
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      lowerCamelCase__       (       __lowerCAmelCase						:						List[Any]					,							__lowerCAmelCase						:						str       ):




			"""simple docstring"""


			try:
						with open(__lowerCAmelCase					,							"rb"       ) as flax_state_f:
									lowerCAmelCase_      			=			from_bytes(__lowerCAmelCase					,							flax_state_f.read()       )
			except UnpicklingError as e:
						try:
									with open(__lowerCAmelCase       ) 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(__lowerCAmelCase					,							__lowerCAmelCase       )






def      lowerCamelCase__       (       __lowerCAmelCase						:						int					,							__lowerCAmelCase						:						Any       ):




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

			lowerCAmelCase_      			=			""

			lowerCAmelCase_      			=			flatten_dict(__lowerCAmelCase					,							sep="."       )
			lowerCAmelCase_      			=			pt_model.state_dict()

			# 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.split("."       )

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

						if "time_embedding" not in flax_key_tuple_array:
									for i, flax_key_tuple_string in enumerate(__lowerCAmelCase       ):
												lowerCAmelCase_      			=			(
												    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"       )
												)

						lowerCAmelCase_      			=			".".join(__lowerCAmelCase       )

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

			pt_model.load_state_dict(__lowerCAmelCase       )

			# re-transform missing_keys to list
			lowerCAmelCase_      			=			list(__lowerCAmelCase       )

			if len(__lowerCAmelCase       ) > 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(__lowerCAmelCase       ) > 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__       (       __lowerCAmelCase						:						int       ):




			"""simple docstring"""


			if upper_limit < 0:
						raise ValueError("Limit for the Catalan sequence must be ≥ 0"       )

			lowerCAmelCase_      			=			[0] * (upper_limit + 1)

			# Base case: C(0) = C(1) = 1
			lowerCAmelCase_      			=			1
			if upper_limit > 0:
						lowerCAmelCase_      			=			1

			# Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
			for i in range(2					,							upper_limit + 1       ):
						for j in range(__lowerCAmelCase       ):
									catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

			return catalan_list


if __name__ == "__main__":
			print("\n********* Catalan Numbers Using Dynamic Programming ************\n")
			print("\n*** Enter -1 at any time to quit ***")
			print("\nEnter the upper limit (≥ 0) for the Catalan number sequence: ", end="")
			try:
						while True:
									_A     =  int(input().strip())
									if N < 0:
												print("\n********* Goodbye!! ************")
												break
									else:
												print(f"""The Catalan numbers from 0 through {N} are:""")
												print(catalan_numbers(N))
												print("Try another upper limit for the sequence: ", end="")
			except (NameError, ValueError):
						print("\n********* Invalid input, goodbye! ************\n")

			import doctest

			doctest.testmod()



