


'''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        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE=False		)				->					List[str]:
					"""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:
										__a       =   os.path.abspath(snake_case_		)
										logger.info(F'''Loading PyTorch weights from {pt_path}'''		)

										__a       =   torch.load(snake_case_				,  map_location="""cpu"""		)
										logger.info(F'''PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()		):,} parameters.'''		)

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




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  )				->					Any:
					"""simple docstring"""




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

					# layer norm
					__a       =   pt_tuple_key[:-1] + ("""scale""",)
					if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(snake_case_		):
										return renamed_pt_tuple_key, pt_tensor

					# batch norm layer mean
					__a       =   pt_tuple_key[:-1] + ("""mean""",)
					if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(snake_case_		):
										return renamed_pt_tuple_key, pt_tensor

					# batch norm layer var
					__a       =   pt_tuple_key[:-1] + ("""var""",)
					if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(snake_case_		):
										return renamed_pt_tuple_key, pt_tensor

					# embedding
					__a       =   pt_tuple_key[:-1] + ("""embedding""",)
					if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(snake_case_		):
										return renamed_pt_tuple_key, pt_tensor

					# conv layer
					__a       =   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(snake_case_		):
										__a       =   pt_tensor.transpose(2				,  3				,  1				,  0		)
										return renamed_pt_tuple_key, pt_tensor

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

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

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

					return pt_tuple_key, pt_tensor




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					List[str]:
					"""simple docstring"""




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

					__a       =   flax_model.base_model_prefix

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

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

					__a       =   {}

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

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

										# Correctly rename weight parameters
										__a       =   rename_key_and_reshape_tensor(
										    snake_case_				,  snake_case_				,  snake_case_				,  snake_case_		)

										# add model prefix if necessary
										__a       =   (model_prefix,) + flax_key in random_flax_state_dict
										if load_base_model_into_model_with_head and require_base_model_prefix:
															__a       =   (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]:
																				__a       =   jnp.asarray(snake_case_		)
																				continue
															# remove num_batches_tracked key
															if "num_batches_tracked" in flax_key[-1]:
																				flax_state_dict.pop(snake_case_				,  snake_case_		)
																				continue

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

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

					return unflatten_dict(snake_case_		)




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					str:
					"""simple docstring"""




					import torch

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

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

															__a       =   flatten_dict(snake_case_		)
															random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""]		)		)
										else:
															__a       =   flax_model.params
															__a       =   flatten_dict(snake_case_		)

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

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

															# Correctly rename weight parameters
															__a       =   rename_key_and_reshape_tensor(
															    snake_case_				,  snake_case_				,  snake_case_				,  snake_case_		)
															# add model prefix if necessary
															__a       =   (model_prefix,) + flax_key in random_flax_state_dict
															if load_base_model_into_model_with_head and require_base_model_prefix:
																				__a       =   (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]:
																									__a       =   jnp.asarray(snake_case_		)
																									continue
																				if "var" in flax_key[-1]:
																									__a       =   jnp.asarray(snake_case_		)
																									continue
																				# remove num_batches_tracked key
																				if "num_batches_tracked" in flax_key[-1]:
																									flax_state_dict.pop(snake_case_				,  snake_case_		)
																									continue

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

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




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					Optional[int]:
					"""simple docstring"""




					__a       =   os.path.abspath(snake_case_		)
					logger.info(F'''Loading Flax weights from {flax_checkpoint_path}'''		)

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

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




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					Tuple:
					"""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
					__a       =   flatten_dict(jax.tree_util.tree_map(lambda __SCREAMING_SNAKE_CASE		: x.dtype == jnp.bfloataa				,  snake_case_		)		).values()
					if any(snake_case_		):
										# 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."""		)
										__a       =   jax.tree_util.tree_map(
										    lambda __SCREAMING_SNAKE_CASE		: params.astype(np.floataa		) if params.dtype == jnp.bfloataa else params				,  snake_case_		)

					__a       =   flatten_dict(snake_case_		)
					__a       =   pt_model.state_dict()

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

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

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

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

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

										if flax_key in special_pt_names:
															__a       =   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
																				__a       =   np.asarray(snake_case_		) if not isinstance(snake_case_				,  np.ndarray		) else flax_tensor
																				__a       =   torch.from_numpy(snake_case_		)
																				# remove from missing keys
																				missing_keys.remove(snake_case_		)
										else:
															# weight is not expected by PyTorch model
															unexpected_keys.append(snake_case_		)

					pt_model.load_state_dict(snake_case_		)

					# re-transform missing_keys to list
					__a       =   list(snake_case_		)

					if len(snake_case_		) > 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(snake_case_		) > 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'''







def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					str:
     """simple docstring"""




     return " ".join(input_str.split()[::-1]		)


if __name__ == "__main__":
 import doctest

 doctest.testmod()






