'''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


snake_case__					:      Optional[int]				      =  logging.get_logger(__name__)


def  _lowerCamelCase   (				lowerCamelCase_ :							List[str]     ,					lowerCamelCase_ :							Tuple     ,					lowerCamelCase_ :							List[Any]     ,					lowerCamelCase_ :							Optional[int]=False       ):



     """simple docstring"""





     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:
          UpperCAmelCase_							:       Optional[int]       =				os.path.abspath(lowerCamelCase_       )
          logger.info(F'''Loading PyTorch weights from {pt_path}'''       )

          UpperCAmelCase_							:       str       =				torch.load(lowerCamelCase_     ,					map_location='cpu'       )
          logger.info(F'''PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()       ):,} parameters.'''       )

          UpperCAmelCase_							:       List[str]       =				convert_pytorch_state_dict_to_flax(lowerCamelCase_     ,					lowerCamelCase_       )
     else:
          # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
          UpperCAmelCase_							:       Dict       =				convert_pytorch_sharded_state_dict_to_flax(lowerCamelCase_     ,					lowerCamelCase_       )
     return flax_state_dict


def  _lowerCamelCase   (				lowerCamelCase_ :							Tuple[str]     ,					lowerCamelCase_ :							np.ndarray     ,					lowerCamelCase_ :							Dict[str, jnp.ndarray]     ,					lowerCamelCase_ :							str     ,					):



     """simple docstring"""





     def is_key_or_prefix_key_in_dict(lowerCamelCase_ :							Tuple[str]       ) -> bool:
          return len(set(lowerCamelCase_       ) & {key, (model_prefix,) + key}       ) > 0

     # layer norm
     UpperCAmelCase_							:       List[Any]       =				pt_tuple_key[:-1] + ('scale',)
     if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(lowerCamelCase_       ):
          return renamed_pt_tuple_key, pt_tensor

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

     # batch norm layer var
     UpperCAmelCase_							:       Optional[Any]       =				pt_tuple_key[:-1] + ('var',)
     if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(lowerCamelCase_       ):
          return renamed_pt_tuple_key, pt_tensor

     # embedding
     UpperCAmelCase_							:       Union[str, Any]       =				pt_tuple_key[:-1] + ('embedding',)
     if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(lowerCamelCase_       ):
          return renamed_pt_tuple_key, pt_tensor

     # conv layer
     UpperCAmelCase_							:       Dict       =				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(lowerCamelCase_       ):
          UpperCAmelCase_							:       Tuple       =				pt_tensor.transpose(2     ,					3     ,					1     ,					0       )
          return renamed_pt_tuple_key, pt_tensor

     # linear layer
     UpperCAmelCase_							:       Any       =				pt_tuple_key[:-1] + ('kernel',)
     if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(lowerCamelCase_       ):
          UpperCAmelCase_							:       Optional[Any]       =				pt_tensor.T
          return renamed_pt_tuple_key, pt_tensor

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

     # old PyTorch layer norm bias
     UpperCAmelCase_							:       Optional[int]       =				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
     UpperCAmelCase_							:       int       =				None
     if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
          UpperCAmelCase_							:       List[Any]       =				pt_tuple_key[-2] + '_g'
     elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
          UpperCAmelCase_							:       Optional[Any]       =				pt_tuple_key[-2] + '_v'
     if name is not None:
          UpperCAmelCase_							:       str       =				pt_tuple_key[:-3] + (name,)
          return renamed_pt_tuple_key, pt_tensor

     return pt_tuple_key, pt_tensor


def  _lowerCamelCase   (				lowerCamelCase_ :							List[Any]     ,					lowerCamelCase_ :							List[Any]       ):



     """simple docstring"""





     UpperCAmelCase_							:       Any       =				{k: v.numpy() for k, v in pt_state_dict.items()}

     UpperCAmelCase_							:       int       =				flax_model.base_model_prefix

     # use params dict if the model contains batch norm layers
     if "params" in flax_model.params:
          UpperCAmelCase_							:       Dict       =				flax_model.params['params']
     else:
          UpperCAmelCase_							:       List[Any]       =				flax_model.params
     UpperCAmelCase_							:       Tuple       =				flatten_dict(lowerCamelCase_       )

     # add batch_stats keys,values to dict
     if "batch_stats" in flax_model.params:
          UpperCAmelCase_							:       Dict       =				flatten_dict(flax_model.params['batch_stats']       )
          random_flax_state_dict.update(lowerCamelCase_       )

     UpperCAmelCase_							:       int       =				{}

     UpperCAmelCase_							:       Tuple       =				(model_prefix not in flax_model_params) and (
         model_prefix in {k.split('.'       )[0] for k in pt_state_dict.keys()}
     )
     UpperCAmelCase_							:       str       =				(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():
          UpperCAmelCase_							:       Tuple       =				tuple(pt_key.split('.'       )       )

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

          # Correctly rename weight parameters
          UpperCAmelCase_       ,      UpperCAmelCase_							:       int       =				rename_key_and_reshape_tensor(
              lowerCamelCase_     ,					lowerCamelCase_     ,					lowerCamelCase_     ,					lowerCamelCase_       )

