
"""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


SCREAMING_SNAKE_CASE      =     logging.get_logger(__name__)



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_=False				)->							Union[str, Any]:







       """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:
              UpperCamelCase					 =		os.path.abspath(UpperCAmelCase_				)
              logger.info(F"Loading PyTorch weights from {pt_path}"				)

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

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



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      )->							(Tuple[str], np.ndarray):







       """simple docstring"""

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

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

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

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

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

       return pt_tuple_key, pt_tensor



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_				)->							Union[str, Any]:







       """simple docstring"""
       # convert pytorch tensor to numpy
       UpperCamelCase					 =		{k: v.numpy() for k, v in pt_state_dict.items()}

       UpperCamelCase					 =		flax_model.base_model_prefix

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

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

       UpperCamelCase					 =		{}

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

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

              # Correctly rename weight parameters
              UpperCamelCase ,       UpperCamelCase					 =		rename_key_and_reshape_tensor(
                  UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_				)

              # add model prefix if necessary
              UpperCamelCase					 =		(model_prefix,) + flax_key in random_flax_state_dict
              if load_base_model_into_model_with_head and require_base_model_prefix:
                     UpperCamelCase					 =		(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]:
                            UpperCamelCase					 =		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
                     UpperCamelCase					 =		jnp.asarray(UpperCAmelCase_				)

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

       return unflatten_dict(UpperCAmelCase_				)



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_				)->							List[Any]:







       """simple docstring"""
       import torch

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

              UpperCamelCase					 =		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:
                     UpperCamelCase					 =		flax_model.params["params"]

                     UpperCamelCase					 =		flatten_dict(UpperCAmelCase_				)
                     random_flax_state_dict.update(flatten_dict(flax_model.params["batch_stats"]				)				)
              else:
                     UpperCamelCase					 =		flax_model.params
                     UpperCamelCase					 =		flatten_dict(UpperCAmelCase_				)

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

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

                     # Correctly rename weight parameters
                     UpperCamelCase ,       UpperCamelCase					 =		rename_key_and_reshape_tensor(
                         UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_				)
                     # add model prefix if necessary
                     UpperCamelCase					 =		(model_prefix,) + flax_key in random_flax_state_dict
                     if load_base_model_into_model_with_head and require_base_model_prefix:
                            UpperCamelCase					 =		(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]:
                                   UpperCamelCase					 =		jnp.asarray(UpperCAmelCase_				)
                                   continue
                            if "var" in flax_key[-1]:
                                   UpperCamelCase					 =		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
                            UpperCamelCase					 =		jnp.asarray(UpperCAmelCase_				)

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



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_				)->							int:







       """simple docstring"""
       UpperCamelCase					 =		os.path.abspath(UpperCAmelCase_				)
       logger.info(F"Loading Flax weights from {flax_checkpoint_path}"				)

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

       # load flax weight dict
       with open(UpperCAmelCase_		,      "rb"				) as state_f:
              try:
                     UpperCamelCase					 =		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 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_				)->							Any:







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

       UpperCamelCase					 =		flatten_dict(UpperCAmelCase_				)
       UpperCamelCase					 =		pt_model.state_dict()

       UpperCamelCase					 =		(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()}
       )
       UpperCamelCase					 =		(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
       UpperCamelCase					 =		[]
       UpperCamelCase					 =		set(pt_model_dict.keys()				)

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

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

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

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

              if flax_key in special_pt_names:
                     UpperCamelCase					 =		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
                            UpperCamelCase					 =		np.asarray(UpperCAmelCase_				) if not isinstance(UpperCAmelCase_		,      np.ndarray				) else flax_tensor
                            UpperCamelCase					 =		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
       UpperCamelCase					 =		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







"""simple docstring"""


import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


SCREAMING_SNAKE_CASE      =     4
SCREAMING_SNAKE_CASE      =     3



class   __a  (      _lowerCAmelCase	):
       pass





def 	lowerCamelCase__			(							UpperCAmelCase_				)->							List[str]:







       """simple docstring"""
       for shard in shards:
              for i in range(UpperCAmelCase_				):
                     yield {"i": i, "shard": shard}



def 	lowerCamelCase__			(							)->							Union[str, Any]:







       """simple docstring"""
       UpperCamelCase					 =		int(os.environ["RANK"]				)
       UpperCamelCase					 =		int(os.environ["WORLD_SIZE"]				)

       UpperCamelCase					 =		ArgumentParser()
       parser.add_argument("--streaming"		,      type=UpperCAmelCase_				)
       parser.add_argument("--local_rank"		,      type=UpperCAmelCase_				)
       parser.add_argument("--num_workers"		,      type=UpperCAmelCase_		,      default=0				)
       UpperCamelCase					 =		parser.parse_args()
       UpperCamelCase					 =		args.streaming
       UpperCamelCase					 =		args.num_workers

       UpperCamelCase					 =		{"shards": [F"shard_{shard_idx}" for shard_idx in range(UpperCAmelCase_				)]}
       UpperCamelCase					 =		IterableDataset.from_generator(UpperCAmelCase_		,      gen_kwargs=UpperCAmelCase_				)
       if not streaming:
              UpperCamelCase					 =		Dataset.from_list(list(UpperCAmelCase_				)				)

       UpperCamelCase					 =		split_dataset_by_node(UpperCAmelCase_		,      rank=UpperCAmelCase_		,      world_size=UpperCAmelCase_				)
       UpperCamelCase					 =		torch.utils.data.DataLoader(UpperCAmelCase_		,      num_workers=UpperCAmelCase_				)

       UpperCamelCase					 =		NUM_SHARDS * NUM_ITEMS_PER_SHARD
       UpperCamelCase					 =		full_size // world_size
       expected_local_size += int(rank < (full_size % world_size)				)

       UpperCamelCase					 =		sum(1 for _ in dataloader				)
       if local_size != expected_local_size:
              raise FailedTestError(F"local_size {local_size} != expected_local_size {expected_local_size}"				)


if __name__ == "__main__":
      main()





