





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

def   a_   (			lowerCAmelCase_				:      List[str],     lowerCAmelCase_				:      str,     lowerCAmelCase_				:      List[Any],     lowerCAmelCase_				:      Optional[int]=False     ):

			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:
						__lowerCAmelCase							     =     os.path.abspath(_lowerCamelCase     )
						logger.info(F"""Loading PyTorch weights from {pt_path}"""     )

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

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

def   a_   (			lowerCAmelCase_				:      Tuple[str],     lowerCAmelCase_				:      np.ndarray,     lowerCAmelCase_				:      Dict[str, jnp.ndarray],     lowerCAmelCase_				:      str,     ):

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

			# layer norm
			__lowerCAmelCase							     =     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
			__lowerCAmelCase							     =     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
			__lowerCAmelCase							     =     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
			__lowerCAmelCase							     =     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
			__lowerCAmelCase							     =     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     ):
						__lowerCAmelCase							     =     pt_tensor.transpose(2,     3,     1,     0     )
						return renamed_pt_tuple_key, pt_tensor

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

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

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

			return pt_tuple_key, pt_tensor

def   a_   (			lowerCAmelCase_				:      List[Any],     lowerCAmelCase_				:      Optional[Any]     ):
			__lowerCAmelCase							     =     {k: v.numpy() for k, v in pt_state_dict.items()}

			__lowerCAmelCase							     =     flax_model.base_model_prefix

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

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

			__lowerCAmelCase							     =     {}

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

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

						# Correctly rename weight parameters
						__lowerCAmelCase							     =     rename_key_and_reshape_tensor(
						    _lowerCamelCase,     _lowerCamelCase,     _lowerCamelCase,     _lowerCamelCase     )

						# add model prefix if necessary
						__lowerCAmelCase							     =     (model_prefix,) + flax_key in random_flax_state_dict
						if load_base_model_into_model_with_head and require_base_model_prefix:
									__lowerCAmelCase							     =     (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]:
												__lowerCAmelCase							     =     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
									__lowerCAmelCase							     =     jnp.asarray(_lowerCamelCase     )

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

			return unflatten_dict(_lowerCamelCase     )

def   a_   (			lowerCAmelCase_				:      List[Any],     lowerCAmelCase_				:      Tuple     ):
			import torch

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

						__lowerCAmelCase							     =     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:
									__lowerCAmelCase							     =     flax_model.params["params"]

									__lowerCAmelCase							     =     flatten_dict(_lowerCamelCase     )
									random_flax_state_dict.update(flatten_dict(flax_model.params['batch_stats']     )     )
						else:
									__lowerCAmelCase							     =     flax_model.params
									__lowerCAmelCase							     =     flatten_dict(_lowerCamelCase     )

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

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

									# Correctly rename weight parameters
									__lowerCAmelCase							     =     rename_key_and_reshape_tensor(
									    _lowerCamelCase,     _lowerCamelCase,     _lowerCamelCase,     _lowerCamelCase     )
									# add model prefix if necessary
									__lowerCAmelCase							     =     (model_prefix,) + flax_key in random_flax_state_dict
									if load_base_model_into_model_with_head and require_base_model_prefix:
												__lowerCAmelCase							     =     (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]:
															__lowerCAmelCase							     =     jnp.asarray(_lowerCamelCase     )
															continue
												if "var" in flax_key[-1]:
															__lowerCAmelCase							     =     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
												__lowerCAmelCase							     =     jnp.asarray(_lowerCamelCase     )

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

def   a_   (			lowerCAmelCase_				:      str,     lowerCAmelCase_				:      Optional[Any]     ):
			__lowerCAmelCase							     =     os.path.abspath(_lowerCamelCase     )
			logger.info(F"""Loading Flax weights from {flax_checkpoint_path}"""     )

			# import correct flax class
			__lowerCAmelCase							     =     getattr(_lowerCamelCase,     'Flax' + model.__class__.__name__     )

			# load flax weight dict
			with open(_lowerCamelCase,     'rb'     ) as state_f:
						try:
									__lowerCAmelCase							     =     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   a_   (			lowerCAmelCase_				:      Tuple,     lowerCAmelCase_				:      Any     ):

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

			__lowerCAmelCase							     =     flatten_dict(_lowerCamelCase     )
			__lowerCAmelCase							     =     pt_model.state_dict()

