


"""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     =  logging.get_logger(__name__)


def __lowercase     (      lowerCamelCase_  :      Optional[Any]						,					lowerCamelCase_  :      List[str]						,					lowerCamelCase_  :      Optional[Any]						,					lowerCamelCase_  :      Union[str, Any]=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__     							=				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 __lowercase     (      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
				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 __lowercase     (      lowerCamelCase_  :      int						,					lowerCamelCase_  :      Union[str, Any]  ):
				# convert pytorch tensor to numpy
				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 __lowercase     (      lowerCamelCase_  :      Optional[Any]						,					lowerCamelCase_  :      List[Any]  ):
				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 __lowercase     (      lowerCamelCase_  :      Union[str, Any]						,					lowerCamelCase_  :      List[Any]  ):
				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 __lowercase     (      lowerCamelCase_  :      Dict						,					lowerCamelCase_  :      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
				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"""






# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser


def __lowercase     (      ):
				SCREAMING_SNAKE_CASE__     							=				ArgumentParser("Accelerate CLI tool"						,					usage="accelerate <command> [<args>]"						,					allow_abbrev=lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				parser.add_subparsers(help="accelerate command helpers"  )

				# Register commands
				get_config_parser(subparsers=lowerCamelCase_  )
				env_command_parser(subparsers=lowerCamelCase_  )
				launch_command_parser(subparsers=lowerCamelCase_  )
				tpu_command_parser(subparsers=lowerCamelCase_  )
				test_command_parser(subparsers=lowerCamelCase_  )

				# Let's go
				SCREAMING_SNAKE_CASE__     							=				parser.parse_args()

				if not hasattr(lowerCamelCase_						,					"func"  ):
								parser.print_help()
								exit(1  )

				# Run
				args.func(lowerCamelCase_  )


if __name__ == "__main__":
	main()
