


from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


_lowercase       :      Optional[int]	=  logging.get_logger(__name__)





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     List[Any]						) ->   Optional[Any]:





	"""simple docstring"""







	try:
		with open(UpperCamelCase__							,					"""rb"""						) as flax_state_f:
			A								=				from_bytes(UpperCamelCase__							,					flax_state_f.read()						)
	except UnpicklingError as e:
		try:
			with open(UpperCamelCase__						) as f:
				if f.read().startswith("""version"""						):
					raise OSError(
					    """You seem to have cloned a repository without having git-lfs installed. Please"""
					    """ install git-lfs and run `git lfs install` followed by `git lfs pull` in the"""
					    """ folder you cloned."""						)
				else:
					raise ValueError from e
		except (UnicodeDecodeError, ValueError):
			raise EnvironmentError(f'Unable to convert {model_file} to Flax deserializable object. '						)

	return load_flax_weights_in_pytorch_model(UpperCamelCase__							,					UpperCamelCase__						)





def   _lowerCAmelCase				(   UpperCamelCase__:     int							,					UpperCamelCase__:     List[Any]						) ->   str:





	"""simple docstring"""







	try:
		import torch  # noqa: F401
	except ImportError:
		logger.error(
		    """Loading 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 UpperCamelCase__						: x.dtype == jnp.bfloataa							,					UpperCamelCase__						)						).values()
	if any(UpperCamelCase__						):
		# convert all weights to fp32 if they 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 UpperCamelCase__						: params.astype(np.floataa						) if params.dtype == jnp.bfloataa else params							,					UpperCamelCase__						)

	A								=				""""""

	A								=				flatten_dict(UpperCamelCase__							,					sep="""."""						)
	A								=				pt_model.state_dict()

	# 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.split("""."""						)

		if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
			A								=				flax_key_tuple_array[:-1] + ["""weight"""]
			A								=				jnp.transpose(UpperCamelCase__							,					(3, 2, 0, 1)						)
		elif flax_key_tuple_array[-1] == "kernel":
			A								=				flax_key_tuple_array[:-1] + ["""weight"""]
			A								=				flax_tensor.T
		elif flax_key_tuple_array[-1] == "scale":
			A								=				flax_key_tuple_array[:-1] + ["""weight"""]

		if "time_embedding" not in flax_key_tuple_array:
			for i, flax_key_tuple_string in enumerate(UpperCamelCase__						):
				A								=				(
				    flax_key_tuple_string.replace("""_0"""							,					""".0"""						)
				    .replace("""_1"""							,					""".1"""						)
				    .replace("""_2"""							,					""".2"""						)
				    .replace("""_3"""							,					""".3"""						)
				    .replace("""_4"""							,					""".4"""						)
				    .replace("""_5"""							,					""".5"""						)
				    .replace("""_6"""							,					""".6"""						)
				    .replace("""_7"""							,					""".7"""						)
				    .replace("""_8"""							,					""".8"""						)
				    .replace("""_9"""							,					""".9"""						)
				)

		A								=				""".""".join(UpperCamelCase__						)

		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(UpperCamelCase__						) if not isinstance(UpperCamelCase__							,					np.ndarray						) else flax_tensor
				A								=				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
	A								=				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)."""						)
	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."""						)

	return pt_model







def   _lowerCAmelCase				(   UpperCamelCase__:     str						) ->   str:





	"""simple docstring"""







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


if __name__ == "__main__":
				import doctest

				doctest.testmod()



