'''simple docstring'''



import os
from typing import Dict, List, Tuple, TypeVar, Union


lowerCAmelCase_     :       int								=						TypeVar('T')

lowerCAmelCase_     :       Union[str, Any]								=						Union[List[T], Tuple[T, ...]]
lowerCAmelCase_     :       Optional[int]								=						Union[T, List[T], Dict[str, T]]
lowerCAmelCase_     :       str								=						Union[str, bytes, os.PathLike]









'''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


lowerCAmelCase_     :       List[str]								=						logging.get_logger(__name__)
def 					UpperCAmelCase   (						A  :				Optional[Any]					,		A  :				List[Any]					,		A  :				Any					,		A  :				Dict=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:
														SCREAMING_SNAKE_CASE					:      List[str]     					=			os.path.abspath(A				)
														logger.info(F"""Loading PyTorch weights from {pt_path}"""				)

														SCREAMING_SNAKE_CASE					:      List[str]     					=			torch.load(A					,		map_location='''cpu'''				)
														logger.info(F"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()				):,} parameters."""				)

														SCREAMING_SNAKE_CASE					:      Any     					=			convert_pytorch_state_dict_to_flax(A					,		A				)
							else:
														# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
														SCREAMING_SNAKE_CASE					:      List[str]     					=			convert_pytorch_sharded_state_dict_to_flax(A					,		A				)
							return flax_state_dict
def 					UpperCAmelCase   (						A  :				Tuple[str]					,		A  :				np.ndarray					,		A  :				Dict[str, jnp.ndarray]					,		A  :				str					,		):

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

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

							# batch norm layer mean
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			pt_tuple_key[:-1] + ('''mean''',)
							if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(A				):
														return renamed_pt_tuple_key, pt_tensor

							# batch norm layer var
							SCREAMING_SNAKE_CASE					:      Dict     					=			pt_tuple_key[:-1] + ('''var''',)
							if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(A				):
														return renamed_pt_tuple_key, pt_tensor

							# embedding
							SCREAMING_SNAKE_CASE					:      Any     					=			pt_tuple_key[:-1] + ('''embedding''',)
							if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(A				):
														return renamed_pt_tuple_key, pt_tensor

							# conv layer
							SCREAMING_SNAKE_CASE					:      Any     					=			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(A				):
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			pt_tensor.transpose(2					,		3					,		1					,		0				)
														return renamed_pt_tuple_key, pt_tensor

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

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

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

							return pt_tuple_key, pt_tensor
def 					UpperCAmelCase   (						A  :				Union[str, Any]					,		A  :				List[str]				):
							# convert pytorch tensor to numpy
							SCREAMING_SNAKE_CASE					:      str     					=			{k: v.numpy() for k, v in pt_state_dict.items()}

							SCREAMING_SNAKE_CASE					:      str     					=			flax_model.base_model_prefix

							# use params dict if the model contains batch norm layers
							if "params" in flax_model.params:
														SCREAMING_SNAKE_CASE					:      Tuple     					=			flax_model.params['''params''']
							else:
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			flax_model.params
							SCREAMING_SNAKE_CASE					:      int     					=			flatten_dict(A				)

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

							SCREAMING_SNAKE_CASE					:      Any     					=			{}

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

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

														# Correctly rename weight parameters
														SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Dict     					=			rename_key_and_reshape_tensor(
														    A					,		A					,		A					,		A				)

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

																					# also add unexpected weight so that warning is thrown
																					SCREAMING_SNAKE_CASE					:      str     					=			jnp.asarray(A				)

														else:
																					# also add unexpected weight so that warning is thrown
																					SCREAMING_SNAKE_CASE					:      Tuple     					=			jnp.asarray(A				)

							return unflatten_dict(A				)
def 					UpperCAmelCase   (						A  :				Any					,		A  :				Union[str, Any]				):
							import torch

							# Load the index
							SCREAMING_SNAKE_CASE					:      str     					=			{}
							for shard_file in shard_filenames:
														# load using msgpack utils
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			torch.load(A				)
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			{k: v.numpy() for k, v in pt_state_dict.items()}

														SCREAMING_SNAKE_CASE					:      List[str]     					=			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:
																					SCREAMING_SNAKE_CASE					:      Tuple     					=			flax_model.params['''params''']

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

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

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

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

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

																					else:
																												# also add unexpected weight so that warning is thrown
																												SCREAMING_SNAKE_CASE					:      Optional[int]     					=			jnp.asarray(A				)
							return unflatten_dict(A				)
def 					UpperCAmelCase   (						A  :				List[str]					,		A  :				str				):
							SCREAMING_SNAKE_CASE					:      str     					=			os.path.abspath(A				)
							logger.info(F"""Loading Flax weights from {flax_checkpoint_path}"""				)

							# import correct flax class
							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			getattr(A					,		'''Flax''' + model.__class__.__name__				)

							# load flax weight dict
							with open(A					,		'''rb'''				) as state_f:
														try:
																					SCREAMING_SNAKE_CASE					:      Optional[int]     					=			from_bytes(A					,		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(A					,		A				)






def 					UpperCAmelCase   (						A  :				int					,		A  :				int				):

							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
							SCREAMING_SNAKE_CASE					:      Any     					=			flatten_dict(jax.tree_util.tree_map(lambda A				: x.dtype == jnp.bfloataa					,		A				)				).values()
							if any(A				):
														# 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.'''				)
														SCREAMING_SNAKE_CASE					:      int     					=			jax.tree_util.tree_map(
														    lambda A				: params.astype(np.floataa				) if params.dtype == jnp.bfloataa else params					,		A				)

							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			flatten_dict(A				)
							SCREAMING_SNAKE_CASE					:      str     					=			pt_model.state_dict()

							SCREAMING_SNAKE_CASE					:      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()}
							)
							SCREAMING_SNAKE_CASE					:      Optional[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
							SCREAMING_SNAKE_CASE					:      int     					=			[]
							SCREAMING_SNAKE_CASE					:      Any     					=			set(pt_model_dict.keys()				)

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

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

														if "batch_stats" in flax_state:
																					SCREAMING_SNAKE_CASE					:      Any     					=			'''.'''.join(flax_key_tuple[1:]				)  # Remove the params/batch_stats header
														else:
																					SCREAMING_SNAKE_CASE					:      List[str]     					=			'''.'''.join(A				)

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

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

							pt_model.load_state_dict(A				)

							# re-transform missing_keys to list
							SCREAMING_SNAKE_CASE					:      Tuple     					=			list(A				)

							if len(A				) > 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(A				) > 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




