
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


_A     =  logging.get_logger(__name__)






def      lowerCamelCase__       (       __lowerCAmelCase						:						int					,							__lowerCAmelCase						:						Any					,							__lowerCAmelCase						:						Optional[Any]					,							__lowerCAmelCase						:						Optional[Any]=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:
						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      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
			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      lowerCamelCase__       (       __lowerCAmelCase						:						Optional[Any]					,							__lowerCAmelCase						:						List[str]       ):




			"""simple docstring"""


			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_			,						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      lowerCamelCase__       (       __lowerCAmelCase						:						Tuple					,							__lowerCAmelCase						:						Union[str, Any]       ):




			"""simple docstring"""


			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_			,						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      lowerCamelCase__       (       __lowerCAmelCase						:						Optional[Any]					,							__lowerCAmelCase						:						Union[str, Any]       ):




			"""simple docstring"""


			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      lowerCamelCase__       (       __lowerCAmelCase						:						Tuple					,							__lowerCAmelCase						:						Optional[int]       ):




			"""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
			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 TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_A     =  {}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     =  ["NllbTokenizer"]

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     =  ["NllbTokenizerFast"]


if TYPE_CHECKING:
			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_nllb import NllbTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_nllb_fast import NllbTokenizerFast

else:
			import sys

			_A     =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



