


'''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 	_A  (       A							,A							,A							,A=False							)  ->				Dict:
	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:
		lowercase						:		Tuple   					=		os.path.abspath(A							)
		logger.info(F'''Loading PyTorch weights from {pt_path}'''							)

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

		lowercase						:		Optional[int]   					=		convert_pytorch_state_dict_to_flax(A							,A							)
	else:
		# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
		lowercase						:		Optional[Any]   					=		convert_pytorch_sharded_state_dict_to_flax(A							,A							)
	return flax_state_dict




def 	_A  (       A							,A							,A							,A							,)  ->				(Tuple[str], np.ndarray):

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

	# layer norm
	lowercase						:		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
	lowercase						:		Optional[int]   					=		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
	lowercase						:		Any   					=		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
	lowercase						:		Optional[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
	lowercase						:		Tuple   					=		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							):
		lowercase						:		str   					=		pt_tensor.transpose(2							,3							,1							,0							)
		return renamed_pt_tuple_key, pt_tensor

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

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

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

	return pt_tuple_key, pt_tensor




def 	_A  (       A							,A							)  ->				Dict:
	# convert pytorch tensor to numpy
	lowercase						:		Any   					=		{k: v.numpy() for k, v in pt_state_dict.items()}

	lowercase						:		Tuple   					=		flax_model.base_model_prefix

	# use params dict if the model contains batch norm layers
	if "params" in flax_model.params:
		lowercase						:		Union[str, Any]   					=		flax_model.params["params"]
	else:
		lowercase						:		str   					=		flax_model.params
	lowercase						:		Tuple   					=		flatten_dict(A							)

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

	lowercase						:		List[Any]   					=		{}

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

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

		# Correctly rename weight parameters
		lowercase					,		lowercase						:		List[str]   					=		rename_key_and_reshape_tensor(
		    A							,A							,A							,A							)

		# add model prefix if necessary
		lowercase						:		List[Any]   					=		(model_prefix,) + flax_key in random_flax_state_dict
		if load_base_model_into_model_with_head and require_base_model_prefix:
			lowercase						:		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]:
				lowercase						:		List[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
			lowercase						:		int   					=		jnp.asarray(A							)

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

	return unflatten_dict(A							)




def 	_A  (       A							,A							)  ->				Union[str, Any]:
	import torch

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

		lowercase						:		List[Any]   					=		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:
			lowercase						:		str   					=		flax_model.params["params"]

			lowercase						:		List[Any]   					=		flatten_dict(A							)
			random_flax_state_dict.update(flatten_dict(flax_model.params["batch_stats"]							)							)
		else:
			lowercase						:		Any   					=		flax_model.params
			lowercase						:		str   					=		flatten_dict(A							)

		lowercase						:		List[str]   					=		(model_prefix not in flax_model_params) and (
		    model_prefix in {k.split("."							)[0] for k in pt_state_dict.keys()}
		)
		lowercase						:		List[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():
			lowercase						:		Optional[int]   					=		tuple(pt_key.split("."							)							)

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

			# Correctly rename weight parameters
			lowercase					,		lowercase						:		Optional[Any]   					=		rename_key_and_reshape_tensor(
			    A							,A							,A							,A							)
			# add model prefix if necessary
			lowercase						:		List[Any]   					=		(model_prefix,) + flax_key in random_flax_state_dict
			if load_base_model_into_model_with_head and require_base_model_prefix:
				lowercase						:		Optional[Any]   					=		(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]:
					lowercase						:		Union[str, Any]   					=		jnp.asarray(A							)
					continue
				if "var" in flax_key[-1]:
					lowercase						:		Optional[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
				lowercase						:		int   					=		jnp.asarray(A							)

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




def 	_A  (       A							,A							)  ->				Union[str, Any]:
	lowercase						:		int   					=		os.path.abspath(A							)
	logger.info(F'''Loading Flax weights from {flax_checkpoint_path}'''							)

	# import correct flax class
	lowercase						:		str   					=		getattr(A							,"Flax" + model.__class__.__name__							)

	# load flax weight dict
	with open(A							,"rb"							) as state_f:
		try:
			lowercase						:		Any   					=		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 	_A  (       A							,A							)  ->				Union[str, 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
	lowercase						:		List[str]   					=		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."							)
		lowercase						:		Union[str, Any]   					=		jax.tree_util.tree_map(
		    lambda A							: params.astype(np.floataa							) if params.dtype == jnp.bfloataa else params							,A							)

	lowercase						:		Optional[int]   					=		flatten_dict(A							)
	lowercase						:		List[Any]   					=		pt_model.state_dict()

	lowercase						:		int   					=		(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()}
	)
	lowercase						:		int   					=		(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
	lowercase						:		Tuple   					=		[]
	lowercase						:		List[Any]   					=		set(pt_model_dict.keys()							)

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

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

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

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

		if flax_key in special_pt_names:
			lowercase						:		Optional[int]   					=		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
				lowercase						:		str   					=		np.asarray(A							) if not isinstance(A							,np.ndarray							) else flax_tensor
				lowercase						:		List[str]   					=		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
	lowercase						:		List[str]   					=		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






'''simple docstring'''
from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase   : Dict             =				{"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase   : int             =				[
					    """VAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """VanForImageClassification""",
					    """VanModel""",
					    """VanPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_van import (
										    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    VanForImageClassification,
										    VanModel,
										    VanPreTrainedModel,
										)

else:
					import sys

					lowerCAmelCase   : Tuple             =				_LazyModule(__name__, globals()["""__file__"""], _import_structure)