          # add model prefix if necessary
          UpperCAmelCase_							:       int       =				(model_prefix,) + flax_key in random_flax_state_dict
          if load_base_model_into_model_with_head and require_base_model_prefix:
               UpperCAmelCase_							:       Optional[int]       =				(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]:
                    UpperCAmelCase_							:       int       =				jnp.asarray(lowerCamelCase_       )
                    continue
               # remove num_batches_tracked key
               if "num_batches_tracked" in flax_key[-1]:
                    flax_state_dict.pop(lowerCamelCase_     ,					lowerCamelCase_       )
                    continue

               # also add unexpected weight so that warning is thrown
               UpperCAmelCase_							:       Union[str, Any]       =				jnp.asarray(lowerCamelCase_       )

          else:
               # also add unexpected weight so that warning is thrown
               UpperCAmelCase_							:       Union[str, Any]       =				jnp.asarray(lowerCamelCase_       )

     return unflatten_dict(lowerCamelCase_       )


def  _lowerCamelCase   (				lowerCamelCase_ :							List[Any]     ,					lowerCamelCase_ :							Optional[int]       ):



     """simple docstring"""





     import torch

     # Load the index
     UpperCAmelCase_							:       Any       =				{}
     for shard_file in shard_filenames:
          # load using msgpack utils
          UpperCAmelCase_							:       str       =				torch.load(lowerCamelCase_       )
          UpperCAmelCase_							:       Tuple       =				{k: v.numpy() for k, v in pt_state_dict.items()}

          UpperCAmelCase_							:       Optional[int]       =				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:
               UpperCAmelCase_							:       Dict       =				flax_model.params['params']

               UpperCAmelCase_							:       str       =				flatten_dict(lowerCamelCase_       )
               random_flax_state_dict.update(flatten_dict(flax_model.params['batch_stats']       )       )
          else:
               UpperCAmelCase_							:       int       =				flax_model.params
               UpperCAmelCase_							:       Union[str, Any]       =				flatten_dict(lowerCamelCase_       )

          UpperCAmelCase_							:       Union[str, Any]       =				(model_prefix not in flax_model_params) and (
              model_prefix in {k.split('.'       )[0] for k in pt_state_dict.keys()}
          )
          UpperCAmelCase_							:       Any       =				(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():
               UpperCAmelCase_							:       Optional[int]       =				tuple(pt_key.split('.'       )       )

               # remove base model prefix if necessary
               UpperCAmelCase_							:       Union[str, Any]       =				pt_tuple_key[0] == model_prefix
               if load_model_with_head_into_base_model and has_base_model_prefix:
                    UpperCAmelCase_							:       List[str]       =				pt_tuple_key[1:]

               # Correctly rename weight parameters
               UpperCAmelCase_       ,      UpperCAmelCase_							:       str       =				rename_key_and_reshape_tensor(
                   lowerCamelCase_     ,					lowerCamelCase_     ,					lowerCamelCase_     ,					lowerCamelCase_       )
               # add model prefix if necessary
               UpperCAmelCase_							:       str       =				(model_prefix,) + flax_key in random_flax_state_dict
               if load_base_model_into_model_with_head and require_base_model_prefix:
                    UpperCAmelCase_							:       int       =				(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]:
                         UpperCAmelCase_							:       List[Any]       =				jnp.asarray(lowerCamelCase_       )
                         continue
                    if "var" in flax_key[-1]:
                         UpperCAmelCase_							:       List[str]       =				jnp.asarray(lowerCamelCase_       )
                         continue
                    # remove num_batches_tracked key
                    if "num_batches_tracked" in flax_key[-1]:
                         flax_state_dict.pop(lowerCamelCase_     ,					lowerCamelCase_       )
                         continue

                    # also add unexpected weight so that warning is thrown
                    UpperCAmelCase_							:       Any       =				jnp.asarray(lowerCamelCase_       )

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


def  _lowerCamelCase   (				lowerCamelCase_ :							Union[str, Any]     ,					lowerCamelCase_ :							Optional[Any]       ):



     """simple docstring"""





     UpperCAmelCase_							:       Dict       =				os.path.abspath(lowerCamelCase_       )
     logger.info(F'''Loading Flax weights from {flax_checkpoint_path}'''       )

     # import correct flax class
     UpperCAmelCase_							:       int       =				getattr(lowerCamelCase_     ,					'Flax' + model.__class__.__name__       )

     # load flax weight dict
     with open(lowerCamelCase_     ,					'rb'       ) as state_f:
          try:
               UpperCAmelCase_							:       Union[str, Any]       =				from_bytes(lowerCamelCase_     ,					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(lowerCamelCase_     ,					lowerCamelCase_       )





def  _lowerCamelCase   (				lowerCamelCase_ :							Dict     ,					lowerCamelCase_ :							Any       ):



     """simple docstring"""





