






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


lowerCamelCase      :       Union[str, Any]    =						logging.get_logger(__name__)





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case							):
     try:
          with open(__snake_case       ,			"rb"							) as flax_state_f:
               __lowerCAmelCase             =       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    __lowerCAmelCase   (    __snake_case       ,			__snake_case							):

     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
     __lowerCAmelCase             =       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."							)
          __lowerCAmelCase             =       jax.tree_util.tree_map(
              lambda __snake_case							: params.astype(np.floataa							) if params.dtype == jnp.bfloataa else params       ,			__snake_case							)

     __lowerCAmelCase             =       ""

     __lowerCAmelCase             =       flatten_dict(__snake_case       ,			sep="."							)
     __lowerCAmelCase             =       pt_model.state_dict()

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

     for flax_key_tuple, flax_tensor in flax_state_dict.items():
          __lowerCAmelCase             =       flax_key_tuple.split("."							)

          if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
               __lowerCAmelCase             =       flax_key_tuple_array[:-1] + ["weight"]
               __lowerCAmelCase             =       jnp.transpose(__snake_case       ,			(3, 2, 0, 1)							)
          elif flax_key_tuple_array[-1] == "kernel":
               __lowerCAmelCase             =       flax_key_tuple_array[:-1] + ["weight"]
               __lowerCAmelCase             =       flax_tensor.T
          elif flax_key_tuple_array[-1] == "scale":
               __lowerCAmelCase             =       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							):
                    __lowerCAmelCase             =       (
                        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"							)
                    )

          __lowerCAmelCase             =       ".".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
                    __lowerCAmelCase             =       np.asarray(__snake_case							) if not isinstance(__snake_case       ,			np.ndarray							) else flax_tensor
                    __lowerCAmelCase             =       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
     __lowerCAmelCase             =       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














from __future__ import annotations





def    __lowerCAmelCase   (    __snake_case							):
     __lowerCAmelCase             =       len(__snake_case							)
     # We need to create solution object to save path.
     __lowerCAmelCase             =       [[0 for _ in range(__snake_case							)] for _ in range(__snake_case							)]
     __lowerCAmelCase             =       run_maze(__snake_case       ,			0       ,			0       ,			__snake_case							)
     if solved:
          print("\n".join(str(__snake_case							) for row in solutions							)							)
     else:
          print("No solution exists!"							)
     return solved





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       len(__snake_case							)
     # Final check point.
     if i == j == (size - 1):
          __lowerCAmelCase             =       1
          return True

     __lowerCAmelCase             =       (not i < 0) and (not j < 0)  # Check lower bounds
     __lowerCAmelCase             =       (i < size) and (j < size)  # Check upper bounds

     if lower_flag and upper_flag:
          # check for already visited and block points.
          __lowerCAmelCase             =       (not solutions[i][j]) and (not maze[i][j])
          if block_flag:
               # check visited
               __lowerCAmelCase             =       1

               # check for directions
               if (
                   run_maze(__snake_case       ,			i + 1       ,			__snake_case       ,			__snake_case							)
                   or run_maze(__snake_case       ,			__snake_case       ,			j + 1       ,			__snake_case							)
                   or run_maze(__snake_case       ,			i - 1       ,			__snake_case       ,			__snake_case							)
                   or run_maze(__snake_case       ,			__snake_case       ,			j - 1       ,			__snake_case							)
               ):
                    return True

               __lowerCAmelCase             =       0
               return False
     return False


if __name__ == "__main__":
 import doctest

 doctest.testmod()






