



'''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


UpperCAmelCase     		=   logging.get_logger(__name__)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Dict							)      ->						Optional[int]:







	"""simple docstring"""
	lowerCAmelCase   =     R"""\w+[.]\d+"""
	lowerCAmelCase   =     re.findall(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
	for pat in pats:
		lowerCAmelCase   =     key.replace(_SCREAMING_SNAKE_CASE							,		"""_""".join(pat.split("""."""							)							)							)
	return key






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Optional[int]							,		_SCREAMING_SNAKE_CASE					:						Dict							,		_SCREAMING_SNAKE_CASE					:						Optional[int]							)      ->						int:







	"""simple docstring"""
	lowerCAmelCase   =     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)
	):
		lowerCAmelCase   =     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:
		lowerCAmelCase   =     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:
		lowerCAmelCase   =     pt_tuple_key[:-1] + ("""embedding""",)
		return renamed_pt_tuple_key, pt_tensor

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

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

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

	# old PyTorch layer norm bias
	lowerCAmelCase   =     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 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Union[str, Any]							,		_SCREAMING_SNAKE_CASE					:						Optional[Any]							,		_SCREAMING_SNAKE_CASE					:						Tuple=42							)      ->						Dict:







	"""simple docstring"""
	# Step 1: Convert pytorch tensor to numpy
	lowerCAmelCase   =     {k: v.numpy() for k, v in pt_state_dict.items()}

	# Step 2: Since the model is stateless, get random Flax params
	lowerCAmelCase   =     flax_model.init_weights(PRNGKey(_SCREAMING_SNAKE_CASE							)							)

	lowerCAmelCase   =     flatten_dict(_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     {}

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

		# Correctly rename weight parameters
		lowerCAmelCase,       lowerCAmelCase   =     rename_key_and_reshape_tensor(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_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
		lowerCAmelCase   =     jnp.asarray(_SCREAMING_SNAKE_CASE							)

	return unflatten_dict(_SCREAMING_SNAKE_CASE							)




'''simple docstring'''

class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						pass







class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						pass







class __snake_case:


						'''simple docstring'''



						def __init__( self				)     ->     int:
							lowerCAmelCase   =     [
							    [],
							    [],
							    [],
							]


						def     __snake_case   ( self     ,	A_     ,	A_				)     ->     None:
							try:
								if len(self.queues[priority]				) >= 100:
									raise OverflowError("""Maximum queue size is 100"""				)
								self.queues[priority].append(A_				)
							except IndexError:
								raise ValueError("""Valid priorities are 0, 1, and 2"""				)


						def     __snake_case   ( self				)     ->     int:
							for queue in self.queues:
								if queue:
									return queue.pop(0				)
							raise UnderFlowError("""All queues are empty"""				)




						def __str__( self				)     ->     str:
							return "\n".join(f'Priority {i}: {q}' for i, q in enumerate(self.queues				)				)







class __snake_case:


						'''simple docstring'''



						def __init__( self				)     ->     Dict:
							lowerCAmelCase   =     []


						def     __snake_case   ( self     ,	A_				)     ->     None:
							if len(self.queue				) == 100:
								raise OverFlowError("""Maximum queue size is 100"""				)
							self.queue.append(A_				)


						def     __snake_case   ( self				)     ->     int:
							if not self.queue:
								raise UnderFlowError("""The queue is empty"""				)
							else:
								lowerCAmelCase   =     min(self.queue				)
								self.queue.remove(A_				)
								return data




						def __str__( self				)     ->     str:
							return str(self.queue				)







def 		_snake_case		( )      ->						Tuple:







	"""simple docstring"""
	lowerCAmelCase   =     FixedPriorityQueue()
	fpq.enqueue(0							,		10							)
	fpq.enqueue(1							,		70							)
	fpq.enqueue(0							,		100							)
	fpq.enqueue(2							,		1							)
	fpq.enqueue(2							,		5							)
	fpq.enqueue(1							,		7							)
	fpq.enqueue(2							,		4							)
	fpq.enqueue(1							,		64							)
	fpq.enqueue(0							,		128							)
	print(_SCREAMING_SNAKE_CASE							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)
	print(_SCREAMING_SNAKE_CASE							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)






def 		_snake_case		( )      ->						Optional[Any]:







	"""simple docstring"""
	lowerCAmelCase   =     ElementPriorityQueue()
	epq.enqueue(10							)
	epq.enqueue(70							)
	epq.enqueue(100							)
	epq.enqueue(1							)
	epq.enqueue(5							)
	epq.enqueue(7							)
	epq.enqueue(4							)
	epq.enqueue(64							)
	epq.enqueue(128							)
	print(_SCREAMING_SNAKE_CASE							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)
	print(_SCREAMING_SNAKE_CASE							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)


if __name__ == "__main__":
				fixed_priority_queue()
				element_priority_queue()