     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
     UpperCAmelCase_							:       List[Any]       =				flatten_dict(jax.tree_util.tree_map(lambda lowerCamelCase_       : x.dtype == jnp.bfloataa     ,					lowerCamelCase_       )       ).values()
     if any(lowerCamelCase_       ):
          # 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.'       )
          UpperCAmelCase_							:       Optional[int]       =				jax.tree_util.tree_map(
              lambda lowerCamelCase_       : params.astype(np.floataa       ) if params.dtype == jnp.bfloataa else params     ,					lowerCamelCase_       )

     UpperCAmelCase_							:       List[Any]       =				flatten_dict(lowerCamelCase_       )
     UpperCAmelCase_							:       Dict       =				pt_model.state_dict()

     UpperCAmelCase_							:       Optional[Any]       =				(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()}
     )
     UpperCAmelCase_							:       Union[str, Any]       =				(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
     UpperCAmelCase_							:       List[Any]       =				[]
     UpperCAmelCase_							:       List[str]       =				set(pt_model_dict.keys()       )

     for flax_key_tuple, flax_tensor in flax_state_dict.items():
          UpperCAmelCase_							:       Any       =				flax_key_tuple[0] == pt_model.base_model_prefix
          UpperCAmelCase_							:       Optional[Any]       =				'.'.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:
               UpperCAmelCase_							:       List[Any]       =				flax_key_tuple[1:]
          elif load_base_model_into_model_with_head and require_base_model_prefix:
               UpperCAmelCase_							:       str       =				(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(lowerCamelCase_       ) not in pt_model_dict:
               # conv layer
               UpperCAmelCase_							:       List[Any]       =				flax_key_tuple[:-1] + ('weight',)
               UpperCAmelCase_							:       Union[str, Any]       =				jnp.transpose(lowerCamelCase_     ,					(3, 2, 0, 1)       )
          elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCamelCase_       ) not in pt_model_dict:
               # linear layer
               UpperCAmelCase_							:       Any       =				flax_key_tuple[:-1] + ('weight',)
               UpperCAmelCase_							:       Dict       =				flax_tensor.T
          elif flax_key_tuple[-1] in ["scale", "embedding"]:
               UpperCAmelCase_							:       Dict       =				flax_key_tuple[:-1] + ('weight',)

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

          if "batch_stats" in flax_state:
               UpperCAmelCase_							:       Optional[int]       =				'.'.join(flax_key_tuple[1:]       )  # Remove the params/batch_stats header
          else:
               UpperCAmelCase_							:       Optional[int]       =				'.'.join(lowerCamelCase_       )

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

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

     pt_model.load_state_dict(lowerCamelCase_       )

     # re-transform missing_keys to list
     UpperCAmelCase_							:       Optional[Any]       =				list(lowerCamelCase_       )

     if len(lowerCamelCase_       ) > 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(lowerCamelCase_       ) > 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'''


snake_case__					:      Optional[Any]				      =  tuple[float, float, float]
snake_case__					:      Tuple				      =  tuple[float, float, float]


def  _lowerCamelCase   (				lowerCamelCase_ :							Pointad     ,					lowerCamelCase_ :							Pointad       ):



     """simple docstring"""





     UpperCAmelCase_							:       Any       =				end_pointa[0] - end_pointa[0]
     UpperCAmelCase_							:       Optional[Any]       =				end_pointa[1] - end_pointa[1]
     UpperCAmelCase_							:       Any       =				end_pointa[2] - end_pointa[2]
     return (x, y, z)


def  _lowerCamelCase   (				lowerCamelCase_ :							Vectorad     ,					lowerCamelCase_ :							Vectorad       ):



     """simple docstring"""





     UpperCAmelCase_							:       Optional[Any]       =				ab[1] * ac[2] - ab[2] * ac[1]  # *i
     UpperCAmelCase_							:       Optional[Any]       =				(ab[0] * ac[2] - ab[2] * ac[0]) * -1  # *j
     UpperCAmelCase_							:       Dict       =				ab[0] * ac[1] - ab[1] * ac[0]  # *k
     return (x, y, z)


def  _lowerCamelCase   (				lowerCamelCase_ :							Vectorad     ,					lowerCamelCase_ :							int       ):



     """simple docstring"""





     return tuple(round(lowerCamelCase_     ,					lowerCamelCase_       ) for x in vector       ) == (0, 0, 0)





def  _lowerCamelCase   (				lowerCamelCase_ :							Pointad     ,					lowerCamelCase_ :							Pointad     ,					lowerCamelCase_ :							Pointad     ,					lowerCamelCase_ :							int = 10       ):



     """simple docstring"""





     UpperCAmelCase_							:       List[str]       =				create_vector(lowerCamelCase_     ,					lowerCamelCase_       )
     UpperCAmelCase_							:       List[Any]       =				create_vector(lowerCamelCase_     ,					lowerCamelCase_       )
     return is_zero_vector(get_ad_vectors_cross(lowerCamelCase_     ,					lowerCamelCase_       )     ,					lowerCamelCase_       )






