

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


_a:    List[str] 							=			logging.get_logger(__name__)



def       __lowerCAmelCase      (						A    ):
	UpperCAmelCase_       				=			r"\w+[.]\d+"
	UpperCAmelCase_       				=			re.findall(A       ,     A    )
	for pat in pats:
		UpperCAmelCase_       				=			key.replace(A       ,     "_".join(pat.split("."    )    )    )
	return key



def       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	UpperCAmelCase_       				=			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)
	):
		UpperCAmelCase_       				=			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:
		UpperCAmelCase_       				=			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:
		UpperCAmelCase_       				=			pt_tuple_key[:-1] + ("embedding",)
		return renamed_pt_tuple_key, pt_tensor

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

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

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

	# old PyTorch layer norm bias
	UpperCAmelCase_       				=			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       __lowerCAmelCase      (						A       ,     A       ,     A=42    ):
	# Step 1: Convert pytorch tensor to numpy
	UpperCAmelCase_       				=			{k: v.numpy() for k, v in pt_state_dict.items()}

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

	UpperCAmelCase_       				=			flatten_dict(A    )
	UpperCAmelCase_       				=			{}

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

		# Correctly rename weight parameters
		UpperCAmelCase_				,   UpperCAmelCase_       				=			rename_key_and_reshape_tensor(A       ,     A       ,     A    )

		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
		UpperCAmelCase_       				=			jnp.asarray(A    )

	return unflatten_dict(A    )


from __future__ import annotations

class 						__UpperCamelCase    :




	def __init__(		self	:						Optional[Any]		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						str							):


		'''simple docstring'''







		UpperCAmelCase_				,   UpperCAmelCase_       				=			text, pattern
		UpperCAmelCase_				,   UpperCAmelCase_       				=			len(lowerCAmelCase							), len(lowerCAmelCase							)




	def       __A					(		self	:						List[str]		,	lowerCAmelCase	:						str							):


		'''simple docstring'''







		for i in range(self.patLen - 1		,	-1		,	-1							):
			if char == self.pattern[i]:
				return i
		return -1




	def       __A					(		self	:						int		,	lowerCAmelCase	:						int							):


		'''simple docstring'''







		for i in range(self.patLen - 1		,	-1		,	-1							):
			if self.pattern[i] != self.text[current_pos + i]:
				return current_pos + i
		return -1




	def       __A					(		self	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			[]
		for i in range(self.textLen - self.patLen + 1							):
			UpperCAmelCase_       				=			self.mismatch_in_text(lowerCAmelCase							)
			if mismatch_index == -1:
				positions.append(lowerCAmelCase							)
			else:
				UpperCAmelCase_       				=			self.match_in_pattern(self.text[mismatch_index]							)
				UpperCAmelCase_       				=			(
				    mismatch_index - match_index
				)  # shifting index lgtm [py/multiple-definition]
		return positions


_a:    Dict 							=			"""ABAABA"""
_a:    Union[str, Any] 							=			"""AB"""
_a:    str 							=			BoyerMooreSearch(text, pattern)
_a:    Dict 							=			bms.bad_character_heuristic()

if len(positions) == 0:
				print("""No match found""")
else:
				print("""Pattern found in following positions: """)
				print(positions)