





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


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





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase=False				)						->  Union[str, Any]:
				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(_UpperCAmelCase				)
								logger.info(F"""Loading PyTorch weights from {pt_path}"""				)

								lowerCamelCase	 =torch.load(_UpperCAmelCase			,      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(_UpperCAmelCase			,      _UpperCAmelCase				)
				else:
								# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
								lowerCamelCase	 =convert_pytorch_sharded_state_dict_to_flax(_UpperCAmelCase			,      _UpperCAmelCase				)
				return flax_state_dict





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      )						->  (Tuple[str], np.ndarray):

				def is_key_or_prefix_key_in_dict(_UpperCAmelCase				) -> bool:
								return len(set(_UpperCAmelCase				) & {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(_UpperCAmelCase				):
								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(_UpperCAmelCase				):
								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(_UpperCAmelCase				):
								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(_UpperCAmelCase				):
								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(_UpperCAmelCase				):
								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(_UpperCAmelCase				):
								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      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase				)						->  Any:
				# convert pytorch tensor to numpy
				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(_UpperCAmelCase				)

				# 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(_UpperCAmelCase				)

				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(
								    _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)

								# 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(_UpperCAmelCase				)
																continue
												# remove num_batches_tracked key
												if "num_batches_tracked" in flax_key[-1]:
																flax_state_dict.pop(_UpperCAmelCase			,      _UpperCAmelCase				)
																continue

												# also add unexpected weight so that warning is thrown
												lowerCamelCase	 =jnp.asarray(_UpperCAmelCase				)

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

				return unflatten_dict(_UpperCAmelCase				)





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase				)						->  Any:
				import torch

				# Load the index
				lowerCamelCase	 ={}
				for shard_file in shard_filenames:
								# load using msgpack utils
								lowerCamelCase	 =torch.load(_UpperCAmelCase				)
								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(_UpperCAmelCase				)
												random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""]				)				)
								else:
												lowerCamelCase	 =flax_model.params
												lowerCamelCase	 =flatten_dict(_UpperCAmelCase				)

								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(
												    _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)
												# 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(_UpperCAmelCase				)
																				continue
																if "var" in flax_key[-1]:
																				lowerCamelCase	 =jnp.asarray(_UpperCAmelCase				)
																				continue
																# remove num_batches_tracked key
																if "num_batches_tracked" in flax_key[-1]:
																				flax_state_dict.pop(_UpperCAmelCase			,      _UpperCAmelCase				)
																				continue

																# also add unexpected weight so that warning is thrown
																lowerCamelCase	 =jnp.asarray(_UpperCAmelCase				)

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





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase				)						->  Optional[Any]:
				lowerCamelCase	 =os.path.abspath(_UpperCAmelCase				)
				logger.info(F"""Loading Flax weights from {flax_checkpoint_path}"""				)

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

				# load flax weight dict
				with open(_UpperCAmelCase			,      """rb"""				) as state_f:
								try:
												lowerCamelCase	 =from_bytes(_UpperCAmelCase			,      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(_UpperCAmelCase			,      _UpperCAmelCase				)





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase				)						->  List[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
				lowerCamelCase	 =flatten_dict(jax.tree_util.tree_map(lambda _UpperCAmelCase				: x.dtype == jnp.bfloataa			,      _UpperCAmelCase				)				).values()
				if any(_UpperCAmelCase				):
								# 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 _UpperCAmelCase				: params.astype(np.floataa				) if params.dtype == jnp.bfloataa else params			,      _UpperCAmelCase				)

				lowerCamelCase	 =flatten_dict(_UpperCAmelCase				)
				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(_UpperCAmelCase				) not in pt_model_dict:
												# conv layer
												lowerCamelCase	 =flax_key_tuple[:-1] + ("""weight""",)
												lowerCamelCase	 =jnp.transpose(_UpperCAmelCase			,      (3, 2, 0, 1)				)
								elif flax_key_tuple[-1] == "kernel" and ".".join(_UpperCAmelCase				) 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(_UpperCAmelCase				)

								# 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(_UpperCAmelCase				)
																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(_UpperCAmelCase				) if not isinstance(_UpperCAmelCase			,      np.ndarray				) else flax_tensor
																lowerCamelCase	 =torch.from_numpy(_UpperCAmelCase				)
																# remove from missing keys
																missing_keys.remove(_UpperCAmelCase				)
								else:
												# weight is not expected by PyTorch model
												unexpected_keys.append(_UpperCAmelCase				)

				pt_model.load_state_dict(_UpperCAmelCase				)

				# re-transform missing_keys to list
				lowerCamelCase	 =list(_UpperCAmelCase				)

				if len(_UpperCAmelCase				) > 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(_UpperCAmelCase				) > 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












import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase=10_24			,      _UpperCAmelCase=10_24			,      _UpperCAmelCase=False			,      **_UpperCAmelCase				)						->  Optional[Any]:
				lowerCamelCase	 =AutoTokenizer.from_pretrained(_UpperCAmelCase				)
				lowerCamelCase	 =SeqaSeqDataset(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      type_path="""train"""			,      **_UpperCAmelCase				)
				lowerCamelCase	 =tok.pad_token_id

				def get_lens(_UpperCAmelCase				):
								lowerCamelCase	 =tqdm(
								    DataLoader(_UpperCAmelCase			,      batch_size=5_12			,      num_workers=8			,      shuffle=_UpperCAmelCase			,      collate_fn=ds.collate_fn				)			,      desc=str(ds.len_file				)			,      )
								lowerCamelCase	 =[]
								for batch in dl:
												lowerCamelCase	 =batch["""input_ids"""].ne(_UpperCAmelCase				).sum(1				).tolist()
												lowerCamelCase	 =batch["""labels"""].ne(_UpperCAmelCase				).sum(1				).tolist()
												if consider_target:
																for src, tgt in zip(_UpperCAmelCase			,      _UpperCAmelCase				):
																				max_lens.append(max(_UpperCAmelCase			,      _UpperCAmelCase				)				)
												else:
																max_lens.extend(_UpperCAmelCase				)
								return max_lens

				lowerCamelCase	 =get_lens(_UpperCAmelCase				)
				lowerCamelCase	 =SeqaSeqDataset(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      type_path="""val"""			,      **_UpperCAmelCase				)
				lowerCamelCase	 =get_lens(_UpperCAmelCase				)
				pickle_save(_UpperCAmelCase			,      train_ds.len_file				)
				pickle_save(_UpperCAmelCase			,      val_ds.len_file				)


if __name__ == "__main__":
							fire.Fire(save_len_file)





