






"""simple docstring"""
import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


_lowerCAmelCase         =							logging.get_logger(__name__)






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						Union[str, Any]	    =   R"""\w+[.]\d+"""
	A_		:						List[str]	    =   re.findall(snake_case__   ,   snake_case__					)
	for pat in pats:
		A_		:						Any	    =   key.replace(snake_case__   ,   """_""".join(pat.split("""."""					)					)					)
	return key






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						Any	    =   pt_tuple_key[:-1] + ("""scale""",)
	if (
	    any("""norm""" in str_ for str_ in pt_tuple_key					)
	    and (pt_tuple_key[-1] == "bias")
	    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
	    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
	):
		A_		:						Any	    =   pt_tuple_key[:-1] + ("""scale""",)
		return renamed_pt_tuple_key, pt_tensor
	elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
		A_		:						Tuple	    =   pt_tuple_key[:-1] + ("""scale""",)
		return renamed_pt_tuple_key, pt_tensor

	# embedding
	if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
		A_		:						List[str]	    =   pt_tuple_key[:-1] + ("""embedding""",)
		return renamed_pt_tuple_key, pt_tensor

	# conv layer
	A_		:						List[Any]	    =   pt_tuple_key[:-1] + ("""kernel""",)
	if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
		A_		:						int	    =   pt_tensor.transpose(2   ,   3   ,   1   ,   0					)
		return renamed_pt_tuple_key, pt_tensor

	# linear layer
	A_		:						List[str]	    =   pt_tuple_key[:-1] + ("""kernel""",)
	if pt_tuple_key[-1] == "weight":
		A_		:						str	    =   pt_tensor.T
		return renamed_pt_tuple_key, pt_tensor

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

	# old PyTorch layer norm bias
	A_		:						Optional[int]	    =   pt_tuple_key[:-1] + ("""bias""",)
	if pt_tuple_key[-1] == "beta":
		return renamed_pt_tuple_key, pt_tensor

	return pt_tuple_key, pt_tensor






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__=42					):
	# Step 1: Convert pytorch tensor to numpy
	A_		:						List[str]	    =   {k: v.numpy() for k, v in pt_state_dict.items()}

	# Step 2: Since the model is stateless, get random Flax params
	A_		:						Optional[Any]	    =   flax_model.init_weights(PRNGKey(snake_case__					)					)

	A_		:						Optional[Any]	    =   flatten_dict(snake_case__					)
	A_		:						Tuple	    =   {}

	# Need to change some parameters name to match Flax names
	for pt_key, pt_tensor in pt_state_dict.items():
		A_		:						Any	    =   rename_key(snake_case__					)
		A_		:						str	    =   tuple(renamed_pt_key.split("""."""					)					)

		# Correctly rename weight parameters
		A_    ,     A_		:						Tuple	    =   rename_key_and_reshape_tensor(snake_case__   ,   snake_case__   ,   snake_case__					)

		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}."""					)

        # also add unexpected weight so that warning is thrown
		A_		:						Optional[int]	    =   jnp.asarray(snake_case__					)

	return unflatten_dict(snake_case__					)









"""simple docstring"""
def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	return not any(
	    neighbour == 1 and colored_vertices[i] == color
	    for i, neighbour in enumerate(snake_case__					)					)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__					):

	# Base Case
	if index == len(snake_case__					):
		return True

	# Recursive Step
	for i in range(snake_case__					):
		if valid_coloring(graph[index]   ,   snake_case__   ,   snake_case__					):
			# Color current vertex
			A_		:						Dict	    =   i
			# Validate coloring
			if util_color(snake_case__   ,   snake_case__   ,   snake_case__   ,   index + 1					):
				return True
			# Backtrack
			A_		:						Union[str, Any]	    =   -1
	return False






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	A_		:						int	    =   [-1] * len(snake_case__					)

	if util_color(snake_case__   ,   snake_case__   ,   snake_case__   ,   0					):
		return colored_vertices

	return []

