




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


UpperCamelCase__    			=			logging.get_logger(__name__)







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Dict      ,      SCREAMING_SNAKE_CASE_							:				List[str]     ):




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







def 	_a      (						SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				Union[str, Any]     ):




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

					__lowerCAmelCase				   =       ""

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

					pt_model.load_state_dict(SCREAMING_SNAKE_CASE_     )

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

					if len(SCREAMING_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(SCREAMING_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








from __future__ import annotations


class        _a							:

							def __init__(							self:	Union[str, Any]    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	str							)			->   List[Any]:





									"""simple docstring"""







									lowercase__     ,		lowercase__            =      text, pattern
									lowercase__     ,		lowercase__            =      len(UpperCamelCase_							), len(UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	Optional[int]    ,						UpperCamelCase_:	str							)			->   int:





									"""simple docstring"""







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

							def       lowerCamelCase_ (							self:	Optional[int]    ,						UpperCamelCase_:	int							)			->   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       lowerCamelCase_ (							self:	List[Any]							)			->   list[int]:





									"""simple docstring"""







									lowercase__            =      []
									for i in range(self.textLen - self.patLen + 1							):
											lowercase__            =      self.mismatch_in_text(UpperCamelCase_							)
											if mismatch_index == -1:
													positions.append(UpperCamelCase_							)
											else:
													lowercase__            =      self.match_in_pattern(self.text[mismatch_index]							)
													lowercase__            =      (
													    mismatch_index - match_index
													)  # shifting index lgtm [py/multiple-definition]
									return positions
lowerCAmelCase       =							'ABAABA'
lowerCAmelCase       =							'AB'
lowerCAmelCase       =							BoyerMooreSearch(text, pattern)
lowerCAmelCase       =							bms.bad_character_heuristic()

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


