)

							return load_flax_weights_in_pytorch_model(_a			,	_a			)







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			str:

							try:
														import torch  # noqa: F401
							except ImportError:
														logger.error(
														    """Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see"""
														    """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
														    """ instructions."""			)
														raise

							# check if we have bf16 weights
							snake_case_          =						flatten_dict(jax.tree_util.tree_map(lambda _SCREAMING_SNAKE_CASE			: x.dtype == jnp.bfloataa			,	_a			)			).values()
							if any(_a			):
														# convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
														# and bf16 is not fully supported in PT yet.
														logger.warning(
														    """Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
														    """before loading those in PyTorch model."""			)
														snake_case_          =						jax.tree_util.tree_map(
														    lambda _SCREAMING_SNAKE_CASE			: params.astype(np.floataa			) if params.dtype == jnp.bfloataa else params			,	_a			)

							snake_case_          =						flatten_dict(_a			)
							snake_case_          =						pt_model.state_dict()

							snake_case_          =						(pt_model.base_model_prefix in flax_state) and (
							    pt_model.base_model_prefix not in {k.split("""."""			)[0] for k in pt_model_dict.keys()}
							)
							snake_case_          =						(pt_model.base_model_prefix not in flax_state) and (
							    pt_model.base_model_prefix in {k.split("""."""			)[0] for k in pt_model_dict.keys()}
							)

							# keep track of unexpected & missing keys
							snake_case_          =						[]
							snake_case_          =						set(pt_model_dict.keys()			)

							for flax_key_tuple, flax_tensor in flax_state_dict.items():
														snake_case_          =						flax_key_tuple[0] == pt_model.base_model_prefix
														snake_case_          =						".".join((pt_model.base_model_prefix,) + flax_key_tuple			) in pt_model_dict

														# adapt flax_key to prepare for loading from/to base model only
														if load_model_with_head_into_base_model and has_base_model_prefix:
																					snake_case_          =						flax_key_tuple[1:]
														elif load_base_model_into_model_with_head and require_base_model_prefix:
																					snake_case_          =						(pt_model.base_model_prefix,) + flax_key_tuple

														# rename flax weights to PyTorch format
														if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(_a			) not in pt_model_dict:
																					# conv layer
																					snake_case_          =						flax_key_tuple[:-1] + ("weight",)
																					snake_case_          =						jnp.transpose(_a			,	(3, 2, 0, 1)			)
														elif flax_key_tuple[-1] == "kernel" and ".".join(_a			) not in pt_model_dict:
																					# linear layer
																					snake_case_          =						flax_key_tuple[:-1] + ("weight",)
																					snake_case_          =						flax_tensor.T
														elif flax_key_tuple[-1] in ["scale", "embedding"]:
																					snake_case_          =						flax_key_tuple[:-1] + ("weight",)

														# adding batch stats from flax batch norm to pt
														elif "mean" in flax_key_tuple[-1]:
																					snake_case_          =						flax_key_tuple[:-1] + ("running_mean",)
														elif "var" in flax_key_tuple[-1]:
																					snake_case_          =						flax_key_tuple[:-1] + ("running_var",)

														if "batch_stats" in flax_state:
																					snake_case_          =						".".join(flax_key_tuple[1:]			)  # Remove the params/batch_stats header
														else:
																					snake_case_          =						".".join(_a			)

														# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
														snake_case_          =						{}
														# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
														for key in pt_model_dict:
																					snake_case_          =						key.split("""."""			)
																					snake_case_          =						None
																					if key_components[-3::2] == ["parametrizations", "original0"]:
																												snake_case_          =						key_components[-2] + "_g"
																					elif key_components[-3::2] == ["parametrizations", "original1"]:
																												snake_case_          =						key_components[-2] + "_v"
																					if name is not None:
																												snake_case_          =						key_components[:-3] + [name]
																												snake_case_          =						".".join(_a			)
																												snake_case_          =						key

														if flax_key in special_pt_names:
																					snake_case_          =						special_pt_names[flax_key]

														if flax_key in pt_model_dict:
																					if flax_tensor.shape != pt_model_dict[flax_key].shape:
																												raise ValueError(
																												    f"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
																												    f"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."""			)
																					else:
																												# add weight to pytorch dict
																												snake_case_          =						np.asarray(_a			) if not isinstance(_a			,	np.ndarray			) else flax_tensor
																												snake_case_          =						torch.from_numpy(_a			)
																												# remove from missing keys
																												missing_keys.remove(_a			)
														else:
																					# weight is not expected by PyTorch model
																					unexpected_keys.append(_a			)

							pt_model.load_state_dict(_a			)

							# re-transform missing_keys to list
							snake_case_          =						list(_a			)

							if len(_a			) > 0:
														logger.warning(
														    """Some weights of the Flax model were not used when initializing the PyTorch model"""
														    f""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
														    f""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
														    """ (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
														    f""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
														    """ to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
														    """ FlaxBertForSequenceClassification model)."""			)
							else:
														logger.warning(f"""All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n"""			)
							if len(_a			) > 0:
														logger.warning(
														    f"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
														    f""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
														    """ use it for predictions and inference."""			)
							else:
														logger.warning(
														    f"""All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"""
														    """If your task is similar to the task the model of the checkpoint was trained on, """
														    f"""you can already use {pt_model.__class__.__name__} for predictions without further training."""			)

							return pt_model







"""simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation







def   _a			( _SCREAMING_SNAKE_CASE = 8			)      ->			str:
							snake_case_          =						ascii_letters + digits + punctuation
							return "".join(secrets.choice(_SCREAMING_SNAKE_CASE			) for _ in range(_SCREAMING_SNAKE_CASE			)			)







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			str:
							# Password Generator = full boot with random_number, random_letters, and
							# random_character FUNCTIONS
							# Put your code here...
							i -= len(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						i // 3
							snake_case_          =						i % 3
							# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
							#     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
							snake_case_          =						(
							    chars_incl
							    + random(_SCREAMING_SNAKE_CASE			,	quotient + remainder			)
							    + random(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)
							    + random(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)
							)
							snake_case_          =						list(_SCREAMING_SNAKE_CASE			)
							shuffle(_SCREAMING_SNAKE_CASE			)
							return "".join(_SCREAMING_SNAKE_CASE			)

							# random is a generalised function for letters, characters and numbers







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			str:
							return "".join(secrets.choice(_SCREAMING_SNAKE_CASE			) for _ in range(_SCREAMING_SNAKE_CASE			)			)







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			Any:
							pass  # Put your code here...







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			Optional[int]:
							pass  # Put your code here...







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			List[Any]:
							pass  # Put your code here...







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE = 8			)      ->			bool:

							if len(_SCREAMING_SNAKE_CASE			) < min_length:
														# Your Password must be at least 8 characters long
														return False

							snake_case_          =						any(char in ascii_uppercase for char in password			)
							snake_case_          =						any(char in ascii_lowercase for char in password			)
							snake_case_          =						any(char in digits for char in password			)
							snake_case_          =						any(char in punctuation for char in password			)

							return upper and lower and num and spec_char
							# Passwords should contain UPPERCASE, lowerase
							# numbers, and special characters







def   _a			( )      ->			Union[str, Any]:
							snake_case_          =						int(input("""Please indicate the max length of your password: """			).strip()			)
							snake_case_          =						input(
							    """Please indicate the characters that must be in your password: """			).strip()
							print("""Password generated:"""			,	password_generator(_SCREAMING_SNAKE_CASE			)			)
							print(
							    """Alternative Password generated:"""			,	alternative_password_generator(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)			,	)
							print("""[If you are thinking of using this passsword, You better save it.]"""			)


if __name__ == "__main__":
				main()



