
from pickle import UnpicklingError

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

from ..utils import logging


_UpperCAmelCase      :				int          = logging.get_logger(__name__)


def    A (      lowercase					, lowercase				)     ->						Optional[Any]:
		'''simple docstring'''

		try:
				with open(lowercase					, 'rb'				) as flax_state_f:
						UpperCamelCase						=  from_bytes(lowercase					, flax_state_f.read()				)
		except UnpicklingError as e:
				try:
						with open(lowercase				) as f:
								if f.read().startswith('version'				):
										raise OSError(
										    'You seem to have cloned a repository without having git-lfs installed. Please'
										    ' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'
										    ' folder you cloned.'				)
								else:
										raise ValueError from e
				except (UnicodeDecodeError, ValueError):
						raise EnvironmentError(f'''Unable to convert {model_file} to Flax deserializable object. '''				)

		return load_flax_weights_in_pytorch_model(lowercase					, lowercase				)


def    A (      lowercase					, lowercase				)     ->						Union[str, Any]:
		'''simple docstring'''

		try:
				import torch  # noqa: F401
		except ImportError:
				logger.error(
				    'Loading 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
		UpperCamelCase						=  flatten_dict(jax.tree_util.tree_map(lambda lowercase				: x.dtype == jnp.bfloataa					, lowercase				)				).values()
		if any(lowercase				):
				# convert all weights to fp32 if they 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.'				)
				UpperCamelCase						=  jax.tree_util.tree_map(
				    lambda lowercase				: params.astype(np.floataa				) if params.dtype == jnp.bfloataa else params					, lowercase				)

		UpperCamelCase						=  ''

		UpperCamelCase						=  flatten_dict(lowercase					, sep='.'				)
		UpperCamelCase						=  pt_model.state_dict()

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

		for flax_key_tuple, flax_tensor in flax_state_dict.items():
				UpperCamelCase						=  flax_key_tuple.split('.'				)

				if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
						UpperCamelCase						=  flax_key_tuple_array[:-1] + ['weight']
						UpperCamelCase						=  jnp.transpose(lowercase					, (3, 2, 0, 1)				)
				elif flax_key_tuple_array[-1] == "kernel":
						UpperCamelCase						=  flax_key_tuple_array[:-1] + ['weight']
						UpperCamelCase						=  flax_tensor.T
				elif flax_key_tuple_array[-1] == "scale":
						UpperCamelCase						=  flax_key_tuple_array[:-1] + ['weight']

				if "time_embedding" not in flax_key_tuple_array:
						for i, flax_key_tuple_string in enumerate(lowercase				):
								UpperCamelCase						=  (
								    flax_key_tuple_string.replace('_0'					, '.0'				)
								    .replace('_1'					, '.1'				)
								    .replace('_2'					, '.2'				)
								    .replace('_3'					, '.3'				)
								    .replace('_4'					, '.4'				)
								    .replace('_5'					, '.5'				)
								    .replace('_6'					, '.6'				)
								    .replace('_7'					, '.7'				)
								    .replace('_8'					, '.8'				)
								    .replace('_9'					, '.9'				)
								)

				UpperCamelCase						=  '.'.join(lowercase				)

				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
								UpperCamelCase						=  np.asarray(lowercase				) if not isinstance(lowercase					, np.ndarray				) else flax_tensor
								UpperCamelCase						=  torch.from_numpy(lowercase				)
								# remove from missing keys
								missing_keys.remove(lowercase				)
				else:
						# weight is not expected by PyTorch model
						unexpected_keys.append(lowercase				)

		pt_model.load_state_dict(lowercase				)

		# re-transform missing_keys to list
		UpperCamelCase						=  list(lowercase				)

		if len(lowercase				) > 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).'				)
		if len(lowercase				) > 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.'				)

		return pt_model


from string import ascii_uppercase

_UpperCAmelCase      :				Dict          = {char: i for i, char in enumerate(ascii_uppercase)}
_UpperCAmelCase      :				Tuple          = dict(enumerate(ascii_uppercase))


def    A (      lowercase					, lowercase				)     ->						str:
		'''simple docstring'''

		UpperCamelCase						=  len(lowercase				)
		UpperCamelCase						=  0
		while True:
				if x == i:
						UpperCamelCase						=  0
				if len(lowercase				) == len(lowercase				):
						break
				key += key[i]
				i += 1
		return key


def    A (      lowercase					, lowercase				)     ->						str:
		'''simple docstring'''

		UpperCamelCase						=  ''
		UpperCamelCase						=  0
		for letter in message:
				if letter == " ":
						cipher_text += " "
				else:
						UpperCamelCase						=  (dicta[letter] - dicta[key_new[i]]) % 26
						i += 1
						cipher_text += dicta[x]
		return cipher_text


def    A (      lowercase					, lowercase				)     ->						str:
		'''simple docstring'''

		UpperCamelCase						=  ''
		UpperCamelCase						=  0
		for letter in cipher_text:
				if letter == " ":
						or_txt += " "
				else:
						UpperCamelCase						=  (dicta[letter] + dicta[key_new[i]] + 26) % 26
						i += 1
						or_txt += dicta[x]
		return or_txt


def    A (      )     ->						None:
		'''simple docstring'''

		UpperCamelCase						=  'THE GERMAN ATTACK'
		UpperCamelCase						=  'SECRET'
		UpperCamelCase						=  generate_key(lowercase					, lowercase				)
		UpperCamelCase						=  cipher_text(lowercase					, lowercase				)
		print(f'''Encrypted Text = {s}'''				)
		print(f'''Original Text = {original_text(lowercase					, lowercase				)}'''				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()
