





"""simple docstring"""



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


a_			=      logging.get_logger(__name__)







def 		__lowercase					(       snake_case_  :  int    ,snake_case_  :  int						)       ->Dict:



    '''simple docstring'''







    try:
        with open(snake_case_    ,'''rb'''						) as flax_state_f:
            __A   :						Optional[Any]									=					from_bytes(snake_case_    ,flax_state_f.read()						)
    except UnpicklingError as e:
        try:
            with open(snake_case_						) 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(snake_case_    ,snake_case_						)







def 		__lowercase					(       snake_case_  :  Any    ,snake_case_  :  Dict						)       ->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
    __A   :						int									=					flatten_dict(jax.tree_util.tree_map(lambda snake_case_						: x.dtype == jnp.bfloataa    ,snake_case_						)						).values()
    if any(snake_case_						):
        # 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.'''						)
        __A   :						Tuple									=					jax.tree_util.tree_map(
            lambda snake_case_						: params.astype(np.floataa						) if params.dtype == jnp.bfloataa else params    ,snake_case_						)

    __A   :						Union[str, Any]									=					''''''

    __A   :						Optional[int]									=					flatten_dict(snake_case_    ,sep='''.'''						)
    __A   :						List[str]									=					pt_model.state_dict()

    # keep track of unexpected & missing keys
    __A   :						Any									=					[]
    __A   :						Dict									=					set(pt_model_dict.keys()						)

    for flax_key_tuple, flax_tensor in flax_state_dict.items():
        __A   :						Optional[Any]									=					flax_key_tuple.split('''.'''						)

        if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
            __A   :						Any									=					flax_key_tuple_array[:-1] + ['''weight''']
            __A   :						int									=					jnp.transpose(snake_case_    ,(3, 2, 0, 1)						)
        elif flax_key_tuple_array[-1] == "kernel":
            __A   :						Optional[int]									=					flax_key_tuple_array[:-1] + ['''weight''']
            __A   :						Any									=					flax_tensor.T
        elif flax_key_tuple_array[-1] == "scale":
            __A   :						List[Any]									=					flax_key_tuple_array[:-1] + ['''weight''']

        if "time_embedding" not in flax_key_tuple_array:
            for i, flax_key_tuple_string in enumerate(snake_case_						):
                __A   :						int									=					(
                    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'''						)
                )

        __A   :						str									=					'''.'''.join(snake_case_						)

        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
                __A   :						List[Any]									=					np.asarray(snake_case_						) if not isinstance(snake_case_    ,np.ndarray						) else flax_tensor
                __A   :						Optional[Any]									=					torch.from_numpy(snake_case_						)
                # remove from missing keys
                missing_keys.remove(snake_case_						)
        else:
            # weight is not expected by PyTorch model
            unexpected_keys.append(snake_case_						)

    pt_model.load_state_dict(snake_case_						)

    # re-transform missing_keys to list
    __A   :						Any									=					list(snake_case_						)

    if len(snake_case_						) > 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(snake_case_						) > 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











"""simple docstring"""



import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
a_			=      {
    """E""": 12.70,
    """T""": 9.06,
    """A""": 8.17,
    """O""": 7.51,
    """I""": 6.97,
    """N""": 6.75,
    """S""": 6.33,
    """H""": 6.09,
    """R""": 5.99,
    """D""": 4.25,
    """L""": 4.03,
    """C""": 2.78,
    """U""": 2.76,
    """M""": 2.41,
    """W""": 2.36,
    """F""": 2.23,
    """G""": 2.02,
    """Y""": 1.97,
    """P""": 1.93,
    """B""": 1.29,
    """V""": 0.98,
    """K""": 0.77,
    """J""": 0.15,
    """X""": 0.15,
    """Q""": 0.10,
    """Z""": 0.07,
}
a_			=      """ETAOINSHRDLCUMWFGYPBVKJXQZ"""
a_			=      """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""







def 		__lowercase					(       snake_case_  :  str						)       ->dict[str, int]:



    '''simple docstring'''







    __A   :						Any									=					{letter: 0 for letter in string.ascii_uppercase}
    for letter in message.upper():
        if letter in LETTERS:
            letter_count[letter] += 1

    return letter_count







def 		__lowercase					(       snake_case_  :  tuple						)       ->str:



    '''simple docstring'''







    return x[0]







def 		__lowercase					(       snake_case_  :  str						)       ->str:



    '''simple docstring'''







    __A   :						Union[str, Any]									=					get_letter_count(snake_case_						)
    __A   :						dict[int, list[str]]									=					{
        freq: [] for letter, freq in letter_to_freq.items()
    }
    for letter in LETTERS:
        freq_to_letter[letter_to_freq[letter]].append(snake_case_						)

    __A   :						dict[int, str]									=					{}

    for freq in freq_to_letter:
        freq_to_letter[freq].sort(key=ETAOIN.find    ,reverse=snake_case_						)
        __A   :						Optional[int]									=					''''''.join(freq_to_letter[freq]						)

    __A   :						str									=					list(freq_to_letter_str.items()						)
    freq_pairs.sort(key=snake_case_    ,reverse=snake_case_						)

    __A   :						list[str]									=					[freq_pair[1] for freq_pair in freq_pairs]

    return "".join(snake_case_						)







def 		__lowercase					(       snake_case_  :  str						)       ->int:



    '''simple docstring'''







    __A   :						Any									=					get_frequency_order(snake_case_						)
    __A   :						str									=					0
    for common_letter in ETAOIN[:6]:
        if common_letter in freq_order[:6]:
            match_score += 1

    for uncommon_letter in ETAOIN[-6:]:
        if uncommon_letter in freq_order[-6:]:
            match_score += 1

    return match_score


if __name__ == "__main__":
       import doctest

       doctest.testmod()




