




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


a__		     =     logging.get_logger(__name__)





def 				A__ (snake_case   :		Tuple		,	snake_case   :		Optional[Any]				)     ->						Union[str, Any]:
					try:
										with open(snake_case		,	"""rb"""				) as flax_state_f:
															__UpperCamelCase					:	str             =      from_bytes(snake_case		,	flax_state_f.read()				)
					except UnpicklingError as e:
										try:
															with open(snake_case				) 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(snake_case		,	snake_case				)







def 				A__ (snake_case   :		List[str]		,	snake_case   :		Dict				)     ->						Tuple:

					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
					__UpperCamelCase					:	int             =      flatten_dict(jax.tree_util.tree_map(lambda snake_case				: x.dtype == jnp.bfloataa		,	snake_case				)				).values()
					if any(snake_case				):
										# 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."""				)
										__UpperCamelCase					:	Any             =      jax.tree_util.tree_map(
										    lambda snake_case				: params.astype(np.floataa				) if params.dtype == jnp.bfloataa else params		,	snake_case				)

					__UpperCamelCase					:	List[str]             =      """"""

					__UpperCamelCase					:	Dict             =      flatten_dict(snake_case		,	sep="""."""				)
					__UpperCamelCase					:	int             =      pt_model.state_dict()

					# keep track of unexpected & missing keys
					__UpperCamelCase					:	Tuple             =      []
					__UpperCamelCase					:	Union[str, Any]             =      set(pt_model_dict.keys()				)

					for flax_key_tuple, flax_tensor in flax_state_dict.items():
										__UpperCamelCase					:	Tuple             =      flax_key_tuple.split("""."""				)

										if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
															__UpperCamelCase					:	Union[str, Any]             =      flax_key_tuple_array[:-1] + ["""weight"""]
															__UpperCamelCase					:	Dict             =      jnp.transpose(snake_case		,	(3, 2, 0, 1)				)
										elif flax_key_tuple_array[-1] == "kernel":
															__UpperCamelCase					:	Dict             =      flax_key_tuple_array[:-1] + ["""weight"""]
															__UpperCamelCase					:	List[Any]             =      flax_tensor.T
										elif flax_key_tuple_array[-1] == "scale":
															__UpperCamelCase					:	Optional[int]             =      flax_key_tuple_array[:-1] + ["""weight"""]

										if "time_embedding" not in flax_key_tuple_array:
															for i, flax_key_tuple_string in enumerate(snake_case				):
																				__UpperCamelCase					:	Dict             =      (
																				    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"""				)
																				)

										__UpperCamelCase					:	List[Any]             =      """.""".join(snake_case				)

										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					:	Any             =      np.asarray(snake_case				) if not isinstance(snake_case		,	np.ndarray				) else flax_tensor
																				__UpperCamelCase					:	List[str]             =      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
					__UpperCamelCase					:	Optional[int]             =      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)."""				)
					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."""				)

					return pt_model












def 				A__ (snake_case   :		float		,	snake_case   :		int				)     ->						float:
					if digit_amount > 0:
										return round(number - int(snake_case				)		,	snake_case				)
					return number - int(snake_case				)


if __name__ == "__main__":
	print(decimal_isolate(1.53, 0))
	print(decimal_isolate(35.345, 1))
	print(decimal_isolate(35.345, 2))
	print(decimal_isolate(35.345, 3))
	print(decimal_isolate(-14.789, 3))
	print(decimal_isolate(0, 2))
	print(decimal_isolate(-14.123, 1))
	print(decimal_isolate(-14.123, 2))
	print(decimal_isolate(-14.123, 3))