			__lowerCAmelCase							     =     (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()}
			)
			__lowerCAmelCase							     =     (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
			__lowerCAmelCase							     =     []
			__lowerCAmelCase							     =     set(pt_model_dict.keys()     )

			for flax_key_tuple, flax_tensor in flax_state_dict.items():
						__lowerCAmelCase							     =     flax_key_tuple[0] == pt_model.base_model_prefix
						__lowerCAmelCase							     =     ".".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:
									__lowerCAmelCase							     =     flax_key_tuple[1:]
						elif load_base_model_into_model_with_head and require_base_model_prefix:
									__lowerCAmelCase							     =     (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
									__lowerCAmelCase							     =     flax_key_tuple[:-1] + ("weight",)
									__lowerCAmelCase							     =     jnp.transpose(_lowerCamelCase,     (3, 2, 0, 1)     )
						elif flax_key_tuple[-1] == "kernel" and ".".join(_lowerCamelCase     ) not in pt_model_dict:
									# linear layer
									__lowerCAmelCase							     =     flax_key_tuple[:-1] + ("weight",)
									__lowerCAmelCase							     =     flax_tensor.T
						elif flax_key_tuple[-1] in ["scale", "embedding"]:
									__lowerCAmelCase							     =     flax_key_tuple[:-1] + ("weight",)

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

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

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

						if flax_key in special_pt_names:
									__lowerCAmelCase							     =     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
												__lowerCAmelCase							     =     np.asarray(_lowerCamelCase     ) if not isinstance(_lowerCamelCase,     np.ndarray     ) else flax_tensor
												__lowerCAmelCase							     =     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
			__lowerCAmelCase							     =     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








from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map

def   a_   (			lowerCAmelCase_				:      Dict[str, torch.Tensor]     ):
   __lowerCAmelCase							     =     []
   __lowerCAmelCase							     =     []
   __lowerCAmelCase							     =     []

   for rt in rc.restypes:
      __lowerCAmelCase							     =     rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
      restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]     )
      __lowerCAmelCase							     =     {name: i for i, name in enumerate(lowerCAmelCase_     )}
      restype_atomaa_to_atomaa_list.append(
          [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]     )

      restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]     )

   # Add dummy mapping for restype 'UNK'
   restype_atomaa_to_atomaa_list.append([0] * 14     )
   restype_atomaa_to_atomaa_list.append([0] * 37     )
   restype_atomaa_mask_list.append([0.0] * 14     )

   __lowerCAmelCase							     =     torch.tensor(
       lowerCAmelCase_,     dtype=torch.intaa,     device=protein['aatype'].device,     )
   __lowerCAmelCase							     =     torch.tensor(
       lowerCAmelCase_,     dtype=torch.intaa,     device=protein['aatype'].device,     )
   __lowerCAmelCase							     =     torch.tensor(
       lowerCAmelCase_,     dtype=torch.floataa,     device=protein['aatype'].device,     )
   __lowerCAmelCase							     =     protein['aatype'].to(torch.long     )

   # create the mapping for (residx, atom14) --> atom37, i.e. an array
   # with shape (num_res, 14) containing the atom37 indices for this protein
   __lowerCAmelCase							     =     restype_atomaa_to_atomaa[protein_aatype]
   __lowerCAmelCase							     =     restype_atomaa_mask[protein_aatype]

   __lowerCAmelCase							     =     residx_atomaa_mask
   __lowerCAmelCase							     =     residx_atomaa_to_atomaa.long()

   # create the gather indices for mapping back
   __lowerCAmelCase							     =     restype_atomaa_to_atomaa[protein_aatype]
   __lowerCAmelCase							     =     residx_atomaa_to_atomaa.long()

   # create the corresponding mask
   __lowerCAmelCase							     =     torch.zeros([21, 37],     dtype=torch.floataa,     device=protein['aatype'].device     )
   for restype, restype_letter in enumerate(rc.restypes     ):
      __lowerCAmelCase							     =     rc.restype_atoa[restype_letter]
      __lowerCAmelCase							     =     rc.residue_atoms[restype_name]
      for atom_name in atom_names:
         __lowerCAmelCase							     =     rc.atom_order[atom_name]
         __lowerCAmelCase							     =     1

   __lowerCAmelCase							     =     restype_atomaa_mask[protein_aatype]
   __lowerCAmelCase							     =     residx_atomaa_mask

   return protein




def   a_   (			lowerCAmelCase_				:      Dict[str, torch.Tensor]     ):
   __lowerCAmelCase							     =     tree_map(lambda lowerCAmelCase_     : torch.tensor(lowerCAmelCase_,     device=batch['aatype'].device     ),     lowerCAmelCase_,     np.ndarray     )
   __lowerCAmelCase							     =     tensor_tree_map(lambda lowerCAmelCase_     : np.array(lowerCAmelCase_     ),     make_atomaa_masks(lowerCAmelCase_     )     )
   return out

