


"""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


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




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_=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:
										__SCREAMING_SNAKE_CASE		     =       os.path.abspath(lowerCAmelCase_     )
										logger.info(f"""Loading PyTorch weights from {pt_path}"""     )

										__SCREAMING_SNAKE_CASE		     =       torch.load(lowerCAmelCase_						,		map_location="cpu"     )
										logger.info(f"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()     ):,} parameters."""     )

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




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		):




					'''simple docstring'''


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

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

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

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

					return pt_tuple_key, pt_tensor




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


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

					__SCREAMING_SNAKE_CASE		     =       flax_model.base_model_prefix

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

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

					__SCREAMING_SNAKE_CASE		     =       {}

					__SCREAMING_SNAKE_CASE		     =       (model_prefix not in flax_model_params) and (
					    model_prefix in {k.split("."     )[0] for k in pt_state_dict.keys()}
					)
					__SCREAMING_SNAKE_CASE		     =       (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(pt_key.split("."     )     )

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

										# Correctly rename weight parameters
										__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       rename_key_and_reshape_tensor(
										    lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     )

										# add model prefix if necessary
										__SCREAMING_SNAKE_CASE		     =       (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		     =       (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		     =       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
															__SCREAMING_SNAKE_CASE		     =       jnp.asarray(lowerCAmelCase_     )

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

					return unflatten_dict(lowerCAmelCase_     )




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					import torch

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

										__SCREAMING_SNAKE_CASE		     =       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		     =       flax_model.params["params"]

															__SCREAMING_SNAKE_CASE		     =       flatten_dict(lowerCAmelCase_     )
															random_flax_state_dict.update(flatten_dict(flax_model.params["batch_stats"]     )     )
										else:
															__SCREAMING_SNAKE_CASE		     =       flax_model.params
															__SCREAMING_SNAKE_CASE		     =       flatten_dict(lowerCAmelCase_     )

										__SCREAMING_SNAKE_CASE		     =       (model_prefix not in flax_model_params) and (
										    model_prefix in {k.split("."     )[0] for k in pt_state_dict.keys()}
										)
										__SCREAMING_SNAKE_CASE		     =       (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(pt_key.split("."     )     )

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

															# Correctly rename weight parameters
															__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       rename_key_and_reshape_tensor(
															    lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     )
															# add model prefix if necessary
															__SCREAMING_SNAKE_CASE		     =       (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		     =       (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		     =       jnp.asarray(lowerCAmelCase_     )
																									continue
																				if "var" in flax_key[-1]:
																									__SCREAMING_SNAKE_CASE		     =       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
																				__SCREAMING_SNAKE_CASE		     =       jnp.asarray(lowerCAmelCase_     )

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




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       os.path.abspath(lowerCAmelCase_     )
					logger.info(f"""Loading Flax weights from {flax_checkpoint_path}"""     )

					# import correct flax class
					__SCREAMING_SNAKE_CASE		     =       getattr(lowerCAmelCase_						,		"Flax" + model.__class__.__name__     )

					# load flax weight dict
					with open(lowerCAmelCase_						,		"rb"     ) as state_f:
										try:
															__SCREAMING_SNAKE_CASE		     =       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      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




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

					__SCREAMING_SNAKE_CASE		     =       flatten_dict(lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE		     =       pt_model.state_dict()

					__SCREAMING_SNAKE_CASE		     =       (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		     =       (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		     =       []
					__SCREAMING_SNAKE_CASE		     =       set(pt_model_dict.keys()     )

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

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

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

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

										if flax_key in special_pt_names:
															__SCREAMING_SNAKE_CASE		     =       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		     =       np.asarray(lowerCAmelCase_     ) if not isinstance(lowerCAmelCase_						,		np.ndarray     ) else flax_tensor
																				__SCREAMING_SNAKE_CASE		     =       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
					__SCREAMING_SNAKE_CASE		     =       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"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


a__       :							List[str]        =  {}


try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__       :							int        =  ['''MLukeTokenizer''']

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


else:
		import sys

		a__       :							Optional[Any]        =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
